Trends in global income inequality and their political implications - - PowerPoint PPT Presentation

Trends in global income inequality and their political implications

Branko Milanovic

LIS Center; Graduate School City University of New York

Autumn 2014

Branko Milanovic

• A. National inequalities mostly

increased

Branko Milanovic

Ginis in the late 1980s and around now

1985-90 After 2008 Change

Average Gini 36.3 38.8 +2.5

Pop-weighted Gini

33.9 37.3 +3.4

GDP-weighted Gini

32.2 36.4 +4.2

Countries with higher Ginis

32.0 36.2 +4.5

Countries with lower Ginis

42.8 39.5

• 3.3

From final-complete3.dta and key_variables_calcul2.do (lines 2 and 3; rest from AlltheGinis)

Branko Milanovic

Ginis in the late 1980s and around now

twoway (scatter bbb aaa if year==2000, mlabel(contcod) msize(vlarge)) (function y=x, range(20 60) legend(off) xtitle(Gini between 1985 and 1990) ytitle(Gini after 2008)) using allginis.dta

Branko Milanovic

ARG ARM AUS AUT AZE BEL BEL BGD BGR BLR BOL BOL BOL BRA CAN CHL CHN CIV COL CRI CZE DEU DEU DEU DNK DOM ECU ESP ESP EST EST EST FIN FIN FIN FRA GBR GEO GRC GTM HND HRV HUN IDN IND IRL IRN ISR ITA ITA JOR JPN KAZ KGZ KOR LKA LKA LTU LVA MDA MEX MEX MEX MKD MLI MRT MYS NGA NLD NOR PAK PAN PER PHL POL PRT ROU RUS SGP SLV SLV SVK SVN SWE THA TJK TUR TWN TWN UGA UGA UKR URY USA USA VEN

20 30 40 50 60 70 20 30 40 50 60 Gini between 1985 and 1990

Ginis in 1988 and 2008 (population-weighted countries)

From twenty_years/… key_variables_calcul3.do

Branko Milanovic

RUS IND-U MEX BRA NGA IND-R USA CHN-U CHN-R 20 30 40 50 60 20 30 40 50 60 Gini in 1988

Convergence of countries’ Ginis: an empirical

• bservation without theoretical explanation

Branko Milanovic

ARG AUS BEL BGD BGR BHS BOL BRA BRB CAN CHL CHN COL CRI CZE DEU DNK DOM ECU EGY ESP FIN FJI FRA GAB GBR GRC GTM HKG HND HUN IDN IND IRL IRN ISR ITA JAM JPN KOR LKA MEX MYS NLD NOR NPL NZL PAK PAN PER PHL POL PRI PRT SDN SGP SLE SLV SWE SYC THA TTO TUN TUR TWN TZA USA VEN ZMB

• 20
• 10

10 20 20 30 40 50 60 average country Giniall before 1980

twoway (scatter change_gini gini_pre1980 if nvals==1, mlabel(contcod)) (lfit change_gini gini_pre1980, yline(0, lpattern(dash)) ytitle(change in Gini after 1980) legend(off)) Using Allthe Ginis.dta

Market, gross and disposable income Ginis in the US and Germany

Branko Milanovic

.25 .3 .35 .4 .45 .5 1970 1980 1990 2000 2010 year

USA

.25 .3 .35 .4 .45 .5 1970 1980 1990 2000 2010 year

Germany

Define_variables.do using data_voter_checked.dta

Issues raised by growing national inequalities

• Social separatism of the rich
• Hollowing out of the middle classes
• Inequality as one of the causes of the global

financial crisis

• Perception of inequality outstrips real

increase because of globalization, role of social media and political (crony) capitalism (example of Egypt)

• Hidden assets of the rich

Branko Milanovic

Some long-term examples set in the Kuznets framework

Branko Milanovic

38.0 40.0 42.0 44.0 46.0 48.0 50.0 1929 1939 1949 1959 1969 1979 1989 1999 2009

Inequality (Gini) in the USA 1929-2009 (gross income across households)

From ydisrt/us_and_uk.xls

Kuznets and Piketty “frames”

10 20 30 40 50 60 70 1600 1650 1700 1750 1800 1850 1900 1950 2000 2050

Ginis for England/UK and the United States in a very long run

England/UK USA

From uk_and_usa.xls

11

Contemporary examples of Brazil and China: moving on the descending portion of the Kuznets curve

China, 1967-2007

twoway (scatter Giniall lngdpppp if contcod=="CHN" & year>1960, connect(l) ylabel(40(10)60) xtitle(2000 6000 12000) ytitle(Gini) xtitle(ln GDP per capita)) (qfit Giniall lngdpppp if contcod=="CHN" & year>1960, lwidth(thick)) From gdppppreg4.dta

twoway (scatter Giniall lngdpppp if contcod=="BRA", connect(l) ylabel(40(10)60) xtitle(2000 6000 12000) ytitle(Gini) xtitle(ln GDP per capita)) (qfit Giniall lngdpppp if contcod=="BRA", lwidth(thick)) From gdppppreg4.dta

