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Munich Personal RePEc Archive
Billionaires, millionaires, inequality, and
happiness
Popov, Vladimir
21 May 2019
Online at https://mpra.ub.uni-muenchen.de/94081/
MPRA Paper No. 94081, posted 28 May 2019 17:16 UTC
1
Billionaires, millionaires, inequality, and happiness1
Vladimir Popov2
Abstract
The relationship between inequality and happiness is counterintuitive. This applies to both
inequality in income and wealth distribution overall and also inequality at the very top of the wealth
pyramid, as measured by billionaire intensity (the ratio of billionaire wealth to GDP). First,
billionaire intensity appears to be higher in countries with low, not high, levels of income
inequality. Second, happiness indices are higher in countries with high percentages of billionaire
and millionaire wealth as a proportion of GDP, but with low levels of income inequality.
This paper uses databases from the Forbes billionaires list, the Global Wealth Report (GWR), and
the World Happiness Report, as well as from the World Database on Happiness. Using these
datasets, I examine the relationship between income inequality and happiness for over 200
countries from 2000 to 2018.
It turns out that in relatively poor countries – below $20,000-$30,000 per capita income –
inequality raises happiness rather than lowers it, but inequality has a negative impact on happiness
in rich countries. A certain degree of inequality of wealth and income distribution has a positive
impact on happiness feelings, especially in countries with low levels of income. Furthermore,
wealth inequalities, and especially the degree of concentration of wealth at the very top of the
wealth pyramid, raise happiness self-evaluations even when income inequalities lower it.
Keywords: inequality in income and wealth distribution, share of billionaires’ and millionaires’
wealth in GDP, happiness indices
JEL: D31, D63, I31
1 This paper is the logical continuation of my two 2018 papers ‘Paradoxes of happiness. Why do people feel more
comfortable with high levels of inequality and high murder rates?’ DOC Expert Comment, 18 June 2018 and ‘Why
do some countries have more billionaires than others? Explaining variations in the billionaire-intensity of GDP’. DOC Expert Comment, 24 July 2018. 2 Dialogue of Civilizations Research Institute, Berlin, Germany. My thanks go to Tony Shorrocks who kindly
provided me with the excel table of the GWR database. I am also most grateful to Elena Sulimova for collecting the
data and preparing the database for the analysis.
2
Billionaires, millionaires, inequality, and happiness3
Vladimir Popov4
Literature review, puzzles, and hypotheses
There are some important paradoxes in the dynamics of happiness indices and the relative levels
of these indices for various countries and different populations groups. One puzzle, the Easterlin
paradox, is the non-increasing level of happiness in the US in spite of constantly rising personal
incomes (fig. 1).5
Figure 1: Average happiness score (left scale) and GDP per capita, dollars, (right scale) in
the US in 1972- 2016
Source: Sachs (2018).
3 This paper is the logical continuation of my two 2018 papers ‘Paradoxes of happiness. Why do people feel more
comfortable with high levels of inequality and high murder rates?’ DOC Expert Comment, 18 June 2018 and ‘Why
do some countries have more billionaires than others? Explaining variations in the billionaire-intensity of GDP’. DOC Expert Comment, 24 July 2018. 4 Dialogue of Civilizations Research Institute, Berlin, Germany. My thanks go to Tony Shorrocks who kindly
provided me with the excel table of the GWR database. I am also most grateful to Elena Sulimova for collecting the
data and preparing the database for the analysis and to Jonathan Grayson for editing. 5 The happiness index in this paper is taken from the World Database on Happiness. This is a self-evaluation of personal happiness on a scale from 0 to 10, derived from surveys.
3
Sachs (2018) argues that America’s subjective wellbeing is being systematically undermined by
three interrelated epidemics, notably obesity, substance abuse (especially opioid addiction), and
depression. But in other countries without as much obesity, drugs, and depression, there is also a
decline in happiness that goes hand in hand with rising real incomes.
In India, the happiness index score fell from 5 to 4 over the 2006-18 period despite strong growth
of income in this period. In China, over the 1990–2000 decade, happiness plummeted despite
massive improvements in material living standards. Brockmann, Delhey, Welzel, and Hao (2008)
explain this by growing income inequality within China, i.e., in relation to the average national
income, the financial position of most Chinese people deteriorated.
Similarly, in the US the recent increase in income inequalities could be responsible for the decline
in happiness: in 1980-2014, the post-tax incomes of the richest 10% rose by 113%; of the top 1%,
by 194%; and of the top 0.001%, by 617% (Piketty, Saez, Zucman, 2016), whereas the US
happiness index score over this period fell. However, the relationship between inequality and
happiness is also not straightforward and presents another puzzle.
Normally we assume that greater equality – ‘inclusive development that leaves no one behind’ –
is both morally just and desirable for the creation of happy societies. But there is evidence that
income and wealth inequalities are positively associated with happiness, as measured by the
happiness index, at least for a group of countries. There are some poorer countries with high
income inequalities – Bolivia, Honduras, Colombia, Ecuador, Costa Rica and some other Latin
American countries – and yet also with very high happiness index scores (fig. 2). It may well be
that a certain degree of inequality is necessary to keep alive a kind of ‘American dream’: a future-
oriented belief in getting rich and achieving success in life.
Alesina, Di Tella, and MacCulloch (2001) showed that there is a large, negative, and significant
effect of inequality on happiness in Europe, but not in the US. It is also clear that people have
different perceptions of ‘correct’, ‘optimal’, or ‘just/fair’ degrees of inequality. Alesina and La
Ferrara (2001) found that individual support for redistribution is negatively affected by social
4
mobility. People who believe that American society offers equal opportunities to all are more
averse to redistribution in the face of increased mobility.
On the other hand, those who see the social rat race as a biased process do not see social mobility
as an alternative to redistributive policies. Alesina and Giuliano (2009) presented evidence that
individuals who believe other people try to take advantage of them rather than being fair have a
strong desire for redistribution; similarly, believing that luck is more important than work as a
driver of success is strongly associated with a taste for redistribution.
Inequality at the very top does not seem to lead to pressure for redistribution. In Victorian England,
inequality within the elite was associated with more conditionality and less generous welfare
expenditure. Removing institutional advantages that benefited the elite did not appear to reduce
the effect of elite inequality, which suggests results cannot be explained by a classic median voter
model (Chapman, 2018).6 Therefore, an increase in inequality at the very top may be self-
perpetuating; there is no pressure to redistribute and no mechanism to automatically ‘correct’ the
inequality.
6 This model predicts that in a majority-rule voting system, the outcome selected at the polls will be the one preferred by the median voter, i.e., the voter separating one half of the electorate from the other half, if all voters are ranked according to their preferences.
5
Figure 2: Happiness index (Word Happiness Report and World Database on Happiness), 0-
to-10 scale; and GINI coefficient of income inequality in percentage terms, 2000-18
Source: World Database on Happiness (2019); WDI.
There is also some evidence that happiness is positively correlated with murder rates, especially
when this goes hand in hand with inequalities (Popov, 2018a; 2018b). Inequalities lead to higher
murder rates, but this does not lead to a decline in happiness, at least up to a certain point.
Furthermore, happiness scores also seem almost independent of suicide rates, which is often
considered an objective indicators of happiness, in contrast to happiness indices that measure self-
perception through surveys where people measure their own happiness on a 0-to-10 scale.
The relationship between self-reported happiness and objective indicators of frustration and
distress is somewhat counterintuitive. Murder rates are correlated positively with happiness index
scores (fig. 3), although this correlation is not statistically significant, whereas the correlation
between suicide rates and happiness is negative (fig. 4), as one would expect but very weak: in
regressions of happiness on murder rates, suicides rates, and per capita income, R2 is less than 2%
and the suicide rate is significant only in random effects specification – it is apparent with the
ALBDZA
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RUS
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RWASEN
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SVKSVKSVK
SVKSVKSVKSVK
SVKSVK
SVKSVK
SVN
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ZAF
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ESPESPESPESPESPESPESPESPESP
LKALKA LKALKASDN
SWESWESWESWESWESWESWESWESWESWE
SWE
SWE
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CHECHECHECHECHE
CHE
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TZATZA
THATHATHATHA
THA
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TUN TUR
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TURTUR
TURTUR
TURTUR
TURTURTUR
TUR
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UGA
UGAUGA
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UKR
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GBRGBRGBRGBRGBR
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GBR
GBR
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USAUSAUSAUSA
URY
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YEM
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ZWE
24
68
10
25 35 45 55 65GINI Index (World Bank Estimate)
Happiness Score Fitted values
6
naked eye from a comparison of figure 4, which presents cross-section data for the single year of
2018, and figure 5, which presents panel data for the years 2000-18.7
The murder rate is clearly positively correlated with income inequality (fig. 6), but the suicide rate
is correlated negatively, if at all (fig. 7).
