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Chapter 9 Poverty and Inequality in Brazil and Latin America

Author(s):
Antonio Spilimbergo, and Krishna Srinivasan
Published Date:
March 2019
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Author(s)
Ravi Balakrishnan, Frederik Toscani and Mauricio Vargas 

Brazil, and Latin America more broadly, have made impressive progress in reducing inequality and poverty since the turn of the century, although they remain highly unequal. Similar to other commodity exporters in the region, much of the progress in Brazil reflected real labor income gains for lower-skilled workers as well as higher government transfers. With the commodity boom over, a tighter fiscal envelope, and poverty rates already edging up, policies will have to be carefully recalibrated to sustain social progress. Better targeting of social transfers and reforms to decentralization frameworks also have an important role to play.

Introduction

Historically, Brazil has been one of the most unequal countries in the world. Even around the turn of this century, about 35 percent of the population lived below the poverty line. Since then, however, Brazil has made tremendous progress in reducing poverty and inequality. The poverty rate fell by more than 20 percentage points, and income inequality, as measured by the Gini coefficient, dropped from 0.60 to 0.52 during the commodity boom period.1,2

Previous analysis has shown that strong labor income growth in the lower-income segments was crucial for the reduction of inequality and poverty in Brazil. Average real income growth was high for all but the top decile of the income distribution, mirroring results in Latin America more broadly. Góes and Karpowicz (2017) find that most of the change in the Gini in Brazil can be explained by labor income growth, higher schooling levels, and labor formalization. The targeted social program, Bolsa Família, also contributed to income convergence.

Against this backdrop, this chapter puts inequality and poverty developments in Brazil into regional perspective. It shows that the whole of Latin America did well, but improvements were particularly pronounced in commodity-exporting countries. The chapter then asks why and explores the channels through which commodity cycles affect social progress by using a microdata case study for Brazil, which the chapter compares with case studies for Bolivia and Peru.

Of great concern is the recent reversal in poverty and inequality gains. Although similar reversals have occurred in several other countries in the region, the deterioration in Brazil, in the context of a deep economic recession, has been particularly stark. This chapter concludes with a discussion of policies that can help maintain progress in the current period of lower commodity prices. The correct policy mix will be crucial to guaranteeing that Brazil can continue to move toward the twin goals of eradicating poverty and reducing inequality.

Social Gains in Brazil and Latin America During the Commodity Boom

Throughout the 20th century Latin America was associated with some of the highest levels of inequality in the world; but since 2000 it has been the only region to have seen a significant reduction in inequality (Figure 9.1). Poverty has also fallen significantly, although it has also dropped in other regions, and Latin America started from a relatively low base (Figure 9.2).

Figure 9.1.Gini Coefficient

(Gini index; population-weighted average)

Sources: World Bank, PovcalNet database; and World Bank, World Development Indicators (WDI) database.

Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. EAP = East Asia and Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; SAR = South Asia; SSA = sub-Saharan Africa.

Figure 9.2.Poverty Rate

(Percent; headcount ratio at US$3.20 a day; 2011 PPP)

Sources: World Bank, World Development Indicators (WDI) database.

Note: For 2015, Latin America (LA) is the average of available values from WDI. Countries include Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Honduras, Panama, Paraguay, Peru, and Uruguay. No data are available for SAR in 2015. EAP = East Asia Pacific; ECA = Europe and Central Asia; LA = Latin America; MENA = Middle East and North Africa; PPP = purchasing power parity; SAR = South Asia; SSA = sub-Saharan Africa.

Overall, poverty reduction was strong across the region during the commodity boom, especially in South America (Figure 9.3).3 Inequality as measured by the Gini coefficient declined in both Central and South America, but significantly more in South America (Figure 9.4).4 In South America, the difference between the 1990s (when poverty and inequality increased) and the boom period was particularly stark.

Figure 9.3.Change in Poverty Headcount Ratio

(Percentage points; US$3.10 a day)

Source: Inter-American Development Bank, SIMS database.

Note: South America comprises Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay. Central America comprises Belize, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Panama.

Figure 9.4.Change in Average Gini Coefficient

(Percentage points)

Sources: World Bank, World Development Indicators database; and IMF staff calculations.

Note: South America comprises Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay. Central America comprises Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama.

A large literature has shown that the widespread decline in inequality across the region during the 2000s was caused by a reduction in hourly labor income inequality and by more robust and progressive government transfers (Azevedo, Saavedra, and Winkler 2012; Cornia and Martorano 2013; de la Torre, Messina, and Pienknagura 2012; López-Calva and Lustig 2010; Lustig, López-Calva, and Ortiz-Juarez 2013). For poverty reduction, and, to some degree, for inequality declines, an obvious hypothesis is that higher growth across Latin America during the boom period might have been the key driver. Relative to the 1990s, Figure 9.5 shows that during the commodity boom, growth did indeed increase in South America (where poverty fell the most), while in Central America growth was lower but remained high. Figure 9.6 shows that the association between GDP growth and poverty reduction for individual countries across emerging market regions during the boom was positive.5 South American countries, however, are generally below the fitted line, meaning that for every additional percentage point of growth, they reduced poverty by more than other countries. This outcome suggests that factors beyond high growth have been behind the remarkable turnaround in poverty reduction in South America in the 2000s.

Figure 9.5.Average Real GDP Growth

(Percent)

Sources: IMF, World Economic Outlook database; Inter-American Development Bank, SIMS database; and IMF staff calculations.

Note: South America comprises Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay. Central America comprises Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama. CAPDR = Central America, Panama, and the Dominican Republic; PPP = purchasing power parity. The figure controls for convergence effects. Specifically, the variable on the y-axis is the residual of a regression of the change in poverty on the initial poverty rate. Data labels in the figure use International Organization for Standardization (ISO) country codes.

Figure 9.6.Average GDP Growth and Change in Poverty Headcount Ratio (2000–14)

(US$3.10 a day, PPP)

Sources: IMF, World Economic Outlook database; and IMF staff calculations.

Note: South America comprises Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay. Central America comprises Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama.

A key question then is, why were the social gains greater in South America during the boom relative to other regions? Figure 9.7 provides a potential link: South America is home to many commodity exporters that experienced a significant boost in their terms of trade relative to other countries. Figures 9.8 and 9.9 zoom in on the differences in inequality and poverty reduction between individual commodity exporters and non–commodity exporters. The largest gains on both fronts were made in two countries highly dependent on commodity exports, Bolivia and Ecuador. Indeed, 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.6 For inequality, the same pattern holds but the picture is more mixed, with El Salvador and the Dominican Republic seeing bigger reductions in inequality than several commodity exporters (Chile, Colombia, Paraguay, Honduras).7 For both poverty and inequality, progress in Brazil was similar to that in comparator countries, with improvements of similar magnitude to those in Peru, for example.

Figure 9.7.Average Commodity Terms of Trade Growth during Boom (2000–14)

(Percent)

Source: IMF staff calculations.

Note: Terms of trade is the commodity net export price index weighted by GDP (see Gruss 2014). All countries in South America are commodity exporters except Uruguay. All Central American countries are non-commodity exporters except Honduras.

Figure 9.8.Change in Gini Coefficient

(Gini units)

Sources: Inter-American Development Bank, SIMS database; World Bank, World Development Indicators database; and IMF staff calculations.

Note: Colombia uses 2003 and Brazil uses 2001 values for 2000, given data availability. Data labels in the figure use International Organization for Standardization (ISO) country codes.

