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José Manuel Salazar-Xirinachs

Executive Secretary

Javier Medina Vásquez

Deputy Executive Secretary a.i.

Sally Shaw

Chief, Documents and Publications Division

Miguel Torres

Editor

Esteban Pérez Caldentey

Associate Editor

Osvaldo Sunkel

Chair of the Editorial Board

The CEPAL Review was founded in 1976, along with the corresponding Spanish version, Revista CEPAL, and it is published three times a year by the Economic Commission for Latin America and the Caribbean (ECLAC), which has its headquarters in Santiago. The Review has full editorial independence and follows the usual academic procedures and criteria, including the review of articles by independent external referees. The purpose of the Review is to contribute to the discussion of socioeconomic development issues in the region by offering analytical and policy approaches and articles by economists and other social scientists working both within and outside the United Nations. The Review is distributed to universities, research institutes and other international organizations, as well as to individual subscribers.

The opinions expressed in the articles are those of the authors and do not necessarily reflect the views of the Organization or the countries it represents.

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Applications for authorization to reproduce this work in whole or in part should be sent to the Economic Commission for Latin America and the Caribbean (ECLAC), Documents and Publications Division, publicaciones.cepal@un.org. Member States and their governmental institutions may reproduce this work without prior authorization, but are requested to mention the source and to inform ECLAC of such reproduction.

Contents

Maintitle

Copyright

Explanatory notes

Editorial note

On the measurement of growth over the long run1

Economic growth and productive sectors: recent empirical data on middle-income countries

Long-term effects of real exchange rate volatility and institutional quality on economic complexity

The influence of fiscal solvency on financial development: evidence for 140 countries, 1990–20201

Empirical evidence for Okun’s law in Colombia: an analysis of rural areas at the region level

Prices and progress? The link between inflation and structural change in post-war Brazil

Impact of the peace negotiations in Colombia on real GDP, 2013–2019

Book review essay

The World That Latin America Created The United Nations Economic Commission for Latin America in the Development Era, by Margarita Fajardo1

Guidelines for contributors to the cepal Review

Explanatory notes

Explanatory notes

The following symbols have been used in the tables in this publication:

… Three dots indicate that data are not available or are not separately reported.

— A dash indicates that the amount is nil or negligible.

A blank space indicates that the concept under consideration is not applicable or not comparable.

– A minus sign indicates a deficit or decrease, unless otherwise indicated.

. A full stop is used to indicate decimals.

/ A slash between years (e.g. 2022/2023) indicates a 12-month period falling between the two years.

- The use of an en dash between years (e.g. 2022–2023) indicates reference to the complete number of calendar years involved, including the beginning and end years.

Reference to “tons” indicates metric tons and the world “dollars” refers to United States dollars, unless otherwise specified. Individual figures and percentages in graphs and tables may not always add up to the corresponding total because of rounding.

Editorial note

Miguel Torres

Editor of CEPAL Review

At the close of the first four months of 2025, we are pleased to share with our readership a new issue of CEPAL Review, the 145th edition of our ever-expanding catalogue that seeks to promote knowledge and understanding —from an academic, structural and contemporary perspective— of the various economic, social and environmental aspects of development in Latin America and the Caribbean.

This year is marked by a range of changing situations in the global economic landscape on the geopolitical, economic, technological and trade fronts. These conditions are creating high levels of global uncertainty and rendering the achievement of the goals for economic, inclusive and sustainable development for our region more challenging. CEPAL Review will monitor these new trends with a view to offering relevant and quality inputs. We extend an open invitation to our vast community of academics and researchers who identify with our editorial line to contribute ideas and articles.

The first issue of 2025 heralds the start of a new stage in the history of CEPAL Review, which will be marked by the commemoration, in 2026, of the fiftieth anniversary of our publication.

In this regard, we are pleased to announce that Esteban Pérez Caldentey has joined our editorial team as associate editor of CEPAL Review. He will certainly help to renew and further diversify our content and enhance the rigour, discernment and objectivity that are the hallmarks of our publication.

Welcome, Esteban.

We are also grateful for the support of the Office of the Executive Secretary of the Economic Commission for Latin America and the Caribbean (ECLAC), in particular José Manuel Salazar-Xirinachs, Executive Secretary of the Commission, for this and other initiatives.

With this 145th edition of CEPAL Review, we have also introduced new sections to complement the selection of articles that are customarily approved under a strict double-blind peer review system. Thus, in addition to the seven articles selected for this issue, which all deal with macroeconomic aspects of development in our region, a permanent book review section has been included, with reviews from selected reviewers.

In this issue, Esteban Pérez Caldentey and Miguel Torres present a review of Margarita Fajardo’s The World That Latin America Created: The United Nations Economic Commission for Latin America in the Development Era, recently published in Spanish by Fondo de Cultura Económica.

Future issues of CEPAL Review will also include a section containing interviews with key figures and stakeholders in regional and global economic development.

We invite our readers to explore the content of this issue and to remain attentive to that of forthcoming issues.

Santiago, April 2025.

On the measurement of growth over the long run[1]

Rafael R. Guthmann

Received: 05/03/2024

Accepted: 04/12/2024

Abstract

Methodologies for the construction of nominal and real gross domestic product (GDP) time series often differ over time and between countries. This paper discusses the main issues raised by this methodological heterogeneity for long-run measures of economic growth and, informed by these issues, provides a set of internationally comparable GDP estimates from 1820 to 2020. The estimates are based on real product benchmarks relative to the United Kingdom as the reference economy. The GDP time series of the reference economy is a normalized composite of several indices. These estimates suggest that the Maddison Project data sets overestimate economic growth after 1950 relative to the period from 1820 to 1950.

Keywords

Economic growth, national accounts, gross domestic product, time-series analysis, statistical methodology

JEL classification

E01, O47, O57

Author

Rafael R. Guthmann is an assistant professor at the Faculty of Economics and Business of Alberto Hurtado University, Chile. Email: rroos@uahurtado.cl.

I. Introduction

Although gross domestic product (GDP) time series data are universally used by economists as an easy way to visualize economic growth and development, there is great heterogeneity in their quality and the methods used to produce them. GDP is an accounting convention that varies over time and between countries. In addition, owing to the complexity of national economies, GDP is constructed using data that are inevitably incomplete. As a result, the official real GDP time series produced by an economy’s statistical agencies often changes substantially from one vintage of the series to another.[2]

The present paper does two things. First, it discusses how variations in methodologies and data incompleteness change the measured growth rate. Second, it provides alternatives to the data sets of Maddison (2010) and Bolt and Van Zanden (2020), estimating historical GDP time series in constant 1990 dollars after correction for methodological heterogeneity.

It is a well-known feature of the national accounts literature that benchmark estimates of GDP measured in purchasing power parity (PPP) for different economies are inconsistent with the growth rates implied by their national accounts. These discrepancies are natural and occur even if there are no measurement errors, since different economies have different relative prices, and relative prices change over time (Deaton, 2012). Methodologies for estimating GDP growth are heterogeneous, and different PPP benchmarks produced with different methodologies yield different relative levels of GDP (Deaton and Heston, 2010).[3]

This means that long-run estimates of economic performance should be constructed using a uniform methodology for the whole period under analysis. For example, Broadberry, Fukao and Zammit (2015) and Van Zanden and Van Leeuwen (2012) produced series for England and the Kingdom of the Netherlands covering very long periods (from the thirteenth to the mid-nineteenth century and from the fourteenth to the early nineteenth century, respectively). However, because these series were constructed with a very different methodology from the official GDP time series produced by modern nations, their quantitative values are not comparable to those of a contemporary GDP time series.

This paper makes two contributions. First, using a simple theoretical framework, it shows that different methods produce different estimates even when based on the same data. In addition, if statistical agencies have more complete information on the full scope of economic activity in the present than in the past, the measured growth rate of GDP might be distorted relative to a situation where the statistical agency had complete information.

Second, and taking these issues into consideration, this paper presents alternative estimates of real GDP time series over a long period (from 1820 to 2020) for a set of 57 economies that collectively comprised to of the world’s population over this period. One implication of the series estimated by this paper is that the GDP time series data in the data sets presented by Maddison (2010) and Bolt and Van Zanden (2020) appear to overestimate the rate of economic growth since 1950 relative to the rate of growth before 1950.

The estimates presented here address these problems by normalizing the level and growth rates of GDP relative to the GDP of a reference economy. The reference economy’s long-run GDP time series is necessarily a composite of different series, but this composite is corrected for the methodological variance across series. The time series for the other 56 economies are then normalized by means of several benchmark comparisons of real GDP using PPPs.

In principle, a time series for GDP measured at local prices should be a more accurate measure of economic growth than a GDP series anchored in real product comparisons relative to a reference economy. However, that will only be the situation if the time series for GDP measured at local prices is constructed using a uniform methodology for the whole period under consideration, which is not the case for the GDP time series of any economy covering long periods up to the present. The normalization method used here yields more reasonable estimates of long-run GDP growth than simply using several GDP times series without any attempt at normalization.

The author’s estimates are more strongly correlated with national accounts estimates in local prices than are the estimates from the Penn World Table version 10.0 (see table 4). This shows that, despite the numerous issues that can be raised on theoretical grounds, it is possible to construct estimates for the evolution of GDP across time and space over the past couple of centuries that are consistent with the local evidence for economic growth and with the cross-country evidence of relative income levels.

The paper is organized as follows. Section II studies a simple example of an economy with a representative consumer, which illustrates how changes in the methodology used for computing GDP alter the computed growth rate. Section III describes the methodology used to construct the real GDP time series data set. Section IV presents the resulting data set and compares it to Maddison (2010) and the 2020 release of the Maddison Project. Section V discusses the implications of these estimates for the historical applicability of the Balassa-Samuelson effect. Concluding remarks are offered in section VI. Robustness tests are performed in annex A1. First, the exercise is replicated, with the reference economy changed from the United Kingdom to the United States, showing that the results do not alter significantly. Second, it robustness tests are performed on the GDP time series of the United Kingdom to ascertain whether these series exhibit biased relative growth rates for different periods. There is also an online annex that discusses the measurement of productivity per hour worked and presents all the data sources used and the benchmarks of real product levels.

II. The measurement of economic growth

This section briefly describes the most commonly used methodologies for measuring real GDP changes across time and countries. It makes two points. First, it shows that even if a statistical agency had complete information about the economic activity of a country, measured GDP growth rates would vary significantly across different methodologies. Second, it shows that, in the realistic case of statistical information being incomplete, new distortions make measurement more problematic. In particular, the interaction between technological change and incomplete statistical information makes the disparities between methodologies even more pronounced.

1. Measuring growth in an economy with complete data

These points can be illustrated by a simple but useful discrete-time infinite horizon economy with two goods (the bare minimum needed to discuss aggregation issues): food (f) and manufactured goods (m).

On the demand side, a (representative) consumer has Stone-Geary preferences over non-negative bundles of food and manufactured goods. Specifically, the consumer’s per-period utility function is given by

(1)

where is the bundle of food and manufactured goods consumed in period t ∈ {0, 1, 2, …}, α ∈ (0, 1), and cf represents the minimum (“subsistence”) level of food consumption.

On the supply side, one unit of labour (inelastically supplied by the consumer) can be used to produce each good. Production technologies are linear in labour. In other words, the production function of good k ∈ {f, m} at time t is

(2)

where is the amount of labour used to produce good k at time t and is labour productivity in sector k at time t. We assume that , so that labour productivity in food production is high enough to meet the minimum required level of consumption.

Production is performed by competitive firms. The labour share of income is equal to 1, and because the firms are competitive, relative prices in terms of wages are determined by labour productivity. Therefore, , where is the price of good k at time t, and , where wt is the wage at time t.

Given product prices and wages, the consumer maximizes the present value of utility from consuming streams of food and manufactured goods:

(3)

where β ∈ (0, 1) is the discount factor. The solution to the problem above gives equilibrium output of food, yf, and manufactures, ym, over time according to the following equations:

(4)

(5)

For the quantitative exercise, the parametrization shown in table 1 is used without any loss of generality.

Table 1

Parametrization in the quantitative exercise

Parameter

Value

cf

0.75

a

0.25

1

1%

5%

Source: Prepared by the author.

Note: is the growth rate of labour productivity in sector k at time 't.

2. Methodologies for constructing GDP time series

Five series for real GDP between periods 0 and 30 will now be constructed using distinct methodologies, beginning with the simplest.

Series 1. This methodology computes the output index (GDP) at time t using relative prices from one base period. In this particular case, period 0 is taken as the base period. Then, the output index at time t > 0, Yt is computed according to the following formula:

(6)

where and are, respectively, the quantities of food and manufactured goods produced (consumed) at time t, and are, respectively, the prices for food and manufactured goods in base period 0, and Y_0 is set at 100.

This method is not officially used by any country to compute GDP series for long periods of time. Since relative prices tend to change substantially over long time periods, it is more satisfactory to use prices that are chronologically closer to the years selected for incorporation into an index of real GDP.

Series 2. This methodology (like the previous one) uses prices from a selected base period. In this case, however, the output index incorporates multiple base periods. In this example, three different base periods are used —10, 20 and 30— and the output index is set at 100 for period 0.

This methodology is typically used by the statistical offices of most developing countries because it requires less information than the methods used in series 3, 4 and 5. For example, from the beginning of China’s official national accounts up to 2018, nine base periods were used: 1952, 1957, 1970, 1980, 1990, 2000, 2005, 2010 and 2015.[4] Other countries that use this method include Argentina, India, Indonesia, Mexico, Nigeria, Viet Nam and Uruguay.

The method is, first, to compute the output index of period t > 0 using prices from time period 10, i.e.:

(7)

Second, compute the output index of period 0 using prices from period 10, i.e.:

(8)

Third, divide (7) by (8) and multiply the result by the output index of period 0:

(9)

In general, the methodology works as follows. At any period t e {bi-1, …, bi }, where bi = 10 × i para i ≥ t/10 for is one of the three base periods and b_0 = 0, the index of output Yt is given by

(10)

where and are, respectively, the prices of food and manufactured goods in the base period bi.

In other words, given a designated base period bi, GDP at any time t < bi (i.e. previous to the base period) is measured in terms of the prices of the base period. Moreover, the base period changes at regular 10-period intervals. Using equation 10, the reader may find it useful to verify that the output index at any time t ∈ 11, 12, …, 20 is given by

(11)

Series 3. The output index is computed with what are generally known as “chained prices” or the “chained Fisher index”. Under this methodology, the output index is measured using prices from every period of the series instead of prices from a set of fixed base periods. Brazil and Russia, among others, are countries that use this methodology.

Let us assume that we have computed the output index at some time t. Then, the output index in the next period is determined as follows:

First, we compute the output index at t + 1 in terms of prices at t, . This is done by obtaining aggregate output at t + 1 using prices at t and then dividing the result by the aggregate output at t using prices at t as follows:

(12)

Second, we compute the output index at t + 1 in terms of prices at t + 1, . This index is obtained by aggregating output at t + 1 using prices at t + 1 and then dividing the result by aggregate output at t in terms of prices at t + 1, i.e.:

(13)

Lastly, the chained output index Yt+1 is computed by taking the geometric average of and :

(14)

Series 4. In this case, the index is estimated with chained weights like series 3, but with one important difference, which is that it incorporates hedonic methods. These methods are used to account for changes in the quality of goods over time. This methodology is currently used for the national accounts of the United States.

To incorporate hedonics, we assume that the quality of manufactured goods improves at a rate of 1% per period. The assumption means that the representative consumer is indifferent between an increase of 1% in the quantity of manufactured goods and the same quantity of manufactured goods in the consecutive period.[5]

Series 5. This method consists of estimating real output growth from periods 0 to 30 by comparing prices and quantities for only these two periods. For clarity, we can see how the output index of period 30 relative to period 0, is computed by following a series of steps.

First, the period 0 index number, Y_0, is set at Y_0 = 100. Let denote the index of output at period 30 relative to period 0 measured in terms of period 0 prices. This index is computed by the formula:

(15)

Second, let be the index number that uses period 30 prices to measure the change in aggregate output from period 0 to period 30. The index is computed by the formula:

(16)

Lastly, the estimated output index at period 30, Y_30, is the geometric average of the indices calculated in the first two steps:

(17)

If we suppose that the prices and quantities of periods 0 and 30 represent the prices and quantities of, say, two countries A and B, then this method is the one typically used to construct estimates of real GDP levels in terms of PPP across countries.[6]

Not surprisingly, even in a simple two-good representative agent economy, output indices vary substantially with different methodologies. Table 2 gives the estimated growth rates (from period 0 to period 30) measured according to the five indices discussed above.

Table 2

Measured GDP growth rate from period 0 to period 30, by method

(Percentages)

Method

Growth rate

Series 1

4.85

Series 2

3.86

Series 3

4.12

Series 4

4.49

Series 5

3.92

Source: Prepared by the author.

The differences in the estimated growth rates are not negligible. For instance, the growth rate of series 1 is much higher than that of series 2. To understand why this happens, we must recall the assumption that productivity in the manufactured goods sector increases at a higher rate than productivity in the food sector. This assumption implies that the relative price of manufactured goods falls relative to the price of food from one period to the next. Therefore, series 1, which only uses prices from period 0, when manufactured goods were relatively more expensive, gives greater weight to manufactured goods in the output index than the other series. As productivity grows faster in this category of goods, its measured total output also grows at a higher rate. Conversely, series 2 yields a much lower growth rate because it uses prices from future periods at which manufactured goods are cheaper than in period 0.

The chained index of series 3 uses prices from the “present” period (i.e. to measure the change in output from t to t + 1, series 3 uses prices from only these two times), and therefore the measured growth rate is at an intermediate value between the growth rates of series 1 and series 2. Series 4 builds on series 3, but the measured growth rate is higher because it incorporates time-quality improvements in manufactured goods.

Lastly, the growth rate of series 5 is much lower than that of series 1 and just slightly higher than that of series 2. The reason is that this methodology uses manufactured goods prices from period 30, which are lower than the period 0 prices used by series 5 in addition to period 30 prices. However, series 1 exclusively uses prices from period 0. Because productivity growth is higher in the manufactured goods sector, then, it follows that the relative weight of that sector in the output index is lower for series 5 than for series 1.

3. Measuring growth with incomplete data

For a given methodology, other factors can also negatively affect the possibility of obtaining “reasonable” measures of GDP growth. Clearly, for a given time period, the most significant one is incomplete coverage of economic activity by the statistical agency. For instance, it is very well known that black market activities are difficult to measure and value accurately. Moreover, the advent of new and more productive information systems for collecting data, though welcome, generates further intertemporal distortions in the measurement of economic growth.[7]

This can be illustrated by the following exercise. First, suppose that from period 0 to 15, only 50% of all manufactured goods transactions are observed by the statistical office. Thus, only this portion of final output is accounted for in the GDP figures. Second, assume that from period 15 onward, there is an improvement in the quality of the office’s data collection. As a result, from period 15 to 30, 90% of manufactured goods transactions are observed by the statistical agency. These data are used to construct a new GDP series, series 3I, and its GDP growth rates are then compared with the growth rates of series 3.[8]

Note that because the manufacturing sector is underweighted in the index of real output, the growth rate from period 0 to period 30 of series 3I (3.48%) is substantially lower than the growth rate of series 3 (4.12%). Moreover, the discrepancies in the growth rate of series 3I relative to those of series 3 are heterogeneous over time. Relative to series 3, the growth rate of series 3I is nearly 30% lower over the first 15 periods but only 4% lower over periods 15 to 30. This simple example should suffice to show how the presence of incomplete data further distorts the measurement of GDP growth.

III. Methodology for the set of GDP estimates

This section presents estimates of real GDP in 1990 international dollars for 57 economies from 1820 to 2020. These time series are estimated as follows. First, a time series is estimated for a reference economy during the period. Then, the other 56 economies’ real GDP time series are constructed on the basis of benchmark comparisons of real GDP relative to the reference economy. For each of these 56 economies, GDP estimates at local prices are normalized by the benchmark comparisons with the reference economy to construct time series. The details of the estimation procedure are presented in the following subsections, as are the resulting GDP time series using the United Kingdom as the reference economy. In section 1 of annex A1, the same exercise is performed using the United States as the reference economy instead of the United Kingdom.

The method of taking a reference economy and normalizing the GDP figures of other economies relative to it has been used before; for example, the versions of the Penn World Table released since 2015 have taken the United States.[9] The innovation of this paper is to also normalize the series of the reference economy by taking account of discrepancies from historical estimates for the more distant past. We use the United Kingdom as the main reference economy in this study for two reasons. First, there are more historical macroeconomic data for the United Kingdom than virtually any other country and, second, many historical real GDP benchmark comparisons include it.

1. Normalization procedure for the reference economy

The real GDP series of the United Kingdom over this long period (1820–2020) is a composite of several different real GDP series estimated by distinct methods. As shown in the previous section, different methods yield different growth rates. To correct for this inconsistency, the series are first normalized and then used to construct the composite GDP series.

To explain the normalization procedure, an example will be first presented and then formally described. Let us consider the economy described in section II and suppose that:

(i) The statistical office of this economy has computed series 2 as in the previous section (i.e. the GDP series has been computed with prices from fixed base periods). Suppose that the economy existed before period 0 and that the statistical office has also computed series 2 for this economy for 30 periods before period 0 and then up to period 15 (i.e. the GDP time series begins at period -30 and ends at period 15). Thus, series 2 covers periods -30, ...,15 using prices from base periods at 10-year intervals: -20, -10, 0, 10, 20.

(ii) The statistical office has also produced another GDP series for this economy, series 4 (chained prices adjusted by hedonic methods), which only covers periods 0 to 30.

A comparison of the economic growth rates of series 2 for the time up to period 15 with the growth rates of series 4 from periods 15 to 30 yields a substantial overestimation of growth performance in the later periods compared to the earlier periods. Note that series 4 does not cover periods before period 0, and series 2 does not cover periods 15 to 30. Therefore, no single time series covers the entire time interval from periods -30 to 30.

To properly compare growth before period 0 with growth from periods 15 to 30, consider a composite series of series 2 and series 4, where series 2 is used for periods -30 to 15 and a normalized version of series 4 is used for periods 16,17,...,30: the growth rate of series 4 is normalized by a constant proportion given by the ratio between the growth rates of series 4 and series 2 over the first 15 periods. This yields series 2N. The growth rate from period 15 to period 30 is 4.81% according to series 4 but only 4.13% according to series 2N. Series 2 has a growth rate of 4.18% from period 15 to period 30. Thus, this normalization means that series 2N closely approximates the growth rate of series 2.

The normalization procedure is formally implemented as follows. First, suppose there are two series: series , which covers periods {t_0, t_0 + 1, …, t2}, and series S^2, which covers periods {t_1, t_1 + 1, …, t3}, where t_0 < t_1 < t_2 < t_3. Note that because t_1 < t_2, the two series overlap over periods {t_1, t_1 + 1, …, t2}. Let be the log growth in GDP from t_1 to t_2 according to series S j. Then, the composite series covering periods t_0 to t_3, , is given by for each period t ≤ t_2, while for each t e {t_2 +1, …, t_3 }, it is given by

(18)

By normalizing all the series using a normalized series for the reference economy, this method yields more reasonable estimates of long-run growth than using different GDP time series without any attempt to control for methodological differences, as done hitherto in the literature.

2. GDP series for the reference economy

The GDP series for the United Kingdom from 1820 to 2020 is constructed by combining the Bank of England’s estimated GDP series from 1820 to 1948 as published in Thomas and others (2017)[10] with the official national accounts from 1948 to 2020 as computed by the Office for National Statistics (ONS).

The normalization procedure consists in normalizing the GDP growth rates from both the Bank of England series for 1820–1948 and the ONS series for 1948–2020. The normalization coefficients (the ratio of growth rates 1/S^2 in equation 18) are derived from the overlap of these two series with the series from Maddison (2010) for 1855–1965. The series estimated by Maddison, in turn, was based on the real GDP time series estimated by Feinstein (1972). These four series and the resulting normalized series for 1820–2020 are shown in figure 1. Section 2 of annex A1 shows the results of robustness tests on this composite normalized series.

Figure 1

Normalized GDP series for the United Kingdom

Source: Prepared by the author, on the basis of A. Maddison, “World population GDP and per capita GDP 1–2009 ad”, Groningen, University of Groningen, 2010; R. Thomas and others, “A millennium of macroeconomic data for the UK”, Technical Report, London, Bank of England, 2017; C. Feinstein, National Income, Expenditure and Output of the United Kingdom, 1855–1965, Cambridge, Cambridge University Press, 1972; and Office for National Statistics (ONS).

3. Internationally comparable GDP time series: chained PPPs

This study includes GDP estimates for only 57 of the 200 or so sovereign countries that exist today, for two reasons: (i) these are the only economies for which very long-term national accounts estimates and historical estimates of PPPs are available, and (ii) the number of sovereign countries has increased dramatically since the mid-twentieth century, with most lacking any estimated time series for real GDP before independence.

The GDP time series for economies other than the United Kingdom are estimated as follows.

First, the United Kingdom’s GDP series is stated in terms of 1990 dollars, using the 1990 benchmark of PPP relative to the United States. The use of 1990 dollars as a unit of account is also found in Maddison (1995, 2001 and 2010) and much of the literature on historical national accounts that followed. Normalized United States GDP for 1990 is computed here at US$ 5.803 trillion,[11] which in turn implies GDP of US$ 1.018 trillion in 1990 dollars for the United Kingdom in 1990. The GDP figures for all other times and also for the other economies are then normalized on this GDP figure of US$ 1.018 trillion in 1990 dollars for the United Kingdom.

Second, studies that estimate historical national accounts and PPPs for the United Kingdom and other economies are used to produce a set of benchmark estimates of real GDP levels for the other economies. For example, Ma, De Jong and Xu (2016) estimate the PPP level of Chinese per capita GDP relative to the United Kingdom in 1912; this estimate is used here to estimate Chinese GDP in 1990 dollars for that year. For China’s GDP time series, real GDP benchmarks relative to the United Kingdom for 1840, 1912, 1986, 2005, 2011 and 2017 have been used in this study.

Third, a combination of these benchmark estimates of real GDP and the real GDP time series computed in local prices[12] are used to determine levels of GDP in 1990 dollars for other years. The basis of this method is that, for a pair of years, benchmark GDP estimates expressed in the same currency (such as 1990 dollars) for each year imply an average growth rate between them. Where estimates of GDP in local prices imply real GDP growth over a long period of time that greatly differs from the growth rates implied by the benchmark estimates, the growth rate of the estimated series is adjusted by the benchmark. In other words, the growth rate is multiplied by the ratio of the growth rate implied by the benchmark to the growth rate in local prices.

Formally, the interpolation procedure is as follows. Let {tb1, tb2} be the dates of two real income level benchmarks with tb2 > tb1, and let Ytb1, Ytb2 be two benchmark levels of observed GDP. Consider a time series index for GDP in local prices (for example the official national accounts for some economy); then, at some date t e {tb1, tb1 + 1, tb1 + 2, …, tb2}, the estimated GDP level at time t, Yt, is given by

(19)

For example, table 3 shows Japanese GDP levels in 1990 dollars relative to the United Kingdom for the three benchmarks used to construct Japan’s GDP time series in this study. These benchmarks imply an annual growth rate of 5.2% from 1935 to 1990 and a growth rate of 0.8% from 1990 to 2014. For the same periods, the growth rates computed in local prices were 5.1% from 1935 to 1990[13] and 0.9% from 1990 to 2014.[14]

Table 3

Japan: real GDP benchmarks

(Millions of 1990 dollars)

Benchmark year

GDP

1935

145 492

1990

2 376 141

2014

2 853 794

Source: Prepared by the author, on the basis of official figures.

The GDP series for Japan from 1935 to 2014 is based on the GDP series in local prices, with the growth rate from 1935 to 1990 normalized to match the GDP level of the 1990 benchmark and the rate from 1990 to 2014 normalized to match the GDP level of the 2014 benchmark. For years before 1935 and after 2014, GDP levels are based on national accounts estimated at local prices without adjustment for international comparisons with the United Kingdom. However, nearly 70% of the log variation of Japan’s estimated per capita GDP from 1820 to 2020 occurred between 1935 and 2014, as that was the period of greatest economic growth. For countries like China, India and the United States and most of Western Europe, benchmarks in the more distant past are available (for China, our time series uses benchmarks of relative GDP levels at PPPs for 1840, 1986 and 2017). Hence, dependence on the use of local prices in the estimation of long-run growth is smaller.

Exclusively for the period after 1990 and the GDPs of Greece, Italy, Portugal, Romania and the countries of the former Soviet Union, a different chained PPP method is used here. The reason is that the method described by equation (19) would imply even greater volatility of aggregate GDP than is suggested by official national accounts. The method of linear weighting of PPP benchmarks used in these cases can be described as follows: for a year t e {tb1, tb1 + 1, tb1 + 2, …, tb2}, the estimated GDP level at t, Yt, is given by

(20)

The use of several real GDP benchmarks close together in time could dramatically change GDP growth rates over short periods and yield real GDP time series that were inconsistent with the macroeconomic trajectory of these economies. Therefore, the estimates follow the rule that only one benchmark is taken when a pair of PPP benchmarks are close together in time. Only benchmarks that are relatively far apart in time (over 20 years) are used in the construction of these estimates. This method results in modest normalization of growth rates over long periods while keeping relative changes in output levels consistent with the GDP time series constructed in local prices.[15]

Another rule followed by the estimates here is that extrapolations and interpolations between two benchmark estimates are made using the same source for the entire time segment of the GDP time series, if such a source is available. This is done to minimize distortions to relative growth rates, as different series are often constructed using different methodologies.

4. Differences from the estimates in the 2018 release of the Maddison Project

The release of the Maddison Project in Bolt and others (2018) used the GDP time series estimates of Maddison (2010) and several PPP benchmarks to modify the GDP levels estimated. Subsequent editions of the Maddison Project have used the same method as Maddison (2010), which is just to aggregate the series of different countries without any adjustments.

The methodological differences between Bolt and others (2018) and the current study are as follows:

(i) They use the United States as the reference economy but do not normalize the various GDP time series used by Maddison and his followers to construct the United States GDP time series from 1820 to 2016. The present study has carefully normalized the time series for both the United Kingdom and the United States (in section 1 of annex A.1) so as to make the adjusted GDP time series consistent with the theoretical ideal of a GDP time series constructed using a uniform methodology over the whole period under study.

(ii) The present study recomputed all real GDP benchmarks before including them, and this yielded some important differences from the results of the 2018 release of the Maddison Project. For example, Germany and France were found to be much closer to the United States in terms of per capita GDP than suggested by the Maddison Project 2018 estimates. The recomputed GDP benchmarks are in section 5 of annex A.1.

In the present study, the real GDP time series are normalized using the GDP PPP benchmarks in accordance with the procedure described by equation 19, with some exceptions as per equation 20. In Bolt and others (2018), the specific normalization procedure follows Feenstra, Inklaar and Timmer (2015) and is described by equation 20.

IV. The time series

Table 4 presents the estimates arrived at for per capita GDP in 1990 international dollars for selected economies.[16]

Table 4

Selected countries: estimated per capita GDP, 1820–2020

(1990 international dollars)

Year

1820

1850

1870

1890

1913

1929

1938

1950

1970

1990

2010

2020

Asia

Year

1820

1850

1870

1890

1913

1929

1938

1950

1970

1990

2010

2020

Europe

Great Britain

2 371

3 006

4 133

4 729

5 820

6 183

6 942

8 127

12 329

17 897

22 493

22 184

France

1 565

2 201

2 586

3 461

5 151

6 516

6 258

7 066

12 914

17 914

22 070

22 044

Germanya

1 618

2 145

2 462

3 433

5 104

5 725

6 684

4 869

11 832

16 994

23 514

26 925

Italy

1 308

1 281

1 334

1 659

2 735

3 294

3 355

3 758

10 368

17 438

21 768

20 426

Netherlands (Kingdom of the)

1 777

2 234

2 613

3 141

4 175

5 840

5 386

6 115

12 377

17 824

26 896

28 354

Russian Empire (1820–1922), Union of Soviet Socialist

Republics (USSR) (1922–1991) and former USSR (1991–2020)

778

1 067

1 014

1 655

1 655

2 630

3 281

6 025

7 195

8 758

10 537

Spain

1 232

1 416

1 560

2 148

2 726

3 612

2 440

2 905

7 330

12 884

19 196

18 724

Western offshoots

Australia

2 913

4 092

5 144

5 945

6 000

6 639

8 136

12 646

16 991

24 326

26 273

Canada

1 405

2 065

2 632

3 357

5 323

5 910

5 305

8 149

12 933

19 064

23 782

24 179

United States

1 672

2 427

2 890

4 409

6 225

8 094

7 497

11 519

16 469

23 248

29 022

31 348

Latin America

Argentina

1 288

1 615

1 895

3 173

4 902

5 638

5 256

5 702

7 708

7 148

12 180

10 221

Brazil

769

816

849

944

965

1 354

1 516

1 938

3 410

5 412

7 706

7 334

Chile

579

815

1 129

1 721

2 615

3 024

2 766

3 211

4 699

5 723

11 025

11 607

Colombia

1 261

1 535

1 880

2 196

3 236

4 917

6 393

7 014

Mexico

744

778

772

1 158

1 479

1 897

1 898

2 687

4 969

7 161

9 143

9 566

Peru

1 083

2 000

2 019

2 354

3 814

3 063

5 348

5 818

Venezuela (Bolivarian Republic of)

593

1 155

1 084

1 638

1 072

1 803

2 628

5 186

8 979

8 293

10 165

2 632

Asia

China

679

655

711

716

704

713

711

571

820

1 763

5 460

8 359

Indiab

728

736

661

696

765

798

727

698

852

1 130

2 153

2 990

Indonesia

594

618

561

594

849

1 114

1 087

780

1 147

2 463

4 329

5 987

Japan

678

735

759

1 042

1 395

2 037

2 449

1 984

9 890

19 243

21 615

22 563

Thailand

780

832

1 072

1 150

1 085

1 130

1 118

2 332

4 802

7 716

8 749

Turkey

757

972

1 429

1 429

2 031

1 911

3 593

6 522

10 296

14 813

Africa

Egypt

597

815

1 133

1 144

1 529

3 214

5 907

7 038

South Africa

592

1 225

2 287

3 619

5 578

5 017

6 503

6 188

Worldc

787

1 060

1 836

2 151

2 210

2 512

4 105

5 332

7 337

8 275

Source: Prepared by the author, on the basis of A. Maddison, “World population GDP and per capita GDP 1–2009 ad”, Groningen, University of Groningen, 2010; World Bank, World Development Indicators [online database] https://databank.worldbank.org/source/world-development-indicators.

Note: To the extent possible, country and territory names reflect the designations used during the period of analysis.

a To maintain consistency, Germany includes the Federal Republic of Germany and the German Democratic Republic during the period 1950–1990.

b To maintain consistency, India includes Bangladesh and Pakistan during the period 1950–2020.

c World per capita GDP is estimated by adding the GDP of the rest of the world (besides the 57 economies) in accordance with its ratio to the GDP of the United Kingdom. For the period 1820–2000, this ratio is taken from Maddison’s (2010) estimate; for 2001 onward, it is extrapolated using data from the World Bank World Development Indicators.

As shown in table 5, the GDP time series estimated here exhibits a substantially higher correlation with the official national accounts of the countries in the sample from 1960 to 2019 than the estimates from the Penn World Table. The latter systematically overestimates growth rates relative to official national accounts data, while the estimates here, on average, exhibit similar growth rates, while also having a high correlation with historical national accounts estimated at local prices and similar rates of growth.

Table 5

Correlation between GDP time series and official national accounts, 1960–2019

Time series

Correlation with official national accounts

Mean annual growth rate

(Percentages)

This paper

0.960

3.23

Penn World Table version 10.0

0.920

3.82

Official national accounts

1.000

3.40

Source: Prepared by the author, on the basis of official figures.

Table 6

Selected countries: estimated mean annual GDP growth rates, 1960–2019

(Percentages)

Country

This paper

Penn World Table 10.0

Official national accounts

Argentina

2.39

4.57

2.26

Brazil

3.69

4.88

3.84

China

5.60

5.80

7.63

France

2.25

2.96

2.74

Germany (1970–2019)a

2.27

2.82

2.11

India

4.36

4.85

5.05

Indonesia

4.86

5.88

5.07

Italy

2.41

3.15

2.33

Japan

3.42

3.75

3.49

Mexico

3.92

3.92

3.70

Russian Federation (1990–2019)

1.92

1.92

0.79

United States

2.55

3.03

2.97

Mean difference from official national accounts

0.49

0.80

Source: Prepared by the author, on the basis of official figures.

Note: To the extent possible, country and territory names reflect the designations used during the period of analysis.

a To maintain consistency, Germany includes the Federal Republic of Germany and the German Democratic Republic until 1990.

Table 7

World: correlation with the historical national accounts data set and mean annual growth rates, 1820–1953

Time series

Correlation with the historical national accounts data set

Mean annual growth rate

(Percentages)

This paper

0.994

2.25

Historical national accounts

1.000

2.27

Source: Prepared by the author, on the basis of official figures.

As shown in table 8, the estimates of this paper agree with other estimates in finding that the period from 1950 to 2020 was exceptionally prosperous on a global level compared to the 130 years before. However, they still imply that economic growth from 1950 onward was substantially lower relative to the growth from 1820 to 1950 than is indicated by the data sets of Maddison (2010) and Bolt and Van Zanden (2020). This result is expected, since our method normalizes the growth rates of our reference economy to estimates from historical time series, which tend to be lower than those of official national accounts. For example, the mean annual GDP growth rate of the United States from 1929 to 1957 was 3.61% according to the country’s official national accounts,[17] but Kendrick (1961) estimated it to be 2.95%, which means that the official national accounts imply a per capita GDP growth rate nearly 40% higher than that estimated by the methods used to reconstruct historical national accounts. The normalized GDP estimate using the United Kingdom as the reference economy implies a growth rate of 2.90%, which is closer to the estimates produced by economic historians for the period. Maddison (2010) estimated the same growth rate of 2.90%.

Table 8

World: estimated mean annual GDP growth rates

(Percentages)

Period

This paper

Maddison (2010)

Maddison Project 2020

1820-1870

0.60

0.54

0.62

1870-1950

1.07

1.11

1.01

1950-2008

1.85

2.24

1950-2019

1.80

2.22

Source: Prepared by the author, on the basis of A. Maddison, “World population GDP and per capita GDP 1–2009 ad”, Groningen, University of Groningen, 2010.

V. Changes in the implied Balassa-Samuelson effect

It is a stylized fact that there is a strong correlation between levels of per capita GDP and price levels (the “price level” of a country is the market exchange rate relative to the PPP conversion rate). This correlation is called the Balassa-Samuelson effect.[18] Figure 2 illustrates this effect across countries in 2011 (the year that a purchasing power conversion rate benchmark was computed by the World Bank International Comparison Program).

Figure 2

Balassa-Samuelson effect in the 2011 World Bank International Comparison Programme benchmark

(Index: United States=1)

Source: Prepared by the author.

The estimates given here suggest that the Balassa-Samuelson effect was already present but weaker in the more distant past. On the basis of the same nominal per capita GDP estimates as were used to construct the real GDP time series, price levels have been estimated here for 35 economies in 1913 (before the First World War) and for 40 economies in 1936 (before the Second World War), yielding 75 data points. Regressing the price level on real GDP levels yields a lower correlation between the two variables, although it is still positive and statistically significant. To take an extreme example, the price level of China is estimated to have been about the same as Belgium’s circa 1910 (Ma, de Jong and Xu, 2016), although Belgium’s per capita GDP is estimated to have been nine times as high as China’s from direct benchmark comparisons of real output at the time.

Although empirically a well-documented stylized fact, the Balassa-Samuelson effect depends on strong theoretical assumptions. A perfect Balassa-Samuelson effect would be a situation where there was a perfect correlation between relative price levels and per capita GDP. Given such a perfect correlation, nominal per capita GDP converted at market exchange rates would be a sufficient statistic to predict real per capita GDP. However, a perfect Balassa-Samuelson effect requires the following assumptions:

(i) The world economy is in perfectly competitive general equilibrium, and production technology is neoclassical with constant returns to scale.

(ii) The economy can be decomposed into two components: a tradable sector and a non-tradable sector, such that the costs of transportation are zero for the tradable sector’s output and infinite for the non-tradable sector’s output.

(iii) Currency exchange rates are set purely by the market, and trade barriers and tariffs for product markets across countries do not exist.

(iv) Cross-country productivity per worker (marginal product per worker) in the tradable sector varies in proportion to per capita GDP at PPP multiplied by a fixed ratio. In other words, there exists a > 1 such that for two countries A and B having yA > 0, yB > 0 as their pair of PP per capita GDP levels and and as their productivity levels in the tradable sector, .

Below are some conjectures as to why these assumptions might not have applied as strongly in the more distant past as they do today:

(i) A lower degree of economic integration, since the world economy was much less integrated in the more distant past than it is today. This lower degree of integration naturally implies that prices for tradable goods diverged across countries to a greater extent than in more recent years. This applies particularly to economies that were relatively closed to international trade, such as the former Union of Soviet Socialist Republics or China. Even among economies that were more integrated, however, the higher costs of information and transport meant that price levels for tradables could vary more across countries.

(ii) New World-specific and Old World-specific price levels, since by the early twentieth century, Argentina, Australia, Canada, New Zealand and the United States had much higher price levels than European countries at similar income levels, such as Belgium, Switzerland and the United Kingdom. This discrepancy in price levels is supported by both direct benchmark comparisons of prices for final goods (Williamson, 1995; Ward and Devereux, 2021) and income levels extrapolated from the 1990 benchmark. The current state of the evidence suggests that productivity in manufacturing and for other tradable goods was much higher in the New World. For example, productivity per worker in United States manufacturing is estimated to have been over twice as high as in the United Kingdom (Broadberry and Irwin, 2006) and 1.6 times as high as in Germany around 1910 (De Jong and Veenstra, 2016), a much larger difference than for per capita GDP. An additional factor that may help explain this discrepancy in price levels are capital flows from Europe to the rest of the world: capital flowed from European countries to their colonies, increasing their relative supply of gold[19] and resulting in a higher price level.

(iii) Changes in the relative variability of productivity for tradables and non-tradables, since the assumption that tradables prices are lower relative to non-tradables in richer countries is the central hypothesis used to explain the Balassa-Samuelson effect. This property holds for the present data, but it might not have always held in the past. For example, Ma, De Jong and Xu (2016) estimate PPP for the Chinese yuan relative to the British pound in 1912 at 7.85 yuan per pound in the services sector (which typically produces non-tradable goods) and 6.61 yuan for manufacturing (which typically produces tradable goods).

VI. Concluding remarks

This study has shown that different methodologies for computing GDP growth rates yield substantial differences in the measured growth of real GDP and that incomplete data and technological change amplify these problems. Therefore, the use of GDP figures to measure performance over the economic long run should be taken with a grain of salt and disciplined by the normalization of GDP time series across both time and space.

Taking these problems into consideration, this paper presents a set of estimates for the evolution of GDP over the last 200 years in 57 economies constituting 85% to 90% of the world’s population during that two century period. The study has produced estimates that maintain the consistency of estimated GDP across different economies over time relative to national accounts estimates in local prices. The method consists of constructing a normalized real GDP time series for a reference economy and then normalizing the GDP figures for the other economies through real GDP comparisons with the reference economy. This set of normalized GDP time series largely agrees with historical and official national accounts estimates, is found to be robust in several tests performed in the annex and provides a global portrait of economic growth that is comparable over time and between countries.

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Annex A1

Robustness tests for the GDP time series

In this annex, we perform robustness tests on the estimated real GDP time series for the United Kingdom, the reference economy used to normalize the real GDP time series of all other economies. The first robustness test shows that the resulting GDP estimates do not change substantially when we use the United States as the reference economy.

1. The United States as the reference economy

Table A1.1 shows the estimates arrived at when we replicate the method of the paper to calculate per capita GDP in 1990 international dollars for selected economies using the United States as the reference economy instead of the United Kingdom. The overall estimates closely match the estimates made using the United Kingdom as the reference economy.

Table A1.1

Selected countries: per capita GDP estimated with the United States as the reference economy, 1820–2020

(1990 international dollars)

Year

1820

1850

1870

1890

1913

1929

1938

1950

1970

1990

2010

2020

Europe

Great Britain

2 258

2 863

3 936

4 623

5 857

6 560

7 738

8 371

12 474

17 856

22 345

21 877

France

1 507

2 183

2 564

3 494

5 157

6 522

6 264

7 072

12 918

17 914

21 760

21 660

Germanya

1 558

2 065

2 371

3 363

5 105

5 750

6 705

4 893

11 858

16 994

23 229

26 456

Italy

1 259

1 233

1 285

1 625

2 747

3 306

3 367

3 770

10 379

17 438

21 439

20 070

Netherlands (Kingdom of the)

1 711

2 150

2 516

3 082

4 187

5 916

5 458

6 228

12 375

17 824

26 511

27 860

Russian Empire (1820–1922), Union of Soviet Socialist Republics (USSR) (1922–1991)

and former USSR (1991-2020)

803

1 101

1 046

1 708

1 708

2 714

3 385

6 115

7 195

8 642

10 355

Spain

1 293

1 486

1 637

2 254

2 861

3 785

2 577

3 046

7 463

12 884

19 023

18 398

Western offshoots

Australia

2 774

3 897

5 070

5 981

6 032

6 672

8 167

12 667

16 991

23 913

25 815

Canada

1 352

1 988

2 534

3 285

5 346

5 931

5 324

8 170

12 947

19 064

23 457

23 758

United States

1 593

2 359

2 809

4 383

6 269

8 235

7 869

11 864

16 768

23 248

28 523

30 802

Latin America

Argentina

1 356

1 701

1 995

3 341

5 161

5 936

5 534

5 879

7 931

6 822

12 006

10 043

Brazil

810

859

893

994

1 016

1 425

1 595

2 031

3 542

5 559

7 610

7 206

Chile

595

838

1 161

1 769

2 688

3 109

2 844

3 301

4 831

5 840

10 916

11 405

Colombia

1 296

1 578

1 933

2 258

3 327

4 899

6 342

6 891

Mexico

722

756

750

1 125

1 436

1 842

1 843

2 609

4 688

7 072

8 956

9 399

Peru

1 113

2 057

2 075

2 420

3 921

3 162

5 318

5 716

Venezuela (Bolivarian Republic of)

585

1 139

1 070

1 616

1 058

1 779

2 593

5 116

9 467

8 470

9 591

2 912

Asia

China

658

634

686

693

685

695

694

555

810

1 657

5 542

8 213

Indiab

708

715

642

681

754

792

723

688

858

1 136

2 124

2 938

Indonesia

580

604

548

584

844

1 117

1 093

781

1 165

2 498

4 294

5 882

Asia

Japan

700

757

784

1 075

1 439

2 102

2 411

2 045

9 985

19 243

20 573

21 584

Thailand

662

706

910

976

921

959

949

1 979

4 393

7 485

8 597

Turkey

749

961

1 414

1 414

2 009

1 891

3 555

6 454

10 121

14 555

Africa

Egypt

573

783

1 088

1 098

1 468

3 094

5 781

6 915

South Africa

580

1 199

2 239

3 543

5 460

4 911

6 366

6 080

Source: Prepared by the author, on the basis of official figures.

