New The economics of happiness: A lifetime perspective · 2012. 4. 30. · happiness studies, that...

The economics of happiness: A lifetime perspective

Douglas A. Beatton BBus (Finance and Management)

MBus (Research)

School of Economics and Finance

Queensland University of Technology

A

Dissertation

in fulfilment of the requirements

for the

Degree

of

Doctor of Philosophy

11th November 2011

[i]

[ii]

Chapter 7 Verse 16

Confucius said, “Give me a few more years to learn at the age of fifty and I will be unlikely to have major faults”

(Cheung, 2010)

[iii]

[iv]

Abstract

The three studies in this thesis focus on happiness and age and seek to contribute to

our understanding of happiness change over the lifetime. The first study contributes

by offering an explanation for what was evolving to a ‘stylised fact’ in the economics

literature, the U-shape of happiness in age. No U-shape is evident if one makes a

visual inspection of the age happiness relationship in the German socio-economic

panel data, and, it seems counter-intuitive that we just have to wait until we get old to

be happy. Eliminating the very young, the very old, and the first timers from the

analysis did not explain away regression results supporting the U-shape of happiness

in age, but fixed effect analysis did. Analysis revealed found that reverse causality

arising from time-invariant individual traits explained the U-shape of happiness in

age in the German population, and the results were robust across six econometric

methods. Robustness was added to the German fixed effect finding by replicating it

with the Australian and the British socio-economic panel data sets.

During analysis of the German data an unexpected finding emerged, an exceedingly

large negative linear effect of age on happiness in fixed-effect regressions. There is a

large self-reported happiness decline by those who remain in the German panel. A

similar decline over time was not evident in the Australian or the British data. After

testing away age, time and cohort effects, a time-in-panel effect was found. Germans

who remain in the panel for longer progressively report lower levels of happiness.

Because time-in-panel effects have not been included in happiness regression

specifications, our estimates may be biased; perhaps some economics of the

happiness studies, that used German panel data, need revisiting.

The second study builds upon the fixed-effect finding of the first study and extends

our view of lifetime happiness to a cohort little visited by economists, children.

Initial analysis extends our view of lifetime happiness beyond adulthood and

revealed a happiness decline in adolescent (15 to 23 year-old) Australians that is

twice the size of the happiness decline we see in older Australians (75 to 86 year-

olds), who we expect to be unhappy due to declining income, failing health and the

onset of death. To resolve a difference of opinion in the literature as to whether

[v]

childhood happiness decreases, increases, or remains flat in age; survey instruments

and an Internet-based survey were developed and used to collect data from four

hundred 9 to 14 year-old Australian children. Applying the data to a Model of

Childhood Happiness revealed that the natural environment life-satisfaction domain

factor did not have a significant effect on childhood happiness. However, the

children’s school environment and interactions with friends life-satisfaction domain

factors explained over half a steep decline in childhood happiness that is three times

larger than what we see in older Australians. Adding personality to the model

revealed what we expect to see with adults, extraverted children are happier, but

unexpectedly, so are conscientious children.

With the steep decline in the happiness of young Australians revealed and

explanations offered, the third study builds on the time-invariant individual trait

finding from the first study by applying the Australian panel data to an Aggregate

Model of Average Happiness over the lifetime. The model’s independent variable is

the stress that arises from the interaction between personality and the life event

shocks that affect individuals and peers throughout their lives. Interestingly, a

graphic depiction of the stress in age relationship reveals an inverse U-shape; an

inverse U-shape that looks like the opposite of the U-shape of happiness in age we

saw in the first study. The stress arising from life event shocks is found to explain

much of the change in average happiness over a lifetime. With the policy

recommendations of economists potentially invoking unexpected changes in our

lives, the ensuing stress and resulting (un)happiness warrant consideration before

economists make policy recommendations.

KEYWORDS: Happiness, methodology, unobservables, peers, latent variable models, age effects, cohort effects, children, school, life events, stress, lifetime happiness, life satisfaction domains, U-shape, panel data.

[vi]

Table of Contents

Abstract ................................................................................................................................... iv

Table of Contents .................................................................................................................... vi

List of Tables ........................................................................................................................... x

Table of Figures .................................................................................................................... xiv

Glossary of Terms and Abbreviations ................................................................................ xviii

Statement of Original Authorship .......................................................................................... xx

Acknowledgements .............................................................................................................. xxii

Chapter One ............................................................................................................................. 1

Introduction .............................................................................................................................. 1

1.1 Overview of the Thesis ............................................................................................... 1

Chapter Two............................................................................................................................. 3

Preliminary review of the happiness literature......................................................................... 3

2.1 Happiness ..................................................................................................................... 3

2.2 Identifying the research gap addressed in the Chapter 3 study .................................... 8

2.3 Identifying the research gap addressed in the Chapter 4 study .................................. 10

2.4 Identifying the research gap addressed in the Chapter 5 study .................................. 11

2.5 Chapter Two Summary .............................................................................................. 13

Chapter Three ......................................................................................................................... 15

The puzzle of the U-shaped relationship between happiness and age ................................... 15

3.1 Introduction ................................................................................................................ 15

3.2 Literature review ........................................................................................................ 17

3.3 The Three Panel Data Sets ......................................................................................... 22

3.4 Analysis of the puzzle ................................................................................................ 29

3.5 Potential explanations for the U-shape of happiness in age ....................................... 39

[vii]

3.6 Explanation for the negative slope in the GSOEP ..................................................... 61

3.7 Are there time and cohort effects in the HILDA ........................................................ 70

3.8 Are there time and cohort effects in the BHPS .......................................................... 74

3.9 Conclusions and discussion on the negative slope ..................................................... 77

3.10 Chapter 3 Limitations ................................................................................................ 78

3.11 Chapter 3 Summary ................................................................................................... 79

Chapter 3 - Appendix A: Descriptive Statistics ..................................................................... 81

Chapter 3 - Appendix B: Results from Least Squares Regression Analysis .......................... 85

Chapter 3 - Appendix C: Additional information on the robustness analyses in section 3.5.4

............................................................................................................................................. 101

Chapter Four ........................................................................................................................ 111

Unhappy Young Australians ................................................................................................ 111

4.1 Introduction .............................................................................................................. 111

4.2 A Review of Childhood Happiness from the Economics Literature ........................ 112

4.3 The Data Sets ........................................................................................................... 116

4.4 Methodology and analyses ....................................................................................... 139

4.5 Analysis, results and discussion ............................................................................... 140

4.6 Chapter 4 Limitations .............................................................................................. 150

4.7 Chapter 4 Summary ................................................................................................. 152

Chapter 4 - Appendix A: The Smart Train Survey Questions ............................................. 155

Chapter 4 - Appendix B: Regression results ........................................................................ 167

Chapter Five ......................................................................................................................... 171

Do changes in the lives of our peers make us unhappy? ...................................................... 171

5.1 Introduction .............................................................................................................. 172

5.2 The Data ................................................................................................................... 175

5.3 Methodology analyses and results ........................................................................... 178

5.4 Chapter 5 Limitations .............................................................................................. 201

5.5 Chapter 5 Summary ................................................................................................. 203

[viii]

Chapter 5 - Appendix A: Descriptive Statistics ................................................................... 205

Chapter 5 - Appendix B: Regression Results for the Aggregate Model of Happiness ........ 207

Chapter 5 - Appendix C: Results for the Model of Individual Level of Happiness ............. 213

Chapter Six .......................................................................................................................... 217

Summary of Findings ........................................................................................................... 217

Chapter Seven ...................................................................................................................... 223

Discussion, Policy Conclusions and Future Research ......................................................... 223

REFERENCES .................................................................................................................... 240

[ix]

[x]

List of Tables

Table 3.1a&b: Life Satisfaction regression results (t-values) from recent studies ................ 19

Table 3.2: Sample averages from the entire GSOEP, HILDA and BHPS samples ............... 25

Table 3.3: Summary of changes in the GSOEP Age and Age2 coefficients as controls are

progressively added ............................................................................................................... 31

Table 3.4: Comparison of changes in the GSOEP & HILDA Age and Age2 coefficients as

controls are progressively added ............................................................................................ 35

Table 3.5: Summary of changes in the GSOEP, HILDA & BHPS Age and Age2 coefficients

as controls are progressively added ....................................................................................... 38


as controls are progressively added; for ages 22 to 80 years ................................................. 43


progressively added; with fixed effects ................................................................................. 46

Table 3.8: Summary of changes in the GSOEP and HILDA Age and Age2 coefficients as

controls are progressively added; with fixed effects .............................................................. 49


as controls are progressively added; with fixed effects ......................................................... 52

Table 3.10: Coefficients for the key 5 variables (pooled & fixed effects) for the three data

sets. ........................................................................................................................................ 56

Table 3.11: Descriptive statistics for the entire and first-timer GSOEP samples .................. 81

Table 3.12: Descriptive statistics for the entire and first-timer HILDA samples .................. 82

Table 3.13: Descriptive statistics for the entire and first-timer BHPS samples ..................... 83

Table 3.14: The determinants of Life Satisfaction for West-Germans in the GSOEP; Pooled

OLS Regression – entire sample, N = 176,770 ...................................................................... 86

Table 3.15: Determinants of Life Satisfaction for West-Germans in the GSOEP; Pooled OLS

Regressions – ages 22 to 80, N = 160,332 ............................................................................. 87

Table 3.16: The determinants of Life Satisfaction for West-Germans in the GSOEP; Fixed-

effect Regressions – entire sample, N = 176,770 .................................................................. 88

[xi]

Table 3.17: The determinants of Life Satisfaction for West-Germans in the GSOEP; Pooled

Regression – first time respondents, N = 18,821 ................................................................... 89

Table 3.18: The determinants of Life Satisfaction; Pooled OLS regression results for all

individuals in the HILDA; N = 75,529 .................................................................................. 90

Table 3.19: Determinants of Life Satisfaction for Australians in the HILDA; Pooled

Regressions, ages 22 to 80, N = 65.679 ................................................................................. 91

Table 3.20: The determinants of Life Satisfaction for Australians in the HILDA; Fixed-effect

Regressions – entire sample, N = 75,529............................................................................... 92

Table 3.21: The determinants of Life Satisfaction for Australians in the HILDA; Pooled


Table 3.22: The determinants of Life Satisfaction for Britons in the BHPS; Pooled

Regression – entire sample, N = 153,886 .............................................................................. 94


Regressions – ages 22 to 80, N = 138,481 ............................................................................. 95

Table 3.24: The determinants of Life Satisfaction for Britons in the BHPS; Fixed-effect

Regressions – entire sample, N = 153,886 ............................................................................. 96





Table 3.27: Summary of changes in the HILDA Age and Age2 coefficients as controls are


Table 3.28: Summary of changes in BHPS Age and Age2 coefficients as controls are

progressively added ............................................................................................................. 100

Table 3.29: The determinants of Life Satisfaction for West-Germans in the GSOEP; ‘Usual

suspects plus health’ specification, OLS, OLS with categorical health, Ordered Logit, BUC

estimator & OLS with fixed effect – entire sample, N = 176,770 ....................................... 102

Table 3.30: The determinants of Life Satisfaction for West-Germans in the GSOEP, Age-

bands: (1) without control; (2) with controls per the ‘Usual suspects plus health’

specification; (3) plus fixed effects (3), N = 176,770 .......................................................... 104

[xii]

Table 3.31: The determinants of Life Satisfaction for British in the BHPS; ‘Usual suspects

plus health’ specification, OLS, OLS with categorical health, Ordered Logit, BUC estimator

& OLS with fixed effect – entire sample, N = 153,886 ...................................................... 105

Table 3.32: The determinants of Life Satisfaction for British in the BHPS; Age-bands: (1)

without controls; (2) with controls per the ‘Usual suspects plus health’ specification; (3) plus

fixed effects, N = 153,886 ................................................................................................... 107

Table 3.33: The determinants of Life Satisfaction for Australians in the HILDA; ‘Usual

suspects plus health’ specification, OLS, OLS with categorical health, Ordered Logit, BUC

estimator & OLS with fixed effect – entire sample, N = 72,529 ......................................... 108

Table 3.34: The determinants of Life Satisfaction for Australians in the HILDA, Age-bands:

(1) without controls; (2) with controls per the ‘Usual suspects plus health’ specification; (3)

plus fixed effects, N = 75,729 .............................................................................................. 110

Table 4.35a & b: Summary of happiness studies of the young from the economics literature

(EconLit) identifying the study population as adolescents or children ................................ 114

Table 4.36: Observations by age & year for the 15 to 23 year-old sample from HILDA waves

2-8 ........................................................................................................................................ 117

Table 4.37: Sample averages for the 15 to 23 year-old cohort and the entire HILDA sample

............................................................................................................................................. 118

Table 4.38: Cross-country OLS happiness results ordered (1) to (6) by the size of the

standardised beta coefficient ................................................................................................ 129

Table 4.39: Personality factors, related behaviours, & ‘Happiness Survey’ question numbers

............................................................................................................................................. 132

Table 4.40: ‘Happiness Survey’ question numbers & behaviours for the school environment

and interaction with friends life satisfaction domains ......................................................... 134

Table 4.41: Life Satisfaction for the 9 to 14 year old children in the ‘Smart Train’ data .... 136

Table 4.42: Descriptive statistics for selected questions from the ‘Smart Train’ cross-

sectional data; N = 389 ........................................................................................................ 138

Table 4.43: The determinants of Life Satisfaction for children aged 9 to 14 years in the Smart

Train dataset; OLS regression, N = 389 .............................................................................. 167

Table 4.44: The determinants of Life Satisfaction for children aged 9 to 14 years in the Smart

Train dataset; OLS regression, N = 389 .............................................................................. 168

[xiii]

Table 4.45: Other determinant variables of Life Satisfaction for the children aged 9 to 14

years in the cross-sectional Smart Train dataset; OLS regression, N = 389 ........................ 169

Table 4.46: The determinants of Life Satisfaction; Pooled OLS regression results for 15 to 23

year-old cohort and All (15 to 92 year-olds) in the HILDA; N = 12,330 ............................ 170

Table 5.47: Sample averages for individuals in the HILDA; N = 55,177 ........................... 176

Table 5.48: Sample averages for life events affecting individuals in the HILDA; N = 55,177

............................................................................................................................................. 177

Table 5.49: Stress levels defined by the Social Readjustment Rating Scale ........................ 183

Table 5.50: Stress levels defined by the Social Readjustment Rating Scale (continued) .... 184

Table 5.51: The HILDA personality questionnaire (HILDA, 2008a) .................................. 191

Table 5.52: Descriptive statistics for aggregate variables used in models (1) to (6); N = 70

............................................................................................................................................. 205

Table 5.53: OLS regressions results for nested Aggregate Models of Happiness (5) for

Australians aged 15 to 84; N = 70 ....................................................................................... 207

Table 5.54: OLS regressions for the Aggregate Model of Happiness (5.2) with the eleven

most important life events; N = 70 ...................................................................................... 208

Table 5.55: The determinants of Life Satisfaction for Australians; Pooled OLS regression

results for individuals in the HILDA; N = 55,177 ............................................................ 213

Table 5.56: The determinants of Life Satisfaction for Australians; Fixed-effect regression

results for individuals in the balanced HILDA panel; N = 55,177 ..................................... 215

[xiv]

Table of Figures

Figure 3.1: Life satisfaction question from page 35 of wave 18 of the GSOEP Living in

Germany Survey questionnaire on the social situation of households ................................... 27

Figure 3.2: Life satisfaction question from page 74 of wave 8 of the HILDA Continuing

Person Questionnaire ............................................................................................................. 28

Figure 3.3: Life satisfaction question from the British Household Panel Survey: wave 18

questionnaire, p. 64 ................................................................................................................ 28

Figure 3.4: Average life satisfaction by age in the GSOEP for the pooled sample .............. 29

Figure 3.5: Life satisfaction in the GSOEP for the pooled sample with added controls........ 30

Figure 3.6: Life satisfaction in the HILDA for the pooled sample ........................................ 32

Figure 3.7: Life satisfaction in the HILDA for the pooled sample with added controls ........ 34

Figure 3.8: Life satisfaction in the BHPS for the pooled sample ........................................... 36

Figure 3.9: Life satisfaction in the BHPS for the pooled sample with added controls .......... 37

Figure 3.10: Life satisfaction in the GSOEP for the pooled sample for the mid-age range ... 40

Figure 3.11: Life satisfaction in the HILDA for the pooled sample for the mid-age range ... 41

Figure 3.12: Life satisfaction in the BHPS for the pooled sample for the mid-age range ..... 42

Figure 3.13: Life satisfaction in the GSOEP for the balanced panel ..................................... 45

Figure 3.14: Age and observed correlates in the GSOEP ...................................................... 47

Figure 3.15: Life satisfaction in the HILDA for the balanced panel...................................... 48

Figure 3.16: Age and observed correlates in the HILDA ...................................................... 50

Figure 3.17: Life satisfaction in the BHPS for the balanced panel ........................................ 51

Figure 3.18: Age and observed correlates in the BHPS ......................................................... 53

Figure 3.19: Comparison of Age and individual observed correlates across data sets .......... 54

Figure 3.20: Predicted happiness effects of the non-age variables in the GSOEP ................. 57

Figure 3.21: Predicted happiness effects of the non-age variables in the HILDA ................. 58

Figure 3.22: Predicted happiness effects of the non-age variables in the BHPS ................... 58

[xv]

Figure 3.23: Year and life satisfaction in the GSOEP for the pooled sample ........................ 63

Figure 3.24 (top) and 3.24 (bottom): life satisfaction in the GSOEP for first-time respondents

............................................................................................................................................... 66

Figure 3.25: The degree of selection in the GSOEP for stayers in the panel ......................... 68

Figure 3.26: Year and life satisfaction in the HILDA for the pooled sample ........................ 71

Figure 3.27 (top) and 3.25 (bottom): life satisfaction in the HILDA for first-time respondents

............................................................................................................................................... 72

Figure 3.28: The degree of selection in the HILDA for stayers in the panel ......................... 73

Figure 3.29: Year and life satisfaction in the HILDA for the pooled sample ........................ 74

Figure 3.30 (top) and 3.30 (bottom): life satisfaction in the BHPS for first-time respondents

............................................................................................................................................... 75

Figure 3.31: The degree of selection in the BHPS for stayers in the panel ........................... 76

Figure 4.32: Log of average Annual Household Income and proportion of self-reported

pregnancy (self or partner) for Australians aged 15 to 93 years, 2002-2008 HILDA, N =

77,132 .................................................................................................................................. 119

Figure 4.33: Life Satisfaction of 15 to 93 year-old Australians (2002-2008 HILDA panel

data) ..................................................................................................................................... 120

Figure 4.34a: Front side of the ‘Happiness Postcard’ themed with the same graphics as the

‘Happiness’ promotional poster & the icon clicked to initiate the web-based ‘Happiness

Survey’ ................................................................................................................................. 123

Figures 4.35(a) & (b): Average Life Satisfaction for 9 to 14 year old Australian children in

the ‘Smart Train’ data and 15 to 90 year-old Australians in the 2002-2008 HILDA panel data

............................................................................................................................................. 140

Figure 4.36: The predicted changes in childhood happiness from each domain factor as the

children move up in school grade ........................................................................................ 145

Figure 4.37a: The online ‘Happiness Survey’: initial screen and question q1 ..................... 155

Figure 5.38: Average happiness for Australians aged 15 to 84 ........................................... 186

Figure 5.39: Average stress level for Australians aged 15 to 84 ......................................... 186

Figure 5.40: Average stress from positive and negative life events; Australians aged 15 to 84

............................................................................................................................................. 188

[xvi]

Figures 5.41a to e: Change in personality factors over time for Australians aged 15 to 84;

scale is 1 to 7 ........................................................................................................................ 194

Figures 5.42: The role of the direct and indirect effects from personality on life events and

the stress arising from those life events ............................................................................... 198

Figure 5.43a to k: Graphics of the stress at each age arising from the eleven most important

life events ............................................................................................................................. 209

[xvii]

xviii

Glossary of Terms and Abbreviations

BHPS1 British Household Panel Survey data

GDP Gross domestic product

GSOEP2 German Socio-Economic Panel survey data

Happiness Life satisfaction

HILDA3 Household Income & Labour Dynamics in Australia panel survey data

PanelWhiz4 Software used to extract variables from the panel data sets

QUT Queensland University of Technology

SRRS Social Readjustment Rating Scale

UK United Kingdom, Britain

USA United States of America

1 I thank the UK Data Archive, the Economic and Social Research Council and the University of Essex for the use of the BHPS data - Study Number 5151 - British Household Panel Survey: Waves 1-18, 1991-2009. 2 The GSOEP is a longitudinal household survey sponsored by the Deutsche Forschungsgemeinschaft. It is organized by the German Institute for Economic Research (Berlin) and the Center for Demography and Economics of Aging (Syracuse University). I thank these institutes and the director Dr. G. Wagner for making the data available. 3 This thesis uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). The findings and views reported in this paper are those of the author and should not be attributed to either FaHCSIA or the MIAESR. I thank FaHCSIA & the Melbourne Institute director, Professor Deborah Cobb-Clark, and her staff for making the data available. 4 The HILDA and BHPS data was extracted using the Add-On package PanelWhiz v3.0 (Nov 2010) for Stata. PanelWhiz was written by Dr. John P. Haisken-DeNew ([email protected]). The PanelWhiz generated DO file to retrieve the HILDA data used here and any Panelwhiz Plugins are available upon request. Any data or computational errors in this paper are my own. Haisken-DeNew and Hahn (2006) describes PanelWhiz in detail.

xix

xx

Statement of Original Authorship

The work contained in this thesis has not been previously submitted for a Degree or

Diploma at any other higher education institution. To the best of my knowledge and

belief the thesis contains no material previously published or written by another

person except where due reference is made in the thesis itself.

Signed : ______________________________ Date: 11th November 2011

Douglas A. Beatton

xxi

xxii

Acknowledgements

Many people contributed to the completion of this thesis. To all those involved I offer my

sincere gratitude and thanks. There are those, whose contribution was exceptional, I

acknowledge their considerable assistance. First, thank you to my wife, Debbie, and our sons

Stuart and Anthony for their understanding and patience. Your love and support have been

the greatest driving force. To my mentor and supervisor during my PhD studies, Professor

Paul Frijters, thank you for your guidance and advice, for believing that I could learn

something about economics, redeem my lost math skills, develop my empirical abilities, and,

actually write something that others might choose to read. My sincere thanks also go to Dr

David Johnston and Professor Uwe Dulleck who willingly substituted as my Principal

Supervisor after Paul moved to another university. I particularly thank my co-supervisor,

Professor Lisa Bradley, for helping me traverse the inter-disciplinary precipice between

psychology and economics. Thanks for the roller coaster ride; it has been an exhilarating

four years.

To those who helped develop my economics knowledge, thank you. To Professor Stan Hurn,

Dr David Johnston and Dr Vlad Pavlov, thank you for my new found econometric skills. To

Professor Uwe Dulleck, thank you for the helpful discussions and the generous loan of your

personal library of economics texts. To the research students in the School of Economics and

Finance, thanks for the helpful advice and debate that contributed to my learning. My

particular thanks go to Redzo Mujcic, Markus Schaffner, Bin Dong, Jonas Fooken, David

Savage and Wasantha Athukorala who were always available to offer help and

encouragement when things became difficult.

An empirical thesis like this is reliant upon good data. I therefore thank the providers of the

data sets used in this thesis: the German Socio-Economic Panel, the British Household Panel

Survey, and, the Household Income Dynamics in Australia Survey. I also thank Leesa

Watkin, QUT Smart Train Project Manager, and Annie Harris, Senior Project Officer from

the Queensland Government, Department of Tourism, Regional Development and Industry

for their assistance in collecting data from the Queensland children who visited the QUT

Smart Train. Finally, the assistance of the School of Economics & Finance, the QUT

Business School, the Research Students Centre and the Library staff at QUT is gratefully

acknowledged.

xxiii

1

Chapter One

Introduction

This empirical thesis seeks to enhance current econometric models of happiness5.

Recent advances in the content of socio-economic panel data sets have created

research opportunities that allow us to consider happiness from a multidisciplinary

perspective. For example, panel data from the Australian, British and German

socioeconomic surveys now include variables that allow us to econometrically test

how behavioural factors contribute to the theme of this study, happiness change over

the lifetime.

1.1 Overview of the Thesis

This thesis is about happiness and age. The thesis begins with a preliminary review

of the economics of happiness literature (Chapter 2). The review reveals gaps in the

happiness literature that are the basis for the research questions pursued in the

Chapter 3, 4, and 5 studies. The Chapter 3 study addresses the first research gap, the

U-shape of happiness in age. After offering a methodological explanation for the U-

shape of happiness in age in Chapter 3, Chapter 4 extends our view of lifetime

happiness to an age-cohort infrequently visited in the economics of happiness

literature, children. The Chapter 4 study uses unique cross-sectional data to reveal

life satisfaction domain factors that contribute to explaining happiness change in 9 to

14 year-old children. Chapter 5 builds on the findings from the Chapter 3 and 4

studies by offering a life event based explanation of changes in happiness over the

lifetime. Chapter 6 summarises the Chapter 3, 4 and 5 findings and they are

discussed in Chapter 7.

5 Consistent with the example of eminent happiness researchers such as Andrew Clark, Ed Diener, David Blanchflower, Richard Easterlin, Bruno Frey, Paul Frijters, Andrew Oswald, Bernard van Praag, and others, I interchangeably refer to life satisfaction as happiness or wellbeing.

2

Lifetime happiness is topical. The December 2010 issue of The Economist magazine

devoted three pages to ‘The U-bend of life .... Why, beyond middle age, people get

happier as they get older’ (Economist, 2011, pp. 33-36). This article began by

identifying what The Economist called ‘the dismal way ... the economics discipline

talks about happiness ... using money as a proxy for utility. Like The Economist, this

behavioural economics researcher is unconvinced that material wealth is all that

makes us happy. Some policy makers agree. For example, the French President,

Nicolas Sarkozy (2009) has recently commissioned two Nobel-prize-winning

economists (Amartya Sen and Joseph Stiglitz) to develop a broader happiness

measure. In addition, The Economist revealed that, David Cameron, the British Prime

Minister, has shown similar leadership by initiating the collection of wellbeing data.

After revealing that policy makers are beginning to believe that money and GDP

growth are not the panacea to measuring the overall wellbeing of society, The

Economist illustrates that happiness over a lifetime is shaped like the U-bend in our

bathroom washbasin. To explain this U-shape of happiness in age, The Economist

draws from the current economics literature. Unlike The Economist, this thesis is

unbounded from a single happiness perspective. This thesis offers explanations for

the U-shape of happiness in age that are based upon theory from other behavioural

disciplines. The explanations offered are supported by empirical evidence from oft-

used socio-economic datasets6. But first, I begin with a preliminary review of the

happiness literature7 to reveal the research gaps that are the basis for the three studies

documented in this thesis.

6 As well as my own data set, I use panel data from the Household Income Dynamics in Australia Survey (HILDA, 2009), the German Socio-Economic Panel (GSOEP, 2008), and the British Household Panel Survey: Institute for Social & Economic Research (BHPS, 2010). 7 Chapters 3, 4 & 5 also contain a detailed literature review relevant to the research questions addressed in those chapters.

3

Chapter Two

Preliminary review of the happiness literature8

2.1 Happiness

“ ……there is in reality nothing desired except happiness. Whatever is

desired otherwise than as a means to some end beyond itself, and ultimately to happiness, is desired as itself a part of happiness, and is

not desired for itself until it has become so.”

John Stuart Mill (1806-1873), Utilitarianism, Chapter 4.

The notion of happiness transcends culture, geography & time. The above quote

from John Stuart Mill (2009) is reflective of the importance nineteenth century

British society placed upon happiness. The founding fathers of the United States of

America considered it such an important human ideal that they included the ‘pursuit

of Happiness’ in the preamble of the 1776 Declaration of Independence. More

recently the Nobel Prize winner Joseph Stiglitz and the French President (Sarkozy,

2009) urged world leaders to revolutionise their thinking on economic success by

extending economic measures of growth beyond gross domestic product to include

factors such as healthcare, social situation and happiness.

Happiness is not just the reserve of western society. Confucius, the fifth century BC

Chinese philosopher and teacher motivated the practice of a life of virtue by stating

that the souls of departed relatives were largely dependent for their happiness on the

conduct of their living descendants (Shinn, 2009). The Hindu philosophy of

prarabdha karma amplifies the inter-temporal importance of happiness by stating that

happiness is a consequence both of actions performed in the present and past lives

(Srivastava & Misra, 2003). In addition, the Buddha taught that happiness (sukkha) is

not derived from an escalating spiral of consumption but from a pervasive state of

mind that emerges when the trials, tribulations, and events that confront us in our

lives can be borne with ease. The notion of happiness extends across time and

8 Note: There is a detailed literature review for each study in Chapters 3, 4 and 5.

4

transcends geography and cultures. Independent of how we view our social

situations, humans seek happiness. However, ancient western philosophers view

happiness from different perspectives.

Let us briefly return to the roots of western society and review the alternative

happiness positions of two ancient Greek philosophers. To Aristotle, eudaimonia

(happiness) emerged from the pursuit of a good life founded on virtue and excellence

wherein we derive happiness from the incremental achievements of a planned life

that focuses on living up to one’s potential, consideration of others, and contribution

to the overall wellbeing of society. Alternatively, Plato hedonically espoused that

happiness emerges from the active pursuit of all things that bring us much pleasure

and satisfaction9. The objective or societal outcome perspective of Aristotle’s

eudaimonia (happiness) may appear in conflict with Plato’s hedonic focus on

maximising individual utility. Aristotle’s happiness objectively focuses on behaviour

that leads to societal well-being. Plato’s position is subjective and calls on

individuals to take a mentalist approach by constantly thinking about how to

maximise the pleasure (utility) in their lives10. If we are to heed the teachings of these

ancient Greek philosophers, happiness research should not just hedonically focus on

utility maximisation. Happiness research should also consider the human behaviour

behind the pleasures we choose. This empirical thesis unbinds from a single literature

and responds to Daniel Kahneman (2003) who encouraged collaboration and

research consideration from multidisciplinary perspectives.