Figure 9.9.Change in Poverty Headcount Ratio

(Percentage points; US$3.10 a day)

Source: Inter-American Development Bank, SIMS database.

Note: Colombia uses 2003 and Brazil uses 2001 values for 2000, given data availability. Data labels in the figure use International Organization for Standardization (ISO) country codes.

The significant progress in many non–commodity exporters under-scores the various factors that drive social progress, of which commodity cycles is only one. Indeed, Messina and Silva (2018) argue that supply factors, such as an increasing supply of skilled workers, were likely the key drivers of lower inequality in Central America and Mexico and played an important role across the region. Lustig, López-Calva, and Ortiz-Juarez (2012) also point to the expansion of cash transfers in Mexico and Brazil, while IMF (2017) highlights the role of government policies in boosting low wages in Uruguay.

Commodity Cycles, Poverty, and Inequality

Is There a Statistical Association?

What is the relationship between social indicators and the commodity cycle? The correlation between the reduction in poverty and inequality during the boom and the change in commodity terms of trade points to an interesting story (Figure 9.10).8 For non–commodity exporters, no clear association is evident between changes in commodity terms of trade and changes in poverty and inequality. For commodity exporters, however, 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.9 For inequality, the relationship for commodity exporters is not as strong as for poverty but is still clearly visible. A closer relationship between the commodity cycle and poverty (rather than inequality) is an empirical regularity found throughout this chapter.

Figure 9.10.Commodity Terms of Trade, Poverty, and Gini Coefficient

Sources: Inter-American Development Bank, SIMS database; World Bank, World Development Indicators database; and IMF staff calculations.

Note: Dark blue dots correspond to CAPDR and Mexico and green dots to South America. CAPDR comprises Central America, Panama, and the Dominican Republic. Chile uses 2013 values for the 2014 poverty headcount ratio due to data availability. Data labels in the figure use International Organization for Standardization (ISO) country codes.

Table 9.1 reports regressions of the share of income by decile on commodity terms of trade as well as several control variables.10 Income shares of the second to eighth deciles increased significantly, while the share of the top decile declined. Because both low-income and medium-to-high-income segments gained, the poverty result is stronger than the inequality result. Nevertheless, inequality did tend to fall because the share of income going to the highest decile fell substantially, on average.11 Interestingly, the bottom income decile did not see its share rise in a statistically significant way in response to higher commodity terms of trade, although its absolute income increased. As expected, and consistent with Figure 9.9, poverty reduction was driven more by developments closer to the poverty line, namely in the second to fourth decile, depending on the country.12

Table 9.1.Commodity Terms of Trade and Income Share by Decile in Commodity Exporters
Variables(1) Decile 1(2) Decile 2(3) Decile 3(4) Decile 4(5) Decile 5(6) Decile 6(7) Decile 7(8) Decile 8(9) Decile 9(10) Decile 10
(Log) Net Commodity0.1510.395**0.392*0.405*0.476**0.575**0.716***0.790***0.436-4.310**
Price Index(0.120)(0.191)(0.207)(0.226)(0.236)(0.255)(0.267)(0.259)(0.301)(1.735)
Country fixed effectsYesYesYesYesYesYesYesYesYesYes
ControlsGDP per capitaGDP per capitaGDP per capitaGDP per capitaGDP per capitaGDP per capitaGDP per capitaGDP per capitaGDP per capitaGDP per capita
Period2000–142000–142000–142000–142000–142000–142000–142000–142000–142000–14
Observations114114114114114114114114114114
R20.6080.6270.6640.6740.6850.6580.6040.4880.0200.638
Number of countries9999999999
Sources: Socio-Economic Database for Latin America and Caribbean (CEDLAS and World Bank); and IMF staff calculations.*p < 0.10; **p < 0.05; ***p < 0.01.
Sources: Socio-Economic Database for Latin America and Caribbean (CEDLAS and World Bank); and IMF staff calculations.*p < 0.10; **p < 0.05; ***p < 0.01.

What Are the Channels?

The statistical relationship naturally leads to the question, through which channels does the commodity cycle influence social indicators? Essentially, a commodity boom is a positive wealth shock that propagates through the economy via various channels, as described in the sections that follow.13

Market and Private Sector Channels

The positive wealth shock has a direct impact on the commodity sector and creates spillovers to the rest of the economy, many of them transmitted via the labor market:

  • First, the booming commodity sector expands. The expansion draws in labor and other resources. Higher labor demand pushes up real wages or employment, or both. It can also reduce or increase the skills premium, depending on the relative labor intensity of the commodity sector.14
  • Second, improved terms of trade and the expansion of the commodity sector create spillovers to other sectors. Higher wealth and incomes cause domestic demand to increase, benefiting the nontradables sector. Higher investment by the commodity sector can lead, for example, to more construction, which is another way the positive wealth shock feeds into the economy, again expanding the nontradables sector.
  • Third, changes in relative wages (a compression in the skills premium if the commodity sector and the nontradables sector are intensive in unskilled labor) will benefit more skill-intensive sectors and lead to further reallocation (Benguria, Saffie, and Urzua 2017).

Overall, the above channels should lead to more employment in the commodity and nontradables sectors. The impact on the non–commodity tradables sector is not immediately clear. On the one hand, the classic natural resource curse (Dutch disease) could be operating—higher demand expands the nontradables sector but crowds out the non–commodity sector because of a more appreciated real exchange rate (Harding and Venables 2016). On the other hand, if key tradables inputs are provided locally, positive spillovers can occur from the commodity sector to the manufacturing sector, as has been shown for the United States.15 Given the relatively narrow initial manufacturing base in most Latin American countries, both effects might be modest, but commodity booms are likely to hamper export diversification to some degree.

For social outcomes, the expansion of the commodity and nontradables sectors, and the related increase in wages, should reduce poverty if those sectors employ workers from the lower end of the income distribution. Additionally, inequality will fall if the expanding sectors are intensive in low-skilled labor, causing the skills premium to decline.

Fiscal Channels

The positive wealth shock is also transmitted via higher fiscal revenues and expenditures:

  • Higher government investment operates in a manner similar to higher commodity sector investment. It leads to more domestic demand, for example, via increased construction, with a resulting impact on wages and thus on poverty and inequality.16
  • Larger transfers will have a direct impact on poverty and inequality, especially if the transfers are targeted to lower-income individuals.
Other General Equilibrium Effects

While not a focus in the remainder of this chapter, the wealth shock can be transmitted via other general equilibrium effects, for example, via migration or the financial system.17

Regional Macroeconomic Evidence and Key Data for Brazil

In aggregate, then, commodity booms should reduce poverty and inequality through labor market developments and fiscal transfers.18 Indeed, these mechanisms seem to have played out in the region. Public investment and employment growth were higher in commodity exporters than in non—commodity exporters (Figures 9.11 and 9.12). In line with the results of de la Torre and others (2015), commodity exporters also experienced significantly larger real labor income gains than non—commodity exporters across all skill levels (Figure 9.13). Low-skilled workers gained the most, compressing the skills premium and reducing inequality in both commodity exporters and non—commodity exporters (Figure 9.14) but because of different underlying wage dynamics. Specifically, as Messina and Silva (2018) note, the skills premium reduction reflects not just demand factors tied to the commodity boom but also an increase in the supply of high-skilled labor. In addition to labor income, government transfers also increased more in commodity exporters than in non-commodity exporters, further con-tributing to greater poverty and inequality declines in commodity exporters (Figure 9.15).

