## 1. Composition of the Household Total Income per Capita (Selected Years)

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---

### Main findings and drivers of inequality and poverty reduction
- The reduction of inequality and poverty in Bolivia was driven mainly by labor income growth at the lower end of the income distribution.
- The contribution of non-labor income (rents, transfers, remittances) was important for certain groups but relatively small.
- Labor income increases were concentrated in the service and manufacturing sectors, and in the informal sector.
- These labor income changes reduced the skills premium.
- Pro-poor labor policies and transfers played a role:
  - Bono Juancito Pinto (school age children).
  - Renta Dignidad (elderly people) — "Renta Dignidad in particular has made a big difference for the elderly poor."
  - Bono Juana Azurduy (pregnant women and newborns).
- Looking ahead: "the skills premium could well increase as the spillovers to low skill workers from the commodity boom go into reverse." Given a tighter revenue envelope, "it will be essential to ensure that labor and social policies are well designed and targeted."

### Evolution of inequality and poverty — comparative and country-specific evidence
- Bolivia "has dramatically reduced inequality and poverty even compared to peers in Latin-American (LA)."
- Bolivia’s net and market Gini do not differ much, indicating limited redistributive impact from transfers and taxes.
- Under alternative inequality metrics, Bolivia’s average annual reduction during 2000–2012 surpassed that observed in other countries of the region; the biggest reduction occurs in the Rate 90/10 indicator.
- Regional poverty trends (using 2005 PPP):
  - Share living under $US PPP-2005 1.25/day: from 12.5 percent in 1987 to 4.6 percent in 2011 (regional aggregate).
  - Share living under $US PPP-2005 2.5/day: from 30.3 percent in 1987 to 13.2 percent in 2011 (regional aggregate).
- Bolivia-specific poverty changes:
  - $1.25/day poverty: from 17 percent in 1996 to 7 in 2011.
  - $2.5/day poverty: from 33.7 percent in 1996 to 16.2 percent in 2011.

### Macroeconomic context and potential drivers
- Economic growth
  - Bolivia sustained high economic growth during the past 15 years, with an average growth rate of real GDP of 4.2 percent, "1 percent point above for LAC."
  - Bolivia’s exports "grew 800 percent in nominal US$ dollar terms."
  - Export dependence and concentration:
    - Exports were 23 percent of GDP in 1990.
    - Exports were 18 percent of GDP in 2000.
    - Exports were 41 percent of GDP in 2010.
    - Exports peaked at 47 percent of GDP in 2012.
  - Overall, Bolivia’s real GDP grew "slightly more than 80 percent during 2000–2014," and "42 percent in real per capita PPP terms."
  - Investment dynamics:
    - "Total investment grew by 100 percent between 2000 and 2013."
    - Share of public investment as percent of total investment increased from 30 percent to 56 percent between 2000 and 2013.
  - Public investment increases may have contributed to pro-poor impacts via infrastructure (roads, electricity supply, and sanitation).
- Exchange rate policy
  - Bolivia followed a de jure crawling peg exchange rate regime during the sample period, with sustained nominal depreciations between 2000 and 2005, and nominal appreciations between 2006 and (text ends).

### Real exchange rate and trade structure (2011–recent)
- Bolivia has maintained a de facto US$ peg during the last 4 years, which has helped further de-dollarization of the economy and maintain price stability.
- The real effective exchange rate (REER) has appreciated strongly during the last couple of years.
- Potential impacts of real appreciation:
  - Could discourage employment in export-oriented firms, increasing poverty and inequality.
  - Could lower import prices, reducing inflationary pressures and, depending on consumption composition, favor more vulnerable groups.
  - The import sector could benefit from higher economic activity.
- Bolivia’s trade structure implications:
  - A real depreciation would favor at most 20 percent of exports (those concentrated in nontraditional products).
  - A depreciation would negatively affect imports which are intensive in labor.
  - These elements may support a positive effect of the recent real appreciation on the reduction of inequality and poverty.
- REER indices shown: RER with Argentina, RER with Brazil, Multilateral REER (Index, Jan 2010=100).

### Remittances
- Remittances trends:
  - During the first half of 2000–2014, remittances showed an upward trend; in 2007 they were almost 8 percent of GDP.
  - Trend stabilized in U.S. dollar terms after 2008, and reversed as a percent of GDP, representing around 3½ percent of GDP in 2014.
- Drivers of decline in remittances as percent of GDP:
  - Lingering effects of the financial crisis affecting salaries of Bolivian immigrants in Spain and United States.
  - Lower growth rate in Argentina.
  - Lower levels of immigration given relatively favorable economic conditions in Bolivia.
- Figure 9 indicators (as presented): Migrant remittance inflows (US$ million); Remittances as percent of GDP (right axis).

### Active social and economic policies (redistribution)
- Policies affecting labor and non-labor income include:
  - i) Material increases of the minimum wage and capped maximum salaries in the public sector from 2006 onwards (maximum salaries capped from 2006 to 2012).
  - ii) Implementation of unconditional and conditional transfers to vulnerable sectors (elderly people, primary school kids, and pregnant women and newborns).

### Salaries and labor income
- Minimum wage:
  - The minimum wage in Bolivia grew by 122 percent in real terms from 2000 to 2015.
  - The change during 2010–2015 accounts for 100 pp. of the overall increase.
  - Bolivia still has the lowest minimum wage in South America (US$240 per month).
  - Coverage caveat: minimum wage directly affects only workers in the formal sector, which represent about ¼ of the workforce.
  - Informal sector “lighthouse effect” may provide higher reference salaries in the informal sector.
- Labor income dynamics:
  - Average labor income increased by 36 percent from 2000–2013.
  - Sectoral differences: Primary activities, commerce and construction saw large salary increases; public administration and skilled services workers (those with tertiary education) experienced real income declines.
  - Decline in skilled-services incomes may have helped reduce inequality since those salaries are higher than in other groups.
- Real labor income indices and sectoral patterns illustrated in Figure 11 and Figure 20.