Figure 3: Happiness index (Word Happiness Report and World Database on Happiness), 0-
to-10 scale; and murder rate per 100,000 inhabitants in 2000-18
Source: WHO; World Database on Happiness.
7 HappinessIndex = 5.8*** – 9.7e-07 Ycap + 0.007MURDERrate* – 0.01SUICIDErate
N=323 (Panel data for 2000-18 for over 200 countries, some observations are missing), R2=0.014, robust estimates, no control for fixed and random effects
HappinessIndex = 5.7*** + 0.005MURDERrate – 0.02SUICIDErate*
Random effects regression, R2 (between) = 0.02, N=328.
HappinessIndex = 5.7*** – 1.4e-08 Ycap + 0.005MURDERrate – 0.03SUICIDErate**
Random effects regression, R2 (between) = 0.02, N=323.
Here and later the following notations are used: *, **, *** – denotes significance at 10, 5 and 1% level respectively.
AFG
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EST
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DEUDEUDEUDEU
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GHAGHA
GHAGHA
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GHA
GRCGRCGRCGRCGRCGRCGRCGRC
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GIN
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HND
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HKGHKGHKGHKG
HKGHKGHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUN
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IDN
IDNIDNIDNIDNIDNIDNIDNIDN
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ITAITAITAITAITAITAITAITA
ITAITAITAITA
ITA
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JAMJPNJPNJPNJPNJPNJPNJPNJPNJPNJPNJPNJPNJPNJPNJPNJORJOR
JOR
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MUSMUSMUSMUSMUS
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UGA
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GBR
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URYURY
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ZMB
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ZMB
ZMB
ZWE
ZWEZWE
24
68
10
0 20 40 60 80 100Intentional homicide rate (Wikipedia)
Happiness Score Fitted values
7
Figure 4: Happiness index in 2018 and suicide rates in 2016
Figure 5: Happiness index and suicide rates in 2000-18
Source: WHO; World Happiness Database.
Afghanistan
Albania
Algeria
Angola
Argentina
Armenia
AustraliaAustria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Belize
Benin
Bhutan
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Central African Republic
Chad
Chile
China
Colombia
Congo (Brazzaville)
Congo (Kinshasa)
Costa Rica
Croatia
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Haiti
HondurasHungary
Iceland
India
Indonesia
Iran
Iraq
IrelandIsrael
ItalyJamaica Japan
Jordan
Kazakhstan
Kenya
Kuwait
Kyrgyzstan
Laos
Latvia
Lebanon
Lesotho
Liberia
Libya
Lithuania
Luxembourg
Macedonia
MadagascarMalawi
Malaysia
Mali
Malta
Mauritania
Mauritius
Mexico
Moldova
MongoliaMontenegroMorocco
MozambiqueMyanmar
Namibia
Nepal
NetherlandsNew Zealand
Nicaragua
Niger
Nigeria
Norway
Pakistan
Panama
ParaguayPeruPhilippines
Poland
Portugal
Qatar
RomaniaRussia
Rwanda
Saudi Arabia
Senegal
Serbia
Sierra Leone
SingaporeSlovakia
Slovenia
Somalia
South Africa
South Korea
South Sudan
Spain
Sri Lanka
Sudan
SwedenSwitzerland
Syria
Tajikistan
Tanzania
Thailand
Togo
Trinidad & Tobago
Tunisia
TurkeyTurkmenistan
Uganda Ukraine
United Arab EmiratesUnited KingdomUnited States
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
34
56
78
0 10 20 30 40Suicide rate per 100,000 inhabitants in 2016
Happiness score in 2018 Fitted values
AFG
AFGAFG
ALB
ALBALB
DZADZA
DZA
AGOAGO
ARGARGARGARG
ARMARMARM
AUSAUS
AUTAUT
AUTAUT
AZE
AZEAZE
BHRBHRBHR
BGDBGDBGD
BLR
BLRBLR
BELBELBELBEL
BLZ
BLZ
BENBEN
BTNBTN
BOLBOL
BOLBOL
BIHBIHBIH
BWA
BWA
BWA
BRABRABRA
BGRBGRBGRBFA
BFABFA
BDIBDI
KHMKHMKHM
CMR
CMRCMR
CAN
CANCAN
CAFCAF
CAF
TCDTCDTCD
CHL
CHL
CHLCHL
CHN
CHNCHN
COL
COL
COLCOL
COMCOMCOM
CODCODCOGCOG
CRI
CRI
CRICRI
CIVCIV
HRVHRV
HRV
CYP
CYPCYP
CZE
CZECZE
DNKDNK
DNKDNK
DJI
DJI
DOM
DOMDOM
ECU
ECU
ECUECU
EGY
EGYEGY
SLVSLV
SLVSLVEST
ESTEST
ETHETHETH
FIN
FINFINFIN
FRAFRAFRAFRA
GAB
GABGABGEO
GEOGEO
DEUDEUDEU
DEU
GHAGHA
GHA
GRC
GRCGRCGRC
GTMGTM
GTMGTM
GIN
GINGIN
GUY
HTIHTI
HTI
HNDHND
HNDHND HUNHUN
HUN
ISLISL
IND
INDIND
IDNIDNIDN
IRNIRNIRQ
IRQIRQ
IRLIRLIRLIRL
ISRISRISR
ITAITAITAITA
JAM
JAMJAM
JPNJPNJPN
JOR
JORJORKAZ
KAZKAZ
KENKENKEN
KORKORKOR
KWT
KWTKWT
KGZKGZKGZLAOLAO
LVALVA
LVA
LBNLBNLBN
LSO
LSO
LBR
LBR
LBR
LBY
LBYLBY
LTU
LTULTU
LUXLUX
LUXLUX
MKDMKDMKD
MDGMDG
MWIMWI
MYSMYSMYS
MLIMLIMLI
MLT
MLT
MLT
MRT
MRTMRT
MUSMUS
MEX
MEX
MEX
MDA
MDAMDA
MNGMNGMNG
MNEMNEMNE
MARMARMAR
MOZ
MMR
MMRMMR
NAM
NAMNPL
NPLNPL
NLDNLD
NLDNLDNZLNZL
NIC
NIC
NICNIC
NERNERNER
NGA
NGA
NGA
NORNOR
OMN
PAK
PAKPAK
PAN
PANPANPRY
PRY
PRY
PRYPER
PER
PERPER
PHLPHL
POLPOLPOLPRT
PRT
PRTPRT
QATQATQAT
ROU
ROU
ROU
ROURUS
RUS
RWARWA
SAUSAUSAU
SEN
SENSEN
SRBSRB
SLE
SLESLE
SGPSGP
SVKSVKSVK
SVN
SVNSVN
SOM
SOMSOM
ZAFZAF
ESPESPESPESP
LKALKALKASDN
SDN
SDN
SURSURSUR
SWESWE
SWESWE
CHECHE
CHECHE
SYR
SYRSYR
TJK
TJKTJK
TZA
TZATZA
THATHATHA
TGO
TGO
TTOTTO
TUN
TUNTUN
TUR
TURTURTKMTKM
UGAUGAUGA
UKR
UKR
ARE
ARE
GBRGBR
GBRGBR
USAUSAUSAURY
URY
URYURY
UZB
UZBUZB
VENVEN
VEN
VEN
VNMVNMVNM
YEM
YEM
YEM
ZMB
ZMB ZWE ZWE
ZWE
24
68
0 10 20 30 40 50Age-standardized suicide rates (WHO)
Fitted values Happiness Score
8
Figure 6: Murder rate per 100,000 inhabitants and Gini coefficient of income distribution in
percentage terms in 2000-18
Figure 7: Suicide rate per 100,000 inhabitants and GINI coefficient of income inequality in
percentage terms in 2000-18
Source: WHO, WDI.