Figure 9.11.Public Investment in Latin America

(Percent of GDP)

Source: Inter-American Development Bank, SIMS database.

Figure 9.12.Total Employment Growth

(Percent)

Sources: IMF, World Economic Outlook database; and IMF staff calculations.

Figure 9.13.Real Labor Income Growth by Educational Level

(Percent)

Source: Inter-American Development Bank, SIMS database.

Figure 9.14.Skills Premium Change in the 2000s

(Percentage point change in the ratio of hourly wage; high to low education)

Sources: Socio-Economic Database for Latin America and the Caribbean (CEDLAS and World Bank); and IMF staff calculations.

Note: Data labels in the figure use International Organization for Standardization (ISO) country codes.

Figure 9.15.Average Government Transfers in Latin America

(Percent of GDP)

Sources: IMF, World Economic Outlook database; and IMF staff calculations.

Looking more specifically at Brazil, the same patterns hold. Real labor income gains were strong, and they were strongest for lower-skilled workers, resulting in a substantial compression of the skills premium. The expansion of government transfers was more pronounced in Brazil than in most other countries, with the conditional cash transfer program Bolsa Família playing an important role (Barros and others 2010).

Microdata Case Studies: Brazil and Comparison with Bolivia and Peru

This section examines Brazil, Bolivia, and Peru, all of which experienced significant reductions in poverty and inequality. They are also all commodity exporters, although Brazil is more diversified. The analysis first uses Shapley decompositions of household survey data to analyze the drivers of the decline in national inequality and poverty. This exercise helps identify whether labor income or transfer income played a larger role.19 Within-country studies are then conducted for Brazil and Bolivia to disentangle the impact of a fiscal windfall from the pure market impact associated with a commodity boom.

The Fall in Poverty and Inequality in Brazil during the Commodity Boom, according to the Literature

Several authors have exploited household income data to try to understand the drivers of the fall in poverty and inequality in Brazil. Barros and others (2010) estimate that the drop in inequality between 2001 and 2007 was driven by both an expansion in government transfers and a compression in the ratio of labor income of better-educated workers to that of less-educated ones. They explain the latter by an expansion in the supply of educated workers.

Azevedo, Inchauste, and Sanfelice (2013) use Shapley decompositions to explain the roles of different factors in reducing inequality in several Latin American countries, while Azevedo and others (2013) use the same approach to explain the reduction in poverty. For Brazil, the former paper finds that the largest contributor to lower income inequality was higher labor income (contribution of 45 percent), but government transfers (contribution of 20 percent) and pensions (contribution of 18 percent) also played an important role. Similarly, for poverty, employment and earnings growth was the largest single factor, but nonlabor income (notably government transfers) played an important role, more so than in several other Latin American countries. The importance of transfers in Brazil can be noted by observing that the share of transfers in total household income of the bottom 20 percent of the distribution went from 3 percent to 24 percent between 2000 and 2010 (Azevedo, Inchauste, and Sanfelice 2013).

What Household Survey Data Show regarding Wage, Employment, and Government Transfer Developments in Bolivia and Peru20

In Bolivia during the boom, real labor income increased for all skills segments except for the highest one. Workers with intermediate levels of education experienced the largest gains (Figure 9.16), a finding consistent with the cross-country regression results on changes in income share by decile.

Figure 9.16.Bolivia: Index of Monthly Real Labor Income by Educational Level

(Index: 2001 = 100)

Sources: Improving the measurement of living conditions (MECOVI) household survey; and IMF staff calculations.

Note: The size of the bubble corresponds to the relative number of workers in each category.

Figure 9.17 looks at real per capita labor income and employment by sector for Bolivia (panel 1) and Peru (panel 2). The biggest winners in employment growth were construction and the extractive sector in Peru, and the extractive sector and commerce in Bolivia, in line with the previous discussion on channels. The broad services sector created the most jobs in both countries, in part reflecting the sectors size. Overall, employment growth came from the extractive and nontradables sectors.

Figure 9.17.Real Labor per Capita and Sectoral Employment in Bolivia and Peru

Sources: National household surveys (ENAHO) for Peru; improving the measurement of living conditions (MECOVI) household surveys for Bolivia; and IMF staff calculations.

Note: The size of the bubble corresponds to the absolute change between 2006 and 2013 in the number of workers in each sector whose income depends on each of the sectors for Bolivia, and the absolute change between 2007 and 2011 in the number of workers in each sector whose income depends on each of the sectors for Peru. Darker blue means a negative change.

The picture is more mixed for real wage growth. Average wages in the extractive sector fell in Bolivia, likely reflecting a compositional effect, with the number of informal (poorly paid) miners increasing faster than employees in larger, capital-intensive mines. Manufacturing did poorly in both countries, especially in employment growth, again in line with a standard crowding-out story as well as with global trends.21

Finally, Table 9.2 reports the share of labor versus transfers in gross income (which includes transfers from the government and from family members or others). In Bolivia, government transfers increased markedly during the boom, partly reflecting the introduction of a noncontributory pension scheme. In Peru, transfers from the government did not increase substantially. In both countries, however, transfers account for a much smaller share of income than labor income, mechanically limiting their scope for lowering poverty and inequality.

Table 9.2.Composition of Households’ Total Income
20062007201120122013
BoliviaLabor82.882.481.880.979.1
Nonlabor16.417.017.918.420.4
Of which: Transfers from government5.75.49.811.2...
20072008200920102011
PeruLabor83.684.284.984.885.8
Nonlabor16.415.815.115.214.2
Of which: Current transfers19.49.09.08.68.3
Of which: JUNTOS program0.50.70.30.30.3
Sources: National household surveys (ENAHO) for Peru; improving the measurement of living conditions (MECOVI) household surveys for Bolivia; and IMF staff calculations.Note: Figures for Bolivia do not sum exactly to 100 percent because extraordinary retirement benefits, scholarships. and insurance compensation are not included.… = not available.

Includes transfers within the country: pensions and transfers from individuals and institutions, public and private.

Sources: National household surveys (ENAHO) for Peru; improving the measurement of living conditions (MECOVI) household surveys for Bolivia; and IMF staff calculations.Note: Figures for Bolivia do not sum exactly to 100 percent because extraordinary retirement benefits, scholarships. and insurance compensation are not included.… = not available.

Includes transfers within the country: pensions and transfers from individuals and institutions, public and private.

Shapley Decompositions

The formal Shapley decompositions largely confirm the earlier conclusions. For both Bolivia and Peru, labor income played a larger role than nonlabor income in reducing inequality and poverty. Across sectors, changes in labor income of the nontradables (services) sector explain much of the social progress (Figure 9.18).22

Figure 9.18.Shapley Decompositions of Poverty and Inequality by Employment Sector and Skill Level for Bolivia and Peru

Sources: National household surveys (ENAHO) for Peru; improving the measurement of living conditions (MECOVI) household surveys for Bolivia; and IMF staff calculations.

Note: Gini coefficient change based on rescaled Gini coefficients in the range (0–100); poverty changes in percentage points. Unskilled (never attended school or incomplete primary education); low skilled (complete primary or incomplete secondary education); skilled (complete secondary, incomplete tertiary, or complete tertiary education).