### Transfers: Renta Dignidad and CCTs
- Major transfers introduced since 2006:
  - One unconditional: Renta Dignidad (monthly non-contributory social security program for all people above 60 years), created in 2008 (successor to Bono Solidario).
    - Monthly payment (current at time of source): Bs.250 (US$35.9) for persons not receiving contributory pensions, and Bs.200 (US$28.7) for recipients of contributory pensions.
    - Amount flat until 2012; increased by 25 percent in 2013; in 2014 a 13th month payment was added (Christmas bonus “Aguinaldo”).
    - Expenditure on Renta Dignidad is larger than one percent of GDP; number of beneficiaries is above 8 percent of total population.
  - Two conditional cash transfers (CCTs):
    - Bono Juancito Pinto: implemented in 2006 for families with kids in primary school (1st to 6th grades), extended progressively to cover all secondary school students by 2014; paid at the end of each year conditional on not dropping out.
      - Largest population of beneficiaries; spending close to 0.2 percent of GDP; per capita income around US$30 a year.
    - Bono Juana Azurduy: for expectant and new mothers and their infants; started in 2009.
      - Least important in spending terms (0.06 percent of GDP per year in 2014) and beneficiaries (1.6 percent of total population).
- Distributional and developmental roles:
  - Renta Dignidad likely effective in increasing income for particular groups of people (elderly).
  - Bono Juancito Pinto and Bono Juana Azurduy important for improving health and education, which ultimately should reduce poverty and inequality.
- Transfer data visualizations: Figures 12–14 show beneficiaries, transfer amounts (millions of US$), transfer per beneficiary (avg. in US$), and transfer as percent of GDP.

### Explaining inequality and poverty from micro data
- Data comparability:
  - SEDLAC (World Bank) harmonizes household surveys; comparisons between official and SEDLAC data show only marginal differences that do not affect the trend or magnitude of inequality and poverty reduction in Bolivia.
- Trends:
  - Gini coefficient dropped steadily from 2000 to 2013, with more rapid decline during 2007–11.
  - Poverty estimates show constant reduction in number of poor people during most years, with accelerated progress during 2007–11.
  - Major reductions occurred in rural areas: extreme poverty decreased from 75 to 41 percent, and moderate poverty fell from 87 to 61 percent between 2000–2012 (official estimates; Table A1 referenced).
- Income composition and distribution:
  - Non-labor income represents at most around 20 percent of total income on average (Table 1).
  - Government transfers to households doubled since 2006 as a share of income.
  - The average household income per capita of the top 15 percent increased just slightly between 2001 and 2006 and was flat during 2006–2013.
  - Table 1: Composition of Household Total Income per Capita (selected years) shows exact shares for Labor and Non-labor components and subcomponents (Returns of Capital; Transfers from Government; Transfers between households).
- Impact of Renta Dignidad (Table 2 and discussion):
  - For households with at least one member aged 60+ years in 2013:
    - P25: Labor Income 183; Non Labor income 77; Non Labor Income: Renta Dignidad (percent of total income) 24.4%
    - P50: Labor Income 329; Non Labor income 136; Renta Dignidad 15.4%
    - P75: Labor Income 416; Non Labor income 370; Renta Dignidad 9.2%
  - For all population percentiles in 2013, Renta Dignidad shares are 3.8% (P25), 2.2% (P50), 1.2% (P75).
  - Renta Dignidad represents 24 percent of total income for the 25th percentile, 15 percent for 50th percentile and around 10 percent for 75th percentile among elderly people (textual statement).
  - UDAPE (2013) cited finding: Renta Dignidad reduced the poverty incidence in a range of 13.5 to 16.1 percent (according to the poverty line threshold) in the target population.
- Disaggregated real income stylized facts:
  - i) Labor income of skilled people decreased in real terms between 2001 and 2013, especially in the formal sector.
  - ii) Labor income increased for most low-skilled workers, mainly in the informal sector.
  - iii) Labor income increased in all urban areas, but increases were more limited in the Departments of La Paz and Cochabamba.
  - iv) In rural areas, workers in Oruro, Potosi and Santa Cruz saw important gains.
  - v) The youngest (15–25 years) and older workers (51–60 years) experienced the most rapid increases in labor income.
  - vi) Manufacturing and services sectors gained most, although increases were concentrated in the informal sector.
- Growth in number of people earning greater than zero non-labor income (Table 3):
  - Growth rates by age group:
    - 0–14 years: 2001–2006 = 14%; 2006–2013 = 330%; 2001–2013 = 392%
    - +60 years: 2001–2006 = 33%; 2006–2013 = 264%; 2001–2013 = 385%
    - The number of people receiving a positive non-labor income increased by 150 percent between 2001 and 2013, compared to a 50 percent increase in the number of people earning labor income during the same period.
- Figures 20–21 present indices (base year 2001=100) of real labor and non-labor income per capita disaggregated by education, formal/informal sector, department (urban/rural), age, gender, and activity.

### Inequality and poverty decompositions
- Datt-Ravallion decomposition (Figure 22) of poverty changes:
  - Results suggest overall growth of total income drove the reduction in poverty more than changes in the distribution (inequality).
  - Between 2007 and 2013, 83 percent of the large reduction in moderate poverty is explained by growth; 70 percent of the reduction in extreme poverty is explained by growth.
  - Changes in the distribution (inequality) actually increased poverty levels between 2001 and 2006.
- Shapley-Shorrocks decompositions (Azevedo et al., 2013 method) used to attribute changes in inequality (gini) and poverty (moderate and extreme, national poverty lines) to income components (e.g., non-labor income, formal labor, informal labor).
- Key decomposition insights for 2007–2013:
  - By education level (Figure 23): Changes in the distribution of skilled and low-skilled workers were main determinants of decreased poverty and inequality.
    - Skilled workers experienced reduction in real salaries (reducing inequality) but remained well above poverty lines.
    - Low-skilled workers increased salaries mainly in the informal sector (reducing inequality and poverty).
    - Contribution of non-labor income to reducing inequality and poverty was relatively minor but positive.
  - By formal/informal status (Figure 24): Reduction of inequality and poverty was mainly driven by changes in the distribution of formal workers rather than informal workers.
    - Interpretation caveat: formal high-skilled workers experienced decreases in real labor income, while formal low-skilled salaries increased in real terms between 2001–[text truncated at source].
- Methodological notes:
  - Shapley-Shorrocks decomposition averages contributions across all possible orders of replacing components to avoid path dependence.
  - Datt-Ravallion redistribution component is insensitive to changes in the distribution above the poverty line.
  - Caveats on Shapley decompositions noted (Sastre and Trannoy, 2002).

### Findings on labor income, education, and poverty/inequality (2013 summary)
- High-skilled workers in the informal sector reduced their mean income per capita while low-skilled workers increased theirs.
- Since salaries are higher in the formal sector, it should be more likely for a worker to get out of poverty by working in this sector rather than in the informal one; this hypothesis requires further research.
- Labor income played the primary role in reductions in inequality and poverty; non-labor income had a more limited role.
- Inequality reduced by 0.10 percentage points between 2007–2013, of which 0.01 p.p. was associated with changes in non-labor income.
- Extreme (moderate) poverty reduced by 19 (21) percentage points between 2007–2013, of which 17.4 (20.4) percentage points are explained by changes in labor income; just 1.5 (0.6) percentage points are due to non-labor income changes.

