Happiness, social cohesion and income
inequalities in Britain and Japan
Dimitris Ballas
Department of Geography
University of Sheffielde-mail: d.ballas@sheffield.ac.uk
http://www.sheffield.ac.uk/geography/staff/ballas_dimitris

Project ref: 8006/8599

Acknowledgements
•

•
•

•

The research presented here is based on joint work with Danny Dorling(University of Oxford), Helena Tunstall (University of Edinburgh), Tomoki,
Nakaya (Ritsumeikan University), Kazumasa Hanaoka (Tohoku University)
and Tomoya Hanibuchi (Chukyo University) which was supported by a
Daiwa Anglo-Japanese Foundation small grant.
The Family Resources Survey and Household Below Average Income
microdata were made available through the UK Data Archive.
The National Survey of Family Income and Expenditure microdata weremade available through the Japanese Statistics Bureau.
The Japanese General Social Surveys (JGSS) are designed and carried out
at the Institute of Regional Studies at Osaka University of Commerce incollaboration with the Institute of Social Science at the University of Tokyo
under the direction of Ichiro TANIOKA, Michio NITTA, Hiroki SATO andNoriko IWAI with Project Manager, Minae OSAWA. The project is financiallyassisted by Gakujutsu Frontier Grant from the Japanese Ministry of
Education, Culture, Sports, Science and Technology for 1999-2003
academic years, and the datasets are compiled and distributed by SSJ Data
Archive, Information Center for Social Science Research on Japan, Instituteof Social Science, the University of Tokyo.

Relevant publications• Ballas, D, Dorling, D, Nakaya, T, Tunstall, H, Hanaoka, K (2014),
Income inequalities in the UK and Japan: a comparative study of two
island economies, Social Policy and Society, vol. 13, pp. 103-117.
• Ballas, D, (2013), What makes a ‘happy city’?, Cities, vol. 32, s39 –s50.
• Ballas, D, Campbell, M, Clarke, G, Hanaoka, K, Nakaya, T, Waley, P(2012), A spatial microsimulation approach to small area income
estimation in Britain and Japan, Studies in Regional Science, vol. 42,
pp.163-187.
• Ballas, D, Dorling, D. (2013), The Geography of Happiness, in David,
S, Boniwell, I, Conley Ayers, A (eds.), The Oxford Handbook ofHappiness, Oxford University Press, Oxford, pp. 465-481.
• Ballas, D, Tranmer M (2012), Happy People or Happy Places? A MultiLevel Modelling Approach to the Analysis of Happiness and WellBeing, International Regional Science Review, vol. 35, pp. 70-102.

Outline• The Spirit Level hypothesis• Why compare Britain and Japan?
• Social cohesion, income inequalities andwell-being in Britain and Japan• Income inequality and poverty in Britain
and Japan 1989 – 2009
• Happy people or happy places? Models ofhappiness and well-being in Britain andJapan• Spatial microsimulation

‘The Spirit Level’by Richard Wilkinson and Kate Pickett

http://www.equalitytrust.org.uk

‘The Spirit Level’by Richard Wilkinson and Kate Pickett“Politics was once seen as a way of improving people’s social andemotional well-being by changing their economic circumstances. But
over the last few decades the bigger picture has been lost. People are
now more likely to see psychosocial well-being as dependent on what
can be done at the individual level, using cognitive behavioural therapy– one person at a time – or on providing support in early childhood, oron the reassertion of religious or family values. However, it is now
clear that income distribution provides policy makers with a wayof improving the psychosocial wellbeing of whole populations.
Politicians have an opportunity to do genuine good.”(Wilkinson and Pickett, 2009: 233; my emphasis)

17.7

Singapore

15.9

United States

15.0

Portugal

13∙8

United Kingdom

13.4

Israel12.5

Australia

12.5

New Zealand

11.6

Italy

Ratio
of the incomeofthe best-off
Wereallyneedbettertenth to worse of tenth ofhouseholds
comparison
at the moment the best
we have is this:UK

Germany

13∙8

6∙9

USA

15∙9

Japan
France

10.3

Spain

10.2

GreeceSpain

9.4

Canada10∙3

9.4

Ireland

9.2

Netherlands

9.1

France

9.0

Switzerland

8.2

Belgium

8.1

Denmark

7.3

Slovenia

6.9

Austria

6.9

Germany

6.2

Sweden

6.1

Norway5.6

Finland

4.5

Japan

Source: UNDP world development report 2009

9∙1

4∙5

The effects of inequality inaffluence countries
Appear to be related to high:Meat consumption
Water consumption
Waste production
Number of Flights
Ecological impact
in each of the most unequal affluent countries
compared to other less unequal rich nations
You might think: "Surely, if a few people hold most of the wealth we all consume less?"Inequality and meat
Meat consumption in kg per year per person