Across skill levels, changes at the lower end of the distribution were important for understanding changes in social indicators. Specifically, low-skilled workers—defined as having complete primary or incomplete secondary education—were one of the biggest contributors to the drop in poverty and inequality. Interestingly, skilled workers in both countries (with complete secondary or tertiary education) were also important contributors to poverty reduction, even though they have the highest wages, on average, and their wages grew the least. Although average income did not increase for skilled workers, wages at the lower end of their wage distribution moved up during the boom. This allowed a nontrivial fraction of skilled workers to exit poverty.23

Municipal-Level Analysis

This section studies the differences between commodity-producing and non-commodity-producing regions within Brazil and Bolivia. Both Brazil and Bolivia produce commodities with a range of labor intensity and redistribute a large share of the commodity windfall to producing regions.

Did Poverty Fall across the Whole Country or Only in Certain Regions?

Based on census data, poverty reduction was broad based in Brazil, with the entire municipal poverty distribution shifting toward less poverty during the boom period (left shift in Figure 9.19).24 Indeed, poverty fell in 99 percent of Brazilian municipalities between the two census rounds and, on average, poverty fell by an impressive 18 percentage points.25 In addition to the fall in poverty, the whole municipal inequality distribution also shifted left, indicating lower inequality throughout the country. Last, labor formality increased in most municipalities, a testament to the strong labor market. Results are similar for Bolivia, where poverty fell in 97 percent of the municipalities.

Figure 9.19.Density Distributions by Municipality in Brazil

Source: Brazilian Institute of Geography and Statistics (IBGE).

Note: Figures show density of the municipal-level social outcomes.

Did Municipalities That Produce Natural Resources Improve More than Others?

For Brazil, information from the national oil and gas regulator (National Agency of Petroleum, Natural Gas and Biofuels) and the Ministry of Mining was combined to construct the real value of natural resource production per capita for each municipality (Figure 9.20). Data at this level of precision were not available for Bolivia. Instead, a list of all municipalities that produce either hydrocarbons or minerals was constructed, without obtaining the precise volume or value of production.

Figure 9.20.Value of Natural Resource Production per Capita by Municipalities, 2010

Sources: National Agency of Petroleum, Natural Gas and Biofuels (ANP); Brazilian Mining Ministry; Brazilian Institute of Geography and Statistics (IBGE) (2010); and IMF staff calculations.

Note: The map shows natural resource (hydrocarbons + minerals) production per capita in 2010 for 5,565 Brazilian municipalities. Population data are from the 2010 population census. Data on hydrocarbon production volumes by field are from ANP. These data are assigned to municipalities based on geographic information and are valuated according to annual price data by state, also from ANP. Mineral production values data are from the Brazilian Mining Ministry. Values are in constant 2010 Brazilian reais.

In both countries, many municipalities produce natural resources, but value and volume of production are both regionally concentrated, creating a relatively small group of municipalities with high per capita natural resource production. For example, out of Brazil’s more than 5,500 municipalities, the top 20 producers account for 75 percent of total production. In Bolivia, the region of Tarija produced about 70 percent of total natural gas in 2012.

To study the impact of natural resources, the change in poverty in producer municipalities is compared with the change in poverty in other municipalities, controlling for other factors (see Annex 9.1 for details of the identification strategy).

Poverty fell by more in natural resource municipalities (Table 9.3). For Brazil, higher real values of natural resource production are associated with larger declines in poverty, with producer municipalities reducing poverty by 1.4 percentage points, on average, relative to nonproducer ones.26 For Bolivia, the natural resource municipalities reduced poverty by 2.7 percentage points more than other municipalities. Regarding inequality, the results are mixed for Brazil, with statistical significance dependent on which technique is used.

Table 9.3.Impact of Natural Resource Boom on Producer Municipalities in Brazil and Bolivia
BrazilBolivia
PovertyGini CoefficientPoverty
Impact of Increase in Real per Capita Natural Resource Production (range for top 20 increases)-0.39*** to -9.1***0 to -0.05**N/A
Impact of Being a Natural Resource Producer Municipality (dummy variable analysis)-1.44***0-2.75*
Source: IMF staff calculations.Note: N/A = not applicable.*p < 0.10; **p < 0.05; ***p < 0.01.
Source: IMF staff calculations.Note: N/A = not applicable.*p < 0.10; **p < 0.05; ***p < 0.01.

In summary, the social gains in Brazil and Bolivia were broad-based across municipalities, but natural resource producers experienced larger gains.

What within-Country Analysis Can Show about the Channels through Which the Commodity Boom Affected Poverty and Inequality

To isolate the fiscal impact from other channels, natural resources can be divided into offshore oil and gas production and domestic mineral mining for Brazil, and into onshore gas megacampos27 and mineral mining for Bolivia.28 Mineral mining tends to yield smaller fiscal windfalls but generates substantial labor demand in the local extractive sector. Offshore oil and gas has a minimal labor demand effect (and labor may not even be located in the municipality closest to the rig), but it generates important fiscal windfalls for municipalities closest to the oil field. Hence, for Brazil, the impact of offshore oil and gas production is a proxy for the pure fiscal channel, while mining picks up the combined impact (as seen in Table 9.4). Similar logic applies to the distinction between gas megacampos and mineral mining in Bolivia, although the analysis is less precise because neither the value or volume of production nor exact fiscal windfalls at the municipal level are known.

Table 9.4.Impact of Mineral and Offshore Hydrocarbon Production on Municipal Revenues and Extractive Sector Employment
(1) Natural Resource Royalties per Capita(2) Current Revenues per Capita(3) Share of Workers in Extractive Industries
Change in Mineral Production per Capita0.0174***

(0.000922)
0.0241***

(0.006010)
1.33e-05***

(0.000004)
Change in Offshore Oil and Gas Production per Capita0.0209***

(0.001300)
0.0248***

(0.002640)
-2.56E-06

(0.000002)
Geographic ControlsYesYesYes
Dependent Variable in 2000YesYesYes
Change in Dependent Variable between 1991 and 2000NoNoNo
State Fixed EffectsYesYesYes
Observations5,5074,9825,507
R20.8860.8340.223
Source: IMF staff calculations.*p < 0.10; **p < 0.05; ***p < 0.01.
Source: IMF staff calculations.*p < 0.10; **p < 0.05; ***p < 0.01.

In Brazil, the pure fiscal impact (as measured by the impact of offshore oil and gas production) leads to some reduction in poverty and a marginal increase in labor formality (Figure 9.21).29 It also leads to a shift of labor out of agriculture and into nontradables, essentially services and construction, because the increased fiscal resources are partly used for public investment.30 Additionally, part of the fiscal windfall is used to increase public sector employment. In mineral municipalities, the labor market effects are much larger. Labor formality increased significantly and labor shifted from agriculture and manufacturing into construction and services. The results thus point to an important role for both fiscal and market channels, but especially the latter, in reducing poverty.31

Figure 9.21.Brazil: Impact of a One Standard Deviation Increase in Natural Resource Extraction at the Municipal Level

(Percentage points)

Sources: Brazilian Institute of Geography and Statistics (IBGE); and IMF staff calculations.

Note: The change between the 1991 and 2000 censuses is included in the regressions as a control variable when available. Standard errors are clustered at the state level. Estimated coefficients are set to zero when they are not significant at least at the 10 percent level. When they are significantly different from zero, the figure shows the impact of a one standard deviation increase in the value of natural resource production per capita between 2000 and 2010.

Similarly, in Bolivia, while poverty was reduced more in gas megacampo municipalities, the labor market impact was greater in mining municipalities because the fraction of agricultural employment decreased significantly and net migration increased (Figure 9.22). In megacampo municipalities, public sector employment increased significantly, in line with the Brazilian results, and pointing to the fiscal windfall being used for public employment. Indeed, the increase in public employment is notable considering the small share of public sector workers in the average Bolivian municipality—the increase of about 2 percentage points in public sector employment in gas megacampo municipalities is greater than one standard deviation.

