## ccilaea ## Source details **Canonical URL:** [ccilaea](https://www.imf.org/-/media/files/publications/dp/2021/english/ccilaea.pdf) ## Other formats - [Markdown version](/-/media/files/publications/dp/2021/english/ccilaea.pdf.md) - [Structured JSON version](/-/media/files/publications/dp/2021/english/ccilaea.pdf.json) --- ### Overview and Purpose - Investigates the link between commodity prices, and poverty and inequality developments in Latin America. - Uses 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. - Focuses on the impact of the commodity boom of 2000–2014 on poverty and inequality improvements, examines post-boom developments, and discusses policy options in a post-commodity boom world sharpened by the COVID-19 shock. ### Key Regional Findings During the Commodity Boom (2000–2014) - Latin America experienced significant declines in inequality and reductions in poverty in the first decade and a half of the 21st century; the region was the only region in the World to have experienced such significant inequality declines in that period. - Social gains were particularly pronounced in commodity exporters. - Main drivers: - Real labor income gains for lower-skilled workers, especially in services. - Government transfers played a smaller but positive role. - Spillovers from the commodity sector to nontradable sectors increased wages and incomes for lower-skilled workers. - Quantitative attribution (regional averages and ranges cited): - On average, 45 percent of the reduction in the Gini coefficient can be attributed to changes in hourly labor income (range: 22 percent in Panama to 66 percent in Ecuador). - Changes in government transfers contributed, on average, 14 percent of the observed regional decline in inequality; changes in pensions contributed 7 percent. - Labor income accounted for about 80 percent of household income throughout the boom period (Bolivia context). ### Heterogeneity by Commodity Type and Mechanisms - Agricultural price shocks: - Appear to have a larger direct effect on poverty and inequality reduction due to relatively high labor intensity and low incomes of rural populations. - Energy price shocks: - Can reduce poverty but tend to increase income inequality directly because energy sectors are more capital and skill intensive. - Indirect fiscal effects can offset direct inequality-increasing pressures (example: Bolivia). - Mineral mining versus hydrocarbons: - Mining tends to have larger spillover effects than hydrocarbons owing to higher labor intensity and larger shifts in employment composition. - Empirical decile effects for commodity exporters (2000–14, Table 2 coefficients preserved verbatim): - Decile 1: 0.037** 20.013 - Decile 2: 0.059** 20.02 - Decile 3: 0.066** 20.023 - Decile 4: 0.07** 20.025 - Decile 5: 0.075** 20.027 - Decile 6: 0.078** 20.026 - Decile 7: 0.081*** 20.023 - Decile 8: 0.072*** 20.017 - Decile 9: 0.035 20.022 - Decile 10: 20.57*** 20.16 - Interpretation: income shares of the first to eighth deciles increased significantly during the boom while the share of the top decile declined; the poverty result is stronger than the inequality one. ### Case-Study and Model Insights (Bolivia, Brazil, Peru, Paraguay) - Bolivia (stylized facts and outcomes): - Hydrocarbon and metal mining at peak in 2014: summed to close to 15 percent of GDP; accounted for 80 percent of export revenues and 35 percent of fiscal revenues. - Government take in oil and gas production estimated at more than 70 percent; effective royalty rate for hydrocarbon production is 50 percent (18 percent royalty plus 32 percent direct tax). - Over the boom years, Bolivia’s Gini coefficient fell by close to nine basis points (8.7 points). - Shapley decomposition: bulk of improvement explained by labor income variations; labor income share examples for Bolivia: Labor: 2001 83.6; 2002 84.7; 2006 82.8; 2007 82.4; 2011 81.8; 2012 80.9; 2013 79.1. - Model corroboration: commodity price boom, increased skills, and migration can account for about two-third of the fall in the Gini coefficient in 2006–13. - Local municipal-level DID estimates (2001–12): - Mineral mining municipalities: poverty fell by about 4 percentage points relative to other municipalities. - Gas megacampos municipalities: poverty fell by about 8 percentage points relative to other municipalities. - Other oil and gas municipalities without large discoveries: no significant impact. - Brazil (municipal-level evidence and fiscal channels): - National changes during the 2000s: Gini fell by 7 basis points (from 0.6 to 0.53); national poverty rate fell from 28 percent to 14 percent. - Municipal sample size: 5,565 municipalities. - Key municipal-level summary statistics (change in per capita values, 2000–10; constant 2010 Brazilian Real) preserved as reported, including Obs: 5,565 and various Mean and Std. Dev. entries as presented in source. - Main municipal results: - Natural resource producer municipalities experienced statistically significant poverty reductions relative to nonproducer ones: "1.4 percentage points on average relative to nonproducer ones (Table 5)." - A one standard deviation increase in the value of mineral production per capita reduces the poverty rate by only 0.2 percentage points for most municipalities; for the top 5 producers estimated reductions in poverty of "3 and 9 percentage points." - Fiscal and employment channels (key coefficients preserved verbatim): - Change in mineral production per capita → Natural Resource Royalties per Capita: "0.0174*** (0.000922)"; (Current) Revenues per Capita: "0.0241*** (0.00601)"; Share of Workers in Extractive Industries: "1.33e-05*** (4.19e-06)". - Change in offshore oil and gas production per capita → Natural Resource Royalties per Capita: "0.0209*** (0.00130)"; (Current) Revenues per Capita: "0.0248*** (0.00264)"; Share of Workers in Extractive Industries: "22.56-e06 (1.82e-06)" (not significant). - Revenues and expenditures responses to production changes reported in Table 7 with coefficients such as Current Revenues "0.0241*** (0.00601)" and Capital Spending "0.00868*** (0.00181)" for minerals. - Peru (boom-period magnitudes and local transfer effects): - Extractive industries represented close to "14 percent of GDP on average" over 2007–11. - Over 2007–11: export price index improved by "44 percent"; terms of trade increased by "13 percent"; volume of exports grew by "15 percent". - Gini fell from "0.51 to 0.45" during 2007–11; poverty rate fell from "13.8 to 8 percent" (SEDLAC). - Shapley decompositions: changes in labor income explain about "two-thirds" of the reduction in inequality; nonlabor income contributed about "20 percent". - Departmental regression (Table 10 standardized coefficients): - Poverty: "20.28***" - Inequality: "0.07" - Income per Capita: "0.35*" - Unemployment: "0.18" - Interpretation: a 1 standard deviation increase in canon transfers reduces the poverty headcount by about "0.28 standard deviations" and increases income per capita by "0.35 standard deviations"; evidence of absorptive capacity constraints in high-transfer regions (negative correlation between budget execution and canon transfers per capita). - Model calibration and quantitative attributions (Bolivia and Paraguay): - Model matched pre-boom averages and sectoral shares (selected calibration entries preserved verbatim, e.g., Agriculture share in GDP: Bolivia 15.9; Model 16.3. Paraguay Data 27.1; Model 15.9). - Bolivia boom simulation (2006–14) — observed commodity price changes relative to 2000–05: - Agricultural commodity prices (mainly soy): increased by more than 60 percent. - Natural gas prices: increased by nearly 75 percent. - Simulation contributions to change in growth (2006–13, percentage points): - Agricultural commodity prices: model contribution 0.5 percentage points. - Oil price: model contribution 0.2 percentage points. - TFP: model contribution 0.8 percentage points. - More skills: model contribution 1.6 percentage points. - Tax policy: model contribution −0.6 percentage points. - Migration: model contribution 0.8 percentage points. - Cash transfers: model contribution 0.0 percentage points. - Price controls: model contribution 0.6 percentage points. - Model total simulated change in growth: 2.0 percentage points; Data observed change: 2.0 percentage points. - Paraguay model highlights: - Growth averaged 4.7 percent; transitional growth rate increased by 2.7 percentage points in 2006–13. - Agricultural price boom accounted for about 20 percent of the fall in the Gini (about 1 Gini points) in one accounting; in model simulations agricultural boom explained nearly half of observed transitional growth increase. - Land Gini example: land Gini is 93.0; counterfactual with land Gini of 78.0 increases the share of the Gini change attributable to agricultural prices to about 40 percent of the actual change. ### Post-Boom and COVID-19 Developments - After 2014 commodity prices remained low for several years; the speed of social gains slowed and in some cases partially reversed before COVID-19. - COVID-19 impacts (early evidence): - Total employment in LA5 (Brazil, Chile, Colombia, Peru and Mexico) fell by 30 percent on average between January and May 2020 (largest four-month contraction on record). - Employment fell by 15 percent in Bolivia from February to May. - Micro-simulation (Lustig and others, 2020) for Argentina, Brazil, Colombia and Mexico indicates increases of 4–9 percentage points in the poverty rate and an increase in the Gini of 0.02–0.04 when government policy response is not included. - Emergency assistance in Argentina and Brazil strongly mitigated worsening social outcomes while in place; Brazil evidence suggests emergency cash transfers more than offset labor income losses of the bottom 40 percent temporarily. - Perceptions of fairness (survey evidence): - In 2018, an average of about 80 percent of Latin Americans described the income distribution as unfair or very unfair, up from about 70 percent in 2013 and relative to 85 percent in 2001 (Latinbarómetro; margin of error roughly +/- 3 percent per country and year). - Correlation between perceived improvement and objective Gini fall during 2000–14: correlation 0.59; correlation over 2013–18 insignificant (and wrong sign). ### Local Fiscal Windfalls, Decentralization, and Governance Risks - Local fiscal windfalls from extractive sectors produced mixed outcomes: - Often led to large increases in public sector employment in oil and gas municipalities, contributing to fiscal distress when windfalls ended (Bolivia Tarija example: Tarija population share ~5 percent; Tarija’s budget accounted for over a third of all departmental revenues and wages and nearly half of departmental capital expenditure; roughly 70 percent of Bolivian gas production came from Tarija). - In some municipalities with very large windfalls absorptive capacity constraints appeared: unused funds accumulated rather than being productively deployed (Peru evidence on canon transfers and budget execution). - Decentralization and revenue-sharing reform guidance: - Minimize horizontal inequities, avoid boom-bust local revenue cycles, clarify goals of revenue-sharing agreements. - Use precautionary stabilization funds with clear rules and governance arrangements; increase transparency and consider equalization transfers and broader sharing to reduce geographic disparities. - Build subnational capacity and expand own-revenue bases (property taxes average 0.3 percent of GDP across region). ### Fiscal Policy, Pensions, and Redistribution - Fiscal patterns and redistributive effectiveness: - Fiscal policy reduces income inequality by an average of about 15 percent across Latin American countries; majority of reduction comes from spending on health and education rather than taxes and cash transfers. - Fiscal instruments with a direct impact on income reduce inequality by only 4 percent on average; personal income taxes have low effective progressivity (effective rate for top decile 5.4 percent on average). - Redistributive impact of personal income taxes in Latin America reduces inequality by 2 percent versus more than 12 percent in EU countries after income taxes. - Progressive spending on health and education reduces inequality by an average of 10 percent across Latin America. - Pension system issues: - Many systems require long minimum contribution periods, producing regressive outcomes because informality prevents many low-income workers from meeting thresholds. - Colombia example: only about one-third of the pension-age population receive a contributory pension; eligibility threshold 1,350 weeks; replacement rates for eligible range 70−100 percent; about half of implicit subsidy accrues to top income quintile. IMF recommends expanding coverage while ensuring progressivity and fiscal sustainability. - Peru example: only about 30 percent of economically active population contribute; shortening minimum contribution from 20 to 15 years estimated gross cost 0.05 percent of GDP. ### Labor Markets, Informality, and Structural Reforms - Labor-market stylized facts: - Latin America characterized by low labor productivity, high informality, and dual labor markets. - Employment and labor income gains were key to poverty reduction during the boom; low-skilled nontradable-sector jobs were central. - Policy levers: - Reduce redundancy costs and make dismissal procedures more transparent and predictable while building unemployment insurance and retraining programs. - Improve education quality and access (primary and lower-secondary) to reduce inequality of opportunity; tertiary financing reforms (income-contingent loans, targeted scholarships) cited. - Support diversification and resilience through financing, special economic zones, infrastructure (irrigation, roads, ports), and governance improvements. - Redundancy costs and reform sequencing: - Duval and Loungani (2019) suggest reducing expected firing costs and pairing with social protections; political-economy considerations require careful sequencing and communication. ### Model Structure and Selected Mechanisms (Chapter 4 and Appendix) - Model type: Dynamic general equilibrium model with a continuum of heterogeneous households and five production sectors (agriculture, manufacturing, services, energy, agricultural export). - Households: rural and urban; private, public, entrepreneurs; high- and low-skilled workers; idiosyncratic shocks; limited financial access for some farmers. - Government instruments: taxes, royalties, transfers, infrastructure investment (TFP effect), subsidies, public wages. - Key model findings preserved: - Agricultural price shocks are more poverty-reducing due to labor intensity and diffusion to rural low-income households. - Energy price shocks can raise inequality directly but indirect fiscal-investment channels and price controls can offset that effect. - Post-boom (negative wealth shock) effects are generally symmetric through same channels but not necessarily identical due to migration