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

BELBEL

BGD

BGR

BLR

BOLBOLBOL

BRA

CAN

CHL

CHN

CIV

COL

CRI

CZE

DEUDEUDEU

DNK

DOMECU

ESPESPESTESTEST

FINFINFIN

FRA

GBR

GEO

GRC

GTMHND

HRV

HUN

IDN

IND

IRL

IRNISR

ITAITA

JOR

JPN

KAZ

KGZ

KOR

LKALKA

LTU

LVA

MDA

MEXMEXMEX

MKD

MLI

MRT

MYSNGA

NLD

NOR

PAK

PAN

PER

PHL

POL

PRT

ROU

RUS

SGPSLVSLV

SVKSVN

SWE

THA

TJK

TUR

TWNTWN

UGAUGA

UKR

URY

USAUSA

VEN

20

30

40

50

60

70

Gin

i afte

r 2

00

8

20 30 40 50 60Gini 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

Gin

i in

200

8

20 30 40 50 60Gini in 1988

Convergence of countries’ Ginis: an empirical observation without theoretical explanation

Branko Milanovic

ARG

AUSBEL

BGD

BGR

BHS

BOL BRA

BRB

CAN

CHLCHN

COL

CRI

CZE

DEUDNK

DOM

ECU

EGY

ESP

FIN

FJI

FRA

GAB

GBR

GRC

GTM

HKG

HND

HUN

IDNIND

IRL

IRNISR

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

TTOTUN

TUR

TWN

TZA

USA

VEN

ZMB

-20

-10

01

02

0

chan

ge

in G

ini a

fter

19

80

20 30 40 50 60average 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

.3.3

5.4

.45

.5

1970 1980 1990 2000 2010year

USA

.25

.3.3

5.4

.45

.5

1970 1980 1990 2000 2010year

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”

0

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

Gin

i

7.5 8 8.5 9 9.5ln GDP per capita

updated Giniall Fitted values

40

50

60

Gin

i

5 6 7 8 9ln 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, RussiaW.Europe, Japan USA

China

01

02

03

0

Pe

rcen

t

0 10000 20000 30000 40000 50000GDP 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

2

0

30

4

0

50

6

0

70

8

0

90

1

00

p

erc

en

tile

of w

orl

d in

co

me

dis

trib

utio

n

1 20 40 60 80 100 country percentile

Branko Milanovic

Denmark

Mozambique

Mali

Tanzania

Uganda1

10

20

30

40

50

60

70

80

90

100

perc

entile

of w

orl

d in

com

e d

istr

ibutio

n

1 5 10 15 20country ventile

Branko Milanovic

2 2 2 2 2 2

3 3

5

6

7 7

9 9

12

0

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

Gin

i coe

ffic

ien

t

1950 1960 1970 1980 1990 2000 2010year

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

.5.5

5.6

.65

Gin

i coe

ffic

ien

t in

pe

rcen

t

1940 1960 1980 2000 2020year

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

EGYPAK

ETH

LAO

BGD

IND

VNM

UGAKHM

TZA

MDG

NPL

GMB

BDI

LKA

YEM

SLEBTN

TJK

GINBLR

KGZKEN

NIC

THA

IDN

MRT

PHL

JOR

DZA

TUNMKD

MNG

BOLUKR

RWA

MLI

ALBBFA

BEN

MAR

TGO

AZE

SDNSDN

GHA

GTM

GNB

NER

BGR

MDA

HTI MYSNGA

CMR

CIV

MWI

ZMBSAU

OMN

SEN ARMSLV SRB

DOMGEO MNE

TWN

BIH

LBR

HND ECU

DJI

TCD

PRYSWZLSO

CAF

CHN

KAZ

PAN

BWA

MOZ

PER MUS

SUR

BRN

MAC

BLZ

FJI

MDV

COM

TUR

RUS

CPV

COG

TTOHUN

POLMEX

KWT

GNQ

COLJAM

LTU

VEN

NAM

ZAF

QAT

GABCRI

LVA

ARE

HKGSVK

SGP

HRV

CHLAGO

EST

CZEKOR

MLTURY

SVNPRT

BRA

CYP

BHS

GRCESP USAITA

DEUISR

GBR

IRLISL

AUTNLDBEL

NZLFRA

CAN

LUX

FIN

JPNSWE

DNKAUS

NORCHE

-50

05

01

00

150

gain

com

pare

d t

o 2

005

ipc--

norm

aliz

ed b

y th

e u

s leve

l

50000 100000150000gdppc 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

.6.6

5.7

.75

.8.8

5

Gin

i coe

ffic

ien

t

1970 1980 1990 2000 2010Year

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

.5.5

5.6

.65

Gin

i

1990 1995 2000 2005 2010 2015year

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

11

.2

sh

are

of con

sum

ption

in G

DP

1000 10000 50000GDP 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

11

.2

su

rvey m

ea

n o

ve

r N

A c

onsu

mptio

n

1000 10000 50000GDP 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 only

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

0

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

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100

Re

al P

PP

inco

me

ch

ange

(in

pe

rce

nt)

Percentile of global income distribution

-20

-10

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90 100

Re

al P

PP

inco

me

ch

ange

(in

pe

rce

nt)

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

02

04

06

08

0

cu

mula

tive

re

al g

row

th r

ate

betw

een

198

8 a

nd 2

008

2 10 20 30 40 50 60 70 80 90 95 100percentile 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

0

5

10

15

20

25

305

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

99

10

0

Dis

trib

uti

on

(in

pe

rce

nt)

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

An

nu

al p

er

cap

ita

afte

r-ta

x in

com

e in

in

tern

atio

nal

do

llars

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

20080

.2.4

.6.8

1

den

sity

300

100

0

300

0

600

0

100

00

300

00

500

00

100

00

0

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

co

mbin

ed r

ea

l_g

row

th 1

and

2

1 2 3 4 5 6 7 8 9 10decile

200

250

300

350

400

450

co

mbin

ed r

ea

l_g

row

th 1

and

2

1 2 3 4 5 6 7 8 9 10decile

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 opportunity globally and citizenship is a rent (unrelated to individual desert, effort)

• Key issue: Is global equality of opportunity 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)

0

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

0

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

Pe

rce

nt

of

inco

me

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 of history

Branko Milanovic

Global income inequality, 1820-2008 (Source: Bourguignon-Morrisson and Milanovic; 1990 PPPs )

Theil

Gini

02

04

06

08

01

00

1820 1860 1900 1940 1980 2020year

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

0

10

20

30

40

50

60

70

1800 1850 1900 1950 2000 2050

Per

cen

tage

sh

are

of

glo

bal

inco

me

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 0

20

40

60

80

100

1870 2008

Th

eil

0 in

de

x (

me

an

lo

g d

evia

tio

n)

Class

Location

Location

Class

Branko Milanovic

0

10

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90

1800 1850 1900 1950 2000 2050

Bet

wee

n c

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po

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t, in

per

cen

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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