Just like the happiness perspectives of the ancients, seeking truth from

multidisciplinary perspectives could leave a researcher in two minds. To avoid this

stuck-in-the-middle pitfall, I adopt Jeremy Bentham’s11 ‘Greatest Happiness

Principle’, which provides a rational foundation upon which to study happiness:

9 White (2006) provides an historical view of happiness through contemporary eyes. 10 See Kashdana, Biswas-Diener, & Laura A. King (2008) for more on the ‘objectivist’ versus the ‘subjectivist’ or ‘mentalist’ happiness positions. 11 Extended by John Stuart Mill to Utilitarianism (Mill, 2010; UCL, 2009).

5

“A policy of acting with the explicit aim of maximizing society’s happiness….. is more likely to have the consequence of maximizing one’s own happiness than

is any other policy, including a policy of acting with the explicit aim of maximizing one’s own happiness.”

(Mill in Mawson, 2002, p. 402)

In a society where individuals (i) have limited resources and opportunities, we should

consider the overall wellbeing of society when selecting alternatives that efficiently

maximise the utility value (U) we derive from our choice behaviours, and thereby

maximise our and the overall well-being of society:

. 1max

N

ii

U=∑

Choices have outcomes that an individual self-assesses as beneficial to their overall

wellbeing, their happiness12. In seeking to maximise their utility, their happiness,

individuals seek to maximise the positive effects from pleasurable outcomes they

deem good or beneficial while minimising the painful outcomes, the negative effects

that detract from their overall wellbeing. It follows that if an individual is capable of

adjudging the good and bad in their lives, one way to measure their happiness is to

ask them.

However, the scientific disciplines have different views on how happiness and its

determinants should be measured. Early motivation to measure social wellbeing

emerged from the desire to statistically gauge and guide the direction of social

change during the 1930s & 1940s. With so much change taking place in western

societies, there was a move by groups like the University of Chicago under the

auspices of the US Research Committee on Social Trends to collect social indicators

of change to measure how this change was affecting the overall wellbeing of society.

By the 1970s, the focus had shifted from social wellbeing to the quality of life; those

set of wants from which arises the satisfaction that makes us happy. Quality of life is

the combination of the subjective feelings and the objective status of personal well- 12 Economics of happiness researchers view life satisfaction scales as measures of subjective utility; they propose using them as an alternative to the orthodox revealed preferences approach to utility (Varian, 1992, p.132)

6

being that arises from the environment in which we live at a particular point in time.

However, there emerged a difference of opinion across the scientific disciplines on

what constitutes a quality or happy life and how its determinants should be

measured.

The objective approach to measuring the determinants of wellbeing uses selected

objective variables drawn from the regular census and other socio-demographic

surveys. Examples of objective variables include employment status, education

level, health, age, gender, housing, leisure activities, and income. The subjective

approach to measuring the determinants of wellbeing also uses data collected with

polls and surveys, but with this subjective data, people are asked about the quality of

their life, their experiences and what is going on in their lives. Examples of self-

reported variables include asking people about their mental health, their overall

health, self-rated stress, financial well-being and overall life satisfaction (Andrews &

Withey, 1976; Campbell, Converse, & Rodgers, 1976). This research takes a multi-

discipline approach by using combinations of objective and subjective variables to

explain happiness change over the lifetime.

As to measuring happiness itself, economists could choose from numerous happiness

or wellbeing measures. Measures include: the Satisfaction With Life Scale (SWLS)

(Diener, et al., 1985); Life Satisfaction Index (LSI- A) (Leugarten, 1987 #645);

Temporal Satisfaction with Life Scale’ (TSWLS) (Pavot, Diener, & Suh, 1998;

(Watson, 1988) Positive Affect/Negative Affect Schedule (PANAS);

Delighted/Terrible Scale (O-DT) (Andrews, 1976), and; the ‘Day Reconstruction

Method’ (DRM) which measures affective experience in daily life (Csikszentmihalyi

& Larson, 1987). This research uses the global happiness question common to

economics and found in the survey questionnaires used to collect the socioeconomic

panel data used in this study. The global happiness question is based on the early

work of Wessman & Ricks (1966) as interpreted and updated by Fordyce (1973;

1988, p.357). The question takes the form:

All things considered, how satisfied are you with your life?

7

In Chapter 4, I argue and provide evidence for why the global happiness question is

suitable for measuring happiness; ‘the aggregate utility arising from all the good and

bad things that occur in our lives’ (Fordyce 1988). Using data collected with the

global happiness question, economics of happiness research has typically focused on

finding the causes and correlates of happiness. The model of individual happiness

(2.1) is:

2.1

where,

LSit Life satisfaction (individual happiness)

C Constant

Xit Time-variant socio-demographic variables (e.g. income, health, employment status, relationship status)

Leit Life events (changing circumstances in an individual’s life) (e.g. personal injury, death of spouse, fired from job)

εit error term

Individual life satisfaction is a function of time-variant socio-demographic variables

(Xit), self-reported life events shocks (Leit) that affected an individual over the

previous period (over the past year), and, unobservables in the usual error term εi .

The degree of positive or negative change in our happiness is a function of an

individual’s consideration of their happiness expectations (their aspirations) with the

changing economic circumstances (Xit) and the life events (Leit) that affect them

(Easterlin, 2002, p 214). Time-variant socio-demographic variables (Xit) found to

have a positive effect on happiness include: employment (Argyle, Kahneman,

Diener, & Schwarz, 1999; Dockery, 2005), income (Frey & Stutzer, 2000; Frijters,

Haisken-DeNew, & Shields, 2004), marriage (Frey & Stutzer, 2005; Powdthavee,

2009), religious belief (Chamberlain & Zika, 1988; Dehejia, Deleire, & Luttmer,

1 2it it it itLS C X Leβ β ε= + + +

8

2007); good health (Gerdtham & Johannesson, 2001; Veenhoven, 2008) and, social

relationships (Powdthavee, 2008). Socio-demographic variables found to have a

negative effect on happiness include: unemployment (Clark & Oswald, 1994; Di

Tella, MacCulloch, & Oswald, 2001); victim of crime (Powdthavee, 2003); obesity

(Oswald & Powdthavee, 2007); children (Phelps, 2001; Tsang, 2003), divorce

(Gardner & Oswald, 2006), ill-health (Gerdtham & Johannesson, 2001); disability

(Oswald & Powdthavee, 2008), and; the death of a loved one or the imminent onset

of one’s own demise (Frijters, Johnston, & Shields, 2008).

In addition to time-variant socio-demographic variables, the inclusion of life event

questions in socio-economic surveys has provided the opportunity to analyse the

effect(s) of self-reported life events (Leit) on individual happiness. Life events

occurring in the previous period and found to have a negative effect on individual

happiness include: finances just worsened (Frijters, Johnston, & Shields, 2009); just

separated or divorced (Blanchflower & Oswald, 2004); just fired from a job (Clark &

Oswald, 2002); was just a victim of crime (Frijters, et al., 2008), and; spouse, friend

or close relative just died (Frijters, et al., 2008). Life events occurring in the previous

period and found to have a positive effect on happiness include: finances just

improved; just got married; just had a baby (Frijters, et al., 2009), and; just retired

(Beatton & Frijters, 2009). Some of these life event shocks have been priced. Clark

& Oswald (2002) concluded that widowhood (death of spouse) brings a degree of

unhappiness that requires, on average, an extra £170 000 per annum to offset, and,

Frijters et al. (2008a) concluded that ’the average criminal event can be offset by a

windfall income gain of about 14,000 US’. The inclusion of life events in happiness

regressions has contributed to the level of happiness explanation.

2.2 Identifying the research gap addressed in the Chapter 3 study

The inclusion of age effects in happiness regressions has also contributed to the level

of happiness explanation. Until the early 2000s, economic opinion about the effect of

age on happiness was divided. Clark (2006) found a U-shaped pattern for the UK,

whilst Winkelmann and Winkelmann (1998) found no U-shape in happiness but

simply a very strong negative effect of age. Easterlin and Schaefer & Macunovich

(1993), using 20 years of the US General Social Survey even concluded that life

9

satisfaction is almost flat in age, with neither a U-shape nor a negative slope.

Alesina, Di Tella, & MacCulloch, (2004) and van Praag, Frijters, & Ferrer-i-

Carbonell (2000) even found an inverted U-shape. Despite economic opinion on the

effect of age on happiness being divided, age effects began to regularly appear in

models of individual happiness (2.2).

2.2

In 2006, Clark claimed robustness with respect to methodology for the finding that

happiness is U-shaped in age when he concluded that ‘Panel analysis controlling for

fixed effects continues to produce a U-shaped relationship between well-being and

age’.

The weight of economic opinion appeared to have tilted towards acceptance of the

U-shape of happiness in age as a stylised fact. However, such a stylised fact conflicts

with an old psychology literature that finds no happiness-age relationship (Cantril,

1965). Palmore and Luikart (1972) comment in their review; ‘Several variables

thought to be related to life satisfaction had little or no relationship: age, sex, total

social contacts. Studying the age-effect relationship on the happiness of 2,272

individuals aged 25 to 74 years, Mroczek & Kolarz (1998) found that that

personality, contextual, and socio-demographic variables, as well as their

interactions, are all that is needed to fully understand the age-happiness relationship.

In Chapter 3, I examine this difference of opinion between the age-happiness

literatures and provide an empirically-supported explanation for the U-shaped

relationship between age and happiness over a lifetime.

21 2 1 3 4it it it itLS C X Le age ageβ β β β ε−= + + + + +

10


In considering happiness over a lifetime, economists have primarily focussed on

examining the happiness of adults and, occasionally, adolescents13. Adolescent and

adult happiness over a lifetime was initially considered to be stable (Easterlin, 1974).

Blanchflower & Oswald (1998) examined random samples of young men and

women using socio-economic data from the USA and thirteen European countries.

They found that between the ‘1970s to the 1990s the well-being of the young

increased quite markedly’. A cursory glance at the average happiness of the

adolescents from the German socio-economic panel that is examined in Chapter 3

reveals a result opposite to the above-noted findings of Easterlin (1974) and

Blanchflower et al. (1998). Conflicting evidence leads us to question whether the

happiness of the young increases decreases or remains stable in age. Looking for an

answer, I located a paucity of economic literature examining the happiness of the

young (adolescents and children).

This is not surprising. In economics, adolescents and children have usually been

considered in the context of the negative (Stutzer & Frey, 2006; White, 2006) or

positive (Tsang, 2003) effect they have on adult happiness. There have been studies

of the German population that considered the relationship between parents and their

adult children's subjective well-being (Bruhin & Winkelmann, 2009) but they too

were adult-centric studies. There are a small number of studies, which examined

adolescent happiness. Using Australian socio-economic panel data, Ulker (2008)

found that a stable family structure had a significant positive effect on the happiness

of young Australians aged 15 to 27 years. Ebner (2008) examined the effect of

housing decisions on adolescent happiness. Dockery (2005) examined the effect of

education, labour market experience and employment on the happiness of 16 to 19

year old Australian adolescents, and, Bassi & Delle Fave (2004) identified the

importance of providing adolescents with meaningful activities in order to foster

their personal growth and well-being. There has been less research on childhood

happiness.

13 Oft-used socio-economic panel data sets s usually include observations on individuals aged 15 years and over.

11

Lee & Oguzoglu (2007) examined how the receipt of income support payments

affected the well-being of youths with a median age of 14 years. Flouri (2004)

examined the role of parenting in later-life subjective well-being and found

correlations between closeness to parents at age 7 and the happiness of the same

individuals at age 42, and, Csikszentmihalyi & Hunter (2003) found correlations

between proximal environmental factors and the happiness of US grade school

children. There are a small number of studies on adolescent happiness but even less

on childhood happiness, and, these studies do not widen our view of lifetime

happiness to include the young. In Chapter 4, I address this gap in the literature by

extending our view of lifetime happiness to nine-year-old children and ask, is there is

a decrease or an increase in the happiness of young individuals, and, what explains

changes in childhood happiness?


With our view of happiness extended to young children, Chapter 5 builds on the

findings of the first study by offering explanations to changes in happiness over a

lifetime. The aggregate model of happiness over a lifetime that is offered

incorporates life event shocks, peer effects and fixed traits. As we will see in Chapter

3, fixed traits are very important to happiness and the question arises what these

fixed traits might be. Obvious fixed-effect candidates are the traits that psychologists

now consider stable over a lifetime, personality (McCrae, Costa, Mroczek, & Little,

2006). Previously, the prevailing consensus was that life events like marriage,

parenting, retirement, and chronic illnesses would profoundly affect personality.

After many decades of research, psychologists generally agree that personality traits

are invariant across age (McCrae et al., 2002), life events have been found to have no

effect on personality (Costa, Herbst, McCrae, & Siegler, 2000). However,

personality can affect how an individual behaves when confronted with a life event

shock. In their four-year longitudinal study of young adults, Magnus, Diener, Fujita,

& Pavot (1993) examined the causal pathways between personality and life events.

They found the personality trait of extraversion predisposed individuals to

experience life events more positively, whereas neuroticism predisposed individuals

to experience life events more negatively. These findings from the psychology

literature provide some evidence that, while personality has been found to have direct

12

effects on happiness, the effects from life events on happiness may be mediated by

personality.

The study of Howell (2006) goes beyond the above-mentioned studies that

considered interactions between personality and life event shocks; they found that

‘daily social interactions mediated the relationship between personality trait of

extraversion and life satisfaction’. Individual happiness is not only affected by

personality, life event shocks, and their interactions, but also by those individuals

with whom we socially interact on a daily basis, our peers. In clarification, I offer an

example from Rayo & Becker’s seminal peer effect paper (Rayo & Becker, 2007).

Individuals confronted with an ‘improvement in finances’ life event shock are not

just concerned with their absolute level of income but also with the difference

between their income and the income of their peers. Rayo & Becker (2007) theorise

that the basis for this comparison by peers is a social (income) norm, or set point,

that changes over time. This peer comparison example fits well with the ‘Easterlin

Paradox’, a phenomena seen in a number of western countries over the past few

decades (Easterlin, 1995). In France, Germany, Japan, the United Kingdom and the

United States of America higher incomes have not translated to increased happiness.

Individual happiness has not changed as real per capita income grew (Lewer,

Gerlich, & Gretz, 2009). Individuals appear to quickly adapt to changes in social

norms and as a result are no (un)happier.

We can see from the previous paragraphs that happiness researchers have considered

peer effects, personality and life events shocks in isolation, but, not together. In

Chapter 5, I take a unique approach to the study of lifetime happiness by combining

all three (personality, life events shocks and peer effects) to offer an aggregate model

that significantly explains changes in happiness over a lifetime.

13

2.5 Chapter Two Summary

The preliminary review of literature revealed the research gaps that are the basis for

the studies in Chapters 3, 4 and 5. The Chapter 3 study seeks to contribute to the

ongoing debate on whether lifetime happiness is U-shaped in age. The Chapter 3

study extends our view of lifetime happiness by examining an age cohort seldom

visited in the economics of happiness literature, childhood happiness. With our view

of lifetime happiness extended to children, Chapter 5 takes a unique approach to the

study of happiness by combining personality and life event shock interactions with

peer effects in an aggregate model of lifetime happiness that significantly contributes

to the explanation of changes in happiness over a lifetime.

14

15

Chapter Three

The puzzle of the U-shaped relationship between happiness and age

In this chapter, the puzzle of the relationship between age and happiness is

considered. Whilst the majority of psychologists have concluded there is not much of

a relationship at all, the economic literature has unearthed a possible U-shape of

happiness in age. In this chapter, I look for the U-shape in three panel data sets, the

German Socioeconomic Panel (GSOEP), the British Household Panel Survey

(BHPS) and the Household Income Labour Dynamics Australia (HILDA) and

investigate several possible explanations for it14.

3.1 Introduction

What is the relationship between happiness and age? Do we become more miserable

as we age, or is our happiness relatively constant throughout our lives with only the

occasional special event (marriage, birth, promotion, illness) temporarily raising or

reducing our happiness, or, do we actually get happier as life gets on and we learn to

be content with what we have?

14 This chapter has been submitted as the joint paper with my supervisor, Professor Paul Frijters. The paper is titled ‘The mystery of the U-shaped relationship between happiness and age” and is at the review and resubmit stage with the Journal of Economic Behavior and Organisation. We would like to thank conference attendees, anonymous referees, and, seminar participants for useful comments and suggestions.

16

The weight of evidence from the recent economics of happiness literature supports a

belief that the age-happiness relationship is U-shaped15. This finding holds for the

United States, Germany, Britain, Australia, Europe, and South Africa. The stylised

finding is that individuals gradually become unhappier after their 18th birthday, with

a minimum around 50, followed by a gradual upturn in old age. The predicted effect

of age can be quite large. For example, the difference in average happiness between

an 18 year old and a 50 year old can be as much as 1.5 points on a 10-point-scale.

This recent economics literature, however, conflicts with an old psychology literature

that finds no happiness-age relationship (Cantril, 1965). Palmore and Luikart (1972)

comment in their review, ‘Several variables thought to be related to life satisfaction

had little or no relationship: age, sex, total social contacts, ....’.Diener, Sapyta, &

Suh (1998) provide a common-held psychological explanation for changes in

happiness over our lifetime. The changes are a reflection of the positive and negative

effects that emerge from changes in our life situations. More recently, Dear,

Henderson, & Korten (2002) conclude that the prevalence of high life satisfaction

simply becomes less common at higher ages. From this reading, it is clear that either

the psychologists have overlooked something important for a long time or that the

economists have somehow gotten it wrong recently; this chapter intends to clarify

and find out, which it is.

I begin by re-examining the age-happiness relationship and then delve into the

methodological aspects of the problem. Essentially, we want to know if the U-shape

that economic scholars find is an artefact or real and what the actual relationship

between age and life satisfaction is. The age-happiness relationship is examined

using an often-used dataset, the German Socio Economic Panel that has an extensive

set of variables on the individual level. This data-richness allows us to not only

15 Recent papers on this in the economic literature include: (Bell & Blanchflower, 2007; Blanchflower, 2008; Blanchflower & Oswald, 2001; 2004; 2007; 2008; 2009; Clark, 2006; Dear, Henderson, & Korten, 2002; Di Tella, MacCulloch, & Oswald, 2001; Ferrer-i-Carbonell & Frijters, 2004; Ferrer-i-Carbonell, 2005; Gerdtham & Johannesson, 2001; Hayo & Seifert, 2003; Helliwell, 2003; Oswald, 1997; Oswald & Powdthavee, 2008; Powdthavee, 2003; Seifert, 2003; Senik, 2004; Theodossiou, 1998; Van Landeghem, 2008; Winkelmann & Winkelmann, 1998; Wolpert, 2010). An introduction to the found effects of correlates of happiness can be found in Frey & Stutzer (2002). For a recent general introduction to the economic literature on happiness, see Clark et al. (2008). For a full list of earlier papers in the field of happiness, see Veenhoven’s Database of Happiness (introduced in Veenhoven et al. 1994).

17

replicate the findings of other studies based on cross-sectional data, but also allows

us to explore the dynamic interplay between age, covariates, unobserved

heterogeneity and happiness.

The progression in this chapter is to let the puzzle of the age-happiness relationship

unfold. I first briefly review the recent literature where I summarise the methodology

and reveal the main findings from other happiness studies. Then I present the data we

have and show that we can also generate a U-shape of happiness in age when we run

similar regressions to those in the literature. I then go through successive

explanations for the U-shape, including: the possibility that it depends upon

including the happiness decline found in early adulthood (age 18 to 22); that it is an

artefact of not allowing for fixed effects; or that it is a truly robust finding. In the

conclusion, I summarise the findings and explain what they mean for future research

into the economics of happiness.

3.2 Literature review

Whilst much of the economic literature on the age-happiness relationship is recent,

there have been earlier discussions of it (see Theodossiou (1998) for a discussion of

the history of this issue). Until the early 2000s, the opinion of economists about the

effect of age was divided. Clark and Oswald (1994) found a U-shaped pattern for the

United Kingdom (UK), on the other hand, Winkelmann and Winkelmann (1998)

found no U-shape in happiness but simply a very strong negative effect from age.

Using 20 years of the US General Social Survey, Easterlin and Schaefer &

Macunovich (1993) concluded that life satisfaction is almost flat in age, with neither

a U-shape nor a negative slope. Alesina, Di Tella, & MacCulloch, (2004) and van

Praag, Frijters, & Ferrer-i-Carbonell (2000) even found an inverted U-shape.

Despite this early difference of opinion, nearly all recent papers come down on the

side of a U-shape relationship between happiness and age. Blanchflower and Oswald

(2001; 2004) simply state that ‘Wellbeing is U-shaped in age’. Gerdtham and

Johannesson (2001) also report a U-shape in age with a minimum around the age of

55. Hayo and Seifert ( 2003) and Seifert (2003) also report a U-shape and call the U-

18

shaped age effect a ‘typical finding in happiness regressions’. The most

comprehensive study to date is Blanchflower and Oswald (2007) who combine cross-

sectional data for the US, Europe and the World Value Survey. In total, they have

about 800,000 respondents in over 60 countries for which they all report a U-shape in

happiness and age. Clark (2006) claims some robustness with respect to

methodology for this finding when he concludes that ‘Panel analysis controlling for

fixed effects continues to produce a U-shaped relationship between well-being and

age’.

In order to get a feeling for the role of methodology in these findings, I reproduce in

Tables 3.1a & 3.1b the main findings of the recent economic studies on the U-shape

between age and happiness. Importantly, in this literature the existence of a U-shape

is inferred from the combination of a negative coefficient on age and a positive

coefficient on age-squared in a happiness regression. Tables 3.1a & 3.1b note the

study, the found coefficients on age and age-squared, details and source of the data,

and, the estimation method used. In addition, the studies summarised in Tables 3.1a

& 3.1b use other personal control variables in the same regression. The controls

mainly include measures for employment, income, family relationships, the number

of children in a family, education and, sometimes, indicators of where individuals

live.

19

Table 3.1a&b: Life Satisfaction regression results (t-values) from recent studies

Author, date Sample

(size & name)

Coefficients - Pooled(t-value)

Coefficients - Fixed Effects (t-value)

Dependent variable (DV) and controls Age Age Squared Age Age Squared

(Blanchflower

& Oswald,

2009)

data from 8

European

nations

OLS16

-0.00800

OLS15

0.0000815

DV:

Life

Satisfaction

without

controls

(Blanchflower

& Oswald,

2008)

data from 16

countries

Ordered

Logit

-0.0576

(8.85)

Ordered

Logit

0.0006

(9.95)

DV:

Life

Satisfaction

with personal

controls

(Blanchflower

& Oswald,

2001)

USA: General

Social Survey

1972-2006

N = 45,474

Ordered

Logit

(men+women

averaged)

USA -0.0211

(4.39)

Ordered Logit

(men+women

averaged)

USA: 0.0003

(5.92)

DV:

Happiness

Controls: yes

(specification

without

cohort)

(Blanchflower

& Oswald,

2001)

Europe:

Eurobarometer

1976-2002

N = 589,446

Ordered

Logit

(men+women

averaged)

Eur: -0.045

(31.31)

Ordered Logit

(men+women

averaged)

Eur: 0.00052

(10.1)

DV: Life

Satisfaction

Controls: yes

(specification

without

cohort)

(Blanchflower

& Oswald,

2001)

World Value

Survey

1981- 2004

N = 163,852

Ordered

Logit

(men+women

averaged)

WVS:

-0.0505

(10.1)

Ordered Logit

(men+women

averaged)

WVS: 0.0003

(5.92)

DV: Life

Satisfaction

Controls: yes

(specification

without

cohort)

16A measure of significance was not provided by the authors.

20

Table 3.1a&b (continued): Continuation of Life Satisfaction regression results (t-values) from

recent studies

Author, date Sample

(size & name)

Coefficients - Pooled(t-value)

Coefficients - Fixed Effects (t-value)

Dependent variable (DV) and controls

Age Age Squared Age

Age Squared

(Blanchflower

& Oswald,

2004)

UK:

Eurobarometer

Survey

1975-1998

Ordered

Logits - All

UK: -0.0424

(2.84)

N = 54,549

Ordered

Logits - All

UK: 0.0005

(15.38)

N = 54,549

DV: Life

Satisfaction

Controls: yes

(Clark, 2006)

British

Household

Panel Survey

(BHPS)

waves 1 to 14

-0.075

(-25)

N = 82,096

0.00091

(30.33)

N = 82,096

Applied age

cohorts to

derive fixed

effect

coefficients

DV: Life

Satisfaction

Controls: yes

(Di Tella, et

al., 2001)

Eurobarometer

Survey Series

1975-1991

OLS

-0.02

(20.0)

N = 264,710

OLS

0.0002

(33.33)

N = 264,710

DV: Life

Satisfaction

Controls: yes

(Powdthavee

, 2005)

Statistics

South Africa

OHS study of

1997

-0.011

(z-stat: -2.38)

N = 20,634

0.0001

(z stat: 2.03)

N = 20, 634

DV: Life

Satisfaction

Controls: yes

(Senik, 2004)

Russian

longitudinal

monitoring

survey

(RLMS).

Ordered

Probit (2)

-0.050

(8.33)

N = 17,897

Ordered

Probit (2)

.001

(p < .01)

N = 17,897

DV: Life

Satisfaction

Controls: yes

(Winkelmann

&

Winkelmann,

1998)

German Socio-

Economic

Panel

1984-89 waves

of the GSOEP

-0.098

(-9.8)

N = 20,944

0.0012

(12)

N = 20,944

fixed effects

logit model 2

-0.118

(-3.19)

N = 20,944

fixed effects

logit model

2

-0.0001

(0.25)

N = 20,944

DV: Binary

Life

Satisfaction

Controls: yes

21

Tables 3.1a & 3.1b confirm the very strong effect that age is found to have upon life

satisfaction in recent studies and that the effect of linear age is always negative,

whilst that of age-squared is positive, indicating a U-shape. Bearing in mind that the

age at which the minimum occurs is given by the coefficient of linear age divided by

twice the coefficient of age-squared; it appears that the majority of the studies find

that 55 is the age at which minimum happiness occurs. Tables 3.1a & 3.1b also

underscore that the effects are mainly found in cross-sections when controls are

added for individual socio-economic variables.

Despite the reliance in the literature on using age and age-squared in order to unearth

a U-shape, other approaches have been used. Wunder et al. (2009) include a fourth-

order polynomial of age in their happiness regressions, where they find that the

higher order terms are also significant and hence that the U-shape is not a perfect

description of the actual relationships (they find a clear negative slope at the very

high age ranges). Yet, since this chapter is interested in seeing where a particular

finding in the literature comes from, I follow the convention of focussing on just a

second-order polynomial (age and age-squared).

With the literature review coming down heavily in support of the presence of a U-

shape of happiness in age, this chapter proceeds by seeing if we can replicate the U-

shape of happiness in age using the oft-used German Socio-Economic Panel data

(GSOEP) panel data set. While the GSOEP is the base data source for findings and

explanations, robustness added by replicating the GSOEP findings with two

additional socio-economic panel data sets: the Household, Income and Labour

Dynamics in Australia Survey panel data (HILDA), and; the British Household Panel

Survey (BHPS) panel data. These three panel data sets are now described.

22

3.3 The Three Panel Data Sets

3.3.1 The German (GSOEP) data

As the basis for analysis I use the 1984-2002 waves of the German Socio-Economic

Panel (GSOEP, 2008), a representative 18-year panel of the German population. The

first wave (1980) included only the Federal Republic of Germany; it has included the

former East Germany since 1990. I use only the information on West Germany in

order to abstract from the importance of the 1990 German reunification, which had a

tremendous impact on the lives and satisfaction levels of East Germans (Frijters,

Haisken-DeNew, & Shields, 2004). The GSOEP currently tracks about 20,000

individuals and 12,000 households. See Wagner, Burkhauser, & Behringer (1993) or

Plug & Van Praag (1998) for a detailed description of the data. Table 3.2 (p.25)

shows the sample means and standard deviations for the variables used from the

GSOEP data and Table 3.11 contains the descriptive statistics for the whole sample

and the first-time respondents (Chapter 3 - Appendix A, p.81).

3.3.1.1 A brief summary of the GSOEP descriptive statistics

The average self-assessed happiness in the GSOEP (7.16) is on the high side of the 0

to 10 scale. Forty nine per cent of respondents are male and the average age across

the entire unbalanced panel is 44.26 years, with the average age of first-time

respondents 5.79 years lower. Average years of education are 10.93 years. The

average employment level is 47%, 4% are unemployed, and, 33% are non-

participants in the labour force, with the remaining retired. Average household

annual income is 49,800 Euros. Average health (2.59) is on the poorer health side of

the health question response scale (1 to 5), but only 4% self-identify with a disability

(invalid). The majority of households are married (65%), with less than one child on

average (0.65). Just 42% own or are paying off the home in which they live. On

average, the life event that occurs most often is the birth of a child (4%) with the next

highest event occurrence being job loss (2%), a life event occurrence the same as

marriage. Let us now review the HILDA panel data.