frictions and fiscal responses. - Model calibrated results reproduced observed growth and distributional changes for Bolivia and Paraguay when combining commodity shocks with structural changes (skills, migration, fiscal policy, price controls). ### Policy Recommendations and Priorities - Immediate (COVID-19) short-term priorities: - Damp negative impact on the poor and vulnerable (expand sick leave, unemployment benefits, health benefits; introduce transfers and public work programs; provide financing to sustain employment). - Transition from emergency crisis support to less costly, post-crisis support as conditions permit. - Fiscal and tax measures: - Increase personal income tax revenues by scaling back tax exemptions, avoiding preferential treatments, combating tax evasion; consider lowering thresholds to bring high-income individuals into tax net where appropriate. - Rebalance spending to maintain key social transfers and infrastructure spending; use fiscal buffers prudently. - Reduce universal price subsidies (energy) and improve targeting. - Social transfers and targeting: - Better targeting of social transfers and means-testing where feasible; strengthen in-kind transfers in education and health. - Pension reform: - Shorten minimum contribution requirements where feasible; expand non-contributory pillars to protect those excluded by informality; ensure fiscal sustainability. - Structural policies for longer-term resilience and equity: - Labor market reform to reduce informality and adjust redundancy costs with complementary social protections. - Renewed focus on education quality and access. - Promote economic diversification via financing, infrastructure, special zones, and targeted state support. - Decentralization and revenue-sharing: - Design to minimize horizontal inequities, stabilize local revenue cycles, clarify revenue-sharing goals, and strengthen subnational capacity and accountability. - Sequencing and trade-offs: - Recognize trade-offs between growth and inequality impacts; implement well-designed and sequenced reform packages that combine infrastructure and social programs to promote inclusive growth. ### Overall Conclusion - Commodity booms in 2000–2014 contributed importantly to poverty reduction and, to a lesser extent, inequality reduction in Latin America through labor-income gains, nontradable-sector spillovers, and fiscal channels. - There is no silver bullet; a coherent mix of fiscal, social, and structural reforms can limit COVID-19 fallout and help deepen social progress in a world with permanently lower commodity prices. *Source: Executive Summary, "COMMODITY CYCLES, INEQUALITY, AND POVERTY IN LATIN AMERICA" (departmental paper).* ### Executive Summary ...................................................................................................... ### Executive Summary ### Overview and Purpose - Investigates the link between commodity prices, and poverty and inequality developments in Latin America. - Uses 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. - Focuses on the impact of the commodity boom of 2000–2014 on poverty and inequality improvements, examines post-boom developments, and discusses policy options in a post-commodity boom world sharpened by the COVID-19 shock. ### Key Regional Findings During the Commodity Boom (2000–2014) - Latin America experienced significant declines in inequality and reductions in poverty in the first decade and a half of the 21st century, making it the only region in the World to have experienced such significant inequality declines in that period. - Social gains were particularly pronounced in commodity exporters. - Much of the progress reflected real labor income gains for lower-skilled workers, especially in services. - Government transfers played a smaller but positive role in reducing poverty and inequality. - Spillovers from the commodity sector to nontradable sectors are central: demand pressures on nontradables increased wages and incomes for lower-skilled workers and drove observed poverty and inequality reductions. - The impact of a commodity price shock depends on: - the importance of the commodity sector in each country, - the type of commodity and its production technology, - government policies. ### Heterogeneity by Commodity Type and Mechanisms - Positive agricultural price shocks: - Appear to have a larger direct effect on poverty and inequality reduction. - Rationale: relatively high labor intensity (in particular, low-skilled) of the production technology and the low-income level of much of the rural population. - Positive energy price shocks: - Can reduce poverty but have the potential direct effect of increasing income inequality. - Rationale: energy sectors typically employ relatively more skilled labor and are more capital intensive. - Indirect effects can offset direct effects: - Example: Bolivia — increase in energy prices and royalty revenues led to indirect effects (including increased demand for auxiliary and nontradable sectors) that more than offset direct inequality-increasing pressures. - Mineral mining tends to have larger spillover effects than hydrocarbons: - Due to higher labor intensity, mining shows larger shifts in employment composition and greater poverty reduction in mineral-producing municipalities versus hydrocarbons-producing municipalities. ### Case-Study and Model Insights on Local Fiscal Windfalls and Outcomes - Local fiscal windfalls from extractive sectors produced mixed outcomes: - Often led to large increases in public sector employment in oil and gas municipalities, contributing to fiscal distress when windfalls ended. - In some municipalities with very large windfalls, absorptive capacity constraints appeared: unused funds accumulated rather than being productively deployed. - Policy implication for decentralization and revenue-sharing reforms: - Reforms should aim to minimize horizontal inequities, avoid boom-bust local revenue cycles, and clarify the goals of revenue-sharing agreements. ### Post-Boom and COVID-19 Developments - After the end of the boom, commodity prices remained low for several years. - The speed of social gains in Latin America slowed, and in some cases partially reversed, even before the COVID-19 shock. - The COVID-19 shock is likely to dramatically worsen short-term poverty and inequality dynamics and is leading to a further sizable reversal in gains. - The region—especially South American commodity exporters—faces the critical challenges of: - confronting the COVID-19 shock, - achieving further reductions in poverty and inequality thereafter. - Subjective perceptions of inequality: - In a 2018 survey, an average of about 80 percent of Latin Americans described the income distribution in their country as either unfair or very unfair, up from about 70 percent in 2013 (and relative to 85 percent in 2001). ### Comparative and Distributional Notes - The impact of commodity booms is stronger on poverty than on inequality: - Gains for everybody reduce poverty but do not necessarily reduce inequality. - Country- and commodity-specific outcomes matter: technology, labor intensity, and government fiscal responses shape distributional consequences. - Model and microdata results show variation across countries (e.g., Bolivia, Brazil, Peru, Paraguay) tied to sectoral structure and policy choices. ### Policy Recommendations and Priorities - Immediate short-term priorities (in light of COVID-19): - Damp the negative impact of the COVID-19 shock on the poor and vulnerable. - Transition from emergency crisis support to less costly, post-crisis support. - Fiscal and tax measures: - Increase personal income tax revenues by scaling back tax exemptions, avoiding preferential treatments, and combating tax evasion while rebalancing spending to maintain key social transfers and infrastructure spending. - Many pension systems contain regressive components that should be reformed to reduce inequities while protecting fiscal sustainability. - Social transfers and targeting: - Better targeting of social transfers has an important role to play. - Structural policies for longer-term resilience and equity: - Labor market reform. - Renewed focus on education quality. - Development of non-resource sectors, potentially through well-calibrated state support. - Sequencing and trade-offs: - Some policies involve trade-offs between growth and inequality impacts; a well-designed and sequenced package of reforms is essential. - Decentralization and revenue-sharing: - Where substantive reforms to decentralization frameworks are possible, they should minimize horizontal inequities, stabilize local revenue cycles, and clarify revenue-sharing goals. ### Overall Conclusion - There is no silver bullet, but a coherent mix of fiscal, social, and structural reforms can limit the fallout from the COVID-19 pandemic and help deepen the social progress achieved since the turn of the century. *Source: Executive Summary, "COMMODITY CYCLES, INEQUALITY, AND POVERTY IN LATIN AMERICA" (departmental paper).* ### Executive Summary ### Executive Summary ### Historical context and motivating question - Latin America historically associated with some of the world’s highest levels of inequality, 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. - The region is rich in commodities (examples cited: silver in Bolivia; oil in Venezuela; copper in Chile and Peru; coffee in Brazil and Colombia). - Central research question: Could the decline in inequality in the first decade and a half of the 21st century be related to the commodity boom that ran from the mid-2000s until 2014? ### Key stylized facts documented - Latin America was the only region in the World to have experienced significant declines in inequality in the first decade and a half of the 21st century. - Poverty also fell significantly in Latin America in that period; LAC started from a relatively low base. - The period between the turn of the century and around 2014 was one of significant social gains in Latin America, especially in commodity exporters. - 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 has already led to dramatic job losses across Latin America and is likely to substantially worsen short-term poverty and inequality dynamics. ### Measurement caveats and data limitations - Analysis relies primarily on consumption or income-based measures from household survey data because these are most widely available across countries and time. - Wealth inequality is not covered by household survey measures in this paper; recent work suggests wealth inequality in Latin America is even more pronounced than income inequality (ECLAC 2019), but the paper does not have data to comment on changes in wealth inequality during the commodity boom. - Underrepresentation of very high-income households in household surveys affects measurement. - Cross-country comparability can be problematic: Latin American data used are generally harmonized and income-based (main sources: World Bank’s SEDLAC; Inter-American Development Bank’s SIMS), while data for many countries outside Latin America are consumption-based. - Aggregate summary statistics used include the Gini coefficient for inequality and monetary poverty rates defined relative to domestic or international poverty lines (examples and figures in the paper reference headcount at $3.20 a day; 2011 PPP). ### Empirical findings on channels linking commodity booms to social outcomes - Much of the progress during the boom reflected real labor income gains for lower-skilled workers, especially in services. - Government transfers played a smaller but important and positive role. - Spillovers from the commodity sector to nontradable sectors appear core to labor income gains for low-skilled labor and are a key driver of observed poverty and inequality reductions. - The paper distinguishes fiscal channels from direct real-economy channels when analyzing local impacts of different types of natural resource booms (metals, on shore oil and gas, and offshore oil and gas). ### Evidence on top incomes (survey + administrative) - Tax data used to complement household survey data show the top 1 percent hold as much as 20 (Colombia, Chile, Mexico) to 25 percent (Brazil) of income. ### Conceptual focus: outcomes vs. opportunities - The paper focuses on observed inequality of outcomes rather than inequality of opportunities, while noting the conceptual appeal of the latter and measurement challenges. - Existing evidence finds a high correlation between inequality of opportunities and observed inequality; Box 1 discusses available evidence on equality of opportunity in Latin America. - Between-types inequality (a common empirical approach to inequality of opportunity) yields lower-bound estimates; available studies for about 40 countries (six in Latin America) show Latin American countries among those with highest levels of inequality of opportunity. - Examples from inequality-of-opportunity measures: - The lower bound for the share of actual inequality explained by circumstances is about one-third in Guatemala and Brazil (the countries with the highest shares). - Inequality of opportunity in Brazil is found to be more than twice actual inequality in Denmark (comparative statement from the literature reviewed). - The World Bank’s Human Opportunity Index (HOI) shows progress across Latin America between 2000 and 2014 in access indicators (education, sanitation, water); Southern cone countries (Argentina, Chile, Uruguay) have the lowest inequality of opportunity by this measure, while Central American countries (El Salvador, Guatemala, Honduras) lag substantially behind. ### Organization of the paper and analytical strategy - Chapter 1: Documents recent trends in inequality and poverty in Latin America. - Chapter 2: Establishes an empirical link between poverty, inequality, and commodity prices. - Chapter 3: Uses micro data for Bolivia, Brazil, and Peru to decompose changes in poverty and inequality and to study provincial/municipal impacts of different types of natural resource booms (metals, on shore oil and gas, offshore oil and gas), allowing disentangling of fiscal vs. direct real-economy channels. - Chapter 4: Uses a dynamic general equilibrium model with heterogeneous agents to study channels through which commodity cycles affect income distribution; model applied to Bolivia and Paraguay to compare economies with different types of commodity production. - Chapter 5: Brief discussion of policy choices during post-boom years. - Chapter 6: Concludes with policies to support further social gains in a world with permanently lower commodity prices and includes a short discussion of cyclical policy priorities to help mitigate the near-term impact of the COVID-19 shock on the poor and vulnerable. ### Policy-relevant implications signaled in the Executive Summary - Reforms—from fiscal policy to the development of non-resource sectors—are made more urgent by the halt or reversal of social gains since the end of the commodity boom and