Title Page
Western Hemisphere Department
Commodity Cycles, Inequality, and Poverty in Latin America
Ravi Balakrishnan, Sandra Lizarazo, Marika Santoro, Frederik Toscani, and Mauricio Vargas
INTERNATIONAL MONETARY FUND
Copyright Page
Copyright ©2021 International Monetary Fund
Cataloging-in-Publication Data
IMF Library
Names: Balakrishnan, Ravi. | Lizarazo Ruiz, Sandra. | Santoro, Marika. | Toscani, Frederik. | Vargas, Mauricio, 1977- | International Monetary Fund. Western Hemisphere Department, issuing body. | International Monetary Fund, publisher.
Title: Commodity cycles, inequality, and poverty in Latin America / Ravi Balakrishnan, Sandra Lizarazo, Marika Santoro, Frederik Toscani, and Mauricio Vargas.
Other titles: International Monetary Fund. Western Hemisphere Department (Series).
Description: Washington, DC : International Monetary Fund, 2021. | Departmental paper series. | Includes bibliographical references.
Identifiers: ISBN 9781484326091 (paper)
Subjects: LCSH: Prices—Latin America. | Poverty—Latin America. | Equality—Latin America.
Classification: LCC HB235.L25 B35 2021
The Departmental Paper Series presents research by IMF staff on issues of broad regional or cross-country interest. The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Ravi Balakrishnan led the staff team and provided overall guidance. Frederik Toscani coordinated analytical inputs and drafting, while Marika Santoro was the lead author of Chapter 4. Frederik Lambert, Lusine Lusinyan, Adrian Peralta Alva, and Marina Mendes Tavares made important contributions to the paper. Adrian Robles, Steve Brito, Catherine Koh, and Cristhian Vera Avellan provided excellent research assistance. Bas Baaker, Pelin Berkmen, Hamid Faruqee, Krishna Srinivasan, Alejandro Werner, and other colleagues in the IMF’s Western Hemisphere and other departments provided useful comments and feedback, as did seminar participants in in La Paz, Lima, and Asunción.
Publication orders may be placed online, by fax, or through the mail:
International Monetary Fund, Publication Services
P.O. Box 92780, Washington, DC 20090, U.S.A.
Tel. (202) 623-7430 Fax: (202) 623-7201
E-mail: publications@imf.org
Contents
Executive Summary
Introduction
1. Inequality and Poverty Developments in Latin America Since the Turn of the Century
Panoramic View of Social Gains during the Boom
Poverty and Inequality Developments from 2014 to 2019
Early Evidence on the Impact of the COVID-19 Shock on Poverty and Inequality
2. The Link Between Commodity Cycles, Poverty and Inequality
Is There a Statistical Association?
Panel Regression Analysis
What Are the Channels during the Boom Phase?
Disentangling the Channels
3. Micro Data Case Studies of the Boom: Bolivia, Brazil, and Peru
Bolivia
Brazil
Peru
Takeaways
4. Model-Based Case Studies: Bolivia and Paraguay
Motivation
Model
Calibration of the Pre-Boom
Bolivia
Paraguay
Key Takeaways and Differences between Bolivia and Paraguay
5. Explaining Post-Boom Developments
What Do the Findings About the Boom Suggest About the Post-Boom Phase?
How Did Actual Developments Compare to These Predictions?
6. Policies to Tackle Poverty and Inequality: Where to Next and What Needs to Be Done?
Short-Term Policy Priorities Following the COVID-19 Shock
The Road Ahead: Structural Policy Discussion
Concluding Considerations
Appendix 1. Details of the Model from Chapter 4
References
Boxes
Box 1. Measuring (In)equality of Opportunity in Latin America
Box 2. Inequality Perceptions: “How Fair is the Income Distribution in Your Country?”
