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THREATS OF GROWING INCOME INEQUALITIES AND POSSIBLE SCENARIOS
Vladimir Popov
There are at least two threats of growing income inequalities:
social tension within countries
backlash against globalization
Past and recent research shows that inequalities are associated with the array of negative social consequences (Wilkinson, Pickett, 2010).
- decline in the institutional capacity of the state - increase in crime and mortality - decline in educational attainments - proliferation of psychological disorders - obesity - worse attitude to democracy
Besides, inequalities undermine social mobility and lead to the conservation of social stratification: the higher the inequalities:
The higher the probability that ones’ incomes will closely resemble that of their parents (the Great Gatsby curve).
Fig. 4. Murder rate in 2002 and income inequalities in 1990-2005 (WHO, WDI).
Albania
Algeria
Argentina
ArmeniaAustralia
Austria
Azerbaijan
BangladeshBelarus
Belgium
Benin
Boliv ia
Bosnia and Herzegov ina
Botswana
Brazil
Bulgaria
Burkina Faso
BurundiCambodia
Cameroon
Canada
Central Af rican Republic
Chile
China
Colombia
Costa Rica
Cote d'Iv oire
Croatia
Czech RepublicDenmark
Dominican Republic
Ecuador
Egy pt, Arab Rep.
El Salv ador
Estonia
Ethiopia
Finland
France
Gambia, The
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Guinea-Bissau
Haiti
Honduras
Hungary
IndiaIndonesia
Iran, Islamic Rep.
Ireland
Israel
ItalyJamaica
Japan
Jordan
Kazakhstan
Keny a
Korea, Rep.Ky rgy z Republic
Lao PDR
Latv ia
Lesotho
Lithuania
Macedonia, FYR
Madagascar
MalawiMalay sia
Mali
Mauritania
Mexico
Moldov a
Mongolia
MoroccoMozambique
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
Norway
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Romania
Russian Federation
Rwanda
Senegal
Sierra Leone
Singapore
Slov ak Republic
Slov enia
South Af rica
SpainSri Lanka
Swaziland
Sweden
SwitzerlandTajikistan
Tanzania
ThailandTrinidad and TobagoTunisia
Turkey
TurkmenistanUganda
Ukraine
United Kingdom
United States
Uruguay
Uzbekistan
Venezuela, RB
Vietnam
Yemen, Rep.
Zambia
Zimbabwe
20
40
60
80
GIN
I_90_05
0 20 40 60 80Murders per 100 000 inhabitants (WHO)
Albania Algeria
Argentina
Armenia
AustraliaAustria
Azerbaijan
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and HerzegovinaBotswana
Brazil
Bulgaria
Burkina FasoBurundi
Cambodia
Cameroon
Canada
Central African Republic
ChileChina
Colombia
Costa Rica
Cote d'Ivoire
Croatia
Czech RepublicDenmark
Dominican RepublicEcuadorEgypt, Arab Rep.
El Salvador
Estonia
Ethiopia
FinlandFrance
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Haiti
Honduras
Hong Kong, China
HungaryIndiaIndonesia
Iran, Islamic Rep.
Ireland
IsraelItaly
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep.
Kyrgyz Republic
Lao PDR
Latvia
LesothoLithuania
Macedonia, FYR
MadagascarMalawi
Malaysia
Mali
Mauritania
Mexico
Moldova
Mongolia
Morocco
Mozambique
Namibia
Nepal
NetherlandsNew Zealand
Nicaragua
Niger
Nigeria
Norway
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Romania
Russian Federation
Rwanda
Senegal
Sierra Leone
Singapore
Slovak Republic
Slovenia South Africa
Spain
Sri Lanka
Sweden
Switzerland
Tanzania
Thailand
Tunisia
Turkey
Uganda
Ukraine
United KingdomUnited States
Uruguay
Uzbekistan Venezuela, RB
Vietnam
Yemen, Rep.
Zambia
Zimbabwe
Albania Algeria
Argentina
Armenia
AustraliaAustria
Azerbaijan
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia and HerzegovinaBotswana
Brazil
Bulgaria
Burkina FasoBurundi
Cambodia
Cameroon
Canada
Central African Republic
ChileChina
Colombia
Costa Rica
Cote d'Ivoire
Croatia
Czech RepublicDenmark
Dominican RepublicEcuadorEgypt, Arab Rep.
El Salvador
Estonia
Ethiopia
FinlandFrance
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Haiti
Honduras
Hong Kong, China
HungaryIndiaIndonesia
Iran, Islamic Rep.
