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Explaining the decline in earnings 
inequality in Brazil, 1995-2012 
Francisco H.G. Ferreira* 
Sergio Firpo # 
Julian Messina* 
* The World Bank and IZA # Sao Paulo School of Economics at FGV and IZA
Plan of the talk 
1. The question 
2. The suspects 
3. Data 
4. Methodology 
5. Results 
2
The question: 
What accounts for the decline in the Gini 
coefficient for labor incomes between 
1995 (0.50) and 2012 (0.40) in Brazil? 
3
Context: A broader decline in inequality (in 
household income per capita, 2001-2011) 
Three key factors drove the fall in inequality: demographics, labor incomes, and transfers.
∈ 
1
∈ 
 1
-13 -45 -20 -18 -8 4 
Shapley Value Estimates of the Contributions to the decline in 
the Gini Coefficient 
Others non labor income by adult Income from pension by adult Income from transfer by adult 
Labor income by occupied Share of adults Income from capital by adult 
Share of occupied by adults 
Change in 
Gini 
coefficient: 
6.5 pts 
Source: figure based on analysis in Azevedo, Inchauste and Sanfelice (2013)
Labor income levels:1995-2012 
781.5 
486.4 
445.0 
671.9 
467.1 
431.3 
893.6 
670.0 
637.1 
900 Reales (2005) 
800 
700 
600 
500 
400 
1995 2000 2005 2010 
Year 
Average labor 
income 
Median labor 
income 
Average household 
per capita income 
Note: median and average earnings calculated over estimating sample (formal, informal and self employed of ages 18-65) 5
Labor income inequality: 1995-2012 
0.59 
0.50 
0.58 
0.47 
0.52 
.6 
.55 
.5 
.45 
.4 0.40 
Gini Index 
1995 2000 2005 2010 
year 
Labor income 95% CI 
Household per 
capita income 
95% CI 
Note: labor income Gini coefficient calculated over estimating sample (formal, informal and self employed aged 18-65). Household per 6 
capita income Gini calculated over the entire population. The confidence intervals were computed using jack-knife standard errors.
7 
Labor income inequality: 1995-2012 
0.94 
0.92 
0.89 
0.80 
0.66 
0.63 
1 1995=1 
.9 
.8 
.7 
.6 
.5 
1995 2000 2005 2010 
Year 
Gini (1995=0.50) Theil (1995=0.45) P90/P10 (1995=10.00)
The suspects: 
• Human Capital 
- Increases in the supply of education 
- Declines in the returns to education 
- Aging population 
• Labor market institutions 
- A rising real minimum wage 
- Changes in the formal / informal composition of the labor force 
• Demographics 
- Female participation 
- Racial gaps 
• Spatial 
- Urban/Rural gaps 
- Regional disparities 8
Human Capital 
9
10 
Increases in the supply of education 
1 .8 
.6 
.4 
.2 
fraction in the population aged 18-65 
0 
cdf years of education 
0 5 10 15 20 
years of education 
1996 2003 2012
The evolution of (potential) experience 
11 
52.7 50.7 
45.8 
89.8 89.8 
86.6 
25.0 25.2 
20.4 
100 
80 
60 
40 
20 
0 
% population between 18-65 years 
1995 2000 2005 2010 
Year 
56-65 46-55 36-45 
26-35 18-25
Labor market institutions 
12
A rising real minimum wage 
1.20 
0.97 
0.86 
2.03 
1.43 
1.14 
100 
80 
60 
40 
20 
Note: median and average earnings calculated over estimating sample (formal, informal and self employed of ages 18-65) 13 
2 
1.5 
1 
.5 
1995=1 
1995 2000 2005 2010 
Year 
Average Earnings Median Earnigs Minimum Wage 
11.9 
16.8 16.2 
0 
% of the occupied population 
between 18-65 years 
1995 2000 2005 2010 
Year 
At or above min W Below
14 
Density of the distribution of monthly labor 
earnings (with relevant minimum wages) 
02-03 
min w 
95-96 
min w 