23

3.3.2 The Australian (HILDA) data

The second data set I use are waves 2 to 8 from the ‘Household, Income and Labour

Dynamics in Australia’ (HILDA) Survey17. This household–based panel survey

began in 2001 (HILDA, 2008b). It has the following key features. It collects

information about economic and subjective wellbeing, labour market dynamics and

family dynamics. Special questionnaire modules are included each wave including

life events in waves 2 to 8. Interviews are conducted annually with all adult members

of each household. The initial wave (1) panel response consisted of 6,872 households

and 13,969 individuals, and, wave 8 (2008) tracked 12,785 individuals. Of the

13,969 individuals from the first wave, 9,354 (67%) responded in wave 8 of the

HILDA (Watson & Wooden, 2010). The sample means and standard deviations for

the variables used from the HILDA are in Table 3.2 (p.25) and Table 3.12 contains

the descriptive statistics for the whole sample and the first-time respondents (Chapter

3 - Appendix A, p.82).

3.3.2.1 A brief summary of the HILDA descriptive statistics

The average self-assessed happiness in the HILDA (7.91) is on the high side of the 0

to 10 scale. Forty seven per cent of respondents are male and the average age across

the entire unbalanced panel is 45.50 years; with the average age of first-time

respondents 1.44 years lower. Surprisingly, average years of education for

Australians (12.82) is 1.89 years higher than in the German sample, and, the

Australian standard deviation is lower (1.80 versus 2.46). The average Australian

employment is 66%, 3% unemployed, with the remaining non-participating or

retired. Average annual household income is $AUD 63,357, but the distribution is

positively skewed (range 0 to 583,260; 50th percentile 55,535; 75th percentile 81,966,

and; 95th percentile 138,624). Average health (2.64) is towards the poorer health side

of the health question response scale (1 to 5), and, relative to the German sample

(4%), many more Australians (24%) self-identify with a disability (invalid)18.

17 The questionnaire for wave 1 of the HILDA panel survey did not include several important variables often used in happiness regressions (life events). 18 The difference between high disability levels in the Australian population and populations from other western nations is worthy of further study.

24

Compared to the German sample (65%), fewer Australian households are married

(55%), but, on average, Australian families have more children (0.65 for the

Germans versus 0.77 for Australians). Household ownership in the Australian sample

(75%) is much higher than in the German sample (42%); home-ownership is a long-

held social norm in Australia. The life event shock that most affects Australians is

family death (11%), closely followed by divorce (9%). The occurrence of marriage

breakdown in the Australian sample (3%) is half as big again as in the German

sample. This is also the case with job loss; Australian’s are more likely to be

dismissed (fired) from their job (0.02 in the German sample versus 0.03 in the

Australian sample). This all appears to indicate that Australian families may be less

stable than German families. Compared to Germans, Australians have a higher

incidence of marriage breakdown, more children per household, and, are more likely

to lose their jobs then find new employment (they change jobs more often). Let us

now review the BHPS panel data.

3.3.3 The British (BHPS) data

Finally, I use waves 6 to 10 and waves 12 to 18 of the British Household Panel

Survey19. The British Household Panel Survey (BHPS) began in 1991 and is a multi-

purpose longitudinal survey that generates panel data. It follows the same

representative sample of individuals over a period of years. It is household-based,

interviewing every adult member of sampled households. The BHPS contains

sufficient cases for meaningful analysis of certain groups such as the elderly or lone

parent families. Wave 1 of the BHPS panel data included responses from 5,500

households and 10,300 individuals drawn from 250 areas of Great Britain. An

additional sample from 1,500 households in each of Scotland and Wales were added

in 1999, and in 2001 a sample of 2,000 Northern Ireland households was added,

making the panel suitable for United Kingdom-wide research (BHPS, 2010). Table

3.2 (p.25) shows the sample means and standard deviations for the life satisfaction

and the variables used from the BHPS panel and Table 3.13 contains the descriptive

statistics for the whole sample and the first-time respondents (Chapter 3 - Appendix

A, p.83).

19 The BHPS Waves 1 to 5 and 11 did not include the happiness question.

25

Table 3.2: Sample averages from the entire GSOEP, HILDA and BHPS samples

GSOEP HILDA BHPS Mean s. d. Mean s. d. Mean s. d.

overall life satisfaction 7.16 1.85 7.91 1.47 5.23 1.29

age 44.26 16.91 45.50 16.92 46.45 17.80

age*age 2244.67 1659.88 2356.65 1666.50 2474.85 1789.30

Ln (annual household income) 10.68 0.53 10.77 1.07 9.84 1.61

male (1=yes) 0.49 0.50 0.47 0.50 0.45 0.50

level of education (years) 10.93 2.46 12.82 1.80 13.26 2.40

number of children in family 0.65 0.99 0.77 1.12 0.53 0.94

married (1=yes) 0.65 0.48 0.55 0.50 0.55 0.50

employed (1=yes) 0.47 0.50 0.66 0.47 0.58 0.49

unemployed (1=yes) 0.04 0.20 0.03 0.16 0.03 0.18

average regional income 20 4149.99 477.88 1108.65 1113.69 10.04 0.09

own or purchasing dwelling (1=yes) 0.42 0.49 0.75 0.43 0.73 0.44

imputed rent 20 1484.61 2910.48 4.94 39.82 40.70 179.67

Self-reported health 21 2.59 0.95 2.64 0.95 2.20 0.95

invalid (1=yes)23 0.04 0.20 0.24 0.43 0.02 0.14

household member died (1=yes) 22 0.01 0.08 0.11 0.31

divorced (1=yes) 0.05 0.22 0.09 0.29 0.06 0.23

separated from partner (1=yes) 0.01 0.12 0.04 0.19 0.02 0.13

partner dead (1=yes) 0.06 0.24 0.05 0.22 0.07 0.26

just married (1=yes) 22 0.02 0.15 0.03 0.16

just divorced (1=yes) 22 0.00 0.07 0.01 0.08

just separated (1=yes) 22 0.01 0.11 0.04 0.20

partner just died (1=yes) 22 0.00 0.06 0.01 0.09

just had a baby (1=yes) 0.04 0.19 0.04 0.19 0.00 0.07

pregnant (1=yes) 0.01 0.11 0.05 0.22 0.00 0.04

just fired from job (1=yes) 22 0.02 0.12 0.03 0.17

N = 176,770 72,529 153,886

Note: Samples include all observations with non-missing information

20 Monetary denominations are GSOEP, Euros; HILDA, $AUD, and; BHPS, British pounds. 21 Health is reverse coded: 1 = excellent to 5 = poor. 22 The self-report life event variables in the GSOEP and the HILDA are not in the BHPS panel data.

26

The average self-assessed happiness in the BHPS (5.21) is on the ‘completely

satisfied’ side of the 1 to 7 scale23. Forty five per cent of respondents are male and

the average age across the entire unbalanced panel is 46.45 years, with the average

age of first-time respondents 2.52 years lower. The British are higher educated than

Germans and Australians. Average years of education (12.82) for the British is 2.39

years higher than Germans but only 0.44 years higher than for Australians. Education

standard deviation in the British sample is slightly lower than for the German sample

(2.40 versus 2.46) but both are much higher than for the Australian sample (1.80).

The average British employment level is 58%, 3% unemployed, with the remaining

non-participating or retired. Average annual household income is 29,152 pounds, but

the distribution is positively skewed (range, 0 to 1,205,210; 50th percentile, 24,666;

75th percentile, 38,779, and; 95th percentile, 67,905). Average health (2.20) is

towards the ‘excellent health’ side of the (1 to 5) question’s response scale, and,

unlike than Germans (4%) and Australians (24%), fewer British (just 2%) self-

identify with a disability (invalid) 24.

Moving from societal to a household marriage comparison with Germans (65%),

marriage in British households is the same as for Australian households (55%), but,

on average, the British have fewer children per household (0.53) than either

Australians (0.77) or Germans (0.65). Similar to Australians (75%), household

ownership in the British sample (73%) is higher than in the GSOEP (42%). Unlike

the GSOEP and the HILDA, there are no self-reported life-event variables in the

BHPS. One life event was derived from the available data. Given that maternity

leave is less than 12 months, the ‘just had a baby’ was constructed from those who

were on maternity leave. This revealed that less than 1% of Britons in the sample had

just had a baby, less than a quarter of the birth events in the Australian and German

samples. This low birth level may be because only one partner was eligible for

maternity leave, it may be an artefact of this British sample, or; perhaps the British

birth rate really is much lower than Australia and Germany25. Before proceeding with

23 The BHPS happiness response scale is different from the GSOEP & the HILDA; the measurement of happiness in the panel surveys is discussed in the next section (3.3.4) 24 This high level of disability in Australia (relative to Germany and the United Kingdom) may be related to the Australian benefits system and is worthy of future research. 25 The ‘just had a baby’ question in the GSOEP and the HILDA asks ‘if you or your partner has just had a baby’. In the BHPS, with just one partner eligible for maternity leave, only one individual would be identified as ‘ just having a baby’, thus leading to a birth life event occurrence of approximately

27

preliminary analysis using the German data set, I reveal the cross-panel differences

in the measurement of our dependent variable, happiness.

3.3.4 Happiness measurement

The wording of the happiness question in the GSOEP, HILDA & BHPS panel survey

questionnaires are subtly different. Even subtle differences in survey question

wording can result in measurement error (Cavana, et al., 2001; Nunnally, 1978). To

add further complication to cross-panel happiness comparison, the response scale of

the BHPS is not the same as the other two panel surveys. In the absence of rescaling,

which can introduce measurement error, question wording and scale differences can

make absolute cross-panel comparisons difficult. Even if the BHPS happiness data

were rescaled, one cannot control for the measurement error arising from the subtle

differences in the happiness question wording. Therefore, to facilitate easier cross-

panel age-happiness comparison, I also report within survey results with percentages.

Beginning with the GSOEP, let us have a detailed look at the happiness survey

question.

3.3.4.1 The life satisfaction question from the German (GSOEP) survey

The happiness question (Figure 3.1) in the GSOEP survey questionnaire is based on

the Fordyce Global Happiness Scale. The happiness question seeks to measure the

aggregate utility from all the good and bad things that occur throughout our lives

(Fordyce, 1988). The GSOEP happiness question is:

Figure 3.1: Life satisfaction question from page 35 of wave 18 of the GSOEP Living in Germany

Survey questionnaire on the social situation of households

half of that which would be self-reported by both male & female parents in the GSOEP or the HILDA (assuming that at least one partner takes maternity leave). Doubling the BHPS ‘just had a baby’ life event to account for the mothers/fathers who do not both report to be on maternity leave still results in a ‘just had a baby’ occurrence of half that of Germany & Australia. This difference in national birth rates and its effect on economic growth could be worthy of future research.

28

3.3.4.2 The life satisfaction question from the Australian (HILDA) survey

The HILDA life satisfaction question (Figure 3.2) is very similar to the GSOEP life

satisfaction question in Figure 3.1. It asks the respondent to ‘pick a number between

0 and 10 that indicates your level of satisfaction. The more you are satisfied you are,

the higher the number you should pick. The less satisfied you are, the lower the

number’.

Figure 3.2: Life satisfaction question from page 74 of wave 8 of the HILDA Continuing Person

Questionnaire

3.3.4.3 The Life satisfaction question in the British (BHPS) survey

The BHPS life satisfaction question is worded and scaled differently to the GSOEP

and the HILDA. The BHPS question asks: ‘On a scale of 1 to 7 where 1 = not

satisfied at all and 7 = completely satisfied, please tell me the number which you feel

best describes how dissatisfied or satisfied you are with the following aspects of

your current situation’ (plus 0, doesn’t apply, and; 8, don’t know). The overall life

satisfaction question is:

Figure 3.3: Life satisfaction question from the British Household Panel Survey: wave 18

questionnaire, p. 64

With the happiness question differences revealed, let us begin analysis by seeking to

confirm the presence of the U-shape of happiness in age using the German data.

29

3.4 Analysis of the puzzle

3.4.1 Is there a U-shape for Germany?

For all the analyses that follow, the full regression tables are shown in Appendix B at

the end of this chapter, but the story is progressively told using graphs and summary

tables in the main text. I experimented using both simple least squares and latent-

variable analyses (for cross-sectional as well as fixed-effects analyses) but found, per

Ferrer-i-Carbonell & Frijters (2004), no qualitative difference26. I choose to present

the least squares results in the text whilst the latent-variable results in Chapter 3

Appendix C (p.101) are offered as tests of robustness.

I begin the GSOEP analysis by showing a picture of the raw pooled cross-sectional

relationship between age and aggregate happiness for the GSOEP, with the predicted

lines overlaid for least-squared regressions that include either just age or age and

agesquared (Figure 3.4). The shown intercepts are normalised such that satisfaction

at age 20 is always the same27.

5.8

6

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

Life

Sat

isfa

citio

n

AGE

Age and Life Satisfaction: pooled regression

Raw average life satisfaction

Linear age term

Age+Age2

Figure 3.4: Average life satisfaction by age in the GSOEP for the pooled sample

26 I follow the example of many psychologists, the arguments and empirical evidence from Ferrer-i-Carbonell & Frijters (2004), and, the evidence from the robustness results in Appendix C of this chapter, to interpret the meaning of the happiness question answers as interpersonally cardinally comparable. 27 Thus, the thin curved line depicts {Life Satisfaction (age 20) + (βage * (age-20)) + (βage2 * (age2- 202)} where age runs from 18 to 92 in the GSOEP, 18 to 92 in the HILDA and 18 to 90 in the BHPS.

30

The GSOEP findings are quite typical of those from other scholars in the economics

literature. There is sharp decline in raw average happiness from 18 years to 22 years

and in those close to death (where there are not many individuals left), but for the age

range 22 to 60 there is no strong relationship between age and happiness to be seen.

If we overlay the regression results, when we include linear age as the only variable,

we get a significantly negative coefficient. If we overlay a regression line with both

age and age-squared, we find a significant nonlinear pattern. The regression results

are: 2

2

7.747 0.0217 * 0.00016 * (249.9) (15.7) (11.6)

0.0037, 176770

i i iLifeSat age age

R N

= − +

= =

Age-squared is highly significant, but the age at which the minimum occurs is about

70 with this simple specification28, implying that for the vast majority of the sample,

there is not so much of a U-shape but rather a horizontal j-shape. What if we add

additional regressors to this simple specification? Figure 3.5 shows the predicted

age-happiness profiles when we successively add additional variables. The detailed

specifications for the ‘Pooled OLS’ regressions are in Table 3.14 in Chapter 3

Appendix B and Table 3.3 on page 31 summarises Age and Age2 coefficient changes

as controls are progressively added.

5

5.5

6

6.5

7

7.5

8

Life

Sat

isfa

citio

n

AGE

Age and Life Satisfaction: what if more 'controls' are added?


Usual suspects

Us sus + Health

Kitchen sink

Figure 3.5: Life satisfaction in the GSOEP for the pooled sample with added controls

28 The simple specification with just age and age2 effects comes from the recent economic of happiness literature and provides the foundation for a systematic analytical process that seeks to reveal the presence of the U-shape of happiness in age in the panel data sets.

31

In Figure 3.5, the thin solid line is the ‘Usual suspects’ specification which includes

the socio-economic variables commonly found in the happiness regressions noted in

Tables 3.1a and 3.1b. The variables are log-income, gender, education in years, the

number of children, a marriage dummy, and three indicators of work-status

(employed, non-participant and unemployed). With the ‘Usual suspects’

specification we see a dramatic deepening of the U-shape, with the predicted

happiness decline from 18 to 70 year old being about 0.55 (-7%) for Germany.

When we also include indicators of health and measures of wealth, ‘Usual suspects +

health’, we see slightly stronger U-shape, with the predicted happiness decline from

18 to 70 year old being about 0.5 (-6.7%) for Germany. When we finally include a

large set of indicators of life events to form the ‘Kitchen sink’ specification,

(including the loss of a spouse, being fired, and birth of a child), the age at which the

minimum occurs becomes earlier (age 50) and the U-shape becomes less deep but it

is still strongly significant (Table 3.3). Table 3.3: Summary of changes in the GSOEP Age and Age2 coefficients as controls are progressively added Pooled OLS

(All)

Specification coefficient t-value

Age + Age2

age -0.0217 ** 15.69

age*age 0.0002 ** 11.63

Usual suspects

age -0.0541 ** 32.80

age*age 0.0005 ** 29.20

Usual suspects + health age -0.0600 ** 36.77

age*age 0.0006 ** 34.13

Kitchen sink

age -0.0454 ** 25.64

age*age 0.0005 ** 25.39

N 176,770

Level of significance: + p < 0 .1 * p < 0.05 ** p < 0.01

32

What we see in Figure 3.5 is congruent with the findings of other scholars in the

field: when standard regressors are added, a very strong U-shape effect emerges with

predicted age effects far bigger than anything observable in the raw data. The U-

shape of happiness in age is indeed in the German data, which makes it likely that the

U-shape may similarly manifest in the HILDA dataset.

3.4.2 Is there also a U-shape for Australia?

In extending the data sets that are used to consider our puzzle, I look for the U-shape

of happiness in age in the Australian (HILDA) panel data and analysis proceeds per

the GSOEP analysis. Figure 3.6 illustrates the raw pooled cross-sectional relationship

between age and aggregate happiness for the HILDA, with the predicted lines

overlaid for least-squared regressions that include either just age or age and age-

squared. The intercepts are normalised such that satisfaction at age twenty is always

the same.

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

9.00

9.50

10.00

Life

Sat

isfa

citio

n

AGE



Linear age term

Age+Age2

Figure 3.6: Life satisfaction in the HILDA for the pooled sample

33

With the GSOEP, there was a sharp decline in raw average happiness from 18 years

to 22 years; this is not evident in the HILDA29. The HILDA shows arguably the

‘cleanest’ U-shape with a predicted minimum at age 36 and no clear happiness

decrease in old age. Indeed, the linear happiness profile is quite strongly increasing

by age, counter to the general profile in the GSOEP.

Like the GSOEP, when we include linear age as the only variable we get a

significant positive coefficient (Table 3.18). If we next overlay a regression line with

both age and age-squared, we find a significant nonlinear profile (the thin solid line

in Figure 3.6). The regression results are:

2

2

8.33 0.0320 * 0.0004 * (214.51) (18.98) (25.79)

0.0241, 75, 529


R N

= − +

= =

The HILDA Age2 coefficient (0.0004, t-value 25.79) is larger and more significant

than the Age2 coefficient from the German data (0.00016, t-value 11.6). This is

probably because the raw happiness from the HILDA is more U-shaped over a

lifetime. Figure 3.7 returns our focus to the HILDA by showing how the predicted

age-happiness profiles change as we progressively add variables. The detailed

specifications for the ‘Pooled OLS’ regressions are in Table 3.18 (Chapter 3

Appendix B, p.90), and Table 3.4 on page 35 summarises Age and Age2 coefficient

changes for both the GSOEP and the HILDA as we progressively add controls.

29 In Chapter 5 I ask the question; is there a similar dramatic drop in the happiness of young Australians, when does it occur, and, why.

34

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

9.00

9.50

10.00Li

fe S

atis

faci

tion

AGE



Usual suspects

Usual suspects + Health

"Kitchen sink"

Figure 3.7: Life satisfaction in the HILDA for the pooled sample with added controls

In the ‘Usual suspects’ specification, we again include the socio-economic variables

that were used in the GSOEP analysis. Summarising the Age and Age2 coefficient

changes (Table 3.4), as we add the ‘Usual suspects’ controls; the Age2 coefficient for

‘Usual suspects’ has increased by 33% (from 0.0006, t-value 28.105) to (0.0008, t-

value 36.01). We can see the dramatic deepening of the U-shape by comparing of the

thin solid line from the “Usual suspects’ specification in Figure 3.7 with the ‘Age +

Age2’ specification in Figure 3.6. Minimum predicted happiness drops by 0.2 (3%)

from 7.86 (age 26) in the ‘Age + Age2’ specification to 7.66 (age 34) with the “Usual

suspects’ specification.

When we add health and wealth to form the ‘Usual suspects + health’ specification,

the U-shape is still evident but becomes shallower (long dashed line in Figure 3.7).

The negative effect from poor health is twenty-three times larger in the HILDA (-.53,

t-value 88.46) than in the GSOEP (-.023, t-value 30.42). The outcome on the Age2

coefficient is to make it 14% smaller and the Age coefficient is now 27% less

negative (Table 3.4).

35

Table 3.4: Comparison of changes in the GSOEP & HILDA Age and Age2 coefficients as controls are progressively added Pooled OLS Pooled OLS (All) (All)

GSOEP HILDA

Specification coefficient t-value coefficient t-value

Age + Age2

age -0.0217 ** 15.69 -0.0320 ** 19.0

age*age 0.0002 ** 11.63 0.0004 ** 25.8

Usual suspects age -0.0541 ** 32.80 -0.0554 ** 29.6

age*age 0.0005 ** 29.20 0.0007 ** 34.0

Usual suspects + health

age -0.0600 ** 36.77 -0.0403 ** 22.7

age*age 0.0006 ** 34.13 0.0006 ** 30.3

Kitchen sink

age -0.0454 ** 25.64 -0.0311 ** 16.0

age*age 0.0005 ** 25.39 0.0005 ** 24.0

N 176,770 75,529


Next, our specification is expanded to include the life event shocks from the HILDA

data that are also in the GSOEP data. This ‘Kitchen Sink’ specification adds six life

event shocks: 1) just married; 2) just divorced; 3) just separated; 4) loss of a

spouse/child; 5) the birth of a child, and; 6) being fired from your job. Comparing the

‘Kitchen sink’ prediction with the others in Figure 3.7, we see that the U-shape has

decreased slightly. The minimum predicted happiness of 7.80 now occurs earlier, at

age 30. Two of the five life event shocks (just married & just had a baby) have a

significant positive effect on happiness and the other four have a significant and large

negative effect (Appendix B, Table 3.18). So far, we have confirmed the presence of

the U-shape of happiness in age in the GSOEP and HILDA data sets. Adding a third,

I ask, is the U-shape also in the British data.

36

3.4.3 Is the U-shape also in the British data?

In examining the British Household Panel Survey data (BHPS) for the U-shape of

happiness in age, we continue as before by illustrating (Figure 3.8) the raw pooled

cross-sectional relationship between age and aggregate happiness, with the predicted

lines overlaid for least-squared regressions that include either just age or age and

age-squared. The intercepts are normalised such that satisfaction at age twenty is

always the same.

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

Life

Sat

isfa

citio

n

AGE



Linear age term

Age+Age2

Figure 3.8: Life satisfaction in the BHPS for the pooled sample

When we include linear age as the only variable, we get a significant positive

coefficient (Appendix B, Table 3.22). If we next overlay a regression line with both

age and age-squared30; for the second time, we find a significant nonlinear profile

(the thin solid line in Figure 8). The regression results are:

2

2

5.54 0.0221* 0.0003* (239.77) (21.96) (29.00)

0.0137, 153,886


R N

= − +

= =

30 Raw average life satisfaction as depicted in Figure 3.8 could be seen to show two turning points that might be better explained by a specification with an age3 term. Age3 has not been included in this specification because this study focuses on the age2-based economics of happiness literature.

37

Figure 3.9 shows how the predicted age-happiness profiles change as we

progressively add variables. The detailed specifications for the ‘Pooled OLS’

regressions are in Table 3.22 (Chapter 3 Appendix B, p.94), and Table 3.5 on page

38 summarises Age and Age2 coefficient changes as we progressively add controls.

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

Life

Sat

isfa

citio

n

AGE



Usual suspects

Usual suspects + Health

"Kitchen sink"

Figure 3.9: Life satisfaction in the BHPS for the pooled sample with added controls

In the ‘Usual suspects’ specification, I again include the socio-economic variables

that were used in the GSOEP & HILDA analyses. These common variables are log

annual household disposable income, gender, education in years, the number of

children in the household, a marriage dummy, and indicators of work-status

(employed and unemployed). The Age2 coefficient for ‘Usual suspects’ has increased

80% (from 0.0005, t-value 32.71 to 0.0009, t-value 55.56). Comparing the thin solid

line from the ‘Usual suspects’ specification in Figure 3.9 with the ‘Age + Age2

specification from Figure 3.8, we can see the dramatic deepening of the U-shape.

Minimum predicted happiness drops by 0.21 (4%) from 5.16 at age 29 in the ‘Age +

Age2’ specification to 4.95 at age 55 with the ‘Usual suspects’ specification. The

changes in the GSOEP and the HILDA were similar (Table 3.5).

38

Table 3.5: Summary of changes in the GSOEP, HILDA & BHPS Age and Age2 coefficients as controls are progressively added Pooled OLS Pooled OLS Pooled OLS (All) (All) (All)

GSOEP HILDA BHPS

Specification coefficient t-value coefficient t-value coefficient t-value

Age + Age2

age -0.0217 ** 15.69 -0.0320 ** 19.0 -0.0221 ** 21.96

age*age 0.00016 ** 11.63 0.0004 ** 25.8 0.0003 ** 29.00

Usual suspects

age -0.0541 ** 32.80 -0.0554 ** 29.6 -0.0554 ** 49.28

age*age 0.0005 ** 29.20 0.0007 ** 34.0 0.0006 ** 55.15


age -0.0600 ** 36.77 -0.0403 ** 22.7 -0.0438 ** 41.01

age*age 0.0006 ** 34.13 0.0006 ** 30.3 0.0005 ** 48.21

Kitchen sink

age -0.0454 ** 25.64 -0.0311 ** 16.0 -0.0350 ** 31.02

age*age 0.0005 ** 25.39 0.0005 ** 24.0 0.0005 ** 39.43

N 176,770 75, 529 153,886

Level of significance: + p < 0 .1 * p < 0.05 ** p < 0.01 When we add health and wealth to form the ‘Usual suspects + health’ specification,

the U-shape is still evident and, like the HILDA, becomes shallower (the long dashed

line in Figure 3.9). The Age2 coefficient is 17% smaller and the Age coefficient is

20% less negative. Minimum predicted happiness increases by 0.05 (just 1%) from

4.95 at age 55 in the ‘Usual suspects’ specification to 5.00 at age 55 with the ‘Usual

suspects + health’ specification.

Next, the specification is expanded to add some of the variables from the ‘Kitchen

sink’ specification: divorced, separated, partner dead, pregnant, and, just had a baby.

Unlike the GSOEP & the HILDA, we cannot add the other five life event shocks

because they are not in the BHPS data. Comparing the ‘Kitchen sink’ prediction with

the others in Figure 3.9, we see that the U-shape has again decreased. Minimum

predicted happiness has increased 0.07 (1.5%) to 5.07 and now occurs earlier, at age

thirty-nine. We have evidence of a U-shape of happiness in age in all three data sets,

the GSOEP, the HILDA and the BHPS.

39

In summary, the findings from all three data sets are consistent with other scholars in

the field. When standard regressors are added a very strong U-shape effect emerges

with predicted age effects bigger than anything observable in the raw data. The

answer to this puzzle is now to be revealed using the GSOEP data. Then, robustness

is added to the GSOEP findings by replicating them using the HILDA and BHPS

data sets. The main regression with covariates I talk about in the remainder of this

chapter is the preferred specification of the ‘Usual suspects’ because it has the

strongest U-shape in our chosen reference data set, the GSOEP. I now seek

explanations for the U-shape of happiness in age.

3.5 Potential explanations for the U-shape of happiness in age

3.5.1a GSOEP explanation I: it is the very young and the very old

A naive first-thought is that there is a particular issue with the early ages, i.e. age 18

to 22, and with high ages, i.e. those above 80. This is because the happiness decline

is particularly steep for the early years and erratic at the later years, which makes one

wonder if the young are being overly optimistic about their actual levels of happiness

and that the happiness of the very old is hard to tell from the few data points in that

range. To examine this possibility, Figure 3.10 shows the GSOEP results of the

regressions when we drop the under-22 year olds and those over 80 from the data

(about 9% of the panel). Table 3.15 (Chapter 3 Appendix B, p.87) shows the exact

specifications and detailed regression results and Table 3.6 (at the end of section

3.5.1c) summarises changes in the age and age2 coefficients as we change

specifications.

40

5

5.5

6

6.5

7

7.5

8

Life

Sat

isfa

citio

n

AGE

Raw averagelife satisfaction

Usual suspects

Us sus + Health

Kitchen sink

Life satisfaction in the GSOEP: ages 22 to 80

Figure 3.10: Life satisfaction in the GSOEP for the pooled sample for the mid-age range

The U-shape for the full sample and those aged 22 to 80 is almost identical.

Comparing the ‘Usual suspects’ specification in Figures 3.5 & 3.10, minimum

predicted life satisfaction has increased just 0.07 (1%). There is no clear qualitative

difference between the results. Hence, the U-shape cannot be explained by the

extremities of the age range and must be due to relations in large parts of the age

range. However, is the result the same for the HILDA data set?

3.5.1b HILDA explanation I: it is the very young and the very old

When we drop the under-22 year olds and those over 80 from the HILDA (10% of

the panel data) we see (Figure 3.11) a very slight deepening of the U-shape of the

“Usual suspects’, ‘Usual suspects + Health’ and ‘Kitchen sink’ specifications. Table

3.19 (Chapter 3 Appendix B) shows the exact specifications and regression results

and Table 3.6 (at the end of section 3.5.1c) summarises changes in the age and age2

coefficients for each specification.

41

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

9.00

9.50

10.00

Life

Sat

isfa

citio

n

AGE

Life satisfaction in the HILDA; ages 22 to 80

Raw average lifesatisfaction

Usual suspects

Usual suspects+ Health

"Kitchen sink"

Figure 3.11: Life satisfaction in the HILDA for the pooled sample for the mid-age range

Comparing Figures 3.7 & 3.11, for our preferred ‘Usual suspect’ specification,

minimum predicted happiness varied just 0.6% (7.63 for the full sample versus 7.58

for 22 to 80 year olds). There is no clear qualitative difference between the results

when we exclude the very young and the old from the HILDA. Hence, like the

GSOEP, the U-shape in the HILDA cannot be explained by the extremities of the age

range and must be due to relationships in large parts of the age range. Let us see if

the same holds for the third data set, the BHPS.