Ireland
IsraelItaly
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep.
Kyrgyz Republic
Lao PDR
Latvia
LesothoLithuania
Macedonia, FYR
MadagascarMalawi
Malaysia
Mali
Mauritania
Mexico
Moldova
Mongolia
Morocco
Mozambique
Namibia
Nepal
NetherlandsNew Zealand
Nicaragua
Niger
Nigeria
Norway
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Romania
Russian Federation
Rwanda
Senegal
Sierra Leone
Singapore
Slovak Republic
Slovenia South Africa
Spain
Sri Lanka
Sweden
Switzerland
Tanzania
Thailand
Tunisia
Turkey
Uganda
Ukraine
United KingdomUnited States
Uruguay
Uzbekistan Venezuela, RB
Vietnam
Yemen, Rep.
Zambia
Zimbabwe
02
04
06
08
0
Sh
ad
ow
econ
om
y a
s a
% o
f G
DP
in
200
5
20 40 60 80Gini coefficient of income inequalities in 1990-2005, %
Shadow economy as a % of GDP in 2005
Attitude toward democracy and
inequality in some Latin American
countries
Source: Latinobarometro, cited in: Economist,
November 17, 2007; Word Development indicators,
2007; Davies, Sandstrom, Shorrocks, Wolf, 2007
There are at least two threats of growing income inequalities:
social tension within countries
backlash against globalization
Fig. The share of 10% richest households in total personal income in European countries in 1875-2013, %
Source: The World Wealth and Income Database.
-5
0
5
10
15
20
1929 1934 1939 1944 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014
30
32
34
36
38
40
42
44
46
48
50
Share of profits with IVA and CCAdj after taxes
Top 10% income share
PPP GDP per capita in Britain as a % of average
level of 30 West European countries
Fig. Increase in the pride index, times, and annual average growth rates of per capita GDP, %, in 1989-2014
Source: World Values Survey; World Development Indicators database.
Fig. 6. Increase in the pride index, times, and increase in the Gini coefficient of income
distribution, p.p., in 1989-2014
Source: World Values Survey; World Development Indicators database.
Fig. 6. Increase in the pride index, times, and increase in the Gini coefficient of income distribution,
p.p., in 1989-2014
The regression equation linking the increase in the pride index (Ipride) with the growth of per capita GDP (GROWTH) and the increase in inequalities (GINIincr) for the period of 1989-2014 is
Ipride = 0.18 GROWTH – 0.08 GINIincr + 0.34
(2.08) (1.61) (1.81),
N=20, R-squared = 0.32, robust estimate, T-statistics in brackets below,
Ipride – ratio of positive to negative answers – (very proud + quite proud)/(not very proud + not proud at all) – in 2010-14 divided by the same ratio in 1989-94,
GROWTH – annual average growth of per capita GDP in 1990-2014, %,
GINIincr – and the increase in Gini index of income inequalities in percentage points from the beginning to the end of the period of 1989-2014, p.p.
Income and wealth inequalities
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Benin
BoliviaBotswana
Brazil
Bulgaria
Burkina Faso
BurundiCambodiaCameroon
Central African RepublicChile
China
Colombia
Costa Rica
Cote d'Ivoire
Croatia
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt, Arab Rep.
El Salvador
Estonia
Ethiopia
Finland
FranceGambia, TheGeorgia
Germany
Ghana
Greece
Guatemala
Guinea
Guinea-Bissau
HaitiHondurasHong Kong, China
Hungary
India
Indonesia
Iran, Islamic Rep.
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep.
Kyrgyz RepublicLatvia
Lesotho
LithuaniaMacedonia, FYR
MadagascarMalawiMalaysia
Mali
Mauritania
Mexico
Moldova MoroccoMozambique
Namibia
NetherlandsNew Zealand
Nicaragua
NigerNigeria
Norway
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
PolandPortugal
Romania
Russian FederationRwanda
SenegalSierra LeoneSingapore
Slovak RepublicSlovenia
South Africa
Spain
Sri Lanka
Swaziland
Sweden
Switzerland
TajikistanTanzania
Thailand
Trinidad and TobagoTunisia
TurkeyUganda
Ukraine
United KingdomUruguayVenezuela, RB
Vietnam
Yemen, Rep.