Box 3. Inequality Developments in a LAC Country without a ToT Boom: The Case of Mexico
Box 4. General Structure of the DGE Model
Box 5. Peru: Inequality and Poverty after the Commodity Boom
Box 6. Details of Natural Resource Revenue-Sharing in Latin America and Elsewhere
Tables
Table 1. Relationship between Commodity Export Prices and Inequality
Table 2. Commodity Terms of Trade and Income Share by Decile in Commodity Exporters
Table 3. Bolivia: Composition of Household Income per Capita
Table 4. Change in Value of Natural Resouce Production and Natural Resource Revenues in Brazilian Municipalities, 2000–10
Table 5. Impact of Natural Resource Boom on Producer Municipalities in Brazil
Table 6. Brazil: Impact of Mineral and Offshore Hydrocarbon Production on Municipal Revenues and Extractive Sector Employment
Table 7. Brazil: Impact of Natural Resource Extraction on Municipal Revenues and Expenditures
Table 8. Peru: Real Income per Capita Growth (2007–11)
Table 9. Peru: Composition of Households’ Total Income
Table 10. Peru: Impact of Canon Transfer on Select Indicators
Table 11. Initial Steady State for Bolivia and Paraguay
Table 12. Production Structure
Figures
Figure 1. Gini Coefficient
Figure 2. Poverty Rate
Figure 3. Change in Poverty Headcount Ratio
Figure 4. Change in Average Gini Coefficient
Figure 5. Average Real GDP Growth
Figure 6. Average GDP Growth and Change in Poverty Headcount Ratio, 2000–14
Figure 7. Average Commodity Terms of Trade Growth during Boom, 2000–14
Figure 8. Change in Gini
Figure 9. Change in Poverty Rate
Figure 10. Commodity Terms of Trade
Figure 11. Global Commodity Prices
Figure 12. Annualized Change in Poverty Headcount Ratio
Figure 13. Average Annual Real Monthly Wage Growth in the Primary Occupation
Figure 14. Average Annual Change in the Employment Rate
Figure 15. Change in Gini Index
Figure 16. Commodity Terms of Trade, Poverty, and Gini Coefficient
Figure 17. Public Investment in Latin America
Figure 18. Total Employment Growth
Figure 19. Real Labor Income Growth by Educational Level
Figure 20. Skill Premium Change in the 2000s
Figure 21. Average Government Transfers in Latin America
Figure 22. Income Distribution in Bolivia
Figure 23. Bolivia: Index of Monthly Real Labor Income by Educational Level
Figure 24. Real Labor per Capita and Sectorial Employment in Bolivia, 2006–13
Figure 25. Bolivia: Decomposition of Reductions in Poverty and Inequality by Education Level
Figure 26. Bolivia: Further Decomposition of Reductions in Poverty and Inequality
Figure 27. Poverty Reduction and Change in Employment Composition in Bolivia
Figure 28. Bolivia: Impact of Natural Resource Boom on Extractive Sector Municipalities
Figure 29. Departmental Budgets in Bolivia, 2012
Figure 30. Municipality Level Distributions of Income, Poverty, Inequality, and Informality in Brazil from the 2000 and 2010 Census
Figure 31. Value of Natural Resource Production in Brazilian Municipalities, 2010
Figure 32. Brazil: Impact of Natural Resource Extraction on Poverty and Employment
Figure 33. Peru: Value of Exports
Figure 34. Peru: Mining, Oil, and Gas Canon
Figure 35. Peru: Transfers to Departments, 2007–11
Figure 36. Peru: Annualized Change in Real Labor Income Per Capita Between 2007–11
Figure 37. Peru: Poverty and Inequality: Shapley Decomposition by Income
Figure 38. Peru: Poverty and Inequality: Shapley Decomposition by Skill Level
Figure 39. Peru: Poverty and Inequality: Shapley Decomposition by Sector
Figure 40. Canon Per Capita and Total Transfers per Capita at the Regional Level in Peru
Figure 41. Canon per Capita Transfers and Budget Execution at the Regional Level in Peru
Figure 42. Commodity Exports
Figure 43. Bolivia GDP Growth and Commodity Prices
Figure 44. Model Simulations of the Commodity Price Boom in Bolivia, Change in Growth, 2006–13
Figure 45. Model Simulations of the Commodity Price Boom in Bolivia, 2006–13
Figure 46. Paraguay: Employment by Area
Figure 47. Paraguay: Wages Primary Versus High-Skilled Sectors
Figure 48. Model Simulations of the Commodity Price Boom in Paraguay, Change in Growth, 2006–13
Figure 49. Model Simulations of the Commodity Price Boom in Paraguay, 2006–13
Figure 50. Overall Balance
Figure 51. Government Debt
Figure 52. Revenues
Figure 53. Expenditures
Figure 54. Commodity Terms of Trade and Fiscal Deficit in Colombia and Ecuador
Figure 55. Poverty Rate in Colombia and Ecuador
Figure 56. Peru: Commodity Terms of Trade and Fiscal Balance
Figure 57. Effect of Fiscal Tools on Inequality
Figure 58. Labor Market Rigidity across Country Groups
Figure 59. Years of Schooling
Figure 60. Average PISA Scores
Executive Summary
Over the past decades, inequality has risen not just in advanced economies but also in many emerging market and developing economies, becoming one of the key global policy challenges. And throughout the 20th century, Latin America was associated with some of the world’s highest levels of inequality. Yet something interesting happened in the first decade and a half of the 21st century. Latin America was the only region in the World to have experienced significant declines in inequality in that period. Poverty also fell in Latin America, although this was replicated in other regions, and Latin America started from a relatively low base. Starting around 2014, however, and even before the COVID-19 pandemic hit, poverty and inequality gains had already slowed in Latin America and, in some cases, gone into reverse. And the COVID-19 shock, which is still playing out, is likely to dramatically worsen short-term poverty and inequality dynamics.