Figure 9.22.Bolivia: Impact of Gas and Mineral Production at the Municipal Level

(Percentage points)

Sources: National Statistics Office (INE); and IMF staff calculations.

Overall, the results for Brazil and Bolivia are in line with growing evidence from other within-country studies in Latin America.32 For Brazil, Cavalcanti, Da Mata, and Toscani (2016) find that the direct market effect (abstracting from the fiscal channel) of having an oil sector is beneficial for municipalities and leads to structural transformation away from subsistence agriculture and toward the services sector over the long term. Benguria, Saffia, and Urzua (2017) corroborate these findings for Brazil over the recent boom period by showing a compression in the wage premium as well as significant employment gains in the commodity and nontradables sector, combined with employment losses in the tradables sector in regions affected by the positive price shock.

Fiscal Decentralization in the Context of Large Commodity Windfalls

In Latin America, Bolivia, Brazil, and Peru redistribute large parts of the fiscal windfalls from natural resource extraction back to the subnational-level governments in producing regions. Colombia also redistributes royalties to subnationals, but with less focus on producer regions since a reform was enacted in 2012 (see Annex 9.2 for further details, including on the frameworks in advanced economies such as Canada and Norway).

Although fiscal windfalls do have some beneficial effects for producer regions, sharing large amounts of natural resource revenues with subnational producers has several conceptual drawbacks. First, it is unclear whether geographic and geological differences between regions should determine fiscal envelopes given the large horizontal inequities that result. Second, the volatile nature of natural resource revenues calls for careful intertemporal planning, which is even harder to achieve at the local level than at the national level. Third, resource revenues are essentially transfer revenues from a local government’s perspective; thus, they do nothing to encourage accountability and the building of own-revenue bases. Fourth, in per capita terms, large fiscal windfalls can lead to problems with absorptive capacity as well as governance (IMF 2009). Of course, the environmental impact of mining activity needs to be considered and creates a case for an additional transfer to producing regions.

Consider the departmental budget breakdown of Bolivia for 2012 (Figure 9.23). The main gas region (Tarija) has a population share of about 5 percent. Yet its budget accounted for more than a third of all departmental revenues and wages, and nearly half of all departmental capital expenditure. In Peru in the same year, the main natural-resource-producing departments (Moquegua and Cusco) received more than 2,000 nuevos soles (S/.) per capita in commodity-related transfers {canons), while some other departments received less than S/.1 per capita. Indeed, 12 of the 183 provinces in Peru receive about 50 percent of canon revenues (Santos and Werner 2015). In Brazil, most mineral royalties go to producing states and municipalities. For oil and gas, the formulas are complicated, but, in some cases, royalties can account for more than 50 percent of a municipality’s revenues.

Figure 9.23.Bolivia: Departmental Budgets, 2012

(US dollars per capita)

Sources: National authorities; and IMF staff calculations.

Note: IDH = direct tax on hydrocarbons.

For Brazil, Caselli and Michaels (2013) find that large fiscal windfalls at the subnational level have not translated into substantial improvements in living standards, suggesting serious governance or capacity constraints at the local level. In both Peru and Bolivia, some local governments with the biggest windfalls per capita began to accumulate large deposits during the boom, while acute investment needs existed in other regions (Santos and Werner 2015, Chapter 10). Since the boom, the most important commodity-producing regions in Bolivia and Brazil, Tarija and Rio de Janeiro, respectively, have suffered severe fiscal sustainability problems. This issue is consistent with the drawbacks noted above, and several papers provide evidence that governance problems and capacity constraints at the subnational level often limit the effectiveness of public spending, especially in the context of high per capita natural resource revenues.33

Given this problem, when the opportunity for substantive reforms to decentralization frameworks is present, those reforms should aim to minimize horizontal inequities, avoid boom-bust revenue cycles at the local level, and, crucially, clarify the goals of the revenue-sharing agreement. To help avoid boom-bust cycles that lead to large spending shocks, further use could be made of precautionary stabilization funds, such as in Chile, Colombia, and Norway. The reform of royalty-sharing arrangements in Colombia in 2012 is a good example of what can be done to reduce horizontal inequities.34

Notwithstanding the Colombian example, achieving consensus on larger reforms to revenue-sharing arrangements is difficult. Other actions can still play an important role, including building capacity at the subnational level and encouraging local governments to build their own-revenue bases to reduce reliance on transfers (for example, via property taxes). Transfer arrangements should also be made as transparent as possible to facilitate planning and oversight. Such measures will increase ownership and accountability and reduce revenue volatility. Finally, nonresource transfers can potentially be used to offset some of the horizontal inequities by using measurable criteria of local needs in some of the allocation formulas (for example, the equalization scheme in Canada).

Can Social Progress be Sustained with Lower Commodity Prices?

To sum up, Brazil and Latin America in general made tremendous progress in reducing inequality and poverty in the 2000s, especially in commodity-exporting countries. Much of the decline in poverty and the Gini coefficient occurred because labor income inequality fell, linked to a declining skills premium and the expansion of services and lower-skill jobs. But increasing social transfers also played a role.

Because commodity prices have been significantly lower since the end of the boom in 2014, there are concerns that social progress is threatened, especially in commodity exporters. Indeed, since 2014, employment growth has slowed much more in commodity exporters than in non–commodity exporters, while real wage growth has been negative for all skill groups (Figures 9.24 and 9.25). The poverty cycle has also turned in some commodity exporters, with increases in poverty rates in Brazil and Paraguay. As discussed earlier, the impact of commodity cycles on inequality is not as strong as on poverty. Nonetheless, inequality in commodity exporters has largely moved sideways since 2014 following the tremendous reduction in the boom years. At the same time, fiscal space in many commodity exporters has fallen, given a decline in commodity-related revenues and slowing growth. All these factors suggest that, absent policy measures, lower commodity prices carry with them a significant risk of slower poverty reduction and possibly higher inequality in commodity exporters in the coming years.

Figure 9.24.Total Employment Growth

(Percent)

Source: Inter-American Development Bank, SIMS database.

Figure 9.25.Real Labor Income Growth by Educational Level

(Percent)

Source: Inter-American Development Bank, SIMS database.

How should commodity exporters respond to this challenge? While the channels by which commodity prices affected inequality and poverty during the boom will also be present in reverse during the postboom period, they need not be symmetric. For example, many commodity exporters saw significant migration to urban areas from rural areas. This experience may not reverse in the postboom period, owing to the high costs associated with moving. Moreover, countries that built up fiscal cushions during the boom can use the buffers in the postboom period to smooth the adjustment to lower commodity prices. Some countries, such as Bolivia and Peru, have been using their buffers, while the adjustment in countries without fiscal buffers (such as Ecuador) has been more difficult. And as shown in the social progress made in many non—commodity exporters in Latin America despite a negative commodity terms-of-trade shock, other policies still have a clear role to play in mitigating the impact of lower commodity prices on social progress:

  • Central governments, especially in countries with limited fiscal buffers, still could maintain the quality of social and infrastructure spending by increasing revenues and reprioritizing spending.35 Indeed, on the social protection side, Latin America already spends significantly less than emerging Europe or advanced economies (Figure 9.26). Space to maintain such spending levels could be created by, for example, (1) increasing revenues from progressive personal income taxes, which, as Figure 9.27 shows, tend to be less in Latin America compared with other regions;36 and (2) reducing universal price subsidies (such as energy subsidies), which are present in Latin America and typically highly regressive, although at lower levels than in other emerging market regions (Figure 9.28). Increasing the efficiency of spending could also play a role. For example, existing social transfers could be better targeted in many countries by making further use of means testing where feasible (IMF 2014).
  • The allocation of revenue-capacity and spending responsibilities at different levels of government could be improved. Enhancing capacity at the local level is essential. Apart from reforming formulas for revenue sharing to take greater account of spending needs (for example, population size and poverty levels), thought should be given to greater use of stabilization funds, with clear rules and governance arrangements, in commodity exporters.
  • Increasing the flexibility of labor markets and deploying policies aimed at retooling workers would help smooth the necessary adjustment to the rebalancing of demand caused by lower commodity prices. And while always challenging, continuing structural reforms to help diversify the production base would increase the resilience of commodity exporters to commodity price shocks.
  • Given that better education was an important structural factor that helped reduce inequality and lift people out of poverty during the boom, pushing for further improvements in the quality of education should remain a priority, although gains from any policy measures will take time to be realized and will accrue only in the longer term.