11-12 
min w 
1 
.8 
.6 
.4 
.2 
0 
kdensity lnw 
4 6 8 10 
log of wage in 2005 Reales 
1995-96 2002-03 2011-12 
The bandwith is .07 for all the periods
Formalization 
15 
51.8 
27.4 
52.5 
25.3 
42.3 
21.3 
100 
80 
60 
40 
20 
0 
% of occupied population 
between 18-65 years 
1995 2000 2005 2010 
Year 
Formal employees Informal employees Self employment
Demographics 
16
Gender and racial composition of the labor force 
17 
43.5 45.5 
53.3 
5.2 6.0 
8.7 
100 
80 
60 
40 
20 
0 
% of population between 18-65 years 
1995 2000 2005 2010 
Year 
Withe Mestizo Indigenaother African american 
37.9 39.9 
42.6 
100 
80 
60 
40 
20 
0 
% of the occupied population 
between 18-65 years 
1995 2000 2005 2010 
Year 
Male Female
Geography 
18
Urbanization and regional composition 
93.1 92.8 92.3 
77.2 77.3 77.7 
31.1 31.9 34.5 
26.8 26.5 26.5 
19 
100 
80 
60 
40 
20 
0 
% of population between 18-65 years 
1995 2000 2005 2010 
Year 
Center west South Southest 
North other Northest 
18.9 
14.4 14.0 
100 
80 
60 
40 
20 
0 
% population between 18-65 years 
1995 2000 2005 2010 
Year 
Urban Rural
Market Structure (OLS estimators) 
20 
South 
Southest 
North 
Northest 
Rural 
Female 
African american 
Indigenous other 
Mestizo 
Informal 
Self employment 
Below minimum wage 
Terciary Incomplete 
Secondary 
Secontary incomplete 
primary or less 
age 46-55 
age 36-45 
age 26-35 
age 18-25 
-1.5 -1 -.5 0 .5 
1995-96 2011-12 95% CI 
Geography 
Demography 
Labor market institutions 
Human Capital
21 
Returns to observable worker characteristics 
1995-96 2002-03 2004-05 2011-12 
coef/se coef/se coef/se coef/se 
18-25 -0.189*** -0.283*** -0.285*** -0.285*** 
(0.007) (0.006) (0.005) (0.005) 
26-35 0.028*** -0.060*** -0.077*** -0.110*** 
(0.007) (0.005) (0.005) (0.005) 
36-45 0.149*** 0.046*** 0.030*** -0.026*** 
(0.007) (0.005) (0.005) (0.005) 
46-55 0.121*** 0.074*** 0.070*** 0.018*** 
(0.007) (0.006) (0.006) (0.005) 
Primary or less -1.228*** -1.150*** -1.082*** -0.907*** 
(0.007) (0.006) (0.005) (0.005) 
Secondary incomplete -0.959*** -0.976*** -0.922*** -0.790*** 
(0.008) (0.006) (0.006) (0.005) 
Secondary -0.696*** -0.777*** -0.738*** -0.654*** 
(0.008) (0.006) (0.006) (0.004) 
Tertiary Incomplete -0.466*** -0.479*** -0.453*** -0.417*** 
(0.010) (0.008) (0.007) (0.006) 
Below minimum wage -1.082*** -1.073*** -1.070*** -0.974*** 
(0.004) (0.003) (0.003) (0.003) 
Self employment 0.049*** -0.031*** -0.038*** 0.067*** 
(0.004) (0.003) (0.003) (0.003) 
Informal -0.193*** -0.131*** -0.103*** -0.045*** 
(0.004) (0.003) (0.003) (0.003) 
‘Mestiço’ -0.137*** -0.102*** -0.097*** -0.081*** 
(0.003) (0.003) (0.002) (0.002) 
Indigenous  other 0.197*** 0.061*** 0.073*** 0.034** 
(0.025) (0.018) (0.018) (0.013) 
African-American -0.183*** -0.119*** -0.109*** -0.092*** 
(0.006) (0.004) (0.004) (0.003) 
Female -0.413*** -0.328*** -0.323*** -0.287*** 
(0.003) (0.002) (0.002) (0.002) 
Rural -0.235*** -0.120*** -0.103*** -0.125*** 
(0.004) (0.003) (0.003) (0.003) 
Northeast -0.214*** -0.218*** -0.229*** -0.216*** 
(0.005) (0.004) (0.003) (0.003) 
North -0.055*** -0.094*** -0.075*** -0.106*** 
(0.007) (0.004) (0.004) (0.004) 
Southeast 0.085*** 0.024*** -0.005 -0.018*** 
(0.005) (0.004) (0.003) (0.003) 
South -0.001 -0.025*** -0.019*** -0.008** 
(0.005) (0.004) (0.004) (0.004)
Data 
• Pesquisa Nacional por Amostra de Domicílios (PNAD). Annual household survey carried out by the 
Instituto Brasileiro de Geografía e Estatística (IBGE). 