ALBALBALBALB
DZA
ARGARGARMARMARM ARMARMARMARMARMARMARMARMARMARMARMARMARMAUSAUSAUSAUSAUTAUTAUTAUTAUTAUTAUTAUTAUTAUTAUTAUTAUT
AZEAZEAZEAZEAZE BGDBGDBGD
BLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBELBELBELBELBELBELBELBELBELBELBELBELBELBEN
BTNBTNBTN BOLBOL
BOLBOLBOLBOLBOL
BOLBOLBOLBOL
BIH BIHBIHBIHBIH
BWABWA
BRABRABRABRABRABRABRABRABRABRABRABRABRABRA
BGRBGRBGRBGRBGRBGRBGRBGRBGRBFABDI CMR
CANCANCANCANCANCPV
CHLCHLCHLCHLCHLCHLCHNCHN
COLCOLCOL
COL
COLCOL
COLCOLCOLCOLCOLCOLCOLCOLCOL
COGCRI CRICRICRICRICRICRICRI
CRICRICRICRICRICRICRICRICRICIV
HRVHRVHRVHRVHRVHRVHRVCYPCYPCYPCYPCYPCYPCYPCYPCYP CYPCYPCYPCZECZECZECZECZECZECZECZECZECZECZECZEDNKDNKDNKDNKDNKDNKDNKDNKDNKDNKDNKDNKDNK
DOMDOMDOM
DOMDOMDOMDOMDOM
DOMDOMDOMDOMDOM
DOMDOMDOM ECUECU
ECUECUECUECUECUECUECU
ECUECUECU
ECUECUECU
EGYEGYEGYEGY
SLVSLV
SLV
SLV
SLVSLVSLV
SLV
SLV
SLV
SLV
SLV
SLVSLV
SLV
SLV
SLV
ESTESTESTESTESTESTESTESTESTESTESTESTEST
ETH ETH
FJIFJIFINFINFINFINFINFINFINFINFINFINFINFINFIN FRAFRAFRAFRA FRAFRAFRAFRAFRAFRAFRAFRAFRA
GABGMBGMBGEOGEOGEOGEOGEO
GEOGEOGEOGEOGEOGEOGEOGEODEUDEUDEUDEUDEUDEUDEUDEUDEU GHAGRCGRCGRCGRCGRCGRCGRCGRCGRCGRCGRCGRCGRC
GTM
GTM
GTM
GNBHTI
HNDHNDHND
HND
HNDHND
HND
HND
HND
HND
HNDHND
HND
HND
HND
HUN HUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNISLISLISLISLISL ISLISLISLISLISLISL ISL
INDIDN
IRNIRNIRN
IRQ
IRLIRLIRLIRLIRLIRLIRLIRLIRLIRLIRLIRLIRLISR ISRISRISRISRITAITAITAITAITAITAITAITAITAITAITAITAITA
JAM
JAM
JPNJORJORJOR
KAZKAZKAZKAZKAZKAZ
KAZKAZKAZKAZKAZKAZ KENKENKORKOSKOS
KOSKOSKOSKOSKOSKOS
KGZKGZKGZKGZ KGZKGZ KGZKGZKGZKGZ
KGZ
KGZKGZKGZKGZKGZKGZ
LVALVALVALVALVALVALVALVALVALVALVALVALBN
LSO
LBR
LTULTULTULTULTULTULTULTU LTULTULTULTU
LUXLUXLUXLUXLUXLUXLUXLUXLUXLUXLUXLUX MKDMKDMKDMKDMKDMKD
MDGMDG
MWI MWIMYSMYSMYSMYSMYSMDVMDVMLTMLTMLTMLTMLTMLTMLTMLTMLTMLT
MRT
MUSMUS
MEXMEXMEXMEXMEXMEX
MEXMEXMEXMEX
FSM
MDAMDAMDAMDAMDAMDAMDAMDAMDAMDAMDAMDAMDAMDAMDA MCOMCO
MNGMNGMNGMNG
MNGMNGMNEMNEMNEMNEMNEMNEMNEMNEMNEMNE MOZMOZ
MMR
NAM
NPLNPLNLDNLDNLDNLDNLDNLDNLDNLDNLDNLDNLDNLD
NICNICNIC
NICNER
NOR NORNORNORNORNORNORNORNORNORNORNOR
PAKPAKPAKPAKPAKPAKPAKPAK
PANPANPANPANPANPANPANPAN
PANPANPANPANPANPAN
PANPANPAN
PRYPRYPRYPRY
PRYPRYPRYPRYPRYPRYPRYPRYPRY PRYPRY
PERPERPERPERPERPER
PRTPRTPRTPRTPRTPRTPRTPRTPRTPRTPRTPRTPRT ROUROUROUROUROUROUROUROUROUROU
RUSRUSRUSRUSRUS
RUSRUSRUSRUSRUSRUSRUSRUS
RWARWASTP
SEN
SYC
SLESVKSVKSVKSVKSVKSVKSVKSVKSVKSVKSVKSVKSVNSVNSVNSVNSVNSVNSVNSVNSVNSVNSVNSVNSLB
ZAF
ZAFZAFZAFZAF
ESPESPESPESPESPESPESPESPESPESPESPESPESP
LKALKA
LKALKASWESWESWESWESWESWESWESWESWESWESWESWESWE CHECHECHECHECHECHECHECHECHECHE SYRTJKTJKTJKTJK
TZA THATHATHATHATHATHATHATHATHATHATHATHATHATLS
TGOTONTONTUNTUN TURTURTURTURTURTURTURTURTURTUR
UGAUGAUGAUKRUKRUKRUKRUKRUKRUKRUKRUKRUKRUKR
GBRGBRGBRGBRGBRGBRGBRGBRGBRGBRGBRGBRUSAUSAUSAUSAUSAUSA URYURYURYURYURYURYURYURYURYURYURY
UZBUZBUZBVUT
VEN
VEN
VEN
VENVEN
VEN
VNMVNMVNMVNM VNMYEM ZMBZMB
02
04
06
08
01
00
20 30 40 50 60 70GINI Index (World Bank Estimate)
Intentional homicide rate 95% CI
Fitted values
AGO
ARGARGARG
ARMARMARM
AUSAUTAUT
BGDBGDBGD
BLR
BLR
BLRBLR
BELBEL BEN
BOL
BOLBOL
BIH BRABGR
CANCAN CHLCHL
COLCOLCOLCOLCRICRICRICRI
CIV
HRVHRV
CYPCYP
CZECZE
DNKDNKDOM
DOMDOMDOM
ECUECUECU
ECU
EGYEGY
SLV
SLV
SLVSLVESTESTETH ETH
FIN
FINFRA
FRAGMBGMB
GEOGEOGEOGEO
DEUDEUDEU
GRCGRC GTM
GNB
HNDHND
HUN
HUNISL
IRLIRL
ISRITAITA
JOR
KAZ
KAZ
KEN
KOR
KGZ
KGZ
KGZKGZ
LVALVA
LSO
LTULTU
LUXLUX
MKD
MDGMWIMYSMLTMLT MRT
MEXMEXMEX
MDAMDA
MDAMDA
MNG
MNG
MNE MMR
NAMNPL
NLDNLDNORNOR
PAK PAK
PANPAN
PANPAN
PRYPRYPRY
PERPERPERPER
PRTPRT
ROUROU
RUS
RUSRUSRWA
RWA
STPSTP
SVK
SVNSVN ZAF ZAF
ESPESP
LKASWESWE
CHECHE
TJK
TZA
THA
THATHA
TGO
TUNTUN
TUR TURTUR
UGA
UKR
UKRUKR
GBRGBRUSAUSA
USAURY
URYURY
UZB
VUTVNMVNM
ZMBZMB
01
02
03
04
05
0
20 30 40 50 60 70GINI Index (World Bank Estimate)
Age-standardized suicide rates (WHO) 95% CI
predicted SuicideRate
9
Wealth inequalities, especially inequalities at the very top of the wealth pyramid – the share of
wealth belonging to billionaires and millionaires – are positively correlated with happiness indices
(fig. 8-10).
It is important to note that inequalities at the very top – billionaire and millionaire ‘intensity’, the
ratio of billionaire/millionaire wealth to GDP – are not precisely correlated with general measures
of inequalities like Gini coefficients. Whereas Gini coefficients for wealth and income distribution
are positively correlated with one another (fig. 12), and billionaire intensity is positively correlated
with Gini coefficients for wealth distribution (fig.13), the correlation of billionaire intensity with
general income-distribution Gini coefficients is negative, if present at all (fig. 14). That is to say,
there are countries with quite an even distribution of income, but levels of high billionaire intensity
in GDP; e.g., Scandinavian countries.