Note: To the extent possible, country and territory names reflect the designations used during the period of analysis.

a To maintain consistency, Germany includes the Federal Republic of Germany and the German Democratic Republic during the period 1950–1990.

b To maintain consistency, India includes Bangladesh and Pakistan during the period 1950–2020.

The United States GDP series from 1820 to 1834 is taken from Williamson (2025), who in turn drew on estimates from 1800 to 1860 by Weiss (1992) and Berry (1988). The time series used from 1834 to 1909 is Gallman’s, as reported in Rhode (2002). The time series used from 1909 to 1957 is Kendrick’s, as reported in Smits, Woltjer and Ma (2009). The United States official national accounts as reported by the Bureau of Economic Analysis (BEA) and the World Bank are used from 1957 to 2020. Series values are linked at the years 1834, 1909 and 1957; the growth reported in the official national accounts from 1957 onward is normalized relative to the composite of Gallman’s and Kendrick’s series from 1834 to 1957 (since the two series exhibit exactly the same growth rate for the period when they overlap), following the same procedure as was described for the United Kingdom series in section III.1. In this case, the growth rate of United States GDP after 1957 is reduced by 17.9%, while the normalization procedure reduces the growth rate of the United Kingdom GDP series after 1948 by 19.4% compared to the official national accounts.

2. Nominal GDP series deflated by consumer price indices

First, let us consider the estimates of nominal GDP for the United Kingdom and deflate this GDP using a price index constructed with a relatively uniform methodology for the whole period under consideration (1820–2020).

The nominal GDP data used were those of Broadberry and others (2015) for Great Britain from 1820 to 1870, Feinstein’s estimate for the whole United Kingdom from 1870 to 1965 and the official national accounts from 1965 to 2020. These series were then deflated by the Office for National Statistics (ONS) composite price index (CPI) and by the United Kingdom retail price index from Clark (2025). The series for Great Britain was then linked to the series for the United Kingdom in 1870, being adjusted by the ratio of Great Britain’s GDP to the United Kingdom’s total GDP as estimated by Thomas and others (2017).

As shown in figure A1.1 and in table A1.2, this method suggests that the GDP growth rate is slightly underestimated for the two century period. The robustness test suggests that the degree of underestimation appears to be slightly greater from 1948, the earliest date in the GDP series regularly updated by ONS. However, this test does not correct for the methodological heterogeneity in the estimation of the different nominal GDP series. The robustness check performed in the next subsection corrects for this heterogeneity, and the result is consistent with the relative growth rates implied by the estimates of the present study.

Figure A1.1

Normalized GDP series for the reference economy compared to alternative series, 1820–1920

Source: Prepared by the author.

Table A1.2

Great Britain/United Kingdom: annual per capita GDP growth rates, 1820–2019

(Percentages)

Great Britain 1820–1920/United Kingdom 1921–2019

Period

1820–1880

1880–1948

1948–2019

1820–2019

Normalized GDP

1.03

0.83

1.62

1.18

GDP deflated by composite price index (CPI)

1.13

0.74

1.89

1.27

Labour earnings series (CPI)

1.21

0.79

1.75

1.26

Labour earnings series (retail price index)

1.22

1.11

1.72

1.36

Source: Prepared by the author, on the basis of official figures.

3. Labour earnings-based GDP series

The heterogeneity of the methods and the quality of the data used in the estimation of historical national accounts imply that nominal GDP levels for the distant past are not directly comparable with more recent estimates. This study therefore includes an additional robustness check for the estimated GDP series that consists of a bottom-up estimate of GDP growth based on average labour earnings, labour force participation and an estimate for the share of GDP represented by labour earnings. This bottom-up estimate lacks the accuracy and resolution to describe GDP growth rates in the short run. However, because it is constructed using the same methodology for its entire duration, it can serve as an additional robustness check for the plausibility of the estimates of long-run economic growth. Given available estimates of the employed population, the labour share of national income, earnings as a share of labour compensation and real earnings, we constructed a series for Great Britain from 1820 to 2020 that can be used to check for biases in the reported measures of nominal product and is invariant to both methodological changes in national accounting methods and to routine updates to nominal GDP figures.

The earnings-based index of output is estimated by adopting a bottom-up approach to the evolution of domestic product that begins with the average earnings of individual workers taken from Clark (2011). Average total labour compensation is determined from earnings and the ratio of earnings to total labour compensation (which might include, for example, health insurance, social contributions and payroll taxes). Nominal net domestic product (NDP) per worker is then determined by dividing average total labour compensation by the labour share of NDP as computed by Piketty and Zucman (2014) from official national accounts and historical estimates. The capital depreciation rate, also estimated by Piketty and Zucman (2014), is used to estimate the gross to net domestic product ratio. This creates a time series for nominal GDP which, deflated by the CPI provided by ONS, creates a series for real GDP per worker. The aggregate output index series is determined from the GDP per worker and total employed population series.

Formally, let be the estimated output index, Lt the employed population at time t, at the labour share of NDP, wt average nominal labour earnings, gt the share of earnings in labour compensation, dt the capital depreciation rate and Pt the price index. For each t, the output index satisfies

(21)

Figure A1.1 compares four series (the normalized GDP series, the series for GDP deflated by the CPI and the two earnings-based output index series) from 1820 to 2020, and table A1.2 presents percentage growth rates for the modern statistical period (1948–2019) and the historical period. The two earnings-based series are more consistent with the estimates than those produced by simply deflating the time series of nominal GDP estimates, yielding growth rates similar to those of the normalized GDP series for both the pre-statistical and modern statistical periods. Measured in log terms, the standard deviation of the normalized GDP series from 1820 to 2019 relative to the earnings-based CPI output index is 0.077, compared to 0.138 for the estimate produced by the Bank of England over the same period.

4. Comparison with estimates from other studies for the United Kingdom

Our normalized series exhibits slower growth from 1950 onward than the time series produced by the Bank of England; however, it is consistent with output time series estimated by economic historians such as Broadberry, Fukao and Zammit (2015), as shown in table A1.3. Series such as those from the present study and from Broadberry, Fukao and Zammit (2015) aim to produce estimates of output across long periods with a consistent methodology.

Table A1.3

United Kingdom: output time series, 1820–2016

(Index: 1935=100)

Year

This study

Broadberry, Fukao and Zammit (2015)

Thomas and others (2017)

1820

13.0

13.3

1850

23.9

24.2

1870

39.2

39.6

1891

57.1

57.2

57.5

1911

80.1

79.8

80.3

1935

100.0

100.0

100.0

1950

135.3

128.8

136.7

1960

177.2

165.6

191.0

1973

252.9

238.0

297.0

1990

337.7

325.5

425.4

2007

479.8

499.4

650.9

2016

516.3

708.8

Source: Prepared by the author, on the basis of S. Broadberry, K. Fukao and N. Zammit, “How did Japan catch-up on the west? A sectoral analysis of Anglo-Japanese productivity differences, 1885–2000”, The University of Warwick Working Paper Series, No. 231, Coventry, University of Warwick, 2015; R. Thomas and others, “A millennium of macroeconomic data for the UK”, Technical Report, London, Bank of England, 2017.

5. Benchmarks for GDP at PPP used in this study

Table A1.4

Selected countries: GDP benchmarks, 1840–1870

China/Great Britain 1840 benchmark

Country

GDP

(Millions of local currency units)

PPP converter

GDP

(Millions

of pounds)

GDP

(Billions of 1990 dollars)

Great Britain

517.7

1.00

517.7

52.32

China

5 379.6

2.03

2 650

267.86

United States/United Kingdom 1850 benchmark

Country

GDP

(Millions of local currency units)

PPP converter

GDP

(Millions

of pounds)

GDP

(Billions of 1990 dollars)

Discrepancy from the estimate

(Percentages)

United States

2 537

5.63

450.4

57.24

7.9

United Kingdom

610.5

1.00

610.5

71.91

Java/Netherlands (Kingdom of the) 1860 benchmark

Country

Nominal per capita GDP

Price level (1858–1862)

Real per capita GDP index

Population

(Millions)

GDP

(Billions of 1990 dollars)

1839–1841

1878–1880

Netherlands (Kingdom of the)

208

271

100.0

100.0

3.33

7.27

Java

28

40

55.8

25.3

25.78a

14.23

India/United States 1870 benchmark

Country

Real per capita GDP index

Population

(Millions)

GDP

(Billions of 1990 dollars)

United States

100.0

42.24

116.32

India

23.5b

253.00

171.85

Source: Prepared by the author, on the basis of A. Maddison, “World population GDP and per capita GDP 1–2009 ad”, Groningen, University of Groningen, 2010; S. Broadberry, H. Guan and D. Li, “China, Europe, and the great divergence: a study in historical national accounting, 980–1850”, Journal of Economic History, vol. 78, No. 4, Cambridge, Cambridge University Press, 2018; R. Thomas and others, “A millennium of macroeconomic data for the UK”, Technical Report, London, Bank of England, 2017; S. Carter and others, Historical Statistics of the United States: Earliest Times to the Present, Cambridge, Cambridge University Press, 2006; J. van Zanden, “Rich and poor before the industrial revolution: a comparison between Java and the Netherlands at the beginning of the 19th century”, Explorations in Economic History, vol. 40, No. 1, Amsterdam, Elsevier, 2003; A. Heston and R. Summers, “Comparative Indian economic growth: 1870 to 1970”, American Economic Review, vol. 70, No. 2, Nashville, American Economic Association (AEA), 1980.

Note: Great Britain nominal GDP is from Thomas and others (2017), and China nominal GDP and PPP for 1840 is from Broadberry, Guan and Li (2018). United States GDP is from Carter and others (2006) and population from Maddison (2010). Java and the Kingdom of the Netherlands are from Van Zanden (2003). To the extent possible, country and territory names reflect the designations used during the period of analysis.

a The total population includes other islands that compose the current territories of Indonesia.

b Heston and Summers (1980) estimate India’s per capita GDP at 23.5% of the per capita GDP of the United States in 1870.

Table A1.5

Selected countries: GDP benchmarks, 1872

Country

GDP

(Millions of local currency units)

Dollar exchange rate

Price level

GDP at PPP

(Millions of current dollars)

Population

(Millions)

Per capita GDP

(Current dollars)

Australia

93.6

0.183

121

424

1.74

243

Belgium

5 586

4.609

103

1 176

5.14

229

Canada

447

0.892

82

612

3.87

158

Denmark

722

3.336

79

291

1.82

160

France

24 955

4.611

84

6 443

37.64

171

Germany

17 535

3.748

83

6 193

41.19

150

Italy

10 310

5.792

87

2 046

26.88

76

Netherlands (Kingdom of the)

1 121

2.232

85

577

3.68

157

New Zealand

17.1

0.183

121

77

0.32

242

Norway

636

3.319

95

202

1.75

115

Sweden

1 209

3.267

80

463

4.23

109

Switzerland

1 835

4.609

86

463

2.70

172

United Kingdom

1 312

0.183

96

7 468

31.87

234

United States

8 163

1.000

100

8 163

42.14

194

Source: Prepared by the author, on the basis of M. Ward and J. Devereux, “New income comparisons for the late nineteenth and early twentieth century”, The Review of Income and Wealth, vol. 67, No. 1, Hoboken, Wiley, 2021; M. Curtis and P. Lindert, “Nominal GDP historical series”, Davis, Global Price and Income History Group (GPIH), 2018 [online] http://gpih.ucdavis.edu/GDP.htm; P. Rhode, “Gallman’s annual output series for the United States, 1834–1909”, NBER Working Paper, No. 8860, Cambridge, National Bureau of Economic Research (NBER), 2002; M. Hiestand, M. Müller and U. Woitek, “A.1 statistische Grundlagen und Methoden”, Wirtschaftsgeschichte der Schweiz im 20. Jahrhundert, P. Halbeisen, M. Müller and B. Veyrassat (eds.), Basel, Schwabe Verlag, 2012; L. Schön and O. Krantz, “New Swedish historical national accounts since the 16th century in constant and current prices”, Lund Papers in Economic History, No. 140, Lund, Lund University, 2015.

Note: Price levels as estimated in Ward and Devereux (2021), using the United States as the benchmark economy. The estimate of the price level for France is taken from the working paper version of Ward and Devereux (2021), as the published version of the paper has a price level that is implausibly high and much higher than the working paper version. Nominal GDP is taken from Curtis and Lindert (2018), except for the United States, where it is from Rhode (2002) (foreign factor income is assumed to be irrelevant, with estimates putting it at less than 1% of GNP, so GDP and GNP are approximately equal); Switzerland, where it is from Curtis and Lindert (2018), adjusted for the average discrepancy from Hiestand, Müller and Woitek (2012) over the period 1890–1913; and Sweden, where it is from Schön and Krantz (2015). Germany’s estimate is net national product (NNP) at market prices, while Denmark’s is GDP at factor cost. NNP and GDP at factor cost were converted into GDP at market prices using, respectively, the ratio of NNP to GDP for the United States and the estimate by Thomas and others (2017) for the ratio between GDP at market prices and factor cost in the United Kingdom. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.6

Selected countries: GDP benchmarks, 1910

Country

GDP

(Millions of local currency units)

Dollar exchange rate

Price level

GDP at PPP

(Millions of current dollars)

Population

(Millions)

Per capita GDP

(Current dollars)

Australia

312.7

0.206

98

1 549

4.43

350

Belgium

7 735

5.170

61

2 453

7.44

330

Canada

2 023

1.000

99

2 043

7.19

284

Denmark

1 922

3.739

73

760

2.74

277

France

40 218

5.170

75

10 372

39.68

261

Germany

47 300

4.200

71

17 764

64.57

275

Italy

19 676

5.324

75

5 126

34.38

149

Netherlands (Kingdom of the)

2 000

2.484

60

1 342

5.95

226

New Zealand

66.5

0.206

98

329

1.05

315

Norway

1 469

3.738

70

562

2.38

236

Sweden

3 298

3.740

73

1 208

5.50

220

Switzerland

4 817

5.170

72

1 294

3.74

346

United Kingdom

2 224

0.206

80

13 495

44.90

301

United States

31 304

1.000

100

31 304

92.77

337

Source: Prepared by the author, on the basis of M. Ward and J. Devereux, “New income comparisons for the late nineteenth and early twentieth century”, The Review of Income and Wealth, vol. 67, No. 1, Hoboken, Wiley, 2021; M. Curtis and P. Lindert, “Nominal GDP historical series”, Davis, Global Price and Income History Group (GPIH), 2018 [online] http://gpih.ucdavis.edu/GDP.htm; M. Hiestand, M. Müller and U. Woitek, “A.1 statistische Grundlagen und Methoden”, Wirtschaftsgeschichte der Schweiz im 20. Jahrhundert, P. Halbeisen, M. Müller and B. Veyrassat (eds.), Basel, Schwabe Verlag, 2012; T. Piketty and G. Zucman, “Capital is back: wealth-income ratios in rich countries 1700–2010”, The Quarterly Journal of Economics, vol. 129, No. 3, Oxford, Oxford University Press, 2014; L. Schön and O. Krantz, “New Swedish historical national accounts since the 16th century in constant and current prices”, Lund Papers in Economic History, No. 140, Lund, Lund University, 2015; J. Smits, E. Horlings and J. van Zanden, “Dutch GNP and its components, 1800–1913”, Groningen Growth and Development Centre Monograph Series, No. 5, Groningen, University of Groningen, 2000; C. Romer, “The prewar business cycle reconsidered: new estimates of gross national product, 1869–1908”, Journal of Political Economy, vol. 97, No. 1, Chicago, The University of Chicago Press, 1989.

Note: Price levels as estimated in Ward and Devereux (2021). Nominal GDP is taken from Curtis and Lindert (2018), except for Switzerland, where it is from Hiestand, Müller and Woitek (2012); France, from Piketty and Zucman (2014) (income approach); Sweden, from Schön and Krantz (2015); and the Kingdom of the Netherlands, where the net national product (NNP) estimate from Smits, Horlings and van Zanden (2000) is used. Denmark’s estimate is GDP at factor cost, and NNP is used instead of GDP for Germany. This benchmark of comparative real product levels uses Balke and Gordon’s GNP estimate and subtracts foreign factor income to arrive at a GDP estimate. Estimates of United States product vary from Balke and Gordon’s (1989) GNP estimate of US$ 31.240 billion and the Historical Statistics of the United States (Carter and others, 2006) GDP estimate of US$ 31.609 billion to substantially higher values for GNP of US$ 33.36 billion and US$ 33.187 billion in Smits, Woltjer and Ma (2009) and Romer (1989), respectively, with a divergence of only 6% from Balke and Gordon (1989). To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.7

Selected countries: GDP benchmarks, 1927

Country

GDP

(Millions of local currency units)

GDP

(Millions of 1990 dollars)

PPP

Benchmark

This study

Argentina (1938)

11 000

9.27

71 274

71 274

Australia (1927)

795.6

1.27

37 628

39 307

Belgium (1927)

70 110

117.6

35 809

47 479

Brazil (1937)

41 288

42.29

58 641

58 641

Canada (1927)

5 561

5.67

58 910

56 315

Denmark (1927)

5 318

16.86

21 203

20 335

France (1927)

321 281

89.1

224 112

242 041

Germany (1927)

80 466

18.08

302 021

354 610

Ireland (1927)

172.2

0.90

11 493

11 493

Italy (1927)

154 089

73.21

132.68

122.89

Norway (1926)

4 573

22.08

12 440

13 384

Netherlands (Kingdom of the) (1927)

6 032

8.99

40 301

42 865

Portugal (1927)

13 886

74.97

11 125

11 125

Spain (1927)

33 485

25.58

78 626

78 626

Sweden (1927)

9 497

20.45

27 894

32 043

United Kingdom (1927)

4 500

1.00

270 292

United Kingdom (factor cost)

4 021

United Kingdom (net national product)

3 983

United States (1927)

96 802

7.57

768 081

923 504

Source: Prepared by the author, on the basis of M. Curtis and P. Lindert, “Nominal GDP historical series”, Davis, Global Price and Income History Group (GPIH), 2018 [online] http://gpih.ucdavis.edu/GDP.htm; R. Thomas and others, “A millennium of macroeconomic data for the UK”, Technical Report, London, Bank of England, 2017; T. Piketty and G. Zucman, “Capital is back: wealth-income ratios in rich countries 1700–2010”, The Quarterly Journal of Economics, vol. 129, No. 3, Oxford, Oxford University Press, 2014; J. Williamson, “The evolution of global labor markets since 1830: background evidence and hypotheses”, Explorations in Economic History, vol. 32, No. 2, Amsterdam, Elsevier, 1995.

Note: GDP in local currency units is taken from Curtis and Lindert (2018), except for the United States, where it is taken from the Historical Statistics of the United States series. Thomas and others (2017) is used for the nominal GDP of the United Kingdom and Piketty and Zucman (2014) (income approach) for that of France and the United States. Denmark’s estimate is GDP at factor cost, and NNP is used instead of GDP for Germany. The benchmark is constructed using PPP converters from Williamson (1995), these being Fisher PPPs for private consumption that take the United Kingdom as the star country, so that they should be regarded as less accurate in representing economy-wide relative price levels than the real product benchmarks for 1872 and 1910. These benchmarks were not used for countries where those more accurate earlier benchmarks were available. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.8

Selected countries: GDP benchmarks, 1935

China/United States benchmark

Country

Per capita GDP

Benchmark

(1935 dollars)

This study

(1990 dollars)

Difference

(Percentages)

United States

574.7

6 794

China

63.6

693

4.9

Japan/United States benchmark

Country

Per capita GDP in PPP

(1935 dollars)

Population

(Millions)

GDP index

United States

574.7

127.86

100.00

Japan

180.8

69.24

17.04

Japan/United Kingdom benchmark

Country

GDP

(Millions of local currency units)

PPP

GDP index

United Kingdom

4 619

1.00

100.00

Japan

18 104

8.12

48.27

Japan/Korea/Taiwan benchmark

Country

GDP

(Millions of local currency units)

PPP

GDP index

Japan

18 104

1.00

100.00

Korea

2 233

0.87

14.17

Taiwan

796

0.86

5.11

Source: Prepared by the author, on the basis of K. Fukao, D. Ma and T. Yuan, “Real GDP in pre-war east Asia: a 1934–36 benchmark purchasing power parity comparison with the U.S.”, The Review of Income and Wealth, vol. 53, No. 3, Hoboken, Wiley, 2007; “International comparison in historical perspective: reconstructing the 1934–1936 benchmark purchasing power parity for Japan, Korea, and Taiwan”, Explorations in Economic History, vol. 43, No. 2, Amsterdam, Elsevier, 2006; S. Broadberry, K. Fukao and N. Zammit, “How did Japan catch-up on the west? A sectoral analysis of Anglo-Japanese productivity differences, 1885–2000”, The University of Warwick Working Paper Series, No. 231, Coventry, University of Warwick, 2015.

Note: For the benchmark relative to the United States, nominal GDP and price level estimates are from Fukao, Ma and Yuan (2007). For the benchmark relative to the United Kingdom, PPP is from Broadberry, Fukao and Zammit (2015). Korea and Taiwan price level estimates relative to Japan are from Fukao, Ma and Yuan (2006). To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.9

Industry-of-origin benchmarks for GDP in PPP, 1935–1937

Germany/United Kingdom benchmark

Country

Employed population

(Millions)

Index of GDP per worker

GDP

(Billions of 1990 dollars)

Benchmark

Estimate

Difference

(Percentages)

Germany (1935)

29 382

75.7

351.40

367.85

4.7

United Kingdom (1935)

19 079

100.0

301.41

United States/United Kingdom benchmark

Country

Employed population

(Millions)

Index of GDP per worker

GDP

(Billions of 1990 dollars)

Benchmark

Estimate

Difference

(Percentages)

United States (1937)

45 683

132.6

993.69

1 032.76

3.9

United Kingdom (1937)

19 942

100.0

327.13

327.13

Source: Prepared by the author, on the basis of S. Broadberry, “Anglo-German productivity differences 1870–1990: a sectoral analysis”, European Review of Economic History, vol. 1, No. 2, Oxford, Oxford University Press, 1997; “Forging ahead, falling behind and catching-up: a sectoral analysis of Anglo-American productivity differences, 1870–1990”, Research in Economic History, vol. 17, Bradford, Emerald Insight, 1997; J. Williamson, “The evolution of global labor markets since 1830: background evidence and hypotheses”, Explorations in Economic History, vol. 32, No. 2, Amsterdam, Elsevier, 1995.

Note: Industry-of-origin benchmark estimated from Broadberry (1997a) for the relative productivity of Germany and the United Kingdom in 1935 and Broadberry (1997b) for the relative productivity of Germany and the United Kingdom in 1937. Note that they are much closer to the estimates produced in this study than the benchmark constructed using Williamson’s (1995) PPPs for private consumption in 1927. The 1937 benchmark was not used for the data set because the deviation of the interpolated United States time series from the benchmark was too small. The deviation between the benchmark and the interpolated United Kingdom time series in the United States benchmark economy data set was substantially greater, so the benchmark was used to correct the estimate of the United Kingdom’s inter-war product level in that data set. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.10

United States and United Kingdom: GDP benchmark, 1950

Country

Employed population

(Millions)

Index of GDP per worker

GDP

(Billions of 1990 dollars)

United Kingdom (1950)

23 232

100.0

407.90

United States (1950)

61 477

162.5

1 753.98

Source: Prepared by the author, on the basis of A. Maddison, The World Economy: A Millennial Perspective, Paris, Organisation for Economic Co-operation and Development (OECD), 2001.

Table A1.11

Cuba and United States: GDP benchmark, 1953

Country

Index of

per capita GDP

Population

(Millions)

Aggregate GDP ratio

GDP

(Billions of 1990 dollars)

Cuba (1953)

27

6.13

0.0103

20.82

United States (1953)

100

160.18

1.000

2 014.91

Source: Prepared by the author, on the basis of M. Ward and J. Devereux, “The road not taken: pre-revolutionary Cuban living standards in comparative perspective”, The Journal of Economic History, vol. 72, No. 1, Cambridge, Cambridge University Press, 2012; A. Maddison, The World Economy: A Millennial Perspective, Paris, Organisation for Economic Co-operation and Development (OECD), 2001.

Note: Cuba’s relative per capita GDP is taken from Ward and Devereux (2012).

Table A1.12

Selected countries: World Bank International Comparison Programme GDP benchmarks, 1975

Country

GDP

(Millions of local currency units)

Gini-Éltetö-Köves-Szulc (GEKS) PPP

GDP

(Billions of 1990 dollars)

Iran

3 485 052

39.70

243.81

Mexico

1 007 036

7.53

370.40

Syria

20 617

1.243

47.30

Thailand

303 312

7.86

109.97

United Kingdom

115 176

0.406

738.21

Source: Prepared by the author, on the basis of A. Maddison, The World Economy: A Millennial Perspective, Paris, Organisation for Economic Co-operation and Development (OECD), 2001.

Note: Maddison (2001) adjusted the official national accounts estimates for Mexico downward by 12.2%, arguing that they overestimated the size of the informal sector, so we do the same in this benchmark. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.13

Selected countries: World Bank International Comparison Programme GDP benchmarks, 1980

Country

GDP

(Millions of local currency units)

Geary-Khamis PPP

GDP

(Billions of 1990 dollars)

Argentina

3 840 000

26.04

237.51

Brazil

12 805 000

29.55

697.82

Chile*

1 132 432

26.67

59.34

Colombia

1 579 084

21.99

115.66

Hungary

720 956

14.78

78.43

India*

1 451 011

3.88

586.54

Indonesia

48 913 811

294

267.78

Ireland*

10 596

0.461

34.74

Mexico*

4 142 579

13.18

475.08

Norway*

318 279

6.16

78.56

Peru

5 920 159

137.0

74.19

Poland

2 482 452

17.42

232.47

Romania

126.82

United Kingdom*

259 962

0.487

806.79

United States

2 686 154

1.000

4 439.7

United States*

2 857 307

1.000

4 439.7

Uruguay

89 654

6.491

22.25

Venezuela (Bolivarian Republic of)

297 800

3.14

152.75

Yugoslavia

1 800 000

19.35

149.55

Source: Prepared by the author, on the basis of A. Maddison, Monitoring the World Economy, 1820–1992, Paris, Organisation for Economic Co-operation and Development (OECD), 1995.

Note: Data from Maddison (1995) with adjusted GDP estimates are used for the informal sector in Argentina, Brazil and Mexico. The estimate for Mexico relative to the United States is not used, as the 1975 benchmark is preferred (the two benchmarks diverge by only 4%). GDP levels for countries using old figures (not marked with *) are estimated relative first to the old national accounts figures for the United States and then to the figures for the United Kingdom using the United States to United Kingdom GDP ratio implied by the latest national accounts figures. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.14

Selected countries: World Bank International Comparison Programme GDP benchmarks, 1985

Country

GDP

(Millions of local currency units)

Geary-Khamis PPP

GDP

(Billions of 1990 dollars)

Egypt

33 130

0.2828

156.22

Republic of Korea*

87 239 600

459.5

230.77

Pakistan*

472 157

3.761

152.60

Sri Lanka

157 763

5.288

39.78

United Kingdom*

414 329

0.568

886.66

United States

3 962 217

1.000

5 073.2

United States*

4 346 734

1.000

5 073.2

Source: Prepared by the author, on the basis of A. Maddison, Chinese Economic Performance in the Long Run, 960–2030 AD, Second Edition, Revised and Updated, Paris, Organisation for Economic Co-operation and Development (OECD), 2007; Chinese Economic Performance in the Long Run, Paris, Organisation for Economic Co-operation and Development (OECD), 1998.

Note: GDP levels for countries using old figures (not marked with *) are estimated relative first to the old national accounts figures for the United States and then to the figures for the United Kingdom using the United States to United Kingdom GDP ratio implied by the latest national accounts figures. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.15

China and United States: GDP benchmark, 1986

Country

GDP

(Millions of local currency units)

Geary-Khamis PPP

GDP

(Billions of 1990 dollars)

China

1 156 140

0.7926

1 651.8

United States

4 590 155

1.000

5 197.9

Source: Prepared by the author, on the basis of A. Maddison, Chinese Economic Performance in the Long Run, 960–2030 AD, Second Edition, Revised and Updated, Paris, Organisation for Economic Co-operation and Development (OECD), 2007; Chinese Economic Performance in the Long Run, Paris, Organisation for Economic Co-operation and Development (OECD), 1998.

Note: GDP levels for countries using old figures (not marked with *) are estimated relative first to the old national accounts figures for the United States and then to the figures for the United Kingdom using the United States to United Kingdom GDP ratio implied by the latest national accounts figures. Nominal GDP for China is taken from Maddison’s (2007) benchmark for China updated by the estimates from this study for United States GDP in 1929 international dollars and United States GDP in current local currency units from the World Bank World Development Indicators. The Geary-Khamis PPP converter is taken from Maddison (1998).

Table A1.16

Selected countries: World Bank International Comparison Programme GDP benchmarks, 1990

Country

GDP

(Millions of local currency units)

Geary-Khamis PPP

GDP

(Billions of 1990 dollars)

Australia

403 934

1.352

289.95

Austria

1 888 097

13.899

131.84

Belgium

6 898 418

38.362

174.52

Canada

692 997

1.274

527.91

Czechoslovakia

1 027 897

6.120

163.00

Denmark

855 557

8.700

95.44

Finland

541 121

6.219

84.44

France

6 910 809

6.450

1 039.8

Germany

2 851 678

2.052

1 348.7

Greece

15 517 392

129.55

116.25

Ireland

29 840

0.688

42.09

Italy

1 410 629 836

1 384.11

989.10

Japan

453 608 500

185.27

2 376.1

Netherlands (Kingdom of the)

572 258

2.084

266.50

New Zealand

76 167

1.5574

47.46

Portugal

11 222 139

91.737

118.72

Spain

54 544 966

105.71

500.77

Sweden

1 527 964

8.979

165.15

Switzerland

357 608

2.160

161.08

Union of Soviet Socialist Republics

644 200

0.520

2 076.5

United Kingdom

615 673

0.587

1 017.9

United States

5 979 589

1.000

5 803.2

Source: Prepared by the author, on the basis of A. Maddison, The World Economy: A Millennial Perspective, Paris, Organisation for Economic Co-operation and Development (OECD), 2001; Monitoring the World Economy, 1820–1992, Paris, Organisation for Economic Co-operation and Development (OECD), 1995.

Note: Updating the nominal GDP figures with the latest data often implies per capita GDP relative to the United States that is closer to Maddison (1995) than to Maddison (2001), whose figures are often higher. We conjecture that between 1995 and 2001, the United States national accounts were updated and incorporated methodological changes more quickly than other countries’ and hence there is a slight upward bias in Maddison’s (2001) 1990 benchmark for the relative GDP levels of the United States. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.17

Selected countries: World Bank International Comparison Programme GDP benchmarks, 1996

Country

GDP

(Millions of local currency units)

Geary-Khamis PPP

GDP

(Billions of 1990 dollars)

Lebanon*

21 512 808

1 026

16.77

Syria

663 457

15.68

36.35

United Kingdom*

903 029

0.652

1 108.0

United States

7 544 268

1.000

6 638.23

United States*

8 100 201

1.000

6 638.23

Source: Prepared by the author, on the basis of official figures.

Note: GDP levels for countries using old figures (not marked with *) are estimated relative first to the old national accounts figures for the United States and then to the figures for the United Kingdom using the United States to United Kingdom GDP ratio implied by the newest national accounts figures. To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.18

Selected countries: World Bank International Comparison Programme GDP benchmarks, 2011

Country

GDP

(Millions of local currency units)

Geary-Khamis PPP

GDP

(Billions of 1990 dollars)

Japan

491 408.50

107.454

2 793.3

United Kingdom

1 635 060

0.698

1 430.5

United States

15 517 930

1.000

8.935.1

Venezuela (Bolivarian Republic of)

1 357 500

2.713

305.59

Source: Prepared by the author, on the basis of official figures.

Note: GDP levels for countries using old figures (not marked with *) are estimated relative first to the old national accounts figures for the United States and then to the figures for the United Kingdom using the United States to United Kingdom GDP ratio implied by the newest national accounts figures.To the extent possible, country and territory names reflect the designations used during the period of analysis.

Table A1.19

Selected countries: World Bank International Comparison Programme GDP benchmarks, 2017

Country

GDP

(Millions of local currency units)

Gini-Éltetö-Köves-Szulc (GEKS) PPP

GDP

(Billions of 1990 dollars)

Argentina

10 936 200

10.254

539.62

Australia

1 808 607

1.466

641.55

Austria

370 296

0.770

249.93

Bangladesh

6 528 900

37.94

369.49

Belgium

446 365

0.773

300.30

Brazil

6 536 000

2.185

1 569.1

Bulgaria

102 308

0.674

78.91

Canada

2 141 981

1.205

924.47

Chile

180 211 290

411.3

227.84

China

82 075 400

4.184

10 200.4

Colombia

920 194 000

1.315

363.91

Denmark

2 175 106

6.852

165.06

Egypt

4 127 100

3.267

656.93

Finland

225 785

0.863

135.96

France

2 295 063

0.766

1 557.0

Germany

3 244 990

0.741

2 278.4

Greece

180 218

0.576

162.67

Hungary

38 835 221

134.4

150.29

India

166 225 600

20.65

4 186.0

Indonesia

13 587 212.6

4.696

1 504.6

Iran (Islamic Republic of)

16 954 812

13.06

674.97

Iraq

206 530 100

560.76

191.50

Ireland

297 131

0.791

195.29

Italy

1 736 602

0.687

1 315.3

Mexico

21 911 894

8.871

1 284.3

Netherlands (Kingdom of the)

738 146

0.778

493.02

New Zealand

282 741

1.453

101.15

Norway

3 295 382

9.922

172.70

Pakistan

4 942 900

44.05

515.03

Peru

688 000

1.749

204.50

Poland

1 989 351

1.737

595.38

Republic of Korea

1 835 698 237

871.7

1 095.0

Romania

857 896

8.94

278.026

South Africa

4 715 200

6.427

381.50

Spain

1 161 878

0.630

958.78

Sri Lanka

13 317 300

49.39

140.18

Sweden

4 621 046

8.719

275.56

Switzerland

669 542

1.180

295.05

Taiwan Province of China

17 501 200

15.73

578.52

Thailand

15 452 000

12.845

625.52

Turkey

3 110 650

1.373

1 178.0

United Kingdom

2 071 667

0.682

1 579.1

United States

19 519 423

1.000

10 149.3

Uruguay

1 707 100

23.29

38.11

Former Union of Soviet Socialist Republics

2 960.68

Armenia

6 241 800

158.82

18.56

Azerbaijan

65 600

0.491

72.38

Belarus

105 500

0.602

90.27

Estonia

22 800

0.528

23.19

Georgia

45 200

0.810

26.36

Kazakhstan

50 044 800

118.12

233.21

Kyrgyzstan

700 800

18.313

16.28

Latvia

26 800

0.479

28.75

Lithuania

41 300

0.435

49.76

Moldova

220 800

5.827

16.69

Russian Federation

87 180 000

23.64

1 991.2

Tajikistan

61 200

2.359

14.25

Turkmenistan

132.7

1.623

42.53

Ukraine

2 983 900

5.905

262.27

Uzbekistan

302 536 800

1 432.9

109.77

Former Czechoslovakia

299.44

Czechia

5 047 267

12.38

212.03

Slovakia

84 517

0.503

87.41

Former Yugoslavia

204.62

Bosnia and Herzegovina

31 376

0.676

24.12

Croatia

366 426

3.327

57.26

Montenegro

4 299

0.351

6.38

North Macedonia

618 106

19.04

16.88

Serbia

4 754 369

40.80

60.60

Slovenia

42 987

0.568

39.38

Source: Prepared by the author, on the basis of official figures.

1. [1] The author is grateful to Carlos Ponce, Marcelo de Paiva Abreu and the participants in the sixth Brazilian Economic History Workshop in São Paulo for helpful feedback and discussion. This paper has had no specific source of financial support.

2. [2] See Semieniuk (2024) for an example of how different GDP vintages induce different measured growth rates and thus change the measured degree of decoupling between CO_2 emissions and economic growth.

3. [3] For example, the case of China was studied by Feenstra and others (2013).

4. [4] See National Bureau of Statistics of China [online] https://www.stats.gov.cn/english/ [date of reference: December 2018].

5. [5] Formally represented by a per-period utility function u(.) such that u(x) = u(x') for any pair of baskets of goods x = (xf , 1.01 × xm,t) and x' = (xf , xm,t+1), with xm,t+1 = xm,t.

6. [6] The International Comparison Programme (ICP) of the World Bank employs what is known as the Gini-Éltetö-Köves-Szulc (GEKS) method to compute real GDP in PPP terms. This method uses Fisher indices across countries to construct PPP conversion factors. It can be briefly described as follows: (i) for a set of countries in the study, construct Fisher PPPs for each pair of economies, after which (ii) the PPP conversion factors are determined by taking the geometric average of all the indirect PPPs implied by the Fisher PPPs. For example, if there are three countries, A, B, C, in an ICP study and the Fisher PPP conversion factor between countries A and B is taken to be 2 units of A’s currency for 1 unit of B’s currency, the PPP between A’s currency and C’s currency is 2.5, and between B’s and C’s it is 1.4. Then the GEKS PPP between A and B is the geometric average of 2 and 2.5/1.4 (the indirect PPP conversion factor of the currency of country to that of through the Fisher PPPs of both to country C).

7. [7] Incompleteness in coverage of economic activity can arise, for example, from incomplete reporting of economic activity to the State’s statistical agency due to tax evasion or imperfect State capacity.

8. [8] Series 3I is of course constructed by the same methodology as series 3.

9. [9] However, the author’s estimates have a higher correlation with official national accounts than the estimates from the Penn World Table, as shown in table 5.

10. [10] This is a composite of many estimates, including Broadberry and others (2015) for Great Britain up to 1870, Feinstein (1972) for the United Kingdom from 1855 to 1948 and Andersson and Lennard (2016) for Ireland in the nineteenth century.

11. [11] Like Maddison (2010), we note that more recent revisions to the United States national accounts give a slightly higher figure for nominal United States GDP in 1990.

12. [12] These series may be official national accounts or historical national accounts estimates produced by economic historians.

13. [13] From Maddison’s estimate, which combines official national accounts with estimates by economic historians for earlier years.

14. [14] From the official national accounts data reported by the World Bank.

15. [15] This method avoids results that are completely inconsistent with national accounts data. For example, Brazil’s GDP growth rate from 1980 to 2017 according to Penn World Table version 9.1 (the variable rgdpe, or real expenditure-side GDP in chained PPPs, which aims to measure GDP in terms of living standards) was 4.1%, but the official national accounts show a dramatically slower growth rate of only 2.2%; the series for Brazil estimated in this study also has a GDP growth rate of 2.2%.

16. [16] To access the full GDP and population time series for all 57 economies, see R. Guthmann, “GDP, population, and employment estimates 1820–2020”, 2024 [online] https://docs.google.com/spreadsheets/d/1UQtQ2HdLeLz7bsbsB8qES2jHwJZqzeTk/edit?gid=2070000566#gid=2070000566.

17. [17] According to the version available from the Bureau of Economic Analysis (BEA) [online] https://www.bea.gov/ [date of reference: September 2023].

18. [18] It was independently described by Balassa (1964) and Samuelson (1964).

19. [19] The world operated under a gold currency standard at the time.

Economic growth and productive sectors: recent empirical data on middle-income countries

Aline de Souza Vicente, Luciano Ferreira Gabriel and Luciano Dias de Carvalho

Received: 15/05/2022

Accepted: 26/09/2024

Abstract

This article examines how structural change affected economic growth in middle-income developing countries between 1960 and 2019, especially in Brazil (between 1948 and 2020), using the vector autoregression (VAR) model and panel data. The VAR model suggests that structural change in Brazil resulted in the services sector having a greater impact on economic growth owing to the transfer of resources from high-productivity sectors to low-productivity sectors, which reduced the rate of economic growth. Panel data suggest that economic growth in the sample of countries continues to be heavily influenced by the industrial sector and that the weaker momentum of the industrial sector in Brazil is one of the reasons why the country is falling behind.

Keywords

Economic growth, economic structure, structural adjustment, productivity, industrial sector, middle-income countries, Brazil.

JEL classification

L6, L8, O1, O11, O14

Authors

Aline de Souza Vicente holds a master’s degree in economics (stricto sensu postgraduate programme) from the Federal University of Viçosa in Brazil. Email: alinnetr@hotmail.com.

Luciano Ferreira Gabriel is a professor and researcher in the Department of Economics and Finance at the Federal University of Juiz de Fora (UFJF), and a professor in the economics postgraduate programme at the Federal University of Viçosa and the applied economics postgraduate programme at UFJF. He is also a productivity fellow at the National Council for Scientific and Technological Development in Brazil. Email: lucianofg@gmail.com.

Luciano Dias de Carvalho is a professor and researcher in the Department of Economics and a professor in the economics postgraduate programme at the Federal University of Viçosa in Brazil. Email: lucianodc@gmail.com.

I. Introduction

Classical development economics literature and structuralist literature (Economic Commission for Latin America and the Caribbean (ECLAC))[1] establish a close relationship between the sectoral composition of an economy and its economic growth rate. There are strong arguments in the economic literature for sector specificity in economic growth behaviour, which implies that a unit of value added is not necessarily equivalent across economic sectors, in terms of its growth-inducing or growth-enhancing effects (Tregenna, 2009).

Porcile, Spinola and Yajima (2023) explain that a country’s sectoral composition may be captured by its pattern of international specialization, as expressed in the income elasticity of its exports and imports. Hence, institutional dimensions, power relations between classes and the government and, primarily, productive structures play a fundamental role in shaping economic outcomes.

Over the past few decades, the relationship between structural changes and economic growth has become an important topic for the economy and has piqued the interest of various authors, such as Kaldor (1966), Kuznets (1966), Baumol (1967), Pasinetti (1993) and, more recently, Ngai and Pissarides (2007), Rodrik (2013a and 2013b), Nassif, Feijó and Araújo (2015), Vu (2017), Lavopa and Szirmai (2018), and Gabriel and Ribeiro (2019).

Rodrik (2013a) maintains that one key determinant of economic growth is structural change —the emergence and expansion of new sectors and the transfer of labour from traditional activities to modern ones.[2] More specifically, Su and Yao (2016) examined the specific role of the manufacturing industry as the leading driver of economic growth for middle-income countries. They found that industry is imbued with three main characteristics that are key to economic growth: industry drives services, not the other way around; there is a strong link between a decline or an increase in manufacturing sector growth and a decline or an increase in service sector growth in both the short run and the long run; and development of the manufacturing industry not only promotes incentives for saving, but also accelerates the pace of technological accumulation and, compared with other sectors, enhances the utilization of human capital and national institutions.

Felipe and others (2012) make it clear that the classical literature on economic development shows that what differentiates countries, and what determines their fortunes, is their production structure and the specific characteristics of the products that they export, primarily tradable goods such as manufactured goods. In addition, Gala (2017) explains that a country’s economic development and per capita income levels depend to a large extent on its productive capabilities and its capacity to produce more complex and sophisticated goods.

This study investigates empirically how structural change between 1960 and 2019 affected economic growth in 31 middle-income developing countries, meaning developing countries with per capita income of between US$ 3,996 and US$ 12,375 (at 2019 prices). Special attention is paid to Brazil, which has been falling behind in the past few decades,[3] and for which the period examined runs from 1948 to 2020 owing to the availability of data. We use the econometric tools of panel data for the heterogenous sample of countries and vector autoregression (VAR) methodology for Brazil. Using these two methodologies, we are able to contrast the results of Brazil with those of a representative sample of developing countries in the same income bracket.

Given the large number of countries that are experiencing premature deindustrialization, some authors have recently asserted that industrial growth has become a more difficult route to development (Eichengreen and Gupta, 2013; Rodrik, 2016). Despite the importance of manufacturing, there is a growing literature presenting the increasing influence of the services sector as the new driver of economic growth in developing economies (Maroto-Sánchez and Cuadrado-Roura, 2009; Szirmai and Verspagen, 2015). The services sector accounts, on average, for over 60% of gross domestic product (GDP) in advanced economies and some activities, such as software development, financial services and tourism, play an important role as leading economic activities that boost economic growth, for example in India (Dasgupta and Singh, 2005).

In view of the recent debate on the importance of the manufacturing sector and its implications for the sustained growth of developing countries, as well as the increasing influence of the services sector on growth, this article contributes to the literature by presenting a renewed understanding of the role of the manufacturing sector in relation to other sectors, both in Brazil and in a heterogenous sample of middle-income countries. The dynamic relationships presented in the article complement the empirical literature on several issues concerning the importance of the manufacturing sector for developing economies, and for Brazil in particular.

II. Economic sectors and growth: brief review of the literature and empirical data

From early contributions in the literature on economic development (such as Chenery, 1960; and Kuznets, 1966) to more recent studies on sources of economic growth and recovery (such as McMillan, Rodrik and Verduzco-Gallo, 2014), a long tradition in economic thought links economic development with structural change. This process refers to the progressive shift of the economy from the production of simple goods, typically labour— or natural resource-intensive, to the manufacture of complex and more sophisticated goods, typically capital— and technology-intensive goods.

On the basis of a few classical authors in this debate, such as Kaldor (1966) and Kuznets (1966), it can be said that economic growth and structural change are closely intertwined. This can be seen when there is a transfer of resources and labour from a less productive sector, such as agriculture, to a more productive sector, such as the manufacturing sector, across the different stages of development. Kaldor (1967) in particular empirically demonstrated the manufacturing sector’s overriding influence on the rate of economic growth and presented various empirical arguments on the special role of manufacturing for economic growth, including the sector’s high productivity (Kaldor, 1967, p. 12), its forward and backward linkage effects (Kaldor, 1967, p. 23), and its influence on demand according to Engel’s law[4] (Kaldor, 1967, p. 29).

On this basis, almost no country has been able to achieve and sustain a high standard of living without significant developments in its manufacturing sector, except for a few oil-rich countries and small tax havens (Chang, 2016). The tradable manufacturing sector is a major channel through which a developing economy reduces its technological gap. The main vehicle for learning and adopting technology is investment. In the manufacturing industry, high levels of capital and investment are required and this creates strong externalities for other sectors. Furthermore, the manufacturing sector has a higher potential for productivity growth because it has a greater capacity to achieve higher levels of capital accumulation, economies of scale and technological progress, compared with agriculture and some services (Haraguchi, Cheng and Smeets, 2017; Lavopa and Szirmai, 2018; Szirmai, 2012).