3.5.1c BHPS explanation I: it is the very young and the very old

When we similarly drop the under-22 year olds and those over 80 from the BHPS

(10% of the panel data) we see (Figure 3.12) a very slight deepening of the U-shape

of the ‘Usual suspects’, ‘Usual suspects + Health’ and ‘Kitchen sink’ specifications.

Table 3.23 (Chapter 3 Appendix B) shows the exact specifications and regression

results and Table 3.6 ( p.43) summarises changes in the age and age2 coefficients for

each specification.

42

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

Life

Sat

isfa

citio

n

AGE

Life satisfaction in the BHPS; ages 22 to 80


Usual suspects

Usual suspects+ Health

"Kitchen sink"

Figure 3.12: Life satisfaction in the BHPS for the pooled sample for the mid-age range

The minimum still occurs around the same age (44 for the entire sample and 42 when

those below 22 and over 80 are excluded). For our preferred ‘Usual suspect’

specification, minimum predicted happiness varied just 2% (4.87 for the full sample

versus 4.77 for 22 to 80 year olds). This is larger than the variation in the GSOEP

and the HILDA but the U-shape is still clearly evident. When we exclude the very

young and the old, there appears to be no clear qualitative difference between the

BHPS results and those from the GSOEP and the HILDA.

Summarising, the age and age2 coefficients remain strongly significant across all

specifications with all three data sets (Table 3.6). Therefore, the U-shape evident in

all three data sets cannot be explained away by the extremities of the age range and

must be due to relationships in large parts of the age range. We now consider

explanation 2, and refocus back on the full sample and consider omitted variables

and reverse causality.

43

Table 3.6: Summary of changes in the GSOEP, HILDA & BHPS Age and Age2 coefficients as controls are progressively added; for ages 22 to 80 years Pooled OLS Pooled OLS Pooled OLS (Ages 22 to 80) (Ages 22 to 80) (Ages 22 to 80)

GSOEP HILDA BHPS


Age + Age2

age -0.0306** 15.46 -0.0417 ** 17.50 -0.0373 ** 26.21

age*age 0.0003** 13.89 0.0006 ** 23.45 0.0005 ** 32.70

Usual suspects

age 0.0745** 33.91 -0.0680** 26.69 -0.0772 ** 51.31

age*age 0.0008** 32.30 0.0008** 31.09 0.0009 ** 58.50


age -0.0768** 35.27 -0.0496** 20.48 -0.0620 ** 43.38

age*age 0.0008** 33.73 0.0007** 26.72 0.0007 ** 50.57

Kitchen sink

age -0.0618** 26.75 -0.0386** 14.91 0.0521 ** 34.94

age*age 0.0007** 27.03 0.0006** 21.61 0.0007 ** 42.99

N 160,332 65,679 138,481


44

3.5.2a GSOEP potential explanation 2: it is all about unobserved

heterogeneity

An important finding in the literature is that happiness is strongly affected by stable

personality traits (Argyle, Kahneman, Diener, & Schwarz, 1999; Ferrer-i-Carbonell

& Frijters, 2004; Frey & Stutzer, 2002). These fixed individual traits are usually part

of the error term. A stylised finding from both the economic and the psychological

literature is that accounting for fixed traits has a very strong impact on the

coefficients found for socio-economic variables (Ferrer-i-Carbonell & Frijters, 2004;

Clark, Frijters, & Shields, 2008). A leading explanation for this is the possibility of

reverse causality arising from unobserved heterogeneity. For example, the individual

personality traits that make you happier (Lyubomirsky, et al., 2005) also make it

more likely that you will have a higher income, a job, a partner, better health, greater

wealth, and a higher level of education.

Could the problem of reverse causality caused by unobserved fixed traits explain

something about the U-shape? At first glance, one would think not because fixed

personality traits are by design uncorrelated with age and it seems unrealistic to

suggest that happiness causes age. However, the personality traits that affect

happiness can be correlated with variables that are correlated with age, such as

income, a job, a partner, good health and wealth. How would this work? Consider the

problem in its simplest form. Suppose for the purposes of this subsection the truth is

that the following relationship holds

[ ]

21

2

*

, cov( , ) 0, cov( , ) 0, | , , 0it it i it

i it i it it it it it it i

y age x f u

f age f x age x E u age x f

α β= + + +

⊥ > > =

where we have for simplicity subsumed a linear age term into xit and all variables are

normalised to have expectation 0 implying there is no constant term either; there are

individual fixed traits if unrelated to age-squared but related to a composite time-

varying socio-economic variable called xit . There is an error term uit orthogonal to

everything else. What are now the estimated coefficients if we mistakenly run a

regression without accounting for fixed-effects? The asymptotic values are,

45

2

1 1

2

1 1

cov( , ) cov( , )lim ( lim )var( ) var( )

cov( , )lim ( lim )var( )

i it it it

it it

it it

it

f x age xx x

x ageage

ρ β β α ρ α

ρ α α β ρ β

= + + −

= + −

which shows that even though 2

itage is not correlated with the omitted fixed effect,

the coefficient on 2itage can nevertheless be biased when it is related to included

time-varying variables that are correlated with the omitted fixed-effect. The

equations become rather elaborate if we add a linear age term and a constant but the

basic principle remains that a bias in the age-term can occur if the added variables

are correlated with age and with the omitted fixed-effect.

Intuitively, there are two steps in the possible emergence of the bias. The first is that,

as shown just above, the inclusion of fixed effects will change the coefficients of the

non-age variables xit. The second is that xit itself changes systematically with age-

squared, which leads to a bias in the estimated coefficient of age-squared. To explore

this possibility I run fixed-effect analyses on the GSOEP to see how this changes the

U-shape findings. Figure 3.13 depicts the fixed-effect regressions, the specifications

are in Table 3.16 (Chapter 3 Appendix B. p.88) and Table 3.7 provides a summary of

changes in the age and age2 coefficients across specifications.

5

5.5

6

6.5

7

7.5

8

Life

Sat

isfa

citio

n

AGE

Can reverse causality explain the U-shape?


Us sus + healthof pooled

Us sus +healthof fixed effect

age+age2 offixed effect

Figure 3.13: Life satisfaction in the GSOEP for the balanced panel

46

The results for Figure 3.13 are both confirming and surprising. The graph shows the

raw relationship between age and happiness with three overlaid three lines. The U-

shaped line is the same one depicted previously and is the pooled regression with the

‘Usual suspects + health’ specification. Overlaid are two lines from fixed-effect

regressions. The thick dark dashed line is the result of running the same regression as

for the pooled regression but including fixed effects. As one can see, the U-shape

completely disappears, i.e. the age-squared coefficient becomes tiny and insignificant

(Table 3.7). It however replaces the U-shape by a similarly puzzling effect, which is

a very strongly significant negative linear relationship. The third thin solid line,

which shows the result of just running a fixed effect regression with only age and

age-squared as regressors, confirms this. The U-shape slightly reverses into an

inverted U shape, but a very strong negative ‘age’ relationship emerges (Table 3.7).

Table 3.7: Summary of changes in the GSOEP Age and Age2 coefficients as controls are progressively added; with fixed effects Fixed Effect

(All)GSOEP

Specification coefficient t-value

Age + Age2

age -0.0166** 6.60

age*age -0.0003** 9.71

Usual suspects

age -0.00328** 11.61

age*age -0.0001* 2.22


age -0.0298** 9.95

age*age 0.00006 0.74

Kitchen sink

age -0.0184** 5.81

age*age -0.00002* 2.40

N 176,770


47

Before I turn to explain the new puzzle of the strong negative linear relationship in

the Section 3.7, I first want to confirm that the disappearance of the U-shape is

indeed because of reverse causality. To begin, Table 3.14 (OLS) and Table 3.16

(Fixed effects) in the Chapter 3 Appendix B reflect the findings of many other

studies (Ferrer-i-Carbonell, 2005): the coefficients of most socio-economic variables

become much smaller when one adds fixed effects. The income coefficient drops by

45% and the importance of marriage, a job, health and education all reduce.

However, do these variables correlate with age?

Figure 3.14 depicts changes in lifetime happiness for the four most significant socio-

economic variables from the regression of the ‘Usual suspects + health’

specification.

-1.2

-0.7

-0.2

0.3

0.8

Stan

dard

dev

iatio

ns fr

om th

e m

ean

AGE

How do the observed variables behave over the life-cycle?Life satisfaction; mean = 7.16; sd = 1.85ln Household income; mean = 8.20; sd = 0.53Employed; mean = 0.47; sd = 0.50

Education; mean = 10.93; sd = 2.46Health (inverse); mean = 2.20; sd = 0.95

Figure 3.14: Age and observed correlates in the GSOEP

Figure 3.14 indeed shows a strong relationship between age and employment (reverse

U-shaped), education (reverse J-shaped), and health (which has a reverse U-shape in

the main age range between 30 and 50). Household income declines in age with an

inverted U-shape in the middle age range. The relationships all go in the direction we

anticipated above: an artificially high coefficient for employment, education, income

and health would all give rise to a false U-shape in age. However, is there evidence

that reverse causality explains the U-shape of happiness in age in our other data sets?

48

3.5.2b HILDA potential explanation 2: it is also about unobserved

heterogeneity

When we run the same fixed-effect analyses with the HILDA, we get similar

results31. Figure 3.15 shows the raw relationship between age and happiness and has

three overlaid lines.

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

9.00

9.50

10.00

Life

Sat

isfa

citio

n

AGE


Rawaverage lifesatisfaction

Usualsuspects +health ofpooled

Usualsuspects +health offixed effect

age+age2 offixed effect

Figure 3.15: Life satisfaction in the HILDA for the balanced panel

The thin-dashed U-shape line is the same as we showed previously, the pooled OLS

regression with the preferred ‘Usual suspects + health’ specification. The other two

lines are from the fixed-effect regressions. The thick-dashed line is the result of

running a fixed effect regression. Like the GSOEP, the HILDA age2 coefficient goes

non-significant (.00007, t-value 0.94). Just like the GSOEP, the U-shape disappears

(Table 3.8).

31 The exact specifications are in Table 3.20 (Chapter 3 Appendix B, p.92).

49

Table 3.8: Summary of changes in the GSOEP and HILDA Age and Age2 coefficients as controls are progressively added; with fixed effects Fixed Effect Fixed Effect

(All)GSOEP

(All)HILDA

Specification coefficient t-value coefficient t-value

Age + Age2

age -0.0166** 6.60 -0.0076 1.02

age*age -0.0003** 9.71 -0.0001 1.30

Usual suspects

age -0.00328** 11.61 -0.0173* 2.12

age*age -0.0001* 2.22 0.00001 0.17


age -0.0298** 9.95 -0.0202* 2.50

age*age 0.00006 0.74 0.00007 0.94

Kitchen sink

age -0.0184** 5.81 -0.0033 0.40

age*age -0.00002* 2.40 -0.00006 0.72

N 176,770 75,529


Like the GSOEP regression results, the coefficients of the socio-economic variables

from the HILDA are also much smaller with fixed effects. With the GSOEP data, the

income coefficient dropped by more than 40% and the importance of marriage, a job,

health and education all reduced. With the HILDA data, the income coefficient

reduction is similar to the GSOEP, 42%. In addition, the health coefficient dropped

49% from the 0.53 in the pooled regression to 0.27 with fixed effect32. Like the

GSOEP, the HILDA variables correlate with age. Figure 3.16 shows the simple

averages by age of the four most significant socio-economic variables in the

regressions.

32 Comparing the HILDA OLS results in Table 3.17 with the HILDA fixed-effect results in Table 3.20 of Chapter 3 Appendix B.

50

-1.5

-1

-0.5

0

0.5

1St

anda

rd D

evia

tions

from

the

mea

n

AGE

How do the observed variables behave over the life-cycle?

Life satisfaction; mean = 7.91; sd = 1.47ln Household income; mean = 10.77; sd = 1.07Employed; mean = 0.66; sd = 0.47

Education; mean = 12.82; sd = 1.80

Health (inverse); mean = 2.64; sd = 0.95

Figure 3.16: Age and observed correlates in the HILDA

Like the GSOEP (Figure 3.14), the HILDA Figure 3.16 shows a strong relationship

between age and employment (reverse U-shape), education (reverse J-shape), and

health (which consistently declines slowly over time). Like the GSOEP, household

income declines in age with an inverted U-shape in the middle age range. Overall,

the relationships all go in the same direction as we anticipated and saw in the

GSOEP. An artificially high coefficient for employment, education, income and

health would all give rise to a false U-shape in age. Consistent with the GSOEP

findings in Chapter 3, the U-shape is considered to arise from reverse causality with

the included covariates. However, does this hold for the BHPS?

51

3.5.2c BHPS potential explanation 2: it is also about unobserved

heterogeneity

I again run the same fixed-effect analyses on the BHPS as we ran on the GSOEP and

the HILDA. We get similar results33. As before, Figure 3.17 shows the raw

relationship between age and happiness and has three overlaid lines.

3.50

4.00

4.50

5.00

5.50

6.00

6.50

Life

Sat

isfa

citio

n

AGE


RawaveragelifesatisfactionUsualsuspects +health ofpooledUsualsuspects +health offixed effectage+age2of fixedeffect

Figure 3.17: Life satisfaction in the BHPS for the balanced panel

The thin-dashed U-shape line is the same as we showed previously, the pooled OLS

regression with the preferred ‘Usual suspects + health’ specification. The other two

lines are from the fixed-effect regressions. The thick-dashed line is the result of

running a fixed effect regression on our preferred ‘Usual suspects + health’

specification; the U-shape disappears. Like the GSOEP and the HILDA, the BHPS

age2 coefficient is also non-significant (.000008, t-value 0.27). The results with the

BHPS panel data are consistent with those from the GSOEP and the HILDA data; the

U-shape disappears (Table 3.9).

33 The exact specifications are in Table 3.24 (Chapter 3 Appendix B, p.96).

52

Table 3.9: Summary of changes in the GSOEP, HILDA & BHPS Age and Age2 coefficients as controls are progressively added; with fixed effects Fixed Effect Fixed Effect Fixed Effect

(All)GSOEP

(All)HILDA

(All) BHPS


Age + Age2

age -0.0166** 6.60 -0.0076 1.02 0.0044 1.42

age*age -0.0003** 9.71 -0.0001 1.30 -0.0001** 4.61

Usual suspects

age -0.00328** 11.61 -0.0173* 2.12 -0.0064+ 1.88

age*age -0.0001* 2.22 0.00001 0.17 -0.0001+ 1.75


age -0.0298** 9.95 -0.0202* 2.50 -0.0127** 3.86

age*age 0.00006 0.74 0.00007 0.94 0.00008 0.27

Kitchen sink

age -0.0184** 5.81 -0.0033 0.40 -0.0082* 2.49

age*age -0.00002* 2.40 -0.00006 0.72 0.000008 0.31

N 176,770 75,529 153,886


Like the GSOEP and the HILDA regression results, the coefficients of the socio-

economic variables from the BHPS are also much smaller with fixed effects34. With

the GSOEP data, the income coefficient dropped by 45% (40% with the HILDA) and

the importance of marriage, a job, health and education all fell in both data sets. With

the BHPS data, the income coefficient reduction is 58%. Like the GSOEP and the

HILDA, GSOEP variables correlate with age. Figure 18 shows the simple averages

by age of the four most significant socio-economic variables in the BHPS

regressions. The correlation of the changes in lifetime happiness with the four most

significant socio-economic variables in the GSOEP regressions can also be seen with

the BHPS and the HILDA (Figures 3.18 & 3.19a, b, c & d).

34 Comparing the BHPS OLS results in Table 3.22 with the BHPS Fixed-effect results in Table 3.24 of Chapter 3 Appendix B.

53

-1.40

-0.90

-0.40

0.10

0.60St

anda

rd D

evia

tions

from

the

mea

n

AGE

How do the observed variables behave over the life-cycle?Life satisfaction; mean = 5.23; sd = 1.29ln Household income; mean = 9.84; sd = 1.61Employed; mean = 0.58; sd = 0.49

Education; mean = 13.26; sd = 2.40Health (inverse); mean = 2.20; sd = 0.95

Figure 3.18: Age and observed correlates in the BHPS

54

'Household income' changes over the life-cycle: all panels

-1.40

-0.90

-0.40

0.10

0.60

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90

AGE

Stan

dard

Dev

iatio

ns fr

om th

e m

ean

BHPS: lifesatisfaction

BHPS:householdincome

HILDA: lifesatisfaction

GSOEP:lifesatisfaction

HILDA:householdincome

GSOEP:householdincome

'Employed' changes over the life-cycle: all panels

-1.45

-0.95

-0.45

0.05

0.55

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90

AGE

Stan

dard

Dev

iatio

ns fr

om th

e m

ean


BHPS:employed



HILDA:employed

GSOEP:employed

'Education' changes over the life-cycle: all panels

-1.10

-0.90

-0.70

-0.50

-0.30

-0.10

0.10

0.30

0.50

0.70

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90

AGE

Stan

dard

Dev

iatio

ns fr

om th

e m

ean


BHPS:education



HILDA:education

GSOEP:education

'Health' changes over the life-cycle: all panels

-0.90

-0.40

0.10

0.60

1.10

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90

AGE

Stan

dard

Dev

iatio

ns fr

om th

e m

ean BHPS: life

satisfaction

BHPS:health



HILDA:health

GSOEP:health

Figure 3.19: Comparison of Age and individual observed correlates across data sets

55

Whilst a solution has been found to the original puzzle, i.e. the U-shape is an artefact

of unobserved heterogeneity and reverse causality with the included covariates, let us

consider more deeply how the unobserved heterogeneity could bias the pooled

results.

3.5.3 How does the unobserved heterogeneity bias the pooled results?

The mechanism hypothesised in the previous sub-section was that fixed traits lead to

a reverse causality between variables and life-satisfaction. For example, individuals

have high incomes and get married partially because they have high levels of

happiness. The biases in the coefficients of these reverse causality variables would

lead to a bias in the age profile because those variables change systematically with

age. Let us see if we can confirm whether those mechanisms are visible in the data.

Two steps lead to the emergence of bias in the age-coefficients. The first is whether

the coefficients of other variables change when fixed-effects are included. Table 3.10

summarises the estimates of particular coefficients with and without fixed-effects.

The variables shown are those often applied in economic research: employment,

unemployment, marriage, income, and education. These are also the most significant

variables in the ‘usual suspects’ specification.

Looking at Table 3.10, we can see large changes in coefficients for all three datasets

when fixed-effects are included. For income, the coefficient drops 37% in the

GSOEP (0.28 in fixed-effects compared to 0.44 in the pooled regressions), 40% in

the HILDA and 58% in the BHPS. For marriage, the coefficient drops 16% in the

GSOEP (0.25 in fixed-effects compared to 0.29 in the pooled regressions), 32% in

the HILDA and 55% in the BHPS. Interestingly, the absolute coefficients of all these

five variables reduce in all three datasets when including fixed effects. There is a

clear change in the coefficients of variables from pooled to fixed-effects.

56

Table 3.10: Coefficients for the key 5 variables (pooled & fixed effects) for the three data sets.

OLS OLS with Fixed Effects

GSOEP HILDA BHPS GSOEP HILDA BHPSSpecification coefficient t-value coefficient t-value coefficient t-value coefficient t-value coefficient t-value coefficient t-value

Age + Age2

age -0.0217** 15.69 -0.0320** 19.0 -0.0221** 21.96 -0.0166** 6.60 -0.0076 1.02 0.0044 1.42

age*age 0.00016** 11.63 0.0004** 25.8 0.0003** 29.00 -0.0003** 9.71 -0.0001 1.30 -0.0001** 4.61

Usual suspects

age -0.0541** 32.80 -0.0554** 29.6 -0.0554** 49.28 -0.00328** 11.61 -0.0173* 2.12 -0.0064+ 1.88

age*age 0.0005** 29.20 0.0007** 34.0 0.0006** 55.15 -0.0001** 2.22 0.00001 0.17 -0.0001+ 1.75

income 0.4619** 52.2 0.0805** 15.00 0.0943** 19.44 0.2414** 23.13 0.0257** 4.26 0.0164** 2.64

employed 0.0650** 4.8 0.1355** 9.27 0.2397** 27.19 0.0991** 6.69 0.0536* 2.33 0.0618** 4.63

married 0.3106** 27.9 0.4429** 36.20 -0.0033* 2.27 0.2385** 14.95 0.2056** 6.76 0.3567** 46.48


age -0.0600** 36.77 -0.0403** 22.7 -0.0438** 41.01 -0.0298** 9.95 -0.0202* 2.50 -0.0127** 3.86

age*age 0.0006** 34.13 0.0006** 30.3 0.0005** 48.21 0.00006 0.74 0.00007 0.94 0.00008 0.27

income 0.4420** 45.91 0.0329** 4.77 0.0399** 5.47 0.2750** 23.67 0.0191** 3.22 0.0166** 2.7

employed 0.0791** 7.60 -0.1338** 9.20 0.0295* 2.02 0.1001** 6.75 -0.0238 1.06 0.0355** 2.77

married 0.2915** 26.50 0.3624** 30.32 -0.3063** 41.48 0.2457** 15.42 0.1963** 6.52 0.1390** 8.47

Kitchen sink

age -0.0454** 25.64 -0.0311** 16.0 -0.0350** 31.02 -0.0184** 5.81 -0.0033 0.40 -0.0082* 2.49

age*age 0.0005** 25.39 0.0005** 24.0 0.0005** 39.43 -0.00002** 2.40 -0.00006 0.72 0.000008 0.31

income 0.4307** 44.53 0.0309** 5.69 0.0251** 5.25 0.2585** 22.15 0.0151* 2.55 0.0060 0.98

employed 0.0688** 5.11 -0.1166** 8.00 0.0372** 4.38 0.0925** 6.24 0.0394+ 1.75 0.0365** 2.86

married 0.1180** 7.65 0.2514** 14.85 -0.0195** 14.27 0.0327 1.37 -0.1095** 2.84 0.2014** 22.43

N = 176,770 75,529 153,886 176,770 75,529 153,886


57

The second step is to see if changes in the coefficients of these non-age variables

lead to a difference in the predicted age-profile. The clearest way to depict if this

occurs is to show the predicted effect of all non-age variables in the pooled

regressions versus the fixed-effects regressions. In Figures 3.20 to 3.22, two

prediction lines are shown for our three datasets. The first is from the ‘usual

suspects’ regressions that do not include fixed effects (column 3 of Tables 3.14, 3.18,

and 3.22 in the Appendix B), and the second from the ‘usual suspects’ regressions

that do include fixed effects (column 3 of Tables 3.16, 3.20, and 3.24 in the Chapter

3 Appendix B). In all cases, we let the prediction lines start at the same point at age

18 to aid the interpretation.35

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Life

Sat

isfa

citio

n ch

ange

rela

tive

to a

ge 1

8

AGE

Predictions without age and agesqpost the GSOEP OLS and FE 'Usual suspects' regressions

OLS OLS with Fixed Effect

Figure 3.20: Predicted happiness effects of the non-age variables in the GSOEP

35 The model is Life Satisfaction = βage * age + βage

2 * age2 +xit΄βx+eit where eit is the error term that either includes fixed-effects or not. The prediction lines show the average over i of xit΄βx by age, which uses the fact, that xit changes by age.

58

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0Li

fe S

atis

faci

tion

chan

ge re

lativ

eto

age

18

AGE

Predictions without age and agesqpost the HILDA OLS and FE 'Usual suspects' regressions


Figure 3.21: Predicted happiness effects of the non-age variables in the HILDA

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

Life

Sat

isfa

citio

n ch

ange

rela

tive

to a

ge 1

8

AGE

Predictions without age and agesqpost the BHPS OLS and FE 'Usual suspects' regressions


Figure 3.22: Predicted happiness effects of the non-age variables in the BHPS

Looking at the results for the GSOEP in Figure 3.20 first, the main thing to note is

that the predicted OLS line looks very much like an inverted U-shape: the increase

from age 18 to the top at age 48 is about 0.32 and the subsequent decrease to age 80

is about 0.9. Since the regression coefficients of age-and age-square essentially try to

59

fit the actual age-profiles conditional on this predicted effect from the non-age

variables, this inverted U-shape forces a finding of a U-shape in the age coefficients.

When including fixed-effects one can see that the inverted U-shape is much less

pronounced (though not entirely gone): the increase from age 18 to the top is about

0.22 and the subsequent decrease is 0.54. This reduction in the predicted inverted U-

shape from the non-age variables in turn will lead to a reduction in the U-shape

found for age when including fixed-effects.

Qualitatively, the same results appear for the HILDA (Figure 3.21). The reduction in

the predicted happiness contribution of the non-age variables from the top to age 90

is 0.5 with the pooled regression results and only 0.35 with the fixed-effects. Just like

the GSOEP and the HILDA, the results from the BHPS (Figure 3.22) show the

inverted U-shape of the happiness contribution of the non-age variables is much

stronger without fixed-effects than with fixed-effects. Both the upswing and the

downswing are more pronounced. Summarising, we can indeed see that the inclusion

of fixed-effects reduces the coefficients of variables that themselves systematically

vary by age (incomes and marriage peak in middle age) and that this in turn reduces

the predicted inverted U-profile of their effects.

3.5.4 Robustness analyses

I briefly mention the robustness analyses (results are in Chapter 3 Appendix C,

p.101). The first robustness analysis was to re-do everything with latent-variable

techniques rather than linear regressions. I used ordered logits as a cross-sectional

model and the recent BUC estimator from Baetschmann, Staub, & Winkelmann

(2011), which is a fixed-effect conditional logit estimator. As in the main text above,

the highly significant and positive effect on age-squared found in the cross-section

disappeared with the inclusion of fixed-effects. Another robustness analysis was to

vary the treatment of the included health variable. Instead of including self-reported

health as a continuous variable, we included each of the 5 possible health states

(from very bad to very good) as separate dummy variables (as recommended by

Terza, 1987). Again, this made almost no difference to the age-squared effects.

60

As a final robustness check, OLS analysis of the effect of age-bands on life

satisfaction was completed per Clark (2006). See Tables 3.30, 3.32, & 3.34 for the

regression results using the GSOEP, BHPS and HILDA panel data. Consistent with

Clark’s BHPS results (2006, p. 21), a U or wave-shape is evident both with and

without demographic controls. The age band coefficients became more negative from

low age bands through to midlife where they begin to increase then go positive

around age 7036 then decrease again into old age. However, when Clark added fixed

effects, the age-band coefficients increased in size and remained significant37 (Clark,

2006, p. 21, Table 2). This research revealed different results. Running the same

regression with fixed effects on waves 6 to 10 and waves 12 to 18 of the BHPS, the

age-band coefficients and t-values become very small and the U or wave-shape

disappears. This result is interpreted as evidence of time invariant heterogeneity, and,

the results are the same with all three data sets. With age bands, the U-shape

previously evident across age-bands disappears with the inclusion of fixed effects.

3.5.5 Interim Summary of Chapter 3

This chapter started out with the puzzling findings of other researchers of a U-shaped

relationship between age and happiness. I replicated this relationship for Germany,

Australia, and Britain using well-known panel datasets, the GSOEP, the HILDA, and

the BHPS. The raw data in Germany looked like a wave, with a clear decline at high

ages. The raw data in Australia looks very close to a U-shape, whilst the data for

Britain again most resembled a wave. Naive regressions using only age and age-

squared showed relatively weak U-shapes in all three countries with a very late

minimum in Germany (around 70) and an early one for Australia (around 35). In all

three cases, the age-happiness profile became a much clearer U-shape when adding

commonly used socio-economic variables. This emergence of the U-shape was not

dependent on the inclusion of individuals aged 18-22 or those above 80.

The main finding was that the U-shape disappeared when using fixed-effects because

of a reverse causality issue: happiness-increasing variables, like getting a job, a high

income, and getting married, appear to happen mostly to middle-aged individuals

36 The positive peak is at 70 years for the BHPS & GSOEP and age 65 in the HILDA. 37 Perhaps the results vary because Clark only used age bands up to age 65.

61

who were already happy. In all three data sets, this reverse causality shows up in

cross-sections as inflated coefficients for income, marriage, and getting a job. In

order to fit the actual age profile of happiness, the bias in coefficients for socio-

economic variables forces the predicted age profile to become U-shaped. When one

controls for fixed-effects, the non-linearity all but disappears for all three data sets.

The bottom line is that the supposed happiness decline in middle age is far less of a

real finding than has been proposed and that it is not the most prominent age-related

feature of either the raw happiness data or the results of fixed-effect regressions. The

raw data in Germany and Britain is much more supportive of a wave-pattern in

happiness (a ‘happiness peak’ around the age of 70), whilst the main finding from

fixed-effects regressions is a large and steady decrease in happiness as people get

old. The reasons for such a happiness decline in panel datasets needs further study.

3.6 Explanation for the negative slope in the GSOEP

3.6.1 Explanation for the negative slope I: time and cohort effects

Before studying why happiness changes over the lifetime in Chapters 4 and 5, I

pursue an additional research question that arose from the more immediate puzzle

that emerged from the analysis of the GSOEP data. This new puzzle is the strong

negative relationship between age and happiness over time in fixed-effects

regressions even though the relationship in the pooled cross-sectional data is much

less pronounced. Where does this strong negative slope come from? We have seen

that it is not due to any other included variable because it remains when we do not

include other variables. We know for the same reason that it is not due to reverse

causality. It also cannot be due to some simple missing variable, like average

income, because that would go in the opposite direction (average income rose). What

candidate explanations remain?