Figure 8: Happiness index (0-to-10 scale) and billionaire wealth from the Global Wealth
Report as a percentage of PPP GDP in 2018
Argentina
AustraliaAustria
Belgium
Brazil
Canada
Chile
China
Colombia
Cyprus
Czech Republic
Denmark
Egypt
Finland
France
Germany
Greece Hong Kong SAR, China
Iceland
India
Indonesia
IrelandIsrael
ItalyJapanKazakhstan
Kuwait
Lebanon
MalaysiaMexico
Morocco
NetherlandsNew Zealand
Nigeria
Norway
PeruPhilippines
Poland
Portugal
Russia
Saudi Arabia Singapore
South Africa
South Korea
Spain
SwedenSwitzerland
Taiwan Province of China
Thailand
Turkey
Ukraine
United Arab EmiratesUnited KingdomUnited States
34
56
78
0 10 20 30 40Billionaires wealth from GWR in 2018 to PPP GDP in 2016, %
Happiness score in 2018 Fitted values
10
Figure 9: Happiness index (0-to-10 scale) and billionaire wealth from the Forbes list as a
percentage of PPP GDP in 2018
Figure 10: Happiness index (on a scale of 0 to 10) and net worth of millionaires according to
the Global Wealth Report as a percentage of PPP GDP
Argentina
AustraliaAustria
Belgium
Brazil
Canada
Chile
China
Colombia
Cyprus
Czech Republic
Denmark
Egypt
Finland
France
Germany
Greece Hong Kong SAR, China
Iceland
India
Indonesia
IrelandIsrael
ItalyJapanKazakhstan
Kuwait
Lebanon
MalaysiaMexico
Morocco
NetherlandsNew Zealand
Nigeria
Northern Cyprus
Norway
Palestinian Territories
PeruPhilippines
Poland
Portugal
Russia
Saudi ArabiaSingapore
Somalia
South Africa
South Korea
South Sudan
Spain
SwedenSwitzerland
Taiwan Province of China
Thailand
Turkey
Ukraine
United Arab EmiratesUnited KingdomUnited States
34
56
78
0 20 40 60Net wealth of billionaires as a % of GDP in 2018
Happiness score in 2018 Fitted values
Argentina
AustraliaAustria
Belgium
Brazil
Canada
Chile
China
Colombia
Cyprus
Czech Republic
Denmark
Egypt
Finland
France
Germany
GreeceHong Kong SAR, China
Iceland
India
Indonesia
IrelandIsrael
ItalyJapanKazakhstan
Kuwait
Lebanon
MalaysiaMexico
Morocco
NetherlandsNew Zealand
Nigeria
Norway
PeruPhilippines
Poland
Portugal
Russia
Saudi Arabia Singapore
South Africa
South Korea
Spain
SwedenSwitzerland
Taiwan Province of China
Thailand
Turkey
Ukraine
United Arab Emirates United KingdomUnited States
34
56
78
0 50 100 150 200 250Ratio of millionaires wealth to GDP, %
Happiness score in 2018 Fitted values
11
Figure 11: Happiness index (on a scale of 0 to 10) and Gini coefficient of wealth distribution
in 2018, percentage terms
Source: GWR; World Happiness Database; WDI; Forbes billionaires’ list.
Figure 12: Income and wealth inequalities, Gini coefficients in 2000-18, percentage terms
Argentina
AustraliaAustria
Belgium
Brazil
Canada
Chile
China
Colombia
Cyprus
Czech Republic
Denmark
Egypt
Finland
France
Germany
Greece Hong Kong SAR, China
Iceland
India
Indonesia
IrelandIsrael
ItalyJapanKazakhstan
Kuwait
Lebanon
MalaysiaMexico
Morocco
NetherlandsNew Zealand
Nigeria
Norway
PeruPhilippines
Poland
Portugal
Russia
Saudi ArabiaSingapore
South Africa
South Korea
Spain
SwedenSwitzerland
Syria
Taiwan Province of China
Thailand
Turkey
Ukraine
United Arab EmiratesUnited Kingdom United States
34
56
78
50 60 70 80 90 100Gini index of wealth distribution in 2018, %
Happiness score in 2018 Fitted values
12
Figure 13: Wealth of billionaires as a percentage of GDP and Gini index of wealth
distribution, in percentage terms, in 2018
Figure 14: Gini coefficient of income distribution and ratio of billionaire wealth to PPP GDP,
percentage terms
Source: WDI; GWR.
AFGAFGAFGAFGAFGAFGAFGAFGAFGAFGAFGAFGAFGAFGAFGAFGAFGALBALBALBALBALBALBALBALBALBALBALBALBALBALBALBALBALBALBALB DZADZADZADZADZADZADZADZADZADZADZADZADZADZADZADZADZADZADZAAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGO ATGATGATGATGATGATGATGATGATGATGATGATGATGATGATGATGATGATGATGARGARGARGARGARGARGARGARGARGARGARGARG
ARGARGARGARGARGARGARGARMARMARMARMARMARMARMARMARMARMARMARMARMARMARMARMARMARMARMAUSAUSAUSAUS
AUSAUSAUSAUSAUSAUS
AUSAUSAUS
AUSAUSAUS
AUS
AUSAUS
AUTAUT
AUT
AUT
AUT
AUTAUT
AUT
AUTAUTAUTAUT
AUTAUTAUT
AUT
AUTAUTAUT
AZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZEAZE BHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHSBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBHRBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGDBGD BRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBRBBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBLRBELBELBELBELBEL
BELBELBEL
BELBELBELBELBEL
BELBELBELBEL
BELBEL
BLZBLZBLZ BLZBLZBLZBLZBLZBLZBLZBLZBLZBLZBLZBLZBLZBLZBLZBLZBENBENBENBENBENBENBENBENBENBENBENBENBENBENBENBENBENBENBEN BTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTNBTN BOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBOLBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIHBIH BWABWABWABWABWABWABWABWABWABWABWABWABWABWABWABWABWABWABWABRABRABRABRABRABRABRA
BRABRABRABRABRA
BRA
BRA
BRA
BRA
BRABRABRA
BRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBRNBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGRBGR BFABFABFABFABFABFABFABFABFABFABFABFABFABFABFABFABFABFABFABDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDI KHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMKHMCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMRCMR
CANCANCANCANCANCAN
CANCAN
CANCANCANCAN
CANCANCANCANCANCANCAN
CPVCPVCPVCPVCPVCPVCPVCPVCPVCPV CPVCPVCPVCPVCPVCPVCPVCPVCPV CAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAFTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDCHLCHL
CHL
CHLCHL
CHLCHLCHL
CHL
CHLCHLCHL
CHL
CHL
CHL
CHL
CHL
CHL
CHL
CHNCHNCHNCHNCHNCHNCHNCHN
CHNCHN
CHNCHNCHNCHN
CHNCHNCHNCHNCHN
COLCOLCOL
COLCOLCOLCOL
COL
COL
COLCOLCOLCOLCOLCOL
COLCOL
COLCOL
COMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCOMCODCODCODCODCODCODCODCODCODCODCODCODCODCODCODCODCODCODCODCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCOGCRICRICRI CRICRICRI CRICRICRICRICRICRICRICRICRICRICRICRI CRICIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRVHRV CYPCYPCYPCYPCYPCYP CYP
CYP
CYP
CYP
CYP
CYP
CYPCYP
CYP
CYP
CZECZECZECZECZE
CZE
CZE
CZE
CZE
CZECZECZECZECZECZECZE
CZE
CZECZE
DNK
DNKDNK
DNK
DNK
DNK
DNK
DNK
DNK
DNKDNK
DNKDNKDNK
DNK
DNKDNKDNK
DNK
DJIDJIDJIDJIDJIDJIDJIDJIDJIDJI DJIDJIDJIDJIDJIDJIDJIDJIDJI DMADMADMADMADMADMADMADMADMADMADMADMADMADMADMADMADMADMADMADOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMECUECUECU ECUECUECUECUECUECUECUECUECUECUECUECUECUECUECUECUEGYEGYEGYEGYEGYEGYEGY
EGY
EGYEGYEGYEGY
EGYEGYEGY
EGY
EGY EGYEGYSLVSLVSLV SLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLVSLV SLV GNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQERIERIERIERIERIERIERIERIERIERIERIERIERIERIERIERIERIERIERI ESTESTESTESTESTESTESTESTESTESTESTESTESTESTESTESTESTESTESTETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETH FJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFJIFINFINFINFINFINFINFINFINFIN
FIN FINFINFINFINFINFIN
FIN
FINFIN
FRAFRAFRAFRAFRA
FRA
FRA
FRAFRA
FRAFRAFRAFRA
FRAFRAFRAFRA
FRAFRA
GABGABGABGABGABGABGABGABGABGABGABGABGABGABGABGABGABGABGAB GMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGMBGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEOGEO
DEUDEUDEUDEUDEUDEUDEUDEUDEU
DEUDEU
DEU
DEU
DEU
DEU
DEUDEU
DEU
DEU
GHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHA
GRCGRCGRC
GRC
GRC
GRC
GRC
GRCGRC
GRC
GRCGRCGRC
GRCGRCGRC
GRCGRC
GRC
GRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGRDGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGTMGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNB GUYGUYGUY GUYGUYGUYGUYGUYGUYGUYGUYGUYGUYGUYGUYGUYGUYGUYGUY HTIHTIHTI HTIHTIHTIHTIHTIHTIHTIHTIHTIHTIHTIHTIHTIHTIHTIHTIHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHNDHND
HKG
HKGHKG
HKGHKGHKGHKG
HKG
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THA
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USA
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10
15
20
40 60 80 100GINI Index (wealth distribution)
Wealth of billionares as a % of PPP GDP in 2018 Fitted values
ALBALBALBALBDZA AGOAGOARGARGARGARGARGARGARGARGARGARGARGARG
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DJI DJIDJI DOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOMDOM ECUECUECUECUECUECUECUECUECUECUECUECUECUECUECU
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10
15
20
20 30 40 50 60 70GINI Index (World Bank Estimate)
Wealth of billionares as a % of PPP GDP in 2018 Fitted values
13
There are at least two different types of income inequality, presented in the schema below: the
same Gini coefficients of income distribution can result from the concentration of inequalities at
the high end or the low end of the income pyramid. The graphical interpretation of the Gini
coefficient is the ratio of the area between the line of complete equality and the standard Lorenz
curve to the area of the shaded triangle.
