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What determines income distribution and how income distribution might affect growth Branko Milanovic World Bank Training Poverty and Inequality Analysis Course March 3, 2011 A. From Kuznets to Piketty: determinants of income distribution 1. Relationship between income and inequality: the rise and fall of the Kuznets hypothesis Kuznets inverted U-shaped curve (defined in 1955) • As income increases, inequality at first goes up and then declines • “It seems plausible to assume that in the process of growth, the earlier periods are characterized by…forces that may have widened the inequality…for a while because of the rapid growth of the non-A [non-agricultural] sector and wider inequality within it. • It is even more plausible to argue that the recent narrowing in income inequality observed in the developed countries was due to a combination of • the narrowing inter-sectoral inequalities in product per worker, • the decline in the share of property incomes in total incomes of households, and • the institutional changes that reflect decisions concerning social security and full employment." Kuznets curve: history • Evidence for Kuznets curve in cross-sectional data analyzed in the 1970s, 1980s (Paukert, Lecaillon, Koeble & Thomas) • More than 90% of pooled time-series and crosssectional Gini variability is due to differences between countries => factors that determine country inequality are stable (Li, Squire, Zhou) • Elusive evidence in time-series (Oshima) • Strong historical evidence for Western Europe before and during Industrial revolution (Lindert and Willianson, van Zanden, Prados) • Modifications of the Kuznets curve: “strong” and “weak” formulations The same idea; Tocqueville 120 years earlier • If one looks closely at what has happened to the world since the beginning of society, it is easy to see that equality is prevalent only at the historical poles of civilization. Savages are equal because they are equally weak and ignorant. Very civilized men can all become equal because they all have at their disposal similar means of attaining comfort and happiness. Between these two extremes is found inequality of condition, wealth, knowledge-the power of the few, the poverty, ignorance, and weakness of all the rest. (Memoir on pauperism, 1835). • General formulation (used by Ahluwalia 1976) Giniit o 1 ln Yit 2(ln Yit) kZit eit 2 k • We expect β1>0 and β2<0 • Control variables include socialist dummy, government transfers, share of state sector employment, openness, age structure of population (Milanovic 1994; Williamson and Higgins 1999) 60 Relationship between Gini and GDP per capita; (about 1100 Ginis between 1970 and 2005) 40 30 20 Gini 50 Kuznets curve 6 7 8 9 10 ln of GDP per capita in international dollars 11 twoway (scatter Giniall lngdpppp if Giniall<65) (qfit Giniall lngdpppp, yline(20 60, lpattern(dash))), legend(off) xtitle(ln of GDP per capita in international dollars) ytitle(Gini) From global_new2.dta • No controls; a weak inverted U relationship (more than 1300 Gini obs) • Huge variability in inequality; R2 only 0.11 • The upward sloping part of the curve generally hard to discern • Turning point quite unstable; here about $PPP 4,000 (level of Sri Lanka or Paraguay in 2008) • Some disenchantment with the hypothesis: hard to see inverted U in timeseries for a single country 40 35 30 30 35 40 Gini from my allGini file 45 45 No downward portion plotted against time or income: example of the USA 1950 1960 1970 1980 1990 year when the survey was conducted 2000 twoway scatter Giniall year if contcod=="USA", connect(l) ylabel(30(5)45) From allginis.dta. 25000 30000 35000 40000 constant 2005 ppp, based on icp05 45000 twoway scatter Giniall gdpppp if contcod=="USA", connect(l) ylabel(30(5)45) From global_new2 Example of China Against income, 1970-2004 40 35 30 30 35 40 45 Gini from my allGini file 45 Against time, 1950-2004 1950 1960 1970 1980 1990 year when the survey was conducted twoway scatter Giniall year if contcod=="CHN" & year<2005, connect(l) ylabel(30(5)45) Based on giniall.dta 2000 0 1000 2000 3000 constant 2005 ppp, based on icp05 4000 twoway scatter Giniall gdpppp if contcod=="CHN" & year<2005, connect(l) ylabel(30(5)45) From global_new2.dta 2. Credit market imperfections theory : “pull yourself by your bootstraps” Credit market imperfections • Poor households do not invest in human K even if the returns are high; they invest in subsistence-related types of investment • Indivisibilities: minimum threshold of K needed for investment; convex returns • Societies with these problems both more unequal and wasteful in terms of human and capital resources • Example of win-win strategy (inequality&growth) • Solutions: asset redistribution, no school fees, deeper capital markets, micro finance Credit constraint, education, democracy (Li, Squire & Zhou) pooled