1. Global income inequality: from the fall of the Berlin Wall to the Great Recession Paper by Christoph Lakner and Branko Milanovic Winter 2013-14 Branko Milanovic

2. Objectives of the paper Create a new unbalanced panel of country- deciles with their mean incomes all expressed in 2005 PPP dollars Calculate the changes in the level and composition (between/within countries; by region) of global inequality Find out who were the winners and losers of globalization (anonymous and quasi-non anonymous GICs) Try to adjust for the declining HS/NA ratio and the underestimate of top incomes (1 st such attempt at global level) Branko Milanovic

3. Number of surveys Branko Milanovic 19881993199820032008 Mature economies 3137 3436 China/India 44444 Other Asia 811151716 MENA 56686 Sub-Saharan Africa 627223125 Latin America 1722232018 Russia/CIS/ SE Europe 48141916 World75115121133121 China, India, Indonesia enter with rural & urban surveys

4. Population coverage (in %) 19881993199820032008 Mature economies 95100 97 China/India 100 Other Asia 758589 MENA 6070646848 Sub-Saharan Africa 2973688074 Latin America 8893959694 Russia/CIS/ SE Europe 2887 9984 World8192 9491 Non-triviality of the omitted countries

5. GDI coverage (in %, in $PPP terms) Branko Milanovic 19881993199820032008 Mature economies 96100 9897 China/India 100 Other Asia 91969897 MENA 5255454922 Sub-Saharan Africa 2283788277 Latin America 959899 98 Russia/CIS/ SE Europe 5194 10091 World9197 9693

6. Other characteristics of the database 580 surveys, 165 countries No cross-overs: surveys for each country have to be either consumption- or income-based About ½ of surveys are consumption-based with rising share of such surveys in more recent benchmark years Surveys cannot be more than 2 years apart from benchmark years, nor can two surveys from the same country be less than 3 and more than 7 years apart 98% of data from POVCAL and WYD Branko Milanovic

7. 1. General inequality statistics and the shape of the distribution, 1988-2008 Branko Milanovic

8. 19881993199820032008% change 88-08 Gini 72.271.971.571.970.5-2.3 Theil 0 114.2110.7107.1107.6102.7-10.1 Mean inc bottom 10% 20120321722825125 Median income 605725836871109982 Mean income 3295328734713631409724 Mean inc 81-90 pc 741471587177709777544 Mean inc top 10% 187851899020231216322367230 Mean inc top 1% 389643960146583516415421365 Branko Milanovic

9. Between-country inequality still by far the most dominant type but its importance is decreasing while that of within is increasing Branko Milanovic Theil 0 is an internally consistent index for decomposition, because the elimination of one component leaves the absolute value of the other component unchanged

10. The end of the two-peaked global income distributions and the emergence of the “median class” twoway (kdensity logRRinc [w=pop] if logRRinc>2 & bin_year==2008 & keep==1 & mysample==1) (kdensity logRRinc [w=pop] if logRRinc>2 & bin_year==1988 & keep==1 & mysample==1, legend(off) xtitle(log of annual PPP real income) ytitle(density) text(0.95 2.5 "1988") text(0.85 3 "2008")) Or using adding_xlabel.do; always using final_complete7.dta Emerging global “median class” between $3 and $16 Branko Milanovic 1988 2008 0.2.4.6.8 1 density 300 100030006000 1000030000 50000 100000 log of annual PPP real income Emerging global “median class” between $3 and $16

11. Asian income distributions in 1988 and 2008 twoway (kdensity logRRinc [w=pop] if logRRinc>2 & region_old==2 & bin_year==2008 & keep==1 & mysample==1) (kdensity logRRinc [w=pop] if logRRinc>2 & bin_year==1988 & keep==1 & region_old==2 & mysample==1, legend(off) xtitle(log of annual PPP real income) ytitle(density) text(0.35 2.5 "2008") text(0.3 2.2 "1988")) Using final_complete7.dta The explosion of the Asian “global median class” Branko Milanovic

12. Disappearance of a twin-peaked distribution on more detailed 2008 percentile data Branko Milanovic kdensity loginc [w=popu1] if loginc>2, xlabel(3 "1000" 4 "10000" 4.7 "50000") xtitle(Per capita income in 2005 PPPs) title(Global income distribution in 2008) From final08.dta

13. How growth of China and India filled in the middle of the global income distribution Branko Milanovic

14. Global Lorenz curves in 1988 and 2008 Branko Milanovic

15. Lorenz curves Intersection of the Lorenz curves around the 80 th percentile of global income distribution It is the reflection of strong growth of the global median class and stagnation of income among global upper-middle class. Branko Milanovic

17. Main results Slightly decreasing Gini from 72 to 70.5 All indexes except GE(2), which is sensitive to the top, go down Within-country inequality component in GE(0) increases by 6 pp (from 17% to 23%). Between- country component remains the most important End of the two-peaked distribution brought about by the emergence of a global “median class” (between $PPP 3 and $PPP 16 per day) Global 1% share increases while the mean-to- median ratio goes down Branko Milanovic