In the first chart, the ABC triangle has about the same area as the ‘Lorenz curve–complete equality’
area – and the same Gini coefficient – but inequality in concentrated at the top end. So the 90% of
the population at the poorer end of the income distribution are totally equal in their income among
themselves, but only account for 50% of total income, whereas the richest 10% have the other 50%
of total income; the per-capita income of the rich is exactly nine times higher than the per capita
income of the poor.8
In the second chart, the ABD triangle also has about the same area as the ‘Lorenz curve–complete
equality’ area – and the same Gini coefficient – but inequality exists because the lower half of
society has no income at all, and the other half has all the income.
The data seem to suggest that the first type of income distribution – ‘90% poor and equal, 10%
rich’ – is better for happiness than the second type.
8 (50:10) / (50:90) = 9
14
Schema: Two types of inequality with the same Gini coefficient
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Sh
are
in
in
com
e,
%
Share of population, %
Complete equality among the poor, but high income of the few rich
Share in income - standard Lorenz
curve
Complete equality
Share in income: 90% of poor have
the same income
C
B
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Sh
are
in
in
com
e,
%
Share of population, %
High inequalities between rich and poor
Share in income - standard Lorenz curve
Complete equality
Share in income: half rich-half poor
B
D
15
An explanation of the ‘inequality–happiness’ relationship in terms of statics (space – geography)
and dynamics (time – history) could be the ‘big fish in a small pond’ effect. This is a model
developed by Marsh and Parker (1984) to explain why good students prefer to stay in a class in
which they are above the average level, rather than be in a more challenging learning environment
where they are below the average level. This effect can be used to explain one of the paradoxes of
happiness: Strong growth is usually accompanied by growing income inequalities (Popov, 2018a,
fig. 10), so rapid growth is often associated with low happiness scores. A paper by Brockmann,
Delhey, Welzel, and Hao (2008) refers to the concept of “frustrated achievers” and explains the
decline in happiness scores in China through the deterioration of relative incomes for the majority
of the population due to rises in income inequality.
But there is a different relationship with regards to levels and change, stocks and flows, and space
and time dimensions: with low levels of inequality people feel unhappy – the dream of the ‘big
fish in a small pond’ is out of reach – but the transition to higher levels of inequality, when the
relative position of the majority deteriorates in relation to the average, makes people even more
unhappy temporarily, during the transition. When transition to a higher level of inequality is over,
people – maybe new generations – start to feel happier.
The other explanation could be a different relationship between happiness and inequality in rich
and poor countries. The dependence of happiness on income is characterised by a rising but
concave curve (fig. 14) that reaches its maximum at a level of income of about $75,000 (the level
of very rich Norway and Kuwait) and which increases only marginally after the income level of
about $30,000 (the level of the poorest OECD members – Greece, Chile, and Estonia). Whereas
happiness index scores rise from 3 to 6 with a rise of PPP GDP per capita from less than $1,000 to
$30,000, they only increase from 6 to 7 when per-capita income rises from $30,000 to $75,000
(fig. 15).
16
Figure 15: Happiness index (Word Happiness Report and World Database on Happiness),
0-to-10 scale, and PPP GDP per capita in 2018, Dollars
Source: World Database on Happiness, 2019; WDI.
It may well be that in rich countries, the ‘money can’t buy happiness’ story is more true than in
poor countries; i.e., a marginal increase in happiness due to a unit increase in income in rich
countries is lower than in poor countries.
First, for rich people, non-income determinants of happiness probably play a larger role, so the
negative effects of inequality are not counterweighted by higher incomes. Second, in rich
countries, people derive pleasure from being better off than most of the world population – i.e.,
their ‘American dream’ has been achieved already – so inequality in their own country is less
important and has only negative consequences.
Afghanistan
Albania
Algeria
Angola
Argentina
Armenia
AustraliaAustria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Belize
Benin
Bhutan
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Central African Republic
Chad
Chile
China
Colombia
Congo (Brazzaville)
Congo (Kinshasa)
Costa Rica
Croatia
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Ethiopia
Finland
France
Gabon
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Haiti
Honduras Hong Kong SAR, ChinaHungary
Iceland
India
Indonesia
IranIraq
IrelandIsrael
Italy
Ivory Coast
Jamaica Japan
Jordan
Kazakhstan
Kenya
Kosovo
Kuwait
Kyrgyzstan
Laos
Latvia
Lebanon
Lesotho
Liberia
Libya
Lithuania
Luxembourg
Macedonia
MadagascarMalawi
Malaysia
Mali
Malta
Mauritania
Mauritius
Mexico
Moldova
MongoliaMontenegroMorocco
MozambiqueMyanmar
Namibia
Nepal
NetherlandsNew Zealand
Nicaragua
Niger
Nigeria
Norway
Pakistan
Panama
ParaguayPeruPhilippines
Poland
Portugal
Qatar
RomaniaRussia
Rwanda
Saudi Arabia
Senegal
Serbia
Sierra Leone
SingaporeSlovakiaSlovenia
South Africa
South Korea
Spain
Sri Lanka
Sudan
SwedenSwitzerland
Taiwan Province of China
Tajikistan
Tanzania
Thailand
Togo
Trinidad & Tobago
Tunisia
TurkeyTurkmenistan
UgandaUkraine
United Arab EmiratesUnited KingdomUnited States
Uruguay
Uzbekistan
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
34
56
78
0 50000 100000 150000PPP GDP per capita in 2018, dollars
Happiness score in 2018 95% CI
predicted happinessscore
17
This paper uses the databases from the Forbes billionaires list and the Global Wealth Report
(GWR), as well as from the World Happiness Report and the World Database on Happiness. I use
the data to examine the relationship between income inequality, and happiness for nearly 20 years
(2000-18) for over 200 countries.
It turns out that inequality grows together with happiness in relatively poor countries, but harms
happiness in rich countries. On the other hand, inequality of wealth distribution – wealth is a stock
variable whereas income is a flow – is positively linked to happiness in all countries. Wealth
distribution at the very top of the pyramid – billionaire and millionaire wealth as a proportion of
GDP – is one of the most important determinants of happiness index scores in all countries.
The stylised fact is that there are two ‘typical’ statistical portraits of a happy country. One is of a
country with relatively high income compared to the world average, a low level of income
inequality, but high wealth inequality and especially high wealth inequality at the very top –
millionaire and billionaire intensity. This is very much the image of a Scandinavian country – high
income compared to the rest of the world, low income inequality within the country, but pretty
high wealth inequality and billionaire intensity within the country. The other type of happy country
has lower levels of income, but high income and wealth inequalities, especially at the top of the
wealth pyramid. This is the Latin American and African model; e.g., Honduras, Bolivia, Ecuador,
Costa Rica, South Africa, and Zimbabwe.
Data
Income. Data on income are from the World Development Indicators database – purchasing power
parity (PPP) GDP per capita.
Happiness. Data on happiness come from the World Happiness Report, as well as from the World
Database on Happiness. This represents individuals’ self-perception of how happy they are. The
scale is from 0 to 10, and the estimates are derived from the Gallup World Poll, the World Values
Survey, and other sources.