### Selected tables and econometric evidence
- Table 4 — Real labor income per capita (selected observations):
  - Mean of monthly real labor income grew by around 5 percent between 2001–2006, and almost 25 percent between 2006–2013.
  - Formal sector (selected education groups, Mean Labor Income (Bs.), Share of workers (percent), Labor Income growth rate with respect to 2001 (percent)):
    - No education: 391, 0, 48, 0, 109, 0
    - Educ 1–3 years: 1873, 2, -49, 1, -12, 2
    - Educ 4–6 years: 1110, 7, 16, 5, 38, 4
    - Educ 7–9 years: 1147, 6, 5, 5, 52, 3
    - Educ 10–12 years: 1338, 20, 11, 19, 17, 17
    - Educ 13–15 years: 1966, 24, 1, 16, -14, 17
    - Educ More than 16: 2870, 40, 0, 53, -24, 57
  - Informal sector (selected education groups, Mean Labor Income (Bs.), Share of workers (percent), Labor Income growth rate with respect to 2001 (percent)):
    - No education: 395, 11, 17, 7, 7, 8
    - Educ 1–3 years: 565, 17, 5, 11, 19, 13
    - Educ 4–6 years: 711, 25, 16, 20, 44, 20
    - Educ 7–9 years: 701, 15, 5, 15, 77, 11
    - Educ 10–12 years: 868, 21, -1, 31, 49, 29
    - Educ 13–15 years: 1166, 6, -6, 7, -3, 11
    - Educ More than 16: 2157, 5, -6, 10, -23, 8
  - Source: IMF staff calculations based on INE Household Surveys.
- Wage equations (Mincer) — main results (Table 5 and Table A2 highlights):
  - Extended Mincer equation estimated for 2001, 2006 and 2013; constants and coefficients normalized to benchmark average worker (average urban worker in Chuquisaca, no formal education, 15–20 years old, working in agriculture & mining in the formal sector).
  - Regional & sectoral highlights (selected coefficients and proportion of workers in 2013):
    - La Paz: coefficients around 0.02** (2001 OLS), -0.044*** (2006 OLS), 0.018*** (2013 OLS); Proportion of workers in 2013: 26.7
    - Cochabamba: 0.047*** (2001), 0.010 (2006), 0.062*** (2013); Proportion 17.5
    - Tarija: 0.098*** (2001), 0.032 (2006), 0.106*** (2013); Proportion 4.9
    - Santa Cruz: 0.091*** (2001), 0.041*** (2006), 0.11*** (2013); Proportion 27.0
    - Pando: 0.058** (2001), 0.09** (2006), 0.13*** (2013); Proportion 1.1
    - Rural: -0.064*** (2001), -0.032*** (2006), -0.057*** (2013); Proportion 32.6
    - Education coefficients: Educ More than 16: 0.252*** (2001), 0.19*** (2006), 0.165*** (2013); Proportion 12.8
    - Experience: 0.011*** (2001 OLS), 0.005** (2006), 0.005*** (2013)
    - Manufacturing: 0.019* (2001 OLS), 0.082*** (2006); Proportion 16.9
    - Services: 0.012 (2001 OLS), 0.048*** (2006); Proportion 48.2
    - Informal: -0.103*** (2001), -0.107*** (2006), -0.088*** (2013)
    - Adjusted R-squared: 0.29 (2001), 0.29 (2006), 0.33 (2013)
    - Mean of Monthly Real Labor Income (Bs.): 974 (2001), 1048 (2006), 1310 (2013)
  - Workers in the informal sector have a lower labor income than those in the formal sector, but the gap reduced during 2001–2013 in a statistically significant way.
  - Ratio of formal to informal salaries was around 1.8 in 2001 and reduced to 1.6 in 2013 (Table A2 coefficients and appendix discussion).

### Conclusions and policy implications
- Bolivia registered large reductions in inequality and poverty since 2000, but levels remain around or above the average for LA and significantly above AEs.
- Key macro and policy facts:
  - GDP increased by 80 percent in real terms during 2000–2014.
  - The real minimum wage increased by 100 percent during 2000–2014.
  - Average real labor income rose by 40 percent during 2000–2014.
  - Remittances peaked at around 8 percent of GDP in 2007; as of 2014, they were around 3.5 percent of GDP.
  - The three main redistributive tools (Renta Dignidad, Bono Juancito Pinto and Bono Juana Azurduy) reached around 30 percent of the population, corresponding to 1½ percent of GDP of cash transfers in 2014.
- Main drivers of inequality and poverty reduction:
  - Changes in labor income more important than non-labor income.
  - Low-skilled workers benefited more from higher labor income earnings, with gains concentrated in the informal sector.
  - Skilled workers, mainly in the formal sector, faced lower real salaries, reducing the skills premium.
  - Manufacturing and services sectors registered higher labor income growth, although much of it occurred in the informal sector.
  - Departments with strongest labor income growth: Beni, Santa Cruz and Tarija.
- Non-labor income contribution:
  - Positive but small relative to labor income; important for specific target populations (elderly via Renta Dignidad; youngest via Bono Juana Azurduy and Bono Juancito Pinto).
  - The share of government transfers in household income doubled since 2006.
- Fiscal and policy outlook:
  - The skills premium could increase if commodity-boom spillovers to low-skill workers reverse.
  - With a tighter revenue envelope ahead, it will be essential to ensure labor and social policies are well designed and targeted.