IV formulation Schooling 1960 -4.6** -4.4** Democracy 1.6** 1.5** Land Gini 60 0.16** 0.15** Financial depth (M2/GDP) -7.7** -10.1** R2 0.62 No. of obs. 166 166 3. Political theory of income distribution Methodologically, move from household survey data to fiscal data Long-run studies using income and inheritance tax data (Picketty et al.): France 1901-98 • Secular decline in inequality • Due to the declining share of top 1% • Due to the decreasing importance of large capital income • Due to progressive taxation and progressive (and high) inheritance taxes • Produces no effect on average K stock but truncates large K holdings (lower concentration of capital income) Story for the US (Piketty & Sanz) • Top K incomes decreased during the Depression and WW2 and never recovered (top estates still lower in real terms than around 1900) • Total K income did not decrease; its concentration did • Change in factoral income composition among the top 1%; no longer mostly capitalists but salaried workers. Δ more pronounced in the US than in France • Conclusion: No spontaneous decline in inequality. Role of depression, wars and progressive taxation. Policy and politics matter the most. A political theory of income distribution Explanation by E. Saez in “Striking it richer” The labor market has been creating much more inequality over the last thirty years, with the very top earners capturing a large fraction of macroeconomic productivity gains. A number of factors may help explain this increase in inequality, not only •underlying technological changes but also the retreat of institutions developed during the New Deal and World War II - such as • progressive tax policies, • powerful unions, • corporate provision of health and retirement benefits, and • changing social norms regarding pay inequality. Recent findings • A number of similar studies for developed countries reaches the same conclusion: a U-shaped inequality in the 20th century in English-speaking countries (UK: Atkinson 03; Netherlands: Atkinson & Salvedra 03; Italy: Brandolini) • But also for India: Banerji and Piketty 2005 • Long L shaped curve for the rest of developed countries A. Top 0.1% incomne share in English Speaking Countries 12% United States United Kingdom Canada Income Share 10% 8% 6% 4% 2% Source: Piketty and Saez (2006) 1998 1993 1988 1983 1978 1973 1968 1963 1958 1953 1948 1943 1938 1933 1928 1923 1918 1913 0% Fig 5. Top 0.1% income share in Germany and Japan 12% Japan Germany Income Share 10% 8% 6% 4% 2% 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 0% Source: Piketty and Saez (2006) Business Income Capital Income 1951 Salaries 4.5% 1926 Fig 3: Share and Composition of top 0.01% in the US Capital Gains 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% Source: Piketty and Saez (2006) 1996 1991 1986 1981 1976 1971 1966 1961 1956 1946 1941 1936 1931 1921 1916 0.0% But this finding crucially depends on strong ρ btw. Gini and top income share; while true among recent data, not true historically! 30 Kenya Byzantium Chile Kenya 20 China Nueva España Peru Japan Netherlands France Roman Empire India-Moghul India-British Florence Kingdom of Naples Hol1732 10 Eng1290 Bihar Java1924 Java1880 BrazilEng1759 Eng1688 Levant Siam Eng1801 Maghreb Old Castiille 0 South Serbia 20 30 40 50 60 70 gini2 twoway scatter top_percent gini2 if Dancient==1, msize(vlarge) mlabel( country) xlabel(20(10)70) Source: Milanovic, Williamson and Lindert (2009) B. How inequality might affect growth Channel 1: The median voter hypothesis (Meltzer-Richard) Political mechanism • Greater inequality in factor income=> • Relatively poor μ voter=> • Chooses relatively high tax rate Economic mechanism • High redistribution and distorsionary effect of taxes => • Lower growth rate Extent of redistribution = = fct (inequality in market income) • Hypothesis 1. More market-unequal countries redistribute more (using two definitions of market income, without and with government pensions) • Hypothesis 2. An increase in market share of a given decile is associated with a lower sharegain • Question. If countries do redistribute more, is the mechanism through which it happens, the median voter hypothesis? 