Against this background, this departmental paper investigates the link between commodity prices, and poverty and inequality developments in Latin America. To study the impact of the commodity boom of 2000–2014 on the impressive improvements in poverty and inequality the region enjoyed over the same period, it takes a threefold approach: a high-level regional assessment, detailed microdata case studies for Bolivia, Brazil, and Peru, and a heterogenous agent dynamic general equilibrium model calibrated to Bolivia and Paraguay. The paper also discusses the most recent, less-favorable poverty and inequality developments in Latin America in the boom’s aftermath and concludes with a discussion of policy options to achieve further social gains in a post-commodity boom World––a task made ever more urgent by the impact of the COVID-19 shock.
During the commodity boom, social gains in Latin America were particularly pronounced in commodity exporters, and much of the progress reflected real labor income gains for lower-skilled workers, especially in services, with a smaller but positive role for government transfers. Spillovers from the commodity sector to the nontradable sectors seem to be at the core of labor income gains for low-skilled labor and are a key driver for the observed poverty and inequality reductions.
Both the case studies and the model simulations show that the impact of a commodity price shock depends on the importance of the sector affected in each country, the type of commodity and its production technology, and government policies. The impact is also stronger on poverty than inequality, not surprisingly, given that the impact on inequality depends on what happens to the full income distribution––gains for everybody reduce poverty but not inequality, for example. Regarding policies, some can have important trade-offs between the impact on growth and inequality.
The model results show that positive agricultural price shocks appear to have a larger direct effect on poverty and inequality reduction given the relatively high labor intensity (in particular, low-skilled) of the production technology and the low-income level of the majority of the population living in rural relative to urban areas. While reducing poverty, positive energy price shocks have the potential direct effect of increasing income inequality as energy sectors typically employ relatively more skilled labor and the technology is more capital intensive. However, as shown in the case of Bolivia, an increase in energy prices and revenue from royalties seems to lead to a more-than-offsetting indirect effect, including ultimately increasing the demand for auxiliary and other nontradable sectors. Indeed, demand pressures on the nontradable sector appear to be an important indirect effect causing an increase in wages and incomes.
Looking at local data in the country case studies also suggests mineral mining tends to have larger spillover effects than hydrocarbons production because of its higher labor intensity. This shows up in larger shifts in the employment composition and greater poverty reduction in mineral versus hydrocarbons producing municipalities.
An important concern is that local fiscal windfalls associated with the extractive sector might not always have had fully satisfying results. For example, they often led to large increases in public sector employment in oil and gas municipalities, which then contributed to fiscal distress in some cases when the windfall dried up with the end of the boom. In other municipalities with particularly large windfalls on the other hand, issues with absorptive capacity are apparent with unused funds accumulating that could be used productively elsewhere. Given this, when the opportunity exists for substantive reforms to decentralization frameworks, those reforms should aim to minimize horizontal inequities, avoid boom-bust revenue cycles at the local level, and, crucially, clarify the goals of the revenue-sharing agreement.
Following the end of the boom, commodity prices remained low for several years. The speed of social gains in Latin America had slowed and, in some cases, partially reversed even before the COVID-19 shock hit. And the COVID-19 shock is unquestionably leading to a further sizable reversal in these gains. Going forward, the region—and especially South American commodity exporters—thus face the critical challenges of first confronting the COVID-19 shock and then achieving further reductions in poverty and inequality. Even before the COVID-19 shock, these challenges remained acute, especially in the case of inequality which remains among the highest in the world. On a subjective level, an average of about 80 percent of Latin Americans described the income distribution in their country as either unfair or very unfair in a 2018 survey, up from about 70 percent in 2013 (and relative to 85 percent in 2001). The immediate task is to dampen the negative impact of the COVID-19 shock on the poor and vulnerable, and transition from emergency crisis support to less costly, post-crisis support. But returning to the structural reform agenda to tackle some of the longstanding factors contributing to high inequality also remains as important as ever.