Recall the details, a strong negative relationship emerged between age and happiness

over time in fixed-effects regressions with the GSOEP data (Section 3.5.2a). The U-

shape of happiness in age reversed into an inverted U shape, and, a very strong

negative relationship could be seen (Figure 3.13, p.45); While GSOEP age2

62

coefficient went non-significant with fixed effects; the age coefficient became

strongly significant across all specifications (Table 3.7, p.46) 38.

A popular explanation in the recent literature is that there are important time and

cohort effects on happiness (Cribier, 2005). Right at the outset, one should point out

that such explanations are somewhat unsatisfactory because both time and cohort

effects are in a sense ’aggregate unobservables’. For instance, a cohort effect is just a

missing aggregate variable specific to an age group, where we do not know what the

missing variable is. The missing variable could be the mental experience from a

particular traumatic event or the effect of a particular diet in a certain era, or some

cultural trait particular to an era (like expectations), etc. It would be preferable to

measure the supposed elements making up a cohort effect before becoming

convinced cohort effects actually exist. We also need to know what the cohort effects

consist of if we are to make any policy-relevant inferences about how ‘happy’ cohort

effects might be created.

Another problematic aspect of the notion of cohort-effects and time-effects is that

they are statistically difficult to identify. It has been known for a long time that it is

not possible to simultaneously identify age, year, and cohort effects because one can

be written as a function of the other two. Only by introducing somewhat arbitrary

restrictions on cohort effects can they be separated from age and time-effects.

What we can do is to accept that we cannot tease apart the age and cohort effects and

simply presume that the effects of age pick up cohort effects. One can then divide the

age distribution into different cohorts and label the effect of being born in a

particular interval as due to a cohort effect. Blanchflower & Oswald (2007) for

instance define cohorts by age-intervals, i.e. 10-year intervals, and label the effects of

these age-intervals as cohorts. This practically means that there is a sharp dividing

line in the influence of whatever causes a cohort effect on particular days in the

century. Someone born on January 5, 1910 for instance could be in one cohort, and

38 Looking back at the summary fixed effects regression results (Table 3.9) on page 52 we can see a very large and significant age coefficient (-0.0166, t-value 6.60) with the German (GSOEP) data. We do not see this with the Australia and British data, the age coefficients are very small and non-significant.

63

someone born the day after in another. If one does not include age as a regressor, this

procedure is akin to using a semi-parametric function of age (Clark, 2006). However,

if one then also adds linear and quadratic age variables to a regression containing

these age-intervals and proceeds with assuming that the age-intervals pick up

something very different from age effects, then these arbitrary age-intervals become

binding: the assumption that cohort effects jump at particular ages is then what

separately identifies age effects from time and cohort effects.

So far, I have ignored the possibility of cohort or time effects. As far as there are

linear time effects, then these would be indistinguishable in a fixed-effect framework

from age effects. This is because we can write 0it iage age t= + where the first

terms 0( )iage is fixed and absorbed by the fixed-effect term, whilst, the second term

(t) is a straightforward time effect.

In order to ascertain whether there are likely to be time or cohort effects, I next look

at the evolution over time of aggregate life satisfaction of the complete pooled panel.

If there are strong time or cohort effects capable of explaining the large decline that

we saw under the fixed-effects regressions, then we should see such a decline in the

aggregate data.

6.7

6.8

6.9

7

7.1

7.2

7.3

7.4

7.5

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Aver

age

Life

Sat

isfa

ctio

n

Year

Are there strong time or cohort effects?

Life sat of all in GSOEP

Figure 3.23: Year and life satisfaction in the GSOEP for the pooled sample

64

Figure 3.21 shows the evolution over time for the GSOEP. As we can see, there is

indeed a strong decline. Bearing in mind that the standard deviation of the mean life

satisfaction in a year with so many observations (10,000 per year) is less than 0.02,

the year on year changes are highly significant. The overall decline also fits

somewhat, though it is not quite enough: in 19 years, the aggregate drop is only 0.4

whilst the drop predicted by the fixed-effect regression is about 0.6 for 19 years.

There might hence be cohort or time effects39 responsible for the found drop by age,

but the predicted future drop in satisfaction would be enormous, i.e. at current trends

a predicted drop by more than 1 point in the next 30 years. In addition, this predicted

drop does not tally with what we know from other surveys (like the Eurobarometer

Survey), where it has been found that aggregate life satisfaction is quite constant in

Western countries over time, including Germany40. There is hence still something not

quite right about this ‘explanation’ because whilst it fits the GSOEP as a whole, it

seems to violate what we know to hold at the aggregate for happiness cross-sections.

What other explanation is left?

3.6.2 Explanation for the negative slope II: is something wrong with the

panel data

Forced to reject or seriously doubt all other reasonable explanations, I now turn to

the most uncomfortable potential explanation, which is that there is something wrong

with repeat happiness responses in the GSOEP panel data. What if individuals who

remain in the panel, and, who keep answering the survey, are different from those

who drop out of the panel or do not keep answering the satisfaction question?

The GSOEP has a large numbers of dropouts each year. For instance, of the roughly

10,000 individuals in the original 1984 GSOEP sample, only about 4,000 remain in

our data for the full 19 years. The GSOEP replaces those who no longer answer with

39 Time and cohort effects cannot be meaningfully separated if we also include non-linear age effects. 40 Clark et al. (2008) show that life-satisfaction profiles have been virtually flat in cross-sections in the last 30 years for Germany, France the US, and many other Western countries.

65

new respondents, based on a desire to keep a representative sample in terms of

variables included in a census, such as gender, age, income, and education.

What kind of selection could cause the large decline seen in the fixed-effect

regressions and in the aggregate data shown above? A naive thought would be that

only those who are unhappy keep answering the GSOEP, i.e. a selection based on the

fixed effects if . This is not a valid possibility however, because fixed-effects drop

out in the fixed-effect specification, making it irrelevant whether there is a selection

on fixed traits. A second naive thought is that there could be a selection on

particularly unpleasant observed life events, i.e. only those with bad events

happening to them keep answering the survey. Whilst there is limited support for this

in the graphs above (when adding some life events, the predicted life satisfaction

decline reduces), there is no full support for this: as far as observed life events are

concerned, the negative age effect remains when they are taken into account. A third

naive thought is that there could be a selection on transitory unobserved negative

shocks. If this were the case though, then there should only be a one-only drop in life

satisfaction and not the sustained decline we see from the fixed-effect regression.

The most problematic thought is that there could be a selection on unobserved

strongly persistent negative shocks. This implies selection on a strongly persistent

(but not fixed) part of itu . If we are for instance thinking of writing ( ) tit itu eρ= ∑

with ite being i.i.d. shocks, then a high ρ (close to 1) would indicate a strong

persistence in shocks and the selection we would worry about is on ite . If, wave after

wave, it is the case that individuals are more likely to stay another year in the

GSOEP when ite is lower, then we would indeed be able to get the strong negative

age slope observed in the fixed-effects regression.

How can we verify this possibility? We verify it by comparing the answers of those

who stay in the panel with those who enter for the first time. Every year, there are

several thousand new entrants in the GSOEP who have never answered the

questionnaire before. Some of these are new samples, some are partners of regular

respondents, and some are children becoming old enough to be in the sample. What

66

is the relationship between age and life satisfaction for them, and, what does

aggregate satisfaction over the years look like for them?

5

5.5

6

6.5

7

7.5

8

Life

Sat

isfa

citio

n

AGE

Is the linear decline due to sample selection: does selecting on first observation explain it?

Raw average life satisfaction total sample

age and age2 of first-timers

Raw of first-timers

age+age2 of full sample

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

Aver

age

Life

Sat

isfa

ctio

n

Year

Is selection over time important?Surprisingly: yes! No significant increase for first-time entrants with a very significant decrease for

all!

Life sat of first-time inGSOEP

Life sat of all in GSOEP

Figure 3.24 (top) and 3.24 (bottom): life satisfaction in the GSOEP for first-time respondents

67

Figure 3.24 (top) shows the aggregate level of satisfaction by age of those who

answer for the first time, as well as for the entire pool. As we can see, there is still a

decline in happiness at a very young age and at very old ages (which has very few

observations and therefore looks erratic), but there is no decline at all from age 25 to

75. If we then overlay the predicted regression lines, the predicted regression line for

the whole sample shows the horizontal j-shape, but the predicted regression line for

the first-time panel entrants is almost flat. Not quite flat, because there is still a

significantly negative age trend, but the coefficient is about 85% smaller than that for

the full sample. When adding a quadratic term all age effects become insignificant

for the new entrants (the exact specifications are in Table 3.16 (Chapter 3 Appendix

B). Hence, there is a small age effect on happiness, concentrated at the very young

and the very old, but it is not U-shaped. Rather, it is simply a decline.

Figure 3.24 (bottom) confirms the impression of the top graph: when looking at the

average satisfaction over the years for first-time respondents, there is no time profile

of happiness anymore, in line with what is found for cross-sectional studies which by

design only question individuals once. This is consistent with the notion that there

are no age, time, or cohort effects, or that they at least cancel each other out. The

decline in the aggregate-panel is thus most likely due to selection on time-varying

unobservables. How bad is the decline in satisfaction for repeat respondents? Finally,

Figure 3.25 shows aggregate satisfaction depending on the number of years someone

has answered the questionnaire.

68

6.4

6.6

6.8

7

7.2

7.4

7.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Aver

age

Life

Sat

isfa

ctio

n

Years in the panel

How much does satisfaction decline with years in the panel?

Life sat of stayers in GSOEP

Figure 3.25: The degree of selection in the GSOEP for stayers in the panel

Figure 3.25 confirms the running hypothesis: there is a large decline in reported

satisfaction as an individual is in the panel for longer. We now know this is not age

related or related to observables, but is a selection on unobservables. Given that there

is no discernible ‘bounce-back’, it furthermore has to be a relatively persistent time-

varying unobservables responsible for this decline. The decline is large enough to

explain the fixed-effect profile: in eighteen years, there is a 0.64 reduction in life

satisfaction41, which translates to a decline of 2.2 over 60 years, almost exactly the

predicted amount from the fixed-effect regression.

3.6.3 Is there simple corroborating information of a dynamic selection on

negative shocks?

A natural question to ask is whether there is any information outside the panels

themselves that can be used to verify if the panel is indeed retaining the

‘unfortunate’, i.e. those who have experienced unobserved negative shocks. What we

can look at to verify such a possibility is the observed negative happiness relevant

shocks that are observed in the panel for which we can find some official outside

statistics to check them against. For the vast majority of the time-varying happiness-

41 This 2.2 predicted reduction in happiness over a lifetime is an out of (age) range forecast; the 19 years of the GSOEP panel are assumed to be representative of a typical lifetime.

69

related variables in the panel, there is no reliable outside information with which to

check the panel’s selection. The one negative shock for which there is some

information is divorce rates. According to UN statistics, the yearly divorce rate

(official annulments of registered marriages) per 1000 is 5.2 in Germany42. If we take

into account that the panel does not contain individuals below 18 (the 0-17 year olds

make up some 18% in Germany), then this would imply that divorce rates should be

6.2 per 1000 in the GSOEP. As it is, the yearly self-reported divorce rate in the

GSOEP is about right, i.e. about 6 per 1000 people on average and rising over time in

the panel. Hence, self-reported divorce rates are not higher for the sample than for

the population. This means we cannot claim firm outside evidence of negative

dynamic selection.

3.6.4 Alternative interpretations

Can I think of alternative interpretations for the findings above not based on selection

on time-varying unobservables? Note that this is the worst kind of selection possible

in panel analyses because there is not much that can be done about it and one relies

on a leap of faith that it does not affect the coefficients of time-varying observables.

One alternative is that we are not looking at unobservable negative errors at all, but

rather that we are looking at the disappearance of positive errors. It might be the case

that Germans over time become more comfortable talking about their levels of

happiness and other personal matters because they have not noticed any breach of

privacy or other adverse effects of answering the questionnaire. Singer, von Thurn, &

Miller (1995) suggest these are important factors in the quality of responses. Hence,

perhaps the GSOEP respondents are becoming more truthful by not glossing over

their actual level of happiness as much as they do in the earlier waves. This

possibility ties in with the notion of panel learning of which Juster (1986, p. 401)

observes that ‘later interview waves appear to have higher quality than earlier ones’.

The weak point in this explanation is that one would think the process of getting

comfortable with responding levels off after a few years. This would imply that there

42 The UN numbers refer to the number of granted annulments. Since one annulment affects the marital status of two people, we have doubled this to arrive at the number of individuals getting divorced.

70

would eventually have to be some ‘bottoming out’ of the happiness decline for

people who stay in the panel. This is not noticeable in the graph above. Whilst the

interpretation that we are looking at more truthful responses over time is probably

more soothing to the collectors of the GSOEP because it takes away the suspicion

that the representativeness of the panel is in doubt. The implications for the

happiness literature of this second interpretation is worse than the first interpretation.

If it were just the case that panels suffer from non-random attrition based on time-

varying unobservable happiness determinants then there is still a lot of useful

information in cross-sectional surveys and one can hope that the selection does not

involve cross-terms between observable time varying variables and unobservable

ones, allowing useful interpretations of panel coefficients.

If it is, alternatively, the case that we cannot trust the responses on happiness

questions of the first 19 years of responses, then we effectively cannot trust over 99%

of the data in this field. In addition, the changes over time are big, certainly big

enough to get seriously worried: an overstatement of happiness by 0.7 could drop a

country for instance from being the happiest country in the world to being one of the

unhappier countries in the world, nullifying the validity of all the rank-tables. Now,

of course, if the overstatement is a ‘universal constant’ and does not differ by

country, then the implications are less strong but the hypothesis of a universal

constant overstatement cannot be claimed a priori. If a possible tendency to overstate

is furthermore correlated with observables, then cross-sectional and panel analyses

all become highly suspect. Alternatively, is this just an artefact of the GSOEP? To

test this, we look for cohort and time effects in the BHPS and the HILDA data sets.

3.7 Are there time and cohort effects in the HILDA

With the GSOEP data (Section 3.5.2a), the U-shape of happiness in age reversed into

an inverted U shape and a very strong negative relationship emerged (Figure 3.13);

The GSOEP age2 coefficient went non-significant with fixed effects, the age

coefficient became strongly significant across all specifications (Table 3.7). This

occurred to a much lesser extent with the HILDA, the (thin solid) line of the ‘age +

age2’ fixed-effect specification in Figure 3.15 only exhibits a very slight negative

relationship. Like the GSOEP, the HILDA age2 coefficient does go non-significant

71

with fixed effects, but the age coefficient was not as strongly significant across all

specifications (Table 3.8). Therefore, if our GSOEP explanation is to hold, we should

not see strong time or cohort effects with the HILDA data because the large decline

we saw under the fixed-effects regressions with the GSOEP are not evident in the

HILDA data; we should not see a decline in the aggregate HILDA data.

7.5

7.6

7.7

7.8

7.9

8

8.1

2002 2003 2004 2005 2006 2007 2008

Aver

age

Life

Sat

isfa

ctio

n

Year


Life satisfaction of all in HILDA

Figure 3.26: Year and life satisfaction in the HILDA for the pooled sample

With the GSOEP, cohort effects were clearly visible (Figure 3.23); we do not see a

similar decline in the aggregate HILDA data (Figure 3.36). The GSOEP had a 0.4

drop in happiness over the 19-year panel, the HILDA only exhibits a very small

(0.08) happiness increase in 2002-3 but the life satisfaction level across the whole

panel period is almost the same. There appears to be little if any time or cohort

effects in the HILDA. If so, we should not see large differences between the first

timers and those who remain the HILDA panel.

72

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

9.00

9.50

10.00

Life

Sat

isfa

citio

n

AGE

Is the linear decline due to sample selection: does selecting on first obs explain it?


age + age2 of first-timers

Raw average life satisfaction of first-timers


7.5

7.6

7.7

7.8

7.9

8

8.1

2002 2003 2004 2005 2006 2007 2008

Aver

age

Life

Sat

isfa

ctio

n

Year

Is selection over time important?

Life satisfaction of first-timers in HILDA

Life satisfaction of all in HILDA

Figure 3.27 (top) and 3.25 (bottom): life satisfaction in the HILDA for first-time respondents


answer for the first time, as well as for the entire HILDA pool. The raw average

lifetime satisfaction of first timers (thin dashed line) exhibits lower volatility than the

GSOEP, and, unlike the GSOEP (Figure 3.22), the time effects (age + age2) of the

73

HILDA stayers and first timers overlay one another43. Figure 3.27 (bottom) confirms

the impression of the top graph: when we look at the average satisfaction over the

years for first-time respondents, there is only a very small change in life satisfaction

over the 8-year panel: 0.13, less than 10% of a standard deviation. We do not see

large differences between the first timers and those who remain the HILDA panel.

As a final check that time and cohort effects do not strongly manifest in the HILDA,

we look for the evidence that we found with the GSOEP data; how much does life

satisfaction decline with years in the panel.

7.50

7.60

7.70

7.80

7.90

8.00

8.10

1 2 3 4 5 6 7

Aver

age

Life

Sat

isfa

ctio

n

Years in the panel


Life sat of stayers in HILDA

Figure 3.28: The degree of selection in the HILDA for stayers in the panel

Unlike the GSOEP (Figure 3.25), there is no large decline in reported satisfaction as

an individual is in the HILDA panel for longer (Figure 3.28). There are little if any

cohort and time effects evident in the HILDA data. Of course, the lack of time and

cohort effects may be due to the brevity of the HILDA panel. The 12-year BHPS

offers a panel length between that of the GSOEP (18 years) and the HILDA (8

years). I again ask, are the cohort and time effects found in the GSOEP also in the

BHPS.

43 This may be a testament to the high level of attention the Melbourne Institute places on retaining individuals in the HILDA panel and ensuring dropouts are replaced with individuals of similar demographic characteristics (Watson & Wooden, 2010) .

74

3.8 Are there time and cohort effects in the BHPS

With the GSOEP data (Section 3.6), the U-shape of happiness in age reversed into an

inverted U shape and a very strong negative relationship emerged (Figure 3.13). The

GSOEP age2 coefficient went non-significant with fixed effects, the age coefficient

became strongly significant across all specifications (Table 3.7). Like the HILDA,

this occurred to a much lesser extent with the BHPS, the (thin solid) line of the ‘age

+ age2’ fixed-effect specification in Figure 3.29 only shows a very slight negative

relationship. Like the GSOEP, the BHPS age2 coefficient does go non-significant

with fixed effects, but the age coefficient is not as strongly significant across all

specifications (Table 3.9) as it was with the GSOEP. Therefore, if our GSOEP

explanation is to hold, we should not see the strong time or cohort effects with the

BHPS data because the large decline we saw under the fixed-effects regressions with

the GSOEP are not evident in the BHPS data; we should not see a decline in the

aggregate BHPS data.

5

5.1

5.2

5.3

5.4

1996 1997 1998 1999 2000 2002 2003 2004 2005 2006 2007 2008

Aver

age

Life

Sat

isfa

ctio

n

Year


Life satisfaction of all in BHPS

Figure 3.29: Year and life satisfaction in the HILDA for the pooled sample

75

With the GSOEP, cohort effects were clearly visible (Figure 3.23); we do not see a

similar decline in the aggregate BHPS data (Figure 3.29). The GSOEP had a 0.4 drop

in happiness over the 19-year panel but, while there are small changes year-on-year,

life satisfaction levels across the 12-year BHPS panel period remains almost the

same. There appears to be little if any time or cohort effects in the BHPS. If so, we

should not see large differences between the first timers and those who remain the

BHPS panel.

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

Life

Sat

isfa

citio

n

AGE

Is the linear decline due to sample selection: does selecting on first obs explain it?


age + age2 of first-timers

Raw average life satisfaction of first-timers


5

5.1

5.2

5.3

5.4

1996 1997 1998 1999 2000 2002 2003 2004 2005 2006 2007 2008

Aver

age

Life

Sat

isfa

ctio

n

Year

Is selection over time important?

Life satisfaction of first-timers in BHPS

Life satisfaction of all in BHPS

Figure 3.30 (top) and 3.30 (bottom): life satisfaction in the BHPS for first-time respondents

76


answer for the first time, as well as for the entire BHPS pool. The raw average life

satisfaction of first timers (thin dashed line) does not exhibit the high level of

volatility evident in the GSOEP; the time effects (age + age2) of the BHPS stayers

and first timers overlay one another. Figure 3.30 (bottom) confirms the impression of

the top graph; when we looking at the average satisfaction over the years for first-

time respondents, there is only a very small increase in life satisfaction over the 8-

year panel: 0.07, less than 5% of a standard deviation. We do not see large

differences between the first timers and those who remain the BHPS panel. As a final

check that time and cohort effects do not strongly manifest in the BHPS, we look for

the evidence that we found with the GSOEP data: how much does life satisfaction

decline with years in the panel.

5.00

5.10

5.20

5.30

5.40

1 2 3 4 5 6 7 8 9 10 11 12

Aver

age

Life

Sat

isfa

ctio

n

Years in the panel


Life satisfaction of stayers in BHPS

Figure 3.31: The degree of selection in the BHPS for stayers in the panel

Unlike the GSOEP, which had a decline of 0.6 (32% of a standard deviation), there is

no large decline in reported satisfaction as an individual remains in the BHPS panel

for longer (Figure 3.31). Like the HILDA, there appears to be little if any cohort and

time effects in the BHPS data.

77

3.9 Conclusions and discussion on the negative slope

In trying to explain the new puzzle of the negative age slope in the GSOEP, I was

able to discount the likelihood of cohort and time effects: there simply is no

sufficiently strong time profile in the aggregate responses to explain the fixed-effect

results and, there is no time trend at all for first-time responses. This left sample

selection as the reason for the anomalously high age slope. I confirmed that those

who answer the GSOEP for the first time showed a smaller age profile in happiness.

There is a small decrease in happiness after age eighteen and after about age eighty,

but no significant change between the ages of twenty-five and seventy-five for first-

time responses. On the other hand, those who stayed in the panel reported lower

levels of life satisfaction with the decline being 0.64 for those in the GSOEP for

nineteen years. This perfectly fit the fixed-effects regressions.

The two possible interpretations of this were either that there was a selection of

stayers on somewhat persistent time-varying happiness unobservables or that those

who keep answering the GSOEP questionnaire become progressively more honest

about their actual, lower, level of satisfaction. If there is a selection on unobservables

then this is problematic for the reliability of long panels and analyses based on them.

If individuals are untruthful for the first nineteen years of responding to

questionnaires then this more or less affects all the analyses in the field, especially

cross-sectional studies that make up the bulk of the literature. The exact time profile

of the happiness of the stayers (a virtually continuous decline) slightly favours the

notion of selection on time-varying unobservables because one would have expected

the effect of becoming open and honest to the interviewer to gradually level off

before nineteen years.

Can we think of a reasonable third alternative explanation that is neither damning for

the collection of long representative panels or for the happiness field as a whole? I

cannot think of one. The found effect of age in fixed-effect regressions is simply too

large and too out of line with everything else we know to be believable. The

difference between first-time respondents and stayers and between the number of

years someone stays in the panel does not allow for explanations based on fixed traits

or observables. There has to be either a problem on the left-hand side (i.e. the

78

measurement of happiness over the life of a panel) or on the right-hand side

(selection on time-varying unobservables).

Recent research by Kassenboehmera & Haisken-DeNew (October 10, 2011) has

revealed a possible answer to the puzzle of why the happiness responses of Germans

who stay in the GSOEP panel longer decrease over time; there appears to be

interviewer characteristics effects. It would appear that the GSOEP survey is

conducted via personal interview and it is normal for the same interviewer to

interview the same individual over time. When the interviewer changes there is

evidence of a significant change in self-reported happiness. The interviewers' gender

or interviewing experience appears to have a significant impact on response

behaviour and should be taken into account where the interviewer, gender and

interviewing experience variables are available in panel data. Future research will

have to investigate whether the findings of Kassenboehmera & Haisken-DeNew hold

for other panel data sets or whether there are similar problems with other variables

(such as self-reported health).

3.10 Chapter 3 Limitations

This study has limitations. There are happiness question wording and format

differences across the Australian, British and German panel survey questionnaires.

Format and wording differences bring into question whether the three surveys are

measuring the same phenomena. Wording and format differences alter the

psychometric properties of a survey question and initiate different responses from a

subject (Nunnally, 1978). It is possible to measure the instrument bias arising from

wording and format differences if subjects respond to all the happiness questions at

the same time. Confirmatory factor analysis can quantify the size of any instrument

bias and we could include this as a control variable in our regressions. Unfortunately,

the data to do this analysis is not in the German, British or Australian panel data. The

instrument bias arising from differences in the format and wording of the life

satisfaction questions remains unmeasured and is worthy of future research.

79

In addition to format and wording differences in the happiness questions, the

response scale (1-7) for the happiness question in the British panel survey

questionnaire is different from the scale (0-10) used for the happiness question in the

German and Australian panel survey questionnaires. The British happiness variable

could be rescaled 0-10 but this could introduce error in cross-panel comparisons.

Using beta coefficients or expressing within panel happiness changes as percentages

or coefficients of variation can assist with cross-panel comparisons. However,

analysis would be simplified if the authors of the socio-economic survey

questionnaires standardised on questions with the same scale, format and wording.

The next limitation concerns the graphics depicting happiness over a lifetime; they

are out of range predictions. In example, the figures depicting happiness over a

lifetime for the German population use nineteen years of data to depict a typical

lifetime. Some individuals may have appeared in the panel once, others many times.

Ideally, we could work with a balanced panel of data where a large number of

individuals stay in a panel for their complete lifetime. However, given the short

duration and the high subject attrition rate in socio-economic panels like the German

GSOEP, the British BHPS and the Australian HILDA, this ideal situation may never

be realised. We will just have to continue to use short unbalanced panels to depict a

typical lifetime.

3.11 Chapter 3 Summary

Chapter 3 questioned the relationship between happiness and age. A review of recent

economic literature revealed the finding of several economists that happiness is U-

shaped in age; we get happier as we age. The psychological literature disagrees; their

consensus opinion is that age has little to do with happiness. Using a nested model

approach, we re-examined the age-happiness relationship in the often-used German

(GSOEP) panel data set and found evidence that reverse causality caused by

unobserved fixed traits44 explained the U-shape. Robustness was added to the

GSOEP fixed effect finding by replicating it with the Australian (HILDA), and,

44 The effects of observed fixed traits on the life event shocks affecting happiness are examined in Chapter 5.

80

British (BHPS) panel data sets. In addition, the years-in-panel effect identified in the

GSOEP did not manifest in the HILDA and BHPS data.

I now pursue a difference between German and Australian lifetime happiness noted

earlier in this chapter. Germans have a steep decline in happiness between the ages of

18 and 22 that is not evident in the Australian HILDA data. In Chapter 4, I ask the

question; is there a similar dramatic drop in the happiness of young Australians,

when does it begin, and, what could lie behind the steep decrease in the happiness of

the young?

81

Chapter 3 - Appendix A: Descriptive Statistics

Table 3.11: Descriptive statistics for the entire and first-timer GSOEP samples Entire

Sample First-time

Respondents Mean s.d. Mean s.d.

overall life satisfaction

(self-assessed and scaled 0 to 10)

7.16 1.85 7.45 2.01

age 44.26 16.91 38.45 17.28

age*age 2244.67 1659.88 1769.01 1577.34

Ln(monthly household income, Euros) 10.68 0.53 10.64 0.51

male (1=yes) 0.49 0.50 0.49 0.50

level of education (years) 10.93 2.46 10.51 2.41

number of children in family 0.65 0.99 0.71 1.01

married (1=yes) 0.65 0.48 0.55 0.50

employed (1=yes) 0.47 0.50 0.44 0.50

non-participant in the labour-force (1=yes) 0.33 0.47 0.37 0.48

unemployed (1=yes) 0.04 0.20 0.04 0.21

average regional income (Euro) 4149.99 477.88 3980.37 332.64

own or purchasing dwelling (1=yes) 0.42 0.49 0.34 0.48

asset income (Euro) 2359.80 10700.08 1258.23 6706.26

imputed rent (Euro) 1484.61 2910.48 921.85 2134.08

current state of health 45 (stated) 2.59 0.95 2.22 0.94

invalid (1=yes) 0.04 0.20 0.04 0.19

household member died this year (1=yes) 0.01 0.08 0.00 0.06

divorced (1=yes) 0.05 0.22 0.04 0.19

separated from partner (1=yes) 0.01 0.12 0.01 0.11

partner dead (1=yes) 0.06 0.24 0.05 0.22

just married (1=yes) 0.02 0.15 0.05 0.21

just divorced (1=yes) 0.00 0.07 0.00 0.06

just separated (1=yes) 0.01 0.11 0.01 0.08

partner just died (1=yes) 0.00 0.06 0.00 0.03

just had a baby (1=yes) 0.04 0.19 0.02 0.14

pregnant (1=yes) 0.01 0.11 0.01 0.01

just fired from job (1=yes) 0.02 0.12 0.00 0.07

Sample Size: 176,770 18,824


45 Health is reverse coded: 1 = excellent to 5 = poor.

82

Table 3.12: Descriptive statistics for the entire and first-timer HILDA samples Entire

Sample First-time




7.91 1.47 7.88 1.58

age 45.50 16.92 43.06 16.92

age*age 2356.65 1666.50 2140.16 1631.59

Ln (annual household income46) 10.77 1.07 5.23 3.09

male (1=yes) 0.47 0.50 0.48 0.50



married (1=yes) 0.55 0.50 0.50 0.50

employed (1=yes) 0.66 0.47 0.65 0.48

unemployed (1=yes) 0.03 0.16 0.04 0.20

average regional (State ) income ($AUD) 1108.65 1113.69 1026.75 1009.84


imputed rent ($AUD) 4.94 39.82 4.35 37.67

current state of health47 (stated) 2.64 0.95 2.61 0.97

invalid (1=yes) 0.24 0.43 0.21 0.41

household member died this year (1=yes) 0.11 0.31 0.11 0.31

divorced (1=yes) 0.09 0.29 0.09 0.29


partner dead (1=yes) 0.05 0.22 0.05 0.21

just married (1=yes) 0.03 0.16 0.04 0.19

just divorced 0.01 0.08 0.001 0.04




pregnant (1=yes) 0.05 0.22 0.06 0.24


Sample Size: 72,108 18,821


46 Includes wages and salary, income from investments, as well as, Government transfers. 47 Health is reverse coded; 1 = excellent to 5 = poor.