Figure 9.26.Composition of Social Spending, 2010

(Percent of GDP)

Sources: IMF, Fiscal Affairs Department database; and IMF staff calculations.

Note: AE = advanced economies; EMEU = emerging Europe; SA = South America; CA and Carib. = Central America and the Caribbean; MENA = Middle East and North Africa; AP = Asia and Pacific; SSA = sub-Saharan Africa.

Figure 9.27.Global Revenue Mix by Region, 2015

(Percent of GDP)

Sources: IMF, Fiscal Affairs Department database; and IMF staff calculations.

Note: AE = advanced economies; EMEU = emerging Europe; LA = Latin America; SSA = sub-Saharan Africa; AP = Asia and Pacific; MENA = Middle East and North Africa.

Figure 9.28.Composition of Social Spending

(Percent of GDP)

Sources: IMF, Fiscal Affairs Department database; and IMF staff calculations.

Note: Data labels in the figure use International Organization for Standardization (ISO) country codes.

South America faces an important challenge in managing the impact of lower commodity prices on social progress, especially their impact on inequality and poverty reduction since the turn of the century. Implementing the right policies will be key to meeting this challenge.

References

    AlberolaE. andG.Benigno. 2017. “Revisiting the Commodity Curse: A Financial Perspective.” Journal of International Economics 108.

    • Search Google Scholar
    • Export Citation

    AllcottH. andD.Keniston. 2018. “Dutch Disease or Agglomeration? The Local Economic Effects of Natural Resource Booms in Modern America.” Review of Economic Studies85(2): 596731.

    • Search Google Scholar
    • Export Citation

    AlvarezR.A.Garcia andS.Ilabaca. 2017. “Commodity Prices Shocks and Poverty in Chile.” Unpublished.

    AragonF. M. andJ. P.Rud. 2013. “Natural Resources and Local Communities: Evidence from a Peruvian Gold Mine.” American Economic Journal: Economic Policy5(2): 125.

    • Search Google Scholar
    • Export Citation

    Arellano-YanguasJ.2011. “Aggravating the Resource Curse: Decentralisation, Mining and Conflict in Peru.” The Journal of Development Studies47(4): 61738.

    • Search Google Scholar
    • Export Citation

    AzevedoJ. P.G.Inchauste andV.Sanfelice. 2013. “Decomposing the Recent Inequality Decline in Latin America.” Policy Research Working Paper 6315World BankWashington, DC.

    • Search Google Scholar
    • Export Citation

    AzevedoJ. P.G.InchausteS.OlivieriJ.Saavedra andV.Sanfelice. 2013. “Is Labor Income Responsible for Poverty Reduction? A Decomposition Approach.” Policy Research Working Paper 6414World BankWashington, DC.

    • Search Google Scholar
    • Export Citation

    AzevedoJ. P.J.Saavedra andH.Winkler. 2012. “When Job Earnings Are Behind Poverty Reduction.” Operational Studies Paper No. 97World BankWashington, DC.

    • Search Google Scholar
    • Export Citation

    BarrosR.M.De CarvalhoS.Franco andR.Mendonça. 2010. “Markets, the State and the Dynamics of Inequality in Brazil.” In Declining Inequality in Latin America: A Decade of Progress?Washington, DC: Brookings Institution and UNDP.

    • Search Google Scholar
    • Export Citation

    BenguriaF.F.Saffie andS.Urzua. 2017. “Commodity Shocks Firm-level Responses and Labor Market Dynamics.” Unpublished.

    Caselli. F. andG.Michaels. 2013. “Do Oil Windfalls Improve Living Standards? Evidence from Brazil.” American Economic Journal: Applied Economics51: 20838.

    • Search Google Scholar
    • Export Citation

    CavalcantiT.D.Da Mata andF.Toscani. 2016. “Winning the Oil Lottery: The Impact of Natural Resource Discoveries on Growth.” IMF Working Paper 16/61International Monetary FundWashington, DC.

    • Search Google Scholar
    • Export Citation

    CorniaG. andB.Martorano. 2013. “Development Policies and Income Inequality in Selected Developing Regions, 1980–2010.” Economic Working Paper. University of Florence, Department of Economics and Business SciencesFlorence.

    • Search Google Scholar
    • Export Citation

    CustJ. andS.Poelhekke. 2015. “The Local Economic Impacts of Natural Resource Extraction.” Annual Review of Resource Economics7(5): 21568.

    • Search Google Scholar
    • Export Citation

    de la TorreAugustoA.IzeG. R.Beylis andD.Lederman. 2015. “Jobs Wages and the Latin American Slowdown.” Washington, DC: World Bank Group.

    • Search Google Scholar
    • Export Citation

    de la TorreAugustoJ.Messina andS.Pienknagura. 2012. “The Labor Market Story Behind Latin America’s Transformation.” LAC Semiannual Report (October). World BankWashington, DC.

    • Search Google Scholar
    • Export Citation

    FeresJ. C. andX.Mancero. 2001. “The Unmet Basic Needs Method (NBI) and Its Applications in Latin America.” Economic Commission for Latin America and the Caribbean Santiago.

    • Search Google Scholar
    • Export Citation

    GóesC. andI.Karpowicz. 2017. “Inequality in Brazil: A Micro-Data Analysis.” Special Issues Paper. International Monetary FundWashington, DC.

    • Search Google Scholar
    • Export Citation

    GrussB.2014. “After the Boom – Commodity Prices and Economic Growth in Latin America and the Caribbean.” IMF Working Paper No. 14/154International Monetary FundWashington, DC.

    • Search Google Scholar
    • Export Citation

    HainmuellerJ. andY.Xu. 2013. “ebalance: A Stata Package for Entropy Balancing.” Journal of Statistical Software54(7).

    HanniM.R.Martner andA.Podesta. 2015. “The Redistributive Potential of Taxation in Latin America.” CEPAL Review116: 826.

    HardingT. andA. J.Venables. 2016. “The Implications of Natural Resource Exports for Nonresource Trade.” IMF Economic Review64 (2).

    • Search Google Scholar
    • Export Citation

    Inter-American Development Bank (IDB). 2015. Decentralizing Revenue in Latin America: Why and How. Washington, DC.

    International Monetary Fund (IMF). 2009. “Macro Policy Lessons for a Sound Design of Fiscal Decentralization.” Washington, DC.

    International Monetary Fund (IMF). 2014. “Fiscal Policy and Income Inequality.” IMF Fiscal Affairs Department. Washington, DC.