• Periods compared are “paired years”: 1995-1996, 2002-2003,2004-2005 and 2011-2012 
• Wage measure: total (gross) individual monthly labor earnings. 
Sample for analysis: 
• Working age population: 18-65 
• Men and women, in rural and urban areas. 
• Employees and self employed. 
• Further, we distinguish between formal and informal employees 
– An employee is informal if (s)he does not have Carteira de Trabalho. 
• Trimming: top and bottom percentiles of the distribution omitted. 
• We exclude the rural North of the country, which was included in PNAD only after 2004 
• Monetary values are in constant 2005 R$. 
22
Methodology 
A generalized Oaxaca-Blinder decomposition 
• Consider two time periods, A and B. We can express the overall change in the 
distributional statistic  of wages Y over time as 
Δ  
  | −  | , 
where F is the cumulative distribution of wages   and  !  1 is an 
indicator of group B membership. 
– Think of | as the integral of the density function 
– Define a counterfactual distribution | analogously, as the integral of: 
23 
f (y) g (y X ) (X )dX B B B = ∫∫∫ f 
f (y) g (y X ) (X )dX s A B = ∫∫∫ f
Methodology 
A generalized Oaxaca-Blinder decomposition 
• Adding and subtracting the counterfactual distribution statistic  | : 
– Δ  
=  | −  | +  | −  | 
– Structure effect:  | −  | 
– Composition effect:  | −  | 
• If interested in mean wages the OB decomposition is straightforward. 
Applying the law of iterated expectations we can use the regression 
coefficients and sample means to perform this decomposition: 
– Δ= #$ 
% 
− % 
#$ 
#$ 
 
! !  + ! − % 
 
24
Methodology: 
Firpo, Fortin and Lemieux (2009) 
• Other distributional statistics are more complex. Non-linearities preclude us 
from applying the law of iterated expectations. 
• FFL suggest using the recentered influence function (RIF) 
• The influence function of a distributional statistic represents the influence of 
an individual observation to that distributional statistic: '(; ) 
• The RIF adds back the distributional statistic: +' =  + '(; ) 
• A convenient feature of this RIF function is that E(+') =  
• Hence, we can apply OLS to obtain regression coefficients from RIF 
transformed variables and go back to our traditional Oaxaca-Blinder 
decompositions. This allows us to build counterfactuals for any distributional 
statistic with a known influence function. 
• In our case, we build counterfactuals for the mean and the Gini coefficient. 
25
Methodology: Detailed Specification 
With indicator variables representing a categorical variable (dummies), the 
Oaxaca decomposition is not invariant to the choice of the excluded category 
(Oaxaca and Ransom (1999) and Yun (2005)). 
26 
-./ 01 2345 = 6 + 789:31 ;3=30 + ?@5:A4B3C=DE + 
FGHIJKLMN + OPQQRSR TLJH + OU'VIKRLWQX + Y 
Omitted Categories: Best performers (white males , tertiary education completed in the age 
bracket 56-65, urban center-west, being at or above the minimum wage and being a formal 
employee)
Changes in Average Earnings in Brazil. 