by the worsening near-term outlook due to COVID-19. - Structural policies are needed to durably further reduce poverty and inequality in Latin America in a lower commodity-price environment. *ccilaea - Executive Summary* ### Introduction ### Introduction ### Panoramic view of social gains during the boom - Poverty and inequality reduction was strong across Latin America during the commodity boom period, especially in South America. - Inequality—as measured by the Gini coefficient—declined in both Central and South America, but significantly more in South America. - The commodity boom for Latin America started during the first decade of the 2000s; for comparability the end of the boom is defined as the start of the 2014 oil price shock. - Country coverage includes Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. - Commodity exporters are defined where net commodity exports surpass 10 percent of total exports plus imports (October 2015 WEO), with Brazil added due to large estimated natural resource reserves. The full list of commodity exporters (with main commodity exports in brackets) is: Argentina (soybean meal, corn, soybean oil), Brazil (soybeans, iron ore, crude petroleum), Bolivia (petroleum gas, zinc ore, gold), Chile (copper ore, refined copper, fish), Colombia (crude petroleum, coal briquettes, gold), Ecuador (crude petroleum, bananas, crustaceans), Honduras (coffee, palm oil, bananas), Paraguay (soybeans, soybean meal, bovine meat), and Peru (copper ore, gold, refined petroleum). ### Drivers of inequality and poverty reduction during the boom - Evidence indicates a widespread decline in inequality in the 2000s was largely due to a reduction in hourly labor income inequality and more robust and progressive government transfers. - On average, 45 percent of the reduction in the Gini coefficient can be attributed to changes in hourly labor income (ranging from 22 percent in Panama to 66 percent in Ecuador). - Changes in government transfers contributed, on average, 14 percent of the observed regional decline in inequality; changes in pensions contributed 7 percent. - Changes in returns to capital in Argentina, Brazil, and Mexico are estimated to be small and mostly leading to increased inequality; household surveys likely under-estimate income from capital. - The decline in hourly labor income inequality is driven by the skill premium, or the returns to education. ### Growth versus redistribution: regional differences - Relative to the 1990s, during the commodity boom period growth increased in South America (where poverty fell the most); in Central America growth was lower although remaining high. - Cross-country analysis during the boom shows a positive association between GDP growth and poverty reduction, but South American countries lie below the fitted line—meaning for every additional percentage point of growth they reduced poverty by more than other countries—implying factors beyond high growth contributed to poverty reduction in South America. - Commodity exporters experienced a significant boost in terms of trade relative to other countries during the boom (terms of trade measured by a commodity net export price index weighted by GDP). ### Heterogeneity across countries - The largest gains in poverty and inequality reduction were in two commodity-dependent countries: Bolivia and Ecuador. - Commodity exporters made larger gains in poverty reduction across the board except for Chile and Honduras, which experienced smaller gains than some non-commodity exporters such as Nicaragua and Panama. - For inequality the pattern is more mixed: El Salvador and the Dominican Republic saw bigger reductions in inequality than several commodity exporters (Chile, Colombia, Paraguay, and Honduras). - Supply-side factors, such as an increasing supply of skilled workers, were likely key drivers of lower inequality in Central America and Mexico. Policy expansions (for example, cash transfers in Mexico and policies to boost low wages in Uruguay) also played roles. ### Poverty and inequality developments from 2014 to 2019 - Commodity terms of trade for Latin American commodity exporters peaked in April 2011 when metal prices started to decline; commodity terms of trade then declined sharply in June 2014 as a result of the oil price shock. - The biggest post-2014 reversals in terms of trade occurred in large oil and natural gas exporters (Bolivia, Colombia, Ecuador). - For the major metal exporters, Chile and Peru, commodity terms of trade were broadly unchanged between 2014 and 2019—after falling over 2011–14—because metals prices recovered over much of the post-2014 period. - In non-commodity exporters, poverty and inequality developments between the boom and post-boom period show little difference; most social indicators continued to improve over 2014–18/19. - In commodity exporters, poverty reduction came to a halt between the end of the commodity boom and the onset of the COVID-19 crisis. - On average, the impressive gains of the boom did not reverse so much as stagnate; while poverty increased somewhat in a few crisis cases (Argentina and Brazil), inequality in commodity exporters continued to fall on average. - Labor market changes after 2014 point to a crucial source of stagnation: - Real wages weakened and employment growth slowed—both in stark contrast to the boom times. - Figures cited show annualized and average changes comparing 2000–14 to 2014–18/19: poverty headcount reductions slowed for commodity exporters; average annual real monthly wage growth in primary occupation and employment rate changes decelerated. - Measurement and data notes: - Comparable cross-country data on poverty and inequality were available until the end of 2018 for most countries and until end-2019 for some. - Commodity terms of trade index used is from Gruss (2014) updated in Gruss and Kebhaj (2019); Bolivia’s natural gas valuation uses a different vintage due to price proxy issues. ### Early evidence on the impact of the COVID-19 shock on poverty and inequality - The COVID-19 shock led to an unprecedented contraction in economic activity in 2020 and historic job losses across the region. - Total employment in LA5 (Brazil, Chile, Colombia, Peru and Mexico) fell by 30 percent on average between January and May 2020, the largest four-month contraction on record. - Employment fell by 15 percent in Bolivia from February to May; an equivalent contraction was seen in Ecuador from December to May/June. - Job losses were concentrated among more vulnerable segments—informal workers in the services sector with lower levels of education. - An online survey (Bottan, Hoffman and Vera-Cossio, 2020) found job and income losses were more likely among respondents who had lower income pre-COVID. - Micro-simulation (Lustig and others, 2020) for Argentina, Brazil, Colombia and Mexico indicates: - An increase of 4–9 percentage points in the poverty rate and an increase in the Gini of 0.02–0.04 when the government’s policy response is not included. - In Argentina and Brazil, emergency government assistance strongly mitigated worsening social outcomes while in place; in Brazil preliminary evidence suggests broad-based emergency cash transfers more than offset labor income losses of the bottom 40 percent, avoiding increases in poverty and inequality temporarily. - There is an important risk that the shock will materially worsen poverty and inequality, not just immediately but over the next years. ### Perceptions of fairness - After improving strongly over 2001–13, perceptions of the fairness of the income distribution worsened over 2013–18. - More than 80 percent of Latin Americans say they perceive the income distribution in their country to be unfair or very unfair (Latinbarómetro). - Survey dynamics: - In 2001, 11 percent of respondents perceived the income distribution as fair or very fair. - This rose to 23 percent at the end of the commodity boom in 2013 and fell back to 16 percent in 2018. - Perception improvements varied across countries; largest positive changes in Ecuador, Panama, and Uruguay over 2001–13. - Perceptions worsened across the region during 2013–18 with largest reversals in Venezuela, Ecuador, and Panama. - In 2018, respondents in Bolivia and Ecuador were the most likely to perceive income as fairly distributed; respondents in Venezuela and Chile were the least likely. - Correlations: - Over the boom period, the perceived improvement in fairness correlates well with the objective fall in income inequality (correlation 0.59). - Over 2013–18 the correlation between perception changes and measured Gini changes is insignificant (and has the wrong sign). - Latinobarómetro data note: the margin of error per country and year is roughly +/- 3 percent. *Source: ccilaea - Introduction (IMF PDF).* ### Box 2. Inequality Perceptions: “How Fair is the Income Distribution in Your Country?” ### Box 2. Inequality Perceptions: “How Fair is the Income Distribution in Your Country?” ### Is There a Statistical Association? - Scatter evidence for the boom period (2000–14) indicates: - For non-commodity exporters, there is no clear association between changes in commodity terms of trade and changes in poverty and inequality. - For commodity exporters, the relationship is strong, particularly for poverty: "The size of poverty reduction is directly proportional to the growth rate of the commodity terms of trade in commodity exporters." - For inequality, the relationship for commodity exporters is weaker than for poverty but still visible. - Note on Honduras: classified as a commodity exporter but experienced a negative wealth effect because it "exports non-extractive commodities and imports extractive ones that saw their prices increase by more," and "poverty fell significantly less than in most other Latin American countries." ### Panel Regression Analysis - Estimated panel regression specification: - y_i,t = α_i + β x_i,t + δ v_i,t−1 + ε_i,t - y_i,t is the (log) Gini coefficient; x_i,t is country i’s commodity terms of trade; v_i,t−1 is lagged GDP per capita. - Data sources and sample: - SWIID (Standardized World Income Inequality Database) for largest sample. - SEDLAC (CEDLAS and World Bank) for Latin America decile shares. - Full sample available from 1961 until 2018 as far as data permit. - Key interpretation: - A 1 percentage point change in the commodity terms of trade index can be interpreted as a change in aggregate disposable income equivalent to 1 percentage point of GDP. - Regression results (summary interpretation): - Estimates confirm a negative relationship between commodity price changes and the Gini coefficient for Latin American commodity exporters: "a positive increase in commodity net export prices is associated with a fall in income inequality." - The relationship is not significant when all commodity exporters (global sample) are included; the point estimate is smaller, suggesting a distinctive Latin American pattern. ### Commodity Terms of Trade and Income Share by Decile in Commodity Exporters (2000–14) - Table 2 coefficients (Commodity Terms of Trade, weighted by GDP) and accompanying second number shown in the source for each decile (presented verbatim): - Decile 1: 0.037** 20.013 - Decile 2: 0.059** 20.02 - Decile 3: 0.066** 20.023 - Decile 4: 0.07** 20.025 - Decile 5: 0.075** 20.027 - Decile 6: 0.078** 20.026 - Decile 7: 0.081*** 20.023 - Decile 8: 0.072*** 20.017 - Decile 9: 0.035 20.022 - Decile 10: 20.57*** 20.16 - Other table details preserved from source: - Country Fixed Effects: Yes for all deciles. - Control: GDP per capita for all deciles. - Period: 2000–14 for all deciles. - Observations: 114 for each decile regression. - R-Squared by decile: 0.236, 0.213, 0.205, 0.197, 0.206, 0.217, 0.246, 0.288, 0.094, 0.258 respectively. - Number of Countries listed as: 9 9 9 9 9 9 9 9 9 9 - Interpretation from text: - Income shares of the first to eighth deciles increased significantly during the boom, while the share of the top decile declined (quantitatively strongest for upper middle deciles 5–7). - Since low- and medium- to high-income segments gained, "the poverty result is stronger than the inequality one." Poverty reduction depends on developments close to the poverty line (2nd–4th decile depending on the country). ### Channels during the Boom Phase - Market/Private-sector Channels: - Commodity sector expansion draws labor and other resources, raising real wages and/or employment and potentially reducing the skills premium. - Spillovers to nontradable sector via higher domestic demand and commodity-sector investment (e.g., construction). - Changes in relative wages can compress the skills premium if commodity and nontradable sectors are intensive in unskilled labor. - Net effect: more employment in commodity and nontradable sectors; ambiguous effect on non-commodity tradable sector (Dutch disease vs. positive local input spillovers). - Fiscal Channels: - Higher government investment increases domestic demand and wages. - Larger transfers directly affect poverty and inequality, especially if targeted to lower-income individuals. - Other General Equilibrium Effects: - Transmission via financial system and second-round effects such as migration to urban areas. ### Regional Macroeconomic Evidence - Aggregate expectations: commodity booms should reduce poverty and inequality through labor market developments and fiscal transfers. - Empirical patterns highlighted in the source: - Public investment and employment growth were higher in commodity exporters than importers (Figures 17 and 18). - Commodity exporters experienced significantly larger real labor income gains than non-commodity exporters across all skill levels in the boom (Figure 19). - Low-skilled workers gained the most, compressing the skills premium and reducing inequality in both commodity exporters and non-exporters (Figure 20). - Government transfers increased more in commodity exporters than non-exporters (Figure 21). - Labor market context for Latin America: - Characterized by low labor productivity, high informality, and strong duality between protected formal jobs and unprotected informal jobs. - A large pool of workers in low productivity occupations shaped the impact of the commodity boom. ### Disentangling the Channels - Multi-pronged approach described in the source: - National-level household survey Shapley decompositions (Chapter 3) to separate changes due to fiscal redistribution versus labor income changes and to assess sectoral composition of labor income changes. - Local-level data exploit regional variation in resource type and intensity (mineral mining vs onshore oil vs offshore oil) to identify direct market effects and fiscal windfalls. - Complementary simulations from a heterogeneous agent DGE model (Chapter 4) to further analyze mechanisms. - Strengths and limitations noted: - Shapley decompositions provide national-level attribution but cannot discriminate channels through which labor income increases occurred. - Local-level identification offers cleaner causal evidence on channels but does not directly provide national-level impact estimates. *Italic: Source — Box 2. Inequality Perceptions: “How Fair is the Income Distribution in Your Country?”, ccilaea - Box 2. Inequality Perceptions: “How Fair is the Income Distribution in Your Country?”