    International Monetary Fund (IMF). 2017. “Uruguay: Selected Issues.” Washington, DC.

    LoayzaN. andJ.Rigolini. 2016. “The Local Impact of Mining on Poverty and Inequality: Evidence from the Commodity Boom in Peru.” Peruvian Economic Association Working Paper No. 33.

    • Search Google Scholar
    • Export Citation

    López-CalvaL. andN.Lustig. 2010. Declining Inequality in Latin America: A Decade of Progress?Washington, DC: Brookings Institution Press.

    • Search Google Scholar
    • Export Citation

    LustigN.2012. “Taxes, Transfers, and Income Redistribution in Latin America.” Inequality in Focus1 (2).

    LustigN.L. F.López-Calva andE.Ortiz-Juarez. 2012. “Declining Inequality in Latin America in the 2000s: The Cases of Argentina, Brazil, and Mexico.” Working Paper No. 266 Society for the Study of Economic InequalityPalma de Mallorca.

    • Search Google Scholar
    • Export Citation

    LustigN.L. F.López-Calva andE.Ortiz-Juarez. 2013. “Deconstructing the Decline in Inequality in Latin America.” Policy Research Working Paper No. 6552World BankWashington, DC.

    • Search Google Scholar
    • Export Citation

    MessinaJ. andJ.Silva. 2018. Wage Inequality in Latin America: Understanding the Past to Prepare for the Future. Washington, DC: World Bank.

    • Search Google Scholar
    • Export Citation

    MichaelsG.2011. “The Long-Term Consequences of Resource-Based Specialization.” Economic Journal121 (551): 3157.

    Organisation for Economic Co-operation and Development (OECD). 2018. Revenue Statistics in Latin America and the Caribbean 2018. Paris: OECD Publishing.

    • Search Google Scholar
    • Export Citation

    PellandraA.2015. “The Commodity Price Boom and Regional Workers in Chile: A Natural Resources Blessing?” Unpublished.

    PerryG. andM.Olivera. 2009. “The Impact of Oil and Mining on Regional and Local Development in Colombia.” CAF Working Paper 2009/06CAF Development Bank of Latin AmericaCaracas.

    • Search Google Scholar
    • Export Citation

    SantosA. andA.Werner (eds). 2015. Peru: Staying the Course of Economic Success. Washington, DC: International Monetary Fund.

    ToscaniF.2017. “The Impact of Natural Resource Discoveries in Latin America and the Caribbean: A Closer Look at the Case of Bolivia.” IMF Working Paper No. 17/27International Monetary FundWashington, DC.

    • Search Google Scholar
    • Export Citation

    van der PloegF.2011. “Natural Resources: Curse or Blessing?Journal of Economic Literature49 (2): 366420.

    VargasJ.P.M.and S.Garriga. 2015. “Explaining Inequality and Poverty Reduction in Bolivia.” IMF Working Paper No. 15/265International Monetary FundWashington, DC.

    • Search Google Scholar
    • Export Citation

    VialeC.2015. “Distribution of Extractive Industries Income to Subnational Governments in Latin America: Comparative and Trend Analysis.” Pontifical Catholic University of PeruLima.

    • Search Google Scholar
    • Export Citation
Annex 9.1. The Local Impact of Natural Resource Booms in Latin America: Methodology

Brazil: The following equation is estimated to capture the local impact of the resource boom:

in which ∆ yi,2010 is the change in the dependent variable between 2000 and 2010 in municipality i and ∆xi,2010 is the change in the explanatory variable (natural resource production per capita measured in constant 2010 Brazilian reais) in municipality i. β is the coefficient of interest. The equation includes both the level of the dependent variable in 2000 (yi,2000) to capture convergence effects and the change in the dependent variable between the previous census rounds (1991 to 2000, ∆ y i,2000) to control for municipality-specific pretreatment trends. Additionally, the analysis includes state fixed effects θs to account for regional dynamics and a vector of geographic controls Zi that measure, for example, whether a municipality is located on the coast. Standard errors are clustered at the state level.

Bolivia: The following simple difference-in-differences regression model is estimated using data from the 2001 and 2012 population censuses:37

in which yit is the dependent variable, EMi is a dummy variable equal to 1 for extractive sector municipalities, Tt is a time dummy equal to 1 in 2012, and the interaction Dit=(EMi*Tt) is the treatment variable, so that ρ is the coefficient of interest. Xit is a vector of municipality and time-varying covariates. A differentiation is made between mineral producers—”small” oil and gas producers, and the natural gas megacampo producers.

Because data from before 2001 are not available for Bolivia, the parallel trend assumption or control for pretreatment trends in the estimation cannot be explicitly tested. To improve identification, the control group is limited to those municipalities that have the best covariate overlap with the treatment group. In other words, the aim is to compare extractive sector municipalities to municipalities that looked very similar to them before the resource boom. To do this, an entropy balancing technique is used (Hainmueller and Xu 2013). The method assigns weights between 0 and 1 to municipalities in the control group to achieve optimal covariance overlap and is well suited to the setup with many more control municipalities than treatment municipalities.38

Annex 9.2. Details of Natural Resource Revenue Sharing in Latin America and Elsewhere

Natural resource revenues are largely centralized in Chile, Ecuador, Mexico, Norway, Trinidad and Tobago, and Venezuela, with either very limited or no redistribution to subnational-level governments in producing regions. In the three case study countries and Colombia, significant revenue amounts go to subnational governments (see Viale 2015 for an overview). In Canada, provinces manage nonrenewable natural resources.

Bolivia: A hydrocarbon royalty of 18 percent of revenues is levied in Bolivia. Of those, 11 percentage points go to producing departments, 6 percentage points stay with the central government, and 1 percentage point goes to the lightly populated departments of Pando and Beni. Additionally, a 32 percent hydrocarbon tax (impuesto directo a los hidrocarburos, or IDH) is levied and is allocated in a more complicated way. It goes to both producing and nonproducing departments as well as to municipalities, with 20 percentage points remaining with the central government. Mining royalties are distributed only to producing departments and municipalities, with an 85–15 split between the two. For more details, see IDB (2015).

Brazil: Most mineral royalties go to producing states and municipalities. For oil and gas, the allocation formula is complicated, but since the 1997 royalties law, substantial amounts of oil and gas revenues have been distributed to municipalities that either host an onshore oil and gas field or face an offshore oil and gas field. In some cases, royalties can account for more than 50 percent of a municipality’s revenues.

Canada: In addition to being subject to federal and provincial corporate income taxes, natural resource income is subject to mining taxes, royalties, and land taxes at the provincial level. A fiscal stabilization program also enables the federal government to provide financial assistance to any province faced with a year-over-year decline in nonresource revenues greater than 5 percent and caused by an economic downturn. Finally, Canada has an equalization program to reduce fiscal disparities between provinces. The equalization transfers are unconditional and are determined by measuring provinces’ ability to raise revenues.

Colombia: Before the 2012 reform, roughly 80 percent of royalties went directly to producer departments and municipalities, which had only 17 percent of the population. Following the 2012 reform, this amount was reduced to roughly 10 percent, with the remainder of the resources assigned to several central funds with specific goals. About 30 percent is saved in a stabilization fund, 10 percent goes to a science and innovation fund, 10 percent goes to a regional pension fund, and the remainder is allocated to subnational investment projects using a relatively complex distribution formula based on poverty levels and other factors. As a result, 1,089 municipalities received a share of commodity royalties in 2012 compared with 522 in 2011.