### Appendix — Description of inequality indicators and supporting figures/tables
- Theil Index (TT):
  - TT is described as a statistic used to measure economic inequality; same as redundancy in information theory; special case of generalized entropy index.
  - Boundary cases: TT = 0 if everyone has the same income; TT attains maximum if one person has all the income.
  - Notation: yi is individual income (i=1..N) and ȳ is the mean income.
- The Atkinson index:
  - Developed by Anthony Barnes Atkinson; imposes a coefficient ε ("inequality aversion") to weight incomes.
  - For ε =0 the Atkinson index A(ε) = 0. For ε→∞ A(ε)=1. A(ε) varies between 0 and 1.
  - Notation: yi is individual income (i=1..N) and ȳ is the mean income.
- Figures A1 and A2: Bolivia income distributions (Monthly Real Labor Income per capita (Bs.) and Monthly Real Non-Labor Income per capita (Bs.)) by education, formal/informal sector, department (urban/rural), age, gender, and activity; axes and panel ticks include values explicitly shown in the figures.
- Table A1. Poverty Headcount Ratio (SEDLAC harmonized income estimations and UDAPE official estimations) — raw yearly entries and official estimations are reported exactly as presented in the source (see source content for sequences).
- Table A2. Econometric Model – (Mincer Equation for 2001, 2006, and 2013):
  - OLS and Heckman specifications; LHS is log of real labor income (CPI base year 2000=100).
  - Selected coefficients and statistics preserved exactly in source; significance notation: *** p<0.01, ** p<0.05, * p<0.1.
  - Adjusted R-squared: 0.29 (2001), 0.29 (2006), 0.33 (2013).
  - Mean of Monthly Real Labor Income (Bs.): 974 (2001), 1048 (2006), 1310 (2013).

*Source: IMF staff working paper chapter titled "1. Composition of the Household Total Income per Capita (Selected Years)" from the provided PDF content._*

### 1. Composition of the Household Total Income per Capita (Selected Years) .........................15

### 1. Composition of the Household Total Income per Capita (Selected Years)

### Main findings and drivers of inequality and poverty reduction
- The reduction of inequality and poverty in Bolivia was driven mainly by labor income growth at the lower end of the income distribution.
- The contribution of non-labor income (rents, transfers, remittances) was important for certain groups but relatively small.
- Labor income increases were concentrated in the service and manufacturing sectors, and in the informal sector.
- These labor income changes reduced the skills premium.
- Pro-poor labor policies and transfers played a role:
  - Bono Juancito Pinto (school age children)
  - Renta Dignidad (elderly people) — "Renta Dignidad in particular has made a big difference for the elderly poor."
  - Bono Juana Azurduy (pregnant women and newborns)
- Looking ahead: "the skills premium could well increase as the spillovers to low skill workers from the commodity boom go into reverse." Given a tighter revenue envelope, "it will be essential to ensure that labor and social policies are well designed and targeted."

### Evolution of inequality and poverty — comparative and country-specific evidence
- Bolivia "has dramatically reduced inequality and poverty even compared to peers in Latin-American (LA)."
- Bolivia’s net and market Gini do not differ much, indicating limited redistributive impact from transfers and taxes.
- Under alternative inequality metrics, Bolivia’s average annual reduction during 2000–2012 surpassed that observed in other countries of the region; the biggest reduction occurs in the Rate 90/10 indicator.
- Regional poverty trends (using 2005 PPP):
  - Share living under $US PPP-2005 1.25/day: from 12.5 percent in 1987 to 4.6 percent in 2011 (regional aggregate).
  - Share living under $US PPP-2005 2.5/day: from 30.3 percent in 1987 to 13.2 percent in 2011 (regional aggregate).
- Bolivia-specific poverty changes:
  - $1.25/day poverty: from 17 percent in 1996 to 7 in 2011.
  - $2.5/day poverty: from 33.7 percent in 1996 to 16.2 percent in 2011.

### Macroeconomic context and potential drivers
- Economic growth
  - Bolivia sustained high economic growth during the past 15 years, with an average growth rate of real GDP of 4.2 percent, "1 percent point above for LAC."
  - Bolivia’s exports "grew 800 percent in nominal US$ dollar terms."
  - Export dependence and concentration:
    - Exports were 23 percent of GDP in 1990.
    - Exports were 18 percent of GDP in 2000.
    - Exports were 41 percent of GDP in 2010.
    - Exports peaked at 47 percent of GDP in 2012.
  - Overall, Bolivia’s real GDP grew "slightly more than 80 percent during 2000–2014," and "42 percent in real per capita PPP terms."
  - Investment dynamics:
    - "Total investment grew by 100 percent between 2000 and 2013."
    - Share of public investment as percent of total investment increased from 30 percent to 56 percent between 2000 and 2013.
  - Public investment increases may have contributed to pro-poor impacts via infrastructure (roads, electricity supply, and sanitation).
- Exchange rate policy
  - Bolivia followed a de jure crawling peg exchange rate regime during the sample period, with sustained nominal depreciations between 2000 and 2005, and nominal appreciations between 2006 and (text ends).

### Additional empirical patterns highlighted
- Cross-country comparison: similar growth rates do not guarantee inequality reduction — example: Peru had the highest economic growth but inequality barely fell.
- A reduction in the wage gap (ratio of wages of the highly-educated to lowly-educated) is associated with a reduction in the Gini coefficient, with country-specific nuances:
  - Bolivia reduced inequality strongly within high-skilled workers, and moderately in low and mid-skill groups.
  - Ecuador reduced its Gini more evenly across education subgroups.

*Source: IMF staff working paper chapter titled "1. Composition of the Household Total Income per Capita (Selected Years)" from the provided PDF content.*

### 2011. During the last 4 years, however, it

### _wp15265 - 2011. During the last 4 years, however, it

### Real exchange rate and trade structure
- Bolivia has maintained a de facto US$ peg during the last 4 years, which has helped further de-dollarization of the economy and maintain price stability.
- The real effective exchange rate (REER) has appreciated strongly during the last couple of years.
- Potential impacts of real appreciation:
  - Could discourage employment in export-oriented firms, increasing poverty and inequality.
  - Could lower import prices, reducing inflationary pressures and, depending on consumption composition, favor more vulnerable groups.
  - The import sector could benefit from higher economic activity.
- Bolivia’s trade structure implications:
  - A real depreciation would favor at most 20 percent of exports (those concentrated in nontraditional products).
  - A depreciation would negatively affect imports which are intensive in labor.
  - These elements may support a positive effect of the recent real appreciation on the reduction of inequality and poverty.
- REER indices shown: RER with Argentina, RER with Brazil, Multilateral REER (Index, Jan 2010=100).

### Remittances
- Remittances trends:
  - During the first half of 2000–2014, remittances showed an upward trend; in 2007 they were almost 8 percent of GDP.
  - Trend stabilized in U.S. dollar terms after 2008, and reversed as a percent of GDP, representing around 3½ percent of GDP in 2014.
- Drivers of decline in remittances as percent of GDP:
  - Lingering effects of the financial crisis affecting salaries of Bolivian immigrants in Spain and United States.
  - Lower growth rate in Argentina.
  - Lower levels of immigration given relatively favorable economic conditions in Bolivia.
- Figure 9 indicators (as presented): Migrant remittance inflows (US$ million); Remittances as percent of GDP (right axis).