* ### Chapter 3 focuses on the impact of the extractive sector, but in Chapter 4 ### Chapter 3 focuses on the impact of the extractive sector, but in Chapter 4 ### Overview of commodities and model purpose - Commodities are defined to include both extractive industries and the agricultural sector. - The model traces and quantifies channels by which agricultural and energy commodity price shocks affected inequality, poverty, and growth during the 2000s. - The model allows assessment of the impact of various tax and spending policies. ### Microdata case studies and countries covered - Case studies: Bolivia, Brazil, and Peru (commodity exporters). - Contrast case: Mexico (commodity importer). ### Bolivia — Overview of extractive sector - Bolivia produces hydrocarbons (mainly natural gas) and minerals. - Large gas discoveries of the 1990s led to gas production increasing by a factor of 8 between 1999 and 2015. - Bolivian gas covers one-third of total Brazilian gas demand. - At the peak in 2014: - Hydrocarbon and metal mining summed to close to 15 percent of GDP. - Accounted for 80 percent of export revenues. - Accounted for 35 percent of fiscal revenues. - Government take in oil and gas production estimated at more than 70 percent. - Effective royalty rate for hydrocarbon production is 50 percent—an 18 percent royalty plus a 32 percent direct tax on hydrocarbons. - Royalty allocation (simplified): - Out of the total 18 percent hydrocarbon royalty: 11 percent go to producing departments, 6 percent stays with the central government and 1 percent goes to the lightly populated departments of Pando and Beni. - The 32 percent hydrocarbon tax (IDH) is allocated to producing and non-producing departments as well as municipalities. - Mining royalties distribution: 85–15 split between producing departments and municipalities. ### Stylized facts on inequality, poverty, and natural resource production in Bolivia - Over the boom years, Bolivia’s Gini coefficient fell by close to nine basis points (8.7 points). - In the mid-90s, Bolivia had a Gini coefficient well above the Latin America average; by the end of the boom it was below several peers. - Bolivia’s net and market Gini do not differ much, suggesting transfers and redistributive policies might not be the key driver for lower inequality. - Household survey data show strong increases in labor income of low-skilled workers over the boom period (2001–13). - Change in labor income by decile: gains across most categories except the very top ones. - By education level: - Income gains were large for low-skilled workers. - Income changes were negative for high-skilled workers. - Employment and wage outcomes by sector (2006–13): - Biggest employment growth winners: extractive sector and commerce. - Broad services sector contributed the most in terms of numbers of jobs created. - Employment growth came from extractive and nontradable sectors. - Average wages in the extractive sector fell, likely due to a compositional effect: number of informal (poorly paid) miners increased faster than employees in larger, capital-intensive mines. - Note: On average, labor income per capita in the informal sector represented about 60 percent of its corresponding figure in the formal sector. ### Decomposing reduction in inequality and poverty (Shapley decomposition) - Method: Shapley-Shorrocks decomposition correcting path dependence in standard Barros and others (2006) calculation (per Azevedo, Inchauste, and Sanfelice (2013)). - Income component decomposition: household income per capita = sum of components Y_Hpc,1 + Y_Hpc,2 + ... + Y_Hpc,j. - Let θ be any measure of inequality or poverty; F(.) the cumulative density function of household income per capita. - The method alters the distribution in the later year by replacing components with earlier-year data in all possible orders, computes counterfactual indicators, and averages contributions across all orders to obtain each component's contribution. - Results: - Bulk of improvement is explained by labor income variations. - Higher government transfers had a positive effect, but labor income movements dominate because labor income accounted for about 80 percent of household income throughout the boom period. - Table: Bolivia: Composition of Household Income per Capita (selected years and components) - Labor: 2001 83.6; 2002 84.7; 2006 82.8; 2007 82.4; 2011 81.8; 2012 80.9; 2013 79.1 - Nonlabor (aggregate) and subcomponents (Returns of Capital, Transfers from Government, Transfers between households) shown for 2001, 2002, 2006, 2007, 2011, 2012, 2013 with exact values in source table. - Model corroboration: Chapter 4 model results will show that for Bolivia the commodity price boom, the increase in skills among the working population, and migration from rural to urban centers can account for about two-third of the fall in the Gini coefficient in 2006–13. - Mechanism: increase in labor income, especially for the low skilled, and consequent reduction of the skill premium. - Further decompositions: - Jobs in the formal and services sectors contributed most to inequality and poverty reduction. - Figure 26 highlights role of formal sector employment with only a small contribution from informal sector. - Largest contribution to poverty reduction came from the services sector. ### Impact of the resource boom at the local level — empirical strategy - Data: Bolivia population census (2001 and 2012) used for municipal-level analysis; census-based poverty measure constructed as percentage of population without access to basic necessities (sanitation, water, electricity, adequate living space, etc.). - Advantages of census data: usable at any desirable level of geographic disaggregation; household survey not representative at municipal level. - Difference-in-differences regression specification: - y_it = α + γ EM_i + θ T_t + ρ (EM_i * T_t) + X_it' β + ε_it - y_it: dependent variable - EM_i: dummy = 1 for extractive sector municipalities - T_t: time dummy = 1 in 2012 - Interaction D_it = (EM_i * T_t) is the treatment variable; ρ is coefficient of interest - X_it': vector of municipality and time-varying covariates - Distinction among municipalities: - Mineral producers - "Small" oil and gas producers - Natural gas megacampo producers - Identification issues: - No pre-2001 census data, so parallel trend assumption or control for pretreatment trends cannot be explicitly tested. - Control group limited to municipalities with best covariate overlap with treatment group using entropy balancing (Hainmueller and Xu 2013); weights between 0 and 1 assigned to control municipalities to achieve optimal covariance overlap. - Entropy balancing achieves virtually perfect overlap for first and second moments but implicitly makes a strong linearity assumption. - Suggested alternative: exclude municipalities adjacent to resource municipalities to alleviate concerns about spillovers. ### Local-level results for Bolivia (2001–12) - Both mining and hydrocarbon production significantly reduced poverty in producing municipalities and led to some labor reallocation. - Estimated effects (in percentage points, relative to other Bolivian municipalities): - Mineral mining municipalities: poverty fell by about 4 percentage points. - Also experienced reduction in agricultural employment and increases in construction and manufacturing employment, and higher net migration. - Gas megacampos municipalities: poverty fell by about 8 percentage points (double the fall in mining municipalities). - No fall in agricultural employment. - No increase in net migration. - Increase in share of employment in public sector administration by roughly 2 percentage points, suggesting fiscal windfall might have been used for public employment. - Other oil and gas municipalities without large discoveries: no significant impact. - Standard-deviation-scaled context (Figure 28, panel 2): - Increase in public sector administration in megacampo municipalities: over one standard deviation. - Increase in construction employment in both gas and mineral municipalities: close to 0.9 standard deviations. - Reduction in poverty, scaled by standard deviation, is about 0.2 for mining and gas megacampo municipalities (less impressive when evaluated in standard-deviation terms). *Source: https://www.imf.org/-/media/files/publications/dp/2021/english/ccilaea.pdf* ### 0.4 standard deviations, respectively. ### ccilaea - 0.4 standard deviations, respectively. ### Mechanisms observed in Bolivia during the commodity boom - Labor intensity difference: - Share of workers employed in mining is close to 20 percent. - Share of workers employed in gas production is only about 3 percent. - An increase in metal production therefore has a much bigger impact on direct labor demand than an increase in gas production. - Fiscal windfall differences: - 2012 total fiscal revenues from mineral mining were about 1 percent of GDP, half of which were royalties that are redistributed exclusively to producing departments and municipalities. - In 2012 total fiscal revenues from hydrocarbons were above 10 percent of GDP—of which about half was distributed to subnational governments. - Tarija departmental example (2012): - Tarija has a population share of about 5 percent. - Tarija’s budget accounted for over a third of all departmental revenues and wages, and nearly half of all departmental capital expenditure. - Roughly 70 percent of total Bolivian gas production at the time came from Tarija. - Tarija pays 45 percent of its hydrocarbon royalties directly to the semi-autonomous region of Gran Chaco. - Gran Chaco, with a population of roughly 150,000 people, ends up with an estimated 7 percent of all of Bolivia’s hydrocarbon revenues—somewhat below 1 percent of national GDP. - Policy-relevant observation: - Tarija suffered from severe fiscal imbalances immediately after the end of the commodity boom, highlighting the difficulty of managing revenue volatility at the subnational level. ### Cross-country recurring themes - The important role of increased labor income in an expanding nontradable sector in explaining lower poverty and inequality is a recurring theme across case studies. - Problems associated with a large fiscal windfall at the subnational level recur in multiple country contexts. ### Overview of extractive sector in Brazil (boom context) - Terms-of-trade and production: - Brazil’s terms of trade for goods improved by 24 percent between 2000 and 2010 (20 percent between 2000 and 2014). - Brazil is one of the largest producers of iron ore, aluminums, and bauxite and an important producer of other metals such as gold and copper. - Brazil has important (mainly offshore) oil reserves; achieved self-sufficiency in 2006. - Geographic concentration: - Out of Brazil’s 5,565 municipalities, the top 20 producers concentrate 75 percent of total production. - Main offshore oil fields concentrated off the coast of Rio de Janeiro; most onshore production concentrated in the northeast and Amazon regions. - Mineral mining mainly in Minas Gerais and the Amazon region. - Royalties: - Brazil collects royalties on both mineral and onshore and offshore hydrocarbon production. - For the major producer municipalities revenues from oil can account for as much as 50 percent of their income. ### Stylized facts on inequality, poverty, and natural resource production in Brazil - Inequality and poverty movements during the boom: - Gini coefficient fell by 7 basis points, from 0.6 to 0.53, during the 2000s. - National poverty rate fell from 28 percent to 14 percent. - Drivers identified in the literature: - Barros and others (2010): fall in inequality between 2001 and 2007 driven by expansion in government transfers and compression in the ratio of labor income of better educated workers relative to less-educated (explained by an expansion in the supply of educated workers). - Goes and Karpowicz (2017): most change in the Gini explained by labor income growth, higher schooling levels and labor formalization; Bolsa Família also contributed. - Azevedo, Inchauste, and Sanfelice (2013): for Brazil, largest contributor to lower income inequality was higher labor income (contribution of 45 percent); government transfers contribution of 20 percent; pensions contribution of 18 percent. - For poverty reduction: employment and earnings growth was the largest single factor; nonlabor income (notably government transfers) played a very important role. - Share of transfers in total household income of the bottom 20 percent went from 3 percent to 24 percent between 2000 and 2010. ### Local-level data and empirical strategy for Brazil - Data sources and scope: - Municipal-level data come from the 2000 and 2010 population census (IBGE) for poverty, income, inequality, and employment by sector. - Data on mineral production and mineral royalties by municipality: Brazilian Mining Ministry (DNPM). - Oil royalties and oil production by field: Agencia Nacional de Petroleo (ANP). - Municipal fiscal data: Ipeadata. - Per capita production is used as the relevant measure for determinants of poverty and inequality. - Distributional patterns (2010): - More than 250 municipalities produce more than 1,000 real per capita (annual per capita income of about 6,000 real in the average municipality in 2010). - 66 municipalities produced over 15,000 real per capita worth of natural resources in 2010. - 10 municipalities produced more than 100,000 real per capita (depending on the exchange rate, about $30,000). - “Mega-producers” include Paraupebas (Para) and Campos dos Goytacazes (Rio de Janeiro). - Change in production during the boom: - Value of per capita production increased significantly during the boom period, driven by offshore oil and mineral production; on average onshore oil production contracted slightly. - Natural resource royalties increased together with the value of production. - Empirical specification: - Estimated equation: ∆y_{i,2010} = α + β ∆x_{i,2010} + γ ∆y_{i,2000} + δ y_{i,2000} + θ_s + ρ Z_i + ε_i - ∆y_{i,2010}: change in the dependent variable between 2000 and 2010 in municipality i. - ∆x_{i,2010}: change in natural resource production per capita (constant 2010 Brazilian real) in municipality i. - y_{i,2000}: level of dependent variable in 2000 to capture convergence effects. - ∆y_{i,2000}: change in the dependent variable between 1991 and 2000 to control for municipality-specific trends. - θ_s: state fixed effects. - Z_i: vector of geographic controls (e.g., whether a municipality is located on the coast). - Standard errors clustered at the state level. - Descriptive municipal sample size: - Number of municipalities analyzed: 5,565. ### Key municipal-level summary statistics for Brazil (change in per capita values, 2000–10; constant 2010 Brazilian Real) - Natural resource production (change, per capita): - Obs: 5,565 - Mean: 28,513 - Std. Dev.: 13,824 - Min: 214 - Max: 8,455 - Hydrocarbon production (change, per capita): - Obs: 5,565 - Mean: 18,410 - Std. Dev.: 12,812 - Min: 214 - Max: 8,455 - Onshore hydrocarbon production (change, per capita): - Obs: 5,565 - Mean: 221 - Std. Dev.: 973 - Min: 238,809 - Max: 24,411 - Offshore hydrocarbon production (change, per capita): - Obs: 5,565 - Mean: 20,5 (table formatting in source unclear for this row) - Std. Dev.: 10,765 - Min: 214 - Max: 8,455 - Mineral production (change, per capita): - Obs: 5,565 - Mean: 10,1018,650 (table formatting in source unclear for this row) - Std. Dev.: 2140,951 - Min: 392,654 - Max: (table values span multiple cells in source) - Natural resource royalties (change, per capita): - Obs: 5,565 - Mean: 15290 - Std. Dev.: 23,152 - Min: 14,312 - Hydrocarbon royalties (change, per capita): - Obs: 5,565 - Mean: 13246 - Std. Dev.: 23,152 - Min: 14,312 - Mining royalties (change, per capita): - Obs: 5,565 - Mean: 2158 - Std. Dev.: 22,540 - Min: 7,265 ### Preliminary result reported (text fragment) - Higher real values of natural resource production are associated with larger declines in poverty, with producer municipalities reducing poverty by *Italic: Source — Excerpt from the IMF chapter “Micro Data Case Studies of the Boom: Bolivia, Brazil, and Peru” (PDF filename: ccilaea - 0.4 standard deviations, respectively.).