According to Haraguchi, Cheng and Smeets (2017), the linkage effects are especially strong in manufacturing, which offers better backward linkages (in general) as well as forward linkages for resource-based industries than the agriculture and services sectors. This means that an increase in manufacturing output boosts output even more both in the manufacturing sector itself and in other sectors, through direct output linkages and indirect multiplier effects, such that the manufacturing sector drives growth across the broader economy (Gabriel and Ribeiro, 2019). In countries with a relatively high level of income in particular, high-tech industries —such as chemicals and motor vehicles— make significant contributions to employment in industry-related services, especially business services, owing to the linkage effect (UNIDO, 2013).

Even more importantly, modern services such as telecommunications, financial services, software development, logistics and transport are seen as having considerable technological momentum and thus are also regarded as potential drivers of economic growth. According to Lavopa and Szirmai (2018), the successful development of services in India and other countries has resulted in the proposition that other developmental paths, not necessarily driven by the manufacturing sector, are also possible.

As for the services sector, Haraguchi, Cheng and Smeets (2017) explain that business services are increasingly becoming an integral part of manufacturing activities. Development of the manufacturing industry and structural change therefore play a key role in the development of these modern services. Nonetheless, as globalization advances, manufacturing in global value chains is increasing. This shift, which has accelerated in certain industries in particular since the end of the 1980s, can reduce national output linkages and induction effects to stimulate the economy. Nevertheless, the manufacturing sector continues to show a higher potential for productivity growth owing to its capacity for stronger capital accumulation, economies of scale and technological progress, compared with agriculture and less productive traditional services[5] (Szirmai, 2012).

Szirmai (2012) and Szirmai and Verspagen (2015), using empirical analyses and stylized facts, present arguments to support the hypothesis that industry acts as a driver of growth, as also noted in Haraguchi, Cheng and Smeets (2017). According to them, there is a strong positive correlation between the level of development of the manufacturing sector and per capita income in emerging countries. The transfer of resources from agriculture to the manufacturing sector provides a structural change advantage, particularly for developing countries. However, the transfer of resources from the industrial sector to services may produce a structural change disadvantage, if the latter generates few higher-productivity activities (Baumol, 1967). According to Szirmai and Verspagen (2015, p. 47), the manufacturing sector offers special opportunities for capital accumulation, one of the aggregate sources of economic growth, and greater economies of scale. Productive investments in the manufacturing sector, such as in mining and construction, are capital-intensive. In addition, the manufacturing or processing industry tends to include more technologically advanced activities, which facilitates externalities and indirect diffusion of technology.

With regard to technological progress, Lima, Gabriel and Jayme (2022) analyse empirically how the manufacturing industry, disaggregated into subsectors by research and development (R&D) intensity, influences the level of economic complexity of a sample of 28 countries between 1963 and 2012. The empirical results suggest that allocation of workers to manufacturing activities with higher R&D investment has a positive impact on the level of productive sophistication of all the countries analysed, whereas in the sectors with lower R&D investment, the positive impact is greater in emerging countries, and lower (or negative) in advanced countries.

In the same vein as Szirmai (2012) and Szirmai and Verspagen (2015), Marconi, Reis and Araújo (2016) examine the hypothesis of manufacturing as a driver of growth using Kaldor’s laws for a sample of 63 countries: 32 low- and middle-income countries (with per capita income of between U$ 1,036 and US$ 4,085) and 31 high- and middle-to-high-income countries (per capita income above US$ 4,085). The study covers the period 1990–2011. The results confirm the validity of Kaldor’s laws, since they prove that stronger growth in industrial output leads to higher economic growth (Kaldor’s first law) and to higher industrial productivity (Kaldor’s second law) in both income groups. The effect was greater in low- and middle-to-low-income countries.

Rodrik (2013a) emphasizes two key trends that affect economic growth in the long run. First, growth depends on the accumulation of fundamental capabilities in the form of human capital and institutions, as underscored by Romer (1990) and Acemoglu, Johnson and Robinson (2005), respectively. However, these capabilities have high set-up costs and exhibit complementarities that tend to yield slow growth pay-offs until a certain level of accumulation has been reached. The second important point is that economic growth results from structural change, the transfer of labour from lower-productivity activities to modern higher-productivity activities, as noted in structuralist literature.

According to Rodrik (2013a, p. 5), high-output growth is enabled by the fact that the positive effects of industrialization can be generated even in the presence of a low level of fundamental capabilities. This means that a country can experience significant structural changes even with low levels of human capital and weak institutions. However, as Rodrik (2013a) points out, the policies needed to accumulate fundamental capabilities and those required to foster structural change overlap. The first entail a much broader range of investments and time for positive results to be achieved, and the second may take the form of much more limited measures, such as promoting the export of manufactured goods.

Rodrik (2013b) demonstrated empirically that the manufacturing industry exhibits strong unconditional convergence:[6] industries that start at lower levels of initial value added per worker experience more rapid growth in productivity. Naturally, the rate of conditional convergence is faster in countries with better institutions and human capital, stronger trading ties and higher levels of financial development, but manufacturing continues to play its role as a driver of growth even in countries with weak governance and lower levels of human capital development (Rodrik, 2013a and 2013b).

Along the same lines as Rodrik (2013a and 2013b), Szirmai and Verspagen (2015) tested the relationship between the value added share of manufacturing and per capita GDP growth with a panel of 92 countries. The relationship was examined for three periods (1950–1970, 1970–1990 and 1990–2005) and compared with the results of the services sector. The study, which focused primarily on the results of conservative Hausman-Taylor (1981) estimations, presents the contribution of the manufacturing industry to per capita GDP growth conditional on the level of education and stage of development. The authors showed that the manufacturing industry acts as a driver of growth for low- and middle-income countries, provided they have a sufficient level of human capital. Such growth driving features were not found for the services sector. The findings for more recent periods indicate that a higher level of human capital (at least seven to eight years of education) is necessary for the manufacturing industry to drive growth in developing countries.

Haraguchi, Cheng and Smeets (2017) analysed empirically whether the importance of the manufacturing industry in developing countries has changed in terms of development quality (role of manufacturing as a driver of growth) and development quantity (relative share of manufacturing value added and employment in GDP and in total employment, respectively). Upon assessing these factors, the authors argue that the importance of the manufacturing industry in economic development remained unaltered during the periods analysed (1970–1990 and 1990–2013).

According to Haraguchi, Cheng and Smeets (2017), even after 1990, the manufacturing sector in developing countries still meets the conditions to be described as a driver of economic development, especially to achieve high sustained growth while retaining at least the same size in GDP and total employment as in the period from 1970 to 1990. Thus, the authors conclude that premature deindustrialization in many developing countries has not been caused by changes in the capability of the sector to act as a driver of growth, or even by the quantity of manufacturing activities (in terms of employment and value added), but is mostly attributable to failures of manufacturing development[7] in a large number of developing countries amid rapid development of the manufacturing sector in a small number of countries, thus resulting in a concentration of manufacturing activities in countries such as China.

Unlike Rodrik (2016) and Eichengreen and Gupta (2013), Haraguchi, Cheng and Smeets (2017) are not pessimistic about the possibility of emerging countries achieving economic development through the industrial sector. According to them, following its success in labour-intensive industries, China is likely to upgrade its industrial structure following in the footsteps of high-income countries, which focus more on capital. Once this happens, there may be greater opportunities for current low- and middle-income countries to pursue manufacturing activities in global value chains and regain their importance despite their premature deindustrialization.

The authors also acknowledge that despite these opportunities, further liberalization of international trade and the growing role of global value chains in production and in manufacturing trade may have intensified price competition in international trade and given clear advantages to specific countries with economies of scale and agglomeration.

According to Lavopa and Szirmai (2018), countries that fall into the poverty or middle-income trap are countries that have been unable to sustain their structural transformation with regard to structural change and reduction of the technological gap.[8] In the specific case of middle-income countries, the trap would be failing to move from competitiveness in low-wage activities and routinized tasks to competitiveness in knowledge-based production using cutting-edge technological innovations.

As for middle-to-high-income countries, Lavopa and Szirmai (2018) explain that the main challenge is to narrow the productivity gap (promotion of innovation and structural change which increases productivity) rather than to increase the size of the modern sector.[9] As production across global value chains becomes more important, reducing productivity gaps entails upgrading within these chains and capturing segments within them with greater competitive edge potential.

Countries that have overcome the middle-income trap, such as the Republic of Korea, have used trade, industrial and technological policies with growing international competitiveness.[10] In that case, the expansion of the modern sector was accompanied by a narrowing of the technological gap. According to Lavopa and Szirmai (2018), this trajectory contrasts with that observed in Brazil, where the reduction of the technological gap could not be sustained and, furthermore, where there has been intense deindustrialization since the 1980s.

Doré and Teixeira (2023), upon analysing the specific case of the long-run economic growth of Brazil (from 1822 to 2019), found that structural change is of critical importance as an element to explain the country’s industrialization. According to the authors, although the abundant natural resources in Brazil have helped to boost the primary sector of the economy throughout its history, specific policies and investments propelled manufacturing activities, especially after the 1930s, helping to enhance industrial development and, consequently, economic growth. Lastly, the results of Doré and Teixeira (2023) suggest that the premature deindustrialization of Brazil after the 1980s contributed to a large extent to the country’s near-stagnation growth in the years that followed.

III. Methodology

1. Econometric model

The first method used in this study was the VAR model, as explained and justified below, taking into account the econometric tests performed. This method is used to analyse the effects of structural change in the Brazilian economy on the country’s economic development. The VAR model is appropriate when there are uncertainties surrounding the identification of the endogenous and exogenous variables. In the VAR (p) system, the behaviour of the variables, broadly speaking, is driven by the p-lags of the explained variable and by the p-lags of the explanatory variables. According to the VAR methodology, the following specification is used for real GDP (the main variable of interest):

(1)

where is the real GDP growth rate of Brazil from one year to another; is the rate of change of real GDP over the period t lagged for up to k periods (years); is the real rate of change of the sector of interest over the period t lagged for up to t-k +l years; represents the real rate of change of the nth sector (n.sector) over the period t lagged for up to t-k +l years; and is the random error term. In addition, are the parameters to be estimated up the nth sector (). The economic sectors are shown in table 1. To test for stationarity, the augmented Dickey-Fuller test and the Phillips-Perron test were used. Given that there can be drawbacks to the augmented Dickey-Fuller test stemming from a higher propensity towards failing to reject a null hypothesis when it is false, the decision was made to use the Phillips-Perron test as well. Table 1 shows the variables used in the VAR model.

In addition to the tests for stationarity and for selecting the optimal lag for the VAR model (see section IV.1), a cointegration analysis of the Brazilian economic series was performed. The Johansen cointegration tests of the complete model, both in logarithm (at level) and without any transformation (series in real terms), found no cointegration between the time series. The results for the complete model, including the results of the trace test and the eigenvalue test, are shown in tables A2.1 and A2.2, respectively. Given that the time series, at level, were not cointegrated, but rather stationary at first difference, the VAR (p) method is the most appropriate method (Enders, 2004).[11]

The econometric method chosen to determine how structural change can affect a country’s economic growth was a panel data model using a sample of 31 middle-income countries (economies with a per capita income of between US$ 3,996 and US$ 12,375 at 2019 prices) over the period 1960–2019. That way, the results for Brazil could be contrasted with a heterogenous sample of developing countries.

The following specification was used:

(2)

where are the parameters to be estimated; nt is the specific time effect; hi captures the unobserved effects for each country i that do not change over time; and nit is the idiosyncratic error term.

In equation (2), represents the per capita GDP growth rate; is the explanatory variable, representing the share of the agriculture sector; represents the share of the industrial (manufacturing) sector, and represents the share of the services sector. All sector shares are in real terms and as a percentage of each country’s real GDP. Control variables were also added, such as , which represents gross fixed capital formation; , which represents the terms of trade; , which represents the openness of the economy; and , which represents inflation. Lastly, subscript i represents the countries, while subscript t represents the years.

2. Description of the data

For the application of the VAR model, data on the Brazilian economy were obtained from the System of National Accounts, available on the platform of the Brazilian Institute of Geography and Statistics and the Institute of Applied Economic Research. The purpose of the System of National Accounts is to provide basic data on the country’s economic development. The available data cover the period 1948–2020, which is why that period was chosen for the analysis.

Table 1 contains a description of the variables used in the two approaches to the VAR model. The estimation of the model was divided into two parts: in the first, a limited model with the rate of change of economic growth and the rate of change of the three economic sectors (agriculture, industry and services) was used; in the second, more specific variables were used. A total of eight variables were used: GFCF, gross fixed capital formation; AGDP, agriculture GDP; IGDP, industry GDP; GFCFME, gross fixed capital formation in machinery and equipment; SGDP, services sector GDP; MGDP, manufacturing GDP; TGDP, trade GDP; and GDP, total GDP.

Table 1

Description of the variables used in the vector autoregression model

Source: Prepared by the authors using data from Institute of Applied Economic Research (IPEA), IPEADATA [online], available at www.ipeadata.gov.br [consulted on 17 December 2021], and Brazilian Institute of Geography and Statistics (IBGE), “SCN - Sistema de Contas Nacionais” 2020 [online], available at https://www.ibge.gov.br/estatisticas/economicas/industria/9052-sistema-de-contas-nacionais-brasil.html?edicao=35337.

Note: Data for all variables were obtained by consulting the national accounts for the period 1948–2020, available in IBGE (2020) and IPEA (2021).

Country data for the panel data model were obtained from the World Bank World Development Indicators database[12] or the Penn World Table 10.01.[13] The former contains a series of World Bank development indicators, while the latter is a country data set developed by Feenstra, Inklaar and Timmer (2015). Annual data cover the period 1960–2019 and a sample of 31 middle-income countries.[14] Table 2 contains a summarized description of all variables used in the panel data model.

Table 2

Description of the variables used in the panel data model

Variable

Description

Source

realgdppc

Per capita GDP (Constant dollars at 2010 prices)

Penn World Table 10.01

aggdps

Agriculture, forestry and fishing, value added (Percentages of GDP)

Penn World Table 10.01

indshare

Industry, value added (Percentages of GDP)

Penn World Table 10.01

servshare

Services, value added (Percentages of GDP)

Penn World Table 10.01

gfkforshare

Gross fixed capital formation (Percentages of GDP)

World Development Indicators

ttrade

Terms of trade adjustment

World Development Indicators

openness

Trade openness index (Percentages of GDP)

World Development Indicators

inflationrate

Inflation, consumer prices (Annual percentages)

World Development Indicators

Source: Prepared by the authors using World Bank, “World Development Indicators (WDI)” [online database], available at https://databank.worldbank.org/source/world-development-indicators; and R. C. Feenstra, R. Inklaar y M. P. Timmer, “The next generation of the Penn World Table”, The American Economic Review, vol. 105, No. 10 (2015).

IV. Results and discussion

1. Vector autoregression model for Brazil

The VAR model was used to evaluate the effects of the structural change of the Brazilian economy on economic performance over the period 1948–2020. To that end, the analysis of the model was divided into two parts: the first is a limited model, with only four variables, while the second is a more complete model, with eight variables.

Figure 1 below shows trends in GDP of certain sectors and of the economy as a whole. As shown, in recent years, GDP has risen across all sectors and subsectors, as has total GDP. Agriculture GDP and trade GDP have recorded constant growth, which contrasts with the accelerated growth in services sector GDP. The same results have not been achieved, however, by the industrial sector and the manufacturing subsector, which have declined as a share of total GDP.

Figure 1

Brazil: trends in GDP of various sectors and of the economy as a whole, 1970–2020

Source: Prepared by the authors using data from Brazilian Institute of Geography and Statistics (IBGE).

Note: Real variables at constant prices (base year 2010) were used. The period 1970–2020 was chosen owing to a lack of data on industry GDP and services sector GDP.

According to Nakabashi, Scatolin and Cruz (2010), the growth of the services and trade sector accelerated precisely as industry was losing momentum in the Brazilian economy, during the 1980s, as shown in figure 1. According to Arbache (2015), in Brazil from 1985 onward, growth in services sector GDP accelerated at the expense of the industrial sector and the agriculture sector, whose respective shares of total GDP declined. That acceleration, however, could also be linked to the rise in urbanization and to the demographic transformation of the country, so much so that the services sector already accounted for 70% of GDP from 2013 onward.

Given that one of the prerequisites for using the VAR model is that the series used in the model must be stationary, the annual data were first tested for stationarity using the augmented Dickey-Fuller test. Decisions regarding the selection of the lag and whether to include a constant or trend were made using the Schwarz information criterion, with a limit of four lags. Table 3 shows that all series are stationary at 1%, and thus there is no impediment to using the VAR model.[15]

Table 3

Brazil: series stationarity testing, augmented Dickey-Fuller test

Source: Prepared by the authors.

Note: Null hypothesis: the variable has a unit root (i.e. it is not stationary). An asterisk denotes the rejection of the null hypothesis (1%).

The augmented Dickey-Fuller test can, however, have drawbacks, such as a higher propensity towards failing to reject a null hypothesis when it is false. For that reason, the Phillips-Perron test was also performed. As shown in table 4, once again all series were stationary at 1%, and thus there is no impediment to using the VAR model.

Table 4

Brazil: series stationarity testing, Phillips-Perron test

Source: Prepared by the authors.

Note: Null hypothesis: the variable has a unit root (i.e. it is not stationary). An asterisk denotes the rejection of the null hypothesis (1%).

To determine how many lags to consider in the estimation of the VAR model, the following tests were performed: the Akaike information criterion, the Schwarz information criterion, the Hannan-Quinn information criterion and the final prediction error criterion. Table 5 shows the selection tests for the limited model (i.e. the model with fewer variables). As shown, one lag, two lags and four lags were the only results indicated by the selection tests. Accordingly, the decision was made to consider the number of lags indicated by the most tests (i.e. where the number of lags identified by the different tests varied, the number of lags identified by the majority was selected for the model). Therefore, only one lag should be considered in the VAR model with fewer variables. Table 6 shows the selection tests for the complete model (i.e. the model with more variables). It also indicates that the results of the estimation should only consider one lag.

Table 5

Brazil: lag selection tests for the limited model, annual, 1948–2020

Lag

Final prediction error criterion

Akaike information criterion

Hannan-Quinn information criterion

Schwarz information criterion

1

1283522

25.415

25.675*

26.073*

2

1148578*

25.299

25.767

26.483

3

1186586

25.318

25.995

27.029

4

1170226

25.278*

26.164

27.516

5

1417068

25.428

26.522

28.192

6

1388969

25.344

26.646

28.634

Source: Prepared by the authors.

Note: The variables considered in this test are: GFCF, AGDP, IGDP and SGDP. The asterisk denotes the number of lags selected under that criterion.

Table 6

Brazil: lag selection tests for the complete model: annual, 1948–2020

Lag

Final prediction error criterion

Akaike information criterion

Hannan-Quinn information criterion

Schwarz information criterion

1

5.50e+09

45.118

46.043*

47.450*

2

5.09e+09

44.970

46.717

49.373

3

4.87e+09*

44.733

47.302

51.209

4

5.69e+09

44.486*

47.877

53.034

Source: Prepared by the authors.

Note: The variables considered in this test are: GFCF, AGDP, IGDP, SGDP, MGDP, TGDP, GFCFME and GDP. The asterisk denotes the number of lags selected under that criterion.

In the first approach, gross fixed capital formation () was used, considering the growth rate of each sector (, and ). Table 7 shows the estimated results for each of these variables, taking into account just one lag in accordance with the selection criteria.

Table 7

Brazil: results of the estimations of the VAR model, limited model, 1948–2020

Source: Prepared by the authors.

Note: Standard deviation indicated within parentheses. Significance of 10% (*), 5% (**) and 1% (***). Limited model. Only one lag is considered, in accordance with the selection criteria.

Table 7 shows that five of the variables have a statistical significance level of 10%, 5% or 1%. Three of those fall under the first lag of , influenced by , and . In other words, it is shown that the rate of growth in services sector GDP, lagged for one period, has a significant impact on the rates of growth in agriculture GDP, industry GDP and services sector GDP. Moreover, a 1-percentage-point increase in the rate of growth in services sector GDP provokes a 0.2-percentage-point decrease in the rate of growth in agriculture GDP and a 0.53-percentage-point increase in the rate of growth in industry GDP.

Other studies have reached similar conclusions. Cruz and others (2007), for example, analysed the decline in industry in terms of its share of employment and of value added in the economy (GDP) and the expansion of the services sector. They found evidence of a decline in manufacturing in terms of its share of employment, as well as evidence of an increase in the share of services subsectors, such as public administration and trade.

Satistically significant variables were the rate of growth in agriculture GDP, which had a negative impact on its own rate of growth, and industry GDP, which had a positive impact on agriculture GDP.

The results align with those of Nakabashi, Scatolin and Cruz (2010), in that the agriculture sector affects its own performance, but not that of any other sector. This means that the agriculture sector does not have any influence on the growth rate of the economy as a whole; in other words, it has no indirect impact on the growth rate of other sectors. The industrial sector, however, has a positive impact on the agriculture sector.

The limited model shows that the most significant sector, in terms of its pull on other sectors, was the services sector, which had a negative impact on the agriculture sector but a positive impact on both the industrial sector and its own lagged behaviour. It is thus shown that, in Brazil, the services sector has a significant impact on the industrial sector: a 1-percentage-point increase in the services sector has a 0.538-percentage-point impact on the industrial sector.

Table 8 shows the results of the estimations of the complete model, which has the following additional variables: the rates of change in manufacturing GDP (), trade GDP (), gross fixed capital formation in machinery and equipment () and total GDP (). This model yields more data for analysing the relationship between structural changes and economic growth.

According to Nakabashi, Scatolin and Cruz (2010), gross fixed capital formation in machinery and equipment may be the most interesting variable for determining the growth rates of the various economic sectors; manufacturing GDP may provide more information on technology and productivity in the sector; and total GDP may be interesting for observing how other sectors interact with the economy as a whole and how total GDP can interact with each sector. Trade GDP was also included to analyse whether it behaves similarly to services sector GDP.

Table 9 shows that the first lag of has a significant, positive impact on , , and on its own coefficient (), as well as a significant, negative impact on . This suggests that gross fixed capital formation mainly influences the industrial and services sectors.

In contrast, the first lag of has the opposite effect of : a significant, positive impact on and a significant, negative impact on , , and on its own coefficient (). This suggests that, between 1948 and 2020, gross fixed capital formation in machinery and equipment mainly influenced the agriculture sector.

Another important result pertains to the behaviour of total GDP. In addition to its lag having a positive impact, significant to 1%, on its own coefficient, it also influences and : a 1—percentage—point increase in the rate of growth in total GDP raises the rate of growth in trade GDP by 1.12 percentage points and the rate of growth in manufacturing GDP by 1.74 percentage points. This suggests that the total GDP growth rate has a positive influence primarily on two economic subsectors: trade and manufacturing.

Other significant variables, in addition to the constant, were the rate of growth in agriculture GDP () on its own coefficient and the rate of growth in services sector GDP () on its own coefficient. The other variables (, and ) did not have any significant coefficient, which suggests that they did not influence the growth of the Brazilian economy between 1948 and 2020.

Table 8

Results of the estimations of the VAR model, complete model, 1948–2020

Source: Prepared by the authors.

Note: Standard deviation indicated within parentheses. Significance of 10% (*), 5% (**) and 1% (***). Complete structural model. Only one lag is considered, in accordance with the selection criteria.

Notably, and did not have any significant coefficient, which could reflect their decline in terms of their share of total GDP in recent years. Bonelli and Pessôa (2010) found the same result.

In the limited model, results show the importance of the services sector for its own growth rate and demonstrate its relevance in determining the agriculture GDP growth rate and the industry GDP growth rate. In other words, the services sector acted as a driver of economic growth over the period 1947–2020. In the complete model, gross fixed capital formation had a positive impact on the services sector and, especially, on industry, while gross fixed capital formation in machinery and equipment had a positive and statistically significant impact on agriculture GDP. Total GDP was a driver of growth for the manufacturing and trade subsectors.

That last finding suggests that economic growth (or the expectation of such growth) may create more investment opportunities for manufacturing in particular. In other words, the growth of the manufacturing sector is endogenous to the behaviour of the economy, so it is not the most relevant sector in terms of its influence on .

The results for Brazil suggest that structural change documented over the course of the period diminished the importance of the industrial sector for economic growth, because the transfer of the workforce from high-productivity sectors (industry) to low-productivity sectors (mainly trade and services) dampened the rate of economic growth (see figure 1), as well as the industrial sector’s influence on other sectors (as shown in the analysis of the VAR model).

Figure 2 shows the impulse response analysis of the VAR (1) model. Impulse response functions reveal the behaviour of the analysed time series when the value of the residuals of a given variable change in response to an exogenous shock (one standard deviation). In this manner, it is possible to observe the behaviour of and the relationships between the time series related to GDP of Brazil.[16]

Figure 2

Brazil: impulse response functions, VAR model (1), complete model

Accumulated response to generalized 1-standard-deviation innovations with a standard error of ±2

Source: Prepared by the authors on the basis of estimates made using the EViews 9.0 software.

To estimate the impulse response functions of the subfigures of figure 2, the generalized impulse response function method was chosen, in which, in this case, results are not affected by the ordering of the variables in the VAR systems. Cumulative 10-year periods were considered. Looking at the macrosectors, a 1-standard-deviation shock to produces a cumulative positive impact of no more than 4% on , while the same shock to produces a cumulative positive impact of 8% on . In addition, a 1-standard-deviation shock to produces a cumulative impact of 5% after 10 periods. As shown, a 1-standard-deviation shock to produces a very low, near-zero cumulative impact at the end of the period (0.1%) on . In addition, the cumulative impact of a shock to on amounted to 7.47% after 10 periods. Lastly, the shocks to and have a similar impact on , in particular a cumulative impact of approximately 4%.

The results of the impulse response functions suggest that the stimuli (exogenous shocks) to manufacturing () have a considerable (the strongest) impact on the behaviour of GDP over time, in accordance with the estimations of the VAR (1) model.

The analysis of the stability of the VAR (1) model is shown in figure A1.1, which confirms that all roots lie within the unit circle. The resulting eigenvalues confirm that the estimations are stable. Given that all eigenvalues are within the unit circle, the estimated VAR (1) model satisfies the stability condition.

For the autocorrelation test, the Breusch-Godfrey test, also known as the Lagrange multiplier test, was used, and the resulting p-value was 0.4373. Considering a significance of 5%, the test did not reject the null hypothesis that autocorrelation does not exist. In other words, there is no autocorrelation between the residuals. For the heteroscedasticity test, the autoregressive conditional heteroscedasticity test was used, considering a maximum of eight lags. Using this test, the null hypothesis of homoscedasticity, up to a significance level of 5%, was not rejected until the third lag (with a p-value of 0.87).

In view of these results, a fundamental question that should be asked, mainly for the purposes of drawing a contrast with the positive results of the services sector, as shown, is whether industry has ceased to be relevant for economic growth in a representative sample of middle-income countries, such as Brazil. That question is addressed in section IV.2.

2. Panel data estimations

The Hausman test was used to decide whether the panel data models should be estimated with fixed or random effects. Under the null hypothesis that the models are equal, the fixed- and random-effects models are consistent if correctly specified, but the fixed-effects model is inefficient relative to the random-effects model (Wooldridge, 2010).

In the Hausman test, the fixed-effects model was found to be the most appropriate, with a 5% significance level. Nevertheless, the authors also performed the modified Wald test for heteroscedasticity in a fixed-effects regression model, the Wooldridge test for serial correlation in the panel model and the Collin test for multicollinearity. In order to detect heteroscedasticity and autocorrelation at the 5% significance level, the least squares method with panel-corrected standard errors was used. The variance inflation factor (VIF) test did not detect multicollinearity problems.

Linear regression with panel-corrected standard errors calculates estimates for linear cross-sectional time series models where the parameters are estimated by ordinary least squares regression or Prais-Winsten regression. When computing the standard errors and the variance-covariance estimates, the disturbances are assumed, by default, to be heteroscedastic and contemporaneously correlated across panels. To solve this problem, the least squares method with panel-corrected standard errors was used. The results of these estimations are presented in the present section.

Table 9 shows the results of the estimation of equation (1) using the least squares method with panel-corrected standard errors for the panel data of the sample of middle-income countries. In all estimations, the share of the agriculture sector (aggdps) has a negative and significant impact on the per capita GDP growth rate (realgdppc). This suggests that the agriculture sector negatively influences the per capita GDP growth rate in middle-income economies.

Table 9

Sample of middle-income countries (31 countries): panel-corrected standard errors estimation for panel data, 1960–2019

(1)

(2)

(3)

(4)

(5)

realgdppc

realgdppc

realgdppc

realgdppc

realgdppc

aggdps

-63.22

***

-66.71

***

-114.7

***

-119.2

***

-119.4

***

(-8.56)

(-8.07)

(-13.09)

(-13.89)

(-16.89)

indshare

21.55

***

20.84

***

30.45

***

28.62

***

31.22

***

(5.35)

(5.41)

(5.59)

(5.43)

(6.78)

servshare

3.557

4.448

10.65

*

13.04

**

15.86

***

(0.96)

(1.20)

(2.06)

(2.68)

(3.86)

gfkforshare

37.54

***

43.08

***

43.92

***

45.36

***

(9.39)

(10.26)

(9.83)

(10.50)

ttrade

-2.34e-13

-1.69e-13

-2.51e-13

*

(-1.56)

(-1.10)

(-1.71)

openness

-4.286

*

0.0845

(-2.57)

(0.06)

inflationrate

0.0518

(0.44)

_cons

5 353.7

***

4 405.2

***

5 075.0

***

5 217.1

***

4 432.1

***

(14.51)

(13.41)

(11.25)

(12.31)

(13.39)

N

1 211

1 166

1 029

1 029

860

Source: Prepared by the authors.

Note: T-statistic in parentheses. Asterisks: * p < 0.05; ** p < 0.01; *** p < 0.001.

In contrast, the relationship between the share of the industrial (manufacturing) sector (indshare) and the per capita GDP growth rate (realgdppc) was positive and significant in all estimations. This suggests that the industrial sector, as a driver of growth in the economy and whose impact on the per capita GDP growth rate was almost 100% greater than that of the services sector (see estimation 5), positively influenced the per capita GDP growth rate. The services sector (servshare) had a positive influence in all estimations, but was statistically significant only in specifications (3), (4) and (5).

As for the control variables, gross fixed capital formation (gfkforshare), a proxy for investment, presented a positive and significant coefficient in all estimations. According to Vieira and Veríssimo (2009), the investment rate is important for growth in any country. These authors highlight the importance of increasing the investment rate in the medium and long term for achieving and sustaining higher rates of economic growth.

The results for the terms of trade variable (ttrade) vary depending on the estimation. In the more complete estimated model (see the fifth column), this variable is negative and statistically significant, which suggests that the terms of trade have a negative effect on the economic growth rate. This effect may be associated with possible overvaluations of the exchange rate owing to improved terms of trade in this type of economy. However, further empirical research is needed in that regard.

The openness of the economy (openness) and inflation (inflationrate) did not present significant coefficients in the more complete model (see the fifth column). This may indicate a loss of importance of these two variables for the per capita GDP growth rate of the economy when other control variables are taken into account.

V. Final considerations

In view of the empirical studies that suggest that structural change has occurred differently over time in developed and emerging countries, leading to differing patterns of economic growth, the aim of this study was to identify how structural change affected economic growth in developing countries between 1960 and 2019 such that countries like Brazil are falling behind.

The present paper contributes to the existing literature by empirically analysing the relationship between structural changes and economic growth using two main econometric methods: (i) the vector autoregression (VAR) model and (ii) panel data microeconometrics.

The first method was used to analyse the effects of structural change in the Brazilian economy on its own development. The VAR model, applied to the period 1948–2020, was employed.

The results obtained for Brazil suggest that, in the structural change process analysed, the importance of the industrial sector for economic growth declined owing to the transfer of labour from high-productivity sectors, such as industry itself, to low-productivity sectors such as services and trade (in aggregate). This led to a decrease in the influence of the industrial sector on the growth rate of the economy. In any case, when the manufacturing sector experiences an exogenous shock, it tends to have stronger accumulated responses than other sectors, especially the services sector.

The aim of the second method was to use panel data to analyse the effects of structural change on a sample of middle-income economies during the period 1960–2019. Important results were observed: the relationship between the share of the industrial sector and the per capita GDP growth rate (realgdppc) was positive and significant in all estimations. The results therefore indicate that, between 1960 and 2019, industry was the sector that had the greatest influence on the per capita GDP growth rate in the sample of middle-income countries analysed.

The difference in the results obtained from each method suggests that the increase in the income gap in Brazil (its falling-behind trajectory) when compared with other middle-income countries and with developed countries is due partly to structural change being excessively concentrated in services and trade, sectors that have a smaller impact on the growth rate of per capita income.

In a future empirical study, the same panel data exercise will be carried out using a sample of high-income countries. In the light of the literature studied, it is hypothesized that industry will have a heterogeneous impact on economic growth: the services sector is expected to predominate in part of the sample, while the industrial sector will be even more important in economies that are closer to the technological frontier and have a higher number of patent registrations.

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Annex A1

Vector autoregression model (1) stability conditions

Figure A1.1

Inverse roots of the autoregressive characteristic polynomial

Source: Prepared by the authors on the basis of estimates made using the EViews 9.0 software.

Annex A2

Johansen cointegration tests

Table A2.1

Unrestricted Johansen cointegration rank test

(Trace)

Hypothesized

Trace

0.05

Number of cointegrating equations

Eigenvalue

Statistic

Critical value

Probability*

None

0.389364

83.86497

83.93712

0.0706

At most 1

0.276651

49.33715

60.06141

0.2875

At most 2

0.171011

26.66675

40.17493

0.5457

At most 3

0.097525

13.53834

24.27596

0.5760

At most 4

0.080255

6.355372

12.32090

0.3940

At most 5

0.007107

0.499267

4.129906

0.5428

Source: Prepared by the authors.

Note: Trace test indicates no cointegration at the 0.05 level. * P-values calculated in accordance with J. G. MacKinnon, A. A. Haug and L. Michelis, “Numerical distribution functions of likelihood ratio tests for cointegration”, Journal of Applied Econometrics, vol. 14, 1999.

Table A2.2

Unrestricted Johansen cointegration rank tests

(Eigenvalue)

Hypothesized

Maximum eigenvalue

0.05

Number of cointegrating equations

Eigenvalue

Statistic

Critical value

Probability*

None

0.389364

34.52783

36.63019

0.0862

At most 1

0.276651

22.67040

30.43961

0.3363

At most 2

0.171011

13.12841

24.15921

0.6814

At most 3

0.097525

7.182963

17.79730

0.7926

At most 4

0.080255

5.856106

11.22480

0.3663

At most 5

0.007107

0.499267

4.129906

0.5428

Source: Prepared by the authors on the basis of estimates made using the EViews 9.0 software.

Note: Maximum eigenvalue test indicates no cointegration at the 0.05 level. * P-values calculated in accordance with J. G. MacKinnon, A. A. Haug and L. Michelis, “Numerical distribution functions of likelihood ratio tests for cointegration”, Journal of Applied Econometrics, vol. 14, 1999.

1. [1] See examples of classical economic development authors in Myrdal (1957), Rosenstein-Rodan (1943) and Hirschman (1958), among others.

2. [2] In the manufacturing industry or in the sector of the most sophisticated services, such as information and communications technologies (ICTs), software engineering and finance.

3. [3] See Nassif, Feijó and Araújo (2015).

4. [4] According to Engel’s law, as a family’s income declines, the share of its spending on food increases and the share of its spending on other consumer goods, such as healthcare and leisure, decreases.

5. [5] Less productive traditional services are those linked to restaurants and specific public sector bureaucracies, for example.

6. [6] The sample used by Rodrik (2013b) covers up to 118 countries from 1965 to 2005.

7. [7] Through defective industrial policies or adverse effects of comparative advantages, for example.

8. [8] For that analysis, the authors propose a novel index that captures the salient features of this structural transformation by looking at two variables for which long-term cross-country data are available: the share of labour force employed in modern sectors (structural dimension) and labour productivity in the modern sectors relative to labour productivity at the international frontier (technological dimension). The new index combines both variables into a single measure of structural modernization.

9. [9] Lavopa and Szirmai (2018) identify the modern sectors as mining, manufacturing, utilities, construction and tradable services (that is, transport and telecommunications, and financial and professional services).

10. [10] Porcile, Spinola and Yajima (2023) have developed a formal model for economic growth defined by the balance-of-payments constraint, in which one of the possible scenarios is developmentalist policies, such as those implemented in the Republic of Korea. In this case, the objective is to obtain a competitive exchange rate while implementing a strong industrial policy to foster learning and structural change.

11. [11] According to Enders (2004), if the series were not stationary but instead cointegrated, the vector error correction (VEC) should be adjusted.

12. [12] See [online] https://databank.worldbank.org/source/world-development-indicators.

13. [13] See [online] https://www.rug.nl/ggdc/productivity/pwt/.

14. [14] Albania, Argentina, Belarus, Belize, the Bolivarian Republic of Venezuela, Botswana, Brazil, Bulgaria, China, Colombia, Costa Rica, the Dominican Republic, Ecuador, Gabon, Georgia, Guatemala, Indonesia, the Islamic Republic of Iran, Jamaica, Jordan, Lebanon, Libya, Malaysia, Mexico, Namibia, Paraguay, Peru, the Russian Federation, South Africa, Thailand and Türkiye.

15. [15] Section III.1 contains an explanation of why it is appropriate to use the VAR model instead of the VEC model, as well as an account of the results of the Johansen cointegration test.

16. [16] See Spinola (2020) for an analysis of how exogenous shocks to the trade balance, foreign growth, the real exchange rate and domestic income affect Argentina, Brazil, Colombia and Mexico. See also Teixera, Missio and Dathein (2022) for an analysis of the relationship between functional income distribution, aggregate demand and capacity utilization for the Brazilian economy between 2000 and 2015. In both studies, VAR and VEC methods were used.

Long-term effects of real exchange rate volatility and institutional quality on economic complexity

Rafael Moraes de Sousa and Michele Polline Veríssimo

Received: 15/06/2023

Accepted: 12/08/2024

Abstract

This article analyses whether real exchange rate volatility and institutional quality, combined as conditioning variables of the productive specialization of economies, can have a negative impact on a country’s ability to achieve greater economic sophistication, especially in countries with a commodity-dominated and natural resource-based export basket. The methodology used consists of panel cointegration estimation (pooled mean group) using data from 1995 to 2018 for a sample of 54 countries. The results indicate that real exchange rate volatility is indeed an obstacle to the implementation of structural measures aimed at diversification and sophistication of a country’s productive fabric, while the quality of its institutions has a positive effect on its economic complexity and increases the possibilities for developing local capabilities.

Key words

Foreign exchange markets, commodities, natural resources, international trade, production, economic research

JEL classification

F14, O13

Authors

Rafael Moraes de Sousa is a professor in the Faculty of Economics at the Federal University of Pará, Brazil. Email: rafaelmsousa1@hotmail.com.

Michele Polline Veríssimo is a professor in the postgraduate economics programme at the Institute of Economics and International Relations of the Federal University of Uberlândia, Brazil. Email: micheleverissimo@ufu.br.

I. Introduction

Based on the argument that the key to economic prosperity lies in the efficiency of a country or region in bringing together a variety of capabilities that enable it to diversify its productive and export structure, Hidalgo and Hausmann (2009) developed the concept of economic complexity, which has gained wide acceptance in different currents of thought in the economic literature. This methodology aims to measure the complexity and diversity of the economy in a location and is based on the identification, using international trade data, of the products with the largest share of the export profile of that location that make it possible to achieve a greater degree of diversity (number of products that the country or region exports with a comparative advantage) and less ubiquity (number of countries or regions that export a given product with a comparative advantage), and defines the kinds of skills that are necessary to achieve a certain productive profile. To that end, the economic complexity index has been extensively applied as an analytical tool in studies on international trade and structural changes from different perspectives, including as a proxy for the acquisition of productive capabilities; in the analysis of the factors that determine the complexity of economies with a low level of productive sophistication; and in explaining the growth of economies at varying levels of development.

Given the wide range of applications of this methodology, the aim of this article is to discuss two approaches that link international trade to the productive specialization profile of economies and to the potential for economic growth in the long term. The first approach consists of the economic complexity methodology (Hidalgo and Hausmann, 2009), which correlates economic growth with the development of local capabilities that enable diversification and sophistication of the production and export structure. The second approach concerns the literature on the “curse of natural resources”, which assumes a negative relationship between a country’s natural resource specialization and its economic growth (Sachs and Warner, 1995), observed mainly in oil and mineral exporting economies with low institutional quality.

The purpose of this study is to examine the effects of two transmission channels of the resource curse associated with structural changes in economies —real effective exchange rate volatility and institutional quality— on the level of economic complexity that a country can achieve. It is hypothesized that real exchange rate volatility and low institutional quality have a negative effect on countries whose productive and export structure is centred on commodities and on low-technology manufactures, since these channels become long-term obstacles to reorienting the economy towards a more complex production structure. This is because they hinder efforts to implement and propagate capabilities in the productive fabric with the aim of establishing a more sophisticated production and export structure.

The methodology used consists of cointegration analysis with panel data (pooled mean group) for a sample of 54 countries over the period 1995–2018. The models are estimated taking into consideration the economic complexity index of the Observatory of Economic Complexity, exchange rate volatility based on the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models using data from the Bank for International Settlements (BIS), and institutional quality indicators provided by the World Bank, in addition to other control variables related to the characteristics of the countries analysed.

The article contributes to the literature by providing evidence that the economic complexity and resource curse theories have complementary aspects and may represent an interesting research agenda for discussion of the obstacles to long-term economic growth in countries with natural resource-based economies. In addition, as a new development, using measures of exchange rate volatility and proxies to measure institutional quality, the article empirically analyses the impact of variables that may help to drive structural changes that affect the economic complexity level.

This article is divided in five sections, including the introduction and concluding remarks. Section II systematizes the theories unpinning the development of the estimated econometric model. Section III presents the empirical strategies adopted, and section IV explores the results obtained.

II. Economic complexity, exchange rate, institutions and economic growth

One of the major questions that dominates economic thinking concerns the factors behind the income disparity between economies and the influence of international trade on economic growth. The debate also touches on the role of exports as a source of external demand in the determination of output (Kaldor, 1957; Thirlwall, 1979), as well as the importance of the specialization profile of the production and export structure (Krugman, 1994; Sachs and Warner, 1995; Rodrik, 2009).

An analytical tool has been developed relatively recently that is widely used by scholars of the relationships between economic growth, structural changes and international trade, namely the theory of economic complexity proposed by Hidalgo and Hausmann (2009). These authors hypothesize that the productive structure of an economy is determined by the set of capabilities that are necessary to produce a given good. The more complex these capabilities are, that is, the more diverse and exclusive they are, the more likely it is that the economy will develop new products from them (product space).

A country’s economic growth is thus affected in different ways depending on its productive specialization, since some products have a dense network of connections, which helps the country or region to develop diverse capabilities, while others do not have that effect. Based on the assumption that comparative advantages reveal the level of productive specialization, Hidalgo and Hausmann (2009) note that if an economy is able to export a given product with a comparative advantage, it is because it has the capabilities necessary for its production. That country’s economic complexity is therefore related to the complexity of the products it exports.

Given that, despite the benefits of trade relations, income disparity between economies has continued to grow over time, it is reasonable to assume that certain characteristics influence a country’s economic performance that cannot be imported but must be developed internally, such as infrastructure, regulation and special workforce skills. A country’s performance is thus assumed to be directly related to its efficiency in diversifying its non-tradable local capabilities, and therefore cross-country differences in income can be explained by differences in economic complexity, as measured by the diversity of capabilities present in a country and their interactions (Hidalgo and Hausmann, 2009, p. 10.570).

The great contribution of this view is the creation of a mathematical tool called the method of reflections, which makes it possible to derive the economic complexity index from international trade data. According to the Observatory of Economic Complexity (2021), economic complexity is estimated by measuring the complexity of the location (country, region or city) and that of the activities present in it (products, industries or technologies). It is assumed that the activities produced in or exported from a particular location contain information on the complexity of that location, while the locations where a given activity is present contain information on the complexity required to perform that activity.

Based on the adjacency matrix Mcp, which represents the activities (p) present in a location (c), the method of reflections consists of iteratively calculating the average value of a node’s neighbours, that is, it produces a simultaneous interaction between the international trade data (exports and imports) of the related countries of the matrix, such that the method produces a symmetrical set of variables for the two types of nodes in the network: countries and products. Consideration is given only to products for which the country has a revealed comparative advantage, represented by RCAcp = (Xcplocal/Xclocal)/(Xpworld/Xworld), where Xcplocal represents the local exports of activity p at location c; Xclocal, the total exports in location c; Xpworld, global exports of activity p; and Xworld, total global exports.

The matrix Mcp = 1 when local production of an activity is higher than expected for a location of the same size and an activity with the same total production. Thus, the two measurements of interest can be obtained:

(1)

(2)

where Kc is the complexity of the location and Kp is the complexity of the product. Substituting (2) for (1) is equivalent to diagonalizing the following matrix:

(3)

where represents the number of activities (diversity) of the location, and expresses the ubiquity of an activity.

Hidalgo and Hausmann (2009) note that complex products are the result of a combination of diverse and exclusive capabilities. Diversity is measured by the number of products exported by a country, assuming that the more products exported, the greater the diversity of available capabilities. Exclusivity is measured by the number of countries exporting the same product: the fewer the countries, the more exclusive the available capabilities will be. Product complexity is therefore obtained from the interaction between the measures of diversity and exclusivity.

The economic complexity index is calculated based on the share of complex products in the export profile. We therefore obtain:

(4)

(5)

where PCI is the product complexity index; ECI is the economic complexity index; and are averages; and v(Kc ) and v(Kp ) represent the standard deviation.

The authors ascribe a predictive nature to indicators of economic complexity, meaning that a country or region’s productive sophistication is correlated with its income level and deviations from this relationship are predictors of its future economic growth. This suggests that countries and regions tend to converge towards an income level associated with available local capabilities (Hidalgo and others, 2007; Hidalgo and Hausmann, 2009). However, this methodology measures the availability of capabilities in a given location but does not discuss the process required to achieve sophistication in terms of the trajectory taken, the obstacles overcome and how those capabilities were accumulated. The indicator represents a measure of results for a given period of time, taking into account the diversity and exclusivity of the products exported and intuiting the skill level required for their production. In empirical studies, therefore, the economic complexity index is typically used as a proxy for characteristics such as structural changes, the acquisition of productive sophistication, the impact on income disparity and the path to more sustainable production.