18. 2. Who won and who lost? Branko Milanovic

19. Real income growth at various percentiles of global income distribution, 1988-2008 (in 2005 PPPs) From twenty_years\final\summary_data X “US lower middle class” X “China’s middle class” Branko Milanovic $PPP2 $PPP4.5$PPP12 $PPP 110 Estimated at mean-over-mean

20. Growth incidence curve (1988-2008) estimated at percentiles of the income distribution Branko Milanovic Using my_graphs.doMean-on-mean

21. Global growth at 5-year intervals, 1988-2008 1988 to 2003 1988 to 2008 Branko Milanovic

22. The supine S shape is unusual summary_data.xls World 1988-2008 US 1986-2008 Branko Milanovic US: LIS-based disposable per capita income

23. Upward sloping GICs: in most countries increasing (relative) gains for the rich Philippines and BangladeshMexico and Colombia MEX COL BGD PHL Branko Milanovic From key_variables_calcul2.do Source: ee slide for Thailand!

24. Increasing gains for the rich with a widening urban-rural gap Urban and rural ChinaUrban and rural Indonesia From key_variables_calcul2.do Branko Milanovic urban rural urban rural

25. Although not everywhere… Branko Milanovic Urban and rural India urban Thailand and Pakistan Thailand Pakistan rural twoway scatter real_growth group if contcod=="THA" & bin_year==2008 & keep==1, connect(l) xlabel(1(1)10) lwidth(thick) msize(vlarge)Using final_complete7.dta

26. Turkey: Pro-middle class growth, 1988-2008 Growth incidence curve twoway scatter real_growth group if contcod=="TUR" & bin_year==2008 & keep==1, connect(l) xlabel(1(1)10) lwidth(thick) msize(vlarge) ytitle(real income in 2008 compared to 1988) ylabel(100(20)180) Using final_complete7.dta

27. From summary_data.xls Branko Milanovic

28. Conclusions from looking at the distributions only At any percentile, income level is 2008 greater than in 1988 (first-order dominance) At any percentile, cumulative average income greater in 2008 than in 1988 (second-order dominance; implied by the 1 st ) No Lorenz dominance: intersection around the 80 th percentile 45% of absolute gains went to the top 5% Branko Milanovic

29. Quasi non-anonymous GIC: Average growth rate 1988-2008 for different percentiles of the 1988 global income distribution Branko Milanovic

30. From analysis horizontal quasinonanon gic pop 2do Parts of the distribution that gained the most are dominantly from Asia, parts that stagnated are mostly from mature economies 20 30 40 50 60 70 80 90 Cumulative growth rate (%) 0 10 20 30 40 50 60 70 80 90 100 Share of region in ventile population (%) 0.2.4.6.81 Normalised rank in the 1988 global income distribution AsiaMature economiesGIC Solid line shows predicted value from kernel-weighted local polynomial regression (bw=0.05, epanechnikov, cube polynomial). Only countries observed in 1988 & 2008 (N=63) included. population-weighted, including population distribution in base-year Quasi-non-anonymous growth incidence curve (1988-2008)

31. Branko Milanovic From my_graphs.do

32. Branko Milanovic Quasi non-anonymous growth between 1988 and 2008: real absolute per capita gains at different fractiles of the 1988 distribution From analysis horizontal quasinonanon abs 1.do

33. Quasi non-anonymous growth between 1988 and 2008: real absolute per capita gains at different fractiles of the 1988 distribution Branko Milanovic

34. Conclusions from looking at the evolution of the 1988 country-deciles The shape of the non-anonymous global GIC displays the same supine S form Country-deciles that did the best (90% real growth) were those around the 40 th percentile of the 1988 distribution Around ¾ of people there are from China and India (90% from Asia) Country-deciles that did the worst (20% real growth) were those around the 80-90 th percentile of the 1988 distribution Around 90% of people there are from mature economies (78% from “traditional” rich economies) Branko Milanovic

35. Global income position of the Chinese urban and rural deciles in 1988 and 2008 Branko Milanovic Using my_graphs and final_complete7.dta CHN-u 08 CHN-u 88 CHN-r 08 CHN-r 88 1 20 50 80 100 percentile of global income distribution 12345678910 decile of country's income distribution

36. Global percentile position of US median and Chinese urban middle decile twoway (scatter percentile bin_year if group==6 & contcod=="CHN-U" & keep==1 & mysample==1, msize(vlarge) lwidth(thick) connect(l) mlabel(percentile)) (scatter percentile bin_year if group==5 & contcod=="USA" & keep==1 & mysample==1, msize(vlarge) lwidth(thick) connect(l) mlabel(percentile)), legend(off) ylabel(50(10)100) Branko Milanovic