* ### 1.4 percentage points on average relative to nonproducer ones (Table 5). ### ccilaea - 1.4 percentage points on average relative to nonproducer ones (Table 5). ### Brazil — Main empirical findings - Natural resource producer municipalities experienced statistically significant poverty reductions relative to nonproducer ones: "1.4 percentage points on average relative to nonproducer ones (Table 5)." - Inequality results are mixed and statistical significance depends on estimation technique. - Distinction between channels: - Fiscal windfall channel present for both offshore hydrocarbon and mineral booms. - Direct employment channel present only for minerals; offshore oil and gas production is capital-intensive and labor-scarce with limited onshore employment effects. - Quantification of labor and poverty effects: - "A one standard deviation increase in the value of mineral production per capita reduces the poverty rate by only 0.2 percentage points" for most municipalities. - For the top 5 producers, estimated reductions in poverty between "3 and 9 percentage points." ### Brazil — Table 5 (impact summaries) - Table 5 entries (as reported): - "Impact of increase in real per capita natural resource production (range for top 20 increases) 20.39*** to 29.1***0 to 20.05**" - "Impact of being a natural resource producer municipality (dummy variable analysis) 21.44*** 0" - Note: "The first row of the table shows the product of the estimated coefficient with the real per capita production of the top 20 producer municipalities. *p , 0.10; **p , 0.005; ***p , 0.01." ### Brazil — Fiscal and employment channels (Table 6 key coefficients) - Dependent variables are changes between 2000 and 2010. - Change in mineral production per capita: - Natural Resource Royalties per Capita: "0.0174*** (0.000922)" - (Current) Revenues per Capita: "0.0241*** (0.00601)" - Share of Workers in Extractive Industries: "1.33e-05*** (4.19e-06)" - Change in offshore oil and gas production per capita: - Natural Resource Royalties per Capita: "0.0209*** (0.00130)" - (Current) Revenues per Capita: "0.0248*** (0.00264)" - Share of Workers in Extractive Industries: "22.56-e06 (1.82e-06)" (not significant) - Additional regression details: - Geography controls: "Ye s" (all specifications) - Dependent variable in 2000: "Ye s" - State fixed effects: "Ye s" - Observations: "5,507; 4,982; 5,507" - R-squared: "0.886; 0.834; 0.223" ### Brazil — Revenue and expenditure composition (Table 7 key coefficients) - All dependent variables are changes between 2000 and 2010. IPTU = property tax; ISS = municipal tax on services. - Revenues (selected coefficients): - Change in mineral production per capita: - Current Revenues: "0.0241*** (0.00601)" - Natural Resource Royalties: "0.0174*** (0.000922)" - Transfer Revenues: "0.0218*** (0.00525)" - Tax Revenues: "0.00177*** (0.000575)" - IPTU: "3.61e-05** (1.49e-05)" - ISS: "0.00169*** (0.000560)" - Other Taxes: "2.49e-05 (8.14e-05)" - Change in offshore oil and gas production per capita: - Current Revenues: "0.0248*** (0.00264)" - Natural Resource Royalties: "0.0209*** (0.00130)" - Transfer Revenues: "0.0233*** (0.00251)" - Tax Revenues: "0.000462** (0.000195)" - IPTU: "21.57e-06 (2.09e-05)" - ISS: "0.000293* (0.000158)" - Other Taxes: "0.000131** (5.15e-05)" - Expenditures (selected coefficients): - Change in mineral production per capita: - Current Expenditure: "0.00549*** (0.00129)" - Transfer Expenditures: "0.000161 (0.000106)" - Wage Expenditures: "0.00262** (0.00117)" - Capital Spending: "0.00868*** (0.00181)" - Change in offshore oil and gas production per capita: - Current Expenditure: "0.00688*** (0.000437)" - Transfer Expenditures: "0.000663*** (0.000169)" - Wage Expenditures: "0.00370*** (0.000335)" - Capital Spending: "0.00543*** (0.000619)" - R-squared (selected): Current Revenues "0.834"; Natural Resource Royalties "0.886"; Current Expenditure "0.942"; Wage Expenditures "0.693". ### Interpretation for Brazil - Both minerals and offshore hydrocarbons raise municipal revenues by about "0.024" Real per Real of production (about 2.4 cents per Real). - Composition differs: mineral municipalities show larger increases in tax revenues (ISS and IPTU) while oil municipalities show larger royalty increases. - Wage payments increase with mineral and oil production despite royalties not being supposed to be used for interest payments and wages. - Offshore oil leads to increases in public sector employment and shifts into services and construction driven by fiscal windfall spending rather than direct employment or local investment. ### Peru — Stylized facts and local impacts - Extractive industries represented close to "14 percent of GDP on average" over 2007–11. - Over 2007–11: - Export price index improved by "44 percent". - Terms of trade increased by "13 percent". - Volume of exports grew by "15 percent". - Transfers: - Mining/gas: central government transfers "50 percent of the taxes levied on mining/gas companies to local governments in producing regions." - Oil canon: transfer equal to "12.5 percent of the production value." - In some departments, canon/royalty transfers represent close to "90 percent of total per capita transfers received" (example departments Moquegua, Cusco). - Income and inequality trends: - Gini coefficient fell from "0.51 to 0.45" (World Bank SEDLAC) during 2007–11. - Poverty rate fell from "13.8 to 8 percent" during the same period (SEDLAC); official reports cite a fall of "14.6 percentage points" (differences due to methodology). - Distributional and labor-income patterns: - Household survey evidence: poorest deciles (1st to 4th) saw real income rise by more than "8 percent (annualized rate)"; 10th decile experienced income growth "below 3 percent per year." - Sectoral labor income growth (annualized 2007–11): agriculture and fishing, and construction "more than 10 percent"; commerce and other services "more than 5 percent." - Employment shares: manufacturing shrank; services, extraction/mining, and construction grew strongly. - Education/skill impacts: - Number of jobs rose strongest for higher education levels, but real income per capita growth was strongest for lower-skilled workers (unskilled and low skilled). - Low-skilled workers' income grew at an annualized rate of "about 11 percent"; skilled workers' income grew at about "half that rate." ### Peru — Decompositions and tables - Shapley decompositions: - Changes in labor income explain about "two-thirds" of the reduction in inequality. - Nonlabor income (public assistance, remittances) contributed about "20 percent" of the total reduction in the Gini coefficient. - Nonlabor income's contribution to poverty reduction is negligible. - Table 8. Peru: Real Income per Capita Growth (2007–11) — decile annualized growth rates (as reported): - Decile 1: "18.3%" - Decile 2: "28.9%" - Decile 3: "38.3%" - Decile 4: "48.2%" - Decile 5: "57.8%" - Decile 6: "67.5%" - Decile 7: "77.0%" - Decile 8: "86.0%" - Decile 9: "94.9%" - Decile 10: "102.7%" ### Peru — Local fiscal-transfer empirical strategy and results (Table 10) - Departmental regression specification (changes 2007–11); explanatory variable: real per capita canon transfers. - Key standardized-coefficient results (Table 10): - Poverty: "20.28***" - Inequality: "0.07" - Income per Capita: "0.35*" - Unemployment: "0.18" - Interpretation: - Increasing canon transfers by "1 standard deviation" leads to a reduction in the poverty headcount of about "0.28 standard deviations" and an increase in income per capita of "0.35 standard deviations." - No significant departmental-level effects on inequality or unemployment found. - Additional findings: - Canon transfers are strongly associated with total transfers (Figure 40). - Regions with very high canon transfers show absorptive capacity constraints: a strong negative correlation between budget execution and canon transfers per capita (Figure 41). ### Cross-country and policy takeaways - Common themes across Bolivia, Brazil, and Peru: - Lower income deciles experienced large income gains during the commodity boom; income flat for the top decile. - Extractive sector boom in the 2000s tended to reduce inequality and poverty, stronger effects on poverty. - Drivers: higher fiscal spending and increased labor demand in low-skilled sectors (construction and services) tied to resource production. - Spillovers from commodity to nontradable sectors are central to labor income gains for low-skilled workers and key drivers of poverty and inequality reductions. - Government transfers played a smaller role than labor income gains overall, though transfers may be more important for the very lowest deciles (evidence from Brazil literature). - Local-level considerations: - Direct spillovers to nontradable sectors appear more important than the fiscal windfall in generating local labor-income gains. - Mineral mining, being more labor intensive than hydrocarbons, generated larger employment shifts and poverty reductions in mineral municipalities than in oil and gas municipalities. - Evidence of governance and absorptive-capacity constraints at subnational level: increased public sector employment in some oil and gas municipalities and issues with budget execution in high-transfer regions. - Policy implications (implied by empirical evidence): - Strengthen local governance and absorptive capacity to improve effectiveness of fiscal windfalls. - Design revenue-sharing and transfer mechanisms mindful of absorptive limits and potential labor-market spillovers. - Support policies that facilitate positive spillovers into nontradable sectors (services, construction) to maximize poverty reduction effects. *Source: IMF staff calculations.* ### Box 3. Inequality Developments in a LAC Country without a ToT Boom: ### Box 3. Inequality Developments in a LAC Country without a ToT Boom: The Case of Mexico ### Motivation - Purpose: Use a model-based assessment of the impact of the commodity boom in Bolivia and Paraguay to complement empirical evidence. - Commodities definition: Includes extractive industries (oil and gas but not minerals for the purposes of the model) and the agricultural sector. - Objective: Trace and quantify channels by which agricultural and energy commodity price shocks affected inequality, poverty, and growth during the 2000s, and evaluate the impact of various tax and spending policies. ### Model: structure and key features - Model type: Dynamic general equilibrium model with a continuum of heterogeneous households (including farmers; informal, private and public sector workers; and entrepreneurs). - Calibration target: Pre-boom period using macroeconomic and household survey data for Bolivia and Paraguay. - Principal features: - Significant roles for the agricultural and energy sectors, with exports heavily concentrated in these sectors. - A relatively small manufacturing sector. - A relatively large public sector, and small industrial sector. - A relatively basic financial sector, with limited opportunities for risk sharing. - Energy sector representation: - Composed of large enterprises, capital intensive, small employment share, direct benefits highly concentrated. - Price of energy determined on international markets; firms pay taxes and royalties to government (one of the main sources of government revenue). - Agriculture representation: - Built from the bottom up, output from many households ranging from subsistence farms to higher productivity farms active in domestic and international markets. - Fluctuations in agricultural prices redistribute income toward surplus-producing farmers. - Urban households: Range from high-skilled/high-productivity to low-skill/low-productivity; supply labor to industrial sector, government, or self-employment (informal sector). - Government role: - Investment expenditure on infrastructure increases total factor productivity (TFP) of the private sector. - Social spending promotes inclusive growth and reduces poverty. - Public sector is an important category of employment. - Subsidies and price controls affect domestic energy prices. - Government collects taxes and royalties from the energy sector and other taxes from various agents. ### Calibration of the pre-boom (average 2000–05) - Calibration matched sectoral shares and distributional features (persistence and variance of household idiosyncratic shocks to reproduce observed Gini coefficients). - Key initial steady-state differences (selected figures from Table 11): - Agriculture share in GDP: Bolivia 15.9; Model 16.3. Paraguay 27.1; Model 15.9. - Energy share in GDP: Bolivia 12.9; Model 12.6. Paraguay 2.0; Model 12.9. - Manufacturing share in GDP: Bolivia 18.2; Model 18.7. Paraguay 16.1; Model 18.2. - Services share in GDP: Bolivia 53.1; Model 52.5. Paraguay 54.8; Model 53.1. - Commodity export (percent of GDP): Bolivia Data 22.6; Model 19.9. Paraguay Data 13.0; Model 22.6. - Government revenue (percent of GDP): Bolivia Data 21.8; Model 24.6. Paraguay Data 15.3; Model 15.3. - Royalties (percent of GDP): Bolivia Data 4.7; Model 4.5. Paraguay Data 3.1; Model 3.1. - Informal sector (% of workers): Bolivia Data 74.2; Model 65.5. Paraguay Data 72.2; Model 72.2. - Income Gini (National): Bolivia Data 61.0; Model 62.7. Paraguay Data 54.3; Model 52.6. - Private consumption (percent of GDP): Bolivia Data 73.7; Model 71.9. Paraguay Data 61.6; Model 59.5. - Public investment (percent of GDP): Bolivia Data 5.5; Model 6.3. Paraguay Data 6.0; Model 20.9. - Note: For Paraguay, hydroelectric production and export (Binational Dams) are excluded for commodity-cycle analysis; excluding Binationals reduces Paraguay energy share from close to 15 percent to 2 percent. ### Bolivia — Replicating the Boom (2006–14) - Observed commodity price changes (relative to 2000–05): - Agricultural commodity prices (mainly soy): increased by more than 60 percent. - Natural gas prices: increased by nearly 75 percent. - Observed macro changes: - GDP growth doubled. - Gini coefficient fell by nearly 9 Gini points. - Simulation approach: Depart from calibrated pre-boom steady state and compute new steady state associated with higher commodity and energy prices plus exogenously given structural and policy changes observed during the boom. - Exogenous structural changes included in the simulation: 1. Fraction of skilled workers increased from 38 percent (pre-boom) to 47 percent (boom). 