distribution changes in the new millennium? UNU- WIDER: Inequality in - - PowerPoint PPT Presentation

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 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

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% 1993 1994* 1995 1996 1997 1998 1999 2000* 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010* 2011 2012 2013 2014 2015

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 / 13 2Q / 13 3Q / 13 4Q / 13 1Q / 14 2Q / 14 3Q / 14 4Q / 14 1Q / 15 2Q / 15 3Q / 15 4Q / 15 1Q / 16 2Q / 16 3Q / 16 4Q / 16 1Q / 17 2Q / 17 3Q / 17 4Q / 17 1Q / 18 2Q / 18

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 Used Period of Time Income Concept

Firms Effects J-Divergence Decompositions

RAIS

(matched employer-employee)

1994 – 2015

Individual Formal Earnings Gender Gap Regression Models

RAIS

(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 & 2014

Individual Earnings Missing Incomes Imputation Combine Regressions and Stochastic Imputation

PNAD

(household survey)

2001 - 2015

Per capita (All Sources) Fiscal Policy Instruments Microssimulation Dynamic

PNAD + POF + AR

(income & expenditures surveys and administrative records)

2003 - 2015

Per capita (All Sources) Top Incomes Pareto Interpolation

PNAD + PIT

(household survey and income tax records)

2007 - 2015

Individual (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% 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1992 1993 1995 1996 1997 1998 1999 2001 2002 2003 2004 2005 2006 2007 2008 2009 2011 2012 2013 2014 2015

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%).

• fficial cash transfers

→ accelerated the growth

• f social welfare (+1.65%)
• BPC

direct and indirect taxes changes operated in the

• pposite 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 In 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

• r 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 Used Period of Time Income Concept

Firms Effects J-Divergence Decompositions

RAIS

(matched employer-employee)

1994 – 2015

Individual Formal Earnings Gender Gap Regression Models

RAIS

(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 & 2014

Individual Earnings Missing Incomes Imputation Combine Regressions and Stochastic Imputation

PNAD

(household survey)

2001 - 2015

Per capita (All Sources) Fiscal Policy Instruments Microssimulation Dynamic

PNAD + POF + AR

(income & expenditures surveys and administrative records)

2003 - 2015

Per capita (All Sources) Top Incomes Pareto Interpolation

PNAD + PIT

(household survey and income tax records)

2007 - 2015

Individual (All Sources)

Project Overview

Thanks!