The analysis of the circumstances and factors that can hinder the realization of more dynamic and sophisticated productive structures creates a space for dialogue between discussions on economic complexity and the literature on the effects of productive and export specialization on economic growth. Of particular note is the literature on the resource curse, whose central proposition is that there is a negative relationship between natural resource abundance (specialization) and long-term economic growth (Sachs and Warner, 1995).

Given that an economy’s export profile reflects the orientation of its production structure and the concentration of its local capabilities (e.g. knowledge, technological intensity and innovation systems), the resource curse theory identifies factors linking natural resource-based production structures and low economic growth through transmission channels. These include the long-term decline in commodity prices, barriers to the development of the industrial sector, the effects of Dutch disease and weak institutions (Frankel, 2010; Van der Ploeg, 2011).

The literature on the resource curse associates low growth in natural resource-based economies with the Prebisch-Singer hypothesis of low value added and commodity price volatility, which lead to declining terms of trade for commodities relative to manufactured goods. This results in an unequal distribution of the gains from trade, which translates into unequal labour remuneration in the centre and the periphery. That in turn reinforces the disparity in income between countries specializing in commodities (periphery) and industrialized countries (centre).

The literature on the resource curse also indicates that terms-of-trade improvements or the discovery of new resource deposits can hinder industrial development by shifting the means of production (capital and labour) to the primary sectors (Sachs and Warner, 1997), paving the way for Dutch disease and deindustrialization. Dutch disease occurs when an increase in commodity exports —driven by the discovery of new resource deposits or rising prices— leads to higher foreign currency inflows and, in turn, an overvaluation of the real exchange rate such that the export of manufactured goods ceases to be profitable, since they have no comparative advantage in the international market and require a more competitive exchange rate to ensure profitability for their producers (Corden and Neary, 1982; Bresser-Pereira, 2009).

Reorientation of exports towards commodities, primary goods or low value-added manufactures, together with the effects of Dutch disease, is seen as a negative process that can ultimately lead to deindustrialization of the country, in other words, a fall in the share of industry in employment and output (Oreiro and Feijó, 2010). Given the Kaldorian assumptions that the industrial sector has higher income elasticity of exports, greater economies of scale and greater technology spillover to other sectors of the economy, commodity specialization contributes to lower economic growth rates in the long term.

The weakening of the industrial sector due to Dutch disease has structural effects seen in significant changes in the real exchange rate, so instability in this macroeconomic price can be a barrier to any strategy for sustained growth. In this regard, exchange rate variations can lead to non-negligible changes in exports, whereby real devaluation stimulates the entry of new sectors in the export profile, and can also create opportunities to exploit economies of scale, promote productivity growth and enable permanent transformations in the international specialization profile. In contrast, appreciation of the real exchange rate may contribute to the shrinking of some activities, the downgrading of technical capacities, lost externalities, decreased productivity and reduced competitiveness in some sectors. Exchange rate volatility also generates balance-of-payment instability in countries with natural resource-based economies, making it difficult to obtain foreign currency to finance imports (Oreiro and Feijó, 2010).

The effects of the level, misalignment and volatility of the real exchange rate on economic growth have been examined in several empirical studies. Razin and Collins (1997), for example, using a sample of 93 countries over the period 1975–1992, demonstrated that only significant exchange-rate appreciation is associated with slower economic growth, while moderate depreciation is associated with faster growth. Vieira and Damasceno (2016) analysed the Brazilian economy in the period 1995–2011 and found that exchange rate undervaluation (overvaluation) stimulates (dampens) economic growth and that this dynamic is impaired when exchange rate volatility is greater. Pereira and Missio (2019) studied the relationship between the real exchange rate and structural change in Latin America and found that an overvalued exchange rate hurts long-term economic growth, but a depreciated exchange rate does not necessarily boost growth via indirect effects on structural change towards industrialization. Overall, the empirical evidence seems to support the conclusion reached by Eichengreen (2008) that keeping the real exchange rate at competitive levels and avoiding excessive volatility are important for stimulating economic growth.

The empirical evidence also confirms the relationship between the real exchange rate and the level of sophistication of the export profile. Oreiro, D’Agostini and Gala (2020), for example, analysed the determinants of the deindustrialization of the Brazilian economy in the period 1998–2017 and found that the fall in the share of domestic manufacturing and consequently in productive sophistication was linked to the significant overvaluation of the real exchange rate. The authors therefore highlight the need to adopt a macroeconomic policy regime with a real exchange rate target to avoid harmful overvaluations, and to adopt industrial policies aimed at increasing the country’s economic complexity.

Gabriel and Missio (2017) analysed the relationship between the real exchange rate and the economic complexity level in a model with two regions, the North (developed) and the South (developing), based on the argument that the level of the exchange rate matters for the share of industry in the economy and the attainment of an economic growth rate compatible with balance-of-payments equilibrium. The authors found that an excessively overvalued real exchange rate negatively affects the industrial sector, which may result in regressive productive specialization, especially in developing economies. In addition, exchange rate undervaluation and the larger share of the manufacturing sector in the sample of developing countries have a positive impact on their economic complexity.

Hooy, Baharumshah and Brooks (2016) studied the effect of real exchange rate volatility on the exports of Association of Southeast Asian Nations (ASEAN) countries to China and documented negative effects on high- and medium-technology exports. Thorbecke (2008) also shows that there was a decrease in the flow of electronic components in East Asia when uncertainty increased owing to exchange rate volatility, which points to a decline in the regional benefits of cross-border fragmentation. Similarly, Tang (2014) found that exchange rate volatility had unfavourable effects on intra-Asian exports of intermediate goods and equipment goods, which, according to the author, empirically supports the adverse impact of exchange rate instability on components used as production inputs.

Avom, Kamguia, and Ngameni (2021) also found that output and terms-of-trade volatility negatively affected the economic complexity of a group of 119 countries in the period 1998–2017. The variables related to financial development, foreign direct investment, the Internet and income per capita have a positive effect on economic complexity, while natural resources have a negative effect.

Jun and others (2020) examined bilateral trade links for more than 1,200 products and found that the countries analysed are more likely to increase their exports of a product to a given destination if they already export related products. Therefore, in addition to endowments and advantages, diversification involves learning and spillover between related activities, and overcoming information difficulties to access each export destination. In this regard, the risks and uncertainty stemming from exchange rate volatility can limit opportunities for sophistication of the production structure.

The evidence mentioned thus far suggests that exchange rate volatility can affect factors related to the sophistication process of an economy and have adverse effects on its economic growth, given that volatility tends to heighten risk and uncertainty, which in turn increases the costs associated with the production process, especially in sectors that depend on imported inputs, machinery and technology. Furthermore, the negative relationship between exchange rate volatility and economic complexity may be seen in the efforts to minimize the risks involved in the development of more sophisticated activities, since they are associated with high costs, such as research and development and the effective implementation of investments and projects in activities with higher technological content. These risks may be heightened by the uncertainty resulting from exchange rate volatility and inhibit the expansion of the number of activities in the productive structure (diversification) as well as the progression of activities that require exclusive skills and capabilities (non-ubiquity).

High exchange rate volatility means a reduction in more complex exports, followed by specialization in exports based on comparative advantages, since uncertainty regarding future yields hampers expansion of the production structure towards greater diversity and sophistication. In a context of productive sophistication based on the probability of co-exporting related activities, according to Hidalgo and Hausmann (2009), uncertainty can limit spillover from one activity to another. Even when a country or region has developed sufficient domestic capabilities to perform a given activity, the possibility of spillover to a related activity requiring similar skills may be limited by uncertainty caused by exchange rate volatility.

In addition to effects related to the exchange rate, another obstacle highlighted by scholars of the resource curse when analysing the negative effects of natural resource specialization on a country’s economic growth is the quality of its institutions, in view of the associated failures in the conduct of policies and institutional performance. According to Robinson, Torvik and Verdier (2006), many resource-abundant countries underperform in terms of harnessing the potential advantages of their resources. This is owed, among other factors, to poor contract management; to the formulation of policies unable to promote development due to protection of the dominant primary sectors; to corruption; and to the allocation of resources to non-productive activities. Thus, institutional weakness tends to maintain the power and privilege of the primary sectors, which hinders industrial development and diversification and sophistication of the productive and export structure.

In that connection, Mehlum, Moene and Torvik (2006) studied the relationship between economic growth and institutional quality in a sample of 42 natural resource-exporting countries between 1962 and 2000 and found a negative relationship between a country’s gross domestic product (GDP) growth rate and its dependence on natural resources, especially in countries with low-quality institutions. Zhu and others (2010) analysed the determinants of countries’ export sophistication over the period 1992–2006 and found that the impact of a country’s natural resource abundance seems to be moderated by the quality of its institutions: in the absence of effective institutions, natural resource abundance hampers improvements in the structural quality of exports, while the effect can be positive when good institutions exist.

In his study of the extent to which institutional quality shapes international differences in economic complexity, Vu (2021) argues that better institutions tend to generate a higher level of economic complexity, as they facilitate incentives for innovative entrepreneurship, foster human capital accumulation and allocate human resources to productive activities. Using data for 115 countries, the author found that institutional quality (measured by the Economic Freedom of the World Index) had positive effects on economic complexity, and he thus emphasizes the importance of establishing well-functioning institutions in order to undertake structural transformation in favour of more complex productive activities, which would help to alleviate persistent underdevelopment.

Building on the arguments put forward in the literature on economic complexity and the resource curse, the sections that follow present an empirical analysis of the effects of real exchange rate volatility and institutional quality (channels of the resource curse) on export complexity.

III. Methodology and database

The purpose of this study is to ascertain whether two transmission channels of the resource curse —real exchange rate volatility and institutional quality— can have a negative effect on a country’s ability to achieve greater economic complexity, in particular countries with predominantly commodity-based or resource-based export profiles.

To that end, the ideal empirical strategy is one that can be used to conduct long-run estimations for a large group of countries and that combines a cointegration model with a panel data structure. The methodology selected for this study takes those two interests into account, since it is based on the autoregressive distributed lag modelling approach to cointegration analysis —proposed by Pesaran and Shin (1999) and Pesaran, Shin and Smith (2001)— combined with panel data estimation, which helps to control for individual heterogeneity and identify unobservable characteristics (Baltagi, 2005).

Pesaran, Shin and Smith (1999) developed the pooled mean group model, which is based on an autoregressive distributed lag structure adapted for panel data. The model’s probability estimators are used to obtain long-run coefficients and to obtain short-run and error-correction estimations using homogeneity restrictions through the mean group.

The autoregressive distributed lag model was selected because of the advantages it offers over traditional cointegration tests, such as those developed by Engle and Granger (1987) and Johansen (1991), given that it tends to more efficiently capture long-run relations in small data samples and it can be used with sets of variables with different orders of integration, including stationary, I(0), and nonstationary, I(1), variables (Pesaran and Shin, 1999).

The autoregressive distributed lag model checks for the existence of long-run vectors within a set of variables. Once that relation is confirmed, the long- and short-run coefficients of the models are estimated, as is the speed of adjustment to the long-run equilibrium. For this purpose, the pooled mean group-autoregressive distributed lag model is estimated in the form of error correction vectors, which can be specified as shown:

(6)

where ((y)it ) is the dependent variable, is the first deviation, and (x) are the independent variables; represents the error correction mechanism for the i-th group; are the long-run parameters for the i-th group; j = 1,2,...,p-1 are the short-run parameters for the i-th group; and (ft ) are white noise disturbances.

This makes the autoregressive distributed lag panel efficient and superior to other cointegration methods, since it allows for underlying regressors that exhibit orders of integration I(0), I(1) or a mix of the two (Pesaran and Shin, 1999). Other advantages, which justify the use of the model in this study, are that the pooled mean group model allows for the estimation of macro panel data with a time interval of at least 20 years (Asteriou, Pilbeam and Pratiwi, 2021) and that it is less sensitive to the existence of outliers in a relatively small cross section of data (Pesaran, Shin and Smith, 1999); at the same time, it corrects the serial correlation and endogeneity problems that can arise when using the panel data method (Pesaran, Shin and Smith, 1999). The pooled mean group model limits the long-run equilibrium so that it is homogeneous across countries while allowing for country-specific heterogeneity in the short-run relation, which can be focused on such factors as different infrastructure policies, responses to external shocks and financial crises.

In this study, autoregressive distributed lag-pooled mean group models were used to analyse the long-run effects of real exchange rate volatility and institutional quality on economic complexity. Some control variables were also used to improve the specification of the models. A description of the variables used in the estimations follows.

The dependent variable is the economic complexity index (ECI), obtained from the Observatory of Economic Complexity, which generates ECI indicators for a long list of countries using computational methods based on the mathematical tools described in the previous section. Normalized ECI ranges from -2.5 to +2.5. Values that are positive (negative) or closer to the positive (negative) extreme represent more (less) complexity. The database covers the period 1995–2018.

The first explanatory variable of interest is real effective exchange rate volatility (volatreal), which is used as a measure of macroeconomic instability and a potential obstacle to the diversification and sophistication of the productive structure. The exchange rate volatility of each country in the sample was measured using autoregressive conditional heteroscedasticity (ARCH) models developed for modelling and predicting conditional variations, in which the variance of the dependent variable is modelled as a function of its past values and the past values of the independent or exogenous variables. These models were introduced by Engle (1982) and generalized by Bollerslev (1986) and Taylor (1986) as generalized autoregressive conditional heteroscedasticity (GARCH) models.

ARCH and GARCH models[1] are widely used for analysing time series whose values fluctuate greatly. They are also used as an uncertainty measure, wherein the researcher can opt to consider a wide range of available specifications for modelling volatility. This study analyses a sample of 54 countries with monthly estimations —from January 1995 to December 2018, subsequently transformed into annual averages— of the real effective exchange rate taken from the BIS database (2021). Table 1 provides a summary of significant exchange rate volatility specifications for each country in the sample. The GARCH (1.1) structure is the most common, with the exponential generalized conditional heteroscedasticity (EGARCH) (0.1) model[2] being used in some cases. For the autoregressive (AR) aspect of the estimations, the AR(2) structure was the most common.

Table 1

Countries in the sample (54 countries):a estimations of real effective exchange rate volatility in the autoregressive conditional heteroscedasticity model and the generalized autoregressive conditional heteroscedasticity model, 1995–2018

Country

Models selected

Country

Models selected

Algeria

AR(2), GARCH (2.2)

Latvia

AR(2), GARCH(1.2)

Argentina

AR(1), ARCH(1)

Lithuania

AR(3), ARCH(1)

Australia

AR(3), ARCH(2)

Luxembourg

AR(2), GARCH(1.3)

Austria

AR(3), GARCH(1.4)

Malaysia

AR(2), GARCH(1.1)

Belgium

AR(2), GARCH(1.4)

Malta

AR(1), GARCH(1.2)

Brazil

AR(3), ARCH(1)

Mexico

AR(3), GARCH(2.1)

Bulgaria

AR(4), ARCH(1)

Netherlands (Kingdom of the)

AR(3), GARCH(1.1)

Canada

AR(2), GARCH(1.1)

New Zealand

AR(2), GARCH(2.1)

Chile

AR(2), ARCH(2)

Norway

AR(3), GARCH(3.1)

China

AR(2), GARCH(1.1)

Peru

AR(2), GARCH(0.3)

Colombia

AR(2), GARCH(1.2)

Philippines

AR(3), GARCH(1.3)

Croatia

AR(2), GARCH(1.2)

Poland

AR(2), GARCH(1.1)

Cyprus

AR(1), GARCH(1.3)

Portugal

AR(3), GARCH(1.1)

Czechia

AR(2), ARCH(2)

Republic of Korea

AR(2), GARCH(1.1)

Denmark

AR(3), GARCH(0.2)

Romania

AR(3), ARCH(2)

Estonia

AR(4), MA(2), GARCH(1.1)

Russian Federation

AR(3), GARCH(1.1)

Finland

AR(2), GARCH(0.1)

Saudi Arabia

AR(3), GARCH(0.2)

France

AR(2), GARCH(1.2)

Singapore

AR(3), GARCH(0.2)

Germany

AR(2), EGARCH(0.1)

Slovakia

AR(2), GARCH(1.1)

Greece

AR(3), GARCH(1.2)

Slovenia

AR(3), GARCH(2.2)

Hungary

AR(2), GARCH(1.1)

South Africa

AR(2), ARCH(2)

Iceland

AR(3), GARCH(1.3)

Spain

AR(3), EGARCH(0.1)

India

AR(2), GARCH(1.2)

Sweden

AR(3), GARCH(1.3)

Indonesia

AR(2), GARCH(2.1)

Switzerland

AR(2), GARCH(1.3)

Ireland

AR(2), GARCH(1.1)

Thailand

AR(2), GARCH(2.1)

Israel

AR(3), GARCH(1.1)

Türkiye

AR(3), GARCH(1.1)

Italy

AR(3), GARCH(1.2)

United Kingdom

AR(1), GARCH(2.1)

Japan

AR(2), GARCH(2.1)

United States

AR(3), GARCH(1.0)

Source: Prepared by the authors on the basis of estimates made using the EViews software.

a Owing to limited data availability for certain variables in some countries, the models were estimated using data from 54 countries.

The second explanatory variable of interest is institutional quality, which is aimed at capturing the potential positive effect of the presence of better, more effective institutions on the process of productive sophistication. The measure used to calibrate institutional quality is governance, which, according to the World Bank (2021), consists of the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the State for the institutions that govern economic and social interactions among them. On the basis of that concept, the World Bank developed the worldwide governance indicators, prepared using a large research data set summarizing the views on governance quality of a significant number of businesses, individuals and experts interviewed in various countries. One of the strengths of the indicators is its multitude of data sources, obtained from various research institutes, study groups, non-governmental organizations, international organizations and private enterprises.

The end product of the world governance indicators consists of the six dimensions of governance, three of which were chosen for the purposes of this study: government effectiveness, regulatory quality and control of corruption. The indicators are assigned a value from 0 to 100; values closer to 100 indicate that agents have a better perception of institutional quality in the country in question. The three institutional dimensions are defined as follows:

(i) Government effectiveness (Efigob) captures perceptions of the quality of public services, the degree of government independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies.

(ii) Regulatory quality (Qalireg) captures perceptions of the ability of the government to formulate and implement sound policies and regulations that promote private sector development.

(iii) Control of corruption (Corrup) captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the State by elites and private interests.

The control variables of the estimations reflect characteristics of the countries included in the sample, as shown in table 2. Industrial employment as a share of total employment (Emp) is used to estimate the skill level of the workforce. It is assumed that industrial activities are more complex and require a more skilled workforce than resource-based activities, and thus are a source of capacities for the sophistication of the productive structure. GDP per capita (GDP) is used to control for the size of the economy, since wealthier (developed) economies tend to develop more sophisticated productive structures. Lastly, a variable was created for the interaction between real effective exchange rate volatility and GDP per capita (Volat*GDP), with a view to estimating whether the potential negative effects of exchange rate volatility on economic complexity remain the same when combined with the size of the economy. The purpose of this variable is to capture the possibility that the size of the economy (GDP) acts as a buffer to the effects of exchange rate volatility, since the variable checks whether more advanced economies with more complex productive and export structures are less sensitive to exchange rate volatility.

Table 2

Summary of the variables

Variable

Definition and unit

Source

Expected sign

ECI

Economic complexity index

(normalized range from -2.5 to +2.5)

Observatory of Economic Complexity

Dependent variable

Volatreal

Estimated using the ARCH

and GARCH models

Bank for International Settlements

Prepared by the authors

Negative

Efigob

Perception of government effectiveness (index, 0–100)

World Bank

Positive

Qalireg

Perception of government regulatory quality (index, 0–100)

World Bank

Positive

Corrup

Perception of “capture”

of the State (index, 0–100)

World Bank

Positive

Emp

Industrial employment as

a share of total employment (%)

United Nations Educational, Scientific and Cultural Organization

Positive

GDP

GDP per capita (in dollars

at purchasing power parity)

Organisation for Economic Co-operation

and Development (OECD), OECD.Stat database

Positive

Volat*GDP

Interaction between exchange rate volatility and the country’s GDP per capita

Prepared by the authors

Negative

Source: Prepared by the authors.

The autoregressive distributed lag-pooled mean model approach developed by Pesaran, Shin and Smith (2001) is used to specify some versions of the model with the variables that will be observed for the short- and long-run effects. The general equation for the model is:

(7)

Two models with two distinct data sets were estimated. The first model includes the 54 countries included in the sample (see annex 1) in order to determine whether, regardless of export profile, the channels of real exchange rate volatility and institutional capacity act as an obstacle to increasing economic complexity. For the second model, the sample was reduced to 31 countries, using the classification of the technical structure of exports developed by Lall (2000), and only considers countries whose export baskets include primary products or resource-based manufactures among their main exports (by value and amount). The aim is to estimate whether the effect of the above-mentioned channels is stronger in countries with resource-intensive (specialized) export profiles.

IV. Results

Before estimating the coefficients of the models specified in this article, we verified the order of integration of the variables used via panel unit root tests: the Levin, Lin, Chu (2002) (LLC) test; the Im, Pesaran, Shin (2003) (IPS) test; and the second-generation cross-sectionally augmented IPS (CIPS) test of Pesaran (2007). The assumption of the LLC test is that there is no heterogeneity in the autoregression parameter; the IPS test allows for heterogeneity; and the CIPS unit (safer) relaxes the assumption of cross-sectional independence of the contemporary correlation. For all the tests, the presumed null hypothesis is non-stationarity. The lag longitude was selected using the Bayesian-Schwarz information criterion. The tests are relevant to avoid the use of series with an order of integration other than I(0) and I(1). Table 3 shows the results of the tests conducted and confirms that the series are appropriate for the application of the proposed method.

Table 3

Unit root tests

Levin-Lin-Chu

Im-Pesaran-Shin

Fisher

based on

the augmented Dickey-Fuller test

Fisher

based on the Phillips-Perron test

Decision

ECI

-2.60095

-3.90199

182.450

367.266

Stationary

[0.0046]

[0.0000]

[0.0000]

[0.0000]

Volatreal

1.3E+13

-72.9635

619.748

2 384.57

Stationary

[1.000]

[0.0000]

[0.0000]

[0.000]

Efigob

-1.71789

-0.60183

111.093

143.584

Non-stationary

[0.0429]

[0.2736]

[0.3998]

[0.0125]

Qalireg

-2.07570

1.50555

-121.718

112.790

Non-stationary

[0.0190]

[0.0661]

[0.1732]

[0.3570]

Corrup

3.67000

-4.29350

173.373

259.611

Stationary

[0.0001]

[0.0000]

[0.0001]

[0.0000]

Emp

-4.35443

0.97351

94.1031

115.765

Non-stationary

[0.0000]

[0.8349]

[0.8273]

[0.2873]

GDP

-1.58756

-0.36763

115.245

62.7850

Non-stationary

[0.052]

[0.3566]

[0.2989]

[0.9998]

Volat*GDP

4.54606

-2.77007

176.765

693.457

Stationary

[1.000]

[0.0028]

[0.000]

[0.000]

Source: Prepared by the authors from Eviews estimations.

We then estimated the panel data cointegration tests using the Pedroni (1996) cointegration test. The Engle and Granger (1987) cointegration test is based on an examination of the residuals of a spurious regression performed using I(1) variables. If the variables are cointegrated, the residuals will be I(0); otherwise, they will be I(1). Pedroni (1999) extends the Engle and Granger structure to panel data tests and proposes various cointegration tests that allow for cross-sectional heterogenous intercepts and trend coefficients. We considered the following regression:

(8)

where y, x are variables I(1) by supposition; t is the number of time series observations(t = 1, 2, …, T); i is the number of individual members of the panel (i = 1, 2, ..., N); m is the number of variables (m = 1, 2, ..., M); ai refers to individual effects, which can be zero, and the parameters b1i, b2i, …, bMi, which can vary between individuals in the panel, thus allowing for cross-sectional heterogenous intercepts and trend coefficients.

Once the equation (6) was estimated, we tested the residuals obtained to determine non-stationarity I(1), estimating the following auxiliary regression for each cross section:

(9)

Pedroni (1999) sets out various methods for developing statistics that can be used to evaluate the no cointegration null hypothesis ti of the residuals equation. Table 4 shows the results of the within-group and between-group statistics applied to the dimension test. In at least one of the statistics, there is cointegration in the models estimated. The results therefore suggest the existence of a long-term equilibrium relationship between the economic complexity index (ECI) variable and the other variables of the model.

Table 4

Pedroni cointegration test

Within-group dimension

Full sample

Limited sample

Statistic

Probability

Statistic

Probability

Panel difference statistic

-3.86011

0.9999

-1.57100

0.9419

Spearman’s rho panel statistic

1.28747

0.9010

0.596040

0.7244

Phillips-Perron panel statistic

-22.8909

0.0000

-16.7327

0.0000

Augmented Dickey-Fuller panel statistic

-6.39406

0.0000

-4.42804

0.0000

Between-group dimension

Full sample

Limited sample

Statistic

Probability

Statistic

Probability

Spearman’s rho group statistic

5.256928

1.0000

4.590222

1.0000

Phillips-Perron group statistic

-25.5421

0.0000

-12.0269

0.0000

Augmented Dickey-Fuller group statistic

-3.75944

0.0001

-2.287785

0.0111

Source: Prepared by the authors on the basis of estimations made using the EViews program.

Note: Null hypothesis = without cointegration.

It should be noted that each model estimated had six different specifications, since the inclusion of variables indicating institutional quality (Corrup, Efigob and Qualireg) require an entry in separate equations. On the other hand, the Volatreal and Volat*GDP variables show a strong correlation. Specification I estimates the individual impact of real effective exchange rate volatility on the economic complexity index (ECI), while specifications II, III and IV include variables representing institutional quality. Specification V estimates the combined impact of real exchange rate volatility and the Corrup variable, chosen as a relevant indicator of institutional quality.[3] Specification VI measures the impact of real effective exchange rate volatility weighted by the size of the economy. All the specifications are controlled for the share of industrial employment in total employment (Emp) and for per capita GDP (GDP).

Tables 5 and 6 present the results of estimations of long-term coefficients and short-term error correction mechanisms for the full sample of countries and the limited sample, respectively.

Table 5

Full sample of countries (54 countries): long-term coefficients and error correction mechanisms (economic complexity index: dependent variable)

I

II

III

IV

V

VI

Volatreal

-0.62044*

(0.104816)

-1.658784*

(0.356341)

Volat*GDP

7.45E-08*

(2.56E-08)

Efigob

0.572263*

(0.069690)

Qalireg

0.302528*

(0.54688)

Corrup

0.929427*

(0.189260)

4.514914*

(0.277563)

2.027227*

(0.371823)

Emp

0.044591*

(0.011649)

0.032211*

(0.011143)

0.009835*

(0.003583)

0.079156*

(0.018869)

0.202313*

(0.031321)

0.377927*

(0.050036)

GDP

0.000142*

(1.88E-05)

0.173294*

(0.056501)

0.475174*

(0.015681)

0.513363*

(0.069407)

0.057164

(0.182238)

Lags (autoregressive distributed lag model)

[3.3.3.3]

[1.1.1.1]

[1.1.1.1]

[1.2.2.2]

[2.3.3.3.3]

[3.3.3.3]

Maximum lags

3

3

2

2

3

3

Error correction mechanism

-0.950710*

-0.558718*

-0.588747*

-0.502039*

-0.583062*

-0.519300*

Source: Prepared by the authors on the basis of estimations made using the EViews program.

Note: (*) (**) (***) indicate a 1%, 5% and 10% statistical significance, respectively.

Standard errors in parentheses.

Table 6

Sample of natural resource-based countries (31 countries): long-term coefficients and error correction mechanisms (economic complexity index: dependent variable)

I

II

III

IV

V

VI

Volatreal

-0.672304*

(0.083911)

-1.064955*

(0.129651)

Volat*GDP

-2.97E-08

(7.30E-08)

Efigob

1.274437*

(0.030780)

Qalireg

1.957057*

(0.381055)

Corrup

2.255231*

(0.130677)

1.849103*

(0.162510)

1.322799*

(0.040086)

Emp

0.037319*

(0.008119)

0.005587

(0.004871)

0.817803*

(0.104212)

0.023298*

(0.009404)

0.02627***

(0.014601)

0.077149*

(0.001134)

GDP

0.147418*

(0.036810)

0.469344*

(0.016139)

3.322076*

(0.597010)

0.562788*

(0.058004)

0.034373

(0.067016)

Lags (autoregressive distributed lag model)

[1.4.4.4]

[3.3.3.3]

[2.3.3.3]

[1.4.4.4]

[1.3.3.3.3]

[3.3.3.3]

Maximum lags

4

3

3

4

4

3

Error correction mechanism

-0.615942*

-0.568397*

-0.423875*

-0.538091*

-0.446305*

-0.797330*

Source: Prepared by the authors on the basis of estimates made using the EViews program.

Note: (*) (**) (***) indicate a 1%, 5% and 10% statistical significance, respectively.

Standard errors in parentheses.

The first channel analysed is the behaviour of real effective exchange rate volatility, which shows that the directions and magnitudes of the Volatreal variable are similar in both specifications: when the model is estimated without distinction as to export profile (see table 5), and when the sample is restricted to countries with commodity-intensive natural resource-based economies (see table 6). The variable presents the negative sign expected in the specifications in which it is included (I and V), which suggests that high exchange rate volatility hinders an increase in the degree of sophistication of the economy, and also denotes larger magnitudes when combined with the Corrup institutional quality indicator in version V.

Consequently, in keeping with the hypothesis set out in this work and the tests presented in the literature —such as in Oreiro, D’Agostini and Gala (2020), Hooy, Baharumshah and R. Brooks (2016), and Thorbecke (2008)— it is reasonable to consider that exchange rate instability creates a context of uncertainty with regard to the proper implementation of structural change mechanisms, thus discouraging the development of industrial sectors and more sophisticated products with higher technological and knowledge content.

This reinforces the process of productive specialization in commodities and natural resource-intensive goods, as Gabriel and Míssio (2017) argue, since the product space is limited to the production of less sophisticated goods. In line with the premise defended by Hidalgo and Hausmann (2009), the greater the similarity and proximity between products, in terms of their comparative advantages, in a country’s productive fabric, the greater the possibilities of occurrence of a process of sophistication of the production structure. This is because the basic requisite capabilities are already available locally, which facilitates the expansion and development of other similar activities requiring similar capabilities (product space). Consequently, a context of real exchange rate volatility, which discourages the development of products with higher technological content, may prevent the subsequent diversification of products and sectors with a similar level of sophistication in the productive fabric, such that the production structure would remain focused on low value-added production.

The second channel examined —institutional quality— has a positive correlation with economic complexity: in both the full sample of countries (see table 5) and the limited sample (see table 6), there is a strong relationship between higher-quality institutions and more complex production structures. Hence, countries with institutions that are more efficient in the implementation of their economic policies, in the regulation of the economy to allow for greater stability and, above all, the adoption of effective mechanisms for combating and controlling corruption tend to develop more sophisticated products and sectors. This result is consistent with the test results obtained in the works of Zhu and others (2010) and Vu (2021), which emphasize that higher-quality institutions promote knowledge accumulation, entrepreneurship and innovative capabilities that help to raise the technological content of the product space and drive productive sophistication.

The other control variables employed delivered the positive signals expected. The share of industrial production in total employment (Emp) reveals a positive and statistically significant relationship with the economic complexity index (ECI) indicator; this shows the relevance of the level of qualification of the labour force for the development of more complex activities. Per capita GDP (GDP) also has a positive and statistically significant relationship with ECI, which suggests that the size of the economy (more developed economies) is relevant in the export profile sophistication process. The results of the variable of the interaction between real effective exchange rate volatility and per capita GDP volatility (Volat*GDP), to capture the effects of volatility weighted by size of the economy, were not of much interpretative value.

With regard to the short term, the error correction mechanism (ECM) coefficients of the different specifications were negative and statistically significant. ECM refers to the speed at which the estimated model returns to long-term equilibrium. Specification I of the model of the full sample of countries (see table 5) shows a very rapid speed of adjustment to long-term equilibrium (95.07%), which means that a little more than one year is required for the variables to return to equilibrium. In the remaining specifications, which involve structural variables (institutions), the adjustment speed was slower, around 55%, which indicates that it takes an average of almost two years for recovery from short-term shocks.

In the model based on the restricted sample of countries with natural resource-based economies, the speed of adjustment to long-term equilibrium in specification I was relatively slower than that of the full sample (61.59%), which indicates that almost two years are required for the variables to return to long-term equilibrium. This suggests that countries with that export profile have more difficulties dealing with the effects of shocks in real effective exchange rate volatility. The other specifications presented similar speeds of adjustment, also around 55%.

V. Concluding remarks

The aim of this article was to ascertain whether the two channels of transmission of the natural resource curse —real exchange rate volatility and institutional quality—which have harmful effects relating to the link between specialization in commodities and low long-term economic growth potential, may have a negative impact on a country’s capability to achieve greater economic complexity, especially in countries with an export profile dominated by commodities and natural resource-based manufactures. The empirical strategy used to achieve that objective was based on long-term estimations with a sample of 54 countries combined with a model of cointegration with a panel structure.

The study estimated two models. The first model, which included the full sample of 54 countries, was designed to determine whether a heterogenous group of export profiles, real exchange rate volatility and institutional quality indicators prevent a country’s economic complexity from rising. The second model restricted the sample to 31 countries, using Lall’s (2000) classification of exports by technological intensity , and therefore included only countries specializing in the export of commodities or natural resource-based manufactures.

The results obtained confirm the hypothesis of this article, that real exchange rate volatility can hinder the implementation of structural measures designed to diversify and sophisticate the productive fabric of an economy. As suggested by the hypothesis, supported by empirical tests extracted from the literature, there is a factor limiting productive diversification in countries that suffer from exchange rate instability, especially those whose productive structure is based on natural resources and which are more sensitive to the long-term negative effects of the channels of transmission of the natural resource curse. Thus, the trend towards regressive specialization in natural resources, based on comparative advantages, may accelerate in a context in which instability, associated with risks and uncertainties, obstructs the transfer, development and co-export of related activities, including when domestic skills and capabilities are potentially inherent. Furthermore, this situation worsens in a scenario where diversified activities with higher technological (sophisticated) content and possibly exclusive activities (non-ubiquitous) may be prevented from succeeding because of a given country’s inability to address the unfavourable effects of exchange rate instability. Further research on this hypothesis, focusing on the effects on more sophisticated activities, is needed to complement the results presented in this study.

The results also confirm that institutional quality has a positive relationship with economic complexity, showing that higher-quality institutions offer greater opportunities for the development of local skills, which could translate in the future into products with higher added value and a more sophisticated and diversified export profile. Institutions that are more efficient, more credible and less beholden to private interests may be more active at the time of adoption of measures and guidelines not only to address the effect of economic instability, but also to create an enabling environment for diversification and sophistication. It is presumed, therefore, that some good institutions have sufficient and effective mechanisms for domestic activities and capabilities to be stimulated and turned into a more complex production structure, to be able to benefit from their favourable effects on economic growth in the long term.

It is clear, therefore, that there is strong complementarity between the economic complexity approach and the theory of the natural resource curse. While the natural resource curse and its empirical tests demonstrate the significant long-term negative impact of an export profile dominated by natural resources and especially affected by conditioning channels, the economic complexity methodology reaffirms that concern by showing the need for the productive fabric of the country or region to be diversified and sophisticated. Furthermore, like the natural resource curse, the economic complexity analysis shows the negative effects of maintaining a low-complexity productive structure on long-term economic growth since, as suggested by the literature, countries capable of moving toward nodes and conglomerates representing more sophisticated activities in the product space tend to converge toward a long-term growth trajectory. In other words, the nature of the activity in which the country specializes or diversifies is important for its long-term trajectory.

The opportunity thus emerges for a future research agenda based on two synchronous approaches: the measurement of similarities within the product space and the feasibility of developing potential local capabilities, while identifying and establishing the requisite mechanisms for avoiding obstacles so as to escape the low-complexity trap.

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Annex 1

Table A1.1

Characteristics of the export profiles of the countries included in the full sample, 2019

Country

Primary products

Resource-based manufactures

Low-technology manufactures

Medium-technology manufactures

High-technology manufactures

Algeria

X

X

Argentina

X

Australia

X

X

Austria

X

X

Belgium

X

X

Brazil

X

X

Bulgaria

X

Canada

X

X

X

Chile

X

China

X

X

Colombia

X

X

Croatia

X

X

Cyprus

X

X

Czechia

X

X

Denmark

X

X

Estonia

X

X

Finland

X

X

X

France

X

X

Germany

X

X

Greece

X

X

Hungary

X

X

India

X

X

X

Indonesia

X

X

X

Ireland

X

X

Israel

X

X

Italy

X

X

Japan

X

X

Latvia

X

X

Lithuania

X

X

Malaysia

X

X

Mexico

X

X

Netherlands (Kingdom of the)

X

X

New Zealand

X

Norway

X

Peru

X

X

Philippines

X

Poland

X

Portugal

X

X

Republic of Korea

X

X

Romania

X

Russian Federation

X

X

Saudi Arabia

X

X

Singapore

X

X

Slovakia

X

X

Slovenia

X

X

South Africa

X

X

Spain

X

X

Sweden

X

X

Switzerland

X

X

Thailand

X

X

X

Türkiye

X

X

X

United Arab Emirates

X

X

X

X

United Kingdom

X

X

United States

X

X

X

X

Source: Prepared by the authors using data from the Observatory of Economic Complexity (OEC) and the classification developed by S. Lall, “The technological structure and performance of developing country manufactured exports, 1985–1998”, QEH Working Paper Series, Nº 44, University of Oxford, 2000.

1. [1] See Engle (1982) for a more detailed explanation of the equations.

2. [2] A structure that considers an exponential specification.

3. [3] The corruption indicator (Corrup) was selected because it delivered higher coefficients in specifications II, III and IV than the other two institutional quality indicators.

The influence of fiscal solvency on financial development: evidence for 140 countries, 1990–2020[1]

Renzo A. Jiménez-Sotelo

Received:15/09/2022

Accepted:15/08/2024

Abstract

If financial development facilitates economic development at the international level, as is the consensus view, then it follows that each government’s task is to implement policies that effectively boost national financial development. This study tests the new hypothesis that a more fiscally solvent policy approach positively influences financial development. The results, based on a yearly comparison, show that fiscal solvency, approximated by the credit rating on sovereign debt denominated in local currency, affects three dimensions of financial development: (i) the depth of credit leveraging of economic activity; (ii) the efficiency of the bank lending-deposit spread; and (iii) retail access through bank branches and automatic teller machines (ATMs). The test confirms that there is one tool upon whose importance not all macroeconomic policymakers have agreed in their strategic planning for enhanced general public welfare: fiscal solvency.

Keywords

Finance, fiscal policy, public finance, financial policy, public debt, risk, capital markets, creditworthiness, economic development

JEL classification

F65, G00, O16

Author

Renzo A. Jiménez-Sotelo holds a PhD in Economics from the National University of San Marcos and has held the positions of director in the Ministry of Economy and Finance of Peru and professor at the University of the Pacific (Peru). Email: rjimenezsotelo@yahoo.es.

I. Introduction

Of the 218 in existence, the number of independent countries and territories (referred to as “countries” for the purpose of this analysis) with a sovereign credit rating in 1970 was 8. That number grew to 14 by 1980, 37 by 1990 and 168 by 2020, making it possible to study the influence of fiscal solvency on financial development in a set of countries that accounted for an average 83% of the world’s population and 75% of its available surface area in the period 1990–2020.

Although the relationship between finance and economics is well established (see, for example, Schumpeter, 1911/1934, and Keynes, 1936), the study of the determinants of financial development is more recent and has tended to focus almost exclusively on cross-cutting factors, such as legal tradition (La Porta and others, 1998), religion as a determinant of cultural habits and institutional rules (Stulz and Williamson, 2003), political economy as a determinant of regulatory frameworks (Pagano and Volpin, 2001) and geography as a determinant of institutional frameworks (Acemoglu, Johnson and Robinson, 2001). Other studies have focused on the legal origins of firms’ access to financial markets (Beck, Demirgüç-Kunt and Levine, 2005), laws affecting the development of securities markets (La Porta, López de Silanes and Shleifer, 2006) and the macroeconomic policies associated with financial repression in the context of liberalization (Bencivenga and Smith, 1992).

However, Rajan and Zingales (2003), using longer-term data sets, showed that changes in financial development have not always been monotonic over time, and therefore proposed that a policy of simultaneous openness to trade and financial markets accelerated financial development. Chin and Ito (2006) later proposed that trade liberalization was a precondition of financial liberalization. Baltagi, Demetriades and Law (2009) extended this approach to show that the mechanisms of trade and financial openness could be considered substitutes.

Against this backdrop, the present study tests the hypothesis that there is another factor —fiscal solvency— that engenders non-monotonic changes in the development of national financial systems and that can therefore be used as a policy instrument. For this test, sovereign credit ratings were used to build an annual index to approximate fiscal solvency. This factor is separate from institutional change (Acemoglu, Johnson and Robinson, 2005).

The central question that this study seeks to answer is, can greater fiscal solvency facilitate greater development of the national financial system? The study uses a non-experimental quantitative approach and an explanatory unbalanced panel design with samples of up to 140 countries and data spanning nearly three decades. Considering the matter’s multidimensional nature, the influence of fiscal solvency was analysed through a yearly comparison of indicators approximating the four dimensions of financial development identified by the World Bank (2019).

The results show that the null hypothesis accepted in the literature is rejected with representative indicators in three of the four dimensions: (i) the credit leveraging of economic activity, a typical indicator of financial development; (ii) the bank lending-deposit spread, a direct measure of the efficiency with which the financial sector provides its services to the rest of the economy; and (iii) the banking access index based on the relative number of branches and ATMs (IMF, 2023). The results call for a re-evaluation of the residual role that macroeconomic policy has conventionally assigned to public debt management.

The article is divided into five sections, including this introduction. The second section provides an explanation of the growing influence of fiscal solvency on financial development as a result of the more extensive use of credit ratings. The third presents the available data and the selection criteria for the methodology used in this study. Results are presented and analysed in the fourth section. The fifth section presents concluding remarks.

II. The growing role of fiscal solvency in financial development

Progressive global integration since the 1970s might lead one to think that the effects of countries’ traditional points of divergence have diminished over time, especially as States have accelerated their convergence by adopting common policies. However, some effects may have intensified as a result of this convergence, including the financial policies that, beginning in the 1980s and 1990s, progressively emphasized the widespread use of credit ratings as a solvency indicator (see figure 1).

Figure 1

Countries with a sovereign credit rating, 1970–2020

(Number of countries)

Source: Prepared by the author, on the basis of data from Expansión, “Rating: calificación de la deuda de los países”, Datosmacro.com, 2024 [online] https://datosmacro.expansion.com/ratings and World Government Bonds (WGB), “World credit ratings”, 2024 [online] http://www.worldgovernmentbonds.com/world-credit-ratings/.

As is known, the greatest driver of financial convergence has been the series of principles and standards issued by the Basel Committee on Banking Supervision since 1975. The Basel Committee was established in response to the financial crisis triggered by the 1974 failure of Bankhaus Herstatt in Germany and the subsequent refusal of Chase Manhattan Bank to fulfil payment and cheque orders in its capacity as Herstatt’s correspondent bank in the United States (Walker, 2001, pp. 26 and 27). The Committee, tasked with developing appropriate principles and rules to guide regulatory and supervisory practices in global banking with a view to preventing future crises, introduced the first credit risk-based capital measurement system in 1998 (BIS, 1998). The system’s scope extended beyond Group of 10 countries, applying to all countries with internationally active banks. In 1996, the Committee published an amended capital measurement framework, which went into effect in 1997, to incorporate a capital requirement for market risks (BIS, 1996), and this process has marched on ever since (see, for example, BIS 2001 and 2017).

The growing emphasis on the intensive use of credit ratings to calculate capital requirements in all credit institutions led to an increasingly standardized approach to risk management in other types of financial institutions, which in turn caused global demand for credit ratings to soar —first for public debt and then for private. Since then, sovereign credit ratings have become a key benchmark, establishing a de facto ceiling for the credit ratings of all firms in a given country. This has made them increasingly useful in determining the financing terms —currency, rate, maturity and amount— that lenders extend to borrowers (Borensztein, Cowan and Valenzuela, 2013; OECD, 2009).

One of the least studied ways in which public finances affect financial development is the level of fiscal solvency with which elected politicians (or their advisors) prefer to implement macroeconomic policy in each country. As the latest global financial crisis showed once again, sovereign debt crises are ultimately triggered by the imminent insolvency of a given country’s public finances. In the long run, the insolvency of any State is the product of one of the following conditions: (i) an excessive accumulation of unproductive debt; (ii) insufficient structural revenue; (iii) the expectation that some combination of the above will endure or worsen over time (see, for example, Daniel and others, 2006); or (iv) the expectation that, in the event of an overall deterioration of the global outlook, other States will not fare much better in attempting to navigate some combination of these adverse circumstances, as was the case in the last global crisis and the more recent epidemiological crisis, when would-be capital flight had nowhere to land.

In the past, there has been discussion of adopting a golden rule to prevent public financial crises brought on by excessive unproductive debt or insufficient structural revenue. Under this golden rule, States would be prohibited from borrowing to finance any unproductive expenditure on current services provision, which should instead be covered by structural revenue. Such a policy would mean that public debt could only be taken on to finance investment net of depreciation (Khan and Mayes, 2009). Nevertheless, many countries end up doing the exact opposite when, faced with mounting pressure to adopt a more activist fiscal stance (and a diminishing political appetite for tax reform), their governments begin to: (i) take on debt with terms of maturity that far exceed the useful life of the assets that the debt originally financed; (ii) take on debt to cover expenditures that do not generate the needed revenue increases; (iii) issue new debt to partially finance existing debt (including interest); or (iv) directly refinance existing debt.

In recent decades, assessing and rating a given country’s capacity to meet its sovereign debt obligations has become the most widely used valuation method for determining fiscal solvency. In general, the worse the valuation of the borrower, the greater the return that lenders will require to offset the increased risk assumed; and similarly, the better the valuation, the lower the return required. By the same token, the higher the explicit or implicit interest payments on a given loan, the lower the borrower’s debt-carrying capacity, and vice versa.

Thus, a country’s fiscal solvency and the level of development of its financial system are very closely linked: the lower the systemic fiscal solvency, the harder it is for the financial system to perform effectively, owing to the increased financing costs and the diminished capacities that can result in terms of leveraging, efficiency, access and stability; and vice versa.

III. Available data and applied methodology

The 218 countries in existence from 1990 to 2020 comprise the study’s overall population. The countries selected for inclusion in the sample were those that had the most units with available data to simultaneously construct the different indicators for the variables involved. The maximum sample size was 140 countries, as shown in table 1.