83

Table 3.13: Descriptive statistics for the entire and first-timer BHPS samples 48

Entire

Sample First-time




5.23 1.29 5.24 1.37

age 46.45 17.80 42.92 17.99

age*age 2474.85 1789.30 2165.49 1746.34

Ln (annual household income) 9.84 1.61 9.77 1.14

male (1=yes) 0.45 0.50 0.46 0.50



married (1=yes) 0.55 0.50 0.49 0.50

employed (1=yes) 0.58 0.49 0.57 0.49

unemployed (1=yes) 0.03 0.18 0.05 0.21

average regional income ln(pounds) 10.04 0.09 10.04 0.09


imputed rent (pounds) 40.70 179.67 65.15 242.16

current state of health (stated)49 2.20 0.95 2.17 0.96

invalid (1=yes) 0.02 0.14 0.04 0.21

divorced (1=yes) 0.06 0.23 0.05 0.22


partner dead (1=yes) 0.07 0.26 0.07 0.25


pregnant (1=yes) 0.002 0.04 0.001 0.03

Sample Size 153,886 22,922

48 The self-report life event variables in the GSOEP and the HILDA are not in the BHPS panel data. 49. The health average is for the stated health question is for all BHPS waves except wave 9. The wave 9 questionnaire asked a different (relative) health question and its response distribution was significantly different from the health question in the other waves. To maximise sample size in the regressions and to avoid the loss of a wave of data in the middle of the BHPS panel, a health missing/dummy replacement routine was used to enable retention of wave 9 observations (STATA code available upon request).

84

85

Chapter 3 - Appendix B: Results from Least Squares Regression Analysis

86

Table 3.14: The determinants of Life Satisfaction for West-Germans in the GSOEP; Pooled OLS Regression – entire sample, N = 176,770

Age Age + Age2 Usual Suspects Usual Suspects + Health Kitchen Sink Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value coefficient t-value

age -0.0059 22.68 -0.0217 15.69 -0.0541 32.8 -0.0600 36.77 -0.0454 25.64

age*age 0.0002 11.63 0.0005 29.2 0.0006 34.13 0.0005 25.39

Ln Income (monthly, Euros) 0.4619 52.2 0.4420 45.91 0.4307 44.53

male -0.0603 6.3 -0.0719 7.60 -0.0719 7.47

education -0.0249 13.7 -0.0227 12.47 0.0204 11.17

number of children -0.0640 13.1 -0.0498 10.30 -0.0382 7.50

married 0.3106 27.9 0.2915 26.50 0.1180 7.65

employed 0.0650 4.8 0.0791 5.88 0.0688 5.11

non-participant -0.0033 0.2 -0.0027 0.19 -0.0305 2.14

unemployed -1.0076 42.3 -0.9508 40.37 -0.9225 38.37

regional income -0.0001 11.24 -0.0001 11.15

home owner 0.0943 8.45 0.0956 8.58

asset income 0.0000 1.95 0.0000 1.87

imputed rent 0.0000 15.60 0.0000 15.23

health -0.0228 30.42 -0.0231 30.71

invalid -1.2427 55.93 -1.2444 56.12

family death -0.3158 4.69

divorce -0.2558 10.63

separated -0.4539 11.26

partner dead -0.0468 1.84

just married 0.4061 13.70

just divorced 0.0474 0.75

just separated -0.4112 9.92

spouse just died -0.9895 10.55

just had a baby 0.1354 5.65

pregnant 0.2118 5.05

just fired from job -0.2747 7.86

constant 7.4165 602.43 7.7472 249.95 4.2642 53.5 4.8670 58.03 4.7469 56.33

R2 0.00 0.00 0.0481 0.07 0.08

87

Table 3.15: Determinants of Life Satisfaction for West-Germans in the GSOEP; Pooled OLS Regressions – ages 22 to 80, N = 160,332


age -0.0034 11.21 -0.0306 15.46 -0.07451 33.91 -0.0768 35.27 -0.0618 26.75

age*age 0.0003 13.89 0.0008 32.30 0.0008 33.73 0.0007 27.03


male -0.1196 11.66 -0.1208 11.92 -0.1196 11.64

education -0.0286 15.13 -0.0248 13.07 0.0224 11.78


married 0.3373 29.39 0.3096 27.22 0.1531 9.61

employed 0.1442 9.86 0.1434 9.85 0.1303 8.96

non-participant -0.0204 1.32 -0.0127 0.83 -0.0458 2.94

unemployed -0.9302 37.63 -0.8791 35.91 -0.8539 34.21

regional income -0.0001 12.07 -0.0001 12.01

home owner 0.1152 9.90 0.1173 10.09

asset income 0.0000 2.38 0.0000 2.33

imputed rent 0.0000 14.43 0.0000 14.18

health -0.0233 30.01 -0.0237 30.40

invalid -1.1565 47.22 -1.1579 47.36


divorce -0.2193 9.01

separated -0.4069 10.02









constant 7.2962 502.70 7.8762 178.17 4.4076 50.67 5.0061 54.98 4.8739 53.19

R2 0.00 0.00 0.05 0.07 0.08

88

Table 3.16: The determinants of Life Satisfaction for West-Germans in the GSOEP; Fixed-effect Regressions – entire sample, N = 176,770


age -0.0398 50.43 -0.0166 6.60 -0.0328 -11.61 -0.0298 9.95 -0.0184 5.81

age*age -0.0003 9.71 -0.0001 -2.22 0.0000 0.74 -0.0001 2.40


male (omitted) (omitted) (omitted)

education -0.0017 -0.35 -0.0009 0.19 -0.0024 0.51

number of children -0.0255 -3.9 -0.0231 3.52 -0.0102 1.52

married 0.2385 14.95 0.2457 15.42 0.0327 1.37

employed 0.0991 6.69 0.1001 6.75 0.0925 6.24

non-participant 0.0214 1.46 0.0244 1.67 0.0150 1.00

unemployed -0.6623 -29.28 -0.6455 28.61 -0.6282 27.40

regional income 0.0000 5.31 0.0000 4.98

home owner 0.0115 0.71 0.0292 1.82

asset income 0.0000 0.64 0.0000 0.78

imputed rent 0.0000 4.26 0.0000 3.80

health -0.0124 11.07 -0.0122 10.91

invalid -0.7192 29.20 -0.7296 29.65


divorce -0.0008 0.02

separated -0.3273 7.63



just divorced -0.0573 1.04






constant 8.9180 254.02 8.4541 142.62 6.6296 58.97 6.1697 49.82 6.1589 49.54

R2 0.00 0.00 0.0135 0.03 0.03

89

Table 3.17: The determinants of Life Satisfaction for West-Germans in the GSOEP; Pooled Regression – first time respondents, N = 18,821


age -0.0046 5.43 -0.0030 0.73 -0.0437 8.06 -0.0490 9.05 -0.0367 6.26

age*age 0.0000 0.40 0.0004 6.86 0.0005 8.17 0.0004 6.09


male -0.0629 2.02 -0.0730 2.36 -0.0811 2.58

education -0.0296 4.66 -0.0262 4.12 0.0242 3.78


married 0.4189 11.28 0.3997 10.83 0.2359 4.74

employed 0.0501 1.07 0.0779 1.67 0.0759 1.62

non-participant -0.0581 1.26 -0.0497 1.08 -0.0565 1.23

unemployed -1.2065 15.26 -1.1363 14.46 -1.1409 14.38

regional income 0.0000 0.91 0.0000 0.60

home owner 0.1407 3.66 0.1348 3.50

asset income 0.0000 1.37 0.0000 1.29

imputed rent 0.0000 2.66 0.0000 2.65

health -0.0065 1.98 -0.0074 2.22

invalid -1.3171 17.40 -1.3228 17.46


divorce -0.3366 3.81

separated -0.5037 3.67




just separated 0.0272 0.14


just had a baby -0.1287 1.14


just fired from job 0.0198 0.09

constant 7.6261 213.61 7.5963 91.73 4.1537 15.68 4.2166 13.06 4.1449 12.81

R2 0.00 0.00 0.05 0.06 0.07

90

Table 3.18: The determinants of Life Satisfaction; Pooled OLS regression results for all individuals in the HILDA; N = 75,529 Age Age + Age2 Usual Suspects Usual Suspects + Health Kitchen Sink Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value coefficient t-value

age 0.0107 33.37 -0.0320 -18.98 -0.0554 -29.57 -0.0403 -22.72 -0.0311 16.00

age*age 0.0004 25.79 0.0007 33.95 0.0006 30.26 0.0005 23.98

income 0.0805 15.00 0.0329 6.03 0.0309 5.69

male -0.1471 -13.52 -0.1016 -9.93 -0.0981 -9.53

education -0.0232 -7.52 -0.0582 -19.74 -0.0606 -20.6

number of children -0.0590 -10.81 -0.0623 -12.15 -0.0600 -11.25

married 0.4496 36.2 0.3624 30.32 0.2514 14.85

employed 0.1355 9.27 -0.1338 -9.2 -0.1166 -8

unemployed -0.3244 -9.36 -0.4208 -12.92 -0.3560 -10.87

regional income 0.0000 5.87 0.0000 5.2

home owner 0.1397 10.95 0.1253 9.82

imputed rent -0.0001 -0.42 -0.0001 -0.43

health -0.5278 -88.46 -0.5251 -88.37

invalid -0.1073 -7.92 -0.1046 -7.76

family death 0.0110 0.68

divorced -0.0771 -3.51

separated -0.2947 -9.12

partner dead 0.0237 0.78


just divorced -0.1853 -2.72

just separated -0.4209 -15.2

spouse just died -0.2597 -4.59



just fired from job -0.2875 -9.52

constant 7.4240 477.31 8.3255 217.8 8.0870 110 10.0957 135.44 10.0082 132.16

R2 0.0151 0.0241 0.0531 0.1661 0.1745

Adjusted R2 0.0240 0.0530 0.1661 0.1742

91

Table 3.19: Determinants of Life Satisfaction for Australians in the HILDA; Pooled Regressions, ages 22 to 80, N = 65.679 Age Age + Age2 Usual Suspects Usual Suspects + Health Kitchen Sink Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value coefficient t-value

age 0.0135 35.46 -0.0417 -17.5 -0.0680 -26.69 -0.0496 -20.48 -0.0386 -14.91

age*age 0.0006 23.45 0.0008 31.09 0.0007 26.72 0.0006 21.61

income 0.0815 14.02 0.0349 5.92 0.0329 5.61

male -0.1660 -14.52 -0.1039 -9.64 -0.0992 -9.14

education -0.0218 -6.9 -0.0569 -18.77 -0.0589 -19.49


married 0.4494 35.47 0.3598 29.26 0.2514 14.65

employed 0.1758 11.44 -0.1162 -7.55 -0.0995 -6.46

unemployed -0.3029 -7.8 -0.4141 -11.32 -0.3443 -9.36

regional income 0.0000 5.47 0.0000 4.71

home owner 0.1386 10.12 0.1197 8.75

imputed rent 0.0000 0 0.0000 0.07

health -0.5226 -83.07 -0.5206 -83.1

invalid -0.0922 -6.51 -0.0896 -6.36

family death 0.0142 0.84

divorce -0.0720 -3.25

separated -0.2898 -8.93

partner dead 0.0515 1.6








constant 7.2676 389.98 8.4890 153.54 8.2251 94.47 10.1845 115.78 10.0494 112

R2 0.0188 0.0269 0.0587 0.1678 0.1767

Adjusted R2 0.0269 0.0586 0.1677 0.1764

92

Table 3.20: The determinants of Life Satisfaction for Australians in the HILDA; Fixed-effect Regressions – entire sample, N = 75,529


age -0.0167 -6.90 -0.0076 -1.02 -0.0173 -2.12 -0.0202 -2.50 -0.0033 -0.40

age*age -0.0001 -1.30 0.00001 -0.17 0.00007 0.94 -0.00006 -0.72

income 0.0257 4.26 0.0191 3.22 0.0151 2.55

education -0.0204 -1.72 -0.0198 -1.71 -0.0246 -2.11


married 0.2056 6.76 0.1963 6.52 -0.1095 -2.84

employed 0.0536 2.33 0.0238 1.06 0.0394 1.75

unemployed -0.1344 -3.19 -0.1422 -3.43 -0.1208 -2.91

regional income 0.0000 3.30 0.0000 2.65

home owner 0.0687 3.01 0.0640 2.83

imputed rent -0.0001 -0.79 -0.0001 -0.62

health -0.2694 -28.16 -0.2679 -28.13

invalid -0.0492 -3.29 -0.0488 -3.28

family death -0.0046 -0.33

divorce -0.1608 -2.71

separated -0.3479 -4.83

partner dead -0.3512 -3.85








constant 8.6701 78.75 8.4827 47.34 8.5967 41.04 9.2482 44.12 9.1148 43.45

Overall R2 0.0151 0.0175 0.0047 0.0344 0.0325

With robust standard errors

93

Table 3.21: The determinants of Life Satisfaction for Australians in the HILDA; Pooled Regression – first time respondents, N = 14,857 Age Age + Age2 Usual Suspects Usual Suspects + Health Kitchen Sink Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value coefficient t-value

age 0.0103 13.55 -0.0303 -7.62 -0.0582 -13.12 -0.0469 -11.08 -0.0325 -6.94

age*age 0.0004 10.41 0.0007 14.76 0.0006 14.23 0.0005 10.65

income 0.0986 7.11 0.0525 3.64 0.0476 3.32

male -0.1251 -4.83 -0.0756 -3.07 -0.0823 -3.33

education -0.0326 -4.29 -0.0684 -9.34 -0.0730 -10.01


married 0.5072 16.96 0.4188 14.45 0.2048 5.02

employed 0.1338 3.86 -0.1431 -4.13 -0.1130 -3.25

unemployed -0.3529 -5.03 -0.4572 -6.87 -0.3968 -5.94

regional income 0.0001 3.31 0.0000 3.03

home owner 0.1249 4.25 0.1069 3.64

imputed rent -0.0002 -0.74 -0.0003 -0.88

health -0.5263 -37.18 -0.5218 -37.09

invalid -0.1025 -3.04 -0.1005 -3

family death -0.0358 -0.94

divorced -0.1699 -3.23

separated -0.3920 -5.61







pregnant 0.1567 2.5


Constant 7.4309 210.77 8.2610 94.81 8.0377 43.93 10.0849 53.56 9.9728 52.35

R2 0.0122 0.0194 0.0505 0.1496 0.1629

Adjusted R2 0.0192 0.0499 0.1488 0.1615

94

Table 3.22: The determinants of Life Satisfaction for Britons in the BHPS; Pooled Regression – entire sample, N = 153,886


age 0.0066 35.86 -0.0221 -21.96 -0.0554 -49.28 -0.0438 -41.01 -0.0349 -31.02

age*age 0.0003 29.00 0.0006 55.15 0.0005 48.21 0.0005 39.43

income 0.0943 19.44 0.0399 8.39 0.0251 5.25

male -0.0438 -6.66 -0.0540 -8.69 -0.0625 -9.95

education -0.0033 -2.27 -0.0199 -14.52 -0.0195 -14.27


married 0.3567 46.48 0.3063 41.8 0.2014 22.43

employed 0.2397 27.19 0.0295 3.48 0.0372 4.38

unemployed -0.2823 -14.46 -0.3430 -18.55 -0.3406 -18.46

regional income -0.1659 -5.05 -0.1606 -4.9

home owner 0.1521 18.47 0.1445 17.56

imputed rent 0.0001 5.13 0.0001 4.4

health -0.4653 -130.81 -0.4635 -130.57

invalid -0.3415 -15.82 -0.3373 -15.66

divorced -0.2966 -19.74

separated -0.4714 -19.72




constant 4.9199 537.15 5.5346 239.77 5.0476 95.85 8.2857 25.17 8.2286 25.05

R2 0.0083 0.0137 0.0481 0.1529 0.157

Adjusted R2 0.0137 0.048 0.1528 0.1569

95

Table 3.23: The determinants of Life Satisfaction for Britons in the BHPS; Pooled Regressions – ages 22 to 80, N = 138,481


age 0.0087 39.44 -0.0373 -26.21 -0.0772 -51.31 -0.0620 -43.38 -0.0521 -34.94

age*age 0.0005 32.7 0.0009 58.5 0.0007 50.57 0.0007 42.99

income 0.1134 21.22 0.0561 10.75 0.0384 7.31

male -0.0727 -10.56 -0.0726 -11.16 -0.0810 -12.31

education -0.0042 -2.83 -0.0204 -14.41 -0.0198 -13.97


married 0.3439 43.95 0.2951 39.41 0.1984 21.9

employed 0.3340 35 0.0825 8.91 0.0898 9.7

unemployed -0.1903 -8.86 -0.2907 -14.28 -0.2903 -14.29

regional income -0.1874 -5.45 -0.1798 -5.24

home owner 0.1539 17.38 0.1449 16.38

imputed rent 0.0001 3.86 0.0001 3.23

health -0.4629 -124.22 -0.4613 -124.05

invalid -0.3054 -13.52 -0.3019 -13.39

divorced -0.2824 -18.68

separated -0.4506 -18.8




constant 4.8093 440.65 5.8274 176.68 5.1761 86.01 8.6378 25 8.5603 24.83

R2 0.0111 0.0187 0.0599 0.1632 0.1672

Adjusted R2 0.0187 0.0599 0.1631 0.1671

96

Table 3.24: The determinants of Life Satisfaction for Britons in the BHPS; Fixed-effect Regressions – entire sample, N = 153,886


age -0.0097 -9.65 0.0044 1.42 -0.0064 -1.88 -0.0127 -3.86 -0.0082 -2.49

age*age -0.0001 -4.61 -0.0001 -1.75 0.00008 0.27 0.000008 -0.31

income 0.0164 2.64 0.0166 2.7 0.0060 0.98

male (omitted) (omitted) (omitted)

education -0.0029 -0.76 -0.0016 -0.43 -0.0017 -0.47


married 0.1430 8.58 0.1390 8.47 -0.0041 -0.24

employed 0.0618 4.63 0.0355 2.77 0.0365 2.86

unemployed -0.1754 -7.28 -0.1968 -8.35 -0.1958 -8.33

regional income -0.1389 -1.23 -0.1365 -1.22

home owner 0.0340 2.06 0.0340 2.08

imputed rent 0.0001 2.37 0.0001 2.24

health -0.1995 -40.87 -0.1994 -40.9

invalid -0.1337 -3.66 -0.1316 -3.61

divorced -0.2244 -6.73

separated -0.4065 -11.11




constant 5.6756 121.91 5.3853 72.17 5.7226 69.53 7.4496 6.59 7.4777 6.64

Overall R2 0.0083 0.0114 0.0011 0.0374 0.0353

With robust standard errors

97

Table 3.25: The determinants of Life Satisfaction for Britons in the BHPS; Pooled Regression – first time respondents, N = 22,922


age 0.0073 14.61 -0.0211 -7.76 -0.0531 -17.19 -0.0424 -14.13 -0.0312 -9.89

age*age 0.0003 10.63 0.0006 19.24 0.0005 16.88 0.0004 13.35

income 0.1274 10.56 0.0860 7.18 0.0661 5.48

male -0.0512 -2.84 -0.0630 -3.64 -0.0811 -4.63

education -0.0078 -1.93 -0.0239 -6.1 -0.0237 -6.07


married 0.3461 16.38 0.2930 14.28 0.1498 6.1

employed 0.1969 8.42 0.0043 0.19 0.0134 0.58

unemployed -0.3322 -7.45 -0.3968 -9.22 -0.3960 -9.23

regional income -0.3102 -3.14 -0.2944 -2.99

home owner 0.1745 8.25 0.1654 7.84

imputed rent 0.0001 2.33 0.0001 1.74

health -0.4163 -39.93 -0.4142 -39.86

invalid -0.4888 -11.29 -0.4769 -11.05

divorced -0.4021 -9.26

separated -0.6378 -10.07



pregnant -0.0098 -0.04

constant 4.9264 211.71 5.5010 93.5 4.8111 36.65 9.2465 9.35 9.1019 9.24

R2 0.0092 0.0141 0.0495 0.1285 0.1353

Adjusted R2 0.0140 0.0491 0.1279 0.1345

98

Table 3.26: Summary of changes in the GSOEP Age and Age2 coefficients as controls are progressively added Pooled OLS Pooled OLS Fixed Effect (All) (Ages 22 to 80) (All)


Age + Age2

age -0.0217** 15.69 -0.0306** 15.46 -0.0166** 6.60

age*age 0.0002** 11.63 0.0003** 13.89 -0.0003** 9.71

Usual suspects

age -0.0541** 32.80 0.0745** 33.91 -0.00328** 11.61

age*age 0.0005** 29.20 0.0008** 32.30 -0.0001* 2.22


age -0.0600** 36.77 -0.0768** 35.27 -0.0298** 9.95

age*age 0.0006** 34.13 0.0008** 33.73 0.00000 0.74

Kitchen sink

age -0.0454** 25.64 -0.0618** 26.75 -0.0184** 5.81

age*age 0.0005** 25.39 0.0007** 27.03 -0.00002** 2.40

N 176,770 160,332 176,770


99

Table 3.27: Summary of changes in the HILDA Age and Age2 coefficients as controls are progressively added Pooled OLS Pooled OLS Fixed Effect (All) (Ages 22 to 80) (All)


Age + Age2

age -0.0320 ** 19.0 -0.0417** 17.50 -0.00755 1.02

age*age 0.0004 ** 25.8 0.0006** 23.45 -0.00010 1.30

Usual suspects

age -0.0554 ** 29.6 -0.0680** 26.69 -0.01728 * 2.12

age*age 0.0007 ** 34.0 0.0008** 31.09 -0.00001 0.17


age -0.0403 ** 22.7 -0.0496** 20.48 -0.02017 * 2.50

age*age 0.0006 ** 30.3 0.0007** 26.72 0.00007 0.94

Kitchen sink

age -0.0311 ** 16.0 -0.0386** 14.91 -0.00328 0.40

age*age 0.0005 ** 24.0 0.0006** 21.61 -0.00006 0.72

N 72529 65679 72529


100

Table 3.28: Summary of changes in BHPS Age and Age2 coefficients as controls are progressively added Pooled OLS Pooled OLS Fixed Effect (All) (Ages 22 to 80) (All)


Age + Age2

age -0.0221 ** 21.96 -0.0373 ** 26.21 0.00444 1.42

age*age 0.0003 ** 29.00 0.0005 ** 32.70 -0.00015 ** 4.61

Usual suspects age -0.0554 ** 49.28 -0.0772 ** 51.31 -0.00638 + 1.88

age*age 0.0006 ** 55.15 0.0009 ** 58.50 -0.00006 + 1.75


age -0.0438 ** 41.01 -0.0620 ** 43.38 -0.01274 ** 3.86

age*age 0.0005 ** 48.21 0.0007 ** 50.57 0.000009 0.27

Kitchen sink

age -0.0350 ** 31.02 0.0521 ** 34.94 -0.00824 * 2.49

age*age 0.0005 ** 39.43 0.0007 ** 42.99 -0.00001 0.31

N 153886 138481 153886


101

Chapter 3 - Appendix C: Additional information on the robustness analyses in section 3.5.4

(for cross-sectional as well as fixed-effects)

Appendix C contains robustness analyses on the three datasets. Table 3.29 provide

the robustness calculations for the GSOEP, Table 3.31 for the BHPS, and Table 3.33

for the HILDA; column 1 shows the pooled OLS results (with robust standard errors)

that were included in the main text as the ‘Usual suspects + health’ results. Column 2

re-runs that regression with the health variable as categorical rather than continuous.

Column 3 re-runs column 1 with an ordered logit specification rather than a least

squares specification. Column 4 implements the recent conditional fixed-effect logit

model of Baetschmann, Staub, & Winkelmann (2011), whilst column five compares

this with the fixed-effect results. Per the method of Clark (2006), Tables 3.30, 3.32,

& 3.34 show OLS results of the effect of age-bands on life satisfaction for the

GSOEP, BHPS and HILDA.

102

Table 3.29: The determinants of Life Satisfaction for West-Germans in the GSOEP; ‘Usual suspects plus health’ specification, OLS, OLS with categorical health, Ordered Logit, BUC estimator & OLS with fixed effect – entire sample, N = 176,770 OLS

(1) Usual Suspects +

Health

OLS (1)

with Categorical Health

Fixed effects

Ordered Logit BUC estimator2 OLS with fixed effects

Variable: coefficient t-value coefficient t-value coefficient t-value m-effect1 coefficient t-value coefficient t-value age -0.0497 -15.38 -0.0514 -16.02 -0.0646 -18.06 0.0003 -0.0444 -6.94 -0.030 -9.95

age*age 0.0005 16.11 0.0006 16.69 0.0006 16.4 0.0000 0.00004 0.56 0.00002 0.74

income 0.4088 24.67 0.4073 24.69 0.4407 24.66 -0.0022 0.3483 16.34 0.275 23.67

male -0.0855 -4.44 -0.0816 -4.27 -0.0728 -3.42 0.0004 (omitted) (omitted)

education 0.0074 2.07 0.0078 2.19 0.0229 5.8 -0.0001 -0.0036 -0.4 -0.001 -0.19

number of children -0.0601 -6.79 -0.0607 -6.87 -0.0510 -5.34 0.0003 -0.0365 -2.81 -0.023 -3.52

married 0.2997 14.18 0.2951 14.04 0.2837 12.44 -0.0015 0.3141 10.13 0.246 15.42

employed 0.0698 3.42 0.0722 3.54 0.0638 2.85 -0.0003 0.1368 5.21 0.100 6.75

non-participant 0.0385 1.84 0.0468 2.24 0.0183 0.79 -0.0001 0.0385 1.54 0.024 1.67

unemployed -0.8543 -23.44 -0.8427 -23.18 -0.8626 -24.2 0.0066 -0.7480 -19.36 -0.646 -28.61

regional income -0.0001 -9.51 -0.0001 -9.24 -0.0001 -10.7 0.0000 0.0000 -3.72 0.000 -5.31

home owner 0.0999 5.15 0.0972 5.02 0.0873 4.11 -0.0004 0.0129 0.42 0.011 0.71

asset income 0.0000 -1.54 0.0000 -1.59 0.0000 -0.98 0.0000 0.0000 0.62 0.000 0.64

imputed rent 0.0000 8.67 0.0000 8.44 0.0000 9.66 0.0000 0.0000 1.96 0.000 4.26

health -0.7395 -71.83 -0.0274 -25.47 0.0001 -0.0162 -10.25 -0.012 -11.07

invalid -1.0499 0.05 -0.9913 -21.37 -1.1027 -23.36 0.0096 -0.8048 -17.44 -0.719 -29.2

103

OLS(1)

Usual Suspects + Health

OLS (1)


Fixed effects


Variable: coefficient t-value coefficient t-value coefficient t-value m-effect1 coefficient t-value coefficient t-value Categorical health

health==1 (very good) 0.8695 39.14

health==2 (good) 0.2545 18.29

health==3 (satisfactory) -0.3703 -22.68

health==4 (poor) -1.1258 -44.80

health==5 (very poor) -2.5839 -45.67

constant 6.7867 48.48 5.0755 37.21 6.1697 49.82

R2 / Pseudo R2/Overall R2 0.1373 0.1403 0.0182 0.0287 0.302

1 marginal effect of the average person. 2 BUC estimator implemented using ‘feologit’ program in Stata (Baetschmann, Staub et al. 2011). Estimates with clustered standard errors.

