What are the main drivers of Brazilian income

distribution changes in the new millennium?

UNU- WIDER: Inequality in the Developing Giants – Brazil¹

Principal and Junior Investigators:

Marcelo Neri (lead, FGV)

Cecília Machado (FGV) Valdemar Neto

Pedro Silva (IBGE) Manuel Osorio

Rozane Siqueira (UFPE) Tiago Bonomo

Marcos Hecksher (Ipea) Ricardo Nogueira

1 - The research is part of the UNU-WIDER project “Inequality in the Developing Giants” that also includes studies on China, India, Mexico, and South Africa

0.6227

0.6351

0.5952

0.5144

0.589

0.5281

0.51

0.53

0.55

0.57

0.59

0.61

0.63

0.65

19

76

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Gini Per Capita Income All Sources

Concentration Index Labour Earnings Individual

Inequality of per capita income (Gini) and of individual earnings (Concentration)

Source: PNAD/IBGE microdata. Harmonized series in terms of regional coverage.

Growth, Equity (Gini) and Social Welfare Annual Growth Rates

Source: PNADC/IBGE – Per Capita Income

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

19

93

19

94

*

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95

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96

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Growth Equity Social Welfare

Social welfare growth was evenly divided by falling inequality of household income, the differential of mean incomes between surveys and national accounts and real GDP growth.

Growth, Equity and Social Welfare Annual Growth Rates by Quarters in Brazil

In 2014, a reversal of almost all distributive-growth trends happened, starting with the labor

market, which was the main driver behind former distributive changes.

Source: PNADC/IBGE – Per Capita Earnings based 15 to 60 years

-8%

-5%

-2%

1%

4%

7%

1Q

/ 1

3

2Q

/ 1

3

3Q

/ 1

3

4Q

/ 1

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Growth Equity Social Welfare

Did inequality stop falling? When?

This project pursues the measurement and analysis of the second moment of Brazilian

income distribution without losing sight of the first moment, or existing synergies between

them.

Joint look at inequality, mean and social welfare are key to depict what has been happening

in Brazil since the 1990s.

The second general point in all contributions proposed here is to emphasize changes and

not only levels of these dimensions in different points in time.

First, measurement and causal issues that affect inequality should also have implications on

the mean, and vice-versa. Second, differences across time are a way to deal with

measurement issues and to identify causality. Makes it easier to compare different data sets

and periods of analysis

Objective

Inequality in Brazil by Topic, Technique, Dataset, Period of Time and Income Concept

Inequality Topic Technique Dataset UsedPeriod of

TimeIncome Concept

Firms EffectsJ-Divergence

DecompositionsRAIS

(matched employer-employee)1994 – 2015

Individual Formal

Earnings

Gender Gap Regression ModelsRAIS

(matched employer-employee)1994 – 2015

Individual Formal

Earnings

Intergenerational

Transmission of

Education & Returns

Omitted Variables,

Measurement Error and

Markov Regressions

PNAD special supplements(household survey)

1996 & 2014Individual

Earnings

Missing Incomes

Imputation

Combine Regressions

and Stochastic

Imputation

PNAD(household survey)

2001 - 2015Per capita

(All Sources)

Fiscal Policy

Instruments

Microssimulation

Dynamic

PNAD + POF + AR(income & expenditures surveys

and administrative records)

2003 - 2015Per capita

(All Sources)

Top Incomes Pareto InterpolationPNAD + PIT

(household survey and income

tax records)

2007 - 2015Individual

(All Sources)

Project Overview

Formal Labour Market in Brazil:Cumulative Growth Curve 1994 – 2015

• Lower percentiles

• Top percentiles

Formal Labour Market in Brazil:Cumulative Growth Curve 1994 – 2015

Regression Framework: Analyses of earnings inequality within educational groups. How much do variables explain? Firms fixed effects are key!

Source: Rais microdata 1994 to 2015

Gender Explanatory power for

College Graduates falls as new

variables are added into the model.

0.0570

0.0159

0.0046

0.00

0.01

0.02

0.03

0.04

0.05

0.06

Demographics

Evolution of the Earnings Gender Gap throughout the Life Cycle by Birth

Source: RAIS microdata 1994 to 2015

How did intergenerational mobility in education evolved?Persistence in the Intergenerational Mobility of Education by Cohorts – Interaction between fathers education and cohort effects

Source: PNAD 1996 and 2014 microdata.

What was the evolution of wage premiums with respect to schooling? Differences in the Education Premiums by Cohorts - Interaction between individual schooling and cohort effects

.

Source: PNAD 1996 and 2014 microdata.

Does missing income on data affect distributive trends?

