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, Ind ia, Mexico, and South Africa
Inequality of per capita income (Gini) and of individual earnings (Concentration) 0.65 0.6351 0.63 0.6227 0.61 0.5952 0.59 0.589 0.57 0.55 Gini Per Capita Income All Sources 0.53 Concentration Index Labour Earnings Individual 0.5281 0.51 0.5144 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 Source: PNAD/IBGE microdata. Harmonized series in terms of regional coverage.
Growth, Equity (Gini) and Social Welfare Annual Growth Rates 12% 10% 8% 6% 4% 2% 0% -2% -4% -6% Growth Equity Social Welfare -8% 1993 1994* 1995 1996 1997 1998 1999 2000* 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010* 2011 2012 2013 2014 2015 Source: PNADC/IBGE – Per Capita Income 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 7% Did inequality stop falling? When? 4% 1% -2% Growth Equity Social Welfare -5% -8% 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 Source: PNADC/IBGE – Per Capita Earnings based 15 to 60 years 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.
Objective 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
Project Overview Inequality in Brazil by Topic, Technique, Dataset, Period of Time and Income Concept Period of Inequality Topic Technique Dataset Used Income Concept Time J-Divergence RAIS Individual Formal 1994 – 2015 Firms Effects Decompositions Earnings (matched employer-employee) RAIS Individual Formal 1994 – 2015 Gender Gap Regression Models Earnings (matched employer-employee) Intergenerational Omitted Variables, PNAD special supplements Individual 1996 & 2014 Transmission of Measurement Error and Earnings (household survey) Education & Returns Markov Regressions Combine Regressions Missing Incomes PNAD Per capita 2001 - 2015 and Stochastic Imputation (All Sources) (household survey) Imputation PNAD + POF + AR Fiscal Policy Microssimulation Per capita 2003 - 2015 (income & expenditures surveys Instruments Dynamic (All Sources) and administrative records) PNAD + PIT Individual 2007 - 2015 Top Incomes Pareto Interpolation (household survey and income (All Sources) tax records)
Formal Labour Market in Brazil: Cumulative Growth Curve 1994 – 2015 • Lower percentiles
Formal Labour Market in Brazil: Cumulative Growth Curve 1994 – 2015 • Top percentiles
Regression Framework: Analyses of earnings inequality within educational groups. How much do variables explain? Firms fixed effects are key! 0.0570 0.06 0.05 0.04 0.03 0.02 0.0159 0.01 0.0046 0.00 Gender Explanatory power for College Graduates falls as new Demographics variables are added into the model . Source: Rais microdata 1994 to 2015
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.
No Does missing income on data affect distributive trends? Share with null and unavailable household income on PNAD 6% Missing Household Incomes Null HH Incomes 5% 4% 3% 2.6% 2.3% 2% 1.5% 0.9% 1% 0.7% 0.4% 0% 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
How taxes and transfers steered distributive changes? Income, Equality and Social Welfare: Contribution to Disposable Income (2003 to 2015) 2003 to 2015 (Annual) (Contribution of each Income Concept to Disposable Income Growth) Mean Income Equality Welfare Initial income 0.0276 0.0072 0.0349 official cash transfers → accelerated the growth Cash Transfers 0.0110 0.0055 0.0165 Public Pensions 0.0083 0.0016 0.0099 of social welfare (+1.65%) Poor Elderly/Disability Benefits - BPC 0.0010 0.0013 0.0023 Wage Bonus + Family Wage 0.0004 0.0003 0.0008 direct and indirect taxes Unemployment Benefit 0.0004 0.0004 0.0008 changes operated in the Family Grant (CCT) 0.0013 0.0022 0.0034 opposite direction Gross Income 0.0387 0.0127 0.0514 (0.28% and 1.09%,) (-) 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 more to mean income Disposable Income 0.0348 0.0137 0.0486 growth (72%) than (-) Indirect Taxes 0.0080 0.0029 0.0109 inequality reduction Final Income 0.0269 0.0108 0.0377 (28%). 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%).
Distributive impact of public policies: Concentration Curves for the Family Grant Programme ordered by Disposable Income Family Grant became less targeted as it expanded Source: FGV Social with BRAHMS microsimulations
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 Combining PIT (IRPF) and PNAD In Levels 2015 100,000 Real growth rate of income per tenth of the population per year (2007-2015) 10,000 0.911; 1,980 1,000 PNAD IRPF 100 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
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.
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