12

Brazil 1960-2010

40 50 60 Gini 7.5 8 8.5 9 9.5 ln GDP per capita updated Giniall Fitted values 40 50 60 Gini 5 6 7 8 9 ln GDP per capita updated Giniall lowess Giniall lngdpppp

• B. Between national inequalities

remained very high even if decreasing

Branko Milanovic

From defines.do in interyd

Distribution of people by income of the country where they live: emptiness in the middle (year 2013; 2011 PPPs)

India, Indonesia Brazil, Mexico, Russia W.Europe, Japan USA China 10 20 30 10000 20000 30000 40000 50000 GDP per capita in 2005 PPP

Different countries and income classes in global income distribution in 2008

From calcu08.dta

USA India Brazil China Russia 1 10 20 30 40 50 60 70 80 90 100 percentile of world income distribution 1 20 40 60 80 100 country percentile

Branko Milanovic

Denmark Mozambique Mali Tanzania Uganda 1 10 20 30 40 50 60 70 80 90 100 1 5 10 15 20 country ventile

Branko Milanovic

2 2 2 2 2 2 3 3 5 6 7 7 9 9 12

2 4 6 8 10 12 14 CYP DEU IRL KOR NLD TWN FRA NOR GBR JPN CAN LUX CHE SGP USA

Countries with more than 1% of their population in top global percentile (above $PPP 72,000 per capita in 2008 prices)

From summary_data.xls

• C. Global inequality is the product of

within- and between-county inequalities How did it change in the last 60 years?

Branko Milanovic

Essentially, global inequality is determined by three forces

• What happens to within-country income

distributions?

• Is there a catching up of poor countries?
• Are mean incomes of populous & large

countries (China, India) growing faster or slower that the rich world?

Branko Milanovic

Global and international inequality after World War II

Branko Milanovic

Concept2: 1960-1980 from Bourguignon & Morrisson

Defines.do using gdppppreg5.dta

Concept 2 Concept 1 Concept 3 .45 .55 .65 .75 1950 1960 1970 1980 1990 2000 2010 year

Concept 2 inequality with 2011 PPPs and without China and India

Branko Milanovic

Defines.do using gdppppreg5.dta

Without India and China Without China all countries .45 .5 .55 .6 .65 1940 1960 1980 2000 2020 year

47

Population coverage

1988 1993 1998 2002 2005 2008 2011

Africa 48 76 67 77 78 78 71 Asia 93 95 94 96 94 98 89 E.Europe 99 95 100 97 93 92 87 LAC 87 92 93 96 96 97 97 WENAO 92 95 97 99 99 97 95 World 87 92 92 94 93 94 88

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

Branko Milanovic

Three important technical issues in the measurement of global inequality

• The ever-changing PPPs in particular for

populous countries like China and India

• The increasing discrepancy between GDP per

capita and HS means, or more importantly consumption per capita and HS means

• Inadequate coverage of top 1% (related also

to the previous point0

Branko Milanovic

The issue of PPPs

Branko Milanovic

The effect of the new PPPs on countries’ GDP per capita (compared to the US level)

Branko Milanovic

EGY PAK ETH LAO BGD IND VNM UGA KHM TZA MDG NPL GMB BDI LKA YEM SLE BTN TJK GIN BLR KGZ KEN NIC THA IDN MRT PHL JOR DZA TUN MKD MNG BOL UKR RWA MLI ALB BFA BEN MAR TGO AZE SDN SDN GHA GTM GNB NER BGR MDA HTI MYS NGA CMR CIV MWI ZMB SAU OMN SEN ARM SLV SRB DOM GEO MNE TWN BIH LBR HND ECU DJI TCD PRY SWZ LSO CAF CHN KAZ PAN BWA MOZ PER MUS SUR BRN MAC BLZ FJI MDV COM TUR RUS CPV COG TTO HUN POL MEX KWT GNQ COL JAM LTU VEN NAM ZAF QAT GAB CRI LVA ARE HKG SVK SGP HRV CHL AGO EST CZE KOR MLT URY SVN PRT BRA CYP BHS GRC ESP USA ITA DEU ISR GBR IRL ISL AUT NLD BEL NZL FRA CAN LUX FIN JPN SWE DNK AUS NOR CHE

• 50

50 100 150 50000 100000 150000 gdppc in 2011ppp

C:\Branko\worldyd\ppp\2011_icp\define

The effect of new PPPs

Country GDP per capita increase (in %) GDP per capita increase population- weighted (in %) Indonesia 90