### Active social and economic policies (redistribution)
- Policies affecting labor and non-labor income include:
  - i) Material increases of the minimum wage and capped maximum salaries in the public sector from 2006 onwards (maximum salaries capped from 2006 to 2012).
  - ii) Implementation of unconditional and conditional transfers to vulnerable sectors (elderly people, primary school kids, and pregnant women and newborns).

### Salaries and labor income
- Minimum wage:
  - The minimum wage in Bolivia grew by 122 percent in real terms from 2000 to 2015.
  - The change during 2010–2015 accounts for 100 pp. of the overall increase.
  - Bolivia still has the lowest minimum wage in South America (US$240 per month).
  - Coverage caveat: minimum wage directly affects only workers in the formal sector, which represent about ¼ of the workforce.
  - Informal sector “lighthouse effect” may provide higher reference salaries in the informal sector.
- Labor income dynamics:
  - Average labor income increased by 36 percent from 2000–2013.
  - Sectoral differences: Primary activities, commerce and construction saw large salary increases; public administration and skilled services workers (those with tertiary education) experienced real income declines.
  - Decline in skilled-services incomes may have helped reduce inequality since those salaries are higher than in other groups.
- Real labor income indices and sectoral patterns illustrated in Figure 11 and Figure 20.

### Transfers: Renta Dignidad and CCTs
- Major transfers introduced since 2006:
  - One unconditional: Renta Dignidad (monthly non-contributory social security program for all people above 60 years), created in 2008 (successor to Bono Solidario).
    - Monthly payment (current at time of source): Bs.250 (US$35.9) for persons not receiving contributory pensions, and Bs.200 (US$28.7) for recipients of contributory pensions.
    - Amount flat until 2012; increased by 25 percent in 2013; in 2014 a 13th month payment was added (Christmas bonus “Aguinaldo”).
    - Expenditure on Renta Dignidad is larger than one percent of GDP; number of beneficiaries is above 8 percent of total population.
  - Two conditional cash transfers (CCTs):
    - Bono Juancito Pinto: implemented in 2006 for families with kids in primary school (1st to 6th grades), extended progressively to cover all secondary school students by 2014; paid at the end of each year conditional on not dropping out.
      - Largest population of beneficiaries; spending close to 0.2 percent of GDP; per capita income around US$30 a year.
    - Bono Juana Azurduy: for expectant and new mothers and their infants; started in 2009.
      - Least important in spending terms (0.06 percent of GDP per year in 2014) and beneficiaries (1.6 percent of total population).
- Distributional and developmental roles:
  - Renta Dignidad likely effective in increasing income for particular groups of people (elderly).
  - Bono Juancito Pinto and Bono Juana Azurduy important for improving health and education, which ultimately should reduce poverty and inequality.
- Transfer data visualizations: Figures 12–14 show beneficiaries, transfer amounts (millions of US$), transfer per beneficiary (avg. in US$), and transfer as percent of GDP.

### Explaining inequality and poverty from micro data
- Data comparability:
  - SEDLAC (World Bank) harmonizes household surveys; comparisons between official and SEDLAC data show only marginal differences that do not affect the trend or magnitude of inequality and poverty reduction in Bolivia.
- Trends:
  - Gini coefficient dropped steadily from 2000 to 2013, with more rapid decline during 2007–11.
  - Poverty estimates show constant reduction in number of poor people during most years, with accelerated progress during 2007–11.
  - Major reductions occurred in rural areas: extreme poverty decreased from 75 to 41 percent, and moderate poverty fell from 87 to 61 percent between 2000–2012 (official estimates; Table A1 referenced).
- Income composition and distribution:
  - Non-labor income represents at most around 20 percent of total income on average (Table 1).
  - Government transfers to households doubled since 2006 as a share of income.
  - The average household income per capita of the top 15 percent increased just slightly between 2001 and 2006 and was flat during 2006–2013.
  - Table 1: Composition of Household Total Income per Capita (selected years) shows exact shares for Labor and Non-labor components and subcomponents (Returns of Capital; Transfers from Government; Transfers between households).
- Impact of Renta Dignidad (Table 2 and discussion):
  - For households with at least one member aged 60+ years in 2013:
    - P25: Labor Income 183; Non Labor income 77; Non Labor Income: Renta Dignidad (percent of total income) 24.4%
    - P50: Labor Income 329; Non Labor income 136; Renta Dignidad 15.4%
    - P75: Labor Income 416; Non Labor income 370; Renta Dignidad 9.2%
  - For all population percentiles in 2013, Renta Dignidad shares are 3.8% (P25), 2.2% (P50), 1.2% (P75).
  - Renta Dignidad represents 24 percent of total income for the 25th percentile, 15 percent for 50th percentile and around 10 percent for 75th percentile among elderly people (textual statement).
  - UDAPE (2013) cited finding: Renta Dignidad reduced the poverty incidence in a range of 13.5 to 16.1 percent (according to the poverty line threshold) in the target population (noting this as presented in the source).
- Disaggregated real income stylized facts:
  - i) Labor income of skilled people decreased in real terms between 2001 and 2013, especially in the formal sector.
  - ii) Labor income increased for most low-skilled workers, mainly in the informal sector.
  - iii) Labor income increased in all urban areas, but increases were more limited in the Departments of La Paz and Cochabamba.
  - iv) In rural areas, workers in Oruro, Potosi and Santa Cruz saw important gains.
  - v) The youngest (15–25 years) and older workers (51–60 years) experienced the most rapid increases in labor income.
  - vi) Manufacturing and services sectors gained most, although increases were concentrated in the informal sector.
- Growth in number of people earning greater than zero non-labor income (Table 3):
  - Growth rates by age group:
    - 0–14 years: 2001–2006 = 14%; 2006–2013 = 330%; 2001–2013 = 392%
    - +60 years: 2001–2006 = 33%; 2006–2013 = 264%; 2001–2013 = 385%
    - (Other age-group rates shown in Table 3 exactly as provided.)
  - The number of people receiving a positive non-labor income increased by 150 percent between 2001 and 2013, compared to a 50 percent increase in the number of people earning labor income during the same period.
- Figures 20–21 present indices (base year 2001=100) of real labor and non-labor income per capita disaggregated by education, formal/informal sector, department (urban/rural), age, gender, and activity.