While there is no silver bullet, policymakers in the region could consider several avenues for reform. Increasing personal income tax revenues by scaling back tax exemptions, avoiding preferential treatments and combating tax evasion while rebalancing spending could help maintain key social transfers and infrastructure spending. Better targeting of social transfers also has an important role to play. Many pension systems in the region have regressive components that should be reformed to reduce inequities while at the same time protecting fiscal sustainability. Ultimately, structural policies, including labor market reform, a renewed focus on education quality and the development of non-resource sectors, possibly through well-calibrated support of the state, are crucial to make an economy resilient to large commodity price swings. Overall, a well-designed and sequenced package of reforms can certainly help limit the fallout from the COVID-19 pandemic and further deepen the social progress made since the turn of the century.
Introduction
Throughout the 20th century, Latin America has been associated with some of the world’s highest levels of inequality. Many analysts argue that this is a legacy of colonization and the institutions put in place by the conquistadores (Engerman and Sokoloff 1997, 2000, 2002; Acemoglu, Johnson, and Robinson 2001, 2002). Such a legacy has been linked to: (1) the existence of strong elites, (2) capital market imperfections, (3) inequality of opportunities (in particular, in terms of access to high-quality education), (4) labor market segmentation (for example due to informality), and (5) discrimination against women and non-whites (see Cornia 2014, for a survey).
Latin America has also always been rich in commodities, which attracted the conquistadores in the first place. The commodities ranged from silver in Bolivia to oil in Venezuela, copper in Chile and Peru, and coffee in Brazil and Colombia. The commodity endowments are thought by some to have perpetuated the high levels of inequality. For example, it is argued that commodity-intensive development reduces the returns to education, hampering the impact of expanding education on inequality.1
Yet something interesting happened in the first decade and a half of the 21st century. Latin America was the only region in the World to have experienced significant declines in inequality in that period (Figure 1). Indeed, inequality has risen not just in advanced economies but in many emerging market and developing economies, becoming the number 1 policy challenge in many of them. Poverty also fell significantly in Latin America, although this was replicated in other regions, and LAC started from a relatively low base (Figure 2).
Gini Coefficient
(Gini coefficient; population weighted average)
Citation: Departmental Papers 2021, 009; 10.5089/9781484326091.087.A000
Sources: World Bank, PovcalNet database; and World Bank, World Development Indicators (WDI) database.Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. EAP = East Asia Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; SAR = South Asia; SSA = sub-Saharan Africa.Gini Coefficient
(Gini coefficient; population weighted average)
Citation: Departmental Papers 2021, 009; 10.5089/9781484326091.087.A000
Sources: World Bank, PovcalNet database; and World Bank, World Development Indicators (WDI) database.Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. EAP = East Asia Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; SAR = South Asia; SSA = sub-Saharan Africa.Gini Coefficient
(Gini coefficient; population weighted average)
Citation: Departmental Papers 2021, 009; 10.5089/9781484326091.087.A000
Sources: World Bank, PovcalNet database; and World Bank, World Development Indicators (WDI) database.Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. EAP = East Asia Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; SAR = South Asia; SSA = sub-Saharan Africa.
Poverty Rate
(Percent; headcount ratio at $3.20 a day; 2011 PPP)
Citation: Departmental Papers 2021, 009; 10.5089/9781484326091.087.A000
Source: World Bank, World Development Indicators (WDI) database.Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. No data available for SAR in 2015. EAP = East Asia Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; PPP = purchasing power parity; SAR = South Asia; SSA = sub-Saharan Africa.Poverty Rate
(Percent; headcount ratio at $3.20 a day; 2011 PPP)
Citation: Departmental Papers 2021, 009; 10.5089/9781484326091.087.A000
Source: World Bank, World Development Indicators (WDI) database.Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. No data available for SAR in 2015. EAP = East Asia Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; PPP = purchasing power parity; SAR = South Asia; SSA = sub-Saharan Africa.Poverty Rate
(Percent; headcount ratio at $3.20 a day; 2011 PPP)
Citation: Departmental Papers 2021, 009; 10.5089/9781484326091.087.A000
Source: World Bank, World Development Indicators (WDI) database.Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. No data available for SAR in 2015. EAP = East Asia Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; PPP = purchasing power parity; SAR = South Asia; SSA = sub-Saharan Africa.As noted above, Latin America is a region rich in commodities and from the mid-2000s until 2014 there was a commodity super cycle. Could the decline in inequality and the commodity boom be related? Analyzing this question in the context of Latin America provides the motivation for this departmental paper. The aim is not to explain what drives inequality, which is a vast topic, but to explore the channels via which a commodity boom can impact inequality and other social indicators.