Detailed Decompositions 
27 
.4 
.2 
0 
-.2 
1995-2012 1995-2003 2004-2012 
Total Endowments Structure Total Endowments Structure Total Endowments Structure 
Human Capital GenderRace Urban/RuralRegions Min wage Informality Constant 
Change in log(W)
Changes in Gini Earnings Inequality in 
Brazil. Detailed Decompositions 
28 
.05 
0 
-.05 
-.1 
-.15 
1995-2012 1995-2003 2004-2012 
Total Endowments Structure Total Endowments Structure Total Endowments Structure 
Human Capital GenderRace Urban/RuralRegions Min wage Informality Constant 
Change in Gini
Summary of main findings 
• Decline in earnings inequality between 1995 and 2012 was driven 
primarily by changes in the structure of remuneration in the Brazilian 
labour market. 
• In fact, changes in the distribution of worker characteristics were 
inequality enhancing, in particular human capital (‘paradox of growth’?). 
• The negative pay structure effect occurred because of declines in various 
different wage premia: 
• Falling schooling premia; 
• Reductions in the gender wage gap; 
• Reductions in the racial wage gap; 
• Reductions in the urban-rural wage gap; 
• Reductions in the pay gap between formal and informal workers. 
29
The role of Minimum Wage 
• Real minimum wage, which more than doubled over the period. 
• That increase generated a formidable spike in the density function of 
earnings by 2012 
• As suspected, this rise in the minimum wage contributed to falling 
inequality in the 2004-2012 sub-period. 
• However, for the period between 1995 and 2003, increases in MW rose 
inequality through composition effect. 
• There was a steady increase in the proportion of employed workers 
earning strictly less than MW. 
• Thus, because labour market was ‘softer’ (higher unemployment rates) in 
the first sub-period, it meant that the overall impact of minimum wages in 
the whole period was inequality-increasing. 
30

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Explaining the decline in earnings inequality in Brazil

  • 1. Explaining the decline in earnings inequality in Brazil, 1995-2012 Francisco H.G. Ferreira* Sergio Firpo # Julian Messina* * The World Bank and IZA # Sao Paulo School of Economics at FGV and IZA
  • 2. Plan of the talk 1. The question 2. The suspects 3. Data 4. Methodology 5. Results 2
  • 3. The question: What accounts for the decline in the Gini coefficient for labor incomes between 1995 (0.50) and 2012 (0.40) in Brazil? 3
  • 4. Context: A broader decline in inequality (in household income per capita, 2001-2011) Three key factors drove the fall in inequality: demographics, labor incomes, and transfers.
  • 7. -13 -45 -20 -18 -8 4 Shapley Value Estimates of the Contributions to the decline in the Gini Coefficient Others non labor income by adult Income from pension by adult Income from transfer by adult Labor income by occupied Share of adults Income from capital by adult Share of occupied by adults Change in Gini coefficient: 6.5 pts Source: figure based on analysis in Azevedo, Inchauste and Sanfelice (2013)
  • 8. Labor income levels:1995-2012 781.5 486.4 445.0 671.9 467.1 431.3 893.6 670.0 637.1 900 Reales (2005) 800 700 600 500 400 1995 2000 2005 2010 Year Average labor income Median labor income Average household per capita income Note: median and average earnings calculated over estimating sample (formal, informal and self employed of ages 18-65) 5
  • 9. Labor income inequality: 1995-2012 0.59 0.50 0.58 0.47 0.52 .6 .55 .5 .45 .4 0.40 Gini Index 1995 2000 2005 2010 year Labor income 95% CI Household per capita income 95% CI Note: labor income Gini coefficient calculated over estimating sample (formal, informal and self employed aged 18-65). Household per 6 capita income Gini calculated over the entire population. The confidence intervals were computed using jack-knife standard errors.