2. Rural/urban composition changed: rural share fell from 38 percent (pre-boom) to 31 percent (boom). 3. Policy changes: - Cash transfers expenditure increased from 0 to approximately 2 percent of GDP in the boom period. - Price controls: despite increases of 300 percent in international energy prices and nearly 100 percent in international agricultural commodities, average inflation of Bolivia for the boom period was under 3 percent per year; domestic price of energy and food in the model restricted to go only up to 10 percent during the boom period. - Government wage bill as share of GDP rose from 8.8 percent (pre-boom) to 9.5 percent (boom). - Government revenues rose from about 22 percent of GDP (pre-boom) to close to 30 percent of GDP (boom); effective tax rates in the model increased to match observed revenue sources by tax instrument. - Modeling note: Sector TFPs are set so that sector shares for the new steady state match the data; to illustrate impacts of individual shocks, TFP is kept at pre-boom steady-state levels when isolating shocks. ### Results — Impact on Transitional Growth (Bolivia) - Observed increase in growth during 2006–14 relative to 2000–05: 2 percent higher growth. - Model attribution (change in growth, 2006–13, percentage points — model vs data): - Agricultural commodity prices (simulated +60 percent): model contribution to growth 0.5 percentage points. - Oil price (simulated +240 percent to match WTI increase): model contribution to growth 0.2 percentage points. - TFP: model contribution 0.8 percentage points. - More skills (higher fraction skilled): model contribution 1.6 percentage points. - Tax policy (higher effective tax rates to match revenue increase): model contribution −0.6 percentage points. - Migration (rural → urban): model contribution 0.8 percentage points. - Cash transfers: model contribution 0.0 percentage points. - Price controls: model contribution 0.6 percentage points. - Model total simulated change in growth: 2.0 percentage points; Data observed change: 2.0 percentage points. - Interpretation: - Largest positive contribution to growth came from the increase in skilled individuals in the urban labor force (helped industrial sector expansion and private sector productivity). - Migration (rural population fell by nearly 7 percentage points during the boom) supported growth via labor reallocation to more productive urban sectors. - Higher taxes taken in isolation had a moderate negative impact on growth; higher revenues funded cash transfers and infrastructure investment, with infrastructure increasing TFP and contributing indirectly to growth. ### Results — Relative roles of agricultural versus energy price shocks - Direct effects on growth: - Agricultural price shock has a stronger direct effect on growth than energy shock because: 1. Agricultural sector is more labor intensive; increasing incomes of agricultural households raises aggregate demand more than benefits concentrated in entrepreneurs from energy sector (entrepreneurs population share 5 percent). 2. Agricultural sector is larger as a share of GDP than the energy sector. - Energy price shocks generate opposing forces: - Positive: increase in value of marginal product of capital in energy sector raises entrepreneur incomes, consumption, and investment; higher aggregate demand raises nontradable prices, stimulating nontradable sector. - Negative: higher energy prices increase production costs for capital- and energy-intensive modern sectors (manufacturing and services), reducing growth; price controls on energy partially mitigate this negative effect. - Indirect: higher international energy prices increase government revenues, enabling higher investment in infrastructure that raises private sector productivity. - Net effect on output from higher energy prices: significantly positive when accounting for indirect fiscal-investment channel and price controls. ### Results — Impact on Inequality and Poverty (Bolivia) - Mechanisms: - Higher tradable agricultural commodity prices raise demand and prices for raw agricultural products, increasing incomes of rural farmers relative to urban households. - Higher agricultural (food) prices negatively affect urban household budgets, mitigated in Bolivia by food price controls. - Higher rural incomes boost demand for non-tradable goods, bidding up wages for lowest-skilled workers (including informal sector), which lowers aggregate Gini. - However, since nontradable services are concentrated mostly in urban areas, rising nontradable prices can increase urban-rural inequality, partially offsetting aggregate inequality gains. - Outcomes: - Agricultural price increases generate a significant decline in poverty (poverty measured as percent of households below the poverty line nationally), despite a milder impact on the aggregate Gini. - Energy prices have a lower direct impact on inequality and poverty than agricultural prices in the model. - Additional note: The smaller effect on the Gini relative to poverty reduction in Bolivia (versus Paraguay) can reflect a larger share of small farmers in Paraguay; smaller farmers are more likely to be poor and thus benefit more from positive agricultural price shocks. *Sources: National Institute of Statistics and Geography (INEGI); National Statistics Agency (INE); UN Comtrade; Gruss (2014); IMF, World Economic Outlook; IMF staff calculations.* ### 1. Change in Gini Coefficient ### 1. Change in Gini Coefficient ### Overview - The chapter analyzes how commodity price shocks and accompanying structural changes and policies affected GDP growth, income inequality (Gini coefficient), and poverty in Bolivia and Paraguay during commodity booms. - Key quantitative outcomes for Paraguay during 2006–13: - Growth averaged 4.7 percent. - Transitional growth rate increased by 2.7 percentage points in 2006–13. - The agricultural commodity price increase simulated was 60 percent. - The decline in the Gini coefficient between 2006–13: agricultural price boom accounted for about 20 percent of the fall or about 1 Gini points. - The net simulated impact of combined policies (including increased health care spending and VAT reform) was a reduction of the Gini index by about 1.5 basis points. - Land concentration: land Gini is 93.0; a counterfactual with land Gini of 78.0 increases the share of the Gini change attributable to agricultural prices to about 40 percent of the actual change. - Fiscal and policy magnitudes cited: - VAT base extension increased revenue from indirect taxes by 2 percent of GDP. - Transfer programs remained relatively small (below one-half of GDP). - Health care in-kind transfers grew by 2 percentage points of GDP in 2006–13. - Assumed split of increased health spending: central government (Ministry of Health) increase of 1.7 percent of GDP; Instituto de Prevision Social (IPS) increase of 0.3 percent of GDP. ### Bolivia: Findings and Mechanisms (selected excerpts) - Positive energy price shocks can have a direct effect of increasing income inequality because the energy sector: - Employs relatively more skilled labor. - Is more capital intensive. - Indirect effects can offset the direct inequality-increasing effect when higher government revenues are: - Spent on social transfers or infrastructure, expanding social programs and reducing poverty and inequality. - Specific Bolivia case dynamics: - Price controls and expanded cash transfers contributed materially to the observed decline in inequality and poverty. - Migration and increased education levels complemented commodity price effects in reducing inequality. - Model note: To be consistent with the sectorial composition in the second steady-state, the TFP of manufacturing had to increase by 14.7 percent (all other TFP remained at their levels in the steady-state). ### Paraguay: Findings and Mechanisms - Basic facts during the boom (2006–13): - Agricultural and livestock commodity prices increased by about 60–70 percent. - The country grew at about 5 percent a year. - Despite a high Gini coefficient of 0.50, poverty and income inequality declined during the period. - Labor and structural changes: - Employment grew relatively more in rural areas and for low-skilled workers. - The share of urban workers with low skills fell by more than 10 percentage points to slightly more than 50 percent. - Skills among the labor force increased exogenously, reducing the skill premium. - Migration: share of population living in urban areas grew by 2 percentage points (migration from rural areas). - Model simulation results (2006–13): - The commodity boom accounted for nearly half of the observed increase in transitional growth (2.7 percentage points), with the rise in average skills accounting for the other half. - Migration contributed positively to growth but less than in Bolivia because: - Rural population decline in Paraguay was just below 2 percentage points versus 7 percentage points in Bolivia. - Agriculture in Paraguay is more productive, so urban migration generated smaller productivity gains. - Policies and other shocks: - Increase in TFP (mostly in manufacturing and services) had significant positive impact on growth. - VAT base increase had a small negative impact on growth by discouraging consumption. - Impact on inequality and poverty: - Agricultural commodity price boom reduced the Gini coefficient and drove a large reduction in poverty—explaining more than half of the fall in the percentage of households below the poverty line. - Mechanisms for inequality reduction: - Increase in rural relative to urban incomes. - Rise in number of low-skilled jobs and incomes, including in nontradable sectors. - Reduction in skill premium due to increased supply of skills. - Countervailing forces: - Increase in TFP affected urban sectors more, raising intersectoral inequality. - VAT is regressive and had a negative impact on lower-income urban workers; however, because much of the rural informal sector does not pay VAT, the actual inequality impact was smaller than the potential effect. - Large increase in health care spending reduced inequality, though impact was mitigated by concentration of health centers in urban areas. - Combined policy simulation: increased health care spending dominated the negative impact of the VAT reform, producing a net reduction of the Gini index by about 1.5 basis points. - Specific numeric model outputs cited in figures and text (selected): - Model vs Data contributions to growth change (2006–13) include labeled components such as VAT, More skills, TFP, In-kind transfers, Migration, Agricultural commodity prices. - Model-simulated changes in Gini and poverty are presented with component contributions and corresponding measured data for comparison. ### Key Takeaways and Policy Implications - The effects of commodity price shocks depend on: - The type of commodity (agricultural versus energy). - The labor intensity and production technology of the commodity sector. - The size of the commodity sector in the country. - Government policies and how revenues are spent. - Agricultural price shocks tend to reduce inequality more directly because: - Agriculture is relatively labor intensive, especially in low-skilled labor. - Income gains diffuse to rural and low-income households. - Demand pressures on nontradable sectors increase incomes and demand for low-skilled labor. - Energy price shocks may increase inequality directly (capital- and skill-intensive), but large indirect effects via government revenues can offset this if revenues finance pro-poor transfers or infrastructure. - Policy trade-offs: - Cash transfers, price controls, and progressive tax policies can reduce inequality though they may have mild or negative effects on growth. - Infrastructure spending can boost growth but may raise inequality if benefits disproportionately favor urban areas. - Country-specific outcomes emphasize the importance of: - Sectoral composition (e.g., larger agricultural sectors amplify the redistributive effect of agricultural price booms). - Policy choices that determine whether revenue windfalls are translated into inclusive social spending. ### Model Structure (concise) - Small open economy with five consumption goods: domestic food, imported food, manufacturing, services, and energy. - Household heterogeneity: rural and urban; private sector and government employees; entrepreneurs (capital holders); low-skilled and high-skilled workers; continuum of households facing uninsurable idiosyncratic risk. - Five production sectors with distinct technologies: agriculture, manufacturing, services, energy, and agricultural exports by entrepreneurs. - Financial asset: one-period bonds traded among households for risk sharing. - Government collects taxes and royalties to finance infrastructure (increasing private productivity), public wages, energy subsidies, and pro-poor spending. - The model is a dynamic general equilibrium with heterogeneous households and multiple sectors, drawing on the structural transformation literature. *Source: ccilaea - 1. Change in Gini Coefficient (ccilaea - 1. Change in Gini Coefficient, ccilaea.pdf).