160

140
120

USA
Spain

100

FranceGermany

80

UK

60
Japan

40
20
2

4

6

8

10

12

14

16

18

Inequality
Not if you are concerned about how much meat we farm and consume

20

Inequality and water
water in m3 per year per person

8.0
7.0

USA
Spain

6.0
France5.0
Germany

4.0

UK
Japan

3.0
2.0
2

4

6

8

10

12

14

16

18

Inequality
Not if you are concerned about how much water we use (apart from the UK!)

20

Inequality and waste
1100
900
Municipal waste collected (kg per capita per
year)

Singapore

800
USA

700

600

Spain
FranceGermany

UK

500
Japan

400
300
2

4

6

8

10

12

14

16

Inequality
Not if you are concerned about how much waste we each produce

1820

Inequality and flights
New Zealand

60
40

Irelandannual aircraft departures per thousandpeopleNorway35
Canada

30
USA

25

20
15

UK
France10

Spain

Germany

5

Italy

Japan

0
2

4

6

8

10

12

14

16

18

Inequality
Not if you are concerned about how many flights we each take (on average)20Inequality and ecology
Ecological footprint in global hectares per
capita

11
10
USA

9
8
7

6

Spain
UK
Japan

5

FranceSingapore

Germany

4
3

2

4

6

8

10
12
Inequality

14

16

18

Not if you are concerned about how many planets we might need to exist:
An Ecological Footprint of 2.1 global hectares per capita equals one-planet living

20

Data sources

UNDP/FAO http://www.worldmapper.org/display.php?selected=126
UNDP/LPR http://www.worldmapper.org/display.php?selected=104
UNSD http://unstats.un.org/unsd/ENVIRONMENT/qindicators.htm
World Bank World Development Indicators 2005 (IS.AIR.DPRT)WWF Living Planet Index 2008

More and more geographical data is becoming available, often for the first time.

Why compare Britain and Japan?
• Japan is a world leader in health with currently thehighest life expectancy of any country (United Nations,2011). Life expectancy in Japan first overtook that inother countries in the 1970s and has retained thisranking ever since (Yanagishita and Guralnik, 1988). In
addition, according to a recent study comparing selfrated health and socio-economic status in East Asia,
Japan has relatively low levels of health inequality(Hannibuchi et al., 2010).
• Britain, in contrast, has a place near the bottom of thelife expectancy rankings in comparison to otherindustrialised countries (Marmot and Davey Smith,1989).

Why compare Britain and Japan?
• Japan and Britain have, in different ways, been at thecentre of recent international academic and politicaldebate regarding health and social equality andwellbeing in industrialised countries.
• Comparisons of Japan and Britain pertinent to theserecent debates because of their marked differences indeath rates and social inequality.
• These differences are of particular interest becauseof the characteristics that these countries have incommon: both are high income, island nations,dominated by world cities whose populations benefit
from universal health care.

Japan income data controversy• Recent OECD reports suggested that income
inequality in Japan is higher than suggested in the
Spirit Level as well as above the OECD average• A recent New Scientist report on what is describedas ‘the age of inequality’ uses statistics according towhich income inequality in Japan is higher than thatof Denmark, Germany and Greece and only slightly
lower than the UK.
• 2011 United Nations (UN) development report:
Japan has the lowest quintile income ratio of allwhen compared to the same set of countries
presented in the OECD reports.

Social cohesion, income
inequalities, health and well-being in
Britain and Japan• The distribution of income in Japan has often been
discussed as a possible explanation for high life
expectancy since the 1980s (Marmot and Davey Smith,1989) and has formed the centre of much recent debate
following the publication of “The Spirit Level”.
• Before World War Two Japan had a highly unequalincome distribution but the differences between rich andpoor declined in the post war period (Tachibanki, 2005).
• Income inequality was lower in Japan than in otherindustrialised countries in the 1970s and 1980s (Buss etal, 1989; Baur and Mason, 1992).

Social cohesion, income
inequalities, health and well-being in
Britain and Japan• But, social relationships and culture are often seen as
direct causes of good health rather than mediating
factors linking income distribution to health outcomes.
• Cultural tradition of strong ‘group-orientation’ promotessocial cohesion and ‘cultural equality’, supporting
psychological well being and good health among
Japanese people (Marmot and Davey Smith, 1989;
Horiuchi, 2011).
• It has not been explained however why thesuggested health benefits of these Japanese cultural
traditions should have only have become evident in
the post war period.