18
Income and wealth inequalities. Income inequalities data are from the World Development
Indicators database (WDI) and derived from national household surveys of income and
consumption in various countries. Wealth inequalities are computed through extrapolation: first,
regressions between the components of personal financial and non-financial wealth and its
determinants (real consumption; population density; market capitalisation rate; public pensions as
a percentage of GDP; domestic credits available to private sector; and Gini coefficient of income
distribution) are computed for about 40 countries for which these data are available, then an
extrapolation is made for countries that do not have estimates of these components of personal
wealth (Davies, Sandström, Shorrocks, and Wolff, 2007).
Wealth of high net worth individuals (HNWI) – billionaires and millionaires. Sample surveys
tend to omit HNWIs, so income and wealth distribution at the very top of the pyramid is very much
underestimated. That is why this paper uses the Forbes list of billionaires, which reports the wealth
of all billionaires in the world since 1996 – see Popov (2018a) for details – and the Credit Suisse
Global Wealth Report, which makes a number of adjustments to the Forbes data on billionaires
(GWD, 2018, pp. 110-113) and estimates the number and wealth of multi-millionaires,
millionaires, and other HNWIs.9
9 This is how the estimation procedure is explained in the GWR: “We exploit the fact that the top tail of wealth distribution is usually well approximated by the Pareto distribution, which produces a straight-line graph when the logarithm of the number of persons above wealth level w is plotted against the logarithm of w. Our data yield a close fit to the Pareto distribution in the wealth range from USD 250,000 to USD 5 million. Above USD 5 million the relationship begins to break down, and the correspondence weakens further above USD 50 million, as expected given the limitations of the data sources. However, it still seems reasonable to use a fitted Pareto line to estimate the number of individuals in the highest echelons of the wealth distribution. To determine the precise features of the top wealth tail, we rely heavily on the rich list data provided by Forbes and other sources. We make particular use of the number of billionaires reported by Forbes, since the data are available for many years and are broadly comparable across countries. We recognize that rich list data have limitations. The valuations of individual wealth holdings are dominated by financial assets, especially equity holdings in public companies traded in international markets. For practical reasons, less attention is given to nonfinancial assets apart from major real estate holdings and trophy assets, such as expensive yachts. Even less is known – and hence recorded – about personal debts. Some people cooperate enthusiastically with those compiling the lists; others jealously guard their privacy. There are also different country listings for nationals and residents, which is especially evident for India, for instance. The true legal ownership within families – as opposed to nominal ownership or control – adds further complications. Assigning the wealth recorded for Bill Gates, for example, to all family members might well result in several billionaire holdings, so the number of billionaires would increase in this instance. In other cases, reassigning the family wealth would reduce all the individual holdings below the billionaire threshold. For all these reasons, rich list data should be treated with caution. At the same time, the broad patterns and trends are informative, and they provide the best available source of information at the apex of global wealth distribution” (GWR, 2018, p. 111).
19
Forbes data show a higher ratio of billionaire wealth to GDP than the GWR data. For instance, for
Hong Kong, the comparison is 58% and 30% respectively. But overall, these two estimates are
strongly correlated (fig. 16).
Figure 16: Billionaire intensity in percentage terms of PPP GDP according to the Forbes list
and according to the Global Wealth Report
AfghanistanAlbaniaAlgeriaAngolaArgentinaArmenia
AustraliaAustria
AzerbaijanBahrainBangladeshBelarusBelgium
BelizeBeninBhutanBoliviaBosnia and HerzegovinaBotswana
Brazil
BulgariaBurkina FasoBurundiCambodiaCameroon
Canada
Central African RepublicChad
Chile
ChinaColombiaCongo (Brazzaville)Congo (Kinshasa)Costa RicaCroatia
Cyprus
Czech Republic
Denmark
Dominican RepublicEcuadorEgypt
El SalvadorEstoniaEthiopia
Finland
France
Gabon
GeorgiaGermany
GhanaGreece
GuatemalaGuineaHaitiHonduras
Hong Kong SAR, China
Hungary
Iceland
IndiaIndonesia
IranIraq
Ireland
Israel
Italy
Ivory CoastJamaicaJapan
JordanKazakhstan
KenyaKosovoKuwaitKyrgyzstanLaosLatvia
Lebanon
LesothoLiberiaLibyaLithuaniaLuxembourgMacedoniaMadagascarMalawi
Malaysia
MaliMaltaMauritaniaMauritius
Mexico
MoldovaMongoliaMontenegroMorocco
MozambiqueMyanmarNamibiaNepal
NetherlandsNew Zealand
NicaraguaNiger
Nigeria
Norway
PakistanPanamaParaguayPeru
Philippines
PolandPortugal
QatarRomania
Russia
RwandaSaudi Arabia
SenegalSerbiaSierra Leone
Singapore
SlovakiaSlovenia
South AfricaSouth Korea
Spain
Sri LankaSudan
Sweden
Switzerland
Taiwan Province of China
TajikistanTanzania
Thailand
TogoTrinidad & TobagoTunisiaTurkey
TurkmenistanUgandaUkraineUnited Arab Emirates
United Kingdom
United States
UruguayUzbekistanVenezuelaVietnamYemenZambiaZimbabwe0
20
40
60
0 10 20 30 40Billionaires wealth from GWR in 2018 to PPP GDP in 2016, %
20
Source: GWR; Forbes.
As figure 17 suggests, the ratio of millionaire wealth to GDP is correlated with the ratio of
billionaire wealth to GDP. However, data on the wealth of millionaires have the advantage of
including more countries. In 2018, there were only 24 countries, out of over 150, for which data
were available that did not have a single millionaire; in contrast the number of countries without
billionaires was nearly 100 out of over 150.
Suicide and murder rates. These data come from World Health Organization statistics on causes
of death.10 Murder rates statistics is also available from the UNODC (United Nations Office on
Drugs and Crime), which collects statistics mostly from WHO but from other sources as well.
10 External causes of death include murders, suicides, accidents, and ‘unidentified’.
R² = 0.8933
0
10
20
30
40
50
60
0 5 10 15 20 25 30 35 40
Bil
lio
na
ire
s w
ea
lth
fro
m F
orb
es
list
in
20
18
to
PP
P G
DP
in
20
16
, %
Billionaires wealth from GWR in 2018 to PPP GDP in 2016, %
Billionaires wealth as a % of PPP GDP according to Forbes list of
billionaires and according to Global Wealth Report
Georgia
Hong KongCyprus
21
Figure 17: Millionaire and billionaire intensity of PPP GDP according to the Global Wealth
Report, percentage terms
Source: Global Wealth Report.
AfghanistanAlbaniaAlgeriaAngolaArgentinaArmenia
AustraliaAustria
AzerbaijanBahrainBangladeshBelarusBelgium
BelizeBeninBhutanBoliviaBosnia and HerzegovinaBotswanaBrazil
BulgariaBurkina FasoBurundiCambodiaCameroon
Canada
Central African RepublicChadChileChina
ColombiaCongo (Brazzaville)Congo (Kinshasa)Costa RicaCroatia
Cyprus
Czech Republic Denmark
Dominican RepublicEcuadorEgypt
El SalvadorEstoniaEthiopia
Finland
FranceGabonGeorgia
Germany
GhanaGreece
GuatemalaGuineaHaitiHonduras
Hong Kong SAR, China
Hungary
Iceland
IndiaIndonesia
IranIraq
Ireland
Israel
Italy
Ivory CoastJamaica JapanJordanKazakhstanKenyaKosovo
Kuwait
KyrgyzstanLaosLatvia
Lebanon
LesothoLiberia LibyaLithuania LuxembourgMacedoniaMadagascarMalawi
Malaysia
Mali MaltaMauritaniaMauritiusMexico
MoldovaMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNepalNetherlands
New Zealand
NicaraguaNigerNigeria
Norway
PakistanPanamaParaguay
PeruPhilippinesPolandPortugal
QatarRomania
Russia
RwandaSaudi Arabia
SenegalSerbiaSierra Leone
Singapore
SlovakiaSloveniaSouth Africa
South KoreaSpain
Sri LankaSudan
Sweden
Switzerland
Taiwan Province of China
TajikistanTanzania
Thailand
TogoTrinidad & TobagoTunisia
Turkey
TurkmenistanUgandaUkraine
United Arab Emirates United KingdomUnited States
UruguayUzbekistanVenezuelaVietnamYemenZambiaZimbabwe01
02
03
04
0
0 50 100 150 200 250Ratio of millionaires wealth to GDP, %
Billionaires wealth from GWR in 2018 to PPP GDP in 2016, % Fitted values
0
5
10
15
20
25
30
35
40
0 50 100 150 200 250Ra
tio
of
bil
lio
na
ire
s w
ea
lth
to
PP
P G
DP
, %
Ratio of millionaires wealth to PPP GDP, %
Ratio of billionaires and millionaires wealth to PPP
GDP in 2018, %
Cyprus
Hong Kong
22
Results: More billionaires – less inequality, fewer murders, and more happiness
To begin with, it is important to remember that billionaire intensity is negatively related to income
inequalities – the lower the level of inequality, the more billionaires per unit of GDP the country
has, even controlling for the level of income and for random effects.11 This relationship is
counterintuitive and suggests that inequalities at the very top are more pronounced than
inequalities among other income groups.