4 2 0 sharegain in percent 6 8 Redistribution is greater if market income share of the poor is less -1 From figure.do based on data_voter_checked.dta 0 1 2 share of poorest market decile in percent 3 Source: Milanovic 2000, 2009 It holds for all deciles: if a decile is better-off in terms of marketP income distribution, it loses more through the redistribution 2 3 sharegain in percent 4 0 0 1 2 sharegain in percent 6 4 5 Second decile 8 Bottom (first) decile -1 0 1 2 share of poorest market decile in percent 3 1 5 Second richest decile -1 -2 sharegain in percent -4 -6 20 25 30 share of poorest market decile in percent 35 -3 -8 sharegain in percent -2 0 0 Richest (top) decile 2 3 4 share of poorest market decile in percent 15 16 17 share of poorest market decile in percent 18 More market unequal states of the world associated with greater Gini reduction through redistribution Gini of marketP income Without controls With controls +0.438** (8.9) -0.010** (-5.3) 0.47 +0.473** (8.2) +0.002 (0.9) -0.004 (-0.6) -0.070 (0.3) 0.54 110 100 Openness GDP per capita (in logs) Constant R2 (within) Number of observations Dependent variable: Gini reduction through redistribution. Country fixed effects regression. Source: Milanovic (2009) -4 -6 -8 gain of the middle -2 0 But we cannot show that the middle deciles gains more if market inequality high 36 Source: Milanovic 2000 38 40 42 44 share of the middle in market income 46 8 10 Sharegain of the very poor, 1973-2005 (using market income) 6 Germany 2 4 USA 1970 1980 1990 year 2000 2010 twoway (scatter gain3 year if contcod=="DEU" & decile==1, connect(l)) (scatter gain3 year if contcod=="USA" & decile==1, connect(l)), legend(off) text(4 2000 "USA") text(7 2000 "Germany") ytitle(Distributional gain of the bottom decile) Based on data_voter_checked.dta Channel 2: Inequality and property rights Political mechanism • Greater inequality creates cleavages => • They are particularly strong if coincide with ethnic differences (high horizontal inequality)=> • Insecure property rights Economic mechanism • Insecure property rights => • Lower growth rate Inequality and property rights (Keefer & Knack) Dependent variable: protection of property rights Ln GDP per capita 1985 Cross section 7.61** Ethnic tensions -0.933** Income Gini circa 1985 -0.196** Land Gini 1985 -0.097** R2 No. of obs. 0.80 64 Dependent variable: Property rights: ICRG measure 1986-95. Ranges from 0 to 50. Excursus: the reverse link and the reverse sign: greater protection of property rights increases inequality Dependent Gini Property rights 0.929** Gini (time-dummies included on the RHS) 0.709* Financial development (M2/GDI) -0.064** -0.07** Education 0.026 -0.016 Land inequality -0.016 -0.02 Democracy 0.438** 0.323** Prop. Rights x Democracy -0.056** -0.046** R2 within (N) 0.26 (203) 0.35 (203) • Greater protection of property rights increases inequality • The rich elite is also politically powerful and protects its economic assets • The effect is mitigated by the introduction of democracy • => The negative effect of property rights protection is particularly strong in lowdemocracy environments • But the regression does not include an income term (results based on Savoia and Easaw, 2007; World Development, Feb. 2010) Channel 3. Inequality caused by “morally irrelevant” characteristics • Inequalities which are independent of individual effort, entrepreneurship or luck • “Wasteful” (vs. instrumental or “useful”) inequalities • Examples: education, health, opportunity to better oneself economically, to have a political voice • Horizontal inequalities between ethnic/religious groups, education levels, socio-economic categories, geographical areas Assumed ρ’s for different parts of the world Base case Optrimistic (high mobility) Pessimistic (low mobility) Average Gini (year 2002) Nordic 0.2 0.15 0.3 27.5 Rest WENAO E. Europe 0.4 0.3 0.5 33.7 0.4 0.3 0.5 30.6 Asia 0.5 0.4 0.6 37.6 LAC 0.66 0.5 0.9 53.8 Africa 0.66 0.5 0.9 42.6 Also a super-optimistic: ρ=0.2 for all; and super-pessimistic: ρ=0.9 for all. ρ’s based on literature review. How one’s income depends on circumstances: (dependent variable: own household per capita income, in $PPP, logs) 4 (Base) 5 (Optimistic) 6 (Pessimistic) Mean per capita country income (in ln) 0.986 (0.00) 0.987 (0) 0.991 (0) Gini index (in %) -0.019 (0.00) -0.019 (0.00) -0.019 (0.00) Parents’ estimated income class (ventile) 0.105 (0.00) 0.100 (0.00) 0.109 (0.00) Constant -0.513 (0.00) -0.462 (0.00) -0.582 (0.00) Number of observations 232,000 232,000 232,000 R2 adjusted 0.81 0.80 0.83 Number of countries 116 116 116 Eq. • Circumstances at one’s birth (country + parents’ income class) explain between 83 percent (if world is fairly income-mobile within countries) and 85 percent (if there is less social mobility) of variability in income globally • => thus, only a very small portion of global income differences can be due to effort • Coefficient on country mean income remains 1; coefficient on parental income 0.1 (each notch is worth 10% increase in children’s income); coeff. slightly higher if there is less social mobility • As a proxy, WDR06 looks at the contribution of horizontal inequalities to total inequality, or total “feasible between- inequality” (total Y of country=given; number and sizes of groups=given; ‘pecking order’ by group mean incomes= given; => find new group mean incomes that maximize the between component) • Up to 40-45% of “feasible between inequality” explained by education differences • Inequality traps and the interaction between political and economic power