Social cohesion, income
inequalities, health and well-being in
Britain and Japan“Britain is an unequal country, more so than many otherindustrial countries and more so than a generation ago.
This is manifest in many ways – most obviously in the gap
between those who are well off and those who are less welloff. But inequalities in people’s economic positions are also
related to their characteristics – whether they are men or
women, their ages, ethnic backgrounds, and so on”(Hills et al., 2010)

Research agenda• revisiting the “Spirit Level” evidence
according to which Japan is a more
equitable and hence harmonious society
than any other industrialised country,
focusing on contrasts with a country suchas Britain.
• comparing social and spatial inequalities,
social cohesion and well-being between
Britain and Japan at different geographicallevels.

Data• The Family Resources Survey andHousehold Below Average Income(made available through the UK Data
Archive).
• The National Survey of Family
Income and Expenditure microdata(made available through theJapanese Statistics Bureau).

Key terms (1)• The median quintile ratio: this is the median income of
the richest 20 percent of the population divided by the
median income of the poorest 20 percent. This ratio isalso known as the ratio of top to bottom quintile medians
and is widely used in the analyses of HBAI datasets
conducted by the DWP.
• The mean quintile ratio: this is the mean income of therichest 20 percent of the population divided by the mean
income of the poorest 20 percent. This is also known as
the ratio of top quintile share to bottom quintile share andit was the key measure used in the Spirit Level work
(Wilkinson and Pickett, 2009)

Key terms (2)• People on incomes less than 50% of the mediangross household income: the number of individuals
living on household incomes less than 50% the mediangross household income as a proportion of the total
population.
• People on incomes less than 60% of the mediangross household income: the number of individuals
living on household incomes less than 60% the mediangross household income as a proportion of the total
population.

Comparing gross household income quintile ratios between Britain andJapan.

Inequality measure/ Year

1994

1999 2004

Median quintile ratio in
JapanMedian quintile ratio in theUK
Difference

3.85

4.08 3.99

5.09

5.23 4.99

1.24

1.15 1.00

Mean quintile ratio in Japan

4.56

4.74 4.67

Mean quintile ratio in theUK
Difference

6.65

7.13 6.93

2.09

2.39 2.26

Calculating disposable income for Japan

Estimated quintile group annual disposable income in Japan (in 10,000s
Japanese Yen; Source: calculated by applying tax bands on National
Survey of Family Income and Expenditure )Quintile group medians
Year

1

2

3

4

5*

Population
mean*

Medianquintile
ratio

2004

191

287

365

463

655

401

3.42

Quintile group means
Year

2004

1

2

3

4

5*

Population
mean*

Mean
quintile
ratio

179

286

365

465

712

401

3.97

* incomes over 2,500 were top-codedQuintile group annual disposable income in Britain(GBP; source: Family Resources Survey/HBAI)
Year

1

Quintile group medians
2
3
4

5

Population
mean

Medianquintile
ratio(median)
Income Before Housing Costs
2008/09
2004/05
Income After Housing Costs
2008/09
2004/05

201
177

304
262

407
350

545
465

844
704

507
426

4.20
3.98

139
132

243
217

343
300

474
405

745
630

433
370

5.4
4.8

5

Population
mean

Median quintile ratio

Year

1

Quintile group means
2
3
4

IncomeBeforeHousing
Costs
2008/09
2004/05

180
161

304
262

409
350

550
468

1090
890

507
426

6.05
5.53

Income After Housing Costs
2008/09110
243
2004/05
110
217

344
301

478
409

988
811

433
370

9.0
7.3

Estimated poverty rates in Japan (based on gross income
data from the Japanese National Survey of Family Income
and Expenditure)

2004
1999
1994
1989

50% below median 60% below median
10.3
16.4
10.8
17.0
10.2
16.3
6.4
10.1

Estimated poverty rates in Britain (based on gross income data fromthe UK Family Resources Survey)

2008/09
2004/05
1999/00
1994/95

50% below median
60% below median
14.2
21.8
14.0
22.2
16.2
24.7
15.5
24.9

Income distributions in UK and Japan, 2004
12
10
8
6
UK
4

Japan

Difference
20

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51-2-4

Happiness and social
comparisons“A house may be large or small; as long as
the surrounding houses are equally small
it satisfies all social demands for adwelling. But if a palace arises beside thelittle house, the little house shrinks to ahovel… [and]… the dweller will feel more
and more uncomfortable, dissatisfied andcramped within its four walls.”(Marx, 1847)

Happiness researchquestions:• What are the factors that influence
different types of individuals’ happiness?
• Is the source of happiness or unhappinesspurely personal or do contextual factors
matter? (and if they do, to what extent?)• If social comparisons are important, whatis the spatial scale at which people maketheir social comparisons?
• Happy People or Happy Places?