Table 1

Countries included in the study

Albania

Czechiab

Kuwait

Republic of Koreab

Angola

Denmarkb

Kyrgyzstan

Republic of Moldova

Argentina

Dominican Republic

Latviab

Romania

Armenia

Ecuador

Lebanona

Russian Federation

Arubaa

Egypt

Lesotho

Rwanda

Australiab

El Salvador

Libya

Saudi Arabia

Austriab

Estoniab

Lithuaniab

Senegal

Azerbaijan

Eswatini

Luxembourga b

Serbia

Bahamasa

Ethiopia

Macao, Chinaa

Seychellesa

Bahraina

Fiji

Madagascar

Singaporea b

Bangladesh

Finlandb

Malaysiaa

Slovakiab

Barbadosa

Franceb

Mali

Sloveniab

Belarus

Gabon

Malta

South Africa

Belgiumb

Gambia

Mauritiusa

Spainb

Belizea

Georgia

Mexico

Sri Lanka

Benin

Germanyb

Mongolia

Suriname

Bolivia (Plurinational State of)

Ghana

Montenegro

Swedenb

Bosnia and Herzegovina

Greeceb

Moroccoa

Switzerlanda b

Botswana

Guatemala

Namibia

Taiwanb

Brazil

Honduras

Netherlands (Kingdom of the)a b

Tajikistan

Bulgaria

Hong Kong, Chinaa b

New Zealandb

Thailanda

Burkina Faso

Hungary

Nicaragua

Togo

Cabo Verde

Icelandb

Niger

Tunisia

Cambodia

India

North Macedonia

Türkiye

Cameroon

Indonesia

Norwayb

Uganda

Canadab

Irán (Islamic Republic of)

Oman

Ukraine

Chile

Iraq

Pakistan

United Arab Emirates

China

Irelanda b

Panamaa

United Kingdoma b

Colombia

Israela b

Papua New Guinea

United Republic of Tanzania

Congo

Italyb

Paraguay

United Statesa b

Congo (Democratic Republic of the )

Jamaica

Peru

Uruguaya

Costa Ricaa

Japana b

Philippinesa

Uzbekistan

Côte d’Ivoire

Jordan

Poland

Venezuela (Bolivarian Republic of)

Croatia

Kazakhstan

Portugalb

Viet Nam

Cyprusa b

Kenya

Qatar

Zambia

Source: Prepared by the author, on the basis of World Bank, “Indicators”, 2024 [online] https://data.worldbank.org/indicator, International Monetary Fund (IMF), “Offshore financial centers: IMF background paper”, 23 June 2000 [online] https://www.imf.org/external/np/mae/oshore/2000/eng/back.htm and Moody’s Investors Service, Moody’s Country Credit Statistical Handbook, New York, 2012–2019.

a Tax haven.

b Industrialized country.

Regarding the construction of indicators, for each country, the dependent variable data correspond to the financial system; the independent variable data correspond to the central government component of the public sector; and the controlled variable data correspond to the specific aggregate activity (see table 2).

Table 2

Selected indicators

Variable

Indicator

Formula

Symbol

Databases

Financial development

Financial leveraging

of economic activity

(Private credit from depository institutions and others) / GDP * 100

Ycréd

World Bank (2023a)

Index of depth of financial institutions

Yipro

IMF (2023)

Bank access through branches and ATMs

Index of access to financial institutions

Yiacc

Efficiency of bank

lending-deposit spread (for real and nominal interest rates)

1 / (1 + active real interest rate – passive real interest rate) * 100, average

Ytir_i

World Bank (2023a)

1 / (1 + active nominal interest rate – passive nominal interest rate) * 100, average

Ytin_i

Stability, by financial de-dollarization

1 – (percentage dollarization

of bank deposits) * 100, average

Yddol

Moody’s Investors Service (2012-2019) and central banks

Fiscal solvency

Sovereign risk index

in national currency

Daily weighted average of Moody’s,S&P and Fitch sovereign credit ratings

Xrsmn

Expansión (2024) and WGB (2024)

Sovereign curve

Domestic public debt market

(Domestic public debt securities / total public debt securities) * 100, average

Zmidp

World Bank (2023a)

Economic development

Adjusted net national income per capita

(Net domestic product + net foreign income, 2022 euros purchasing power parity) / total population

Zinpc

World Inequality Lab (2024)

Net income equality

of wealthiest 10% of population

National income before taxes and after pensions of wealthiest 10% / national income before taxes and after pensions of 100%

Zequi

Openness of trade system

International trade

(Exports + imports) / GDP

Zac

World Bank (2024)

Stability

Price level

Consumer price index

Zipc

Institutional development

Public governance

Simple average of public governance indicators

Zgob

World Bank (2023b)

Openness of financial system

External balance

(External assets + external liabilities) / GDP

Zafbe

Milesi-Ferretti (2025)

Capital account openness

Index of financial openness

Zafcc

Chin and Ito (2021)

More

developed countries

Industrialized countries

1, or 0 if other

Bind

Moody’s Investors Service (2012)

Crisis

Banking crisis

1, or 0 if other

Bcri

World Bank (2023a)

Tax havens

Offshore financial centres

1, or 0 if other

Bpf

IMF (2000)

Source: Prepared by the author, on the basis of World Bank, “Global Financial Development”, 2023a [online] https://databank.worldbank.org/source/global-financial-development; “Worldwide Governance Indicators”, 2023b [online] https://databank.worldbank.org/source/worldwide-governance-indicators, and “World Development Indicators”, 2024 [online] https://databank.worldbank.org/source/world-development-indicators; International Monetary Fund (IMF), Financial Development Index Database, 2023 [online] https://data.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b and “Offshore financial centers: IMF background paper”, 23 June 2000 [online] https://www.imf.org/external/np/mae/oshore/2000/eng/back.htm; Moody’s Investors Service, Moody’s Country Credit Statistical Handbook, New York, 2012-2019; Expansión, “Rating: calificación de la deuda de los países”, Datosmacro.com, 2024 [online] https://datosmacro.expansion.com/ratings; World Government Bonds (WGB), “World credit ratings”, 2024 [online] http://www.worldgovernmentbonds.com/world-credit-ratings/; World Inequality Lab, World Inequality Database, 2024 [online] https://wid.world; G. M. Milesi-Ferretti, “The external wealth of nations database”, 13 January 2025 [online] https://www.brookings.edu/articles/the-external-wealth-of-nations-database; M. D. Chin and H. Ito, “The Chinn-Ito index: a de jure measure of financial openness”, Portland State University, 2021 [online] https://web.pdx.edu/~ito/Chinn-Ito_website.htm.

Note: The indicator Xrsmn was constructed to take into account risk subcategories (+/-) and outlooks (+/-), using values 1.00, 0.90, 0.75, 0.60, 0.45, 0.30, 0.15, 0.00 to represent credit ratings AAA, AA, A, BBB, BB, B, CCC, C and C (or lower).

In addition to the openness and institutional development indicators analysed by Rajan and Zingales (2003), Chin and Ito (2006) and Baltagi, Demetriades and Law (2009), income equality and monetary stability were included as possible determinants of financial development that change over time, following the approach of Clarke, Xu and Zou (2006) and Almarzoqi, Naceur and Kotak (2015). The role of the sovereign curve provided by the domestic public debt market was also considered, following the approach of Jiménez-Sotelo (2023). Determinants that do not change over time were considered in the aggregate as part of the heterogeneity of each country.

These data cover the top 98.4% and 97.5% of the respective data ranges observed for the 187 countries and 162 countries of the world with information available for indicators Ycréd (4.93% of GDP or higher) and Xrsmn (stable CC or higher). Table 3 provides selected statistics for the indicators.

Table 3

Statistical summary

Indicator

Mean

Median

Standard deviation

Minimum

Maximum

Observations

Period

Ycréd

63.69

52.51

43.91

4.93

304.57

2 990

1990–2020

Yipro

0.34

0.24

0.27

0.02

1.00

3 040

1990–2020

Yiacc

0.42

0.38

0.28

0.00

1.00

3 040

1990–2020

Ytir

6.22

4.86

6.45

-11.86

53.00

1 863

1990–2020

Ytin

6.68

5.11

7.11

-13.09

56.39

1 869

1990–2020

Yddol

74.47

82.06

26.44

0.00

100.00

2 238

1990–2020

Xrsmn

0.62

0.61

0.26

0.02

1.00

3 065

1990–2020

Zmidp

82.41

88.66

18.15

0.09

100.00

1 196

1990–2020

Zinpc

23 785

17 613

19 038

1 535

151 272

3 065

1990–2020

Zequi

0.44

0.44

0.10

0.23

0.71

2 999

1990–2020

Zac

89.74

76.88

58.11

15.16

442.62

3 065

1990–2020

Zipc

98.41

97.27

49.32

0.00

951.62

3 065

1990–2020

Zgob

59.39

58.48

24.56

3.35

99.65

3 065

1990–2020

Zgob#

57.25

54.74

24.28

3.27

99.76

2 559

1996–2020

Zafbe

6.27

1.62

24.94

0.07

405.40

3 065

1990–2020

Zafcc

0.63

0.70

0.36

0.00

1.00

2 936

1990–2020

Source: Prepared by the author, on the basis of World Bank, “Global Financial Development”, 2023a [online] https://databank.worldbank.org/source/global-financial-development; “Worldwide Governance Indicators”, 2023b [online] https://databank.worldbank.org/source/worldwide-governance-indicators, and “World Development Indicators”, 2024 [online] https://databank.worldbank.org/source/world-development-indicators; International Monetary Fund (IMF), Financial Development Index Database, 2023 [online] https://data.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b and “Offshore financial centers: IMF background paper”, 23 June 2000 [online] https://www.imf.org/external/np/mae/oshore/2000/eng/back.htm; Moody’s Investors Service, Moody’s Country Credit Statistical Handbook, New York, 2012-2019; Expansión, “Rating: calificación de la deuda de los países”, Datosmacro.com, 2024 [online] https://datosmacro.expansion.com/ratings; World Government Bonds (WGB), “World credit ratings”, 2024 [online] http://www.worldgovernmentbonds.com/world-credit-ratings/; World Inequality Lab, World Inequality Database, 2024 [online] https://wid.world; G. M. Milesi-Ferretti, “The external wealth of nations database”, 13 January 2025 [online] https://www.brookings.edu/articles/the-external-wealth-of-nations-database; M. D. Chin and H. Ito, “The Chinn-Ito index: a de jure measure of financial openness”, Portland State University, 2021 [online] https://web.pdx.edu/~ito/Chinn-Ito_website.htm.

Note: Zgob# represents the indicator that excludes all data from the 1990–1995 period, which were stochastically imputed in the Zgob indicator.

Some indicators had time series that were not as short, and macroeconomic series were included in certain cases; this meant, for some, the probable presence of trend or persistence over time following any change or shock. In the case of the dependent variable, in all of the tests (p, Z L* and Pm), level indicators Ytir_i, Ytin_i and Yddol and logarithmic indicators Ycréd, Yipro and Yiacc (abbreviated as lnYcréd, lnYinpc and lnYiacc) rejected the null hypothesis that all panels contained a unit root, at a statistical significance of less than 5%, whether the tests were performed with a time trend or only with drift, and with or without eliminating cross-sectional means in an effort to control for potential contemporaneous correlation among countries.

In the case of the fiscal solvency indicator Xrsmn, no transformation was necessary to reject at a level of statistical significance of less than 5% the null hypothesis that all panels contained a single root with a time trend or only with drift, eliminating cross-sectional means. When de-meaning was not performed, the null hypothesis could not be rejected, but this was not particularly concerning: owing to the very nature of the indicator (publicly available), the methodology used in its construction (pairwise comparison) and the behaviour of its target audience (investors who can only invest in one alternative by divesting from all others), there will always be contemporaneous correlation.

In the case of the controlled variables, the unit root test results were as theorized, and the relevant transformations were applied or not applied as appropriate. Indicators Zinpc, Zequi, Zipc and Zapbe, were stationary with logarithmic differencing (abbreviated as dlnZinpc, dlnZequi, dlnZipc and dlnZapbe), while no transformation was necessary for indicators Zac, Zgob and Zafcc or for the sovereign curve indicator (Zmidp).

Table 4 shows the indicator correlations, with transformations applied or not applied as appropriate.

Table 4

Correlation matrix

lnYcred

lnYipro

lnYiacc

Ytir_i

Ytin_i

Yddol

Xrsmn

Zmidp

lnYcred

1.0000

lnYipro

0.8601***

1.0000

lnYiacc

0.7787***

0.7334***

1.0000

Ytir_i

0.2646***

0.2190***

0.1884***

1.0000

Ytin_i

0.2912***

0.2171***

0.2072***

0.9171***

1.0000

Yddol

0.2652***

0.3531***

0.2465***

0.3276***

0.3047***

1.0000

Xrsmn

0.6678***

0.7359***

0.5361***

0.3000***

0.3185***

0.4443***

1.0000

Zmidp

0.4022***

0.3577***

0.1696***

0.2963***

0.3116***

0.4972***

0.3481***

1.0000

dlnZinpc

dlnZequi

Zac

dlnZipc

Zgob

Zgob#

dlnZafbe

Zafcc

dlnZinpc

1.0000

dlnZequi

-0.0339**

1.0000

Zac

0.0356**

-0.0272*

1.0000

dlnZipc

-0.1620***

0.0407***

-0.0688***

1.0000

Zgob

0.0277**

0.0563***

0.3218***

-0.1842***

1.0000

Zgob#

-0.0187

0.0351**

0.3391***

-0.2087***

0.9988***

1.0000

dlnZafbe

-0.1507***

0.0341**

0.0186

0.0691***

0.0690***

0.0992***

1.0000

dlnZafcc

0.0155

0.0268*

0.2420***

-0.1796***

0.5098***

0.5338***

0.0207

1.0000

Source: Prepared by the author, on the basis of the results of his research.

Note: Statistical significance < 1%(***), 5%(**), 10%(*).

Therefore, following the approach used in previous studies of the determinants of financial development, the theory, updated with the proposed hypothesis, was expressed as follows:

Theory: y = F (x, z1, z2, z3, z4, z5, z6, z7, z8, z9…) = F(X', Z')

Hypothesis:

To disprove the proposed hypothesis, the previously accepted hypothesis in the literature was designated the null hypothesis, and the opposite was designated the alternative hypothesis:

H0: A greater capacity to meet sovereign debt obligations hinders or does not facilitate financial development (c ≤ 0).

H1: A greater capacity to meet sovereign debt obligations facilitates financial development (c > 0).

To address the model uncertainty problem in the explanation of financial development (Huang, 2011), the approach of Rajan and Zingales (2003) was applied for the initial estimation, using static panel data models for several time periods and reflecting that, in the hypothesis of causality, the independent variables (causes) should plausibly precede the dependent variable (effect):

(1)

In equation 1, y represents any of the specifications indicated in table 2 to approximate financial development, x represents the specification indicated to approximate fiscal solvency, and c represents its parameter (interest); Z represents a vector with the specifications indicated to approximate control variables, and b represents a vector with their respective parameters; a represents a scalar, and f represents the error term which, in addition to the idiosyncratic component, includes temporal and individual effects. In addition, i represents the individual dimension of N countries, and t represents the temporal dimension of T years.

To obtain robust results with these statistical models, a panel data model with fixed individual and temporal effects was used, considering the effects induced by the heterogeneity of each country and by financialization (Palley, 2009). For this purpose, the following specification tests were conducted: the Wald test (1940) to verify the joint nullity of the fixed coefficients; the Lagrange multiplier test (Breusch and Pagan, 1980) to verify whether the variance of the random effects estimator and the variance of the grouped data were different; the Hausman test (1978) to determine the viability of a random effects specification in lieu of a fixed effects specification; the modified Wald test (Greene, 2002) to verify whether the individual fixed effects presented heteroscedasticity; and the Woolridge test (2002) to determine whether there was first-order error correlation. According to the literature, there are several temporal determinants of financial development that explain cross-cutting financial development differences among countries; therefore, the specification theory that this study sought to empirically prove was that of fixed effects.

Likewise, depending on the stationarity of the specification indicator used to approximate financial development, the panel data models evaluated not just the static proposal of Rajan and Zingales (2003) but also a dynamic proposal like that of Baltagi, Demetriades and Law (2009). This entailed the addition of the respective lag yi,t–1 with its coefficient as an additional regressor, as shown in equation 2:

(2)

To obtain robust results from these dynamic models, the Arellano-Bond test was performed in the first-order differences model with a view to determining whether there was first-order autocorrelation, indicating no autocorrelation in the original error term. While it should be possible to reject the null hypothesis of no first-order autocorrelation, it should not be the case for second-order or higher autocorrelation, to enable the respective lags to be used as instruments. Should the null hypothesis of no first-order autocorrelation not be rejected, a static model would be required instead of a dynamic one. In the dynamic model, it would also be necessary to conduct the Sargan test (1958) or the Hansen test (1982) to determine the joint validity of the instruments used in the Generalized Method of Moments (GMM) estimation, where only the second of the two is robust to heteroscedasticity or autocorrelation, albeit with a propensity for weakness due to excessive instruments. Therefore, as a minimally arbitrary general rule, it was determined that the number of instruments should not exceed the number of units involved and that the probability value of the Hansen test should ideally be between 0.10 and 0.25 (Roodman, 2009) or at least between 0.05 and 0.80 (Labra and Torrecillas, 2014).

Lastly, given the nature of the hypothesis, unilateral tests were programmed to run in Stata 16.1 for each specific case.

IV. Presentation and discussion of the results

Although the calculations could not be shown here owing to limited space, all model specifications with grouped data or random individual effects were rejected, consistent with the theory and all previous findings. Likewise, there was not enough space to show all the models used to obtain the results of hypothesis contrasts discussed further on, three sets of models representing three financial development aspects (depth, access and efficiency) are shown below.

(a) Influence on financial development depth

Table 5 provides a representative set of regressions for financial depth (lnYcréd). The findings show, after accounting for the presence of endogeneity over time, that countries with greater fiscal solvency experienced more rapid increases in terms of financial depth. The level of statistical significance was maintained irrespective of the method used and of whether only original governance data for 1996–2020 (Zgob#) were used or whether the panel with stochastically imputed data for 1990–1995 (Zgob) was also included.

Table 5

Fiscal solvency and financial depth, 1990–2020

Explanatory and statistical variables

Dependent variable: lnYcrédi,t

Full sample / Regression model

1996–2020 sample / Regression model

Fixed effects

OLS

Exogenous

GMM

Pre-determined GMM

Fixed effects

OLS

Exogenous

GMM

Pre-determined GMM

lnYcrédi,t–1

0.8680613

(0.0143229)

***

0.8589265

(0.0247571)

***

0.885147

(0.0273638)

***

0.8455328

(0.0194745)

***

0.8207319

(0.0373274)

***

0.8786502

(0.0319782)

***

Xrsmni,t–1

0.3325568

(0.0524699)

***

0.3397579

(0.0561122)

***

0.692402

(0.1613847)

***

0.32354

(0.0514716)

***

0.3492504

(0.0607408)

***

0.6824334

(0.1624322)

***

dlnZinpci,t–1

0.2383714

(0.061217)

***

0.2347911

(0.0590409)

***

0.3114254

(0.0653215)

***

0.1973124

(0.0717308)

***

0.1830918

(0.0727845)

**

0.2778341

(0.0767843)

***

Zaci,t–1

-0.0004701

(0.0002401)

*

-0.0004765

(0.0002344)

**

-0.0004644

(0.0002294)

**

-0.0005965

(0.00031)

*

-0.0006074

(0.0003086)

**

-0.0005532

(0.0002891)

*

dlnZipci,t–1

-0.0509099

(0.06017)

-0.0511841

(0.0607316)

-0.017643

(0.0467503)

-0.6296721

(0.2307837)

***

-0.6454909

(0.2282339)

***

-0.5285593

(0.2269714)

**

Zgobi,t–1

-0.000848

(0.0007991)

-0.0008239

(0.0007796)

-0.0033954

(0.0012808)

***

Zgob#i,t–1

-0.0005893

(0.000809)

-0.0009093

(0.000831)

-0.003794

(0.0015129)

**

dlnZafbei,t–1

0.0019696

(0.0257789)

0.0034206

(0.0268439)

-0.0112844

(0.0274954)

0.0121906

(0.0200891)

0.0179426

(0.0203351)

-0.0143273

(0.0231375)

Bcrii,t

-0,037363

(0.0148846)

**

-0.0347609

(0.0176862)

**

-0.0359697

(0.0146322)

**

-0.0142034

(0.0187689)

-0.0077857

(0.0224503)

-0.0221267

(0.018523)

Instruments

117

94

87

85

P-value (AR(1))

0.000

0.000

0.000

0.000

P-value (AR(2))

0.587

0.508

0.284

0.415

P-value (Hansen)

0.136

0.129

0.040

0.141

Observations

2 866

2 726

2 726

2 368

2 228

2 228

Countries

140

138

138

140

138

138

T maximum

30

29

29

21

20

20

T average

20.5

16.9

Balancing

68.2

%

68.1

%

68.1

%

80.5

%

80.7

%

80.7

%

Robust

Yes

Yes

Yes

Yes

Yes

Yes

Source: Prepared by the author, on the basis of the results of his research.

Note: GMM, Generalized Method of Moments; OLS, ordinary least squares method. Standard errors in parentheses. Statistical significance < 1%(***), 5%(**), 10%(*). The models used fixed effects estimator xreg, fe vce (cluster) and a robust orthogonalized GMM differences estimator with collapsed instruments (xtabond2). The constant and fixed effects are not shown.

In this set of models, the estimated coefficients practically double when financial development, economic growth and fiscal solvency are taken into account as pre-determined variables (0.692 or 0.682) rather than exogenous only (0.339 or 0.349).

This means that for any yearly improvement in a given country’s sovereign credit rating at the subcategory level (e.g. from BB+ to BBB- or from A- to A) occurring in the period 1990–2020, there is a corresponding increase in the following year of lnYdapred = cpred*X = 0.6924*0.05 = 0.0346 ≈ 3.46%, instead of lnYdaexóg = cexóg*X = 0.3398*0.05 = 0.0170 ≈ 1.70%, in the country’s degree of financial leveraging (indicated by its credit-to-GDP ratio), at a statistical significance of less than 1%.

For regressions only covering the period 1996–2020, there is little change in the difference. The effects on the credit-to-GDP ratio can therefore be calculated at 3.41% and 1.75%, respectively, with no change in statistical significance.

It is worth noting that, while not shown, the coefficients also vary little when substituting financial openness indicators dlnZafbe and Zafcc in all six models. Thus, if all other effects had been held constant, the alternative effects on the ratio would have been 3.58% and 1.60%, respectively, in the period 1990–2020.

It is also worth noting that, while Baltagi, Demetriades and Law (2009) obtained surprising negative and statistically significant coefficients for the per capita GDP logarithm as an indicator of economic activity in all the specifications performed for the period 1980–2003, the present study’s results are compatible with the theory, with positive and statistically significant coefficients of growth obtained using the per capita GNI logarithm indicator instead.

In comparing coefficients for openness to trade, financial openness and institutional development, statistical significance declines or disappears, but at the same time, the coefficients for the sovereign risk, inflation and banking crisis indicators not previously considered become highly significant.

(b) Influence on financial development access

Table 6 presents a representative set of regressions for financial access (lnYiacc). The findings show, after accounting for the presence of endogeneity over time, that countries with greater fiscal solvency experienced more rapid increases in terms of financial access in the period 1990–2020. Results remained robust irrespective of the method used and of whether only original governance data for 1996–2020 (Zgob#) were used or whether the panel with stochastically imputed data for 1990–1995 (Zgob) was also included.

Table 6

Fiscal solvency and financial access, 1990–2020

Explanatory and statistical variables

Dependent variable: lnYiacci,t

Full sample / Regression model

1996–2020 sample / Regression model

Fixed effects

OLS

Exogenous

GMM

Pre-determined GMM

Fixed effects

OLS

Exogenous

GMM

Pre-determined GMM

lnYiacci,t–1

0.9417711

(0.0154656)

***

0.9795722

(0.0137645)

***

0.9466446

(0.0210961)

***

0.9182313

(0.0177097)

***

0.9591043

(0.0150924)

***

0.9133366

(0.0240353)

***

Xrsmni,t–1

0.0960823

(0.0324232)

***

0.0743886

(0.0299508)

**

0.5477657

(0.1463966)

***

0.0977262

(0.0342875)

***

0.0892555

(0.03424)

***

0.6038011

(0.1391253)

***

dlnZinpci,t–1

0.1476567

(0.0501417)

***

0.1344388

(0.0494193)

***

0.1800687

(0.0587039)

***

0.1380398

(0.0501918)

***

0.125517

(0.0499179)

**

0.1641078

(0.0625293)

***

Zaci,t–1

-0.0002576

(0.0001326)

*

-0.0001797

(0.0001241)

-0.0002383

(0.0001502)

-0.0004058

(0.0001558)

**

-0.0003127

(0.000145)

**

-0.0003513

(0.0001809)

*

dlnZipci,t–1

0.0212351

(0.0240985)

0.0263145

(0.0202883)

0.0625114

(0.0150415)

***

-0.0192667

(0.0722245)

-0.0015206

(0.0594104)

0.085399

(0.0754055)

Zgobi,t–1

-0.0015135

(0.0006899)

**

-0.0017563

(0.0006313)

***

-0.0046347

(0.0012208)

***

Zgob#i,t–1

-0.0010286

(0.0006891)

-0.0019763

(0.0007301)

***

-0.0051143

(0.0012746)

***

dlnZafbei,t–1

-0.0041355

(0.0123553)

-0.010834

(0.0127637)

-0.0200816

(0.0157915)

-0.0016155

(0.0126394)

-0.0131965

(0.0135404)

-0.0284125

(0.0159119)

*

Bcrii,t

-0.0266851

(0.0057134)

***

-0.0281912

(0.0054096)

***

-0.0172087

(0.0088784)

*

-0.0214399

(0.0061949)

***

-0.0245644

(0.0059303)

***

-0.0170789

(0.0104303)

Instruments

117

94

87

85

P-value (AR(1))

0.000

0.000

0.000

0.000

P-value (AR(2))

0.297

0.277

0.567

0.673

P-value (Hansen)

0.185

0.115

0.038

0.116

Observations

2 899

2 762

2 762

2 394

2 257

2 257

Countries

137

135

135

137

135

135

T maximum

30

29

29

21

20

20

T average

21.2

17.5

Balancing

70.5

%

70.5

%

70.5

%

83.2

%

83.6

%

83.6

%

Robust errors

Yes

Yes

Yes

Yes

Yes

Yes

Source: Prepared by the author, on the basis of the results of his research.

Note: GMM, Generalized Method of Moments; OLS, ordinary least squares method. Standard errors in parentheses. Statistical significance < 1%(***), 5%(**), 10%(*). The models used fixed effects estimator xreg, fe vce (cluster) and a robust orthogonalized GMM differences estimator with collapsed instruments (xtabond2). The constant and fixed effects are not shown.

Here, the estimated coefficients increased practically sevenfold when financial development, economic growth and fiscal solvency were taken into account as pre-determined variables (0.548 or 0.604) rather than exogenous only (0.074 or 0.089).

This means that for any yearly improvement in a given country’s sovereign credit rating at the subcategory level in the period 1990–2020, there is a corresponding increase in the following year of lnYabbpred = cpred*X = 0.5478*0.05 = 0.0273 ≈ 2.73%, instead of lnYabbexóg = cexóg*X = 0.0744*0.05 = 0.0037 ≈ 0.37%, in the country’s index of access to financial institutions through bank branches and ATMs, at a statistical significance of less than 1%.

These statistically significant results increase slightly (to 3.02% and 0.45%, respectively) if only the period 1996–2020 is taken into account.

The effects are somewhat diminished when substituting financial openness indicators dlnZafbe and Zafcc in all six models. Thus, the estimated effects with all others held constant would be 2.34% and 0.31% for the period 1990–2020 (regressions not shown).

No previous studies were identified that use this access indicator to approximate financial development.

(c) Influence on financial development efficiency

Table 7 provides a representative set of regressions for financial efficiency (lnYiacc). In this case, no endogeneity over time was observed. The findings show that countries with greater fiscal solvency experienced higher levels of efficiency, in the form of lower bank lending-deposit spreads for both real interest rates (Ytir) and nominal interest rates (Ytin) for the period 1990–2020.

Table 7

Fiscal solvency and financial efficiency, 1990–2020

Explanatory and statistical variables

Dependent variable: Ytir_ii,t

Dependent variable: Ytin_ii,t

Regression model: fixed effects OLS / Scope of sample

Full

1996–2020

0%<Ytir<100%

Full

1996–2020

0%<Ytin<100%

Xrsmni,t–1

6.456193

(2.622335)

**

6.868385

(2.70008)

**

6.384225

(2.664578)

**

7.329493

(2.862969)

**

7.812687

(2.894909)

***

7.164609

(2.893272)

**

dlnZinpci,t–1

0.5215604

(0.9746675)

-0.0944986

(0.8434796)

0.4291664

(10.001451)

0.5194831

(10.060543)

-0.0135964

(0.920152)

0.4173384

(10.09093)

Zaci,t–1

-0.0027528

(0.0068997)

-0.0035413

(0.0061181)

-0.0027841

(0.0065574)

-0.0036243

(0.0075429)

-0.0046071

(0.0068558)

-0.0039797

(0.0069097)

dlnZipci,t–1

-1.386343

(2.290088)

1.361551

(2.882638)

-3.085917

(2.228415)

-6.647282

(3.055656)

**

-2.744423

(3.689871)

-9.242402

(2.810671)

***

Zgobi,t–1

0.0221825

(0.0439581)

0.0292876

(0.046101)

0.018229

(0.0470724)

0.0278745

(0.048874)

Zgob#i,t–1

0.0372028

(0.0459639)

0.0323915

(0.0485553)

dlnZafbe i,t–1

-0.2261722

(0.4826925)

-0.298986

(0.5300002)

-0.4925804

(0.5521385)

-0.075461

(0.56228)

-0.204548

(0.5991724)

-0.4229844

(0.6269885)

Bcrii,t

-0.4113341

(0.436571)

-0.1082244

(0.3936348)

-0.523995

(0.4113773)

-0.4706933

(0.4874545)

-0.1310938

(0.4463179)

-0.6153025

(0.4409232)

Xi,t–1 * Bpfi,t–1

-7.80329

(4.257134)

*

-6.739712

(3.926943)

*

-7.853164

(4.287502)

*

-8.590284

(4.516614)

*

-7.412859

(4.11631)

*

-8.617401

(4.543104)

*

F

1.87

1.84

3.31

2.92

2.19

5.47

Prob. > F

0.007

0.015

0.000

0.000

0.002

0.000

Observations

1 804

1 557

1 721

1 822

1 575

1 739

Countries

101

101

98

102

102

99

T maximum

31

21

31

31

21

31

T average

17.9

15.4

17.6

17.9

15.4

17.6

Balancing

57.6

%

73.4

%

56.6

%

57.6

%

73.5

%

56.7

%

Robust errors

Yes

Yes

Yes

Yes

Yes

Yes

Source: Prepared by the author, on the basis of the results of his research.

Note: Standard errors in parentheses. Statistical significance < 1%(***), 5%(**), 10%(*). The models used fixed effects estimator xreg, fe vce (cluster) and a robust orthogonalized GMM differences estimator with collapsed instruments (xtabond2). The constant and fixed effects are not shown.

Maintaining differentiation of countries with tax havens, findings remained robust irrespective of whether only original governance data for 1996–2020 (Zgob#) were used or whether the stochastically completed panel for 1990–1995 (Zgob) was also included. Results were similar when data were eliminated if the bank lending-deposit spread was negative or above 10,000 basis points.

Thus, for countries without tax havens, for any subcategory improvement in the sovereign credit rating, there is a corresponding financial efficiency estimate (as measured by the bank lending-deposit spread) of yCompleta = cCompleta*x = 6.46*0.05 = 0.32 when considering the full sample, y1996-2020 = c1996-2020*x = 6.87*0.05 = 0.34 when considering the 1996–2020 sample and y0%<Yba<100% = c0%<Yba<100%*x = 6.38*0.05 = 0.32 when considering the sample that excludes extreme values. The statistical significance of the results is less than 5%, even though these samples are, on average, 30%–40% smaller than those used in the two previous cases.

To illustrate, the median bank lending-deposit spread of 4.86% can be used as a point of reference. The initial efficiency of the spread () would become after one year, for every 100 monetary units of assets and liabilities processed.

Thus, the real bank lending-deposit spread would decrease by 35 basis points, from 4.86% to 4.51%, for any sovereign credit rating improvement at the subcategory level (e.g. from BBB+ to A-), when considering the full sample coefficient. Similar results are obtained when considering the sample that excludes extreme values.

The magnitude and sign of the estimated interaction coefficients suggest that there would be almost no effect on countries with tax havens.

Here again, no previous studies were identified that use this efficiency indicator to approximate financial development.

(d) Results of hypothesis contrasts

Lastly, table 8 shows the results of the different hypothesis contrasts with the six indicators used to approximate the four dimensions of financial development: (i) lnYcréd and lnYipro and for depth;(ii) lnYiacc for access; (iii) Ytir_i and Ytin_i for efficiency; and (iv) Yddol for stability.

Table 8

Unilateral probability values for disproving null hypothesis (H0: c ≤ 0)

Model

Regressors added or substituted for those maintained

Dependent variable

lnYcréd

lnYipro

lnYiacc

Dynamic fixed effects OLS

Zgob; dlnZafbe; dlnZipc

0.0000***

0.0025***

0.0018***

Zgob;Zafcc;dlnZipc

0.0000***

0.0001***

0.0036***

Zgob#;dlnZafbe;dlnZipc

0.0000***

0.0001***

0.0025***

Zgob#;Zafcc;dlnZipc

0.0000***

0.0002***

0.0043***

Dynamic GMM

differences (exogenous)

Zgob;dlnZafbe;dlnZipc

0.0000***

0.0092***

0.0065***

Zgob;Zafcc;dlnZipc

0.0000***

0.0002***

0.0155**

Zgob#;dlnZafbe;dlnZipc

0.0000***

0.0003***

0.0046***

Zgob#;Zafcc;dlnZipc

0.0000***

0.0005***

0.0102**

Dynamic GMM

differences (pre-determined)

Zgob;dlnZafbe;dlnZipc

0.0000***

0.4257

0.0001***

Zgob;Zafcc;dlnZipc

0.0000***

0.3034

0.0001***

Zgob#;dlnZafbe;dlnZipc

0.0000***

0.1288

0.0000***

Zgob#;Zafcc;dlnZipc

0.0000***

0.0642*

0.0000***

Ytir_i

Ytin_i

Yddol

Static fixed effects OLS

Zgob,dlnZafbe

0.0567*

0.0334**

0.2504

Zgob; dlnZafbe; dlnZipc

0.0691*

0.0606*

0.2063

Zgob; dlnZafbe; dlnZipc,0%<Yti<100%

0.0786*

0.0710*

Zgob#; dlnZafbe

0.0262**

0.0137**

0.1558

Zgob#; dlnZafbe;dlnZipc

0.0215**

0.0152**

0.2707

Zgob#;dlnZafbe;dlnZipc;0%<Yti<100%

0.0227**

0.0161**

Static fixed effects OLS

(adding interaction between Xrsmn and Bpf)

Zgob,dlnZafbe

0.0038***

0.0018***

0.3355

Zgob; dlnZafbe; dlnZipc

0.0078***

0.0060***

0.2707

Zgob; dlnZafbe; dlnZipc,0%<Yti<100%

0.0092***

0.0075***

Zgob#; dlnZafbe

0.0061***

0.0026***

0.1983

Zgob#; dlnZafbe;dlnZipc

0.0062***

0.0041***

0.1939

Zgob#;dlnZafbe;dlnZipc;0%<Yti<100%

0.0059***

0.0039***

Source: Prepared by the author, on the basis of the results of his research.

Note: GMM, Generalized Method of Moments; OLS, ordinary least squares method. Statistical significance < 1%(***), 5%(**), 10%(*). Regressors Xrsmn; Zinpc; Zac and Bcri are maintained for all models.

Differing from the approach suggested by Clarke, Xu and Zou (2006) and by Beck, Demirgüç-Kunt and Levine (2007), the control for inequality (Zequi) was disregarded, as it did not ultimately prove significant in any of the models.

The results show that, using the logarithm indicator for the leveraging of economic activity (column lnYcréd), the null hypothesis was rejected at a statistical significance of less than 1% in all 12 models, irrespective of the financial openness indicator used (dlnZafbe or Zafcc), the coverage of the governance indicator used as a control variable (Zgob or Zgob#), the consideration of variables Xrsmn and dlnZippa as exogenous or pre-determined, or the application of a fixed effects OLS only.

The samples shrank in five countries when Zafcc was used as a regressor in lieu of dlnZafbe.

When the financial depth logarithm indicator (column lnYipro) was used instead of the leveraging logarithm indicator, the statistical significance of the null hypothesis rejection was maintained at less than 1%, except in dynamic pre-determined models, which did not prove significant.

When the financial access logarithm was used to approximate the degree of financial development of each country (column lnYiacc), the null hypothesis was rejected at a statistical significance of less than 1% in 10 of the 12 models and less than 5% in the other 2 models.

Using the bank lending-deposit spread to measure the efficiency of financial development, in both nominal and real terms (columns Ytir_i and Ytin_i), the null hypothesis was rejected at a statistical significance of less than 5% only for the original series of governance index data (Zgob#). However, the statistical significance of the null hypothesis rejection was less than 1% when an interaction was included to separate the influence of countries with tax havens (Bpf), and the choice of model appeared to have very little effect.

Separating out industrialized countries (Bind) produced no significant differences.

The null hypothesis could not be rejected in any of the models when using the de-dollarization indicator (column Yddol) to approximate financial stability.

(e) Discussion of three particularities observed in the results

Three specific aspects of the results bear further discussion: (i) the partial difference of the contrast results with the financial depth indicators; (ii) the statistical insignificance of the contrast with the financial stability indicator; and (iii) the possibility of omitted-variable bias.

First, the partial rejection of the null hypothesis with indicator lnYipro relative to the total for lnYcréd in table 8 is attributable to the fact that indicator Yipro, constructed by the International Monetary Fund, is not adequately defined for less developed countries, which comprise the majority of the available sample.

As shown in table 9, that depth index subsumes not only indicator Ycréd but three others referring to the total assets of non-credit institutions. In less developed countries, this creates a coverage problem, as only the first indicator has exclusively domestic coverage, while for the other three, investments— which represent a large chunk of less developed countries’ assets— are carried out both domestically and abroad. It is certain that deteriorating fiscal solvency would have an effect on total credit (domestic), whereas this is not necessarily so for total investments (domestic and international), as declining local investments could be offset by rising international investments.

Table 9

Links between financial development indicators

Aggregated indicators

Indicators by dimension

Individual indicators

Financial development index

Financial institution index

Depth index

(Yipro)

Private sector credit / GDP (Ycréd)

Pension fund assets / GDP

Mutual fund assets / GDP

Life insurance and non-life insurance premiums / GDP

Access index

(Yiacc)

Bank branches per capita * 100 000

ATMs per capita * 100 000

Efficiency index

Net interest margin

Bank lending-deposit spread (Ytin)

Non-interest income / total income

Overhead costs / total assets

Earnings / total assets

Earnings / equity

Financial markets index

Depth index

Stock market capitalization / GDP

Stocks traded / GDP

International public debt securities / GDP

Domestic and international debt securities

of non-financial corporations / GDP

Domestic and international debt securities of financial corporations / GDP

Access index

Stock market capitalization of 10 largest corporations / GDP

Total number of issuers of debt (domestic and international, non-financial and financial corporations) / 100 000

Efficiency index

Stocks traded / stock market capitalization

Indicators not included

in any index

Stability indicators

Non-dollarized deposits / total deposits (Yddol)

Non-dollarized credit / total credit

Non-dollarized debt securities / total debt securities

1 - International holders of debt securities / total holders

of debt securities

Domestic deposits and debt securities / domestic credit

and investment

Source: Prepared by the author, on the basis of R. Sahay and others, “Rethinking financial deepening: stability and growth in emerging markets”, IMF Staff Discussion Note, SDN/15/08, International Monetary Fund (IMF), 2015, and K. Svirydzenka, “Introducing a new broad-based index of financial development”, IMF Working Paper, No. WP/16/5, IMF, 2016.

The second aspect of the results bearing further examination is the inclusion and non-rejection of the null hypothesis using indicator Yddol. The IMF indices do not include any financial stability indicator (see table 9), and yet, for less developed countries, the dollarization of financial systems is one of the largest sources of vulnerability. Its consideration in the analysis was therefore deemed essential. However, because the average maturity of bank deposits tends to fluctuate and be shorter than one year, one plausible hypothesis for the non-rejection is that this indicator’s response is much more rapid and unstable.

The case of Peru, a particularly dollarized economy, offers an illustrative example of the volatility associated with financial system dollarization. According to official statistics, 53% of the country’s bank deposits were held in checking and savings accounts in 2015 and 58% in 2019, while in the same two years, fixed deposits with terms of maturity longer than one year accounted for just 9% and 14% of total deposits, respectively. This translates into a sizeable volume of funds subject to rapid changes in currency composition, even in the span of a few days. Moreover, in 2015, the average volume of debits and credits was 9.5 times the year’s average beginning and ending balances, compared to 4.5 in 2019. In parallel to these considerable fluctuations in the annual rotation of deposits through financial institutions, the national currency depreciated by 14.2% against the dollar over the course of 2015, while the exchange rate held relatively steady throughout 2019, appreciating by 0.3%. In the final analysis, less fiscal solvency means a less stable currency in the face of similar exogenous shocks.

The third point for discussion is the possible role of the sovereign curve provided by a domestic public debt market (Zmidp). This indicator was not considered in any of the previous regressions because it would exclude nearly 60% of the sample data from the estimations, and not just any data: the exclusion would tend to affect less developed countries in particular, as shown in figure 2. This bias is consistent with Levine’s observation (2002) that stock market development follows banking sector development.

Figure 2

Financial leveraging and fiscal solvency, 1990–2020

(Percentages and index)

Source: Prepared by the author, on the basis of World Bank, “Indicators”, 2024 [online] https://data.worldbank.org/indicator, Expansión, “Rating: calificación de la deuda de los países”, Datosmacro.com, 2024 [online] https://datosmacro.expansion.com/ratings and World Government Bonds (WGB), “World credit ratings”, 2024 [online] http://www.worldgovernmentbonds.com/world-credit-ratings/.

Note: The vertical axis shows credit-to-GDP ratios, expressed in percentages, and the horizontal axis shows the sovereign risk index, with 0.00 representing default and 1.00 representing the greatest possible solvency.

Is there an omitted-variable bias, then, in the previous findings? The answer depends on the group of countries included and the dimension of financial development under analysis. Assuming that the 87 countries without available data on their domestic public debt markets do not have a national stock market, the influence of fiscal solvency on financial depth would be nearly the same, with a minimal decrease in the coefficients calculated and a null hypothesis rejection with the same levels of statistical significance.

However, for the 52 countries that do have data on their domestic public debt markets, the degree of influence of the same level of fiscal solvency could be greater than estimated, but the net impact would depend on its interaction with the relative size of each domestic market, as shown in table 10. The results, while not shown, vary little when substituting financial openness indicators dlnZafbe and Zafcc in all six models.

Table 10

Fiscal solvency, domestic markets and financial development, 1990–2020

Explanatory and statistical variables

Dependent variable: lnYcredi,t

Full sample / Regression model

1996–2020 sample / Regression model

Fixed effects

OLS

Exogenous

GMM

Pre-determined GMM

Fixed effects

OLS

Exogenous

GMM

Pre-determined GMM

lnYcrédi,t–1

0.9018847

(0.0188208)

***

0.912141

(0.0597999)

***

0.7787768

(0.0988094)

***

0.8797891

(0.0338686)

***

0.8542352

(0.08725)

***

0.8167138

(0.1114332)

***

Xrsmni,t–1

0.4986862

(0.0991847)

***

0.4933744

(0.1015507)

***

0.1277722

(0.4951229)

0.5084313

(0.1216543)

***

0.5266576

(0.1239486)

***

0.2073178

(0.4455961)

Zmidpi,t–1

0.0024215

(0.0008898)

***

0.0024476

(0.0008627)

***

-0.0039023

(0.0048187)

0.0025436

(0.0008397)

***

0.0024728

(0.0008556)

***

0.0009217

(0.0042402)

Zmidpi,t–1 *Xrsmni,t–1

-0.0024938

(0.0010481)

**

-0.0025568

(0.0010293)

**

0.0079079

(0.0072288)

-0.0024145

(0.0009928)

**

-0.0022767

(0.0011026)

**

-0.0003188

(0.0071479)

dlnZinpci,t–1

0.139525

(0.1008437)

0.1495904

(0.0956277)

0.1252281

(0.1329272)

0.0746166

(0.0950835)

0.0428219

(0.1274551)

-0.0425988

(0.1574405)

Zaci,t–1

-0.0004628

(0.0003437)

-0.0004752

(0.0003381)

-0.0000699

(0.0004255)

-0.0005779

(0.0004075)

-0.0005721

(0.0004105)

-0.0006855

(0.0005945)

dlnZipci,t–1

-0.1449321

(0.0223457)

***

-0.1468792

(0.0221391)

***

-0.1100583

(0.0428834)

**

-0.3922298

(0.2041858)

*

-0.4188299

(0.2011735)

**

-0.5188131

(0.2395299)

**

Zgobi,t–1

-0.0004022

(0.0010842)

-0.000385

(0.0010424)

-0.0024038

(0.0025741)

Zgob#i,t–1

-0.0000917

(0.0012183)

-0.0004802

(0.0012357)

0.0010916

(0.0021874)

dlnZafbei,t–1

-0.0450893

(0.0533582)

-0.0470584

(0.0517322)

-0.0204444

(0.066394)

-0.0230906

(0.0576375)

-0.0135565

(0.0598457)

0.0118997

(0.0597777)

Bcrii,t

-0.042974

(0.0111317)

***

-0.045607

(0.0176901)

***

0.0034681

(0.0341812)

-0.0284296

(0.0119378)

**

-0.0234595

(0.0196965)

-0.0175872

(0.0326709)

Instruments

67

49

49

40

P-value (AR(1))

0.010

0.016

0.001

0.000

P-value (AR(2))

0.891

0.987

0.156

0.323

P-value (Hansen)

0.978

0.475

0.178

0.220

Observations

1 114

1 062

1 062

812

760

760

Countries

52

51

51

52

51

51

T maximum

30

29

29

21

20

20

T average

21.4

15.6

Balancing

71.4

%

71.8

%

71.8

%

74.4

%

74.5

%

74.5

%

Robust

Yes

Yes

Yes

Yes

Yes

Yes

Source: Prepared by the author, on the basis of the results of his research.