Share with null and unavailable household income on PNAD

0.9%

2.3% 2.6%

0.7%

1.5%

0.4%

0%

1%

2%

3%

4%

5%

6%1

98

1

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Missing Household Incomes Null HH Incomes

No

Income, Equality and Social Welfare: Contribution to Disposable Income (2003 to 2015)

Source: FGV Social with BRAHMS microsimulations

The Gini index based social welfare grew 4.86% per year. Higher than the respective growth rate associated with

initial income (3.49%) and final income (3.77%), but not of gross income (5.14%).

more to mean income

growth (72%) than

inequality reduction

(28%).

official cash transfers

→ accelerated the growth

of social welfare (+1.65%)

- BPC

direct and indirect taxes

changes operated in the

opposite direction

(0.28% and 1.09%,)

(Contribution of each Income Concept to

Disposable Income Growth)

2003 to 2015 (Annual)

Mean Income Equality Welfare

Initial income 0.0276 0.0072 0.0349

Cash Transfers 0.0110 0.0055 0.0165

Public Pensions 0.0083 0.0016 0.0099

Poor Elderly/Disability Benefits 0.0010 0.0013 0.0023

Wage Bonus + Family Wage 0.0004 0.0003 0.0008

Unemployment Benefit 0.0004 0.0004 0.0008

Family Grant (CCT) 0.0013 0.0022 0.0034

Gross Income 0.0387 0.0127 0.0514

(-) Direct Taxes 0.0038 -0.0010 0.0028

Personal Income Tax 0.0018 -0.0013 0.0005

Social Security Contribution 0.0021 0.0003 0.0023

Disposable Income 0.0348 0.0137 0.0486

(-) Indirect Taxes 0.0080 0.0029 0.0109

Final Income 0.0269 0.0108 0.0377

How taxes and transfers steered distributive changes?

Distributive impact of public policies: Concentration Curves for the Family Grant

Programme ordered by Disposable Income

Source: FGV Social with BRAHMS microsimulations

Family Grant became less

targeted as it expanded

Household Surveys Disposable Income 2003-2015• 1. The trend in Gini, mean and Social Welfare of disposable income is close to

gross income.

• 2. The role of earnings (market incomes) in that trend. 79,3% of mean income; 52,6% of Gini inequality; 71,8% of Social Welfare

• 3. Differences between GDP and Household Income Growth (2003-13): 1,9% annual

It is the deflator!

• 4. Role of top incomes: 1%+: (-1,52%), 10%+: (-1,35%), 40%- (2,69%), 10%- (2,69%)

Top Incomes

Real growth rate of income per tenth of the population per year (2007-2015)

0.911; 1,980

100

1,000

10,000

100,000

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

PNAD IRPF

Combining PIT (IRPF) and PNAD In Levels 2015

Did the rich boost social welfare?

• Annual growth of PIT taxpayers’ average declared income (10.1%) was much higher than that of GDP (3%) from 2007 to 2011. Would the rich filers of PIT have experienced an “economic miracle” unnoticed by the National Accounts or in surveys like PNAD? Not necessarily. Deflators and formalization can explain the difference..

• What drove PIT income growth was exempt incomes. While the population ages and grows, PIT taxpayers become younger and declare more dependents (a reduction in the number of elderly declarants and their reallocation as dependents of their sons and daughters is observed) and non-taxable incomes.

• At least part of these differences can be linked to changes in the incentives provided by Brazilian tax laws. Thus, it is risky to conclude on the trend of Brazilian inequality using PIT available tabulations at face value.

Inequality in Brazil by Topic, Technique, Dataset, Period of Time and Income Concept

Inequality Topic Technique Dataset UsedPeriod of

TimeIncome Concept

Firms EffectsJ-Divergence

DecompositionsRAIS

(matched employer-employee)1994 – 2015

Individual Formal

Earnings

Gender Gap Regression ModelsRAIS

(matched employer-employee)1994 – 2015

Individual Formal

Earnings

Intergenerational

Transmission of

Education & Returns

Omitted Variables,

Measurement Error and

Markov Regressions

PNAD special supplements(household survey)

1996 & 2014Individual

Earnings

Missing Incomes

Imputation

Combine Regressions

and Stochastic

Imputation

PNAD(household survey)

2001 - 2015Per capita

(All Sources)

Fiscal Policy

Instruments

Microssimulation

Dynamic

PNAD + POF + AR(income & expenditures surveys

and administrative records)

2003 - 2015Per capita

(All Sources)

Top Incomes Pareto InterpolationPNAD + PIT

(household survey and income

tax records)

2007 - 2015Individual

(All Sources)

Project Overview Thanks!