  • 10. 7 Labor income inequality: 1995-2012 0.94 0.92 0.89 0.80 0.66 0.63 1 1995=1 .9 .8 .7 .6 .5 1995 2000 2005 2010 Year Gini (1995=0.50) Theil (1995=0.45) P90/P10 (1995=10.00)
  • 11. The suspects: • Human Capital - Increases in the supply of education - Declines in the returns to education - Aging population • Labor market institutions - A rising real minimum wage - Changes in the formal / informal composition of the labor force • Demographics - Female participation - Racial gaps • Spatial - Urban/Rural gaps - Regional disparities 8
  • 13. 10 Increases in the supply of education 1 .8 .6 .4 .2 fraction in the population aged 18-65 0 cdf years of education 0 5 10 15 20 years of education 1996 2003 2012
  • 14. The evolution of (potential) experience 11 52.7 50.7 45.8 89.8 89.8 86.6 25.0 25.2 20.4 100 80 60 40 20 0 % population between 18-65 years 1995 2000 2005 2010 Year 56-65 46-55 36-45 26-35 18-25
  • 16. A rising real minimum wage 1.20 0.97 0.86 2.03 1.43 1.14 100 80 60 40 20 Note: median and average earnings calculated over estimating sample (formal, informal and self employed of ages 18-65) 13 2 1.5 1 .5 1995=1 1995 2000 2005 2010 Year Average Earnings Median Earnigs Minimum Wage 11.9 16.8 16.2 0 % of the occupied population between 18-65 years 1995 2000 2005 2010 Year At or above min W Below
  • 17. 14 Density of the distribution of monthly labor earnings (with relevant minimum wages) 02-03 min w 95-96 min w 11-12 min w 1 .8 .6 .4 .2 0 kdensity lnw 4 6 8 10 log of wage in 2005 Reales 1995-96 2002-03 2011-12 The bandwith is .07 for all the periods
  • 18. Formalization 15 51.8 27.4 52.5 25.3 42.3 21.3 100 80 60 40 20 0 % of occupied population between 18-65 years 1995 2000 2005 2010 Year Formal employees Informal employees Self employment
  • 20. Gender and racial composition of the labor force 17 43.5 45.5 53.3 5.2 6.0 8.7 100 80 60 40 20 0 % of population between 18-65 years 1995 2000 2005 2010 Year Withe Mestizo Indigenaother African american 37.9 39.9 42.6 100 80 60 40 20 0 % of the occupied population between 18-65 years 1995 2000 2005 2010 Year Male Female
  • 22. Urbanization and regional composition 93.1 92.8 92.3 77.2 77.3 77.7 31.1 31.9 34.5 26.8 26.5 26.5 19 100 80 60 40 20 0 % of population between 18-65 years 1995 2000 2005 2010 Year Center west South Southest North other Northest 18.9 14.4 14.0 100 80 60 40 20 0 % population between 18-65 years 1995 2000 2005 2010 Year Urban Rural
  • 23. Market Structure (OLS estimators) 20 South Southest North Northest Rural Female African american Indigenous other Mestizo Informal Self employment Below minimum wage Terciary Incomplete Secondary Secontary incomplete primary or less age 46-55 age 36-45 age 26-35 age 18-25 -1.5 -1 -.5 0 .5 1995-96 2011-12 95% CI Geography Demography Labor market institutions Human Capital
  • 24. 21 Returns to observable worker characteristics 1995-96 2002-03 2004-05 2011-12 coef/se coef/se coef/se coef/se 18-25 -0.189*** -0.283*** -0.285*** -0.285*** (0.007) (0.006) (0.005) (0.005) 26-35 0.028*** -0.060*** -0.077*** -0.110*** (0.007) (0.005) (0.005) (0.005) 36-45 0.149*** 0.046*** 0.030*** -0.026*** (0.007) (0.005) (0.005) (0.005) 46-55 0.121*** 0.074*** 0.070*** 0.018*** (0.007) (0.006) (0.006) (0.005) Primary or less -1.228*** -1.150*** -1.082*** -0.907*** (0.007) (0.006) (0.005) (0.005) Secondary incomplete -0.959*** -0.976*** -0.922*** -0.790*** (0.008) (0.006) (0.006) (0.005) Secondary -0.696*** -0.777*** -0.738*** -0.654*** (0.008) (0.006) (0.006) (0.004) Tertiary Incomplete -0.466*** -0.479*** -0.453*** -0.417*** (0.010) (0.008) (0.007) (0.006) Below minimum wage -1.082*** -1.073*** -1.070*** -0.974*** (0.004) (0.003) (0.003) (0.003) Self employment 0.049*** -0.031*** -0.038*** 0.067*** (0.004) (0.003) (0.003) (0.003) Informal -0.193*** -0.131*** -0.103*** -0.045*** (0.004) (0.003) (0.003) (0.003) ‘Mestiço’ -0.137*** -0.102*** -0.097*** -0.081*** (0.003) (0.003) (0.002) (0.002) Indigenous other 0.197*** 0.061*** 0.073*** 0.034** (0.025) (0.018) (0.018) (0.013) African-American -0.183*** -0.119*** -0.109*** -0.092*** (0.006) (0.004) (0.004) (0.003) Female -0.413*** -0.328*** -0.323*** -0.287*** (0.003) (0.002) (0.002) (0.002) Rural -0.235*** -0.120*** -0.103*** -0.125*** (0.004) (0.003) (0.003) (0.003) Northeast -0.214*** -0.218*** -0.229*** -0.216*** (0.005) (0.004) (0.003) (0.003) North -0.055*** -0.094*** -0.075*** -0.106*** (0.007) (0.004) (0.004) (0.004) Southeast 0.085*** 0.024*** -0.005 -0.018*** (0.005) (0.004) (0.003) (0.003) South -0.001 -0.025*** -0.019*** -0.008** (0.005) (0.004) (0.004) (0.004)
  • 25. Data • Pesquisa Nacional por Amostra de Domicílios (PNAD). Annual household survey carried out by the Instituto Brasileiro de Geografía e Estatística (IBGE). • Periods compared are “paired years”: 1995-1996, 2002-2003,2004-2005 and 2011-2012 • Wage measure: total (gross) individual monthly labor earnings. Sample for analysis: • Working age population: 18-65 • Men and women, in rural and urban areas. • Employees and self employed. • Further, we distinguish between formal and informal employees – An employee is informal if (s)he does not have Carteira de Trabalho. • Trimming: top and bottom percentiles of the distribution omitted. • We exclude the rural North of the country, which was included in PNAD only after 2004 • Monetary values are in constant 2005 R$. 22
  • 26. Methodology A generalized Oaxaca-Blinder decomposition • Consider two time periods, A and B. We can express the overall change in the distributional statistic of wages Y over time as Δ | − | , where F is the cumulative distribution of wages and ! 1 is an indicator of group B membership. – Think of | as the integral of the density function – Define a counterfactual distribution | analogously, as the integral of: 23 f (y) g (y X ) (X )dX B B B = ∫∫∫ f f (y) g (y X ) (X )dX s A B = ∫∫∫ f
  • 27. Methodology A generalized Oaxaca-Blinder decomposition • Adding and subtracting the counterfactual distribution statistic | : – Δ = | − | + | − | – Structure effect: | − | – Composition effect: | − | • If interested in mean wages the OB decomposition is straightforward. Applying the law of iterated expectations we can use the regression coefficients and sample means to perform this decomposition: – Δ= #$ % − % #$ #$ ! ! + ! − % 24
  • 28. Methodology: Firpo, Fortin and Lemieux (2009) • Other distributional statistics are more complex. Non-linearities preclude us from applying the law of iterated expectations. • FFL suggest using the recentered influence function (RIF) • The influence function of a distributional statistic represents the influence of an individual observation to that distributional statistic: '(; ) • The RIF adds back the distributional statistic: +' = + '(; ) • A convenient feature of this RIF function is that E(+') = • Hence, we can apply OLS to obtain regression coefficients from RIF transformed variables and go back to our traditional Oaxaca-Blinder decompositions. This allows us to build counterfactuals for any distributional statistic with a known influence function. • In our case, we build counterfactuals for the mean and the Gini coefficient. 25
  • 29. Methodology: Detailed Specification With indicator variables representing a categorical variable (dummies), the Oaxaca decomposition is not invariant to the choice of the excluded category (Oaxaca and Ransom (1999) and Yun (2005)). 26 -./ 01 2345 = 6 + 789:31 ;3=30 + ?@5:A4B3C=DE + FGHIJKLMN + OPQQRSR TLJH + OU'VIKRLWQX + Y Omitted Categories: Best performers (white males , tertiary education completed in the age bracket 56-65, urban center-west, being at or above the minimum wage and being a formal employee)