### Inequality and poverty decompositions
- Datt-Ravallion decomposition (Figure 22) of poverty changes:
  - Results suggest overall growth of total income drove the reduction in poverty more than changes in the distribution (inequality).
  - Between 2007 and 2013, 83 percent of the large reduction in moderate poverty is explained by growth; 70 percent of the reduction in extreme poverty is explained by growth.
  - Changes in the distribution (inequality) actually increased poverty levels between 2001 and 2006.
- Shapley-Shorrocks decompositions (Azevedo et al., 2013 method) used to attribute changes in inequality (gini) and poverty (moderate and extreme, national poverty lines) to income components (e.g., non-labor income, formal labor, informal labor).
- Key decomposition insights for 2007–2013:
  - By education level (Figure 23): Changes in the distribution of skilled and low-skilled workers were main determinants of decreased poverty and inequality.
    - Skilled workers experienced reduction in real salaries (reducing inequality) but remained well above poverty lines.
    - Low-skilled workers increased salaries mainly in the informal sector (reducing inequality and poverty).
    - Contribution of non-labor income to reducing inequality and poverty was relatively minor but positive.
  - By formal/informal status (Figure 24): Reduction of inequality and poverty was mainly driven by changes in the distribution of formal workers rather than informal workers.
    - Interpretation caveat: formal high-skilled workers experienced decreases in real labor income, while formal low-skilled salaries increased in real terms between 2001–[text truncated at source].
- Methodological notes:
  - Shapley-Shorrocks decomposition averages contributions across all possible orders of replacing components to avoid path dependence.
  - Datt-Ravallion redistribution component is insensitive to changes in the distribution above the poverty line.
  - Caveats on Shapley decompositions noted (Sastre and Trannoy, 2002) in the source.

_Italic: Source: _wp15265 - 2011. During the last 4 years, however, it (PDF chapter/section)._

### 2013. Second, a similar pattern happened in the informal sector with high-skilled workers

### _wp15265 - 2013. Second, a similar pattern happened in the informal sector with high-skilled workers

### Findings on labor income, education, and poverty/inequality
- High-skilled workers in the informal sector reduced their mean income per capita while low-skilled workers increased theirs (see Table 4).
- Since salaries are higher in the formal sector, it should be more likely for a worker to get out of poverty by working in this sector rather than in the informal one; this hypothesis requires further research.
- Labor income played the primary role in reductions in inequality and poverty; non-labor income had a more limited role.
- Inequality reduced by 0.10 percentage points between 2007–2013, of which 0.01 p.p. was associated with changes in non-labor income.
- Extreme (moderate) poverty reduced by 19 (21) percentage points between 2007–2013, of which 17.4 (20.4) percentage points are explained by changes in labor income; just 1.5 (0.6) percentage points are due to non-labor income changes.

### Table 4 — Real labor income per capita (selected observations)
- Mean of monthly real labor income grew by around 5 percent between 2001–2006, and almost 25 percent between 2006–2013.
- Formal sector (selected education groups, Mean Labor Income (Bs.), Share of workers (percent), Labor Income growth rate with respect to 2001 (percent)):
  - No education: 391, 0, 48, 0, 109, 0
  - Educ 1–3 years: 1873, 2, -49, 1, -12, 2
  - Educ 4–6 years: 1110, 7, 16, 5, 38, 4
  - Educ 7–9 years: 1147, 6, 5, 5, 52, 3
  - Educ 10–12 years: 1338, 20, 11, 19, 17, 17
  - Educ 13–15 years: 1966, 24, 1, 16, -14, 17
  - Educ More than 16: 2870, 40, 0, 53, -24, 57
- Informal sector (selected education groups, Mean Labor Income (Bs.), Share of workers (percent), Labor Income growth rate with respect to 2001 (percent)):
  - No education: 395, 11, 17, 7, 7, 8
  - Educ 1–3 years: 565, 17, 5, 11, 19, 13
  - Educ 4–6 years: 711, 25, 16, 20, 44, 20
  - Educ 7–9 years: 701, 15, 5, 15, 77, 11
  - Educ 10–12 years: 868, 21, -1, 31, 49, 29
  - Educ 13–15 years: 1166, 6, -6, 7, -3, 11
  - Educ More than 16: 2157, 5, -6, 10, -23, 8
- Source: IMF staff calculations based on INE Household Surveys.

### Decomposition by labor condition and activity sector
- Figure 24 decomposition highlights contributions by labor condition: non-labor income, labor no formal/informal category, labor formal, labor informal.
- Figure 25 decomposition (2007–2013) by activity sector shows:
  - Services and manufacturing explain most of the reduction in inequality and poverty; together these two sectors represent 3/4 of the whole workforce.
  - Services, Manufacturing, Agric. & Mining, and Non-labor income are the components considered.
- A strong currency and cheaper imports facilitating commerce and service sector activity could be an important explanation for the sectoral shifts.

### Minimum wage and lighthouse effect
- The minimum wage had a similar broad impact on both the formal and informal sectors, supporting the existence of a lighthouse effect (see section III).
- Labor income grew materially during 2001–2013 at most educational levels in the informal sector.
- In both informal and formal sectors, real incomes fell significantly for the most educated group (see Table 4).

### Wage equations (Mincer) — main results
- An extended Mincer equation was estimated for 2001, 2006 and 2013 to identify individual, regional and productive characteristics behind labor income.
- The constant and coefficients are normalized to reflect variations from the benchmark average worker (average urban worker in Chuquisaca, no formal education, 15–20 years old, working in agriculture & mining in the formal sector).
- Regional & sectoral highlights from Table 5 (selected coefficients and proportion of workers in 2013):
  - La Paz: coefficients around 0.02** (2001 OLS), -0.044*** (2006 OLS), 0.018*** (2013 OLS); Proportion of workers in 2013: 26.7
  - Cochabamba: 0.047*** (2001), 0.010 (2006), 0.062*** (2013); Proportion 17.5
  - Tarija: 0.098*** (2001), 0.032 (2006), 0.106*** (2013); Proportion 4.9
  - Santa Cruz: 0.091*** (2001), 0.041*** (2006), 0.11*** (2013); Proportion 27.0
  - Pando: 0.058** (2001), 0.09** (2006), 0.13*** (2013); Proportion 1.1
  - Rural: -0.064*** (2001), -0.032*** (2006), -0.057*** (2013); Proportion 32.6
  - Education coefficients (dy/dx for discrete change from 0 to 1), increasing with education levels; Educ More than 16: 0.252*** (2001), 0.19*** (2006), 0.165*** (2013); Proportion 12.8
  - Experience: 0.011*** (2001 OLS), 0.005** (2006), 0.005*** (2013)
  - Manufacturing: 0.019* (2001 OLS), 0.082*** (2006), proportion 16.9
  - Services: 0.012 (2001 OLS), 0.048*** (2006), proportion 48.2
  - Informal: -0.103*** (2001), -0.107*** (2006), -0.088*** (2013)
  - Adjusted R-squared: 0.29 (2001), 0.29 (2006), 0.33 (2013)
  - Mean of Monthly Real Labor Income (Bs.): 974 (2001), 1048 (2006), 1310 (2013)
- Workers in the informal sector have a lower labor income than those in the formal sector, but the gap reduced during 2001–2013 in a statistically significant way.
- According to coefficients in Table A2 (appendix), the ratio of formal to informal salaries was around 1.8 in 2001 and reduced to 1.6 in 2013.