104

Table 3.30: The determinants of Life Satisfaction for West-Germans in the GSOEP, Age-bands: (1) without control; (2) with controls per the ‘Usual suspects plus health’ specification; (3) plus fixed effects (3), N = 176,770

(1)

No controls

(2)Controls per

(3) (2) plus

‘Usual suspects plus health’ Fixed Effects


Age 18 to 22 0.5815 ** 10.86 0.2228 ** 4.26 3.8592E-01 + 1.95

Age 23 to 27 0.4742 ** 8.97 0.0314 0.61 3.1578E-01 + 1.65

Age 28 to 32 0.4436 * 8.43 -0.0739 -1.44 3.3470E-01 * 1.82

Age 33 to 37 0.4015 ** 7.63 -0.1923 ** -3.71 3.4949E-01 * 1.98

Age 38 to 42 0.3902 ** 7.39 -0.2602 ** -4.99 3.7309E-01 * 2.2

Age 43 to 47 0.3395 ** 6.4 -0.3652 ** -7.01 3.5606E-01 * 2.19

Age 48 to 52 0.2953 ** 5.55 -0.4120 ** -7.9 3.4070E-01 * 2.18

Age 53 to 57 0.2058 ** 3.84 -0.4479 ** -8.56 3.1915E-01 * 2.13

Age 58 to 62 0.3160 ** 5.87 -0.2097 ** -4.02 5.0512E-01 ** 3.5

Age 63 to 67 0.4864 ** 8.9 0.0049 0.09 6.8212E-01 ** 4.92

Age 68 to 72 0.5088 ** 9.08 0.0859 1.61 7.1373E-01 ** 5.35

Age 73 to 77 0.4581 ** 7.88 0.1359 ** 2.46 6.5856E-01 ** 5.15

Age 78 to 82 0.1885 ** 3.01 0.0263 0.44 3.9369E-01 ** 3.23

Age 83 to 87 (omitted) (omitted) 2.8359E-01 ** 2.64

Age 88 to 93 -0.0425 -0.45 0.1384 + 1.56 (omitted) 0

Year in Panel -0.0227 ** -23.79 -0.0169 ** -15.51 -3.0472E-02 ** -10.42

constant 6.9257 133.75 3.72 40.30 4.6327E+00 21.97

R2/ R2 Between 0.009 0.077 0.07

N 153,886 153,886 153,886


105

Table 3.31: The determinants of Life Satisfaction for British in the BHPS; ‘Usual suspects plus health’ specification, OLS, OLS with categorical health, Ordered Logit, BUC estimator & OLS with fixed effect – entire sample, N = 153,886 OLS


Health

OLS (1)


Fixed effects


Variable: coefficient t-value coefficient t-value coefficient t-value m-effect1 coefficient t-value coefficient t-value

age -0.0438 -21.17 -0.0431 -20.89 -0.0705 -21.11 0.0008 -0.00887 -0.93 -0.0127 -3.86

age*age 0.00052 24.08 0.00051 23.77 0.0009 23.9 -0.00001 -0.00013 -1.35 0.00008 0.27

income 0.0399 5.47 0.0415 5.70 0.0545 4.82 -0.0006 0.0227 1.21 0.0166 2.7


education -0.0199 -7.80 -0.0195 -7.65 -0.0353 -8.99 0.0004 0.0147 1.19 -0.0016 -0.43


married 0.3063 21.98 0.3036 21.84 0.4685 21.6 -0.0052 0.3387 6.79 0.139 8.47

employed 0.0295 2.02 0.0190 1.31 -0.0317 -1.41 0.0003 -0.0109 -0.28 0.0355 2.77

unemployed -0.3430 -11.85 -0.3542 -12.27 -0.5273 -12.52 0.0073 -0.4871 -7.49 -0.1968 -8.35

regional income -0.1659 -2.59 -0.1723 -2.69 -0.3035 -3.08 0.0033 -0.3949 -1.16 -0.1389 -1.23

home owner 0.1521 9.52 0.0001 3.74 0.2070 8.29 -0.0023 0.0803 1.61 0.034 2.06

imputed rent 0.0001 3.92 0.0001 3.74 0.0001 3.41 0.0000 0.0001 1.63 0.0001 2.37

health -0.4653 -75.05 -0.7068 -76.63 0.0076 -0.4507 -32.34 -0.1995 -40.87

invalid -0.3415 -8.51 -0.0431 -20.89 -0.5088 -8.52 0.0070 -0.1909 -2.3 -0.1337 -3.66

106

OLS


Health

OLS (1)


Fixed effects




health==2 (good) 1.0413 48.52

health==3 (satisfactory) 0.5586 26.46

health==4 (poor) (omitted)

health==5 (very poor) -0.5990 -13.80

constant 8.2857 12.9 6.3989 9.98 7.4496 6.59


1 marginal effect of the average person. 2 BUC estimator implemented using ‘feologit’ program in Stata (Baetschmann, Staub et al. 2011) Estimates with clustered standard errors.

107

Table 3.32: The determinants of Life Satisfaction for British in the BHPS; Age-bands: (1) without controls; (2) with controls per the ‘Usual suspects plus health’ specification; (3) plus fixed effects, N = 153,886

(1) No controls

(2) (3) Controls per the (2) plus

‘Usual suspects plus health’ Fixed EffectsSpecification coefficient t-value coefficient t-value coefficient t-value

Age 18 to 22 -0.0641 -1.26 -0.4913 ** -16.29 0.2373 * 2.11

Age 23 to 27 -0.1115** -2.2 -0.6036 ** -19.83 0.1817 + 1.72

Age 28 to 32 -0.0867* -1.72 -0.6543 ** -21.54 0.1776 + 1.77

Age 33 to 37 -0.1775** -3.52 -0.7518 ** -24.53 0.1373 1.45

Age 38 to 42 -0.2463** -4.87 -0.8222 ** -26.76 0.1072 1.21

Age 43 to 47 -0.2801** -5.52 -0.8597 ** -28.05 0.0867 1.05

Age 48 to 52 -0.2260** -4.45 -0.8091 ** -26.41 0.0994 1.3

Age 53 to 57 -0.1336** -2.62 -0.6647 ** -21.69 0.1558 * 2.2

Age 58 to 62 0.0678 1.32 -0.4291 ** -14.1 0.2951 ** 4.55

Age 63 to 67 0.2037** 3.95 -0.2387 ** -7.79 0.3553 ** 5.97

Age 68 to 72 0.3429** 6.63 -0.0407 -1.33 0.4109 ** 7.67

Age 73 to 77 0.3058** 5.87 0.0163 0.52 0.3271 ** 6.77

Age 78 to 82 0.2209** 4.12 0.0021 0.06 0.2124** 5.43

Age 83 to 87 0.1181** 2.06 (omitted) omitted

Year in Panel -0.0060** -5.62 -0.0177 ** -16.91 -0.0139 ** -6.34

constant 5.3192 106.67 7.81 25.25 6.7974 5.91

R2/ R2 Between 0.021 0.16 0.189

N 153,886 153,886 153,886


108

Table 3.33: The determinants of Life Satisfaction for Australians in the HILDA; ‘Usual suspects plus health’ specification, OLS, OLS with categorical health, Ordered Logit, BUC estimator & OLS with fixed effect – entire sample, N = 72,529 OLS


Health

OLS (1)

with categorical Health

Fixed effects


Variable: coefficient t-value coefficient t-value coefficient t-value m-effect1 coefficient t-value coefficient t-value

age -0.0403 -13.19 -0.0397 -13.10 -0.0542 -12.69 0.000042 -0.03198 -1.88 -0.0202 -2.5

age*age 0.0006 17.22 0.000546 17.07 0.00077 16.82 -0.000001 0.00006 0.34 0.00007 0.94

income 0.0329 4.77 0.0321 4.65 0.0413 4.29 -0.000032 0.03720 3.19 0.0191 3.22


education -0.0582 -11.84 -0.0565 -11.50 -0.0893 -13.26 0.000069 -0.04512 -1.64 -0.0198 -1.71


married 0.3624 17.79 0.3606 17.77 0.4784 17.13 -0.000381 0.36791 6.23 0.1963 6.52

employed -0.1338 -5.91 -0.1589 -7.08 -0.2888 -9.27 0.000214 0.01390 0.31 0.0238 1.06

unemployed -0.4208 -8.15 -0.4467 -8.66 -0.5628 -8.68 0.000574 -0.25771 -3.61 -0.1422 -3.43

regional income 0.0000 4.49 0.0000 4.74 0.0000 3.51 0.000000 0.00006 3.04 0.0000 3.3

home owner 0.1397 6.59 0.1361 6.42 0.1826 6.48 -0.000148 0.13279 2.93 0.0687 3.01

imputed rent -0.0001 -0.29 0.0000 -0.14 0.0000 -0.16 0.000000 -0.00024 -0.82 -0.0001 -0.79

health -0.5278 -54.71 -0.7339 -56.08 0.000566 -0.53700 -28.33 -0.2694 -28.16

invalid -0.1073 -5.70 -0.0749 -4.03 -0.1027 -4.09 0.000081 -0.07743 -2.52 -0.0492 -3.29

109

OLS(1)

Usual Suspects + Health

OLS (1)

with categorical Health

Fixed effects




health==2 (good) 1.9884 29.08

health==3 (satisfactory) 1.5218 22.36

health==4 (poor) 0.9288 13.68

health==5 (very poor) (omitted)

constant 10.0957 95.55 7.0492 58.69 9.2482 44.12


1 marginal effect of the average person. 2 BUC estimator implemented using ‘feologit’ in Stata (Baetschmann, Staub et al. 2011) Estimates with clustered standard errors.

110

Table 3.34: The determinants of Life Satisfaction for Australians in the HILDA, Age-bands: (1) without controls; (2) with controls per the ‘Usual suspects plus health’ specification; (3) plus fixed effects, N = 75,729

(1)

No controls

(2) (3) Controls per (2) plus

‘Usual suspects plus health’ Fixed EffectsSpecification coefficient t-value coefficient t-value coefficient t-value

Age 18 to 22 -0.4468** -7.37 -0.8740 ** -14.94 0.2433 0.96

Age 23 to 27 -0.5804** -9.54 -0.9962 ** -16.89 0.2021 0.82

Age 28 to 32 -0.5715** -9.41 -1.0528 ** -17.96 0.1529 0.64

Age 33 to 37 -0.5648** -9.36 -1.0616 ** -18.15 0.1466 0.62

Age 38 to 42 -0.7049** -11.66 -1.1689 ** -19.84 0.0979 0.43

Age 43 to 47 -0.6845** -11.33 -1.1116 ** -18.86 0.1748 0.78

Age 48 to 52 -0.6213** -10.21 -1.0253 ** -17.4 0.2441 1.12

Age 53 to 57 -0.4268** -6.96 -0.8304 ** -14.21 0.3548+ 1.67

Age 58 to 62 -0.2739** -4.43 -0.6389 ** -10.98 0.4728 * 2.29

Age 63 to 67 -0.0929 -1.49 -0.4481 ** -7.72 0.5795** 2.89

Age 68 to 72 0.0825 1.31 -0.2382 ** -4.07 0.5409** 2.77

Age 73 to 77 0.0952 1.48 -0.1568 ** -2.64 0.4889** 2.58

Age 78 to 82 0.0789 1.17 -0.0383 -0.61 0.4259 * 2.35

Age 83 to 87 (omitted) (omitted) 0.2647 1.58

Age 88 to 93 -0.0239 -0.2 0.0616 0.55 (omitted)

Year in Panel -0.0044* -1.54 -0.0128 ** -4.83 -0.0185** -4.83

constant 8.3780 141.57 10.4078 118.98 8.2548 30.7

R2/ R2 Between 0.029 0.169 0.182

N 75,729 75,729 75,729


111

Chapter Four

Unhappy Young Australians

4.1 Introduction

In Chapter 3, we saw a decline in the happiness of young Germans (Figure 3.4) and

Australians (Figure 3.6) that was much steeper than the decline in older individuals

whose happiness we could expect to decline due to falling income, failing health or

the imminent onset of death. This exploratory study asks why the happiness of young

Australians exhibits such a steep decline.

Such a question is study-worthy because there is evidence from other scientific

disciplines that the happiness of individuals in their childhood can affect the

happiness of those same individuals in adulthood. For example, Cheng & Furnham

(2004) showed that maternal care during early childhood was directly correlated with

adult happiness, and, Flouri's (2004) study of British children found that maternal

care at age seven predicted higher life satisfaction in 42 year-old males, and,

closeness to your mother at age 16 predicted higher life satisfaction at age 42 in both

men and women. Trzcinski & Holst (2007) examined the level of subjective well-

being among young people in transition to adulthood, and like Flouri (2004), they

found that the quality of parental-adolescent relationships was a predictor of the

happiness of young people in transition to adulthood. There is some evidence that

happiness in childhood should be maximised because it maximises our happiness as

adults. For an economist, it would follow that a policy of keeping families together

and thereby increasing child and adult wellbeing is beneficial to the overall

wellbeing of society. However, childhood happiness is little studied by economics of

happiness researchers.

In this chapter, I seek to extend our view of lifetime happiness to an age cohort little

visited by economists, 9 to 14 year old children. The chapter proceeds with a review

of childhood happiness studies in the economics literature. After revealing four

important factors that affect childhood happiness and conflicting results in the

economics of happiness literature, I explain how a survey was developed to collect

112

data from the 9 to 14 year old Australians. After explaining how the data were

collected, a model of childhood happiness and used to examine the data from the 9 to

14 year olds. The results are interpreted and conclusion offered that explain the

relationship between the life satisfaction domains of children and childhood

happiness. Finally, the 15 to 23 year-old cohort from the HILDA socio-economic

panel is examined with a model of individual happiness. First, let us review

childhood happiness studies from the economics literature

4.2 A Review of Childhood Happiness from the Economics Literature

Childhood happiness studies appear infrequently in the economics literature.

Children have usually been considered in the context of the negative (Stutzer & Frey,

2006; White, 2006) or positive (Tsang, 2003) effect they have on adult happiness.

Studies of the German population considered the relationship between parents and

their adult children's subjective well-being (Bruhin & Winkelmann, 2009), but they

too were adult-centric studies. Tables 4.35a & b provide a summary of peer-reviewed

‘EconLit’ literature that examined adolescent or childhood happiness. Of the twelve

studies listed, five examined childhood happiness and seven considered the

happiness of adolescents.

Reviewing studies listed in Tables 4.35a & b, over the past twelve years there have

been happiness studies of Australian, European, Italian, Scottish, British, and, United

States’ adolescents. Ebner's (2008) longitudinal study used the European

Community Household Panel (ECHP) data to reveal that adolescents are happier

when they make the decision to leave the family home. A study by Dockery (2005)

used data from the (1997 -2004) Longitudinal Surveys of Australian Youth and wave

1 of the HILDA and found evidence of declining levels of happiness in adolescents

during periods of unemployment and the importance of the quality and type of work

to the happiness of adolescents. Ulker (2008) also used the Australian HILDA data to

reveal the importance of the role of family members in the well-being of Australian

adolescents; e.g. adolescents are unhappy when their parents’ divorce. Bassi & Delle

Fave's (2004) longitudinal study used an experience sampling method (aka, daily

113

recording method) to look at how day-to-day activities affected adolescent happiness

and revealed the importance of providing 15 to 18 year-old Italian high school

students with meaningful leisure-orientated activities like the access to new

technologies, social networks and the Internet for study from home. Cheng &

Furnham (2004) also studied high school children and revealed the importance of

school performance to the happiness of British schoolchildren aged 16 to 19 years.

All these studies focussed on young adolescents, those aged 15 years and older.

A few studies have focussed on childhood happiness. Fogle, Scott Huebner, &

Laughlin's (2002) cross-sectional study revealed the positive interrelationships

between the personality trait of extraversion, social self-efficacy and social

competence, to the life satisfaction of children aged 10 to 15 years from public

schools in mid-sized South-eastern United States cities. A positive attitude,

confidence in own abilities and the skills to interact with your peer group were found

to be important to childhood happiness. Also focussing on school children, Huebner,

Valois, Paxton, & Drane's (2005) cross-sectional study of public middle school

students (from South Carolina, U.S.A) found that family, friends, school and the

environment in which children live and learn are important to childhood happiness.

Lee & Oguzoglu's (2007) longitudinal study of Australian youths ventured outside

the school environment and found that income support payments contributed to

childhood happiness. Flouri’s (2004) study focussed on the importance of family to

childhood happiness. Using the GHQ-12 measure of mental and physical wellbeing,

Flouri found that children who were insulated from psychological stress within the

family domain were happier and those (7 year-old) children who were more involved

with their mother were happier (as a 42-year-old) in adulthood. These studies mirror

many of the findings from the studies of adult happiness. Individuals with a job,

sufficient income, and stable relationships tend to be happier. What is somewhat

different to adult happiness is the importance of family, school, interaction with

school friends, and the environment to childhood happiness; these are the factors that

are considered in this chapter.

114

Table 4.35a & b: Summary of happiness studies of the young from the economics literature (EconLit) identifying the study population as adolescents or children

Author Data source Sample Ado

lesc

ent

Chi

ldre

n

Research Question

(Ebner, 2008) European Community

Household Panel (ECHP) years 1995 and 1999 Influential determinants of young adults' housing decisions on the

happiness of adolescents.

(Ulker, 2008) Household, Income and Labour Dynamics in Australia Survey

Individuals aged 15 to 24 years. N = 6,013

Factors that influence young Australians’ mental health and life satisfaction, with an emphasis upon the role of family background.

(Lee & Oguzoglu, 2007)

Longitudinal Surveys of Australian Youth. (1997 -2004)

5,865 Australian youths with a median age of 14. N = 26,146

How the receipt of income support payments affects the well-being of youths.

(Dockery, 2005) Longitudinal Surveys of Australian Youth.

8,567 individuals aged 16 to 19 years. N = 30,406

Effect of education, labour market experience, and, employment on the happiness of young Australians.

(Huebner, Valois, Paxton, & Drane, 2005)

Public middle school students in South Carolina, U.S.A.

School children N = 2278

Levels and demographic effects on their satisfaction with their overall lives as well as five specific domains (family, friends, self, school, and living environment) were assessed.

(Bassi & Delle Fave, 2004)

Italian high school students analysed in 1986 and 2000.

Participants aged between 15 to 18 years. N = 120

Importance of providing adolescents with meaningful activities in order to foster their personal growth and well-being.

(Cheng & Furnham, 2004)

Senior pupils from three schools in the United Kingdom.

Adolescents aged 16 to 19 years. N = 90

Relationship between school performance and self-rated happiness.

115

Table 4.35a & b (continued): Summary of childhood happiness studies from the economics literature (EconLit)

Author Data Sample Ado

lesc

ents

Chi

ldre

n

Research Question (Duncan & Grazzani-Gavazzi, 2004)

Scottish and Italian young adults who completed daily event diaries.

1043 positive incidents collected from 157 students aged 18-32 years, N = 1043

Cross-cultural study on positive emotion, well-being and happiness.

(Flouri, 2004) British National Child Development Study

Longitudinal study of British children; aged 7-42 years N = 17,000

Role of parenting in later-life subjective well-being.

(Csikszentmihalyi & Hunter, 2003)

Multi-year study of American youth from the Alfred P. Sloan Study of Youth and Social Development

6th, 8th, 10th and 12th grade primary school students from 33 United States elementary & secondary schools from 12 communities; ages 12 to 18 years, N = 826

Proximal environmental factors, behaviours and habits that correlate with personal happiness.

(Fogle, Scott Huebner, & Laughlin, 2002)

Middle school students from public schools in mid-sized South-eastern United States cities

Children aged 10 to 15 years. N = 160

Interrelationships among temperament, social self-efficacy, social competence, and life satisfaction.

(Blanchflower & Oswald, 1998)

Eurobarometer Surveys Adolescent females & males aged 15 years and older. N = 28,000

Rising life-satisfaction of the young between 1970 and 1990.

116

While many of the above-noted results mirror the findings from the study of adult

happiness, results from two studies do not. Blanchflower & Oswald's (1998) study of

European adolescents found that the happiness of those aged 15 years and older

increased in the period 1970 to 1990. Dockery (2005) also found evidence in the

Longitudinal Surveys of Australian Youth data that adolescent happiness increased

in age. These findings are consistent with Easterlin (2006) who stated that ‘United

States happiness rises slightly, on average, from ages 18 to midlife’. However, such

an increase conflicts with the Chapter 3 findings of a 6% decrease in the average

happiness of young Germans, the 7% decrease in the happiness of 15 to 23 year old

Australians, and, the 2.9% decrease in the happiness of 18 to 23 year-old Britons.

This chapter will not only consider the effects of family, school, interaction with

school friends and the environment on childhood happiness on Australian children,

the study will also seek to resolve whether the happiness of young Australians (aged

9 to 23 years) increases, decreases or is stable in age. Because the HILDA panel

data set only has data on individuals 15 years and older, data needs to be collected

from Australian children. First, an explanation of the HILDA data set used in this

study.

4.3 The Data Sets

In seeking to explain the effects of family, school, interaction with school friends and

the environment on childhood happiness, and whether the happiness of the young is

increasing, decreasing or stable in age, I use two data sets. Data was collected data

from 9 to 14 year-old children and our view of lifetime happiness is extended beyond

the 18 to 93 year-olds we examined in the previous chapter by adding the data for 15

to 18 year olds from the ‘Household, Income and Labour Dynamics in Australia’

Survey (HILDA).

117

4.3.1 The Australian (HILDA) data

The Household Income & Labour Dynamics in Australia panel data (HILDA) arises

from a household–based survey that began in 2001 (HILDA, 2008b). The annual

HILDA survey collects information about economic and subjective wellbeing, labour

market dynamics and family dynamics and special questionnaire modules are

included each wave, including life satisfaction in waves 1 to 8. To extend our view of

lifetime happiness, an unbalanced panel of 15 to 23 year-old Australians was

extracted from waves 2 to 8 of the HILDA50. The number of observations by age and

wave are in Table 4.36 and sample means and standard deviations are in Table 4.37.

Table 4.36: Observations by age & year for the 15 to 23 year-old sample from HILDA waves 2-8 Year of WaveAge 2002 2003 2004 2005 2006 2007 2008 Total

15 231 217 221 225 257 204 224 1579

16 218 223 203 223 219 259 191 1536

17 216 200 205 204 222 199 242 1488

18 201 206 180 203 199 221 201 1411

19 180 193 193 184 194 184 202 1330

20 165 175 178 201 174 192 188 1273

21 178 171 163 174 214 177 181 1258

22 169 183 158 175 189 193 175 1242

23 146 164 166 171 172 194 200 1213

Total: 1704 1732 1667 1760 1840 1823 1804 12330

50 Wave 1 was excluded because it does not include life event variables.

118

Table 4.37: Sample averages for the 15 to 23 year-old cohort and the entire HILDA sample

HILDA (entire sample

15 to 93 year-olds)

HILDA (15 to 23 year-olds)

Mean s. d. Mean s. d.

overall life satisfaction 7.94 1.47 8.07 1.38

age 43.74 17.84 18.76 2.59

age*age 2231.29 1690.90 358.69 98.13

ln (annual household income) 5.26 3.16 10.85 1.22

male (1=yes) 0.47 0.50 0.48 0.50



married (1=yes) 0.51 0.50 0.03 0.17

employed (1=yes) 0.65 0.48 0.66 0.47

unemployed (1=yes) 0.03 0.18 0.09 0.29

average regional income 44 1126.85 1117.62 1393.19 1158.13


imputed rent 51 4.82 39.24 3.80 32.75

Self-reported health 52 2.61 0.96 2.25 0.88

invalid (1=yes) 0.23 0.42 0.10 0.30

household member died (1=yes) 0.11 0.31 0.10 0.30

divorced (1=yes) 0.09 0.28 0.001 0.02


partner dead (1=yes) 0.05 0.22 0.00 0.00

just married (1=yes) 0.03 0.16 0.02 0.13

just divorced (1=yes) 0.01 0.08 0.00 0.03




pregnant53 (1=yes) 0.05 0.22 0.04 0.20


N = 77,132 12,330


51 These variables are in $AUD. 52 Health is reverse coded: 1 = excellent to 5 = poor. 53 Self or partner.

119

Interpreting descriptive statistics from Table 4.37, the average happiness for 15 to 23

year-olds is 2% higher than the average happiness for the 15 to 93 year-olds in the

entire HILDA sample; the happiness of the young initially appears to decline as they

age. The average income of households with 15 to 23 year old children is higher than

for the entire HILDA sample. However, with self-reported pregnancy peaking at age

29 (Figure 4.32), Australian parents with 15-year-old children would be, on average,

44 years of age. Age 44 is when average annual household income peaks. With 15 to

23 year old children reliant on their parent’s income, the happiness of these children

could decline due to fewer resources being available to satisfy the children’s want

and needs. In addition, with the average number of children per household increasing

from 0.011 for households with a 15 year old to 0.19 for households with a 23 year

old, the happiness of those 15 to 23 years olds could be expected to decline. The

increase in the number of children per household could translate to fewer resources

per dependent child.

9

9.5

10

10.5

11

11.5

0

0.05

0.1

0.15

0.2

0.25

Annual Household Income and self-reported Pregnancy by Age: Australia

Self-reported pregnancy (left axis) ln Annual Household Income (right axis) Figure 4.32: Log of average Annual Household Income and proportion of self-reported

pregnancy (self or partner) for Australians aged 15 to 93 years, 2002-2008 HILDA, N = 77,132

120

While there is the possibility of decreasing happiness in 15 to 23 year-olds due to

household incomes supporting an increasing number individuals per household,

fifteen to twenty-three year-olds self-report as healthier, have a lower incidence of

disability (invalid) and, as expected, have a lower incidence of relationship

breakdown (separation or divorce). The positive effect from these variables would

predict an increase in the average happiness of 15 to 23 year-olds. We appear to

have conflicting evidence from the descriptive statistics. Fifteen to 23 year olds could

be expected to be less happy over time if they are from a household with a

decreasing income that needs to support a greater number of children/young adults.

Evidence from descriptive statistics for health, disability and the stability of personal

relationships indicates the opposite that happiness of the young may increase in age.

Figure 4.33 visually-clarifies whether the average happiness of young Australians

increases or decreases in age by expanding our Chapter 3 view of lifetime happiness

of 18 to 93 years old Australians with that of 15 to 17 year olds from the HILDA.

6

6.5

7

7.5

8

8.5

9

9.5

Life

Sat

isfa

citio

n

AGE

Raw Average Life Satisfaction

Figure 4.33: Life Satisfaction of 15 to 93 year-old Australians (2002-2008 HILDA panel data)

Looking back the three extra years to age 15, we can see that, on average, the

happiness of 15 to 23 year-old Australians does decrease in age, just as we saw for

the German and British populations in Chapter 3. The decrease in the happiness of 15

to 18 year-old Australians is large, 4.7%. Added to the 2.5% decline for young adults

aged 18 and 23 years, this steep 7.2% drop in the happiness of young Australians is

121

much larger than the decrease in happiness we saw in 18 to 23 year-old Germans or

the 2.9% decrease we saw in the 18 to 23 year-old British. This large 7.2% decline

in the happiness of young Australians is twice the size of the 3.6% happiness decline

we see in 75 to 86 year old Australians who we expect to have declining happiness

due to their falling incomes, failing health and the onset of death. This leaves us

wondering, why is there such a large decline in the happiness of young Australians,

when does this steep drop in happiness begin, and, why does it occur? To answer

these questions, we need to supplement the data for 15 to 93 year olds in the

HILDA54 by collecting happiness data from Australian children.

4.3.2 Collecting happiness data from Australian children

Collecting data from children is fraught with ethical, logistical and truthful self-

reporting roadblocks. Including data collecting procedures into a child’s normal

teaching program can help to overcome these impediments, particularly if the

teaching program is themed to encourage the children to complete all steps in the

data gathering process (Gilman & Huebner, 1997; 2000; Haranin, Huebner, & Suldo,

2007). This is how perceived data collecting impediments were surmounted.

The data collecting impediments were surmounted by taking advantage of an event

in the children’s normal teaching program, the ‘Smart Train’. On a biennial basis the

Queensland University of Technology (QUT) in conjunction with the Queensland

State Government sponsor a ‘Smart Train’ with four carriages containing teaching

and learning displays themed on innovation & technology. The 2008 ‘Smart Train’

was the fifth to travel throughout Queensland in the last decade and is one of the

State’s largest community outreach programs, with over 90,000 visitors to date. The

train travels 10,000 kilometres throughout the state of Queensland over five weeks

stopping at 24 regional and rural centres and ends its journey with a one-week stop in

Brisbane, the state capital. As part of the children’s normal teaching program,

54 While the HILDA extended our view of lifetime happiness to 15 to 23 year-olds, it does not include data for young children. Young children are not usually included in large socioeconomic panel data collection, probably because young children have difficulty, answering the survey questions either because the questions are not relevant to children or the language too complex for the children to understand (Gilman & Huebner, 1997). For example, the SF-36 health questionnaire included in the HILDA survey is only suitable for those aged 15 years and over (Ware, 2009).

122

schools organize class groups to visit the train at their local railway station. The

children participate in exciting, interactive and informative teaching and learning

displays55 one of those was our ‘Happiness’ display.

The ‘Happiness’ teaching and learning display explained what makes individuals

happy, the importance of income, health and child-related factors such as school,

family, friends and the environment. As an extension to the ‘Happiness’ teaching and

learning display on the “Smart Train”, we teamed with the Queensland

Government56 to anonymously collect the data from the children who visited the

‘Smart Train’. To ensure the ‘Smart Train’ data came from the target population,

each child visiting the ‘Smart Train’ received a ‘Happiness Postcard’ (Figures 34a &

34b) inviting them to participate in a ‘Happiness Research’ project that included

responses to an online ‘Happiness Survey’ survey. To maximize response rates and

to overcome technical impediments to the children responding, all the data collecting

steps the children had to participate in were ‘Happiness’ themed and we integrated

the ‘Happiness Survey’ response process into the children’s normal teaching and

learning program. We initially raised awareness of our ‘Happiness Research’ project

by giving teachers ‘Happy’ promotion posters to hang on classroom walls. Next, to

help with guiding the children through the survey response process, we provided

teachers with ‘Happy Teaching Guides’ that included instructions on how the

children could use the computers in their classroom or school library to respond to

the online ‘Happiness Survey’.