• Pakistan

66

• Russia

35

• India

26

• China

17

• Africa

23 32 Asia 48 33 Latin America 13 17 Eastern Europe 16 24 WENAO 3 2

Global income inequality using nominal dollars

From two_concepts_exrate.do using Global_new5.dta

Concept 2 Concept 1 Concept 3 .55 .6 .65 .7 .75 .8 .85 1970 1980 1990 2000 2010 Year

63

The gap between national accounts and household surveys

Branko Milanovic

Both the level and change: Use of GDP per capita gives a lower lever and a faster decrease of global inequality

Branko Milanovic

Defines.do based on gdppppreg5.dta

usual Concept 2 GDPs pc countries in HS sample HS means--countries in HS sample .45 .5 .55 .6 .65 Gini 1990 1995 2000 2005 2010 2015 year

How global inequality changes with different definitions of income

62 63 64 65 66 67 68 69 70 71 72 Global inequality GDP ppp Consumption Survey mean

Step 1

Branko Milanovic

Step 2

Step 1 driven by low consumption shares in China and India

(although on an unweighted base C/GDP decreases with GDP)

Branko Milanovic

twoway scatter cons_gdp gdpppp if group==1 & cons_gdp<1.4 [w=totpop], xscale(log) xtitle(GDP per capita in ppp) xlabel(1000 10000 50000) ytitle(share of consumption in GDP) title(C/GDP from national accounts in year 2008) using final08,dta

.2 .4 .6 .8 1 1.2 1000 10000 50000 GDP per capita in ppp

C/GDP from national accounts in year 2008

China India USA

Step 2. No clear (weighted) relationship between survey capture and NA consumption

Branko Milanovic .2 .4 .6 .8 1 1.2 1000 10000 50000 GDP per capita in ppp

survey mean/consumption from national account in year 2008

twoway scatter scale2 gdpppp if group==1 & scale2<1.5 [w=totpop], xscale(log) xtitle(GDP per capita in ppp) xlabel(1000 10000 50000) ytitle(survey mean over NA consumption) title(survey mean/consumption from national account in year 2008)

India China USA

The issue of top underestimation

Branko Milanovic

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

Gini: accounting for missing top incomes

1988 1993 1998 2003 2008 Surveys

• nly

72.5 71.8 71.9 71.9 69.6

NAC instead of survey mean

71.5 70.5 70.6 70.7 67.6

NAC with Pareto

71.8 70.8 71.0 71.1 68.0

NAC with top-heavy Pareto

76.3 76.1 77.2 78.1 75.9

Branko Milanovic

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 consumption 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

How Global Gini in 2008 changes with different adjustments

Branko Milanovic

• 4
• 2

2 4 6 8 10

Increase in global Gini with each “marginal”adjustment

Allocate the gap proportionally along each national income distributions Allocate the gap proportionately and add a Pareto “elongation” Allocate the gap to top 10% and add Pareto “elongation”

With full adjustment (allocation to the top 10% + Pareto) Gini decline almost fully disappears

Branko Milanovic

Survey data only 64 66 68 70 72 74 76 78 80

1988 1993 1998 2003 2008

Top-heavy allocation of the gap + Pareto adjustment

• D. How has the world changed

between the fall of the Berlin Wall and the Great Recession

Branko Milanovic

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

10 20 30 40 50 60 70 80 20 40 60 80 100

Real PPP income change (in percent) Percentile of global income distribution

• 20
• 10

10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90 100

Real PPP income change (in percent) Percentile of global income distribution Real income gains (in $PPP) at different percentile of global income distribution 1988-2008

Without China

World

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

Branko Milanovic

Growth incidence curve (1988-2008) estimated at percentiles of the income distribution

Branko Milanovic mean growth 20 40 60 80 2 10 20 30 40 50 60 70 80 90 95 100 percentile of global income distribution

Using my_graphs.do Mean-on-mean

0 0 1 1 1 1 1 2 2 2 3 3 4 5 4 1 3 5 10 25 27 5 10 15 20 25 30 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99 100

Distribution (in percent) of gain ventile/percentile of global income distribution

Distribution of the global absolute gains in income, 1988-2008: more than ½ of the gains went to the top 5%

From summary_data.xls

Branko Milanovic

500 5000 1988 1993 1998 2003 2008 2011 Annual per capita after-tax income in international dollars US 2nd decile Chinese 8th urban decile

From summary_data.xls

Global income distributions in 1988 and 2008

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

1988 2008 .2 .4 .6 .8 1

300 1000 3000 6000 10000 30000 50000 100000

log of annual PPP real income

Emerging global “middle class” between $3 and $16

Branko Milanovic

Increasing gains for the rich with a widening urban-rural gap

Urban and rural China Urban and rural Indonesia

170 180 190 200 210 220 1 2 3 4 5 6 7 8 9 10 decile

200 250 300 350 400 450 1 2 3 4 5 6 7 8 9 10 decile

From key_variables_calcul2.do

Branko Milanovic

urban rural urban rural

• E. Issues of justice and politics
• 1. Citizenship rent
• 2. Migration
• 3. Hollowing out of the middle classes

Branko Milanovic

Global inequality of opportunity

• Regressing (log) average incomes of 118

countries’ percentiles (11,800 data points) against country dummies “explains” 77% of variability of income percentiles

• Where you live is the most important

determinant of your income; for 97% of people in the world: birth=citizenship.