### Conclusions and policy implications
- Bolivia registered large reductions in inequality and poverty since 2000, but levels remain around or above the average for LA and significantly above AEs.
- Key macro and policy facts:
  - GDP increased by 80 percent in real terms during 2000–2014.
  - The real minimum wage increased by 100 percent during 2000–2014.
  - Average real labor income rose by 40 percent during 2000–2014.
  - Remittances peaked at around 8 percent of GDP in 2007; as of 2014, they were around 3.5 percent of GDP.
  - The three main redistributive tools (Renta Dignidad, Bono Juancito Pinto and Bono Juana Azurduy) reached around 30 percent of the population, corresponding to 1½ percent of GDP of cash transfers in 2014.
- Main drivers of inequality and poverty reduction:
  - Changes in labor income more important than non-labor income.
  - Low-skilled workers benefited more from higher labor income earnings, with gains concentrated in the informal sector.
  - Skilled workers, mainly in the formal sector, faced lower real salaries, reducing the skills premium.
  - Manufacturing and services sectors registered higher labor income growth, although much of it occurred in the informal sector.
  - Departments with strongest labor income growth: Beni, Santa Cruz and Tarija.
- Non-labor income contribution:
  - Positive but small relative to labor income; important for specific target populations (elderly via Renta Dignidad; youngest via Bono Juana Azurduy and Bono Juancito Pinto).
  - The share of government transfers in household income doubled since 2006.
- Fiscal and policy outlook:
  - The skills premium could increase if commodity-boom spillovers to low-skill workers reverse.
  - With a tighter revenue envelope ahead, it will be essential to ensure labor and social policies are well designed and targeted.

*Source: IMF staff calculations and analysis as presented in the supplied PDF content.*

### Appendix 1. Description of Inequality Indicators

### _wp15265 - Appendix 1. Description of Inequality Indicators

### Theil Index
- Definition and interpretation:
  - The Theil index TT is described as a statistic used to measure economic inequality.
  - TT is the same as redundancy in information theory: the maximum possible entropy of the data minus the observed entropy.
  - It is a special case of the generalized entropy index.
  - It can be viewed as a measure of redundancy, lack of diversity, isolation, segregation, inequality, non-randomness.
- Boundary cases:
  - If everyone has the same income, then TT gives 0 (population's income has maximum disorder).
  - If one person has all the income, then TT gives the result (maximum order).
  - Dividing TT by (unspecified divisor in source) can normalize the equation to range from 0 to 1.
- Notation:
  - Where yi is individual income (i=1..N) and ȳ is the mean income.

### The Atkinson index
- Definition and normative interpretation:
  - The Atkinson index is a measure of income inequality developed by Anthony Barnes Atkinson.
  - The index can be turned into a normative measure by imposing a coefficient ε to weight incomes.
  - Greater weight can be placed on changes in a given portion of the income distribution by choosing ε, the level of "inequality aversion", appropriately.
  - The Atkinson index becomes more sensitive to changes at the lower end of the income distribution as ε approaches 1.
  - As ε approaches 0 (inequality aversion falls), the Atkinson becomes more sensitive to changes in the upper end of the income distribution.
- Interpretation of parameter ε:
  - ε is often called the "inequality aversion parameter", since it quantifies the amount of social utility assumed to be gained from complete redistribution.
  - For ε =0 (no aversion to inequality) it is assumed that no social utility is gained by complete redistribution and the Atkinson index (A(ε)) is zero.
  - For ε→∞ (infinite aversion to inequality), it is assumed that infinite social utility is gained by complete redistribution in which case A(ε)=1.
  - The Atkinson index A(ε) then varies between 0 and 1 and is a measure of the amount of social utility to be gained by complete redistribution of a given income distribution.
- Notation:
  - Where yi is individual income (i=1..N) and ȳ is the mean income.

### Figures A1 and A2 — Bolivia income distributions (visual panels)
- Figure A1. Bolivia: Monthly Real Labor Income per capita (Bs.)
  - Panels show Monthly Real Labor Income (in Real Bs.) by:
    - Education Level - Formal Sector (years 2001, 2006, 2013; education categories: No Education; 1-3 years; 4-6 years; 7-9 years; 10-12 years; 13-15 years; More than 16)
    - Education Level - Informal Sector (years 2001, 2006, 2013; same education categories)
    - Department - Urban (2001, 2006, 2013; departments: La Paz, Oruro, Tarija, Beni, Sucre, Cochabamba, Potosi, Santa Cruz, Pando)
    - Department - Rural (2001, 2006, 2013; same departments)
    - Age - Males (2001, 2006, 2013; age groups: 0-14, 15-20, 21-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, +61)
    - Age - Females (2001, 2006, 2013; same age groups)
    - Activity (2006, 2013; categories: Agric. & Min., Manufacturing, Services; FORMAL vs INFORMAL)
  - Axes ranges in panels display values such as 0, 500, 1000, 1500, 2000, 2500, 3000, 3500 (Bs.) and category ticks as shown in figure.
  - A numeric column in the activity panel shows values including: 4809, 2350, 2082, 2337, 2023, 1897, 556, 818, 951, 660, 1311, 1269, -1000, 0, 1000, 2000, 3000, 4000, 5000, 6000 (as depicted).
- Figure A2. Bolivia: Monthly Real Non-Labor Income per Capita (Bs.)
  - Panels show Monthly Real Non-Labor Income (in Real Bs.) by the same breakdowns as Figure A1:
    - Education Level - Formal Sector (2001, 2006, 2013)
    - Education Level - Informal Sector (2001, 2006, 2013)
    - Department - Urban (2001, 2006, 2013)
    - Department - Rural (2001, 2006, 2013)
    - Age - Males (2001, 2006, 2013)
    - Age - Females (2001, 2006, 2013)
    - Activity (2006, 2013; Agric. & Min., Manufacturing, Services; FORMAL vs INFORMAL)
  - Axes ranges and panel ticks include negative values in some panels (e.g., -100, 0, 100, 200, ... up to 600) as shown.