Table 1 reports the results of regressions of happiness indices on various determinants without
controls for fixed and random effects.
Models 1.1-1.6 link happiness indices to income and wealth inequalities and to billionaire and
millionaire intensity – with and without controls for per capita income – with similar results:
wealth inequality and billionaire and millionaire intensity have positive effect on happiness,
whereas income inequality affects happiness negatively. Models 1.1-1.2 are based on cross-section
data from about 150 countries; models 1.3-1.6 are based on panel data. In models 1.3-1.6 (panel
data) coefficients become insignificant, if controls for fixed effects or random effects are
introduced, which probably suggests non-linearity, so an interaction term – per capita income
multiplied by Gini coefficient of income distribution – is introduced in the next model (1.7).
11 Billionare’sIntensity = 2.5***– 0,04GINIincome***
Robust estimate, R2 = 0.03, N=1031 (over 200 countries and 18 years, but some observations are missing).
Billionare’sIntensity = 1.9*** – 1.7e-07 Ycap – 0,03GINIincome**
Random effect regression, R2 (between) = 0.05, N=1031 (over 200 countries and 18 years, but some observations
are missing).
The following notations are used: *, **, *** – denotes significance at 10, 5 and 1% level respectively.
23
Table 1: Regression results of happiness indices on various determinants. Cross country and
panel data without control for random and fixed effects (T-statistics add z- statistics in
brackets)
Dependent variable – Happiness
Model, parameters, N /
Variables
1.1, Cross-country, N=139
1.2, Cross-country, N=151
1.3, Panel, N=1967
1.4, Panel,
N=1967
1.5, Panel,
N=1967
1.6, Panel,
N=760
1.7, Panel,
N=760
PPP GDP per capita, $, Ycap
.00002*** (5.2)
3.8e-06
(1.22)
3.6e-06
(1.3)
.00005***
(14.8)
.0001***
(10.2)
Gini coefficient of income distribution, %, GINIincome
-.02**
(1.9)
.04***
(9.0)
.08***
(10.6)
Gini coefficient of wealth distribution, %, GINIwealth
.03**
(2.4)
.02**
(2.0)
0.02***
(4.3)
.01***
(3.6)
Billionaires’ wealth to PPP GDP ratio, %
Billionaires’Intensity
0.13***
(5.7)
0.12***
(5.2)
0.03***
(9.6)
0.03**
(2.1)
Millionaires’ wealth to PPP GDP ratio, %
Millionaires’Intensity
.01***
(8.56)
.008***
(4.8)
.06***
(3.9)
Interaction term
(Ycap * GINIincome)
-2.6e-06
***
(-6.6)
Constant 3.9***
(5.6)
3.6***
(6.8)
5.4***
(162.9)
5.4***
(91.9)
3.2***
(13.6)
2.2***
(7.1)
1.3***
(4.0)
R2 , % 44 58 9 12 8 45 48
*, **, *** - denotes significance at 10, 5 and 1% respectively.
Model 1.7 is a specification with the threshold of per-capita income. The resulting equation has
the following form:
24
Happiness = 1.3*** + 0.0001Ycap*** + 0.01GINIwealth*** + 0.06Millionaires’Intensity*** +
+ 1.7e-06GINIincome*** (30700– Ycap***)
This implies that wealth inequality, both in general and at the top of the wealth pyramid
(‘millionaire intensity’), has a positive impact on happiness, whereas income inequality has a
positive impact only in countries with per capita income above $30,700.
Table 2 reports the results of regressions using panel data and controlling for random and fixed
effects. Better results are obtained by controlling for random effects; variation within the groups –
19 years for the same country – is not enough in most cases to receive meaningful results. But
regressions with fixed-effects controls are reported as well, for consistency.
Model 2.1 shows a significant and positive dependence of happiness indices on per-capita income
and income inequalities, but once wealth inequalities and billionaire intensity variables are
introduced into the right hand side, coefficients become insignificant.
Model 2.2 suggests a non-linear relationship:
Happiness = 3.7*** + 0.00009Ycap*** +1.6e-06GINIincome*** (25000 – Ycap***)
In countries with per-capita income (PPP GDP per capita) higher than roughly US$ 25,000 for a
particular period, income inequalities have a negative impact on happiness, but in poorer countries
the relationship is positive.
Model 2.3 shows the same non-linear relationship with a similar threshold of per-capita income
($22,100 + 388*Billionaires’ intensity). The interesting twist here is that the higher the level of
billionaire intensity, the higher the threshold separating poor and rich countries with regards to the
sensitivity of their happiness self-evaluation to income inequalities. In rich countries, income
inequalities normally have a negative impact on happiness, but the higher the level of billionaire
intensity, the smaller this negative impact is:
25
Table 2: Regression results of happiness indices on various determinants. Panel data with
controls for random and fixed effects (T-statistics add z- statistics in brackets)
Dependent variable - Happiness
Model, parameters, N /
Variables
2.1, Panel, random effects, N=760
2.2, Panel, random effects, N=760
2.3, Panel, random effects,
N=760
2.4, Panel, fixed effects,
N=760
2.5, Panel, N=658
2.6, Panel, random effects,
N=658
2.7, Panel, fixed effects,
N=658
PPP GDP per capita, $, Ycap .00003***
(9.4)
.00009***
(5.6)
.00009***
(5.7)
.00007***
(3.9)
.00005***
(14.9)
.00003***
(8.2)
.00004***
(2.8)
Gini coefficient of income distribution, %, GINIincome
.02***
(3.2)
.04***
(4.6)
.04***
(4.6)
.02***
(2.7)
.03***
(4.7)
Gini coefficient of wealth distribution, %, GINIwealth
.02***
(4.2)
Billionaires’ wealth to PPP GDP ratio, %,
Billionaires’Intensity
.03**
(2.4)
Murder rate (per 100,000 inhabitants), MURDERrate
.18***
(5.4)
-.07***
(-5.6)
-.07***
(-5.4)
Interaction term
(Ycap * GINIincome)
-1.6e-06
***
(3.5)
-1.7e-06
***
(3.6)
-1.9e-06
***
(3.8)
-1.1e-06 **
(2.4)
Interaction term (GINIincome *
*Billionaires’Intensity)
.0007
(1.6)
.0005
(1.2)
Interaction term (GINIincome *
MURDERrate)
.002***
(6.2)
.001***
(5.2)
Constant 4.2*** (14.9)
3.7***
(11.7)
3.7***
(11.7)
5.0***
(13.8)
2.7***
(8.8)
5.2***
(8.2)
6.1***
(52.8)
R2 , % 41 42 43 29 47 41 17
*, **, *** - denotes significance at 10, 5 and 1% respectively.
26
Happiness = 3.7*** + 0.00008Ycap*** +1.7e-06GINIincome*** (22100 – Ycap +
+ 388* BillionaireIntensity***)
Model 2.4 shows the same relationship as model 2.3 (random effects), but with controls for fixed-
effects:
Happiness = 5.0*** + 0.00007Ycap*** +1.9e-06GINIincome*** (15000 – Ycap*** +
312BillionaireIntensity)
This confirms the robustness of the estimates: the positive effect of income inequalities on
happiness for poor countries and the positive effect of billionaire intensity on happiness for all
countries is captured in space – cross-country comparisons, i.e., controlling for random effects, as
well as in time – time series for a particular country, i.e., controlling for fixed effects. The only
difference is the lower threshold: $15,000 compared to $22,000.
In model 2.5 – panel data without controls for fixed effects and non-linearity – happiness indices
are positively and significantly correlated to per-capita income, income inequality, wealth
inequality, billionaire intensity, and murder rates. The interpretation could be that inequality at all
levels has a stronger positive impact on happiness – a positive impact that outweighs the negative
impact of a higher murder rate.