  • 30. Changes in Average Earnings in Brazil. Detailed Decompositions 27 .4 .2 0 -.2 1995-2012 1995-2003 2004-2012 Total Endowments Structure Total Endowments Structure Total Endowments Structure Human Capital GenderRace Urban/RuralRegions Min wage Informality Constant Change in log(W)
  • 31. Changes in Gini Earnings Inequality in Brazil. Detailed Decompositions 28 .05 0 -.05 -.1 -.15 1995-2012 1995-2003 2004-2012 Total Endowments Structure Total Endowments Structure Total Endowments Structure Human Capital GenderRace Urban/RuralRegions Min wage Informality Constant Change in Gini
  • 32. Summary of main findings • Decline in earnings inequality between 1995 and 2012 was driven primarily by changes in the structure of remuneration in the Brazilian labour market. • In fact, changes in the distribution of worker characteristics were inequality enhancing, in particular human capital (‘paradox of growth’?). • The negative pay structure effect occurred because of declines in various different wage premia: • Falling schooling premia; • Reductions in the gender wage gap; • Reductions in the racial wage gap; • Reductions in the urban-rural wage gap; • Reductions in the pay gap between formal and informal workers. 29
  • 33. The role of Minimum Wage • Real minimum wage, which more than doubled over the period. • That increase generated a formidable spike in the density function of earnings by 2012 • As suspected, this rise in the minimum wage contributed to falling inequality in the 2004-2012 sub-period. • However, for the period between 1995 and 2003, increases in MW rose inequality through composition effect. • There was a steady increase in the proportion of employed workers earning strictly less than MW. • Thus, because labour market was ‘softer’ (higher unemployment rates) in the first sub-period, it meant that the overall impact of minimum wages in the whole period was inequality-increasing. 30
  • 34. Conclusions • In contrast to earlier documented periods – the story of these seventeen years was a happy one in Brazilian labour markets: – Unemployment fell and earnings rose. – Not only did average earnings rise, but they rose by most for those groups of workers who used to earn the least. – There was a compression in the schooling wage premia, which used to be unusually large in Brazil. – Even more impressive were the reductions in wage gaps among workers that are observationally equivalent in terms of their human capital, but differ along such dimensions as race, gender, location and type of job. 31
  • 35. Implications for Africa • Schooling: (a) increases in productivity and (b) if focus is on primary and secondary levels, it leads to greater prosperity and greater equity; • Discrimination: gender, ethnic/racial, or other forms – tend to be both inefficient and inequitable. – Encouraging female education, reduction in fertility rates, and greater labour force participation has contributed to growth in average earnings, and to a less unequal distribution in Brazil. • Regional integration: integration of rural areas, and the workers who live there: – Greater connectivity and less labour market segmentation between cities and the countryside are an ongoing part of Brazil’s recently successful fight against poverty and inequality. • Fiscal redistribution is still important (not to be the only tool for policy): – Well-designed transfer programs are perfectly consistent with vibrant labour markets, with rising average wages and declining dispersion. 32
  • 36. Thank you! • Questions or comments: sergio.firpo@fgv.br • More on interaction between Africa and Brazil coming soon: – The CLEAR Regional Center for Brazil and Lusophone Africa -to be launched soon- hosted by Sao Paulo School of Economics at FGV. 33