### Table A1. Poverty Headcount Ratio (SEDLAC harmonized income estimations and UDAPE official estimations)
- Source lines and notes:
  - "Estimations based on harmonized income - SEDLAC (CEDLAS and the World Bank)"
  - "Source: LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank)"
  - "Source: Unidad de Análisis de Políticas Sociales y Económicas (UDAPE)"
  - Table distinguishes National, Urban, Rural, with notes: "Survey not Comparable", "No survey this year", "Not published yet".
- Raw yearly entries as presented in the source (preserving exact numeric sequences and spacing):
  - 200027.243.359.98.925.145.360.576.586.6
  - 200121.438.356.36.822.442.246.866.080.9
  - 200222.039.757.86.522.442.948.969.883.5
  - 2003
  - 2004
  - 200518.335.053.74.319.440.243.362.877.6
  - 200615.732.048.53.215.333.638.962.976.2
  - 200712.030.347.42.417.734.931.055.372.2
  - 200810.522.840.43.512.329.123.742.961.7
  - 200910.720.635.12.99.823.625.741.757.5
  - 2010
  - 20117.016.129.01.57.018.117.934.451.0
  - 20128.217.129.21.87.818.621.135.950.4
  - 20136.814.427.21.26.015.318.031.851.4
- Official estimations - UDAPE yearly entries as presented (preserving exact numeric sequences and spacing):
  - 200045.266.427.954.575.087.0
  - 200138.863.126.254.359.777.7
  - 200239.563.325.753.962.378.8
  - 2003
  - 2004
  - 200538.260.624.351.162.977.6
  - 200637.759.923.450.362.276.5
  - 200737.760.123.750.963.977.3
  - 200830.157.318.948.751.573.6
  - 200926.151.316.143.545.566.4
  - 2010
  - 201120.945.010.836.841.361.3
  - 201221.643.412.234.740.961.1
  - 2013

### Table A2. Econometric Model – (Mincer Equation for 2001, 2006, and 2013)
- Model description and notes:
  - OLS and Heckman specifications reported.
  - 1/ The LHS variable is the log of real income labor (CPI base year 2000=100).
  - P-values calculated using robust standard errors.
  - 2/ dy/dx is for discrete change of dummy variable from 0 to 1.
  - The constant captures the mean income of urban workers in Chuquisaca with no formal education, and age between 15 to 20 years, working in Agriculture & Mining sector.
- Selected coefficient estimates and related statistics (preserving exact values and significance markers):
  - La Paz 2/: 0.11** (2001 OLS), -0.26*** (2006 OLS), 0.10*** (2013 OLS); Heckman: 0.12** (2001), -0.25*** (2006), 0.11** (2013); Proportion of workers in 2013 (percentage): 26.7
  - Cochabamba 2/: 0.26*** (2001), 0.06 (2006), 0.34*** (2013); Heckman: 0.27***, 0.06, 0.34***; Proportion 17.5
  - Oruro 2/: -0.09 (2001), -0.45*** (2006), 0.23*** (2013); Heckman: -0.09, -0.45***, 0.23***; Proportion 4.8
  - Potosi 2/: -0.26*** (2001), -0.07 (2006), -0.02 (2013); Heckman: -0.25***, -0.06, -0.02; Proportion 8.2
  - Tarija 2/: 0.54*** (2001), 0.19* (2006), 0.58*** (2013); Heckman: 0.55***, 0.19**, 0.59***; Proportion 4.9
  - Santa Cruz 2/: 0.50*** (2001), 0.24*** (2006), 0.60*** (2013); Heckman: 0.51***, 0.25***, 0.60***; Proportion 27.0
  - Beni 2/: 0.48*** (2001), 0.06 (2006), 0.53*** (2013); Heckman: 0.50***, 0.07, 0.53***; Proportion 4.1
  - Pando 2/: 0.32** (2001), 0.53** (2006), 0.71*** (2013); Heckman: 0.33***, 0.53***, 0.71***; Proportion 1.1
  - Rural 2/: -0.35*** (2001), -0.19*** (2006), -0.31*** (2013); Heckman: -0.36***, -0.21, -0.32***; Proportion 32.6
  - Education dummy effects (relative to base):
    - Educ 1-3 years 2/: 0.24*** (2001), 0.26*** (2006), 0.30*** (2013); Heckman: 0.25***, 0.26**, 0.30***; Proportion 14.0
    - Educ 4-6 years 2/: 0.52***, 0.40***, 0.61***; Heckman: 0.53***, 0.41***, 0.61***; Proportion 18.0
    - Educ 7-9 years 2/: 0.62***, 0.38***, 0.76***; Heckman: 0.64***, 0.39**, 0.76***; Proportion 8.2
    - Educ 10-12 years 2/: 0.71***, 0.55***, 0.75***; Heckman: 0.73***, 0.56***, 0.75***; Proportion 22.3
    - Educ 13-15 years 2/: 1.00***, 0.78***, 0.68***; Heckman: 1.03***, 0.79***, 0.69***; Proportion 13.8
    - Educ More than 16 2/: 1.39***, 1.12***, 0.90***; Heckman: 1.42***, 1.13***, 0.91***; Proportion 12.8
  - Experience and experience squared:
    - Experience: 0.06*** (2001), 0.03** (2006), 0.03*** (2013); Heckman: 0.07***, 0.03*, 0.03***
    - Experience^2: -0.00*** (2001), -0.00*** (2006), -0.00*** (2013); Heckman: -0.00***, -0.00**, -0.00***
  - Sectoral dummies (2013 proportion and coefficients where reported):
    - Manufacturing 2/: 0.11* (2001), 0.45*** (2006) reported; Proportion 16.9
    - Services 2/: 0.07 (2001), 0.26*** (2006) reported; Proportion 48.2
  - Informal 2/: -0.57*** (2001), -0.63*** (2006), -0.48*** (2013); Heckman same values reported.
  - Constant terms: 5.51*** (2001), 5.90*** (2006), 5.46*** (2013); Heckman: 5.34***, 5.82***, 5.43***.
  - Adjusted R-squared: 0.29 (2001), 0.29 (2006), 0.33 (2013).
  - Mean of Monthly Real Labor Income (Bs.): 974 (2001), 1048 (2006), 1310 (2013) — repeated under both OLS and Heckman columns.
- Significance notation:
  - *** p<0.01, ** p<0.05, * p<0.1

_Appendix 1. Description of Inequality Indicators — extracted from the source content provided._

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