In model 2.6, an additional explanatory variable is introduced: an interaction term between income
inequalities and the murder rate. Counterintuitively, the impact of the murder rate on happiness is
positive for countries with high income inequalities – those with Gini coefficients of income
distribution of over 45%:
Happiness = 5.2*** + 0.00003Ycap*** + 0.0016MURDERS*** (GINIinc*** – 45)
This relationship most likely captures the patterns of poorer countries with high income
inequalities. For all rich countries, the Gini coefficient of income distribution is lower than 45%,
27
so countries with Gini coefficients of over 45% are all developing countries. Even high murder
rates in these countries do not prevent people from experiencing happiness under high income
inequalities, for example, in Latin America and Africa.
Finally, model 2.7 replicates the results with controls for fixed effects:
Happiness=6.1***+1.12e-06Ycap***(GINIinc***–38)+0.0014MURDERS***(GINIinc***–50)
This result is even stronger than with previous models: In countries with low income inequalities
– i.e., Gini coefficients below 38% – even income growth does not bring happiness, and increase
in murders undermines happiness.
In table 3, some results for the determinants of the murder rate and suicide rate are reported. These
indicators could be regarded as more objective measures of wellbeing/happiness; i.e., if people are
so unhappy with their lives and blame themselves, they commit suicide; if they blame others, they
commit murders. Reasons for suicides and especially for murders may be different of course, but
it is instructive to see the determinants of both indicators anyway.
Models 3.1-3.2 suggest that the impact of income and wealth inequalities on murder rates is
positive, but the impact of wealth inequalities at the top (billionaire intensity) is negative.
Model 3.3 shows that for countries with Gini coefficients of income inequality above 29% – most
countries in the world – billionaire intensity has a negative impact on murder rates:
MURDERrate = 0.3GINinc*** + 0.06GINIwealth*** + 0.08Billionaires’Intensity*** (29 –
GINIinc) – 8.4**
Suicide rates do not necessarily go together with high inequalities. Whereas one might expect that
people may feel less happy in countries with, and in times of, high levels of inequality in income
and wealth distribution, such that suicide rates in these countries and these periods are higher, in
fact, suicide rates do not exhibit any strong correlation with income and wealth distribution.
28
Table 3: Regressions of murder rate and suicide rate on determinants
Dependent variable – MURDERrate Dependent variable – SUICIDErate
Model, parameters, N /
Variables
3.1, Panel, N=876
3.2, Panel, random effects, N=876
3.3, Panel, random effects,
N=876
3.4, Panel,
N=197
3.5, Panel, random effects, N=197
3.6, Panel, fixed effects,
N=197
PPP GDP per capita, $, Ycap -1.8e-06 **
(-2.5)
-3.5e-06 ***
(-4.7)
-2.2e-06
(-1.3)
Gini coefficient of income distribution, %, GINIincome
.8***
(15.4)
.3***
(5.1)
.3***
(5.3)
-.1***
(-2.7)
.06
(1.1)
.22***
(2.9)
Gini coefficient of wealth distribution, %, GINIwealth
.2***
(5.6)
.06
(1.2)
-.2
(-1.6)
Billionaires’ wealth to PPP GDP ratio, %,
Billionaires’Intensity
-.9***
(-6.5)
-.3***
(-2.7)
2.4***
(3.8)
Interaction term
(Ycap * GINIincome)
Interaction term (GINIincome *
*Billionaires’Intensity)
-.08***
(-4.4)
Interaction term (GINIincome *
MURDERrate)
Constant 34.9***
(9.6)
3.5
(1.5)
-8.4**
(-1.9)
16.4***
(8.3)
9.2***
(4.3)
15.4*
(2.0)
R2 , % 38 37 35 5 0 4
*, **, *** - denotes significance at 10, 5 and 1% respectively.
The best equations are reported in table 3: there is some indication that income inequalities cause
the suicide rate to fall (model 3.4), but once controls for random effects are introduced, the
29
relationship totally disappears (model 3.5 – R2 =0, all coefficients are insignificant), whereas in
the fixed effects model 3.6, income inequalities have a positive impact on the suicide rate but
wealth inequalities have a negative impact on the suicide rate.
Conclusions
To summarise, income and wealth inequalities do not always cause happiness levels to fall. It is
true that inequalities have an array of negative social consequences, well described in the literature
– from an increase in crime and mortality to a decline in educational attainment and a proliferation
of psychological disorders and obesity (Wilkinson and Pickett, 2010). Besides, inequalities
undermine social mobility and lead to the conservation of social stratification: the higher the level
of inequality, the higher the probability that one’s income will closely resemble that of one’s
parents – known as the ‘Great Gatsby’ curve. Hence the social structure, and very often the political
structure of society, becomes less flexible as well.
“The frequent claim that inequality promotes accumulation and growth does not get much
support from history. On the contrary, great economic inequality has always been
correlated with extreme concentration of political power, and that power has always been
used to widen the income gaps through rent-seeking and rent-keeping, forces that
demonstrably retard economic growth” (Milanovic, Lindert, and Williamson, 2007).
As Joseph Stiglitz explains,
“widely unequal societies do not function efficiently, and their economies are neither
stable, nor sustainable in the long run…When the wealthiest use their political power
to benefit excessively the corporations they control, much needed revenues are
diverted into the pockets of a few instead of benefiting society at large… That higher
inequality is associated with lower growth – controlling for all other relevant factors
– have been verified by looking at the range of countries and looking over longer
periods of time” (Siglitz, 2012, p. 83, 117).
30
Latin American countries, writes Stiglitz, may show a glimpse of the future to other states
that are just stepping out on the road leading to growing inequalities.
“The experience of Latin American countries, the region of the world with the highest
level of inequality, foreshadows what lies ahead. Many of the countries were mired
in civil conflict for decades, suffered high levels of criminality and social instability.
Social cohesion simply did not exist” (Stiglitz, 2012, p. 84).
Developing countries with high levels of income inequality are more likely than others to end
up in a vicious circle: a bad equilibrium with poor quality institutions, low growth, low levels
of social mobility, and high social tensions. It may take a revolution to break this vicious circle
and to exit the bad equilibrium (Popov, 2014).
But some inequality is obviously not only tolerated by society, but is also indispensable to
obtaining the feeling of happiness. Wealth is increasingly not inherited, but self-made, even in
advanced countries (Freund and Oliver, 2016). The most rapid growth in the number of self-made
billionaires and in the growth in their wealth is in East Asia, but such figures are also growing in
Latin America and Sub-Saharan Africa.12 This may be the reason why self-evaluations of
happiness are often affected positively by income and wealth inequalities, especially at the top of
the distribution pyramid. In particular, there is strong evidence that: (1) income and wealth
inequalities in relatively poor countries – with personal incomes below US$ 20,000-30,000 – affect
happiness positively; and that (2) the relative wealth of billionaires and millionaires – as a
percentage of GDP – contributes to feelings of happiness in poor and rich countries.
This paper contributes to the existing literature by specifying the non-linear impact of inequality
on happiness: income inequality raises happiness, rather than lowers it, in relatively poor countries
with per-capita income below $20,000-30,000, whereas in rich countries lower inequality makes
people feel better.
12 The Middle East and North Africa is the only region where the share of inherited wealth is growing and the wealth share of company founders is falling.
31
Inequality of wealth distribution – which is distinct from inequality of income because wealth is a
stock variable, whereas income is a flow – is positively linked to happiness in all countries.
Furthermore, wealth distribution at the very top of the pyramid – the billionaire and millionaire
wealth as a percentage of GDP – is one of the most important determinants of happiness index
scores in all countries. Wealth inequality is probably less irritating to people than income
inequality because wealth is associated with the past, i.e., it is always inherited or acquired from
the past, whereas income concerns the present, and current injustices hurt more than past ones.
Consequently, there are two statistical portraits of a happy country: one has high income per capita
compared to the world average, a low level of income inequality, but high wealth inequality and
especially high wealth inequality at the very top – millionaire and billionaire intensity. Very much
like a typical Scandinavian country. The other type of a happy country has lower levels of income,
but high income and wealth inequalities, especially at the top of the wealth pyramid. This is the
Latin American and African model; e.g., Honduras, Bolivia, Ecuador, Costa Rica, South Africa,
and Zimbabwe. In both cases, wealth inequality at the very top contributes to happiness more than
inequality among other income groups. The simplified picture of the happy society is that of 99%
of the population having roughly similar incomes at around the average level for these 99%, but
where the remaining 1% are millionaires and billionaires.
32
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