Note: GMM, Generalized Method of Moments; OLS, ordinary least squares method. Standard errors in parentheses. Statistical significance < 1%(***), 5%(**), 10%(*). The models used fixed effects estimator xreg, fe vce (cluster) and a robust orthogonalized GMM differences estimator with collapsed instruments (xtabond2). The constant and fixed effects are not shown.

In terms of possible influence on the other dimensions of financial development (efficiency, access and stability), the results of the hypothesis contrasts (not shown) indicate no change, estimating the influence of the domestic public debt market on those dimensions to be not significantly different from zero.

V. Final reflections

The financial development of any country is linked to how effectively and efficiently its financial system fulfils its core function —the allocation and deployment of economic resources, both spatially and across time— in an uncertain environment (Merton, 1990). To do this, the financial system needs to not only (i) support the accumulation of physical and human capital; (ii) use the resulting productive assets in the most efficient manner; and (iii) provide access to the entire population by establishing and expanding institutions, instruments and markets (FitzGerald, 2006); but also (iv) do so without becoming a source of instability. Thus, financial development is not only a matter of quantity but also a matter of quality, as demonstrated by the global financial crisis of 2007–2008 (Sahay and others, 2015).

In that context, the confirmation of the proposed hypothesis is notable on two fronts. First, with one of the same indicators of the quantity of financial development used in earlier studies, the null hypothesis that was previously accepted as truth regarding the completeness of financial development theories was rejected. Second, using the largest sample identified to date, the study found evidence that greater fiscal solvency also affects the quality of financial development —that is, it affects the quality with which the financial system provides its services to the rest of the economy—, reducing costs in the form of the bank lending-deposit spread and expanding its service coverage in the form of bank branches and ATMs.

The confirmation of the hypothesis indicates that the role of public debt management in resisting economic and financial disruptions and avoiding crises is much larger than previously thought (IMF/World Bank, 2001). Indeed, greater fiscal solvency not only avoids problems but generates direct positive externalities for the development of the local financial system and, with its established relationship to economic development, also contributes to the increased well-being of the populations involved.

While governments can aspire to finance their budget deficits with public debt management strategies (IMF/World Bank, 2002), they cannot realistically achieve this aim while maintaining cost and risk targets unchanged, even through futile attempts to sustain a stable debt-to-GDP ratio (IMF/World Bank, 2009 and 2014). Costs and risks would continue to rise as long as debt and debt requirements continued to grow and as long as credible tax reform to service those requirements remained elusive. Ultimately, fiscal solvency perceptions depend not only on the immediate ability to pay but also on whether the current macroeconomic policy can credibly increase that ability over time (OECD, 2009).

Thus, it would be misleading to refer to low-debt or high-debt countries if a benchmark level of fiscal solvency has not been previously specified, nor would it be suitable to refer to countries’ relative fiscal space if there has not been an increase in income or a decrease in expenditure beforehand (Heller, 2005), complemented by the necessary reforms (Roy, Heuty and Letouzé, 2006). Public debt sustainability is similar in that regard: a public debt trend of a certain level can be unsustainable at one risk rating (e.g. BBB-) but acceptable at a lower rating (e.g. BB+), without an insolvency crisis hanging in the balance in either scenario.

The question of what level of fiscal solvency is desirable or tolerable for a given country should be answered through the strategic foresight exercises conducted by its public treasury (Jiménez-Sotelo, 2017). However, similar to the question of the merits of policy favouring domestic public debt markets to provide a sovereign curve as a public good (Jiménez-Sotelo, 2023), the answer has more to do with moral philosophy and political science than with actual economic theory (ILO, 2012, pp. 3–11), because it depends on the ethical basis used to determine what is good and what is bad for a given society (Jiménez-Sotelo, 2018) and on the power struggle (Bunge, 1999, pp. 176–180) to decide who gets what, when and how (Lasswell, 1936).

The question of why elites in all countries might not seek to increase the capacity to repay public debt with a view to boosting financial development and, by extension, economic growth, was already examined from a political economic theory perspective by Becerra, Cavallo and Scartascini (2010), with data for 97 countries. They concluded that the relative intensity of opposition to financial development among a country’s elites depends on their degree of credit dependency and the capabilities and role of the government in credit markets.

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1. [1] This article contains excerpts from the author’s doctoral thesis, La política financiera aplicada a la deuda soberana y el desarrollo financiero, defended on 21 June 2023, and has benefited from the incorporation of valuable comments received on 12 June 2023 from an anonymous reviewer.

Empirical evidence for Okun’s law in Colombia: an analysis of rural areas at the region level

Diego Andrés Cardoso López and Jesús Antonio López Cabrera

Received: 15/01/2024

Accepted: 30/07/2024

Abstract

This article analyses the relationship between income and unemployment, controlling for the variable of human capital, in rural areas of four Colombian regions. The objective is to test for the existence of the empirical regularity known as Okun’s law and thereby measure income’s impact on unemployment by region. The analysis is based on a monthly series for the period 2010–2022, and the methodologies used to determine the behaviour of this relationship include ordinary least square differences, dynamic ordinary least squares, and error correction and vector autoregressive models, establishing the existence of equilibria in the short and long terms. The results are found to be consistent with Okun’s law, showing a negative relationship between real income and unemployment and a positive one between unemployment and human capital.

Keywords

Economic growth, unemployment, labour market, income, productivity, rural areas, Colombia, Latin America and the Caribbean

JEL classification

E24, E60, C32

Authors

Diego Andrés Cardoso López is an assistant professor at the Department of Economic, Administrative and Accounting Sciences at the Los Libertadores University Foundation, Colombia. Email: diego.cardoso@libertadores.edu.co.

Jesús Antonio López Cabrera is a professor of Economics at the School of Social Sciences and Government at Tecnológico de Monterrey, Mexico. Email: jesus.lopezc@tec.mx.

I. Introduction

The principal goal of economic policy is to boost economic growth by ensuring the efficient use of factors of production. One difficulty in this regard relates to the efficient use of installed capacity (productivity): insufficient resources increase the unemployment rate, a common feature of developing economies (Flórez, Pulido-Mahecha and Ramos-Veloza, 2018; García-Luna and Cardoso-López, 2020; Kendrick, 1961).

In view of this, the information provided by economic growth and unemployment indicators is a critical input in the choice of public policies aimed at increasing productivity in such economies. Okun (1962) argues that there is an inverse relationship between real GDP growth and the unemployment rate. There can be no doubt that the dynamics of this relationship impact national and regional productivity levels. Furthermore, the nature of this relationship in an economy tends to change depending on whether the economy is expanding or contracting (i.e. over the business cycle), which shows that there is an inversely proportional relationship between economic growth and the unemployment rate (Dixon, Lim and Van Ours, 2017).

In this connection, there are factors that can reduce domestic production in a region or country and increase the negative effects caused by unemployment. Thus, according to Porras-Arena and Martín-Román (2019), two of the principal factors supporting this negative relationship are the market for goods and the labour structure, with regional income growth being affected by lack of specialization and a production value added composition that relies on low-productivity employment. This characteristic of productivity perpetuates periods of uncertainty in markets. According to Grant (2018), economic uncertainty also increases negative coefficients in the relationship between real GDP and the unemployment rate.

A related process which helps to reduce unemployment is human capital formation, especially in rural areas. Thus, the presence of institutions providing education and vocational training in rural areas enhances the dynamics of production and the creation of value chains (Gordon, 2010; Salido and Bellhouse, 2016; Otero-Cortés, 2019).

This study focuses on rural areas because the relationship with low income is prevalent there. These areas tend to have lower incomes than urban areas, so that poverty reduction is usually slower. Explanations for this include the lack of high-technology and high-value-added production there and the failure to pursue and implement decent work policies, resulting in slow job creation and revenue declines (ECLAC/ILO, 2016b).

Another persistent characteristic of rural as compared to urban areas is the slower accumulation of productive knowledge and management techniques, resulting in lower marginal returns for rural producers and thus lower household income and greater job instability in these areas (Laszlo, 2008).

In Colombia, unemployment rates tend to be lower in rural than in urban areas, but so do household incomes. Thus, although unemployment is low, revenue growth is also lower, being influenced by the levels of informality in these areas (Otero-Cortés, 2019). In addition, we observe that there was a disparity of approximately 2 percentage points in the unemployment rate across Colombia’s regions in the period from 2010 to 2018, but this disparity has been diminishing owing to a dynamic of convergence between rural and urban areas (Otero-Cortés, 2019).

Colombia’s rural areas have also been shown to have a lower level of knowledge (human capital) than urban areas, and this is particularly true of areas that tend to lag economically. According to Tenjo Galarza and Jaimes (2018), the low levels of education in the rural areas of the country’s departments have set off a chain reaction of decreasing returns to education, mainly owing to a lack of technical support and human capital accumulation in these areas, so that output remains persistently below potential.

The main objective of this study, then, is to test for the existence of the empirical regularity known as Okun’s law in rural areas of the Colombian regions. This is the empirically estimated relationship, in the short and long terms, between unemployment and household income, controlling for a variable such as human capital. We use household income as a proxy for production, since there is very little information on the value of rural production over the years studied (2010 to 2022), and what does exist is annualized. Studying rural areas is a worthwhile pursuit, as there is insufficient research on the relationship between unemployment and household income in rural contexts. There is potential to extend the inference to urban areas of the country or to the economy in general (Ramos, 2017; Flórez, Pulido-Mahecha and Ramos-Veloza, 2018; Ortiz, Jiménez and Uribe, 2020).

Again, disaggregating the analysis at the region level allows us to capture the heterogeneity of Colombia’s internal labour markets. It is also important to show that the relationship proposed by Okun (1962) is consistent not only at the macro level, but also at the meso or regional level. As far as the authors are aware, there are no papers analysing this relationship in rural areas and at the region level in Colombia.

To analyse the relationship, we begin by identifying the behaviour of the model estimation coefficients using first a differential version and then a dynamic version of linear regression as an instrument to determine deterministic relationships. Lastly, the vector autoregressive (VAR) model and the error correction model (ECM) support the short- and long-term analyses. The short-term assessment is built up through the evaluation of Granger causality, impulse-response functions and the variance decomposition analysis. We use regional monthly data for rural areas of Colombia, running from 2010 to 2022 and based on the Large Integrated Household Survey (GEIH) of the National Administrative Department of Statistics (DANE).

The results show a strong negative relationship between real rural household income and family unemployment. Furthermore, the accumulation of human capital has a positive effect on unemployment in all rural areas of the Colombian regions. Another aspect derived from the estimation processes is that we identify the long-term effects of the variables with respect to the unemployment rate.

In addition, there are short-term relationships between the real income variables and rural unemployment in most regions. Similarly, by subjecting the data to causality analysis, we demonstrated that there were causal relationships between all the variables and unemployment in rural areas. Lastly, in the impulse-response analysis and the decomposition of the variance, the Central and Eastern[1] regions were found to be the least likely to evince sudden changes in the unemployment rate in the event of movements in the other variables.

This document is structured as follows. Section II presents a review of the literature on Okun’s law and regional case studies, with a focus on rural areas, and uses monthly statistics to examine the performance of the economic growth structure and the evolution of a number of labour market indicators in Colombia’s rural areas. Section III describes the methodology, presenting data and descriptive statistics. Section IV gives the empirical results from the estimation process. Lastly, section V presents conclusions.

II. Literature review

1. Determinants of the relationship between production and unemployment

It is well known that Okun’s law postulates an inverse relationship between production and unemployment. Thus, Okun (1962) and Malley and Molana (2008) posit that a fall in production in a country or region is accompanied by a significant increase in unemployment rates. Mendonça and Oliveira (2019) argue that this inverse relationship is determined by the costs entailed by inefficiency, with these costs determining the number of people seeking employment and the duration of recessions.

Examining the geographical composition of such inefficiency costs, Schettini and Azzoni (2018) find that the costs of production inefficiency, as reflected in the composition of unemployment, tend to be uneven across regions, owing to the heterogeneity of different areas. Therefore, the inverse relationship between income (along with productivity) and unemployment also tends to differ across geographical areas.

As determinants of these differences between regions, Villaverde and Maza (2009) establish that they are due to increases in productivity, which respond in different ways to Okun’s law, since regions with optimal production and resource utilization will evince only slight differences in income and unemployment. In addition, an asymmetrical relationship between these variables in different regions shows the degree to which structural instabilities, represented in terms of inefficiency costs, have become entrenched (Durech and others, 2014; Tang and Bethencourt, 2017).

Elshamy (2013) adds that a key factor driving these differences between regions, and negatively heightening this relationship, is the installed capacity of the labour market. According to Durech and others (2014), an increase in the population’s general skills, which is something that companies call for, will increase production, reducing unemployment and poverty rates. Also, generating specializations among the population will increase human capital and cushion the negative effects of poverty deriving from any decrease in real wages due, for example, to unemployment (Marth, 2015).

Asymmetries between the supply of and demand for labour provide an overview of the production capacity of an economy. When companies can identify valuable employees of the kind they require for their production activities, their loss aversion decreases. This process increases firm investments, employee numbers and, finally, real wages (Kölling, 2018). However, reforms that make the relationship between buyers and sellers more rigid create distortions in production and increase unemployment (Dal Bianco, Bruno and Signorelli, 2015).

Regarding Latin America, Jiménez Villavicencio and Ochoa Moreno (2017) assert that the countries of the subregion lack the market capacity to adequately increase employment and production, while identifying a significant inverse relationship between unemployment and economic growth. At the same time, the unemployment rate shows lags between periods that influence future unemployment rates and create intertemporal dependence.

Similarly, Briceño, Dávila and Rojas (2016), analyse the relationship between production and unemployment globally and for the Latin America subregion from 1991 to 2014. They find that this relationship lacks significance in Latin America but is statistically significant at the global level. A literature review conducted by Pizzo (2019) for Latin America and the Caribbean reveals consensus that production has been held back by structural shocks, creating a tendency towards lower employment.

There are numerous studies, using data from many countries over different time periods, which confirm that better-educated people earn higher wages and experience less unemployment than their less educated counterparts (Card, 1999). In addition, lower incomes negatively impact individuals’ skills, something that in turn is positively related to unemployment (Cingano, 2014).

There is literature on the long-term relationship between production and unemployment in Colombia. For example, Ramos (2017) shows that when production increases by 1%, there is a reduction of 0.45 percentage points in the unemployment rate. Ortiz, Jiménez and Uribe (2020) argue that the structure of the relationship between economic growth and unemployment is partly contingent on factor costs, with additional factors influencing income imbalances.

Flórez, Pulido-Mahecha and Ramos-Veloza (2018) propose a nonlinear model capturing the cointegration relationship to describe the relationship between the rate of income growth and the unemployment rate in Colombia from 1984 to 2016. Their study confirms the inverse relationship and identifies gaps between the two rates at different speeds of growth (rapid growth in periods of change in economic regimes and low growth in periods preceding economic reforms in the country).

2. Characteristics of rural areas in Colombia

This section describes the socioeconomic characteristics of rural areas within a more general context. A number of authors have indicated that rural areas tend to have lower incomes and be less developed than urban areas (Alpízar and others, 2020; Anríquez and Stamoulis, 2007) and that this characteristic is heightened in developing countries. The explanations given for this include demographic, cultural and geographical aspects, economic conditions and, in part, the inner dynamics of rural areas themselves (FAO, 2019; Tickamyer, 2006).

The gap between rural and urban areas is attributable to the size and degree of informality of their labour markets, the specialization of production and demand, and institutional and social factors (FAO, 2019). In the case of Colombia, we emphasize the size of the rural labour market because this is the focus of the study. ILO (2019) confirms that levels of social protection and decent work tend to be lower in rural than in urban areas, resulting in lower real incomes and a larger gap between these areas.

A key factor that impairs the quality of the labour market and weakens economic growth in rural areas is the low rate of job creation, negatively impacting wage growth and unemployment rates. In the view of ECLAC/ILO (2016a), this factor exists because of rural areas’ dependence on market demand for commodities. Thus, a negative shock to demand for rural goods, which are intensive in raw materials, reduces consumption of these and thence rural employment.

In addition, rural employment is concentrated in low-specialization sectors. This characteristic means that workers are employed as labourers, own-account workers or the like, occupational categories in which pension system contributions are lower than in other categories or non-existent. This situation entrenches labour informality (Merchán Hernández, 2015; Otero-Cortés, 2019).

Another socioeconomic factor linked to the stock of workers that affects production in rural areas is the regions’ capacity to retain population in rural areas. According to Vargas Urrutia (2013), Colombians seeking to improve their income migrate from the countryside to urban areas. This constant process generates serious difficulties in the labour market because it drains talent away from local rural production and sets up a vicious circle in which workers’ bargaining power decreases and the potential for innovation in rural production diminishes.

Map 1 uses data from the Large Integrated Household Survey of the National Administrative Department of Statistics (DANE, 2020) to show the distribution of the rural population, as a percentage of the total population, by department. The map shows that, for the period from 2008 to 2022, the departments with the lowest densities of rural inhabitants were Bogotá, D.C. (Central region) and Atlántico (Caribbean region). In contrast, the departments of Nariño, Cauca and Chocó (Pacific region) had the greatest densities of rural inhabitants.

Map 1

Colombia: rural population by administrative department, 2008–2022

(Percentages of total population)

Source: Prepared by the authors.

Note: Simple averages for the period. The departments of Arauca, Amazonas, Casanare, Guainía, Guaviare, Putumayo, Vichada and Vaupés are excluded from the analysis for lack of information.

The boundaries and names shown on this map do not imply official endorsement or acceptance by the United Nations.

One factor restricting the capacity of rural areas to generate income is the lack of knowledge accumulation. According to Laszlo (2008), the returns to education in rural areas derive from an increase in remuneration from hourly wages; these should be the drivers of long-term income growth. From an income and consumption perspective, human capital formation has a positive effect on employment in rural areas, strengthening wage bargaining power, allowing individuals to enjoy greater consumption and generating productivity growth (Laszlo, 2008).

Another factor that negatively impacts working conditions is limited adoption of technology in rural areas. Sinyolo (2020) argues that the implementation of technologies which improve the productive capacity of rural areas has a positive effect on food security and the real incomes of employed people living in rural households.

There are exogenous factors that perpetuate job insecurity and increasingly keep incomes low in rural areas, for example gender gaps in labour demand, armed groups, labour force migration, lack of physical infrastructure, weak institutions and governance, and environmental risks that negatively affect crop productivity (Arias, Ibáñez and Zambrano, 2019; Farah and Pérez, 2003; Klugman, 2002; Tenjo Galarza and Jaimes, 2018). All these factors are common in rural areas, but they vary in their intensity and consequences between rural areas and regions.

III. Methodology

The methodology employed here to determine the relationship between the unemployment rate and production, or real income, follows the approach of Sadiku, Ibraimi and Sadiku (2015), who use four models to estimate Okun’s law for the North Macedonian economy in the period 2000–2012, determining short- and long-term relationships.

To employ the real income series, we assume that the economy follows a classic paradigm whereby the production of a nation (Yit ) depends mainly on the amount of consumption by households (Cit ), and these households in turn are the owners of the capital employed (Iit ) (Sala-i-Martin, 2000), with i being the region of analysis and the time period, while is the difference between the current period (t) and the preceding period (t – 1). This assumption is made mainly because there are no monthly data on the aggregate production of rural areas by region in Colombia; accordingly, we use the aggregate real income of those employed in rural areas.

Below, we explain the analyses carried out by different econometric methods, namely difference analysis, dynamic analysis, ECM analysis and VAR analysis (Sadiku, Ibraimi and Sadiku, 2015).

1. Difference analysis

Equation (1) presents the classical form of Okun’s law, in which the linear relationship between the rate of change in the number of unemployed, measured in logarithmic difference (Uit ), as an endogenous variable, and other exogenous variables, like the rate of change in real incomes (Yit ) and the difference of the logarithm of the number of individuals with higher education and technical skills (Hit ). Lastly, ft is the stochastic error, made up of all those variables not observed by the model.

(1)

2. Dynamic analysis

Equation (2) captures the same relationship as is established in the differential version, but with the addition of a lag for each variable to observe its evolution over time; this equation allows us carry out a short- to medium-term analysis.

(2)

It is important to mention that the negative relationship of the variables with respect to unemployment is preserved, except for the lag of unemployment.

3. Error correction model

This method makes it possible to model variables that do not have stationary properties, allowing for cointegration and long-term analysis (Stock and Watson, 2012). To establish the long-term relationship, it is necessary to determine whether the estimates cointegrate or not, for which we use the classic Engle and Granger method. We consider a set of variables to establish that the relationship is a long-term one, meaning that these variables do not tend to increase with time. This is because they are the product of a vector of variables and their estimated coefficients are equal to 0 , so that, in turn, the errors derived from the estimation processes must be equal to .

For this, we estimate an ordinary least square model with lagged study variables and proceed to derive the residuals from the estimation process, with stationarity of the residuals meaning that the variables are related in the long term, while the dependent variable is the differential of the logarithm of the number of unemployed (Uit ). Next, we use the augmented Dickey-Fuller (ADF) test to check whether the errors (xit-1) are stationary. Lastly, we check the other variables in differences and calculate the lagged errors present in the cointegration processes, deriving long-term estimates that are structured as follows:

(3)

where and Xtk = Yit and Hit. We carry out the estimation with each exogenous variable to determine the existence of long-term relationships for rural areas in each region of Colombia. Again, if is equal to zero, then there will be long-term equilibria. The application of this test and of the short-term analyses emerges as a method or approach for determining the possible presence of causal processes in the estimation by means of VAR models.

4. Vector autoregressive model

Regarding the specification of VAR models, Hansen (2016) and Wooldridge (2010) describe them as multivariate time series which include a dependent variable and a finite number of independent and lagged variables, forming a finite system of equations defined by a vector. This methodology is used because it allows us to observe the movements and past behaviour of the variables and identify some type of causal relationship between them, in addition to observing their movements in the short term.

For that reason, the approach used in this study is to examine three series: (i) the logarithm of the number of unemployed, (ii) the logarithm of real family income and (iii) the logarithm of human capital, all for rural areas in the regions specified. We estimate the equations simultaneously, choosing the best specification on the basis of its lags, so that:

(4)

where Uit, Yit and Hit are the study variables. The same variables are included as lagged terms, repeated times, and lastly the error terms are found, with mean 0 and constant variance. We make the optimal choice of lags in the VAR model in relation to the behaviour of the Bayesian information criterion[2] (Lütkepohl, 2005).

We perform the Granger causality test and the impulse-response function to determine the effects of innovations in the independent variables on the dependent ones, showing the movement of these shocks. We also observe variance decomposition to assess the contribution of shocks in the independent variables to movements in the dependent variables. The analyses focus on unemployment because this is the variable of interest in this article.

IV. Data and descriptive statistics

As already mentioned, this study aims to test the relationships formalized in Okun’s law from a rural and segmented perspective across the main regions of Colombia from January 2010 to December 2022, with data extracted from the DANE Large Integrated Household Survey. The unemployment variable is the total number of persons without jobs in the economically active population, the income variable is real income in Colombian pesos and the human capital variable is the total number of people educated to a technical, bachelor and graduate level. A series of descriptive statistics showing how rural the regions being studied are will now be presented.

The analysis of descriptive statistics reveals that the Pacific and Central regions have higher levels of rural unemployment than the other regions. As regards real income, the Central region accounts for the most income of all the rural areas. Lastly, the rural area of the Central region has the largest amount of human capital, as measured by the proportion of individuals with higher or technical studies (see table 1).

Table 1

Colombia: descriptive statistics for the variables by region, 2010–2022

(Logarithms)

Caribbean

Pacific

Observations

Average

Standard deviation

Observations

Average

Standard deviation

Uit

155

11.17

0.35

Uit

155

11.50

0.36

Yit

155

26.96

0.24

Yit

155

26.90

0.27

Hit

155

11.65

0.37

Hit

155

11.60

0.40

Central

Eastern

Uit

155

11.40

0.30

Uit

155

11.10

0.38

Yit

155

27.17

0.24

Yit

155

27.01

0.22

Hit

155

11.67

0.37

Hit

155

11.63

0.43

Source: Prepared by the authors.

Estimating the logarithm of unemployment for rural regions over time, we find behaviour that is similar across regions but divergent between unemployment and the other variables (see figure 1). In the case of income, for example, the Central region is the best performer of all the regions (see figure 2). Where human capital accumulation is concerned, there is a growth trend across the regions, with the Central region accumulating the largest proportion of individuals with higher and technical studies (see figure 3).

Figure 1

Colombia: rural unemployment, by region, 2010–2022

(Logarithms)

Source: Prepared by the authors.

Figure 2

Colombia: real monthly incomes in rural areas, by region, 2010–2022

(Logarithms)

Source: Prepared by the authors.

Figure 3

Colombia: human capital accumulation in rural areas, by region, 2010–2022

(Logarithms)

Source: Prepared by the authors.

V. Results

In this section, we present the results of the Okun’s law estimates for rural areas in the Colombian regions. The calculations are structured in the form of logarithms, so that to begin the analysis of the study series it is necessary to establish stationarity in the series, as the presence of this would imply that time was not significant in the formation of the series and that the variation in the series was constant as time went on (Stock and Watson, 2012).

To this end, the test applies unit roots using the parameters set by the analysis of the ADF test to determine whether or not the data series is stationary, before estimating the models proposed by the methodology. We observe seasonal behaviour in the series in a generalized way when the analysis in differences is carried out, finding that the unemployment and real income data are not stationary in levels in most rural regions but become seasonal when differences are applied (see table 2).

Table 2

Colombia: analysis of stationarity in levels and differences for rural areas, by region, 2010–2022

Source: Prepared by the authors.

Note: The figures are the p-values derived from the stationarity test, where the null hypothesis (H_0) is the absence of seasonality in the series, with the alternative hypothesis (Ha) applying otherwise. The test used is the augmented Dickey-Fuller test.

Reviewing the results of the estimation of Okun’s law in its differenced version, we observe an effect of the independent variables on the dependent ones (see table 3). Specifically, rural income shows a statistically significant inverse relationship with unemployment in the Caribbean, Central and Eastern regions. In the case of the Pacific region, however, while the relationship is positive, it is not significant.

Table 3

Colombia: coefficients of the model in differences for rural areas, by region, 2010–2022

Source: Prepared by the authors.

Note: Standard errors are shown in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

For the human capital data, the ratio is clearly positive in the formation of the logarithm of unemployment and is significant for the Caribbean and Eastern regions. This characteristic is due to the intensive use of unskilled labour, with an increase in skill levels leading to the labour supply becoming disconnected from the production requirements of these rural areas (Otero-Cortés, 2019).

In the dynamic version of the model, we observe that the real income and human capital variables perform similarly. The income variable in its differenced version has a negative effect on the endogenous variable in differences in the remaining regions (i.e. Caribbean, Central and Eastern), being highly significant (see table 4).

Table 4

Colombia: coefficients of the dynamic model for rural areas, by region, 2008–2022

Source: Prepared by the authors.

Note: Standard errors are shown in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

In addition, including the lagged difference of the natural logarithm of unemployment in the modelling reveals an inverse relationship with the endogenous variable. This result demonstrates the impermanent character that production may have in rural areas, governed as it is by changes in market conditions for labour-intensive goods (Otero-Cortés, 2019). Lastly, we find that the lagged human capital variable has a positive relationship with the dependent variable, an atypical result which probably arises because rural areas do not have the productive capacity to employ highly qualified workers (see table 4).

The ECM for rural areas of the Caribbean region shows variables cointegrating with the differenced logarithm of unemployment. The statistically significant coefficients and their signs show that production adjusts faster than unemployment (Sadiku, Ibraimi and Sadiku, 2015). In addition, they are significant enough to establish a short-term relationship between rural real incomes and unemployment. Compared with the other iterations of the variables, the short-term relationship is significant throughout the analysis for rural areas (see table 5).

Table 5

Colombia: Granger causality test coefficients for rural areas, by region, 2010–2022

Source: Prepared by the authors.

Note: The figures in parentheses represent the p-values derived from the Granger causality test. This test evaluates two hypotheses, namely the null hypothesis (H_0) that one variable does not Granger-cause another and the alternative hypothesis (Ha) of Granger causality, with n lags being used to maximize causal relationships in the analysis.

There are also regions, such as the Central and Caribbean regions, where long-term relationships can be observed. We see that the independent variables tend to adjust more rapidly in the long term than the unemployment variable, owing to the nature of their signs. In the case of the Pacific region, there are both short-term and long-term relationships, with short-term interactions being positive in the cases of production and human capital. In the long-term analysis, it shows similar behaviour to the other regions (see table 6).

Lastly, short-term relationships in rural areas of the Eastern region are found to evolve comparably to those in the Caribbean and Central regions. In the long term, its dynamics continue to parallel those of rural areas in other regions of Colombia. It is important to mention that these short-term relationships tend to be deterministic, featuring in the causality analysis as approximations to the VAR estimates.

The last step in the analysis of short-term relationships are the results of the VAR model, Granger causality test and impulse-response functions. The Granger causality test shows that all the variables present a causal relationship in the short term, except for the differenced logarithm of human capital in rural areas of the Caribbean region. Real income by region is found to have a Granger-causal relationship with the other variables in the short term (see table 5).

Table 6

Colombia: coefficients for the error correction model in rural areas, by region, 2008–2019

Source: Prepared by the authors.

Note: Standard errors are shown in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

VAR is estimated by region as part of the impulse-response function analysis. First, we analyse short-term relationships and the impacts of innovation on unemployment. Unemployment is a key variable in this study, and it is necessary to observe the dynamics of its response to innovations in other variables, for which we use impulse-response functions to assess the relationships between unemployment and the other variables. In addition, we use variance decomposition to determine the proportions of the derived effect attributable to all the exogenous and endogenous variables.

For the Caribbean region, we used a maximum of two lags (based on the Bayesian information criterion) to examine the effects on the dependent variable and observed that both positive and negative effects were highly significant but tended to tail off from the tenth period (see figure 4).

Figure 4

Colombia: impulse-response functions for unemployment shocks in rural areas of the Caribbean region

Source: Prepared by the authors.

Note: The horizontal line represents the impulse-response relationships, and the surrounding lines indicate the confidence intervals at the estimated levels. The bootstrap estimation periods cover 16 months.

Impulse-response movements in the Central region are found to resemble those in the Eastern region, with the impact on unemployment tailing off (see figure 5). In the Pacific region, changes in unemployment are like those in the Caribbean region, which is possible because other variables have an impact on the number of unemployed in the short term (see figure 6). However, as the periods lengthen towards the long term, the impulse-response effect tails off. This interference tends to be less in the Eastern region, with the result that short-term flows do not vary significantly (see figure 7).

Figure 5

Colombia: impulse-response functions for unemployment shocks in rural areas of the Central region

Source: Prepared by the authors.

Note: The horizontal line represents the impulse-response relationships, and the surrounding lines indicate the confidence intervals at the estimated levels. The bootstrap estimation periods cover 16 months.

Figure 6

Colombia: impulse-response functions for unemployment shocks in rural areas of the Pacific region

Source: Prepared by the authors.

Note: The horizontal line represents the impulse-response relationships, and the surrounding lines indicate the confidence intervals at the estimated levels. The bootstrap estimation periods cover 16 months.

Figure 7

Colombia: impulse-response functions for unemployment shocks in rural areas of the Eastern region

Source: Prepared by the authors.

Note: The horizontal line represents the impulse-response relationships, and the surrounding lines indicate the confidence intervals at the estimated levels. The bootstrap estimation periods cover 16 months.

From the above, it can be argued that this initially unsmoothed movement in the Caribbean and Pacific regions is due to the differences in the principal economic activities of their departments. Human capital characteristics have a similar effect, presenting shocks or innovations with significant repercussions in the short term but tending to normalize in the long term (Otero-Cortés, 2019). The interference from these innovations in the Eastern and Central regions is normalized in the short term, which may be due to the small impact that the remaining variables have on movements in the unemployment variable, as seen in the variance decomposition analysis.

To conclude the analysis, we show the variance decomposition, which allows us to compute the proportional impact of each of the variables on changes in the variable analysed, identifying causal dependencies for short-term movements (Johnston and DiNardo, 1997; Stock and Watson, 2012). Table 7 shows the variance decomposition at the region level. In all the regions of Colombia, changes in the natural logarithm of unemployment have a significant impact on its own future movements.

Table 7

Colombia: 12-period estimated variance decomposition for rural areas, by region

(Percentages)

Source: Prepared by the authors.

VI. Conclusions

The aim of this article is to analyse the relationship between income and unemployment in rural areas of Colombia’s regions from 2010 to 2022 in relation to Okun’s (1962) law. We first identify a robust inverse relationship between income and unemployment in almost all rural regions of Colombia. These results show that, in rural areas, economic growth and related increases in real incomes are followed by a reduction in unemployment.

Conversely, increases in human capital in rural areas lead to higher unemployment. Rural demand for workers is oriented towards individuals with lower levels of qualifications, since production specializes in low value added goods. The results reveal a degree of regional homogeneity in rural areas (Merchán Hernandez, 2015; Otero-Cortés, 2019).

Using the ECM, we find a statistically significant long-term relationship between unemployment and all the variables at the region level. Where short-term relationships are concerned, there is a negative relationship between unemployment and real income in the Caribbean, Central and Eastern regions, but not in the Pacific region. This is consistent with previous results (Sadiku, Ibraimi and Sadiku, 2015).

The Granger causality test identifies short-term causal relationships for almost all the variables in the different regions. The impulse-response function results show that the impact of innovations on unemployment produces movements or changes in the logarithm of the number of unemployed in rural areas. These movements are abrupt and are most volatile in the short term. As time goes on, the effect decreases, for example in the Caribbean and Pacific regions. The strength of these movements is due to the weight of the variables concerned in the variance decomposition.

These results have major economic and social policy implications in rural areas of the Colombian regions. This study allows us to derive the causal and deterministic relationships linking real income and human capital accumulation to unemployment in both the short and long terms. It also makes it possible to state that productivity and economic diversification have positive effects on real income.

Regarding human capital, we show that economic diversification and productivity have a positive impact on its utilization. Concerning the relationship between human capital and rural unemployment, the point can be made that the education system ought to be attentive to labour market needs. More work-based training would increase the use of human capital in productive activities, and increasing the number of formal jobs available would positively impact cumulative work experience. Policies of this type should have the effect of decreasing unemployment among highly skilled people in these labour markets. We conclude that all these actions in combination would promote economic growth and development in rural areas.

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1. [1] The regional classification established by DANE in Colombia, given the availability of data on 24 of the 32 departments, is: Caribbean, containing the departments of Atlántico, Bolívar, Cesar, Córdoba, Sucre, Magdalena and La Guajira; Eastern, containing the departments of Norte de Santander, Santander, Boyacá, Cundinamarca and Meta; Central, containing the departments of Bogotá D.C., Caldas, Risaralda, Quindío, Tolima, Huila, Caquetá and Antioquia; and Pacific, containing the departments of Chocó, Cauca, Nariño and Valle del Cauca.

2. [2] The Bayesian information criterion is calculated using the formula: .

Prices and progress? The link between inflation and structural change in post-war Brazil

Lúcia Regina Centurião

Received: 15/09/2023

Accepted: 04/07/2024

Abstract

This paper employs a multivariate time series model to examine the relationship between price increases and structural change in post-war Brazil (1945–1964). To assess this relationship, the model investigates the link between prices, the industrial share of total output in the economy, net investment and industrial sector wages. With a view to addressing criticisms commonly made of the vector autoregression (VAR) family of models, and particularly the ad hoc nature of Cholesky’s decomposition method, hypotheses drawn from the economic literature on development theory were applied to the matrix of restrictions. The findings indicate that inflation played a significant role in structural change in Brazil, while the influence of structural change on inflation was less pronounced.

Keywords

Prices, inflation, industrial sector, structural adjustment, economic structure, world war (1939–1945), post-conflict reconstruction, industrialization, time-series analysis, Brazil

JEL classification

N1, N16, C22

Author

Lúcia Regina Centurião is a professor at the São Paulo School of Economics, Getulio Vargas Foundation, Brazil. Email: lca1328@gmail.com.

I. Introduction

During the years following the Second World War, Brazil went through a remarkable period of structural transformation. Few periods in Brazilian history have witnessed such significant changes. Between the end of the global conflict and the early 1960s, there was a notable shift in the composition of the Brazilian economy. The share of industry in the country’s overall output increased by approximately 10%, while there was a nearly equivalent decrease in the share of agriculture (Baer, 1972, p. 99).

The import substitution industrialization process played a crucial role in driving this structural change. Initially focusing on the production of non-durable consumer goods, Brazil gradually developed a quite solid capital goods sector. By the mid-1960s, the country had already established vehicle and electrical equipment manufacturing industries, among others, reflecting the depth of its industrialization efforts.

Numerous factors have been proposed to explain this structural change. Some studies emphasize supply side determinants, such as investment and productivity, as drivers of structural transformation. Seminal contributions in this field include Baumol (1967) and the model developed by Acemoglu and Guerrieri (2008). Conversely, other scholars highlight the significance of demand side factors as catalysts for this transformation process, one example being Foellmi and Zweimüller (2008). A number of studies have also attached importance to the country’s initial economic conditions, factor endowments, demography and geography when exploring the intricacies of this structural economic change.

Notwithstanding the importance of these factors, some seminal works on economic development have underscored an element that is often overlooked in analyses of structural transformation: inflation. For instance, Furtado (1964), examining Brazilian industrialization, asserted: “During the last three decades, industrialization has consistently relied on the combination of these two factors: import substitution and the transfer of resources resulting from inflation” (Furtado, 1964, p. 119). Lewis (1954) argued that capital formation in underdeveloped countries could be facilitated not only by profits but also by credit. Analysing Brazilian structural change, Prebisch (1973) presented the country as a prominent instance of inflation becoming an instrument of development, despite the undesirable consequences associated with rising prices. These perspectives shed light on the significant role played by inflation in shaping structural change and its implications for economic development.

There have of course been a great many analyses of structural change in Brazil during different periods, but once again inflation is seldom mentioned as an influence in the process, and the post-war period specifically has been less analysed. Recently, for example, Nassif and others (2020) studied the relationship between the level of productivity and changes in the production structure over the 1990s and 2000s. Santos and Spolador (2018) used a dynamic general equilibrium model to investigate the trend in sectoral productivity between 1981 and 2013 and its role in Brazilian structural change. Carvalho and Kupfer (2011) compared the path of structural change in Brazil with that in other countries that went through a similar transformation. Nakabashi, Scatolin and Vargas da Cruz (2010) analysed the effect of structural change on Brazil’s growth in the period 1948–2007. For the 2000s, Messa (2013) analysed the sources of structural change and concluded that the difference in growth between services and industry was due to the production structure itself. Marconi and others (2020) studied the relationship between the rate of return, the exchange rate and structural change in 1996–2007. For 1971–2008, Lamonica, Feijó and Punzo (2012) used a Kaldorian and neo-Schumpeterian framework to analyse the growth path of the economy and the effects of economic policies on structural change. Exploring the implications of the multisectoral Thirlwall’s law, Gouvêa and Lima (2013) studied structural change and growth in the context of Brazil’s external constraint for 1962–2006, giving particular consideration to the second National Development Plan (1975–1979).

This paper aims to study the connection between inflation and structural change in Brazil from 1945 to 1964. This period was chosen because it was a time of rapid structural transformation during which inflation is usually associated by the standard literature with the industrialization process, as will be discussed. The analysis was only taken up to 1964 because that year was marked in Brazil not only by a military coup but also by the start of a series of reforms to the monetary system that changed the inflationary process in the country, such as the creation of a central bank. The paper seeks to explore the reciprocal association between inflation and structural change: inflation, according to the literature, was important for the allocation of resources and thus for structural change, while the latter in turn put pressure on the price level. We have thus used a structural vector error correction (SVEC) model, which assumes that both variables were endogenous.

Although structural inflation theory usually deals with developing countries as a group, individual analysis of a specific Latin American country, in this case Brazil, is important to capture its idiosyncrasies. In the period chosen, for example, other countries’ economic experiences were very different: while in Brazil there was growth with inflation, for example, Argentina presented inflation with stagnation, Venezuela[1] (before 1958) growth with price stability and Cuba stagnation with price stability.

This paper also addresses one of the main criticisms of autoregressive models, namely the ad hoc nature of Cholesky’s decomposition. Two hypotheses, supported by the literature, were incorporated into the constraint matrices: (i) setting out mainly from Lewis’s (1954) model with an infinite supply of labour, industrial wages were assumed to be exogenous; (ii) the effects of an increase in the money supply were assumed to dissipate in the long run, given that people tend to spend as much money as possible in the short run when inflation is high.[2] Following section II, which considers the relationship between inflation and development, these hypotheses are explored in section III. Section IV presents the SVEC model and its main findings, and section V offers some concluding remarks.

II. Inflation and development

The escalation of inflation in Latin America after the war prompted numerous authors to contend that the surge in prices was not solely a monetary phenomenon but rather a consequence of ongoing structural change. At the same time, a number of other scholars began to emphasize the significant role played by inflation in facilitating industrialization.

In the Latin American literature, Vázquez’s (1956) investigation into the relationship between structural change and inflation is widely acknowledged to be seminal (Danby, 2005, p. 161). According to Vázquez (1956, p. 604): “Inflation is not a monetary phenomenon; it is the result of real imbalances that manifest themselves as increases in the general price level. This real character of the inflationary process is much more apparent in underdeveloped countries than in industrialized countries.” Vázquez (1956, p. 605) identified basic inflationary pressures and propagation mechanisms, and argued that the strength of inflation depended primarily on the basic pressures and secondarily on the propagation mechanisms (p. 605). In this seminal article, Vázquez (1956, p. 604) argued that similar elements could be found in all Latin American inflation experiences, but that each country had its own peculiarities.

Referencing Vázquez, Sunkel (1958) similarly posited that Latin American inflation resulted from rapid structural transformations. While the main focus of his paper was on Chile, it also presented generalizations applicable to the broader Latin American context. Sunkel’s conceptualization categorized inflation into three distinct types: (i) basic or structural inflationary pressures, (ii) short-term pressures and (iii) cumulative inflation, i.e. inflation generated by past inflation (Sunkel, 1958, p. 573). Basic inflationary pressures emanated from the inability of some sectors to adapt to changes in demand. These inflationary forces stemmed from factors such as (i) inadequate food production, (ii) constrained export expansion and limited foreign purchasing power, (iii) suboptimal capital accumulation rates and (iv) an inadequate tax system (Sunkel, 1958, p. 574).

Structural inflation theory repeatedly emphasized the challenge of adapting supply to rapid shifts in demand. Furtado (1965) asserted that the rise in income resulting from import substitution industrialization drove a transformation of aggregate demand that necessitated a significant restructuring of aggregate supply. This pressure emerged as the primary driving force behind inflation, particularly in situations where short-term import flexibility was low (Furtado, 1965, p. 268).

A low rate of capital formation and an inadequate tax system were also issues for consideration: insufficient capital formation created a pressing need for investment, while the government’s inability to generate sufficient revenue often led to investment being funded by inflationary methods. During the post-war period, furthermore, the dearth of foreign capital inflows also exacerbated the problem. Between 1945 and 1947, there was hope that Latin America would receive foreign aid, particularly from the United States. Several conferences were held, but at a meeting in Bogotá in 1948, it became clear that there would be nothing similar to a Marshall Plan for Latin America. By 1951, it was the only part of the world without a United States foreign aid programme. Belgium and Luxembourg, for example, received more funds between 1945 and 1951 than all of Latin America together (Thorp, 1992, p. 191).

Addressing the influence of structural change on inflation, Sunkel (1958, p. 580) mentioned an element that came up quite often in the literature on Brazil: monopolies. Import substitution industrialization led to companies operating on a basis of market exclusivity, which resulted in the formation of monopolistic and oligopolistic structures that had consequences for pricing. However, the issue of monopolies was intricate, since greater competition, while potentially lowering prices, could also hurt productivity by diminishing economies of scale. Macario (1964) contended that the monopolistic structure prevalent in Latin America encouraged price increases. He further asserted that as long as governments continued to grant indiscriminate protection, any measures aimed at combating inflation would prove ineffective. According to Macario (1964, p. 79), companies in Latin America often sold products at inflated prices and showed little concern for efficient production. The absence of external competition facilitated the transfer of increased costs to consumers (Macario, 1964, p. 83).

Hirschman (1958) posited that late-industrializing countries experienced greater inflation and balance-of-payments problems than wealthier nations had during their own industrialization phase. He explored various theories dealing with this phenomenon. One, proposed by Wallich (1952 cited in Hirschman, 1958, p. 138), was that the development of richer countries had been production-oriented, while the process in developing countries was consumption-oriented. In a production-oriented society, economic progress was seen as coming from productive efforts, whereas a consumption-oriented society sought to extract more from the economy than it could viably offer (Hirschman, 1958, p. 156). Hirschman (1958, p. 156) also highlighted the “demonstration effect” argument that consumers in poorer nations desired the same level of consumption as their wealthier counterparts without fully comprehending the sacrifices required to achieve such a standard of living. Hirschman (1958, p. 157) also cited some authors who argued that developing countries were “trying to do too many things at the same time”, with governments adopting over-ambitious development plans. Nonetheless, the author argued that these sociopsychological explanations of inflation were not wholly convincing and that inflation would not be so severe and persistent if these factors were solely responsible, contending instead that economic development followed an uneven growth trajectory, with equilibrium eventually being established through pressures and incentives (Hirschman, 1958, p. 157). The author reaffirmed the thesis that inflation arose mainly from the supply imbalances that characterized the growth process (Hirschman, 1958, p. 164).

In addition to the proponents of structuralist theories, researchers into the so-called two-gap model also identified a connection between development and inflation. Chenery and Strout (1966), for instance, argued that economically disadvantaged countries faced two consecutive constraints on growth. In a first stage, savings were low, so that if there was no inflation, investment would also be low. The second limitation was that inputs for continued growth could only be obtained by accumulating balance-of-payments deficits.

As already mentioned, inflation has been recognized by scholars as having a significant influence on various factors that contribute to increased investment and structural change. Bernstein and Patel (1952, p. 368) argued that inflation could influence many elements that were essential to investment growth. First, inflation could increase the returns on investment and encourage companies to scale up their operations. At the same time, inflation could have the effect of transferring income to the entire group of savers in an economy. Retained earnings allowed businesses to sustain a higher level of investment. Hirschman (1968) also addressed the influence of inflation on investment, arguing that some Latin American countries were frustrated with the slow pace of structural transformation, but Brazil was an exception, having created a “special institutional factor designed to increase the supply of capital, namely inflation” (Hirschman, 1968, p. 9).

Tavares and Serra (2000) likewise recognized the importance of inflation, noting that it played a significant role in preserving an illusorily high rate of profits. The expected return on capital was kept artificially high by real asset valuations.

