Billionaires, millionaires, inequality, and happiness · 2019. 9. 27. · 2 Billionaires,...

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.

https://pages.nes.ru/vpopov/documents/Paradoxes%20of%20happiness.pdf


http://pages.nes.ru/vpopov/documents/Billionaires_Web-upload.pdf



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

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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.

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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


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


Nigeria

Norway

PeruPhilippines

Poland

Portugal

Russia

Saudi Arabia Singapore

South Africa

South Korea

Spain

SwedenSwitzerland

Taiwan Province of China

Thailand

Turkey

Ukraine


34

56

78

0 10 20 30 40Billionaires wealth from GWR in 2018 to PPP GDP in 2016, %


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


Iceland

India

Indonesia

IrelandIsrael


Kuwait

Lebanon

MalaysiaMexico

Morocco


Nigeria

Northern Cyprus

Norway

Palestinian Territories

PeruPhilippines

Poland

Portugal

Russia

Saudi ArabiaSingapore

Somalia

South Africa

South Korea

South Sudan

Spain

SwedenSwitzerland


Thailand

Turkey

Ukraine


34

56

78

0 20 40 60Net wealth of billionaires as a % of GDP in 2018


Argentina

AustraliaAustria

Belgium

Brazil

Canada

Chile

China

Colombia

Cyprus

Czech Republic

Denmark

Egypt

Finland

France

Germany

GreeceHong Kong SAR, China

Iceland

India

Indonesia

IrelandIsrael


Kuwait

Lebanon

MalaysiaMexico

Morocco


Nigeria

Norway

PeruPhilippines

Poland

Portugal

Russia

Saudi Arabia Singapore

South Africa

South Korea

Spain

SwedenSwitzerland


Thailand

Turkey

Ukraine

United Arab Emirates United KingdomUnited States

34

56

78

0 50 100 150 200 250Ratio of millionaires wealth to GDP, %


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


Iceland

India

Indonesia

IrelandIsrael


Kuwait

Lebanon

MalaysiaMexico

Morocco


Nigeria

Norway

PeruPhilippines

Poland

Portugal

Russia

Saudi ArabiaSingapore

South Africa

South Korea

Spain

SwedenSwitzerland

Syria


Thailand

Turkey

Ukraine

United Arab EmiratesUnited Kingdom United States

34

56

78

50 60 70 80 90 100Gini index of wealth distribution in 2018, %


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

HKG

HKG

HUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNHUNISLISLISLISL

ISL

ISL

ISL

ISL

ISLISLISLISLISLISLISL

ISL

ISLISLISL

INDINDINDINDINDIND

INDIND

IND

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40 60 80 100GINI Index (wealth distribution)

Wealth of billionares as a % of PPP GDP in 2018 Fitted values

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10

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20


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

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in

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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

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0

10

20

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50

60

70

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90

100

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

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%

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

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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

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Mauritania

Mauritius

Mexico

Moldova

MongoliaMontenegroMorocco

MozambiqueMyanmar

Namibia

Nepal


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


Tajikistan

Tanzania

Thailand

Togo

Trinidad & Tobago

Tunisia

TurkeyTurkmenistan

UgandaUkraine


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


NicaraguaNiger

Nigeria

Norway

PakistanPanamaParaguayPeru

Philippines

PolandPortugal

QatarRomania

Russia

RwandaSaudi Arabia

SenegalSerbiaSierra Leone

Singapore

SlovakiaSlovenia

South AfricaSouth Korea

Spain

Sri LankaSudan

Sweden

Switzerland


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


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


Variables

2.1, Panel, random effects, N=760


2.3, Panel, random effects,

N=760

2.4, Panel, fixed effects,

N=760

2.5, Panel, N=658


N=658


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)


.02***

(3.2)

.04***

(4.6)

.04***

(4.6)

.02***

(2.7)

.03***

(4.7)


.02***

(4.2)

Billionaires’ wealth to PPP GDP ratio, %,


.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)


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


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


Variables

3.1, Panel, N=876



N=876

3.4, Panel,

N=197



N=197

PPP GDP per capita, $, Ycap -1.8e-06 **

(-2.5)

-3.5e-06 ***

(-4.7)

-2.2e-06

(-1.3)


.8***

(15.4)

.3***

(5.1)

.3***

(5.3)

-.1***

(-2.7)

.06

(1.1)

.22***

(2.9)


.2***

(5.6)

.06

(1.2)

-.2

(-1.6)

Billionaires’ wealth to PPP GDP ratio, %,


-.9***

(-6.5)

-.3***

(-2.7)

2.4***

(3.8)

Interaction term

(Ycap * GINIincome)


*Billionaires’Intensity)

-.08***

(-4.4)


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


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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Opportunities. NBER Working Paper, no. 8267.

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