* ### Box 4. General Structure of the DGE Model ### Box 4. General Structure of the DGE Model ### Post-boom mechanisms and expected impacts - The post-boom phase is characterized as a negative wealth shock that propagates through the economy via the same channels active during the boom but in the opposite direction. - Assuming policies do not adjust, lower commodity prices will reduce potential GDP and medium-term growth for commodity producers, and hence adversely affect poverty through forces opposite to those at play during the boom. - The net impact on inequality is ambiguous: - Example (Bolivia): the decline in agricultural prices reduces rural incomes and increases rural poverty, while a steeper decline in energy prices could cause urban incomes to fall faster than rural incomes, potentially reducing intersectoral inequality and the Gini coefficient. - Indirect effects via demand for nontradable goods may more than offset direct effects of energy price declines on inequality. - Additional considerations: - Channels active during the boom need not be symmetric in the post-boom period (for instance, migration to urban areas during the boom may not reverse due to high moving costs). - Fiscal channels matter: revenue losses from lower commodity prices can reduce social safety net and infrastructure spending. Some countries used windfalls to build fiscal buffers that can offset revenue declines for an extended period. - Commodity type matters: metal price declines versus hydrocarbons corrections can differ in employment impact because of relative labor intensity; agricultural price declines have sizable impacts on poverty since rural incomes are typically close to the poverty line. Because the largest price corrections occurred for hydrocarbons, it is not ex ante clear which commodity exporters are most affected. - Summary conclusion: lower commodity prices generally lead to a decline in potential growth and the pace of poverty reduction in commodity producers; the impact on inequality is much less clear-cut due to multiple, sometimes offsetting, forces. ### How actual post-boom developments compared to predictions - Poverty stopped falling in commodity exporters in 2014–19 and in some cases reversed previous gains; inequality on average kept falling albeit at a slower pace. - On average, commodity exporters protected expenditures even as revenues fell, leading to: - rising fiscal deficits, - increasing debt. - Counter-cyclical fiscal policy likely helped limit reversals in social gains, but excessive accommodation in the face of a permanent shock carries risks for medium-term poverty reduction. - Country comparisons and fiscal outcomes: - Colombia and Ecuador suffered negative terms of trade shocks of a similar magnitude. - Ecuador’s fiscal deficit increased by 3.7 percentage points of GDP in 2013, placing it on a divergent fiscal path relative to Colombia. - When the oil price shock materialized in 2014/15: - Colombia, guided by its fiscal rule, allowed the fiscal deficit to widen to somewhat above 3 percent of GDP from a low base. - Ecuador provided similar accommodation in terms of the change in the fiscal deficit, but given its higher starting point, Ecuador’s fiscal deficit reached 8 percent of GDP in 2016. - In 2017 Ecuador began a multi-year effort to reduce the fiscal deficit and put debt on a downward trajectory. - Outcomes: - Poverty continued to fall in Colombia post-2014, while it increased in Ecuador over 2014–16. - Peru example: - During high commodity prices Peru ran important budget surpluses, building buffers. - After commodity terms of trade peaked in 2011, Peru provided fiscal accommodation without endangering sustainability, supporting further poverty reduction. ### Peru: post-boom dynamics and decomposition - Peru’s export prices peaked in 2011 and fell by 27 percent between 2011 and 2015, mostly driven by metal and oil prices. - Growth and labor/income outcomes: - 4.8 GDP annual average growth rate between 2011–15. - 6.7 percent growth rate between 2007–11 (peak-boom). - Real income and employment growth moderated in line with new external conditions. - Poverty and inequality: - Peru continued to reduce poverty and inequality during the post-boom period, but at a much slower pace. - Remarkable changes between 2007–11—reductions of about 13 and 5.5 percentage points respectively—were cut by nearly two-thirds during the post-boom period. - Composition effects (2011–15): - Variations in nonlabor income gained in relative importance in explaining reductions in poverty and inequality. - The manufacturing sector, one of the weakest performers in number of jobs, contributed negatively to poverty reduction. - Low-skilled and unskilled workers (the most vulnerable) did not contribute as much to poverty reduction as in previous years. ### Short-term policy priorities following the COVID-19 shock - COVID-19 led to unprecedented job losses, hitting nontradable low-skilled service sector jobs hardest—jobs that had helped lift many out of poverty and reduce inequality during the boom. - Immediate cyclical policy priorities likely include: - expanding access to sick leave, unemployment benefits, and health benefits, especially for poorer segments who lack savings and are often in the informal sector, self-employed, or on temporary contracts; - introducing new transfers and boosting public work programs to offer job opportunities; - providing financing opportunities to sustain employment; - progressive tax measures to avoid excessive deficits. - These measures aim to mitigate the distributional consequences of the pandemic. ### Structural policy discussion — central government fiscal policy - Latin American tax and transfer systems are substantially less progressive than OECD countries; in some Latin American countries net income of the poor and near-poor can be lower after taxes and cash transfers (Lustig 2012). - In-kind transfers in education and health are progressive throughout the region. - Fiscal policy impact on inequality (averages and components): - Fiscal policy is estimated to reduce income inequality by an average of about 15 percent across Latin American countries, with the majority of the reduction coming from spending on health and education rather than taxes and cash transfers. - Fiscal instruments with a direct impact on income reduce inequality by only 4 percent (compared to 8 percent in other emerging and developing countries and 35 percent in advanced economies). - Personal income taxes have low effective progressivity: effective rate for the top decile is 5.4 percent on average. - Redistributive impact of personal income taxes in Latin America achieves a reduction of just 2 percent in income inequality, versus more than 12 percent reduction after income taxes in European Union countries. - Progressive spending on health and education reduces inequality by an average of 10 percent across Latin America. - Fiscal instruments with an indirect impact on income (such as indirect taxes and tax exemptions) on average do not impact inequality in Latin America. - Policy avenues to protect social progress: - Revenue side: - Implement progressive personal income taxes; increase revenues by scaling back tax exemptions, avoiding preferential treatments, and combating tax evasion and avoidance to raise effective progressivity. - Consider decreasing thresholds to bring more high-income individuals into the tax net in some cases. - Indirect taxes are already relatively high and tend to be less progressive, so they may not be ideal for raising revenues now. - Spending side: - Further reduce universal price subsidies (for example, energy subsidies), even though they are relatively low compared to other emerging regions. - Increase spending efficiency; better target existing social transfers using means testing where feasible. - Carefully calibrate use of fiscal space generated by reduced interest rate expenditure following a primary balance adjustment; an overall package combining infrastructure spending and social transfers is desirable to ensure beneficial growth and social effects over the medium term. - Medium-term reforms to rigid current expenditure envelopes (for example, Brazil and Colombia) could allow a higher share of capital expenditure. ### Pension reform (overview and fiscal/social relevance) - Pension systems are central to social outcomes because of redistribution between individuals and generations and fiscal costs. - Many defined benefit systems in Latin America appear to face fiscal sustainability concerns given aging populations, while defined contribution systems struggle to produce socially acceptable replacement rates (Figliuoli and others 2018). - Pension reform is complex and must be carefully calibrated to country-specific circumstances. - Two issues with direct implications for poverty and inequality are highlighted from Altamirano and others (2018) (discussion continued beyond this box). *Italic: Source — Box 4. General Structure of the DGE Model, ccilaea - Box 4. General Structure of the DGE Model* ### 1. Many Latin American pension systems have a long-minimum contri- ### ccilaea - 1. Many Latin American pension systems have a long-minimum contri- ### Pension eligibility, minimum-contribution thresholds, and distributional effects - Many systems require a long minimum contribution period (usually specified in terms of numbers of weeks or months) to become eligible for a pension, introducing a strong discontinuity in benefits. - Workers contributing for less than the minimum period face low replacement rates (or even 0 in the case of the defined benefit system in Peru), while those who cross the threshold can receive fairly generous replacement rates in many defined benefit systems. - High informality in Latin America, especially among lower-income workers, leads many affiliates to fail to meet minimum contribution periods, making minimum-contribution rules highly regressive: lower-income workers are de facto taxed to pay pension subsidies to higher-income workers. - Minimum pensions and non-contributory pensions act as progressive tools: - Minimum pensions guarantee a pre-specified minimum pension amount to those who meet retirement requirements, raising replacement rates for lower-income retirees relative to higher-income ones. - Non-contributory pensions can provide a safety net for those excluded from contributory pillars when well-integrated with the overall system. ### Colombia: coverage, generosity, and IMF recommendations - Coverage and poverty: - Only about one-third of the pension-age population receive a contributory pension. - About one-half of the pension-age population is below the poverty line. - Eligibility threshold and benefits: - The relatively high contribution threshold for eligibility is 1,350 weeks. - For the few eligible in the defined benefit pillar, replacement rates are relatively generous at 70−100 percent. - About half of the implicit subsidy (difference between contributions and pension benefits received) is received by the top income quintile. - IMF (2018b) recommendations: - Beyond parametric reforms (changing the retirement age and lowering the replacement rate in the defined benefit pillar), deeper structural reforms should aim to expand pension coverage while ensuring progressivity and fiscal sustainability. - Suggested measures include strengthening the non-contributory pillar, lowering the relatively high eligibility threshold (number of weeks), and guaranteeing a minimum pension. ### Peru: informality, replacement-rate distribution, and reform options - Contribution and coverage facts: - High labor informality: only about 30 percent of the economically active population are contributing to statutory pension schemes. - Less than 10 percent of workers in the bottom income-quintile contribute to statutory pension schemes. - Freudenberg and Toscani (2019) find that workers on average contribute for 4–5 months out of a possible 12 months per year over their working life. - Replacement rates and inequality: - Average replacement rates are likely to be low, at about 30 percent in both the defined benefit and defined contribution pillar. - In the defined benefit pillar: - Average replacement rates for those who reach the 20-year minimum contribution period (and are thus eligible) are roughly 60 percent. - About 60 percent of all affiliates are unlikely to reach the 20-year threshold, leading to a replacement rate of zero for them. - Affiliates from the bottom income quintile are three times less likely to reach the threshold than affiliates from the top quintile, implying highly regressive redistribution. - In the defined contribution pillar: - Affiliates with full contribution careers (generally high-income) are expected to have replacement rates about 40 percent in a conservative scenario. - Workers with below average contribution densities (generally low-income) will have replacement rates substantially below 20 percent. - Reform avenues and fiscal cost estimate: - Shortening the minimum contribution period from 20 to 15 years would allow more people to obtain a pension and raise the average replacement rate at a relatively limited fiscal cost; estimated gross cost: 0.05 percent of GDP. - In the SPP (private pension system), broadening the contribution base or increasing contribution rates would raise replacement rates but could adversely affect labor formalization. - Peru’s current old-age transfer (Pension65) reduces old-age poverty and could be strengthened and developed into a full-fledged non-contributory pension. - Medium-term priority: a larger reform to restructure the system and avoid competition between private and public pension plans. ### Fiscal decentralization, resource transfers, and local fiscal risks - Several countries redistribute fiscal windfalls from natural resource extraction to subnational governments: Bolivia, Brazil, Peru redistribute to producer regions and local governments; Colombia redistributes royalties to subnationals. - Downsides of concentrated and volatile resource transfers to subnational governments: - Large horizontal inequities when geographic/geological differences determine fiscal envelopes. - Volatility complicates intertemporal planning, harder at local than national level. - Resource transfers do little to encourage accountability or the building of own-revenue bases. - Large per capita fiscal windfalls can lead to absorptive-capacity and governance problems. - Environmental impacts of mining activity argue for additional transfers to producing regions. - Conceptual framework for assessing decentralization: vertical gap, vertical balance, horizontal gap, horizontal balance. - Equalization transfers are the main instrument to fill horizontal gaps. - Reducing horizontal gaps strengthens the case for distributing resource rents more broadly than only to producer regions or setting up offsetting equalization transfers. - Reform guidance for decentralization and resource-sharing: - Aim to minimize horizontal inequities (take greater account of spending needs), avoid boom-bust revenue cycles at the local level, and clarify goals of revenue-sharing agreements. - Use precautionary stabilization funds with clear rules and governance arrangements (examples cited: Chile, Colombia, Norway). - Consider reforming royalty-sharing arrangements (Colombia’s 2012 reform is cited as an example; 2019 reform partially reversed aspects to increase production incentives). - Increase transparency of transfer arrangements to facilitate planning and oversight. - Use nonresource transfers to offset horizontal inequities by including measurable criteria of local needs in allocation formulas. - Build subnational capacity and encourage local governments to expand own-revenue bases; property taxes can play an important role. - Current property tax revenues across the region average 0.3 percent of GDP. ### Structural policies, labor markets, and informality - Employment and labor income gains were key for poverty reduction during the commodity boom; labor market reform and worker retooling policies can support adjustment to lower commodity prices and reduce poverty in the medium term. - Labor market rigidities in parts of South America: - Redundancy costs are higher than in advanced economies (AEs) or other EMDEs. - Permanent contracts are mandatory for permanent tasks in many countries. - Dismissal of even one worker often requires third-party approval. - Employment protection dimensions linked to higher informality: - Higher redundancy costs and cumbersome dismissal regulations (e.g., requiring third-party approval for dismissal of even one worker) are associated with higher informality. - Peru and Mexico are noted as having high informality relative to their level of development and strict employment protection in key dimensions. - Informality’s macro role: - The response of informality to GDP cycles is stronger than that of unemployment. - Estimates of Okun’s law show the formal/informal adjustment margin reduces the importance of the employment/unemployment margin for cyclical adjustment. - Higher labor formality—higher contributive pension coverage—is crucial to raise old-age income across the region. *Italic: Content derived from the supplied IMF chapter text.