Rangel’s (1986) interpretation also highlighted the role of inflation in sustaining economic growth in Brazil, but emphasized the demand aspect, his argument being that inflation diminished society’s preference for liquidity, thereby increasing the level of effective demand. According to this author, in Marxist terms, by creating exceptional demand for durable goods, rising prices contributed to the realization of social surplus value (Rangel, 1986).

Theories about the influence of inflation on Latin America’s growth found support in the historiography of the development of industrialized countries. Earl Hamilton, who held the chair of the Economic History Association, was one of the most important theorists of the time to point out that the richest countries had undergone a price revolution leading to industrialization. For him, inflation had been a powerful driver in a large number of historical instances and could play a similar role in developing countries (Felix, 1956, p. 442). The mechanism through which inflation exerted a positive impact was relatively straightforward: during prolonged periods of inflation, prices increased faster than wages adjusted. The result was “profit inflation”, and the increased profits allowed a much faster rate of industrial growth than would have occurred with price stability (Felix, 1956, p. 368).

Hamilton (1942, p. 257) posited that while the lag between profits and wages was not the sole factor responsible for industrialization, it played a crucial role in facilitating the industrial revolution, particularly during the second half of the eighteenth century. The author mentioned, for example, England’s explosive population growth and the heavy immigration of Irish workers as factors that prevented wages from increasing (Hamilton, 1942, p. 259). Meanwhile, the increase in the amount of land given over to pasture in the countryside and the elimination of waste through new technologies increased the supply of labour for industry. The weakening of guild restrictions and the effective opposition put up to nascent trade unionism were also decisive (Hamilton, 1942, p. 259). Hamilton further referenced the perspective of Adam Smith, who argued that “masters are always and everywhere in a sort of tacit, but constant and uniform combination, not to raise the wages of labour above their actual rate” (Hamilton, 1942, p. 260).

Despite associating development and inflation, Hamilton (1942, p. 266) argued that in one respect the lag of wages behind prices hindered industrialization: if wages had followed prices, the incentive for labour-saving inventions would have been much greater. Nevertheless, the author argued that, particularly in the second half of the eighteenth century, technological innovations were the result of rising prices, not the cause (Hamilton, 1942, p. 272). In 1952, in his famous “Prices as a factor in business growth: prices and progress” Hamilton stated:

It is difficult [...] to see how anything else could have been more important than the great lag of wages behind prices in certain economically advanced countries during the Price Revolution. Capitalism required capital, and it would not be easy to imagine a more powerful instrument for providing it than forced saving through a highly favorable price-wage ratio. The high rate of profit when prices were rising and wages, the chief cost, were lagging gave a strong inducement to invest savings in productive enterprise. […] Other things anywhere near equal, capitalism could hardly have failed to flourish (Hamilton, 1952, p. 339).

Griffin (1961) put forth the argument that, throughout history, inflation had played a significant role in various economic take-offs, such as those experienced by the British economy in the 1790s, the American economy in the 1850s and the Japanese economy in the 1870s. In the case of Japan, demand for investment funds chronically exceeded the sums available through voluntary saving and taxation. The country’s authorities did not supervise bank credit growth, and this led to inflation that accelerated the transition to industrialism by increasing production capacity and driving growth (Griffin, 1961, p. 1083). However, Griffin (1961, p. 1083) also asserted that inflation would only be effective in driving development in a country if there existed a large industrialist class with a high marginal propensity to save.

Notwithstanding this extensive literature on the positive effects of inflation for development, some Brazilian authors have denied the existence of such an effect in Brazil. Wells (1977, p. 14) mentioned that a Brazilian school of thought, identified as “liberal”, openly disagreed with the structuralists. These authors, like Simonsen and Campos, accepted that industrialization was necessary and supported the establishment of technologically advanced sectors: they were not “liberal” in the sense of advocating less State intervention. For them, however, the main cause of inflation was excessive expansion of the money supply resulting from government deficits, and the main mechanism in its propagation were unjustified increases in the urban minimum wage. For these authors, inflation, far from being beneficial to growth, was directly and indirectly responsible for slowing down industrial expansion. First, rapid inflation meant that firms were unable to identify the best cost-minimizing strategy and failed to apply competitive pricing policies. Inflation was therefore responsible for inefficiencies in production. Furthermore, inflation and some of the policies used to deal with it, such as price controls, led to misallocation of investment owing to the distortion of the perceived relative profitability of certain projects. Inflation also encouraged speculative investment in urban land, luxury buildings and the like, while negatively affecting the banking and financial systems. Negative deposit rates tended to discourage household saving. With lending rates also negative, firms were able to obtain funds even for projects with a negative real return: the interest rate no longer acted as a rationing device (Wells, 1977, pp. 14–16).

Additionally, the authors of the “liberal” school argued that high and rapidly accelerating inflation introduced uncertainty and led to the downward revision of expected rates of return, ultimately holding back economic growth (Wells, 1977, p. 16). They contended that corporate tax laws did not give firms an incentive to depreciate fixed capital at its real replacement cost. As a result, firms did not set aside a share of their profits to maintain the value of their working capital, and this resulted in lower gross investment (Wells, 1977, p. 17).

This scholarly debate between authors who perceived inflation as having a positive role in funding structural change and those who did not (with these latter often being criticized for relying on oversimplified assumptions and placing too much faith in the universality of theories) lends additional significance to the present study.

III. The assumptions of the study

1. The hypothesis of an unlimited labour supply

Lewis (1954) stated that all the classical authors, from Smith to Ricardo, implicitly or explicitly assumed the availability of an unlimited labour supply at a subsistence level of wages. However, neoclassical theory departed from this hypothesis (Lewis, 1954, p. 140). Lewis developed his analysis on the assumption that the labour supply in the poorest countries could be considered unlimited, with wages in the modern sector being determined solely by the subsistence level of the poorest. Other early theorists, such as Rosenstein-Rodan (1956), Eckaus (1960) (dealing mainly with Asia, the Middle East and Italy), Ranis and Fei (1961) and Sen (1966), also emphasized the elasticity of the labour supply as a crucial characteristic of underdeveloped nations. Furtado was another important figure who adopted the view that wages were determined exogenously in the poorest nations and that this had been the situation since the time these countries operated solely as exporters of raw materials, even prior to industrialization:

In fixing a wage rate higher than the pay earned by the peasant in pre-capitalist agriculture, the export sector was assured of an unlimited labor supply. In effect, the old agricultural sector functioned as a labor reservoir. While this reservoir was not exhausted, the export sector enjoyed a flexible labor supply at a wage rate defined by the living standard which prevailed in the semi-feudal fazendas (Furtado, 1965, p. 164).[3]

The hypothesis of an unlimited labour supply was widely applied during the 1950s and 1960s, and numerous subsequent studies have incorporated this concept into their models. Villamil, Wang and Zou (2020), for instance, developed a Lewisian economic growth model that assumed an increase in labour participation at a constant wage. Their research demonstrates the significance of surplus labour in driving structural change in both developed and underdeveloped countries. In an analysis focused on Africa, Diao and McMillan (2018) constructed a model that incorporated various aspects of Lewis’s (1954) seminal work, while also including additional intersectoral changes. Similarly, Parida (2019) used a Lewisian framework to investigate the economic structure of India.

In Brazil specifically, the hypothesis of an abundant supply of labour is further supported by the size and flexibility of the country’s migration flows. For instance, migration from north-east Brazil to São Paulo was particularly substantial from the 1930s, when the number of foreign immigrants in the State of São Paulo was surpassed by that of domestic migrants. This internal migration became even more significant in the first half of the 1950s, breaking previous records (Ferrari, 2005). As mentioned, Hamilton (1942) drew attention to the role of Irish immigration into England as a factor depressing wages.

2. The hypothesis of long-run dissipation of price shocks

The effort to accelerate investment in poor countries poses significant challenges. Lewis (1954), as mentioned earlier, argued that the solution to this problem could be the creation of credit, which should be the first option if increasing taxes was more difficult than dealing with the problems generated by inflation. Credit creation might also be more beneficial because it could redistribute income to the industrial class, further accelerating capital accumulation. However, Lewis also acknowledged that such redistribution would probably be temporary rather than permanent, suggesting that the effects of inflation would dissipate over the long run (Lewis, 1954, p. 166).

Another Brazilian author who highlighted the demand aspect, emphasizing that inflation led to a rush for material goods in the short run, was Rangel. According to the author, a considerable part of the surplus value appropriated by the richer classes, instead of being retained in the form of money, was applied to the acquisition of fixed assets. This generally took the form of investments in activities that were already operating below capacity (Rangel, 1986). Rangel contended that if the members of the affluent class refrained from making such investments and held their wealth in liquid form, the result would be a decline in national income, primarily impacting the poorest segments of society (Rangel, 1986).

Bernstein and Patel (1952) provided further insights by examining the cases of Brazil, Chile and Colombia. They argued that there was evidence that credit expansion stimulated investment in the early stages of the inflationary process rather than in the later stages (Bernstein and Patel, 1952, p. 377). As prices continued to rise, workers became more vigilant in safeguarding their interests and anticipating further price increases. Similarly, Wells (1977, p. 38) noted that accelerating inflation initially led to an increase in the investment rate, but eventually excess capacity would appear. Discussing the price revolution, Hamilton (1952, p. 339) observed that “rising prices penalized delay in investment and by lowering the effective rate of interest encouraged borrowing for investment in anticipation of earnings.”

According to Lewis (1954), in certain economies where funding sources were insufficient, policymakers faced a dilemma between pursuing stable prices and promoting economic growth (Lewis, 1954, p. 171). However, credit should be earmarked for activities that would yield income quickly, because “to finance school building by creating credit is asking for trouble” (Lewis, 1954, p. 171). Lewis also highlighted the risk of eroding public confidence in money if inflation persisted for an extended period, which was why authorities tended to alternate periods of credit with periods of monetary restraint (Lewis, 1954, p. 166).

These two hypotheses, that of the infinite supply of labour and that of the dissipation of the monetary effect in the long term, will be used in the next section to construct the empirical strategy. More broadly, as discussed, this strategy is based on the idea that inflation may have played a role in industrialization and may also have been influenced by the process, an idea developed to a greater or lesser extent by authors such as Noyola, Vázquez, Sunkel, Furtado, Hirschman, Tavares, Serra and Rangel.

IV. Empirical analysis[4]

A multivariate time series model, specifically an SVEC model, was employed to explore the reciprocal relationship between structural change and inflation in Brazil. This methodology allows variables to be treated as endogenous without assumptions being made about their exogeneity. VAR family models have been applied in this way to investigate different aspects of the Brazilian economy in a number of recent studies. For instance, Nobrega, Maia and Besarria (2020) used this methodology to analyse the dominant fiscal or monetary regime in the Brazilian economy between 2003 and 2015. Chernavsky, Dweck and Teixeira (2020) used VAR modelling to investigate how the elements of the fiscal policy of Dilma Roussef’s government affected product growth, and whether these elements were able to explain the subsequent slowdown in the Brazilian economy. Another example is the work of Filleti and Boldrin (2020), who used a quasi-VAR or vector error correction model (VECM) to analyse the process of deindustrialization in the Brazilian textile industry.

Since the aim is to evaluate the structural inflation hypothesis, the main variables of interest are inflation and the industrial share of total product. The consumer price index (IPC) represented inflation in the model.[5] In addition to these two main variables, wages in the industrial sector and net investment were used as additional control variables to estimate the SVEC model. The exercise also included tests with an alternative model that used a different measure of the price level, the broad producer price index - internal availability (IPA-DI), and a different measure of the investment level, gross capital formation (GCF). The exchange rate was not included in the analysis because quantitative import controls were much more important.[6] Thus, as imports were increasingly confined to basic inputs and production goods, it may be inferred that GCF also reflected Brazil’s import controls in this specific period. All the variables used in the estimation are shown in table 1, which provides their descriptive statistics as well as their sources.

Table 1

Descriptive statistics for the data, 1945–1964

Variable

Mean

Minimum

Maximum

Source

Industrial share of product (industry)a

(Percentages)

28.8

24.9

34.2

Brazilian Institute of Geography and Statistics (IBGE)

Gross capital formation (GCF)b

0.1

0.0

0.2

Institute for Applied

Economic Research (IPEA)

Industrial sector wages (wages)c

17 877.5

12 333.0

28 463.0

Colistete (2009)

Consumer price index (IPC)

30.3

3.7

85.6

Institute of Economic

Research Foundation (FIPE)

Broad producer price index - internal availability (IPA-DI)

27.4

2.7

84.1

Getulio Vargas Foundation (FGV)

Net investment (investment)a d

7 482,9

963,0

13 913,6

Institute for Applied

Economic Research (IPEA)

Source: Prepared by the author, on the basis of Brazilian Institute of Geography and Statistics (IBGE) and R. Colistete, “Salários, produtividade e lucros na indústria brasileira, 1945-1978”, Brazilian Journal of Political Economy, vol. 29, No. 4, Rio de Janeiro, National Bank for Economic and Social Development (BNDES), 2009.

a The values for 1945 and 1946 were calculated by interpolation, using information from the IBGE statistical yearbooks for those years.

b Based on reais at 1980 prices.

c Based on cruzeiros at 1950 prices.

d Based on reais at 2010 prices.

The results of the two estimated models will now be presented. One, called the main model, includes the IPC, wage, industry and investment variables; the other, called the alternative model, includes the industry, wage, IPA-DI and GCF variables.

Once the variables had been selected, their stationarity was analysed with the augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS) and Phillips-Perron tests. The results of these tests (see table 2) suggested that the variables were integrated of order one, like many macroeconomic time series.

Table 2

Results of the stationarity tests

Source: Prepared by the author.

Note: The symbol ∆ indicates first difference. Null hypotheses: for the Phillips-Perron and ADF tests, the series is non-stationary; for the KPSS test, the series is stationary.

Since the variables were non-stationary, they were analysed to ascertain whether a stationary linear combination existed between them, indicative of cointegration. This test is important, since the estimation of a VAR family model in differences in the presence of cointegration implies bias because of the omission of a relevant variable, the long-term relationship. Formally, we adopted the stricter Engle and Granger definition of cointegration, in which the elements of the column vector Xt are said to be cointegrated when they simultaneously meet the two following conditions: (i) all elements of the vector are integrated of order d and (ii) there is a non-zero vector such that ut = X t' b ~ I(d – b), where b > 0.

The Johansen methodology was used to test for the existence of cointegrated vectors. The methodology depends on the relationship between the rank of a matrix and its characteristic roots. First, the number of lags to be included in the model was determined. After analysis of the Akaike information criterion (AIC), Schwarz criterion (SC) and Hannan-Quinn information criterion (HQC), three lags were included in both models (see table 3). Table 4 presents the results of the Johansen cointegration test. Both the trace and the eigenvalue confirmed the hypothesis that there was at least one cointegration vector between the variables in the main model, i.e. there was a long-term equilibrium relationship between the variables. In the alternative model, both the eigenvalue and trace tests indicated the existence of at least two cointegrated vectors.[7] Thus, the notion that post-war inflation was related to structural change was validated.

Table 3

Akaike information criterion (AIC), Schwarz criterion (SC) and Hannan-Quinn information criterion (HQC) for the main model

Lags (n)

AIC (n)

SC (n)

HQC (n)

1

35563760

35661200

36544010

2

36026600

36201990

37791050

3

32236790

32490140

34785450

Source: Prepared by the author.

Table 4

Johansen cointegration test

Source: Prepared by the author.

Note: The critical value is 5%.

Given the existence of the cointegration vector, the following SVEC model was estimated using the Johansen methodology:

(1)

where Xt is the vector of endogenous variables, a is the adjustment matrix, b is the cointegration vector, n is the vector r x 1 with constants and Ki are the short term relationship coefficients.

After the model was estimated, diagnostic tests were performed. The portmanteau test was used to test for the existence of serial correlation, but did not detect any correlation in the error,[8] while the autoregressive conditional heteroskedasticity (ARCH) multivariate test was used to test for the presence of heteroskedasticity, but did not detect the presence of this either.[9]

As indicated, two hypotheses supported by the economic literature were included in the constraint matrices to identify structural shocks: (i) setting out mainly from Lewis’s (1954) infinite labour supply model, industrial wages were assumed to be exogenous; and (ii) inflationary shocks were assumed to dissipate in the long run. The impulse-response function was estimated for the identified model by analysing the response of the target variable to a one standard deviation impulse in another. The same procedure[10] was applied to the alternative model as part of the robustness analysis.

Figure 1 presents the impulse-response functions for the main and alternative model, and figure 2 displays the accumulated impulse-response functions.

Figure 1

Impulse-response functions

A. Main model

B. Alternative model

Source: Prepared by the author.

Figure 2

Accumulated impulse-response function

A. Main model

B. Alternative model

Source: Prepared by the author.

Regarding the effect of inflation on investment and industry, both the primary and alternative models illustrate a cyclical relationship between the variables. With the hypothetical positive inflationary shock, there is first a hypothetical positive effect on investment and the size of industry, indicating that inflation stimulates these variables. These findings might be attributable to various factors discussed in the literature on the period. To mention some potential explanations, inflation could enhance consumer spending, since individuals may anticipate future price increases, which may prompt them to bring forward purchases and stimulate investment with the proceeds of the increased sales, as discussed in the theoretical section. It is crucial to bear in mind that the post-war period in Brazil was characterized by significant growth, especially in the consumer durables industry. Given the relatively high value of these goods, it is fair to conjecture that it takes households some time to decide whether to buy them or not. An increase in prices could serve as an additional incentive for consumers to expedite their purchases, thereby encouraging greater production and investment by companies.

The government’s main instrument of monetary expansion was lending to industrialists by the Bank of Brazil, and later by the National Bank for Economic and Social Development (BNDES), which could also help explain the relationship between inflationary shocks and positive investment outcomes. In this period, there were at least two instances where monetary policy spiralled out of control precisely because of lending by the Bank to the industrial sector, despite the government’s stated intention of curbing inflation. During the immediate post-war period, the Dutra government recognized inflation as its foremost challenge. Paradoxically, however, credit extended by the Bank of Brazil to the industrial sector surged between 1947 and 1950. In 1952, the Vargas government, with Horácio Lafer as finance minister, attempted an austerity policy, but inflation remained unchecked, primarily owing to the expansionary credit policies of Ricardo Jafet as chairman of the Bank of Brazil (the Jafets were one of São Paulo’s leading industrialist dynasties). This highlights the institutional dilemma posed by the Bank of Brazil, with its dual role as monetary authority and commercial bank emerging as a potentially significant factor in the interplay between inflation and investment.

Following the positive effect of inflation on investment and the size of industry, a negative influence is observed. This can also be explained from the literature. As inflation drives up prices in other sectors, it can result in increased input costs that discourage later investments. Moreover, if consumers anticipate rising prices and bring forward their purchases, this will naturally lead to a subsequent decline in consumption. Additionally, investment tends to diminish as overcapacity becomes apparent. In response to mounting inflation and the resulting social unrest (in the Brazilian example, there were a number of major strikes in the early 1950s), the government might adopt short-term measures to mitigate inflationary pressures, as exemplified by the dismissal of a figure like Jafet.

In both models, despite such adverse impacts, the cumulative impulse-response function indicates that a cumulative inflationary shock has a positive effect on structural change in the long run.

Analysis of the effect on inflation of the real variables, namely investment and industry, yields certain conclusions. In the main model, a shock in the real variables seems to have a positive impact on inflation, but the results display a high level of variance. In the alternative model, conversely, the effect appears to be close to zero. To delve deeper into the influence of these real variables on price increases, a forecast error variance decomposition (FEVD) analysis was conducted, as shown in figure 3. The FEVD sheds light on the contribution of each variable to the overall variance of forecast errors.

Figure 3

Forecast error variance decomposition (FEVD) for the main model

Source: Prepared by the author.

Among the variables examined, investment exhibits the lowest self-explanatory FEVD. This finding suggests that investment is particularly sensitive to variations in the other variables considered. In the long run, furthermore, inflation emerges as the primary determinant of the variance in the forecast error decomposition of investment. This observation lends support to the emphasis placed by development theorists on the role of inflation in Brazil’s capitalization process.

Analysis of the variance decomposition offers further intriguing insights into the relationship between inflation and investment, revealing that inflation may have been less influenced by changes in investment than investment fluctuations were by inflation: the very conclusion yielded by the impulse-response functions.

This asymmetrical relationship suggests that inflation was not primarily driven by structural changes but was influenced by other factors. The idea that inflation might have been avoidable resonated with structuralist authors such Furtado and Prebisch, who, while acknowledging the structural underpinnings of inflation, contended that price rises were not inevitable. Celso Furtado, for instance, argued: “The ultimate cause of inflation did not lie in development per se but rather in the absence of a genuine development policy” (Furtado, 1961, p. 223). Furtado also drew attention to the role of the Bank of Brazil in this process, arguing: “Investments that were of vital importance to the country and that were a government responsibility, for example in the energy and steel sectors, long suffered from inadequate financial planning, so that they depended on occasional advances from the Bank of Brazil” (Furtado, 1964, p. 129). Furtado further observed that countries undergoing rapid industrialization elsewhere had reformed their tax collection systems, unlike Brazil and several other Latin American nations, thereby reducing their reliance on inflationary financing for investment (Boianovsky, 2012). Similarly, in his study “Economic development or monetary stability: the false dilemma”, Prebisch asserted that while inflation did play a role in development, it was not a prerequisite for economic change (Prebisch, 1961).

Furthermore, as regards the absence of a financing mechanism, one significant factor in Brazil at the time was the Usury Act, which made it illegal to charge a nominal interest rate higher than 12% per year. With the interest rate thus restricted by law, fixed-income securities and government bonds became less attractive to investors. High inflation meant that lending operations with maturities of more than 90 days became increasingly rare and those with maturities of between 30 and 60 days progressively more common (Wells, 1977, p. 178). Consequently, the ability to reduce or expand the money supply, mainly through Bank of Brazil lending, became the government’s primary monetary instrument.

To understand why inflation might have had an impact on structural change in Brazil, it is important to delve further into some key characteristics of the country. One pivotal factor was the size of the Brazilian market: even marginal price fluctuations could have significant consequences, given scale effects. The large consumer base made the economy highly responsive to changes in inflation, and it presented ample opportunities for substantial returns on new investments. The potential for large profits in such a sizeable market could act as a powerful incentive for businesses and investors to take risks and engage in new ventures in the event of even small changes in potential returns resulting from higher prices.

Another factor that might have influenced potential returns and encouraged investment was the level of organization among industrialists, since organized representation could provide more stable political influence and greater encouragement for investments. Authors such as Griffin (1961) argued that inflation could contribute to capitalization only if an organized industrial class existed. One indicator of this was the formation of industrialists’ associations. In Brazil, for instance, the National Confederation of Industry (CNI) was established in 1938, while the Federation of Industries of the State of São Paulo (FIESP) dates back to 1931 and the Federation of Industries of the State of Paraná (FIEP) to 1944.

Furthermore, the Brazilian energy mix has a characteristic that may help explain the relatively low potential impact of structural pressures on inflation. Hydroelectric plants have been operating in Brazil since the late nineteenth century, and the country relies heavily on them to this day. The country’s first hydroelectric plant was constructed in 1883 in the city of Diamantina in Minas Gerais. Over the period analysed, 18 hydroelectric plants were inaugurated, including Furnas in the Rio Grande region, which remains one of Brazil’s most important energy sources. The fact that the country had prior experience with technology which generated energy in part from natural resources could help explain the hypothetical results discussed, since it might have further alleviated the impact of structural change on price increases.

Although the influence of structural change on inflation was relatively weak, it did exist, with the tests identifying a common trend for all the variables. The point, then, is only that the relationship was asymmetrical.

V. Concluding remarks

This paper employed an SVEC model to analyse the links between structural change and inflation in post-war Brazil. The approach encompassed two key aspects: firstly, examination of the reciprocal relationship between the variables, and secondly, consideration of the criticisms directed at VAR family models. Objections are often raised to autoregressive methods because of the somewhat ad hoc nature of Cholesky’s decomposition method. However, the paper employed an alternative approach by applying hypotheses based on the existing economic literature within the matrix of restrictions.

The impulse-response function suggested that an inflationary shock had a positive cumulative effect on both investment and the industry share of gross domestic product (GDP). Thus, the results suggested that inflation might indeed have played a role in driving structural change in Brazil during the specified period. However, the analysis indicated that changes in the inflationary process were not heavily dependent on real variables. This suggests the possibility of an asymmetrical relationship in which inflation might have influenced the capitalization process in Brazil without itself being influenced by it on the same scale. It can be conjectured that while inflation helped explain structural change, inflation itself was not influenced by structural change to the same extent. This aligns with the perspectives of structuralist scholars such as Furtado and Prebisch, who argued that inflation, although affected by structural change, was not inherently unavoidable. Instead, they attributed it to the absence of a genuine development policy. This reflection leads on to the suggestion that to gain a comprehensive understanding of inflation in post-war Brazil, it is imperative to examine not only structural change but also the impact of institutional factors such as the absence of a development policy and of a unified institution to control monetary policy, considering that Brazil was one of the last countries in the world to create such an institution, in 1964. Further research should explore the implications of the country’s delay in establishing a central bank and investigate the political factors involved in the inflationary process. Such studies would provide valuable insights into the multifaceted nature of inflation in Brazil during the post-war period. This paper also analysed the relationship between inflation and investment at the macroeconomic level. An even more rewarding approach would be to study this relationship across different regions of Brazil.

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1. [1] The name “Venezuela” was changed to “Bolivarian Republic of Venezuela” on 15 December 1999; this article uses the form employed prior to that date.

2. [2] See Kilian and Lütkepohl (2017) on the estimation of VAR family models using restrictions.

3. [3] For Furtado, this dual structure of the economy, with a modern sector existing alongside a precapitalist one, was the defining characteristic of an underdeveloped country (Furtado, 1965).

4. [4] R software was used to carry out the tests, model estimation and innovation accounting in this section.

5. [5] The IPC was chosen because one of the main mechanisms by which inflation affects economic development, according to the approach taken in the standard literature, is through the transfer of income from workers to investors. However, as will be explained later, another model employing the broad producer price index - internal availability (IPA-DI) was tested for robustness.

6. [6] This was common in Latin America, where most other countries likewise relegated customs tariffs to a secondary place as an instrument of trade policy. Countries made increasing use of direct controls to maintain the wartime status quo and reserve the domestic market exclusively for domestic industry. At the same time, the policy was to allow domestic industry to import capital goods and other inputs at preferential rates (Macario, 1964, p. 63). These measures were necessary because of the scarcity of dollars. For example, the purchasing power of Brazil’s exports only surpassed pre-1929 levels in the 1950s, and even then by relatively narrow margins (Macario, 1964, p. 63).

7. [7] Eigenvalue statistic = 24.67, trace statistic = 40.04.

8. [8] p-value: 0.46.

9. [9] p-value: 0.98.

10. [10] For the alternative model, the portmanteau and ARCH tests did not indicate the presence of autocorrelation or heteroskedasticity either.

Impact of the peace negotiations in Colombia on real GDP, 2013–2019

Fernando Martín Mayoral and Carlos Andrés Rivera Lasso

Received: 26/09/2023

Accepted: 09/10/2024

Abstract

The peace negotiations between the Revolutionary Armed Forces of Colombia –People’s Army and the Government of Colombia, which began in late 2012 and concluded with the signing of the Final Agreement for Ending the Conflict and Building a Stable and Lasting Peace in 2016, sought to bring an end to decades of violence, loss of life, destruction of infrastructure and social fragmentation. This study uses the synthetic control method proposed by Abadie and Gardeazabal (2003) and Abadie, Diamond and Hainmueller (2010) to assess the impact of the Agreement on Colombia’s real per capita GDP growth, taking into account economic, demographic and institutional factors. The main findings indicate that Colombia has registered higher growth rates than its synthetic counterpart since 2013, reflecting a shift in the expectations of economic agents from the outset of the peace process.

Keywords

Peace agreements, armed conflicts, economic growth, gross domestic product, Colombia

JEL classification

CO1, N4, O1, O5

Authors

Fernando Martín Mayoral is a Research Professor and PhD Coordinator in the Department of Economics, the Environment and Territory of the Latin American Faculty of Social Sciences (FLACSO) Ecuador. He holds a PhD in economics from the University of Salamanca (Spain). Email: fmartin@flacso.edu.ec.

Carlos Andrés Rivera Lasso holds a master’s degree in development economics from the Latin American Faculty of Social Sciences (FLACSO) Ecuador. Email: carlos.rivera.lasso@outlook.com.

I. Introduction

The effects of armed conflict are multidimensional and can negatively impact production and economic growth, both directly and indirectly. Among other factors, these impacts stem from the destruction of physical capital, dissaving, capital outflow (Collier, 1999; Glick and Taylor, 2010), increased uncertainty caused by political instability (Alesina and others, 1996; Poinsot, 2003; Imai and Weinstein, 2000), and the reallocation of public resources towards conflict-related expenditures, which are often unproductive, coupled with the loss of life and rising levels of crime (Granada, Restrepo and Tobón García, 2009; Lim and others, 2022; Díaz Gómez, 2015). Measuring the true effects of armed conflict on economic activity is challenging given this wide range of factors (Gates and others, 2012).

A number of studies have documented the marked deterioration in Colombia’s economic growth resulting from the armed conflict that began in 1960 (Rubio, 1997; Echeverry, Salazar and Navas, 2000; Gómez, 2001). These factors include the prolonged duration of the conflict and agents’ limited capacity to adapt to the resulting conditions, with direct consequences for gross physical capital formation, productivity growth, the accumulation of human capital and erosion of social capital, all within a context of uncertainty.

The armed conflict has directly affected institutions associated with economic freedom, such as security institutions, which led to an allocation of 5% of GDP to security and justice and the prioritization of efforts against drug trafficking (Rubio, 1997). The conflict has also compelled the government to redirect significant resources towards military spending and other unproductive activities, generating negative social outcomes such as increased inequality and the forced displacement of the population (Querubín Borrero, 2003; Álvarez and Rettger, 2008; Echandía-Castilla and Cabrera-Nossa, 2019).

In late 2012, the administration of Juan Manuel Santos began initial discussions with the Revolutionary Armed Forces of Colombia–People’s Army (FARC-EP), culminating in the signing of a peace agreement in 2016 that marked the end of over 60 years of internal armed conflict. During the years of negotiation leading up to the agreement, a series of economic and social policies were implemented to restore normal economic activity as swiftly as possible, starting with the recovery of physical and non-physical capital (Brandon, 2018). In 2015, however, the Government of Colombia faced a complex policy environment shaped by a number of political and economic challenges: regional elections, tax reform efforts, a widening fiscal deficit and challenging legal and constitutional reform negotiations (ECLAC, 2016; Wills-Otero and Hoyos, 2016; Quintero Serna, 2017; Fajardo-Heyward, 2018).

In the reconstruction period following the 2016 peace accord, priority was placed on addressing the root causes of conflict through actions linked to controlling violence and reparations for societal harms in order to prevent renewed hostilities (Collier, Hoeffler and Söderbom, 2008; Hoeffler, Ijaz and Von Billerbeck, 2010). The peace agreement provided a critical opportunity to return to a path of economic growth, as it ensured macroeconomic stability, reducing the direct and indirect costs associated with violence and improving the functioning of institutions. According to the National Planning Department (DNP, 2015), GDP could have grown by up to 5.9% in the years following the peace agreement thanks to higher investment and savings rates. The Ministry of Finance and Public Credit (2017) projected annual investment of 0.7% of GDP over a 15-year period as a result of the comprehensive rural reform agenda.

However, some studies suggest that the political uncertainty that arose in the aftermath of the peace agreement may have hampered the country’s economic growth. This uncertainty may have been affected by political instability, resulting in lower investment levels (Alesina and others, 1996; Poinsot, 2003; Posada Duque, 2010). Such findings underscore that the economic and social consequences of armed conflict are not automatically resolved with the cessation of hostilities. Post-conflict policies should consider both the context and the stakeholders involved, and should target these groups in order to achieve a lasting peace (Lindsay, Gündüz and Subedi, 2009; Sakalasuriya, Haigh and Amaratunga, 2018). It is also important to recognize that peace may entail short-term economic costs, such as a contraction in GDP, an increase in public spending and losses of capital, given the need to prioritize stability and social policies designed to prevent a relapse into conflict (Collier, 1999; Hoeffler and Reynal-Querol, 2003; Ruiz Díaz, Galeano Raquejo and Gil Mateus, 2015; Millán Celis and Jiménez Quitian, 2017).

This study seeks to measure the impact of the 2012–2019 peace process between the Colombian Government and FARC-EP on real per capita GDP growth. As it is not possible to observe the country’s economic trajectory in the absence of the peace process, the synthetic control method was employed (Abadie y Gardeazabal, 2003; Abadie, Diamond and Hainmueller, 2010). This method makes it possible to create a “synthetic Colombia” based on a set of comparable countries and predictive variables that simulates what the country’s performance would have been without the peace agreement. The article is divided into five sections in addition to this introduction. The following sections review the historical context of the armed conflict, analyse its effects on the economy, examine the methodologies applied to study these effects, present the findings obtained with the synthetic control method and offer final conclusions.

II. Historical context of the armed conflict in Colombia

Colombia has faced recurrent social crises and violence since gaining independence from Spain (Ramírez and Guedán, 2005). The nineteenth century was marked by clashes between conservatives and liberals, culminating in the Thousand Days’ War, which claimed over 100,000 lives between 1899 and 1902 (Ríos Sierra, 2019). A weak State (Oquist, 1978) and the absence of agrarian reforms (Gilhodés, 1974; LeGrand, 1988; Ríos Sierra, 2017) exacerbated political tensions throughout the twentieth century. The assassination of Jorge Eliécer Gaitán in 1948 unleashed a wave of violence in Bogotá that soon spread nationwide, resulting in the largest armed mobilization of rural populations (Hobsbawm, 1974, p. 264, cited in Pizarro, 1991) and the deaths of over 200,000 people (Guzmán Campos, Borda and Umaña Luna, 1962; Ríos Sierra, 2019).

In the mid-1950s, the National Front and conservative and liberal parties signed a pact in Spain (Benidorm in 1956 and Sitges in 1957) as a proposed transition towards democracy, establishing the alternation of these two parties in government. However, this arrangement excluded other political forces (Moreno-Parra, 2018). Starting in 1958, opposition movements emerged both within and outside the National Front, including Movimiento Revolucionario Liberal, Movimiento Obrero Independiente y Revolucionario, Alianza Nacional Popular and FARC-EP, which were initially formed as self-defence groups in response to the neglect and poverty affecting rural communities (Archila Neira, 1996).

The Ejército de Liberación Nacional, Ejército Popular de Liberación and Movimiento 19 de Abril were formed in 1965, 1966 and 1973, respectively, with an anti-imperialist democratic discourse. The 1980s saw the emergence of State-sponsored paramilitary groups to counter guerilla movements. From 1980 onward, drug trafficking became a financing mechanism for the various armed groups, strengthening them and further aggravating the domestic conflict (Cárdenas, 2001). Seeking a political resolution to the conflict, the Belisario Betancur administration (1982–1986) initiated the first ceasefire and truce agreement in 1984, which was signed by the main guerrilla forces and gave rise to the leftist political movement of FARC-EP, known as Unión Patriótica. However, the various guerillas regrouped in 1987, prompting the suspension of dialogue between the parties (Granada, Restrepo and Tobón García, 2009).

The Andrés Pastrana administration (1998–2002) failed to reach a peace agreement with FARC-EP, which expanded both in size and territorial scope to reach 70 active fronts with 17,000 combatants (Ríos Sierra, 2019). Under President Álvaro Uribe (2002–2010), the State weakened FARC-EP through a security policy backed by the United States, which contributed over US$ 10 billion under Plan Colombia: Plan for Peace, Prosperity and the Strengthening of the State. While the policy succeeding in weakening the guerrilla group, it also entailed significant military and economic losses for the country (Garzón Triana, 2011). The structural causes of the conflict remained unresolved and every attempt at peace had failed to address underlying issues. Seeking a way out of the armed conflict, the government of Juan Manuel Santos (2010−2018) initiated confidential talks with FARC-EP in 2010 to minimize media interference (Cruz Rodríguez, 2015). The two parties began formal negotiations in Havana in 2012 (Calderón Rojas, 2016). In 2013, agreements were reached on comprehensive rural reform, along with the opening of democratic space to include the political participation of guerrilla groups; in 2014, an agreement was reached to solve the illicit drug problem; and in 2015, the Comprehensive System of Truth, Justice, Reparation and Non-Repetition was established (Castrillón-Torres and Cadavid-Ramírez, 2018).

On 24 November 2016, the Colombian Government and FARC-EP signed the Final Agreement for Ending the Conflict and Building a Stable and Lasting Peace, structured around six key points: (i) comprehensive rural reform; (ii) political participation and a democratic opportunity to build peace; (iii) bilateral and definitive ceasefire and cessation of hostilities and laying down of arms, reincorporation of FARC-EP into civilian life, and security guarantees and the fight against criminal organizations responsible for homicides and massacres or who attack human rights advocates; (iv) solution to the illicit drugs problem; (v) victims (Comprehensive System of Truth, Justice, Reparation and Non-Repetition) and

(vi) implementation and verification mechanisms. This process marked a milestone for the country, as the initial partial agreements generated trust among the population and economic actors, which in turn fostered a favourable climate for investment and growth. However, it also posed major challenges in terms of development, reparations and justice for the more than 8 million victims of the conflict.

III. Effects of the armed conflict on Colombia’s economy

Various schools of thought concur that economic growth depends on investment in physical capital and technical progress. However, while some authors argue that these factors are exogenously determined (Harrod, 1939; Domar, 1946; Solow, 1956; Swan, 1956; Mankiw, Romer and Weil, 1992), others attribute technical progress to endogenous factors such as the accumulation of knowledge within countries (Romer, 1991), improvements in human capital (Uzawa, 1965; Lucas, 1988), the investment decisions made by economic agents (Romer, 1990), public expenditure and taxes (Barro, 1990) and the degree of trade openness (Grossman and Helpman, 1990; Young, 1991). In addition, foreign direct investment plays a critical role in developing economies that lack the capacity to finance capital formation and has a positive impact on GDP, employment, production and knowledge transfer (Li and Liu, 2005; Jiménez Giraldo and Rendón Obando, 2012). All these factors are strongly impacted by the instability and uncertainty generated by armed conflict.

Other studies have highlighted the importance of institutions as determinants of economic growth (Dosi, 1988; Freeman and Pérez, 1988; Nelson and Winter, 1992; Boyer, 1993; Zysman, 1994). Institutions regulate interactions among economic agents and contribute to technological progress by driving progress in innovation, education, physical capital formation and the protection of private ownership (Nelson and Sampat, 2001; Bandeira, 2008; Galindo Martín, 2010). Evidence has shown that institutions affect economic growth by establishing social rules and norms that ensure the proper functioning of markets through respect for private rights, civil liberties, impartial judicial processes and personal autonomy without State interference (Axialá Pastó and Fabro Esteban, 2011; Morales Sánchez, 2015; Carballo and Fracchia, 2016). Therefore, building strong institutions is essential to achieve sustainable economic growth (Vallejos and Domínguez, 2013; Fernández-Torres, Ramajo-Hernández and Díaz-Casero, 2019).

IV. Methodologies used to study the effects of armed conflict on the Colombian economy

Various methodologies have been used to assess the impact of armed conflict on economic growth. Using Tobit regressions based on a gravity model of international trade, Blomberg and Mody (2005) find that violence had a negative effect on trade and investment in World Trade Organization member countries in the period 1981–1998. Bodea and Elbadawi (2008) apply the generalized method of moments and identify a negative impact of organized political violence on economic growth in a sample of 68 developing and developed countries between 1970 and 1990. Diallo (2018) uses ordinary least squares and instrumental variable regressions based on Barro’s (1990) endogenous growth model for a sample of 77 developing countries between 2008 and 2012. The analysis shows an inverse relationship between economic growth and the internal violence index developed by Feindouno, Goujon and Wagner (2016).

In the case of Colombia, Cárdenas (2001) analyses the effects of armed conflict on economic growth using the time series methodology of Ben-David and Papell (1998) and finds that drug trafficking and internal armed conflict have had a negative impact on the country’s productivity and growth rate. Querubín Borrero (2003), employing linear regression and difference-in-differences methods, concludes that violence and its various forms (crime, armed conflict, drug trafficking) have hindered economic growth in Colombia’s departments. Using a three-stage least squares method based on a variation of the Mankiw, Romer and Weil (1992) growth model, Vargas (2003) shows the adverse effects of internal armed conflict on per capita GDP. Carranza Romero, Dueñas Herrera and González Espitia (2011), employing a univariate and multivariate time series analysis, find a bidirectional causal relationship between homicides and economic activity. The Johansen multivariate cointegration method used by Molina and Hurtado Rendón (2012) indicates that violence —as measured by the homicide rate— has a long-term negative impact on economic growth in Colombia. Gil León and Uribe Peñaranda (2017) estimate an expanded Solow (1956) growth model including variables for violence and crime through ordinary least squares. The results indicate that the rise in kidnappings negatively affects the annual GDP growth rate.

Concerning the effects of reduced violence in Colombia, Querubín Borrero (2003) calculates that every 1% reduction in violence would generate annual real GDP growth of 0.06%. According to Santa María Salamanca, Rojas Delgadillo and Hernández Díaz (2013, cited in Ríos Sierra, 2020) this effect is over 2% of GDP. Betancur, Libos and Ortiz (2020) estimate that the peace process will contribute an average of 0.17–0.28 percentage points per year to the country’s economic growth between 2017 and 2031. Under this scenario, the demobilization of FARC-EP would contribute 0.05–0.16 percentage points per year to GDP growth over a 10-year period, while contributions provided through the Investment Fund for Peace would boost growth by an average of 0.12 percentage points per year, thanks to increased funding in more productive sectors.

1. Economic effects of the armed conflict in Colombia

Figure 1 presents the changes in Colombia’s standardized per capita GDP (1970 = 100) compared with selected countries in the Americas[1] and the average for Latin America and the Caribbean. Colombia maintained a growth trajectory similar to the regional average and that of the United States until 1986. From that year onward, a divergent trend emerged: while average growth for Latin America and the Caribbean stagnated, Colombia continued to grow at a pace comparable to that of the United  States until 1998. Between 1998 and 2010, Colombia experienced a period of relatively slower growth, after which growth accelerated, surpassing both the regional and United States averages. This upward trend continued until the outbreak of the coronavirus disease (COVID-19) pandemic in 2020, when per capita GDP contracted sharply.

Figure 1

Latin America (selected countries) and United States: annual changes in standardized per capita GDP, 1960–2021

(Index: 1970 = 100 at constant prices)

Source: World Bank, “GDP per capita (constant 2015 US$)” [online] https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.

The change in per capita GDP appears to be related to the number of victims of the armed conflict. According to data from the Central Register of Victims up to 31 January 2023, the number of victims stood at 9.423 million people, of which 8.391 million were forcibly displaced and 1.278 million were killed.

Figure 2 shows the number of victims and the events resulting from the armed conflict. Both the number of victims and events associated with the armed conflict increased significantly starting in 1995, peaking in 2002. This trend may explain the relative decline in per capita GDP during that period. Beginning in 2014, the number of victims and events decreased markedly, coinciding with the country’s stronger economic growth.

Figure 3 shows how military spending in Colombia expanded considerably from 1990 onward in response to increased hostilities of the armed conflict, reaching a peak of 4.5% of GDP in 1996 and then stabilizing at around 3.5%. The country’s current military spending is comparable to that of the United States and well above the regional average for Latin America and the Caribbean (1.13%). Despite the peace negotiations and signing of the Agreement, the government’s military spending remains unchanged.

Figure 2

Colombia: victims and events associated with the armed conflict, 1985–2022

(Number)

Source: Victims’ Unit, “Publicación de datos abiertos” [online] https://www.unidadvictimas.gov.co/publicacion-de-datos-abiertos/.

Note: “Victims” refers to persons affected by a victimizing act at the local level; “events” refer to the occurrence of such acts in a given place (municipality) and on a specific date.

Figure 3

Colombia and selected countries: military spending, 1960–2022

(Percentages of GDP)

Source: World Bank, “World Bank Open Data” [online] https://data.worldbank.org/.

V. Results obtained using the synthetic control method

The empirical studies reviewed in the previous section identify a negative effect of violence on economic growth. However, they do not provide a clear picture of how Colombia’s economy would have evolved in the absence of the peace process. Colombia’s particular situation makes it difficult to find an economic unit of comparison (counterfactual) with similar socioeconomic characteristics. However, when considering the broader group of countries in the region, Colombia shares economic, institutional and social characteristics with several of them.

In order to assess the effect of the peace agreements between Colombia and FARC-EP in 2013–2016 (the intervention), we applied the synthetic control method developed by Abadie and Gardeazabal (2003) and Abadie, Diamond and Hainmueller (2010). The method’s primary objective is to construct a synthetic unit that serves as a counterfactual and accurately reflects the trajectory that the treated unit would have followed had the intervention not occurred (Kaul and others, 2017). The “synthetic Colombia” unit is constructed through a linear combination of untreated units (Mok León, 2018) as a weighted average (Abadie, Diamond and Hainmueller, 2011) based on their similarity to the unit of study (Bouttell and others, 2018).

Following the methodology of Abadie and Gardeazabal (2003) and Abadie, Diamond and Hainmueller (2010), we consider J+1 units, j = 1, 2, 3, … j+1 over t time periods (t = 1, 2, 3, …T ), where j = 1 refers to the treated unit (Colombia). The units j = 2, …, J+1 form the control group made up of countries in the sample that did not undergo the intervention, referred to as the “donor group”, which is used to construct the “synthetic Colombia” unit. The intervention occurs in period T0+1, so t_0 = 1, …, T_0 represents the pre-intervention periods, corresponding to the years preceding the signing of the 2016 peace agreement. The start of official negotiations in 2012 through to the signing of the peace agreement in 2016 marks the post-intervention period, t_1 = T0+1, …, T, such that t = t_0 + t_1.

As in Abadie, Diamond and Hainmueller (2011), we define two possible outcomes for the variable of interest: Y1t is the observed per capita GDP of Colombia (at constant 2010 prices), the unit that received the intervention (peace process negotiations); and is the counterfactual, representing what Colombia’s real per capita GDP would have been in the absence of the intervention. Unlike the former, the latter is not observed for the treated unit during the post-intervention period. The goal is to estimate the marginal effect of the treatment (a1t_1) on Colombia’s real per capita GDP in the post-intervention period (t_1) using information provided by the donor group, which is defined as:

(1)

is created by taking a weighted average of the control group of countries that did not receive the treatment (donors) and whose observed characteristics (per capita GDP and other relevant variables) most closely resemble those of Colombia during the pre-treatment period. To do this, a set of observable predictor variables (Zj) that influence the outcome variable prior to the intervention must be selected.