Social and Spatial inequalities in Japan“…the conventional wisdom of more recent studies ofJapanese cities is that they lack a 'social geography'(where this phrase is used to mean that there are noimportant differences from one area to another in wealthand social status)”(Fielding 2004: 64)“Mosaic Japan is a geodemographic segmentation. It
classifies consumers according to the type ofneighborhood in which they live, and is based upon thewell established principle that when people are deciding
where to live they naturally prefer to live amongst people
with similar demographics, lifestyles and aspirations totheir own”
(Mosaic Japan, 2011)

Multilevel Analysis
World  Nation  Region 
DistrictElectoral Wards  Neighbourhood
 Household  Individual

Multilevel modelling enables the analysis ofdata with complex patterns of variability –suitable to explore the variability ofhappiness at different levels

Multilevel modelling happiness and wellbeing in Britain
1. “Null model” – extent of variation
2. Socio-economic variables and health –random intercepts
3. Social context – interaction variables
Ballas, D, Tranmer M (2012), Happy People or Happy Places? A Multi-Level
Modelling Approach to the Analysis of Happiness and Well-Being, International
Regional Science Review, vol. 35, 70-102.
(doi:10.1177/0160017611403737)

Model 2 and 3 significant main effects (1)Happiness and well-being determinants
Model 2Age
HLGHQ1(-),GHQL(-)
Female (Reference = Male)HLGHQ1(-),GHQL(-)
Health good (reference = health excellent) HLGHQ1(-),GHQL(-)
Health fair (reference = health excellent) HLGHQ1(-),GHQL(-)
Health poor (reference = health excellent) HLGHQ1(-),GHQL(-)
Health very poor (reference = health
HLGHQ1(-),GHQL(-)
excellent)
Employment status: unemployed
HLGHQ1(-),GHQL(-)(reference = employed or self employed)
Employment status: family care (reference HLGHQ1(-),GHQL(-)= employed or self employed)
Employment status: sick/disabled
HLGHQ1(-),GHQL(-)(reference = employed or self employed)

Model 3
HLGHQ1(-),GHQL(-)
HLGHQ1(-),GHQL(-)
HLGHQ1(-),GHQL(-)
HLGHQ1(-),GHQL(-)
HLGHQ1(-),GHQL(-)
HLGHQ1(-),GHQL(-)
HLGHQ1(-),GHQL(-)

Model 2 and 3 significant main effects (2)Happiness and well-being determinants
Model 2Model 3
Employment status: on maternity leave
GHQL(+)(reference = employed or self employed)
Employment status: on a government
GHQL(-)
scheme (reference = employed or self
employed)
Employment status: other job status(reference = employed or self employed)
Has lived at current address for more
HLGHQ1(+)
HLGHQ1(+)
than 5 years (reference = lived atcurrent address for less than one year)
Household type: couple no children
HLGHQ1(+),GHQL(+ GHQL(+)(reference = single))
Household type: lone parent with
HLGHQ1(-)
HLGHQ1(-)
dependent child(ren) (reference = single)
Household type: lone parent with non
dependent child(ren) (reference = single)
Household type: other (reference = single) GHQL(+)
Household tenure: private renting
GHQL(+)(reference = owner occupier)Household tenure: LA/HA renting
HLGHQ1(-)(reference = owner occupier)Unemployment status (individual level) Not included
HLGHQ1(+),GHQL(x unemployment rate (district level)+)

Data-linkage approach using Geodemographics(Nakaya and Hanibuchi, 2009)
Happy

+

F24

Suburban Elite

E19

Small Time Business

D14

Small Town Seniors

I37

Original Suburbs

C12

Town Gown Transition

I36

Osaka Terraces

A1

Global Connections

H33

Welfare Dependency

B7

Dinky Developments

K50

Ancestral Homelands

Unhappy
Geodemographics“Mosaic Japan”(Neighbourhood Groups)

JGSS Survey microdata

Happiness Variations
betweenneighbourhoodgroupsGeodemographics: Mosaic Japana small areal residential classification
ACTONWINS Co. Ltd.