37. Global income position of the German and Brazilian deciles in 1988 and 2008 Using my_graphs and final_complete7.dta DEU 88 DEU 08 BRA 08 BRA 88 1 20 50 80 100 percentile of global income distribution 12345678910 decile of country's income distribution

38. 3. The issue of top underestimation Branko Milanovic

39. Rising HS/NA gap and top underestimation If these two problems are really just one & the same problem. Assign the entire positive (NA consumption – HS mean) gap to national top deciles Use Pareto interpolation to “elongate” the distribution No a priori guarantee that global Gini will increase Branko Milanovic

40. The capture ratio (HS/NA) as function of consumption per capita twoway scatter scale2 cons_usd_bin_pc if group==1 & scale2<2 & keep==1, xscale(log) yline(1) ylabel(0(0.5)2) xlabel(500 1000 10000 40000) xtitle(Consumption per capita in US dollars) ytitle(HS mean as ratio of PCE from NA) from\final_complete7.dta

41. No relationship between Gini and the capture ratio (but this is a downward-biased estimate of true Gini) twoway scatter gini scale2 if group==1 & scale2<2, & keep==1, xtitle(HS mean as ratioo of PCE from NA) ytitle(Gini) from\final_complete7.dta

42. Gini: accounting for missing top incomes 19881993199820032008 Surveys only 72.571.871.9 69.6 NAC instead of survey mean 71.570.570.670.767.6 NAC with Pareto 71.870.871.071.168.0 NAC with top-heavy Pareto 76.376.177.278.175.9 Branko Milanovic

43. The results of various adjustments Replacing HS survey mean with private consumption from NA reduces Gini by 1 to 2 points Elongating such a distribution (that is, without changing the mean) adds less than ½ Gini point But doing the top-heavy adjustment (NA-HS gap ascribed to top 10% only) adds between 5 and 7 Gini points It also almost eliminates the decrease in global Gini between 1988 and 2008 Branko Milanovic

44. How Global Gini in 2008 changes with different adjustments Branko Milanovic

45. With full adjustment (allocation to the top 10% + Pareto) Gini decline almost fully disappears Branko Milanovic Survey data only Top-heavy allocation of the gap + Pareto adjustment

46. Cumulative growth rate at various ventiles of global income distribution 1988-2008 (with “tail heavy Pareto adjustment”) From my_graphs.do

47. Quasi non-anonymous growth incidence curve 1988-2008 (with tail-heavy income adjustment) Branko Milanovic

48. Top 1% share with survey means only and with tail-heavy National Accounts adjustment Branko Milanovic From summary_data.xls

49. 4. Political implications Branko Milanovic

50. The contradiction of inequality changes during Globalization II Most countries displayed an upward sloping GIC (US, China, India urban, Indonesia…) Perception that the rich are doing better than anybody else (true) But growth rates of countries are uneven; those that grew the fastest were in the lower middle of global income distribution, and they were also most populous This led to the humped (more exactly, reclining S) shape of the global GIC and decreasing global inequality Branko Milanovic

51. The issues Are growth (1) along the entire Chinese income distribution and (2) stagnation around the median in the rich world as well as stagnation across most of income distribution in E. Europe and LAC, related? In other words, is the hump in middle related to the dip around the 70-80 th percentile? Marching of China and India through the ranks reduces global inequality and the importance of the between-country component in global inequality But it might “cause” increases in within-national inequalities (thus offsetting global inequality decline) Branko Milanovic

52. Back to Mandeville… Can something that is bad nationally (increased inequality) be good globally (decreased inequality) ? Can national vices produce global virtue? Branko Milanovic

53. Political implications Possible crowding out of national middle classes, and the creation of a global one But the middle class is presumably a force for stability when there is a political community. There is no political community at the global level. What does global middle class mean? Would global middle class create a global polity? Or, global plutocracy: in the longer-term, reversal to the pre World War I situation Branko Milanovic

54. Or are we at the end of capitalism’s long “el periodo especial”? Three challengers to global capitalism were beaten off in the 20 th century: depression (by reinventing gov’t), war (by marshalling resources), Communism (through Welfare State) Neither of these threats is any longer present; so why can’t capitalism go back to what it once was? Was the 1930-1980 period capitalism’s long detour? Do we have to get used to permanently higher levels of national inequality? Branko Milanovic

55. 5. Additional graphs Branko Milanovic

57. From analysis horizontal quasinonanon gic pop 1.do

58. Rich countries seldom have Ginis over 40 Branko Milanovic twoway scatter gini gdp_2005ppp_pc if group==1, xscale(log) ytitle(Gini) xtitle(GDP per capita in 2005 PPP dollars) xlabel(1000 5000 20000 50000) Usiong final7_complete.dta

59. GIC across ventiles and percentiles of the base case scenario Branko Milanovic Using my_graphs.do

61. Ginis in 1988 and 2008 Branko Milanovic From key-variables_calcul3.do

62. Population-weighted Ginis in 1988 and 2008 Branko Milanovic From key-variables_calcul3.do