* ### 4. Redundancy Costs ### 4. Redundancy Costs ### Labor market regulation and redundancy costs - Title context: "4. Redundancy Costs (Weeks of salary; median)". - Duval and Loungani (2019) recommendations: - Reduce the expected cost of firing procedures by making them more transparent, predictable, and less administratively burdensome to tackle informality and improve labor market functioning. - Build up unemployment insurance and other benefits at the same time to guarantee adequate protection of workers. - The exact impact depends on the nature of informality in each country and interactions with country institutions and other costs of doing business. - Political economy and reform sequencing: - Reforms should be carefully designed and prioritized with good communication and transparency to ensure broad-based support (Ciminelli and others 2019). - Policymakers should factor in and address possible adverse short-term effects of reforms (for example, some labor market reforms may have adverse short-term effects while being beneficial longer term). - Fiscal incentives and policies (job search assistance, retraining, stronger social safety nets) can mitigate social and distributional costs and help advance reform agendas. ### Education, inequality of opportunity, and policy measures - Key education statistics and observations: - Gini index for distribution of years of education remains high in many countries in LA; Central America can reach as much as 0.51 in Guatemala. - Difference in years of schooling between highest and lowest income quintiles: - Bottom income quintile on average have only 6 years of education. - Top quintile completed 12 years of education. - Difference = 6 years of education (or 100 percent when measured relative to the level in the bottom quintile). - Implications and recommended policies: - Improving access to primary and lower-secondary education, especially for girls and in rural areas, to increase progressivity and equalize opportunities. - Increase efficiency of public education spending given relatively high spending but relatively low international test scores for high school students. - For tertiary education: strong case for graduates to finance some costs because benefits accrue to graduates (IMF 2014a). - Income-contingent student loans that are paid only when students start earning would keep higher education free at point of use and reduce disincentives for poorer students. - Means-testing tuition fees and scholarships can help ensure tertiary provision remains progressive. ### Economic diversification and resilience to commodity busts - Central conclusion: The most effective long-term policy to make an economy resilient to commodity busts is economic diversification. - Challenges for commodity producers: - Natural resource wealth can create "Dutch disease" and constrict growth in other tradable sectors (including high-technology manufacturing and services). - Structural challenges may require an active role for the state ("leading hand of the state") in diversification. - Policy instruments for diversification suggested (from Cherif, Hasanov, and Zhu 2015): - Access to financing and business support services through VC funds, development banks, export promotion agencies. - Creation of special economic zones, industry clusters, and related measures. - Study successful diversifiers such as Korea, Malaysia, and Singapore; Malaysia is highlighted as relevant because it is also a commodity producer. - Case study: Peru agro-export boom - The boom relied on construction of irrigation districts, existence of several free trade agreements, and phytosanitary authority (SENASA) work to open new markets. - Further growth needs new investments in irrigation, roads, and ports to extend farmland and reduce transportation and logistics costs. - Timing and sequencing of diversification policies: - Some diversification policies may be easier during commodity busts (lower opportunity costs, exchange rate depreciation helps nascent exporters). - Other diversification policies are more affordable when commodity prices are high and fiscal resources are ample. - Important sequencing tradeoffs need to be taken into account when designing diversification strategies. - Complementary reforms: - Improved governance and rule of law, better access to and quality of health, expanding financial access (leveraging fintech) can reduce inequality of opportunity and outcomes and poverty. ### Concluding considerations on recent history and outlook - Since the turn of the century Latin America saw significant social gains, especially in commodity exporters; after the commodity boom ended the speed of gains slowed and in some cases partially reversed. - COVID-19 shock likely wiped off years of social progress. - Countries noted for large social gains during the boom include Argentina, Bolivia, and Ecuador; these countries also experienced favorable terms of trade developments and faced sharp reversals when terms of trade deteriorated, leading to increased fiscal deficits and fiscal adjustment needs. - Fiscal policy cyclicality during 2007–16 (Vuletin and Vegh 2017): - Fiscal policy was procyclical in Argentina, Brazil, Bolivia, and Ecuador. - Fiscal policy was counter-cyclical in Chile, Colombia, Paraguay, and Peru. - Trade-off question: rapid social gains versus fiscal sustainability—open question whether quick redistribution can compromise long-term fiscal sustainability and thus social gains. - Commodity terms of trade had recently reached their highest levels since 2011 for Latin American commodity exporters, but volatility means gains can be temporary. - Crisis as opportunity: the COVID-19 crisis can be used to advance structural reforms that address long-term challenges. ### Natural resource revenue-sharing details (selected country facts) - General: Natural resource revenues are largely centralized in Chile, Ecuador, Mexico, Norway, Trinidad and Tobago, and Venezuela, with limited or no redistribution to subnational producers. In some countries (three case-study countries in Chapter 3 and Colombia) significant amounts go to subnational governments. - Bolivia: - Total 18 percent hydrocarbon royalty: 11 percent goes to producing departments, 6 percent stays with the central government, and 1 percent goes to the lightly populated departments of Pando and Beni. - The 32 percent hydrocarbon tax (Impuesto directo a los hidrocarburos – IDH) is allocated in a more complicated way, going to both producing and nonproducing departments as well as municipalities, with 20 percentage points remaining with the central government. - Mining royalties: distributed only to producing departments and municipalities, with an 85–15 split between the two. - Brazil: - 60 percent of mineral royalties are distributed directly to the producing municipality (prior to a recent reform this share was 65 percent). - Remainder allocation: producing states 15 percent, the federal government 10 percent, non-producing municipalities with some connection to minerals 15 percent. - For oil and gas, allocation is more complicated; since the 1997 royalties law substantial amounts of oil and gas revenues have been distributed to municipalities hosting or facing oil and gas fields; royalties can account for over 50 percent of a municipality’s revenues in some cases. - Canada: - Natural resource income is subject to federal and provincial corporate income tax, mining taxes, royalties, and land taxes at the provincial level. - Fiscal stabilization program: federal assistance to any province with a year-over-year decline in nonresource revenues greater than 5 percent caused by an economic downturn. - Canada has an equalization program to reduce fiscal disparities between provinces; equalization transfers are unconditional and determined by measuring provinces’ ability to raise revenues. - Colombia: - Prior to the 2012 reform roughly 80 percent of royalties went directly to producer departments and municipalities (which had only 17 percent of population). - Following the 2012 reform this was reduced to roughly 10 percent, with the remainder assigned to central funds with specific goals. - Distribution after 2012: about 30 percent saved in a stabilization fund, 10 percent to a science and innovation fund, 10 percent to a regional pension fund, remainder allocated to subnational investment projects with a complex distribution formula. - As a result of 2012 changes, 1,089 municipalities received a share of commodity royalties in 2012 compared to 522 in 2011. - A 2019 reform increased the share going to producer regions to 25 percent while also increasing the share distributed to the poorest municipalities and strengthening investment focus of royalty use. - Norway: - Government revenues from petroleum activities are transferred to the Government Pension Fund Global. - Under the fiscal rule, petroleum revenues are phased into the economy gradually; over time government spending must not use any of the fund’s ’capital, only its expected real return, which is currently estimated at 3 percent. - Fiscal rule allows petroleum revenue spending to be increased during downturns and decreased during upturns. - Peru: - Overall, about 60 percent of fiscal revenues from the mining sector go to subnational governments, mainly from mining sector corporate income taxes (canon minero) and mining royalties. - Canons are transferred only to the department where production occurs and then further distributed within producing departments so producing provinces and municipalities receive a large share. ### Appendix: Model structure from Chapter 4 (selected elements) - Model type: Dynamic general equilibrium model of a small open economy with multiple sectors and heterogeneous households. - Household and occupation heterogeneity: - Large number of households heterogeneous within and across sectors. - Urban and rural households differ in occupations and access to financial intermediaries. - Within-sector heterogeneity arises from household-specific productivity shocks. - Four occupations: - Agricultural workers (rural) - Entrepreneurs (urban) - Public-sector workers (urban) - Private-sector workers (urban) - Households are confined to their sectors and cannot easily switch occupations. - Production overview (Table 12 summary): - Agricultural goods: produced by agricultural workers using land, labor, fertilizer; used for consumption and as input for agricultural exports. - Manufacturing: produced by entrepreneurs using private sector labor, capital, and energy; used for consumption, investment, and exports. - Services: produced by private/public sector workers (informal technology) and entrepreneurs (private sector labor, capital and energy); used for consumption and investment. - Energy (oil, natural gas): produced by entrepreneurs using capital; used for consumption and exports. - Agricultural export: produced by entrepreneurs using domestic food product and private sector labor; exported. - Preferences and financial access: - Households are infinitely lived, forward-looking, risk averse, choose consumption and savings to smooth consumption over time. - Access to financial intermediaries allows households to save and borrow as insurance against shocks. - Only private- and public-sector workers and a fraction of agricultural workers have access to finance; remaining farmers can neither save nor borrow. - Households choose allocation of consumption across domestic agricultural goods, imported food, and non-food goods (manufacturing, services, energy). - Workers decide time allocation between formal labor market and informal sector work. - Financial intermediation roles: - Convert manufacturing and services goods into capital. - Allow households to save and borrow. - Fiscal policy instruments in the model: - Tax on entrepreneurs’ capital income. - Tax on private- and public-sector workers’ wage earnings. - Royalties from energy sector. - Sector-specific and means-tested transfers and subsidies. - Idiosyncratic shocks: - Non-entrepreneurial household productivity subject to random time-varying changes; households facing severe shocks can borrow if they have access to finance. - Model note: The model highlights financial inclusion as an insurance mechanism that reduces consumption inequality. *Content derived from the source PDF "ccilaea - 4. Redundancy Costs".* ### Appendix 1. Details of the Model from Chapter 4 ### Appendix 1. Details of the Model from Chapter 4 ### Model setup and shocks - Households face idiosyncratic shocks that render some households "unlucky." - There is no aggregate uncertainty. - Given the large number of households, a law of large numbers applies, so that the distribution of shocks across households within each sector remains constant; "the number of unlucky households is always the same." - The prices of energy, agricultural exports and manufacturing goods are exogenously given. ### Equilibrium conditions - At each point in time, prices, wages, and interest rates are set to ensure that the markets for credit, labor and the goods produced only for domestic consumption clear. - Given these prices (both in the present and future) and government policies, all household decisions are made to maximize the present value of lifetime utility. ### Steady state - The economy is in a steady state. - Aggregate variables and prices are constant over time, as is the distribution of wealth, income, and consumption across households. - The income, wealth, and consumption of individual households however changes over time with the realization of their idiosyncratic shocks. *Appendix 1. Details of the Model from Chapter 4* --- _Source: https://www.imf.org/-/media/files/publications/dp/2021/english/ccilaea.pdf_