F Corporate Success Story
Employees of wellestablished corporations,who have worked their way
up the ranks and obtained
a certain level of social
status.
5.76% of Japanesehouseholds

A Metropolitan Careerists
tend to be under forty andearn a very high income.
10.02% of Japanesehouseholds

11 Groups
Types

/

50

B Graduate Newcomers
Young families withchildren living in modern
apartments in the newresidential areas of small
cities and the suburbs oflarge cities.
8.11% of Japanese(Nakaya and Hanibuchi, 2009)
households(ACTONWINS Co. Ltd.)H Social Housing Tenants
Low wage earners living inlarge cities in middle tolarge apartment blocks ofsocial housing developedby local authorities.
3.97% of Japanesehouseholds

Modern

Well educated

Urban

Young
C Campus Lifestyles

B Graduate Newcomers

A Metropolitan Careerists

F Corporate Success

D Older Communities
E Middle JapanOld
G Burdened
Optimists
K Deeply Rural
J Rural Fringe

Rural(Nakaya and Hanibuchi, 2009)

I Blue Collar
OwnersTraditional

H Social Housing

Poorly Educated(Nakaya and Hanibuchi, 2009)

Source: http://www.mosaicjapan.com/groups.htm

Source: http://www.mosaicjapan.com/groups.htm(Nakaya and Hanibuchi, 2009)

Can composition of individual socio-economic status
explain the geographic inequalities of well-being andhappiness?

Well-being

compositional?
Income & Wealth contextual
Individual-levelsocial gradient of wellbeing(Nakaya and Hanibuchi, 2009)

Geographic
social gradient of wellbeing

Spatial Microsimulation of Kyoto and Edinburgh

Source: http://simcity.ea.com/

Spatial Microsimulation
A technique aiming at building
large scale data sets
• Modelling at the microscale• A means of modelling real life
events by simulating thecharacteristics and actions of
the individual units that make up
the system where the events•

What is microsimulation?
PERSONAHID

PID

AAGE12SEXAJBSTAT…

AHLLTAQFVOCATENUREAJLSEG…

1

1000209

10002251

91

2

4

…

1

1

6

9

…

2

100038110004491

28

1

3

…

2

0

7

-8

…

3

100038110004521

26

1

3

…

2

0

7

-8

…

4

1000667

10007857

58

2

2

…

2

1

7

-8

…

5

1001221

10014578

54

2

1

…

2

0

2

-8

…

6

1001221

10014608

57

1

2

…

2

1

2

-8

…

7

1001418

10016813

36

1

1

…

2

1

3

-8

…

8

1001418

10016848

32

2

-7

…

2

-7

3

-7

…

9

1001418

10016872

10

1

-8

…

-8

-8

3

-8

…

10

1001507

10017933

49

2

1

…

2

0

2

-8

…

11

1001507

10017968

46

1

2

…

2

0

2

-8

…

12

1001507

10017992

12

2

-8

…

-8

-8

2

-8

…

Spatial microsimulation
procedures• The construction of a micro-dataset fromsamples and surveys• Static What-if simulations, in which the impactsof alternative policy scenarios on the population
are estimated: for instance if there is a taxation
policy change today, what would be the “morning
after” effect? Which areas would be mostaffected?
• Dynamic modelling, to update a basic microdataset and future-oriented what-if simulations:for instance if the current government had raised
income taxes this year what would theredistributive effects have been between different
socio-economic groups and between central
cities and their suburbs by 2021?

Spatial microsimulation
procedures• Construction of small area
microdata from using samples,
surveys and small area data
• Static-what-if simulations• Dynamic modelling to update thestatic microdata set and perform
dynamic what-if micro-spatial
policy analysis

Conceptual framework of the Kyoto model (Ballas et al., 2012; Hanaoka,
2011)

b. Professional & technical workers
and managers & officials(%)

a. Production process and related workers (%)¯

0c. Average household sized. Kyoto City, Japan

2.5

5
km

Estimated spatial distribution of average equivilised household income in
Edinburgh city

Conclusions• Income inequality analysis consolidates the Spirit Level
work.
• But need to obtain better quality disposable income
data on both countries (instead of the estimates
produced for Japan in the context of this project basedon the assumption of a single earner in eachhousehold).
• Next steps and future possibilities:- Further explore variations in happiness and well-being
using secondary data from the UK Understanding
Society and Japanese General Social Survey- Explore further the impact of interaction variables (e.g.
unemployed x regional unemployment rate)- Explore additional geographical variations using
multilevel and spatial microsimulation modelling
techniques- Further analysis for finer geographical scales (spatial
microsimulation and agent-based modelling)