Any study of poverty and inequality, especially when conducted on a cross-country basis, faces several data constraints which warrant a word of caution upfront. Our treatment of poverty and inequality throughout the paper will rely on consumption or income-based measures from household survey data for the simple reason that this is the most widely available data across countries and time. But it is important to stress that there are measurement difficulties associated with this approach and additional dimensions of inequality and poverty that we do not capture.
Wealth inequality is one important dimension of inequality that is not covered in this paper. Recent work suggests that wealth inequality in Latin America is even more pronounced than income inequality (ECLAC 2019), but we are not able to comment on how this might have changed with the commodity boom.
In terms of measurement difficulties, the underrepresentation of very high-income households in household surveys has been much discussed. This also applies to the data used in this study.2 Cross-country comparability of household survey data can be an additional challenge. The data we use for Latin America are generally harmonized across countries (our main data sources for Latin America are the World Bank’s SEDLAC data and the Inter-American Development Bank’s Labor Markets and Social Security Information System – SIMS). But when we compare Latin America to other regions comparability is not fully guaranteed given that data for Latin America is generally income-based while for many countries outside Latin America it is consumption based.
In addition, for several parts of this paper we rely on two of the most common ways of aggregating income data into summary statistics on inequality and poverty—the Gini coefficient for inequality and a monetary poverty rate defined relative to either a domestic or an international poverty line for poverty. Of course, there are many other ways to use individual level income data to measure poverty and especially inequality. When possible, we discuss additional measures or present a richer set of indicators focusing on a broader description of the income distribution rather than only one summary statistic.
Finally, a conceptual point is related to our focus on observed inequality of outcomes. An important strand in political philosophy and economics instead argues for a focus on inequality of opportunities. The argument is based on the premise that not all forms of inequality are undesirable. Those related to what one might call “effort” are acceptable or even desirable, while others—those due to circumstances outside an individual’s control—are inequitable (Ferreira and Peragine 2016). Societies should thus strive to equalize opportunities while maintaining a principle of reward for effort. Such a focus on equality of opportunities is conceptually appealing but in practice measurement difficulties, especially when trying to look at trends over time, loom large. Given that existing evidence finds a high correlation between inequality of opportunities and observed inequality (Romer and Trannoy 2015), we focus on the latter throughout the paper. Nevertheless, Box 1 discusses some of the available evidence on equality of opportunity in Latin America.
Measuring (In)equality of Opportunity in Latin America
Building on work by Rawls (1971) and others, equality of opportunity has been theoretically defined as a situation in which outcomes for a population depend only on factors for which persons can be considered responsible (Roemer and Trannoy 2015). Going from this principle to a measurable concept is a non-trivial exercise.
In practice, a two-step process is generally applied. First, an actual distribution of some outcome (income, consumption, etc.) is transformed to obtain a counter-factual distribution that only reflects the unfair component of inequality. In the second step, any desired inequality measure can then be applied to the transformed distribution to obtain a measure of inequality of opportunity. It is easy to see that the practical difficulties associated with the measurement of inequality of opportunities arise in the first step of the process and several different approaches have been proposed in the literature (see Romer and Trannoy 2015; and Ferreira and Peragine for authoritative literature reviews).
Here we focus on the so-called between-types inequality approach. First, individuals are partitioned into types, wherein each type has the same circumstances (such as parental education, race, etc.). Then the actual outcome for each individual is replaced by the mean outcome for his or her type. In other words, the impact of effort is removed by giving everybody with the same exogenous circumstances the same outcome. Given that not all circumstances are observable (and the type-partition is thus imperfect) measuring inequality of opportunity on the resulting distribution will give a lower bound for the actual value (Ferreira and Gignoux 2011).