• Citizenship rent.

Branko Milanovic

Is citizenship a rent?

• If most of our income is determined by

citizenship, then there is little equality of

• pportunity globally and citizenship is a rent

(unrelated to individual desert, effort)

• Key issue: Is global equality of
• pportunity something that we ought to

be concerned or not?

• Does national self-determination dispenses

with the need to worry about GEO?

Branko Milanovic

The logic of the argument

• Citizenship is a morally-arbitrary circumstance,

independent of individual effort

• It can be regarded as a rent (shared by all

members of a community)

• Are citizenship rents globally acceptable or

not?

• Political philosophy arguments pro (social

contract; statist theory; self-determination) and contra (cosmopolitan approach)

Branko Milanovic

The Rawlsian world

• For Rawls, global optimum

distribution of income is simply a sum of national optimal income distributions

• Why Rawlsian world will remain

unequal?

Branko Milanovic

All equal Different (as now) All equal Different (as now)

Mean country incomes Individual incomes within country

Global Ginis in Real World, Rawlsian World, Convergence World…and Shangri-La World (Theil 0; year 2008) 98 68 (all country Ginis=0) 30 (all mean incomes same; all country Ginis as now)

Branko Milanovic

Conclusion

• Working on equalization of

within-national inequalities will not be sufficient to significantly reduce global inequality

• Faster growth of poorer countries

is key and also…

Branko Milanovic

Migration: a different way to reduce global inequality and citizenship rent

• A new view of development:

Development is increased income for poor people regardless of where they are, in their countries of birth or elsewhere

• Migration and LDC growth thus become

the two equivalent instruments for development

Branko Milanovic

A migrant point of view: trade-off between country’s mean income and its inequality

Branko Milanovic

2 4 6 8 10 12 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Percent of income Ventile

How much is one Gini point change worth in terms of mean country income? Decrease in Gini Increase in Gini

From interyd..\ventil_vs_country.xls

Political issue: Global vs. national level

• Our income and employment is increasingly

determined by global forces

• But political decision-making still takes place at

the level of the nation-state

• If stagnation of income of rich countries’ middle

classes continues, will they continue to support globalization?

• Two dangers: populism and plutocracy
• To avert both, need for within-national

redistributions: those who lose have to be helped

Branko Milanovic

Final conclusion

• To reduce global inequality: fast

growth of poor countries + migration

• To preserve good aspects of

globalization: redistribution within rich countries

Branko Milanovic

Additional slides

Branko Milanovic

• H. Global inequality over the long-run
• f history

Branko Milanovic

Global income inequality, 1820-2008

(Source: Bourguignon-Morrisson and Milanovic; 1990 PPPs )

Theil Gini 20 40 60 80 100 1820 1860 1900 1940 1980 2020 year

twoway (scatter Gini year, c(l) xlabel(1820(40)2020) ylabel(0(20)100) msize(vlarge) clwidth(thick)) (scatter Theil year, c(l) msize(large) legend(off) text(90 2010 "Theil") text(70 2010 "Gini"))

Branko Milanovic

Branko Milanovic

10 20 30 40 50 60 70 1800 1850 1900 1950 2000 2050

Percentage share of global income Year

Shares of global income received by top 10% and bottom 60% of world population

Top 10% (B-M data) Top 10% (L-M data) Bottom 60% (B-M data) Bottom 60% (L-M data)

A non-Marxist world

• Over the long run, decreasing importance of

within-country inequalities despite some reversal in the last quarter century

• Increasing importance of between-country

inequalities (but with some hopeful signs in the last five years, before the current crisis),

• Global division between countries more than

between classes

Branko Milanovic

Composition of global inequality changed: from being mostly due to “class” (within-national), today it is mostly due to “location” (where people live)

Based on Bourguignon-Morrisson (2002), Maddison data, and Milanovic (2005)

From thepast.xls

Branko Milanovic

20 40 60 80 100

1870 2008 Theil 0 index (mean log deviation)

Class Location Location Class

Branko Milanovic

10 20 30 40 50 60 70 80 90 1800 1850 1900 1950 2000 2050 Between component, in percent Year

Share of the between component in global Theil (0)

B-M data

L-M data

Very high but decreasing importance of location in global inequality

From thepast.xls under c:\history