Zambia
Zimbabwe
50
60
70
80
90
GIN
I_w
ea
lth_
00
20 40 60 80GINI_90_05
Fig. Different series for the UK top 1% wealth share 1911-2012
Source: Facundo Alvaredo, Anthony B Atkinson, and Salvatore Morelli (2016).
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
Marcus
Crassus (53
BCE)
Andrew
Carnegie
(1901)
John D.
Rockefeller
(1937)
Mikhail
Khodorkovsky
(2003)
Bill Gates
(2005)
Carlos Slim
(2009)
Incomes of the richest as a multiple of average national income
Number of
billionaires
Total
wealth
PPP
GDP,
2012
Number
per 1
trillion
PPP
GDP
Wealth of
billionaire
s to PPP
GDP, %
China 122 260.9 12471 9.8 2.1
Russia 110 403.8 3380 32.5 11.9
Ukraine 10 31.3 338.2 29.6 9.3
Kazakhstan 5 9.2 233 21.5 3.9
Czech Republic 4 14.0 277.9 14.4 5.0
Poland 4 9.8 844.2 4.7 1.2
Georgia 1 5.3 26.6 37.6 19.9
Vietnam 1 1.5 322.7 3.1 0.5
Romania 1 1.1 352.3 2.8 0.3
Uzbekistan 0 0 107 0.0 0.0
Table . Billionaires in former USSR, Eastern Europe China, and Vietnam, 2013
Source: Forbes billionaires list
(http://www.forbes.com/billionaires/#page:1_sort:0_direction:asc_search:_filter:All%20industries_filter:All%20countries_filter
Total Forbes billionaire wealth (% national income): Russia vs other countries, 1990-2016
Number if billionaires in 2007and PPP GDP in 2005 (WDI database ; Forbes billionaires website (http://www.forbes.com/billionaires/).
The relationship is non-linear:
Number of billionaires in 2007 = -0.9 + 0.367y –0.0049y2 +2.6Y2,
Where
y – PPP GDP per capita in thousand $
in 2005,
Y – PPP GDP in 2005 in trillions.
N= 181, R2 = 0.95, all coefficients
significant at 1% level.
Vertical vs. horizontal inequalities
Income and wealth levels are different not withing the groups (ethnic, social, etc.), but between groups
Inequalities not within regions , but between regions
First scenario
Inequalities grow => social tensions (revolutions?)
Inequalities grow => nationalism =>
Backlash against globalization => protectionist measures =>
Collapse of the world tarde and capital flows similar to the 1930s
-5
0
5
10
15
20
1929 1934 1939 1944 1949 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014
30
32
34
36
38
40
42
44
46
48
50
Share of profits with IVA and CCAdj after taxes
Top 10% income share
Second scenario
Some countries (Europe, East Asia, others) manage to limit the increase in inequalities =>
These countries become more competitive (stronger institutional capacity, lower murder rate and shadow economy, better investment climate)
Other countries are compelled to change policies due to the demonstration effect
Gini coefficient of income
distribution in Russia and
China, %
Source: Chen, Hou, Jin, 2008; Госкомстат; China daily, January 19, 2013.
0.25
0.3
0.35
0.4
0.45
0.5
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
China, new series
Russia
China
Fig. 3.18. Gini coefficient of income distribution in China in 2003-12, %, new official sample (China Daily, January 19, 2013)
0.47
0.475
0.48
0.485
0.49
0.495
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
INEQUALITY IN CHINA – BETWEEN PROVINCES AND WITHIN PROVINCES
It is important, though, to take into account the size of the country—interms of both territory and population.
Three Chinese provinces (Guangdong, Shandong, Henan) havepopulations over 95 million, another seven have over 50 million, that isbigger than most states, so China should be compared with multistateregions, like the European Union or ASEAN, rather than with particularstates.
In the EU 27, for instance, the coefficient of income inequality around2005 was about40% with 23 percentage points (p.p.) coming frombetween-country inequalities.
In China (29 provinces) it was over 40% with 24 p.p. coming frombetween-province disparities.
In the USA, the inequality coefficient was similar (over 40%), but only 6p.p. came from disparities in income between the states (Milanovic,2012).
If China manages to reduce the income gap between its provinces (andthe EU-between member countries) to a level close to the disparitiesbetween US states, the general inequality between citizens will fall toquite a low level.
Inequalities in Latin America
CONCLUSIONS
Explanation of the trends in inequality dynamics
Social consequences of inequalities:
When growing inequality leads to a revolution?
When revolutions happen without growing inequality?
Scenarios:
Inequality trends?
Future revolutions?
