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Global income inequality today

Global income inequality today. Branko Milanovic Perugia, June 2009. Email: bmilanovic@worldbank.org Based on the book Worlds Apart , 2005 and updates. BM note: this is an update of moscow2.ppt. 1. Inequalities today. Three concepts of inequality defined. Concept 1 inequality.

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Global income inequality today

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  1. Global income inequality today Branko Milanovic Perugia, June 2009 Email: bmilanovic@worldbank.org Based on the book Worlds Apart, 2005 and updates BM note: this is an update of moscow2.ppt

  2. 1. Inequalities today

  3. Three concepts of inequality defined Concept 1 inequality Concept 2 inequality Concept 3 (global) inequalty

  4. Inequality, 1950-2006:The mother of all inequality disputes 0.7 Global inequality (Concept 3) 0.6 Weighted international inequalty Gini coefficient (Concept 2) Weighted international inequality without China 0.5 Unweighted international inequality (Concept 1) 0.4 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984

  5. Three concepts of inequality using the new 2005 PPPs (1950-2007) 1982 1990 2000 Graph in interyd\dofiles\defines.do

  6. The impact of new PPPs • Concept 2 inequality increases by almost 10 Gini points (a level shift) • Somewhat steeper decline of Concept 2 inequality in the last decade (because India and China now appear poorer) • About 5 Gini points increase in Concept 1 inequality (shift effect; no trend effect) • About 5 Gini points increase in global inequality

  7. (cont.) • World poorer than thought, Asia in particular • Inequality (in all formulations) greater • Two engines of “global equalization”: China and India

  8. Concept 1 and Concept 2 inequality Graph in interyd\dofiles\defines.do

  9. 2. Inequality between world citizens today

  10. Methodological issues • GDI per capita or HS mean • Definitional difference (H&E, undisbursed profits) and • Practical difference (under-surveying of the rich and under-reporting of property Y) • Mixing of the two biases both poverty and inequality down • Moreover, movements in NA and HS statistics are different • If HS mean is it HSY or HSX?

  11. Methodological issues (cont.) • Even if HS welfare indicator is selected definitions of X,Y vary in time & btw. countries • Issues: self-employed Y, home C, imputation of housing, treatment of publicly provided H&E, use of top coding, under-estimation of property incomes • What PPP to use • Equivalence scales & intra-HH inequality

  12. The difficulty stems from contradictory movements • (1) Greater inequality within nations • (2) Greater differences between countries’ mean incomes (think of US vs. Africa) • (3) But catching up of large and poor countries (China and India) • All of these forces determine what happens to GLOBAL INEQUALITY (but they affect it differently)

  13. 3. First calculations of global inequality from household survey data alone

  14. Population coverage Non-triviality of the omitted countries (Maddison vs. WDI)

  15. GDI (US dollar) coverage

  16. Number of surveys (C-based)

  17. Global inequality (with 2005 PPPs)(distribution of persons by $PPP or US$ income per capita)

  18. 4. Importance of the differences in countries’ mean incomes

  19. Concept 1 inequality in historical perspective: Convergence/divergence during different economic regimes

  20. How are Concepts 2 and 3 related? • In Gini terms: • where Gi=individual country Gini, π=income share, yi = country income, pi = population share, μ=overall mean income, n = number of countries, L=overlap term Concept 2

  21. A non-Marxist world • Over the long run, decreasing importance of within-country inequalities despite the reversal in the last quarter century, • Increasing importance of between-country inequalities (with some hopeful signs in the last five years, before the current crisis), • Global division between countries more than between classes

  22. Composition of global inequality changed: from being mostly due to “class” (within-national), today it is mostly due to “location” (where people live; between-national) 2000 1870 Based on Bourguignon-Morrisson (2002) and Milanovic (2005)

  23. A literary illustration: Elizabeth’s dilemma (from Pride and Prejudice) 1810 position estimates based on Colquhoun 1801-3 data. Y2K data from LIS (UK1999), and for 0.1% from Piketty (Data-central).