The weights assigned to the untreated units to construct the synthetic unit are collected in a weight vector W = (w_2, …, wj+1 )', where wj is the weight for each donor country, such that wj ≥ 0 and . Each vector W represents a specific weighted average of control units and thus forms a potential synthetic control unit (Abadie, Diamond and Hainmueller, 2011). Abadie and Gardeazabal (2003) and Abadie, Diamond and Hainmueller (2010) propose finding a W such that for all during the pre-treatment period. In this way, the estimates of for the post-treatment period t ≥ T_0 will be a valid estimate of , from which the marginal effect of treatment in the subsequent years can be calculated (Rosado-Anastacio, 2018). Therefore, the estimated impact would be:

(2)

According to Abadie, Diamond and Hainmueller (2010), â1t is an unbiased estimator of a1t.

There are various ways to select the weight vector. Abadie, Diamond and Hainmueller (2015) choose an optimal weight vector (W*) that minimizes the difference between the observed pre-intervention characteristics of Colombia (X_1) and the untreated units (X_0W*). That is: . Here, X_1 is a k×1 vector of pre-treatment characteristics for Colombia (the treated unit) and X_0 is a k×j matrix with the same characteristics for the untreated units that make up the synthetic unit. Lastly, V is a symmetric, positive semi-definite k×k diagonal matrix that allows different weights to be assigned to the variables X_0 and X_1 depending on their predictive power for the outcome (the off-diagonal elements are zero). An optimal V should be selected so that its weights minimize the root mean squared prediction error of the synthetic control estimator in the pre-treatment period (Kaul and others, 2017). In this way, the synthetic control method is capable of accurately replicating the characteristics of the treated unit when X_1 = X_0W*. Abadie, Diamond and Hainmueller (2010) select V using a regression-based method that assigns equal weights.

It is also necessary to control for various explanatory factors. The model for estimating can be expressed as follows:

(3)

where dt is a time factor that is invariant across countries; Zj is a vector of observable covariates in the pre-treatment period for each of the j + 1 countries, known as matching variables;[2] it is a vector of unknown parameters; mt is a vector of unobserved common factors that vary over time (such as cycles or trends); nj is a vector of unobserved country-specific characteristics; and fjt is a random error term with a mean of zero.

In this way, the synthetic group obtained approximates the outcomes of the treated region before the intervention and serves as a control after the treatment is applied. The difference in outcomes between the treated unit and its synthetic control counterpart reveals the policy’s effectiveness (McClelland and Gault, 2017). Abadie and Vives-i-Bastida (2022) further stress that achieving a good pre-treatment fit is essential for synthetic control estimators to perform well. However, this alone is not sufficient, as the model may still suffer from overfitting when the donor group is too large, unobserved factors are present or the pre-treatment period is too short.

To ensure that the results obtained represent a causal effect, Abadie and Gardeazabal (2003) and Abadie, Diamond and Hainmueller (2010 and 2015) recommend conducting placebo tests to determine whether the gap observed in Colombia could have been caused by factors other than the peace negotiations between 2013 and 2016. This involves applying the treatment to randomly selected donor units to assess whether the estimated effect is smaller than that observed in Colombia (Abadie, Diamond and Hainmueller, 2010). Abadie, Diamond and Hainmueller (2015) suggest comparing the difference in the p-value of the synthetic control estimator’s root mean squared prediction error before and after the intervention for the treated unit and each placebo unit. If the treatment has an effect, the post-treatment error for Colombia should be larger relative to its pre-treatment value, while the placebo units should not exhibit a substantial increase in error after the treatment.

1. Data

The variable selected to assess the economic impact of Colombia’s 2012–2016 peace process is the logarithm of per capita GDP at constant 2010 prices. The explanatory variables with the greatest predictive power for the outcome variable in the treated unit should be selected (Abadie, Diamond and Hainmueller, 2015), as they also determine the selection of donor regions and the weights used to form the synthetic group. The period chosen for the analysis is 1995–2019.[3]

The selected predictors largely align with those used in the literature mentioned earlier to measure the effects of Colombia’s armed conflict (Vargas, 2003; Querubín Borrero, 2003; Sánchez Torres and Díaz Escobar, 2005; Chirinos, 2007; Gates and others, 2012; Gil León and Uribe Peñaranda, 2017). The economic factors considered include investment in physical and human capital (primary and secondary school enrolment rates), trade openness (imports plus exports as a percentage of GDP), industrial output as a share of GDP, inflation rate, foreign direct investment inflows and military spending as a share of GDP. Two demographic variables, obtained from the World Bank, are also included: population growth and mortality rate. In addition, two variables representing institutional quality are considered: checks and balances (“checks”) and government stability (“stabs”), both sourced from the Database of Political Institutions developed by the Inter-American Development Bank (IDB, 2020). The first indicator measures government credibility (Keefer, 2007), with values ranging from 1 (low credibility) to 5 (high credibility). The second measures the percentage of “veto players” who leave the government in a given year. Ultimately, the latter variable was excluded given its adverse effect on the fit of the synthetic control model.

The “synthetic country” was constructed from a group of Latin American countries with similar characteristics in some of the control variables and that had not undergone a process similar to Colombia’s in 2013–2016: Argentina, Brazil, Chile, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, the Plurinational State of Bolivia and Uruguay.

To ensure a good model fit, McClelland and Gault (2017) recommend including the lagged dependent variable (the outcome variable) among the regressors because it absorbs the effect of any predictor variables included or omitted from the model. Abadie, Diamond and Hainmueller (2010) use three specific years of the outcome variable as lags. Other authors, such as Mok León (2018), O’Neill and others (2016) and Kreif and others (2016), use all lags of the dependent variable as predictors. However, Kaul and others (2017) demonstrate that including all lagged values of the outcome variable before treatment is not advisable because it renders all other predictors (covariates) irrelevant, resulting in a biased estimator. Instead, they suggest using an average of the outcome across all pre-treatment years or the value from the last pre-treatment year. Ferman, Pinto and Possebom (2020) similarly advise against including all lags of the outcome variable when the synthetic control unit needs to balance a specific set of covariates. McClelland and Gault (2017) recommend selecting a small number of lags that reflect the pre-treatment trend of the outcome variable. Furthermore, McClelland and Gault (2017) and Bonander (2018) recommend including results from different model specifications to select the one that minimizes the root mean squared prediction error of the synthetic control estimator in the pre-treatment period.

Following these recommendations, various sensitivity tests are then performed with different model specifications. The aim is to check for differences in the pre-treatment fit (root mean squared prediction error of the synthetic control estimator) and select the model with the lowest error.[4]

The first aspect to consider is the year selected for treatment. Although the peace agreement was signed in 2016, important commitments were made between 2012 and 2015 (Calderón Rojas, 2016). Different models were estimated for these years, with the lowest prediction error found in 2013, indicating that the peace negotiations had a positive impact on real per capita GDP from the outset by enhancing economic agents’ confidence in the viability of the process.

Next, various specifications of the synthetic control model are analysed, incorporating different lags for both the dependent variables and covariates, as recommended by Bonander (2018) and Ferman, Pinto and Possebom (2020). The lowest root mean squared prediction error of the synthetic control estimator (0.0109193) is obtained by including all lags of the dependent variable up to 2012 and excluding covariates. Including the remaining predictors increases the error (0.0114789), which demonstrates the minimal impact that the other covariates have on the outcome variable, thus corroborating the findings of Kaul and others (2017).[5] Among the alternative specifications, the lowest root mean squared prediction error of the synthetic control estimator is obtained using matching based on five-year intervals from 1995 to 2010, along with the year 2012. In contrast, the specification that included only covariates and excluded lags of the outcome variable produces a higher error, reinforcing the guidance of McClelland and Gault (2017) to include lags of that variable. A similar increase in error occurs when lags are structured in five-year intervals and one pre-treatment variable is randomly omitted. Based on these results, all subsequent analyses will use the specification that includes five-year lags of the outcome variable (1995–2010), the year 2012 and the full set of covariates.

Table 1 presents the weights assigned to each donor country in the “synthetic Colombia” unit, based on the minimization of the squared error. The system assigns weights to five countries.

Table 1

Weights of countries comprising the “synthetic Colombia” unit

Unit

Unit weight

Argentina, Bolivia (Plurinational State of), Chile, Dominican Republic, Guatemala, Mexico, Nicaragua, Panama, El Salvador, Uruguay

0

Honduras

0.04

Brazil

0.088

Peru

0.183

Paraguay

0.266

Ecuador

0.423

Source: Prepared by the authors.

Table 2 presents the values of the predictor variables for real per capita GDP for Colombia (column 1), the corresponding synthetic counterpart (column 2) and the percentage similarity between the two (column 3). Columns 4 and 5 show, respectively, the simple (unweighted) average of the donor group and the percentage similarity with Colombia.

Table 2

Comparison of predictors of average pre-intervention economic growth between Colombia, “synthetic Colombia” and the unweighted donor group

Variable

Colombia

“Synthetic Colombia”

Percentage synthetic control model/Colombia

Average of control countries

Average synthetic control model/Colombia

(Percentages)

lPIBpc(1995)

8.335745

8.32397

100

8.305922

100

lPIBpc(2000)

8.29501

8.298157

100

8.388889

99

lPIBpc(2005)

8.400076

8.408705

100

8.469287

99

lPIBpc(2010)

8.558248

8.571558

100

8.621749

99

lPIBpc(2012)

8.642467

8.639261

100

8.687569

99

FBK

20.47394

21.88805

94

22.32291

92

Industry/GDP

29.55137

31.87236

93

27.51741

107

Mortality rate

5.196167

5.476039

95

6.177907

84

Population growth

1.445254

1.56128

93

1.367605

106

Primary school enrolment

119.1628

113.7988

105

109.7265

109

Secondary school enrolment

82.77796

71.84343

109

77.08553

107

Inflation

10.08567

7.202676

140

7.331686

138

FDI inflows/GDP

3.321019

2.226504

149

3.692775

90

Military spending/GDP

3.410736

1.770442

193

1.261664

27

Checks

3.777778

3.789278

100

3.490667

108

Source: Prepared by the authors.

The results indicate a high degree of alignment between the synthetic control model and Colombia across most variables. However, Colombia shows substantially higher values for three variables (inflation, FDI inflows as a percentage of GDP and military expenditure as a percentage of GDP) relative to its synthetic counterpart. Despite this difference, these variables were retained in the analysis as their exclusion worsened model fit. Compared to the values calculated for the donor group without weighting criteria, the synthetic control model yields a significantly better overall fit, confirming the added value of this approach.

Figure 4 illustrates how the synthetic control group closely replicates the trajectory of Colombia’s real per capita GDP throughout most of the pre-intervention period. From 2013 onward, however, the real per capita GDP of Colombia begins to diverge significantly from that of its synthetic counterpart. While the synthetic donor group exhibits stagnation in real per capita GDP, Colombia shows sustained growth through to 2019. This result indicates that the peace negotiations initiated officially in late 2012 and culminating in the signing of the Agreement in September 2016 have a positive impact on Colombia’s real per capita GDP starting in 2013. Figure 5 supports this result, showing that the gap in real per capita GDP between Colombia and its synthetic control remain close to zero until 2013. From that year onward, the gap widens substantially, reflecting the onset of the peace negotiation process.

A robustness analysis is then conducted, employing placebo tests following the methodology proposed by Abadie, Diamond and Hainmueller (2010 and 2015). This entails estimating the same model for all untreated units within the synthetic group —excluding Colombia— while assuming that they were treated in 2013. If the distribution of placebo effects is comparable in magnitude to that of Colombia, the observed effect may plausibly be attributed to chance (Galiani and Quistorff, 2017). To avoid issues of poor fit for some countries during the pre-intervention period, Galiani and Quistorff (2017) recommend calculating standardized p-values (Pvals_std), in which the original p-values are weighted by the quality of the pre-intervention fit. This value provides a measure of the likelihood that the estimated effect for Colombia occurred by chance and offers insight into the adequacy of the model’s fit during the pre-intervention period.

Figure 4

Colombia: real per capita GDP and its synthetic counterpart, 1995–2019

(Logarithms)

Source: Prepared by the authors.

Figure 5

Colombia and “synthetic Colombia”: gap in real per capita GDP, 1995–2019

(Logarithms)

Source: Prepared by the authors.

Table 3 presents the inference results. The “estimate” column quantifies the effect depicted in figure 5 for the post-treatment period (2013–2019). The p-value indicates the proportion of control units that exhibit an effect at least as large as that of the treated unit. In 2013, this value exceeds 50%. With regard to the standardized p-values, the results are low throughout the post-treatment period. The highest value is observed in 2013 (0.267), a year that marked the beginning of the peace talks that would eventually lead to the 2016 agreement. From 2015 onward, the standardized p-values approach zero. This finding is consistent with expectations, as the effects of policies aimed at fostering stability typically take time to reach their potential.

Table 3

Colombia: inference of real per capita GDP, post-treatment period

Year

Estimate

p-value

Pvals_std

2013

0.0361569

0.533

0.267

2014

0.0578107

0.400

0.133

2015

0.0866418

0.400

0.067

2016

0.1071765

0.267

0.000

2017

0.0931482

0.400

0.000

2018

0.0896696

0.400

0.067

2019

0.1054311

0.400

0.000

Source: Prepared by the authors using the synth_runner command from Synth_Runner package, described in S. Galiani and B. Quistorff, “The synth_runner package: utilities to automate synthetic control estimation using synth”, The Stata Journal, vol. 17, No. 4, December 2017.

VI. Conclusions

The aim of this study has been to analyse the effect of the peace negotiations during the period 2012–2016 on Colombia’s real per capita GDP. There are differing views regarding the effects following the signing of the peace agreement. Authors such as Collier (1999), Hoeffler and Reynal-Querol (2003), Ruiz Díaz, Galeano Raquejo and Gil Mateus (2015) and Millán Celis and Jiménez Quitian (2017) argue that, at least in the short term, the effects of peace have been negative for the country’s economic growth owing to the lack of institutional and societal adaptation in the post-conflict period. Persistent uncertainty related to political instability may also dampen investment decisions (Alesina and others, 1996; Poinsot, 2003; Posada Duque, 2010). These studies employ traditional methods based on linear regressions or impact evaluation techniques, which do not allow a precise estimation of what Colombia’s economic growth trajectory would have been had the peace process not taken place.

The outcomes of this process depend on the design and implementation of policies during and after the negotiations, which should aim to address the factors that led to the armed conflict in Colombia, reduce violence and provide reparations for the wrongs inflicted on society (Collier, Hoeffler and Söderbom, 2008; Hoeffler, Ijaz and Von Billerbeck, 2010). Such actions should also consider the context and dynamics of the actors involved, with the ultimate goal of achieving a sustainable and stable peace from both economic and social perspectives (Sakalasuriya, Haigh and Amaratunga, 2018).

In Colombia, throughout the negotiation period preceding the peace agreement (2012–2016), institutional, social and economic policies were consolidated with the shared objective of mitigating the root causes of the conflict. These were related to the abandonment and poverty of rural areas, as well as the political exclusion of social groups owing to the long-standing institutional dominance of the conservative and liberal parties. This period of reconstruction aimed to restore the normal and effective functioning of the economy as quickly as possible, which started with the restoration of physical and non-physical capital (Brandon, 2018) and culminated in the signing of the 2016 peace accord.

The use of the synthetic control method enabled us to identify the positive effects of Colombia’s peace negotiations, the objective of which was to implement a series of measures designed to prevent a resurgence of violence. The process began to bear fruit in 2013, with the approval of thorough rural reform and reparations for victims of the conflict. These initial agreements gradually fostered a sense of public trust that the process was on the right track, thereby generating a positive impact on real per capita GDP. The reduction in violence from 2014 onward, coupled with the prioritization of social spending, reintegration of combatants into civil and political life and recovery of infrastructure, helped widen the gap with the synthetic counterpart, a trend that appears to be consolidated with the 2016 agreement. In the 2016–2019 period, Colombia had a positive annual GDP growth rate of 9.19% according to data from the World Bank (n.d.) (expressed in dollars at 2010 prices), while growth was not observed in its synthetic counterpart. Improved conditions for investors stemming from reduced destruction of physical and human capital, lower crime rates and greater political stability (Bernal Zepeda and Castillo Ponce, 2012; Jiménez Giraldo and Rendón Obando, 2012) encouraged new investments and greater trade openness.

These findings illustrate that the Colombian peace negotiations involved dynamic institutional transformations from the outset, which must be sustained over time to ensure the long-term success of the process. In addition, the results indicate that the benefits of peace became apparent in subsequent years, as policies promoting social inclusion, development and sustainability were implemented and began to show significant progress. This explains how, from the early stages of a credible peace process, the improved climate of certainty prompted economic agents to adjust their expectations in a more positive direction.

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1. [1] Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Nicaragua, Paraguay, Peru, the Plurinational State of Bolivia, the United States and Uruguay.

2. [2] Hahn and Shi (2017, as cited in McClelland and Gault, 2017) recommend having a relatively large number of predictors compared to the number of donor countries, as this improves the selection of the weights assigned to each country in the synthetic control model, even though it runs counter to analysts’ typical concerns regarding degrees of freedom.

3. [3] Data from 2020 onward were excluded to avoid any bias generated by the impact of the COVID-19 pandemic.

4. [4] For the synthetic control method estimation, the “synth” Stata statistical package, developed by Abadie, Diamond and Hainmueller (2015), was used. The “nested” option was employed to create the weight matrix that makes it possible to minimize the root mean squared prediction error of the synthetic control estimator (Abadie, Diamond and Hainmueller, 2010).

5. [5] Since the work of Islam (1995), growth models have often used regressor data in five-year intervals.

Book review essay

The World That Latin America Created

The United Nations Economic Commission

for Latin America in the Development Era,

by Margarita Fajardo[1]

Esteban Pérez Caldentey and Miguel Torres

Received: 05/02/2025

Accepted: 20/03/2025

Abstract

This book review essay analyses and builds on the work of Margarita Fajardo on the history of the establishment of the Economic Commission for Latin America (ECLAC)[2] and the growth and decline of its influence in the region and around the world in the two decades following its founding.

Keywords

ECLAC, development, underdevelopment, center-periphery system, industrialization, dependency, Latin America

JEL classification

B15, F13, N16, O10, O54

Authors

Esteban Pérez Caldentey is a Senior Economic Affairs Officer at ECLAC. Email: esteban.perez@cepal.org.

Miguel Torres is a Senior Economic Affairs Officer at ECLAC. Email: miguel.torres@cepal.org.

I. Introduction

The World That Latin America Created: The United Nations Economic Commission for Latin America in the Development Era recounts the history of the establishment of the Economic Commission for Latin America (ECLAC) in the late 1940s and the growth and decline of its regional and global influence following the Second World War. The book analyses the work of the economists who worked under the aegis of the Commission, known as cepalinos, in particular Celso Furtado (1920–2004), Raúl Prebisch (1901–1986), Juan Noyola Vázquez (1922–1962), Aníbal Pinto Santa Cruz (1919–1996) and Osvaldo Sunkel (born in 1929) (see chapters 1 to 4), who developed a corpus of critical thinking on the dominant paradigm of the time, offering an alternative interpretation and vision of the centre-periphery concept to address the region’s specific problems. Others who played an important role in developing ECLAC thinking were Jorge Ahumada (1915–1965), Regino Boti (1923–1999), José Medina Echavarría (1903–1977), José Antonio Mayobre (1913–1980), Oscar Soberón (1922–1985) and Víctor Urquidi (1919–2004). The ECLAC vision was organized around three pillars: industrialization of the periphery, economic cooperation between the centre and the periphery, and regional integration (Fajardo, pp. 7–10).

The book presents a detailed explanation of the constraints and barriers —external and internal— that arose in designing and implementing the ECLAC project and notes the differences of opinion among ECLAC economists, stemming from their diverse training, approaches and ideologies, which undermined the cohesiveness and influence of the Commission’s work. In particular, it underscores the difficulty of steering a middle course, an exercise which, amid the ideological polarization in Latin America at the time, ultimately proved to be a tightrope walk.

While the ECLAC approach was vigorously criticized by economists from the region and elsewhere who supported the dominant paradigm, such as Jacob Viner and Eugenio Gaudin, some of the institution’s core ideas were incorporated into the economic development consensus of the 1960s. The programme of action that gave rise to the Alliance for Progress (CIESS, 1961) was, essentially, the Commission’s development agenda, and the foundations of trade policy established in the context of the United Nations Conference on Trade and Development (UNCTAD, 1964 and 1968) reflected the global reach of ECLAC thinking.

Cepalinos also faced sharp criticism from dependency theorists such as André Gunder Frank (1929–2005), Fernando Henrique Cardoso (born in 1931), Enzo Faletto (1935–2003) and Theotônio dos Santos (1936–2018). According to Fajardo (pp. 212 and 213), “the public offensive against the cepalino project began with dependentistas and their denunciation that ... state-led development perpetuated, rather than abolished, the periphery’s dependence on global markets and forces”. For the theory’s adherents, the ideas promoted by ECLAC had come to represent the orthodoxy of development (Fajardo, p. 143) that had to be replaced. Dependency theory was far from uniform, instead encompassing different meanings, which gave rise to a variety of economic and social transformation projects that were not necessarily coherent. It was nonetheless underpinned by the Commission’s ideas, in particular the centre-periphery dichotomy. As such, although dependency theorists aimed to replace cepalinos as the dominant thinkers, they “could not fully dismiss their project” (Fajardo, p. 165).

This analysis emphasizes the relevance of this important book’s contributions to understanding the origin, evolution and high and low points of the work of ECLAC, examines how ECLAC thinking has been applied in certain countries —notably Chile and Brazil— as well as regionally and globally, and builds on some of the book’s main arguments.

II. The founding and early days of ECLAC

Chapter 1 of Fajardo’s book describes and analyses the international political context that led to the establishment of ECLAC and the early challenges faced in justifying its existence and ensuring its continuity. The chapter then focuses on the appointment of Raúl Prebisch as Executive Secretary of ECLAC and on the issue of the region’s declining terms of trade outlined by Prebisch himself and by Hans W. Singer (1910–2006).

In 1947, in line with the foreign policy agenda of then-President of Chile Gabriel González Videla, Hernán Santa Cruz, the Permanent Representative of Chile to the United Nations, proposed to the Economic and Social Council of the United Nations the establishment of the Economic Commission for Latin America, on equal footing with the regional commissions for Europe and Asia. For Santa Cruz and other Latin American representatives on the Economic and Social Council, although the region had been spared the devastation wrought by the Second World War in Europe and Asia, it had nevertheless suffered the economic dislocation caused by the conflict, with repercussions that included trade restrictions and higher import prices. Trade patterns had also shifted, with the loss of European markets driving greater dependence among the region’s countries, as in all others, on the United States as the primary supplier of goods and capital, and as an export market.

The objectives and functions of ECLAC emphasized the region’s industrialization, diversification and economic development with a view to enhancing the use of its resources (United Nations, 1948; Fajardo, p. 23). As reflected later in the thinking of Prebisch and ECLAC, Santa Cruz and other

Latin American representatives on the Economic and Social Council argued that the path towards greater per capita income and economic and social development lay in industrialization and in productive and export diversification. Notwithstanding the objections of the United States on the grounds that the proposed commission’s functions might overlap with those of the Pan American Union, ECLAC was formally established in February 1948, with support from Europe, and headquartered in Santiago.

The Commission’s continuity relied on the fulfilment of its first mandate: the preparation of the Economic Survey of Latin America. Following a few setbacks, including a failed request for cooperation from the region’s governments and the International Monetary Fund (IMF) to overcome the Commission’s funding and statistics shortfalls, the Economic Survey of Latin America was presented at its second session, held from 29 May to 14 June 1949, in Havana.

III. The centre-periphery and the industrialization strategy

The Economic Survey addresses two key dimensions of the centre-periphery relationship: the asymmetry in countercyclical capacity between the major cyclical centres —particularly the United States— and the peripheral economies, including those of Latin America; and the deterioration in the region’s terms of trade from the 1930s onward. This focus reflects the influence of Raúl Prebisch, then a consultant with ECLAC, who had declined to serve as its first Executive Secretary, opting instead to apply for a position at IMF, which he failed to obtain after Argentine President Juan Domingo Perón vetoed his appointment (Fajardo, p. 35).

For the second session of ECLAC, Prebisch had also written The Economic Development of Latin America and its Principal Problems (1950). Known as the “Prebisch manifesto”, it was a critique of the international division of labour —which, historically, had tended to result in the distribution of earnings to the industrialized centres— and of the legitimacy of that system and the theory of comparative advantage underpinning it. With a polemical tone that contrasted with the more neutral tenor of the Economic Survey of Latin America (Dosman, 2008, p. 247), the manifesto encouraged the region’s countries to adopt industrialization policies.

From our perspective, while the document analysed the development problems of Latin America, it was also a critique of the predominant economic paradigm. In particular, it questioned that paradigm’s false sense of universality and called for new generations of economists to adopt an original approach in analysing the specific issues of Latin America (Furtado, 1988, p. 53).

As a complement to Fajardo’s work, it is worth noting that one of Prebisch’s most significant attempts to present his key arguments on Latin American development is found not in his manifesto but rather in the first section of the second Economic Survey of Latin America, entitled “Growth, disequilibrium and disparities: interpretation of the process of economic development”, which was presented at the third session of ECLAC, held from 5 to 21 June 1950, in Montevideo (ECLAC, 1951; Furtado, 1988, p. 66).

His argument was based on the spread of technical progress from the centre towards the periphery as a result of changes in the global economy and the development model following the Great Depression, which later intensified with the outbreak of the Second World War. At the time, the United States had become the major cyclical centre, replacing the United Kingdom, which had held that position since the nineteenth century. The separation of the United States economy from that of the United Kingdom was reflected in a markedly lower import coefficient, which, compounded by its downtrend over time, lessened the United States’ ability to revitalize the global and Latin American economy. This shift undermined the function of international trade as the principal conduit for the spread of technical progress from the major cyclical centre to peripheral countries, compelling them to seek alternative means of absorbing technology.

In a context of constrained export activity owing to the low income elasticity of demand for commodities —compounded by unfavourable terms of trade—, industrialization emerged as the principal avenue to technical progress, enabling an increase in aggregate labour productivity. For Prebisch, Latin America had reached a juncture at which the decline in revenue owing to the growth of primary exports outweighed the loss of revenue stemming from the higher cost of producing industrial goods domestically than that of importing them.

IV. The limits of import substitution

Building on the above reasoning, Prebish advocated for an import substitution policy stimulated by moderate protectionism (Prebisch, 1959). This approach would allow the region to counter its tendency towards external imbalance rooted in the disparity in income elasticity of demand for imports and for exports, and could offset falling terms of trade by channelling additional productive resources towards the industrial sector (Fajardo, p. 34). As Fajardo argues (p. 36), Prebisch had been developing this line of thought since the early 1930s, which we believe calls into question the attribution of authorship of the Prebisch-Singer hypothesis on the deterioration of terms of trade to Hans Singer (Pérez Caldentey and Vernengo, 2025).

As recognized by Prebisch, his policy proposal provided a theoretical justification for the industrialization strategy already being implemented by the region’s largest countries, to encourage the others to follow suit, and to provide them all with an orderly strategy for carrying it out. In short, “industrialization was fact before it was policy, and policy before it was theory” (Love, 1995, p. 395; Fajardo, p. 9). Chapter 2 of Fajardo’s book analyses the methods (training courses, recruitment of economists and mobilization of local agents) used by ECLAC to implement its recommendations in the region, which “paved the road toward the institution’s regional hegemony in the field of economic ideas” (Fajardo, p. 71). Thanks in part to Furtado’s initiatives, ECLAC became remarkably influential in Brazil, a crucial factor in the consolidation of the Commission’s predominant role.

Importantly, the inward-oriented industrialization strategy had not yielded the results originally envisaged in the Commission’s plan. Both the cepalinos themselves and Prebisch recognized some of its shortcomings early on, such as the limited capacity of emerging industrial areas to absorb agricultural labour, which gave rise to urban marginalization, poverty and inequality. Meanwhile, the agricultural sector failed to provide real opportunities for the well-being of poor campesinos owing to highly concentrated land tenure and the technological lag in agricultural production. Moreover, aggregate growth was insufficient and had stagnated owing to the external constraints faced by most of the region’s economies (Prebisch, 1963). The need for imports of capital and intermediate goods exceeded the export capacity of the region’s countries, giving rise to an external bottleneck caused by foreign exchange shortages. Fajardo calls this “the development paradox” (pp. 17, 44 and 71, among others).

Later, in the 1960s, criticism of the inward-oriented industrialization strategy became widespread (Prebisch, 1959; Tavares, 1964; Macario, 1964). It had failed to develop the manufacturing sector, spur job creation or establish efficient industries capable of competing in foreign markets. According to Prebisch (1961), one principal constraint of import substitution policy was the fact that it had been based on the structure and context of nineteenth-century international trade, and therefore its implementation in Latin America had resulted in the splitting of the industrialization process into isolated components.

The difficulties in implementing inward-oriented industrialization prompted Prebisch and ECLAC to focus on the regional market and encourage regional trade integration, a notion that Prebisch had been developing since the 1940s. Stimulating exports of manufactures to cyclical centres was also emphasized. International cooperation mechanisms could improve market access for these products while averting a deterioration in the terms of trade through price stabilization agreements for raw materials. These elements were at the heart of the UNCTAD programme, which ultimately projected the ECLAC development agenda onto the global stage.

V. The structural approach to inflation

Inflation was another key area of concern for the cepalinos, one which brought to light their differences of opinion on its underlying causes, analysis and solution, as addressed by Fajardo in chapter 3 of her book.

In keeping with his training as a central banker, Prebisch rejected the structural approach to inflation (Fajardo, pp. 74–77) developed in the 1950s by Furtado (1954), Noyola Vázquez (1955 and 1956) and Sunkel (1958), who argued that inflation stemmed from non-monetary factors, and that in particular, it reflected an imbalance between the composition of aggregate supply and demand. Although Noyola Vázquez claimed that Prebisch had finally accepted that inflation could be analysed without referring to monetary figures (Fajardo, p. 84), Prebisch continued to believe that “there is a very close correlation in Latin America between variability in the money supply and price fluctuations, the latter usually following the former” (ECLAC, 1954, p. 69).

Our reading of the available information is that this was the official ECLAC position on the causes of inflation until the publication of the Economic Survey of Latin America, 1957 (ECLAC, 1958), with the exception of the role attributed to the distribution struggle among social classes in the analysis of inflation in Chile, as presented in the Economic Survey of Latin America, 1954 (ECLAC, 1955). The 1958 publication presents the core elements of the structural approach to inflation: the distinction between basic (or structural) inflationary pressures —such as external demand instability and stagnation and inelastic agricultural supply— and propagation mechanisms, including the speed of price and wage adjustments, the degree of flexibility in bank lending (with a passive money supply) and the impact of the fiscal deficit. According to this approach, traditional stabilization programmes tend to engender economic stagnation instead of fostering nominal stability.

As Fajardo argues (p. 82), the shift in the inflation narrative and the institutionalization of the structural theory of inflation is largely the result of Prebisch’s loss of “moral authority” following his support in October 1955 for dictator Eduardo Lonardi, who took office in Argentina after then-president Juan Domingo Perón was ousted from power. That support was reflected in an orthodox economic plan (the Prebisch plan) that garnered the opprobrium of cepalinos owing to “the absence of the constraints imposed by Argentina’s insertion in global capitalism” and “the excessive focus on monetary instruments to address the inflationary crisis” (Fajardo, p. 82).

Prebish kept a distance from the debates between structuralists and monetarists and did not attend the Conference on Inflation and Growth, held in Rio de Janeiro, Brazil, in 1963, at which distinguished economists from outside the region, including Albert Hirschman, Arnold Harberger, Gottfried Haberler and Nicholas Kaldor, debated both approaches. Later, referring to that debate, Prebisch said that “the pendulum swung rather too far ... on the structuralist side” (Prebisch, 1976, p. 52). He also rebuffed attempts to categorize him as a structuralist: “I refuse to classify myself or be classified” (Prebisch, 1976, p. 52).

Looking back on the phases of his thinking, he remarked that his treatment of inflation had been “rather conventional … with some occasional incursions into structural factors and external vulnerability” (Prebisch, 1984, p. 182).

The study of inflation also revealed differences and conflicts with other institutions, such as IMF, which contributed, incorrectly, to a radicalized view of ECLAC. As Fajardo notes (p. 100), “the structural approach to inflation neither fully disregarded the monetary aspects nor completely embraced the social conflict approach, but rather represented a Solomonic compromise between visions that were tearing cepalinos apart”.

VI. ECLAC, the Cuban Revolution and the Alliance for Progress

The response of ECLAC to the Cuban Revolution and its relations with the regime of Fidel Castro also elicited significant internal tensions. These matters once again underscored the difficulty of shaking off the ideologically charged perceptions of ECLAC that often shaped the interpretation of its history. Fajardo covers this interesting and revealing episode in fulsome detail in chapter 4 of her book.

In response to a request from Regino Boti and Felipe Pazos —who were members of Fidel Castro’s team of economists— and despite his misgivings, Raúl Prebisch sent an official mission to Cuba in 1959, led by Noyola, aiming to establish a national planning institution and provide training for civil servants (Fajardo, pp. 103–118; Dosman, 2008, pp. 351 and 352). Disagreement between Prebisch and Noyola brought the ECLAC mission to Cuba to a premature end. Prebisch criticized Noyola for his over-enthusiasm for the Cuban Revolution, which had jeopardized the traditional independence and objectivity of ECLAC (ECLAC, 1961). This, in turn, called into question the Commission’s relations with the United States.

As an alternative, Prebisch and Furtado indicated their support for the Alliance for Progress, which they viewed as a transformative project within an established institutional framework. The Charter of Punta del Este, on the establishment of the Alliance for Progress (1961), included several of the core themes of the development agenda of Prebisch and ECLAC, for example achieving sustained growth of per capita income and accelerating the process of rational industrialization so as to increase the productivity of the economy.

The Charter’s objectives also included diversifying production and exports —to lessen the dependence on a small number of commodities and capital goods imports— and reducing the volatility of foreign-exchange earnings from primary exports. It also addressed the need to encourage agrarian reform and to strengthen regional integration, with the ultimate aim of establishing a Latin American common market (CIESS, 1961).

Prebisch indicated that the basic ideas underpinning the Charter of Punta del Este had been developed in Latin America and that he found it concerning that they should be presented as though they had been conceived in the United States (Fajardo, p. 125). In fact, Furtado (1988, p. 118) said that the ideas of ECLAC had guided the United States’ foreign policy (Fajardo, p. 119). Ultimately, Prebisch played a lesser role in designing and implementing the Alliance for Progress, which significantly deflated his initial enthusiasm.

The Alliance for Progress was far from meeting its objectives in the disbursement of funding and scope of cooperation projects. In the end, it focused more on the symptoms than the structural causes of economic and social lag in Latin America (see Fajardo, pp. 128–137).

VII. Cepalinos and dependency theorists

After tracing the pivotal developments that shaped the establishment of ECLAC and the rise of its intellectual and political influence in the region in the 1950s, Fajardo dedicates the last two chapters of her book to analysing the milestones that marked the emergence of dependency theory in Latin America. Paradoxically, dependency theorists were questioning the ECLAC development programme just when “Latin America reached the peak of its global influence in the international development agenda” (Fajardo, p. 13).

Dependency theory originated in Brazil and focused on the ideas of Celso Furtado. Fajardo (pp. 140 and 141) attributes the theory’s emergence to the fact that in the early 1960s, the ECLAC project was severely compromised by disappointing economic performance in the countries with a stronger cepalino presence and by the Commission’s position on the Cuban Revolution and the Alliance for Progress. New social movements demanding more radical change than that proposed in the ECLAC agenda emerged in Brazil.

The book centres mainly on the contributions of André Gunder Frank and Fernando Henrique Cardoso, and on their ideas on the “development of underdevelopment” and on dependency and development.

From Frank’s perspective, underdevelopment in Latin America was less linked to the international system shaped by the centre-periphery model and more so to the region’s incorporation into the global capitalist system beginning in the sixteenth century (Fajardo, p. 147). According to Cardoso (Fajardo, p. 161), cepalinos had incorporated global power structures into their analysis by defining underdevelopment based on the position of the periphery, but had missed a critical component: national or internal power structures. Each interpretation entailed different policy recommendations, reflecting the diverse and sometimes conflicting views of dependency theorists. While Frank’s policy recommendations were more extreme, Cardoso’s centred on fostering social mobility and reducing social inequality.

On this point, Fajardo also rightly notes that Brazil was the country most influenced by ECLAC thinking from the Commission’s earliest days and also, paradoxically, the birthplace of its first critiques, which laid the groundwork for Latin American dependency theory, from Brasilia and São Paulo, in particular.

At the University of Brasilia, a group that included Theotônio dos Santos, Vania Bambirra and Rui Mauro Marini began gathering to read Karl Marx’s Capital. They were joined by German economist André Gunder Frank, who had been influenced by the neo-Marxist analysis of Paul Baran. A dependency movement had also emerged in São Paulo, led by Fernando Henrique Cardoso.

Fajardo weaves an engaging and captivating narrative of the unbridgeable divide that emerged between the two groups of dependency theorists, although their perspectives aligned more often than they diverged during their time in Brazil, especially regarding Frank’s hypothesis on the development of underdevelopment and his critique of “the old intellectual left and its project of economic development based on a bourgeois revolution” (Fajardo, p. 151). The groups nonetheless experienced a rupture following their exile to Chile. Cardoso aligned with the cepalinos, shifting from an approach that viewed dependency through the “development of underdevelopment” lens to a hypothesis of dependency and development, as described in the classic work Dependency and Development in Latin America (Cardoso and Faletto, 1979).

Fajardo’s narrative of this intellectual and political schism tells a story of dependency theory that is rich with anecdotes and substantive analysis of the ideas developed in Chile by both groups in the mid-1960s and early 1970s, and of the intellectual currents underlying each version. In short, chapters 5 and 6 of the book effectively present the origin, rise and decline of dependency theory through a historical and intellectual account that enriches the other studies that systematize it, including the works of Kay (1989), Rodríguez (2006) and Ahumada and Torres (2024).

VIII. Concluding remarks

Little more than a decade following the establishment of ECLAC, its economists “had become an obligatory point of passage for a myriad of actors across the region, creating a world for and from Latin America” (Fajardo, p. 214).

The history of economic thinking reveals that to establish new lines of thought and intellectual arenas, new ways must be found to express its basic concepts. John Maynard Keynes, the most renowned and oft-cited economist of our time, said that “practical men, who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist” (Keynes, 1936). It should not be forgotten that they are also, unavoidably, the slaves of the language used to express those ideas.

ECLAC made its mark on economic development theory by defining unique analytical categories, including the concepts of the external bottleneck, structural inflation, structural heterogeneity, and, above all, the centre-periphery model. For Furtado, this model was Prebisch’s principal legacy, as it characterized the distinct production structures of developed and developing countries, which shaped the groups’ global integration and economic interactions throughout history. As demonstrated by Furtado’s 1958 project, the centre-periphery categorization was flexible enough to reveal subregional disparities. In his view, the Brazilian economy was reproducing the disparity between the industrial hub (the centre) and the raw-material-producing colonies (the periphery). He believed that North-East Brazil, which produced raw materials for foreign markets and exported manufactures from the country’s central-southern region, had suffered a decline in its terms of trade that was hampering its development. Moreover, an exchange-rate regime that allowed the central-southern region to capture a greater share of foreign-currency inflows only worsened the situation. In the early 1960s, to reduce those disparities, Furtado proposed the establishment of the Development Agency for the Northeast (SUDENE) and became its director. The language of ECLAC, which was essential in legitimizing Furtado’s thinking, came to dominate the discourse and rhetoric of economic development, as reflected in two projects very much at ideological odds: the Cuban Revolution and the Alliance for Progress (Fajardo, p. 213.)

In the early 1960s, ECLAC began reviewing the formulations and recommendations in the original programme devised by Prebisch and his team of advisers in the late 1940s and early 1950s. Also in the early 1960s, ECLAC proposed a set of reforms to make development viable (Bielschowsky, 1998; Rodríguez, 2006), which included agrarian reform and policies for redistribution and development financing in the region’s countries. Using this characterization of Latin America in the 1960s as a starting point, Fajardo describes the development of dependency theory, noting that against a backdrop of stagnating economic growth in the region, the ideas of ECLAC, once on the vanguard, had become orthodox from the viewpoint of the new intellectuals who were emerging in its various countries.

Even so, the three dimensions that underpinned the efforts of ECLAC —technical progress and its dissemination to peripheral countries, cooperation between developing and developed countries, and regional integration— remain the cornerstones of its work. Fajardo’s book serves as a valuable reminder of the need to read and appreciate the origin story of ECLAC and the development of its ideas on its own terms to counter the biases of hindsight, which often obscure its contributions to economic thought and policy.

Indeed, this highly original work is an essential contribution to understanding how ECLAC thinking has evolved and assessing its national, regional and global impact. The richness, depth and detail of Fajardo’s narrative and her extensive use of unpublished bibliographic material make this text an essential reference for academics and policymakers and for all readers interested in the development of the Commission’s central ideas.

Bibliography

Ahumada, J. M. and M. Torres (2024), “Dependency theory: an underdevelopmental thought from Latin America to the entire world”, Global Handbook of Inequality, S. S. Jodhka and M. Rehbein (eds.), Springer.

Bielschowsky, R. (1998), “Evolución de las ideas de la CEPAL”, CEPAL Review, special issue (LC/G.2037-P), Santiago, Economic Commission for Latin America and the Caribbean (CEPAL), October.

Cardoso, F. H. and E. Faletto (1979), Dependency and Development in Latin America, University of California Press.

CIESS (Inter-American Center for Social Security Studies) (1961), “The Charter of Punta del Este, Establishing an Alliance for Progress Within the Framework of Operation Pan America”.

Dosman, E. J. (2008), The Life and Times of Raúl Prebisch 1901-1986, McGill-Queen’s University Press.

ECLAC (Economic Commission for Latin America and the Caribbean) (1961), “Provisional summary record of the second meeting” (E/CN.12/AC.47/SR.2), Economic and Social Development Committee, 10 May.

____(1958), Economic Survey of Latin America, 1957 (E/CN.12/489/Rev.1), New York.

____(1956), “Some aspects of the acceleration of the inflationary process in Chile”, Economic Bulletin for Latin America, vol. I, No. 1.

____(1955), Economic Survey of Latin America, 1954 (E/CN.12/362/Rev.1), New York.

____(1954), Economic Survey of Latin America, 1953 (E/CN.12/358), New York.

____(1951), “Growth, disequilibrium and disparities: interpretation of the process of economic development”, Economic Survey of Latin America, 1949 (E/CN.12/164/Rev.1), New York.

Furtado, C. (1988), La fantasía organizada, Buenos Aires, Editorial Universitaria de Buenos Aires (Eudeba).

____(1954), “Capital formation and economic development”, International Economic Papers, No. 4.

Kay, C. (1989), Latin American Theories of Development and Underdevelopment, Routledge.

Keynes, J. M. (1936), The General Theory of Employment, Interest, and Money, New York, Harcourt Brace Jovanovich.

Love, J. L. (1995), “Economic ideas and ideologies in Latin America since 1930”, The Cambridge History of Latin America, vol. 6, part 1, L. Bethell (ed.), Cambridge, Cambridge University Press.

Macario, S. (1964), “Protectionism and industrialization in Latin America”, Economic Bulletin for Latin America, vol. IX, Nº 1, Santiago, Economic Commission for Latin America and the Caribbean (ECLAC).

Noyola Vázquez, J. (1956), “Economic development and inflation in Mexico and other Latin American countries”, Investigación Económica, vol. 16, No. 4, Mexico City, National Autonomous University of Mexico (UNAM).

____(1955), “Inflación y desarrollo en Chile”, unpublished.

Pérez Caldentey, E. and M. Vernengo (2025), “En el centro de la periferia: Raúl Prebisch y la génesis de la teoría del subdesarrollo”, unpublished.

Prebisch, R. (1984), “Five stages in my thinking on development”, Pioneers in Development, G. M. Meier and D. Seers (eds.), Washington, D.C., World Bank.

____(1976), “A critique of peripheral capitalism”, CEPAL Review, Santiago, Economic Commission for Latin America (ECLAC).

____(1963), Towards a dynamic development policy for Latin America (E/CN.12/680), Santiago, Economic Commission for Latin America and the Caribbean (ECLAC).

____(1961), “Exposición del Dr. Raúl Prebisch, Subsecretario de las Naciones Unidas a cargo de la Secretaria Ejecutiva de la Comisión Económica para América Latina, en la sesión plenaria del 27 de octubre de la Segunda Conferencia Interparlamentaria Americana, celebrada en Santiago de Chile” [online] https://repositorio.cepal.org/server/api/core/bitstreams/bd4fc87f-8120-49a7-8073-340acedc6b3e/content.

____(1959), “Commercial policy in the underdeveloped countries”, The American Economic Review, vol. 49, No. 2, May.

____(1950), The economic development of Latin America and its principal problems (E/CN.12/89/Rev.l), New York, United Nations.

Rodríguez, O. (2006), El estructuralismo latinoamericano, Mexico City, Siglo XXI/Economic Commission for Latin America and the Caribbean (ECLAC).

Sunkel, O. (1958), “Inflation in Chile: an unorthodox approach”, El Trimestre Económico, vol. 25, No. 100, October-December.

Tavares, M. C. (1964), “The growth and decline of import substitution in Brazil”, Economic Bulletin for Latin America, vol. IX, No. 1, Santiago, Economic Commission for Latin America and the Caribbean (ECLAC).

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____(1964), Towards a New Trade Policy for Development. Report by Secretary-General of the United Nations Conference on Trade and Development (E/CONF.46/3), Nueva York.

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1. [1] M. Fajardo, The World That Latin America Created: The United Nations Economic Commission for Latin America in the Development Era, Harvard University Press, 2022.

2. [2] In 1984, the entity was renamed the Economic Commission for Latin America and the Caribbean.

Guidelines for contributors to the cepal Review

Guidelines for contributors to the CEPAL Review

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Table of Contents

Maintitle

Copyright

Contents

Explanatory notes

Editorial note

On the measurement of growth over the long run1

Economic growth and productive sectors: recent empirical data on middle-income countries

Long-term effects of real exchange rate volatility and institutional quality on economic complexity

The influence of fiscal solvency on financial development: evidence for 140 countries, 1990–20201

Empirical evidence for Okun’s law in Colombia: an analysis of rural areas at the region level

Prices and progress? The link between inflation and structural change in post-war Brazil

Impact of the peace negotiations in Colombia on real GDP, 2013–2019

Book review essay

The World That Latin America Created The United Nations Economic Commission for Latin America in the Development Era, by Margarita Fajardo1

Guidelines for contributors to the cepal Review