Somewhat comparable measures of inequality of opportunity as defined above exist for about 40 countries, among them six in Latin American (it is important to stress the limitations of cross-country comparability, not least because the underlying data come from different points in time, often more than a decade apart). In general, Latin American countries are found to have among the highest levels of inequality of opportunity, even among countries of a similar level of development. The lower bound for the share of actual inequality explained by circumstances is found to be about one-third in Guatemala and Brazil, the countries with the highest shares. The studies also yield a number of striking cross-country comparisons in terms of the absolute level: inequality of opportunity in Brazil is found to be more than twice actual inequality in Denmark.
The World Bank has been studying inequality of opportunity in Latin America for many years, using a somewhat different methodology as explained in Barros and others (2009). They look at the question from the perspective of access to basic goods such as sanitation, education, and water. Given the focus on children, it is reasonable to assume that all inequality is due to circumstance rather than effort. The resulting Human Opportunity Index (HOI) correlates highly with general measures of development (if coverage for sanitation is close to 100 percent as it tends to be in richer countries then there can also be no inequality in access to sanitation). Averaging across the education, sanitation, and water components shows progress across Latin American between 2000 and 2014 (latest available year). Southern cone countries (Argentina, Chile, and Uruguay) have the lowest inequality of opportunity according to this measure while countries in Central America (El Salvador, Guatemala, and Honduras) lag substantially behind.
With these caveats in mind, we document in this paper that the period between the turn of the century and around 2014 was one of significant social gains in Latin America, especially in commodity exporters. Much of the progress reflected real labor income gains for lower-skilled workers, especially in services, with a smaller but important and positive role for government transfers. Spillovers from the commodity sector to the nontradable sectors seem to be at the core of labor income gains for low-skilled labor and are a key driver for the observed poverty and inequality reductions. Since the end of the commodity boom, poverty and inequality have stopped declining and, in a few cases, reversed part of the previous gains. The COVID-19 shock—which has already led to dramatic job losses across Latin America—is likely to substantially worsen short-term poverty and inequality dynamics, making reforms, from fiscal policy to the development of non-resource sectors, even more urgent.
Organization of the Paper
The core of the paper consists of case studies of poverty and inequality developments in select Latin American countries during the commodity boom, both from an empirical (Chapter 3) and a model-based (Chapter 4) perspective. The case studies are framed by a more high-level, descriptive view of cross-country developments since the turn of the century and a discussion of policy priorities.
The paper was written with one coherent narrative in mind, but with room for each chapter to be read individually. In addition, the end of Chapters 3 and 4 have short summaries of the main takeaways of the empirical and model-based case studies, respectively, for readers interested in a more concise overview. Boxes throughout the paper present issues that while not central to the overall narrative, either complement it or offer promising avenues for future work.
The paper proceeds as follows. Chapter 1 sets the stage by first documenting recent trends in inequality and poverty in Latin America. Chapter 2 establishes an empirical link between poverty, inequality, and commodity prices. Chapter 3 then uses micro data for Bolivia, Brazil, and Peru to decompose changes in poverty and inequality and to study the impact of different types of natural resource booms (metals, on shore oil and gas, and offshore oil and gas) on social indicators at the provincial/municipal level. This allows us to disentangle different possible channels—notably a fiscal channel from a direct real economy channel. Chapter 4 complements Chapter 3 by further studying the channels by which commodity cycles affect the distribution of income through the lens of a dynamic general equilibrium model with heterogenous agents. This model is applied to the cases of Bolivia and Paraguay, which again allows a comparison between economies with different types of commodity production. Chapter 5 briefly discusses policy choices during the post-boom years while chapter 6 concludes by drawing out policies which could support Latin America in making further social gains in a world with permanently lower commodity prices.
It would have been desirable to have detailed case studies also for the post-boom period, but at this point it is simply too early to be able to do a comprehensive assessment, due to both the short time period and large lags in data availability of key distributional indicators. This is particularly true with regard to the impact of the COVID-19 shock, given it is still playing out and hit while most of this paper had already been written. Nevertheless, the paper comments on how the COVID-19 shock is impacting the region where possible. In particular, a short discussion of cyclical policy priorities to help mitigate the unprecedented near-term impact of the shock on the poor and vulnerable is included in Chapter 6, before turning to a more detailed look at the structural policies needed to durably further reduce poverty and inequality in Latin America.