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. Specifically, over time, government spending must not use any of the fund’s capital, only its expected real return, which is currently estimated to be 3 percent. The fiscal rule also provides for petroleum revenue spending to be increased during economic downturns and decreased during economic upturns.

Peru: Overall, about 60 percent of fiscal revenues from the mining sector go to subnational governments, mainly consisting of mining sector corporate income taxes (canon minero) and mining royalties. There are various canons and they are only transferred to the department where production of the natural resource takes place. Resources are then further distributed within producing departments; consequently, producing provinces and municipalities receive a large share of the pie. See Santos and Werner (2015, Chapter 10) for more details.

1The source for poverty and inequality data here is Institute of Applied Economic Research (IPEA). Several different measures of poverty exist. The one referred to here is based on the number of people who have income per capita that is insufficient to satisfy calorific necessities. The poverty line is defined by IPEA as twice the extreme poverty line, and calculations are based on household survey data (PNAD). The cross-country analysis presented later in this chapter uses internationally comparable poverty measures, most notably the share of people living on less than US$3.1 per day in purchasing-power-parity terms. Although the exact numbers differ depending on the poverty measure used, the trend is the same for all.
2In this chapter, the boom period is defined as the period 2000–14.
3Given data availability, 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 determined according to whether net commodity exports surpassed 10 percent of total exports plus imports at the time of the October 2015 World Economic Outlook. Brazil does not fulfill that criterion, but it has the largest estimated natural resource reserves in the region. Hence, the full list of commodity exporters is Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras, Paraguay, and Peru.
4This chapter examines income inequality (income Gini) rather than wealth inequality.
5To control for the initial level of poverty, the variable on the y-axis is the residual of a regression of the change in poverty on the initial poverty ratio.
6That poverty fell less in Chile than in other commodity exporters largely reflects Chile’s relatively low poverty rates before the boom: poverty in 2000 stood at 10.3 percent and fell to 2.6 percent by 2013.
7The mean reduction in poverty during the boom period was statistically significantly larger in commodity exporters than in non–commodity exporters. For inequality, the mean reduction is also larger, but the result is not statistically significant.
8The commodity terms of trade capture the income gain or loss a country experienced during the period owing to commodity price movements (Gruss 2014).
9Although Honduras is classified as a commodity exporter given its high net commodity exports, its commodity terms of trade declined because it exports nonextractive commodities and imports extractive ones whose prices increased by more. Consequently, commodity price changes led to a negative wealth effect for Honduras, and poverty fell significantly less than in most other Latin American countries.
10Because there is no statistical association for non–commodity exporters, the sample here includes only commodity exporters. The regression includes country fixed effects and lagged GDP per capita as a control variable.
11It is not possible to infer what happened to the income level of the top decile from these income-share regressions. Nonetheless, Figure 9.13 shows that real wages grew across all skill levels in commodity exporters, on average, during the boom, suggesting that in most countries the result in Table 9.1 reflects a relative rather than an absolute loss for the top decile.
12For example, in Bolivia nearly 40 percent of the population was below the poverty line in 2000.
13On the larger question of the long-term impact of natural resource abundance on GDP growth and development, there is no consensus. Van der Ploeg (2011), for example, shows that results supporting “the natural resource curse” are sensitive to sample periods and countries.
14Oil and gas production, for example, is substantially less labor intensive than agriculture but is more intensive in skilled labor.
15Allcott and Keniston (2018) demonstrate positive spillovers of the oil and gas sector to manufacturing in the United States. Michaels (2011) finds a similar positive result for the United States.
16Of course, public and private investment can also expand supply, not just demand.
18Note that the vast majority of households in Latin America outside the highest-income segments do not receive any capital income, so transfers and labor income account for the overwhelming share of their total income.
19Broadly speaking, a Shapley decomposition is a rigorous way to calculate how much any one factor contributed to changes in the income distribution. It isolates the contribution of one specific factor (for example, an increase in wages in the agricultural sector) by calculating a counterfactual distribution, holding all other factors constant. See Azevedo, Inchauste, and Sanfelice (2013) for more details.
20Official household survey data are used. For Bolivia, 2013 data are compared with 2007 data, while in Peru the comparison is between 2011 and 2007. For both countries, the official poverty lines are used to define poverty thresholds.
21A decline in manufacturing employment has been a phenomenon not only in commodity exporters (see Chapter 3 of the April 2018 World Economic Outlook).
22See Vargas and Garriga (2015) for more details on the Shapley decomposition for Bolivia.
23For example, in Peru skilled workers make up about a third of the poor, with many close to the national poverty line.
24Population census data are used because household survey data are generally not representative at the municipal level. Typically, such data are available only at one-decade intervals (2001 and 2012 for Bolivia; 2000 and 2010 for Brazil). Importantly, poverty measures from the Brazilian and Bolivian censuses are not directly comparable. Specifically, the Bolivian population census does not provide data on monetary income, so it is not possible to calculate inequality or a standard income-based poverty measure. To capture poverty, measures of access to basic necessities were used (sanitation, water, electricity, adequate living space, and so on). See Feres and Mancero (2001).
25The “hump-shape” in the Brazilian distribution mostly reflects large regional differences.
26To construct the natural resource producer dummy variable in Brazil, a municipality is denned as a producer if it produces more than the mean amount of natural resources per capita (this essentially captures larger producers as opposed to municipalities with only for example, very small-scale mining).
27So-called gas megacampos are the largest gas fields in Bolivia.
28For each country there is an additional category (onshore oil and gas production for Brazil and non-megacampo onshore oil and gas production for Bolivia) for which no impact is found (production is significantly smaller), so that category for each country is omitted from the discussion.
29All coefficients shown in Figures 5.21 and 5.22 are statistically significant. When a coefficient is not statistically significant, the corresponding bar chart is zero (for example, public employment in Brazilian mineral municipalities).
30From regressions with local budget data, fiscal windfalls mainly tend to increase capital expen-diture but also current expenditure, including wages.
31The effects are small for most municipalities—a one standard deviation increase in the value of mineral production per capita reduces the poverty rate by only 0.2 percentage point. For the big producers, however, the impact is economically significant, with an estimated reduction in poverty of between 3 and 9 percentage points for the top five producers.
34Colombia’s royalty-sharing arrangements are not fully integrated into the annual budget. A unified budget would be a preferable option for most countries.
35Latin American tax and transfer systems are substantially less progressive than such systems in Organisation for Economic Co-operation and Development countries (Lustig 2012; Hanni, Martner, and Podesta 2015; OECD 2018). Lustig (2012) finds that in some Latin American countries, the net income of the poor and near-poor can be lower than it was before taxes and cash transfers. In-kind transfers in education and health, however, are progressive throughout the region.
36Hanni, Martner, and Podesta (2015) find that although maximum legal personal income tax rates in Latin America range from 25 to 40 percent, the effective tax rates tend to be substantially lower, with the effective rate for the top decile only 5.4 percent, on average. Consequently, the redistributive impact of personal income taxes in Latin America is very limited, achieving a reduction of just 2 percent in income inequality, which contrasts markedly with the countries of the European Union, whose distribution improves more than 12 percent after income taxes (OECD 2018). IMF (2014) recommends progressive personal income taxes as an important tool for achieving fiscal redistribution.
37See Toscani (2017) for more details.
38Entropy balancing achieves virtually perfect overlap both for the first and the second moment of the distribution. Like the now-popular synthetic control method, however, entropy balancing implicitly makes a strong linearity assumption.

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