  24. Define four worlds: • First World: The West and its offshoots • Take the poorest country of the First World (e.g. Portugal) • Second world (the contenders): all those less than 1/3 poorer than Portugal. • Third world: all those 1/3 and 2/3 of the poorest rich country. • Fourth world: more than 2/3 below Portugal.

  25. Four Worlds in 1960

  26. Four Worlds in 2003

  27. Growth over 1980-2002 period as function of initial (1980) income

  28. Population according to income of country where they live (2007): an empty middle India, Indon, Bgd China Brazil, Russia, Mexico USA W Europe, Japan histogram gdpppp [w=popu] if year==2007 & gdpppp<50000 & Dcont==1, bin(20) percent ylabel(0(10)40) xtitle(GDP per capita in 2005 PPP)

  29. The key borders today • First to fourth world: Greece vs. Macedonia and Albania; Spain vs. Morocco (25km), Malaysia vs. Indonesia (3km) • First to third world: US vs. Mexico. In 1960, the only key borders were Argentina and Uruguay (first) vs. Brazil, Paraguay and Bolivia (third world), and Australia (first) vs. Indonesia (fourth)

  30. * BLS, News Release March 2009; data for 2008 inclusive of undocumented aliens.

  31. 5. Global inequality (cont.)

  32. More than fifty-fifty world (new PPPs)

  33. How big is a Gini of 70? (Year 2002, 2005PPPs)

  34. Different countries and income classes in global income distribution (year 2002; new PPPS) 100 USA 90 Germany 80 70 Russia Brazil 60 percentile of world income distribution 50 India 40 30 20 10 1 1 10 20 30 40 50 60 70 80 90 100 country percentile

  35. Note… • Richest people in India barely intersect with poorest people in Germany • Bottom 20% of Americans worse off than equivalent people in Germany • But this is not true for Brazil and Russia: about half of the population of Brazil better off than the very poorest percentile in Germany; for Russia, it is 4/5. • Russian better-off than Brazilians except at the top (note convexity at the top in Brazil) • Important later for rules re. global transfers

  36. Distribution of percentile of global income distribution across five world regions (02 WYD) . graph box inc_c if maxgroup==20, over(region); use world2002.dta

  37. 100 Germany 90 Italy 80 Hungary 70 60 Serbia 50 percentile of world income distribution 40 30 Russia 20 10 1 1 5 10 15 20 country ventile

  38. GDP per capita and Gini

  39. Global inequality of opportunity • How much of variability of income person’s global income can we explain with two circumstances only: person’s country of citizenship and income class of his/her parents? • Both circumstances basically given at birth • With citizenship person receives several public goods: income of country, its inequality level, and its intergenerational income mobility.

  40. The answer is: about 80 percent! (dependent variable: HH per capita income in $PPP ) Base (optimistic, pessimistic) case about intergenerational income mobility in different parts of the world

  41. 6. Global financial crisis and global inequality

  42. 15 largest annual GDP per capita declines in the United States history

  43. Plutocratic and people’s recessions are not the same thing

  44. Plutocratic and people’s global growth rate in years of plutocratic recessions 0.080 People's growth 0.060 rate 0.040 0.020 0.000 1982 1991 1930 1931 1932 1954 1958 1960 1975 -0.020 Plutocratic growth rate -0.040 -0.060 -0.080

  45. Shrinking GDPs

  46. Who is affected more: poor or rich countries?

  47. The financial crisis 1890-95: relationship between initial GDP per capita and its change during the crisis

  48. The Great Depression 1928-33: relationship between initial GDP per capita and its change during the crisis

  49. World, by population, in the crisis 1930 twoway (scatter dlngdpppp laggdpppp if year==1930 [w=pop]) (lowess dlngdpppp laggdp > ppp if year==1930), yline(0) ytitle(growth rate) legend(off) xtitle(GDP per capita > in 1929) [from maddison_polity2.dta

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