To complete the online ‘Happiness Survey’ the children needed the ‘Happiness

Postcard’ given to them during their ‘Smart Train’ visit. The postcard directed them

to the ‘Smart Train’ website57 where they gained entry to the online survey by

clicking on the ‘How happy are you?’ visual link, the same graphic we used for the

front of the ‘Happiness Postcard’ (Figure 4.34a). 55 The 22 research displays on the 2008 “Smart Train” came from the QUT Faculties of Business, Built Environment and Engineering, Creative Industries, Education, Health, Humanities and Human Services, Information Technology, Law and Sciences; see http://www.train.qut.edu.au/ for details of what was in each display. 56 Our thanks go to: Markus Schaffner, QUT post graduate student; Leesa Watkin, QUT Smart Train Project Manager, and; Annie Harris, Senior Project Officer, Science Partnerships and Engagement, Department of Tourism, Regional Development and Industry, Queensland State Government for their considerable financial and technical assistance in making this research project a success. 57 More information on the ‘Smart Train’ can be found on the website: http://www.train.qut.edu.au/

123

Figure 4.34a: Front side of the ‘Happiness Postcard’ themed with the same graphics as the

‘Happiness’ promotional poster & the icon clicked to initiate the web-based ‘Happiness Survey’

Figure 4.34b: The obverse of the ‘Happiness Postcard’ containing instructions on how to respond to the online survey

The obverse side of the ‘Happiness Postcard’ provided instructions on how to

respond to the online ‘Happiness Survey’ (Figure 4.34b). After completing the

survey questions, the children received a unique survey-system-generated ‘Happy

Number’ to write on the back of their ‘Happiness Postcard’ (Figure 4.34b). The child

was next instructed to mail their ‘Happiness Postcard’ to the Queensland

124

Government, Department of Tourism, Regional Development and Industry58. I used

the ‘Happy Number’, the teacher’s name, the school, the demographic information

from the survey response and the IP address of the responding personal computer, to

authenticate respondents and exclude multiple survey responses from the same child.

Post authentication, we had responses from 217 female and 172 male children, from

49 postcode locations, who visited the ‘Smart Train’ at one of fifteen regional or one

metropolitan Queensland railway station. With the ‘Smart Train’ data collection

process explained, let us look at how the online ‘Happiness Survey’ was developed.

4.3.2.1 The online ‘Happiness Survey’

The online ‘Happiness Survey’59 was developed because existing survey instruments

were considered inappropriate for the 9 to 14 year-old children in our target

population. As discussed earlier, asking children to respond to happiness surveys

occurs infrequently in economics and the survey instruments that economists use are

targeted at individuals 15 years and older. However, children are regularly surveyed

by other scientific disciplines. In the discipline of school psychology Gilman &

Huebner (2003) provide a meta-analysis of life satisfaction research with children

and adolescents. Their summary identifies the happiness-affecting variables

addressed by existing research and lists variables and factors that should be included

in surveys that collect data for the study of childhood happiness. Their list includes

the socio-economic variables that economists usually incorporate into their models of

individual happiness (age, gender, parent’s income, health) and other variables

foreign to the economic approach to happiness research (for example, psychological

constructs such as temperament, mood, emotional disturbance). However, Gilman &

Huebner (2003) do suggest a happiness research approach that is not foreign to

economics, the inclusion of life domains.

58 After the three-week survey response period, the Department randomly drew a ‘Happy Postcard’ for the student and school prizes. The Queensland Government Department presented the lucky student with an Apple iPod and their school received $1000 to spend on science resources. 59 The online “Happiness Survey” screens are in Figures 35a to 35l in Chapter 4 Appendix A.

125

A small number of economics researchers have applied the life-satisfaction domain

approach to happiness research. Early leaders were Headey, Veenhoven, & Wearing

(1991). They examined whether domain satisfactions, social support, life events, and

levels of expectation and aspiration were causes or consequences of subjective well-

being. Frijters and van Praag examined the effect of domains such as health, financial

situation, job, leisure, housing, the environment (Frijters, 1999c; van Praag, Frijters,

& Ferrer-i-Carbonel, 2003). They concluded by stating ‘If we are happy in the

individual domains of our life, then overall we are happy’. Lu & Hu (2005) looked at

the (positive) effect of the leisure domain on individual happiness. More recently

eminent economist Richard Easterlin (2006) has been encouraging the economics

discipline to expand its life satisfaction considerations to include ‘a bottom up model

in which happiness is the net outcome of both objective and subjective factors in

various life domains’. To date, few economics researchers have responded. Trzcinski

& Holst (2007) did find ‘quality of parental-adolescent relationships (family life

domain) as a predictor of adolescent well-being’. However, as was evident from the

earlier summary of childhood happiness studies in Tables 4.28a & 4.28b, there is a

paucity of economics research considering the effect of life-satisfaction domains on

childhood happiness.

It is surprising that there is so little economics research into the life-satisfaction

domains affecting childhood happiness, particularly given the evidence from other

scientific disciplines that life satisfaction in childhood can affect individual life

satisfaction in adulthood. In remediation of the paucity of economics research

considering the effect of life-satisfaction domains on childhood happiness, I chose to

pursue Gilman & Huebner’s (2003) research recommendations and incorporate

childhood happiness domains into this study of lifetime happiness.

Gilman & Huebner (2003) recommended four life-satisfaction domains be included

in future research into childhood happiness. The four life-satisfaction domains were;

the ‘family life’ domain; the ‘school environment’ domain, the ‘living (natural)

environment’ domain and the leisure or ‘interaction with friends’ domain. For

ethical reasons, it was decided not to question the children on their ‘family life’

domain. Questioning young children on the marital status of their parents or whether

they live with both mum and dad was considered outside our university’s ethical

126

standards of conduct for economics researchers. While we chose to not to question

the children on their ‘family life’ domain, we did question them on the other

childhood life-satisfaction domains recommended by Gilman & Huebner (2003):

‘school environment’; ‘natural environment’, and; ‘interaction with friends’.

Having selected three life-satisfaction domain factors, I needed to develop survey

questions to tap them. The initial approach was to choose questions from reliable

instruments used in past studies; none could be found. With no reliable instruments

available, I developed questions to add to existing questions that were considered

suitable for children. The first question that was considered suitable for children was

the life satisfaction question used in the socioeconomic panel surveys (GSOEP,

HILDA). This single ‘Global Life Satisfaction Scale’ question (Fordyce, 1988) asks:

All things considered, how satisfied are you with your life?

Scaled 0-10, the happiness question seeks to measure the aggregate utility, or overall

wellbeing, arising from all the good and bad things that occur throughout our lives.

There has been much discussion in the economics literature concerning measurement

error arising from the use of this single ‘Global Life Satisfaction’ question. There

have been suggestions that responses to the happiness question are subject to bias

arising from the positive and negative effects from recent events60. For example, a

generally happy individual could self-report as unhappy at a specific time because

they missed their usual bus, were late to work, and, in forfeit, lost an hour of pay.

Others assert that economists should measure happiness by choosing one of the many

scales used by psychologists.

Economists do have a choice of scales to measure life satisfaction (what

psychologists separate into subjective wellbeing and psychological wellbeing).

Happiness scale choices include: the ‘Satisfaction With Life Scale’ (SWLS) (Diener,

et al., 1985); Life Satisfaction Index (LSI- A) (Neugarten, Havighurst, & Tobin,

60 The past president of the American Psychological Association, Dr Martin Seligman (2011) states, “Life satisfaction essentially measures cheerful moods, so it is not entitled to a central place in any theory that aims to be more than a happiology .... By that standard ... a government could improve its numbers just by handing out the kind of euphoriant drugs that Aldous Huxley described in “Brave New World”. While Dr Seligman’s comments may be considered extreme, but they do amplify the concern about happiness and its measurement.

127

1961); Temporal Satisfaction with Life Scale (TSWLS) (Pavot, Diener, & Suh,

1998); Watson, Clark, & Tellegen's (1988) Positive Affect/Negative Affect Scale

(PANAS); Delighted/Terrible Scale (Andrews & Withey, 1976), and; the

‘Experience/Day Reconstruction Method’ (DRM) which measures affective

experience in daily life (Csikszentmihalyi & Larson, 1987; Csikszentmihalyi &

Hunter, 2003). Michael Fordyce offers guidance in choosing a happiness scale. In his (1988) paper, Michael Fordyce reviewed 18 years of the use of the ‘Global life

Satisfaction Scale’ (Fordyce, 1973) and other measures of life satisfaction. Fordyce

asserted ‘the validity of the ‘Global Life Satisfaction Scale’ as a measure of

emotional well-being and global health’. Fordyce supported his claim by citing the

collection of evidence from three independent research teams who examined the

accumulated findings from dozens of studies that compared happiness measures.

Generally, the independent research teams were of the opinion that Fordyce’s

‘Global Life Satisfaction Scale’ instrument showed good reliability and stability,

with a good record of convergent, construct, and discriminative validity. The

independent research teams found a high convergence between Fordyce’s ‘Global

Life Satisfaction Scale’ and other happiness measures. In summary, Fordyce

supported the continued use of his ‘Global Life Satisfaction Scale’ with this

statement: ‘it has construct validity, the ability to discriminate between known happy

and unhappy groups, and it has association with widely accepted characteristics of

mental health. Accumulated findings on this instrument are believed to show good

reliability and stability, and a record of convergent, construct, and discriminative

validity’. The ‘Global Life Satisfaction Scale’ is suggested to be a potential

touchstone of measurement consistency in a field that generally lacks it.’

Even if we recognise that Michael Fordyce may be somewhat biased towards

recommending the use of his own happiness measure, he does cite considerable

independent evidence to support its continued use. As to how economists first came

to use Fordyce’s ‘Global life Satisfaction Scale, I found no firm evidence in the

economics literature. Nor could I find any justification for why the question was

selected for inclusion in socio-economic panel survey questionnaires. Whatever the

reason for originally choosing the question, and in the absence of further research

that justifies a question that is more acceptable to the economics discipline, there

appears to no evidence to compel economists to change from measuring global

128

happiness with Fordyce’s single ‘Global Life Satisfaction Scale’ question. However,

economists have been asking adults to answer the ‘Global Life Satisfaction Scale’

question; is it suitable for children?

The Fordyce’s single ‘Global Life Satisfaction Scale’ question is considered suitable

for our 9 to 14 year-old respondents. In their 2004 study, Kornilaki & Chlouverakis

(2004) found that children as young as 7 years understood the concept of happiness. I

am of the opinion that the 9 to 14 year-old children we surveyed could understand

the ‘Global Life Satisfaction Scale’ question in the ‘Happiness Survey’. Together

with the other survey questions, the happiness question was pre-tested on six to

fifteen year-old children before the question wording was finalised. After simplifying

the wording for a small number of questions (but not the happiness question), the

children said they understood the words and the meaning behind all the questions in

our online ‘Happiness Survey’.

Prior to verifying whether the happiness and other questions were suitable for

children, questions were added to collect socio-demographic information about the

children. Careful to satisfy the strict ethical requirements for child anonymity, the

children were asked: which railway station they visited the ‘Smart Train’; their

gender; the postcode where they lived, and; their grade at school. Next, we sought to

include questions to collect data on the variables that have been found to have the

largest effect on adult happiness.

There is an extensive literature identifying variables with the largest effect on adult

happiness (see Clark, Frijters, & Shields, 2008 for a review). Table 4.38 summarises

the results from three national datasets used in this study and ranks the six variables

with the largest effect on overall happiness. The ranking varies across datasets, but

overall health or unemployment have a greater effect on the overall happiness of

adults followed by relationship status in varying orders depending upon the country

(married, separated & divorced) and finally income. Having identified the variables

with the largest effect on adult happiness, I sought to locate child-suitable questions

that could be incorporated into the online ‘Happiness Survey’.

129

Table 4.38: Cross-country OLS happiness results ordered (1) to (6) by the size of the standardised beta coefficient

Country: Australia61 Britain62 Germany63

Variable:

income (6) 0.032**

(6) 0.024**

(6) 0.04**

health (1) 0.52**

(5) 0.05**

(4) 0.23**

unemployed (2) -0.31**

(2) -0.35**

(1) -0.92**

married (4) 0.26**

(4) 0.20**

(5) 0.12**

separated (3) -0.28**

(1) -0.48**

(2) -0.45**

divorced (5) -0.06*

(3) -0.30**

(3) -0.26**

Number in brackets identifies the highest (1) to the lowest (6) standardised beta coefficient.

* significant at 5%; ** significant at 1%

In selecting questions suitable for children, I began by considering how we could ask

children about their family’s annual household income. Children rely on parent’s or

guardian’s income to provide them with sustenance, shelter, education and

healthcare. With (relative) income being so important to happiness, how can we

accurately collect this data from children? Researchers have long been concerned

with inaccuracies in how adults report their income (Moore, Stinson, & Welniak,

2000). If adults misreport income we can hardly expect children to know what mum

or dad earns. However, while children aged 6 to 14 years may not completely

understand the economics of supply and demand, they do understand the concept of

money, it’s what they ‘swap’ with others to get what they want (Leiser & Beth

Halachmi, 2006). Given children understand the concept of money, it is reasonable to

expect that a child would be able to gauge how well off (wellbeing) their family was

compared with their friend’s family. Children would be aware that their friend would 61 Pooled OLS regression results using the HILDA Panel 2002-2008; N = 75,529 (Beatton & Frijters, 2011, p.36). 62 Pooled OLS regression using the British Household Panel Survey (BHPS) waves 1 to 14; N = 82,096 (Frijters & Beatton, 2011, p.39). 63 Pooled OLS regression results using the German SocioEconomic Panel 1984-2002; N = 176,770 (Frijters & Beatton, 2011, p.33).

130

be wealthier if that friend lived in a bigger house, had a more modern car, or had a

holiday home. Children understand that wealthier parents have more money to buy

better things. Children understand that poorer people do not have much money so

they cannot buy a big house with a swimming pool. Children should be able to gauge

whether their family is wealthier, or poorer, than their friend’s family. To gauge the

relative income of their parents we asked the children:

Would you say that your family is? (tick one box)

wealthier than others in our neighbourhood

the same

poorer than others in the neighbourhood

While we are of the opinion that children can gauge their family’s relative wealth,

are the children capable of providing us with a self-assessment of what is arguably

the variable with the strongest effect on happiness, health. The authors of the SF-36

health questionnaire64 don’t think so; their health questionnaire has only been

validated for use on individuals 15 years and older (Ware, 2009). In addition,

findings indicate that, unless they have psychological issues, the majority of children

self-report with excellent health (Flouri, 2004). In expectation of minimal variance in

health question response data, and because the health survey experts (the SF-36

authors) consider their health survey questions unsuitable for children, plus with a

requirement to minimise the number of questions in the survey65, I chose not to

include a health question in the ‘Happiness Survey’.

I next considered asking the children about their family life. The literature is clear in

identifying that a stable home environment and parental relationship status are

important to the happiness of children. For example, Ulker, ( 2008, p210) identified

64 For details of the SF-36 health scale, see section A of the Self Completion Questionnaire from the Australian HILDA survey. It includes the SF-36 health items that gauge general health and well-being (http://www.melbourneinstitute.com/downloads/hilda/Questionnaires/SelfCompletionQuestionnaireW8.pdf). 65 Survey response levels reduce as the number of questions increase; respondents get bored and stop answering or resort to the same response for each question (Cavana, Sekaran, & Delahaye, 2001).

131

the detrimental effects of divorce on the happiness of teenage Australians. However,

we were questioning very young children, Ulker questioned teenagers over the age of

fifteen years. For ethical reasons I chose not to ask young children about their family

life (whether their parents are married, separated divorced). Instead, the questions

focussed on the research recommendations of Gilman & Huebner (2003) and asked

the children about another life satisfaction domain where children spend a large

amount of time socializing and interacting with friends, school.

(Huebner (1991) and Natvig, Albrektsen, & Qvarnstram (2003) found that the school

environment is an important domain of childhood happiness. In a subsequent study

involving 518 American elementary school students, Seligson, Huebner, & Valois

(2005) found a positive relationship between the environment at school and the

global life satisfaction of the children. School environment factors found to affect

childhood happiness include teacher support, student interaction, and competition in

class as well as a student’s psychological adjustment to school. The ‘school

environment’ domain is central to children’s life satisfaction, particularly the support

of teachers and classmates (Suldo & Huebner, 2006).

To measure the effects of the ‘school environment’ domain on children’s happiness, I

needed a scale validated for children. Scales designed for adult response are difficult

for children to understand. Barbaranelli, Carpara, Rabasca, & Pastorelli (2003)

provide a solution. They developed the Big Five Questionnaire for Children (BFQ-C)

as a self-report psychological scale to measure Goldberg's (1990) Big Five

personality factors in youths aged 8 years and above. To check that children could

understand the questions, Barbaranelli et al. (2003) cross-validated their BFQ-C

scale by having both the students and their parents complete the survey; the two sets

of responses were highly correlated. In seeking to further validate the BFQ-C scale,

del Barrio Carrasco Miguel and Holdago (2006) surveyed 852 Spanish school

children aged 8 to 15 years. They used structural equation modelling to run

confirmatory factor analysis models of the five personality factors. They found that

the model structure was suitable across different gender and age groups in the

children. The pattern of factor loading was shown to be invariant across these groups

and the theoretical constructs could be considered equivalent for gender. The

132

children's personality structure perception at ages 8 to 15 is as well differentiated as

the personality structure perception of adults. Therefore, the BFC-Q is considered an

acceptable personality scale for children. Table 4.39 lists the five personality

dimensions that emerge from the BFC-Q scale, the behaviours for each personality

factor, and the ‘Happiness Survey’ question numbers for each.

Table 4.39: Personality factors, related behaviours, & ‘Happiness Survey’ question numbers Personality Factor Behaviours

‘Happiness Survey’ Question Numbers

Extroversion talkative, bashful, quiet, not shy, lively.

q30, q36, q46, q52, q56

Agreeableness sympathetic, kind, cooperative, and warm.

q32, q37, q42, q52

Conscientiousness orderly, systematic, efficient, neat, organised, and efficient.

q33, q38, q43, q48

Emotional stability envious, moody, touchy, jealous, temperamental, and fretful.

q34, q39, q44, q49

Openness to experience

deep, philosophical, creative, intellectual, complex, and imaginative.

q35, q40, q55, q62

While the BFQ-C provided us with a personality scale suitable for children, we still

needed survey questions for our ‘school environment’ and ‘interaction with friends’

domain factors. Looking closely at the question wording of Barbaranelli et al.’s

(2003) BFQ-C personality scale, one notices that the questions are phrased66 to

measure the type of behaviours we would expect from children interacting with their

friends and classmates before, during or after school. I was of the opinion that the

wording of selected questions from the BFQ-C scale could be used to form our

‘school environment’ and ‘interaction with friends’ scales.

Selected questions from the BFQ-C scale were chosen to form two of the three life

domain factors recommended by Gilman & Huebner (2003): 1) ‘school

environment’, and; 2) ‘interaction with friends’. The questions were chosen based on

the theoretical fit of the question wording and the extent to which the question(s)

converged to a single factor. To operationalize the questions, the children were asked 66 See the personality question wording in Figures 35g to 35k in Chapter 4 Appendix A.

133

to tell us something about themselves and the extent to which a statement described

them. Responses were measured using a Likert scale of: definitely not true; probably

not true; don’t know, maybe; probably true; definitely true). The complete survey

questionnaire is in Appendix A of Chapter 4; example questions include:

Example School environment questions:

Q35. When the teacher explains something, I understand immediately

Q52. If a classmate has some difficulty I help her/him

Q55. I easily learn what I study at school

Example interaction with friends questions:

Q30. I make friends easily

Q37. I share my things with other people

Q56. I like to meet with other people

Principal factor analysis was used to maximise convergence and a Cronbachs alpha

test (Cavana, Sekaran, & Delahaye, 2001) was used as a measure of the internal

consistency and reliability of our school environment and interaction with friends

domain factors as a suitable psychometric test for our ‘Smart Train’ subjects. Both

factors exhibited high scale reliability; Table 4.40 lists the question numbers for each

life satisfaction domain factor and their Cronbachs alpha test scores.

134

Table 4.40: ‘Happiness Survey’ question numbers & behaviours for the school environment and interaction with friends life satisfaction domains

Life Domain (variable name) Behaviours

‘Happiness Survey’ Question Number

School environment1 (schoolenv)

understands the teacher does many things, active

concentrates in class work first play last understands things

works hard helpful to classmates

finishes tasks learns easily

checks homework knowledgeable

q35 q36 q38 q43 q45 q48 q52 q53 q55 q60 q62

Interaction with friends2 (friends)

makes friends easily trusting

shares with others likes to talk to others

helpful kind

likes to meet with others forgiving

q30 q32 q37 q41 q42 q47 q56 q59

1 Cronbachs alpha scale reliability coefficient: 0.8559 2 Cronbachs alpha scale reliability coefficient: 0.7638

In addition to selecting questions from the BFC-Q scale to form our school

environment and interaction with friends domain factors, the children were

questioned on their natural environment life satisfaction domain. To gauge the

children’s attitudes to and the level of concern for their natural environment the

children were asked: if they were engaged in discussions on their natural

environment; if they were aware of environmental problems; what they were doing

about them; whether it was an acceptable behaviour to pollute their river or a river

in a neighbouring state, and; the importance of animals and plants in their lives67.

The questions on the ‘natural environment’ were coded as dummy variables and

summed to form the natural environment life satisfaction domain factor; where 1 is

lowest level of concern for the natural environment and 13 the highest level of

concern for the natural environment.

67 The ‘natural environment’ questions in the ‘Happiness Survey’ are q13_1, q13_2, q13_3, q13_4, q13_5, q17, q18, q21_1, q21_2, q21_3, q22, q23, and q24.

135

After including the questions for the natural environment domain factor, some ‘fun’

questions were added to amuse the children and encourage them to complete the

survey. The fun questions68 asked about, magic, belief in a greater being, lucky

charms, if they wrote with their right or left hand and a graphical question to help

the children gauge if their index finger was longer than their middle finger. With the

questions for our ‘Happiness Survey’ finalised and the ‘Smart Train’ beginning its

journey across the state of Queensland, we posted the ‘Happiness Survey’ on the

‘Smart Train’ website and proceeded to collect the ‘Smart Train’ data.

4.3.3 The ‘Smart Train’ data

Three hundred and twenty seven children visited the ‘Smart Train’ at one of twenty-

five regional railway stations (Table 4.41). The remaining sixty-two children visited

the train at an urban station (the state capital, Brisbane). The 389 observations in our

sample came from 217 female and 172 male children (44%) with an average age of

11.76 years. Twelve per cent of the children are left-handed, and, 47% have a ring

finger longer than their index finger; an indicator of the higher testosterone levels

typical of males.

Average life satisfaction for our 9 to 14 year old sample is a very high 9.0; 14%

higher than the 7.91 for the 15 to 23 year-olds in the HILDA and 12% higher than

the 8.07 for the complete HILDA sample. Average happiness for female children in

the ‘Smart Train’ data (9.31) is 8% higher than for male children (Table 4.41). There

was some variation in happiness by railway station and school grade (4 to 9) but this

variation arises mainly from the large sample size differences in the railway station

where the children visited the ‘Smart Train’ and the child’s school grade (50% of the

sample were in grade seven). At the more granular level, there was no significant

difference (ANOVA: F = 0.06, p = 0.82) between the happiness of urban (mean =

9.01, s.d. = 1.99) and regional (mean = 8.94, s.d. = 2.10) children.

68 The fun questions are q6, q7, q8, q10, q11, q19, q20, q21, q27, q28, q29, q63, and q64.

136

Table 4.41: Life Satisfaction for the 9 to 14 year old children in the ‘Smart Train’ data

Mean (s.d.) Happiness Count Age69 All Females Males

Dependent variable: Life Satisfaction

9.00 (2.0)

9.31 (1.88)

8.60 (2.09)

Railway station where the child visited the ‘Smart Train’70

Brisbane Roma Street71 62 8.94 (2.10) 9.19 (1.99) 8.50 (2.28) Bundaberg 5 9.68 (1.20) 10.00 (1.27) 8.80 (.) Cairns 5 9.24 (1.84) 6.60 (.) 9.90 (1.27) Charters Towers 12 8.98 (1.98) 10.00 (1.18) 7.48 (1.97) Emerald 59 8.50 (2.25) 8.51 (2.23) 8.49 (2.30) Gladstone 16 8.80 (2.13) 9.63 (1.14) 7.98 (2.61) Ingham 4 8.80 (0.00) 8.80 (.) 8.80 (.) Mackay 58 8.99 (1.76) 9.75 (1.11) 8.17 (1.98) Maryborough 41 8.85 (2.43) 9.28 (2.20) 8.31 (2.67) Mount Isa 4 10.00 (1.10) 10.00 (1.27) 10.00 (.) Old Gympie Station 6 8.07 (1.80) 8.80 (2.20) 7.33 (1.27) Rockhampton 60 9.61 (1.62) 9.62 (1.73) 9.90 (1.56) Roma 3 8.80 (2.20) 9.90 (1.56) 6.60 (.) Toowoomba 3 7.33 (2.54) 4.40 (.) 8.80 (.) Townsville 41 9.28 (1.81) 9.40 (1.82) 9.15 (1.84) Other72 8 8.25 (2.56) 7.92 (3.34) 8.80 (.)

Regional Children 327 9.01 (1.99) 9.35 (1.85) 8.61 (2.28) Urban Children (Brisbane) 62 8.94 (2.10) 9.19 (1.98) 8.50 (2.07

School Grade Grade 4 17 9 9.45 (1.51) 9.24 (1.84) 9.53 (1.43) Grade 5 19 10 9.38 (1.43) 9.80 (1.51) 8.80 (1.18) Grade 6 91 11 9.21 (2.08) 9.59 (1.84) 8.68 (2.32) Grade 7 196 12 8.83 (2.04) 9.17 (1.89) 8.37 (2.16) Grade 8 50 13 8.70 (1.68) 9.50 (1.42) 8.80 (1.83) Grade 9 20 14 8.57 (2.73) 8.80 (2.81) 8.17 (2.76)

N = 389 217 172

69 For ethical reasons we could not collect identifying information other than gender from the children; age was calculated from the mandated school starting age and the child’s school grade. 70 Care should be taken when interpreting regression results by age & railway station; the minimum sample size requirements were calculated using G*Power 3.08 software; with our low effect size, the sample sizes at each age were too small, except for age 12 (Faul, Buchner, Erdfelder, Faul, & Lang, 2006). The sample sizes for analysis by gender are adequate. 71 Brisbane was the only urban centre; 84% of the respondents came from regional Queensland. 72 Some children did not visit the “Smart Train” but responded to the survey. To do this the children needed the ‘Happiness Postcard’ provided by their teacher.

137

As well as asking the children to answer the happiness question, the children were

asked how they perceived their natural environment (Table 4.42). The children had a

high awareness of environmental issues, they were aware of climate change (68%),

water restrictions (63%), native animals dying out (44%), declining fish stocks (22%)

and land salinity (19%), but ten per cent of the children had noticed none of these

environmental issues. The children perceived climate change (48%) then the loss of

native fauna (24%) as the worst environmental issues. These results are what we

would expect from our majority non-urban sample (327 of the 389). We would

expect that non-urban children would have a higher awareness of issues such as the

loss of native animals (81% of non-urban children said animals were an important

part of their lives), and salinity and climate change; all are current issues affecting

Australia’s agricultural communities.

Sixty-eight per cent of the children stated they had started a conversation about

climate change but, surprisingly only 50% of the children reported that their family

talked about the environment. Either the children are discussing the environment

among themselves or they are exposed to discussions on the environment in their

classrooms. Either way, Australia’s next generation does appear to have a higher

level of concern (73%) for the natural environment than the 53% of Australia’s

current adult population who show a concern for our natural environment (ABS,

2010).

The children in the ‘Smart Train’ data are not just showing more concern for the

natural environment, they are acting on that concern. In example, sixty-one per cent

of the children are engaging in recycling. Sixty-eight per cent of the children have

tried to reduce their water consumption, and, 98% of the children said it was wrong

to pollute a river, even if that river was in another state. These environmental and the

other variables collected with our ‘Happiness Survey’ form the cross-sectional

‘Smart Train’ data set, which I applied to the model of childhood happiness.

138

Table 4.42: Descriptive statistics for selected questions from the ‘Smart Train’ cross-sectional data; N = 389 Mean (s.d.) All Female Male

Dependent variable:Life Satisfaction

9.00 (2.0)

9.31 (1.88)

8.60 (2.09)

q5: Would you say that your family is wealthier etc.? 1 2.0 (0.45) 1.94 (0.40) 2.06 (0.51) q6: Good luck charms sometimes do bring good luck 3 3.00 (1.10) 3.15 (1.04) 2.81 (1.14) q7: Do you have a lucky charm such as a mascot or a talisman? 2 0.29 (0.46) 0.28 (0.44) 0.31 (0.47) q8: Do you believe that a lucky charm can protect or help you? 3 2.91 (1.21) 3.07 (1.16) 2.71 (2.81) q9: Apart from weddings, funerals and christenings, how often do you attend religious services these days? 4 4.39 (2.72) 4.53 (2.65) 9.24 (1.84) q10: Some fortune tellers really can foresee the future 3 2.70 (1.22) 2.91 (1.21) 2.42 (1.18) q11: Is there someone who cannot be seen by others watching over you, making sure you are ok? 2 0.76 (0.43) 0.81 (0.40) 0.71 (0.46) q15: Are animals an important part of your life? 2 0.93 (0.25) 0.93 (0.26) 0.94 (0.24) q16: Are plants an important part of your life? 2 0.82 (0.39) 0.85 (0.36) 0.78 (0.42) q17: Does your family talk about the environment much? 2 0.50 (0.50) 0.49 (0.50) 0.51 (0.50) q18: Have you ever started a conversation about nature or the environment? 0.68 (0.47) 0.71 (0.45) 0.65 (0.48) q22: Let’s say that in your neighbourhood everyone throws their garbage in the river; would that be all right? 5 0.98 (0.98) 0.99 (0.10) 0.97 (0.17) q23: Let's say that in New South Wales, a whole neighbourhood throws its garbage in the river. Do you think it is all right or not all right for them to throw their garbage in the river? 5

0.99 (0.10) 1.00 (0.00) 0.98 (0.15)

q24: Do you think that throwing garbage in the river is harmful to the birds that live around the river? 5 0.93 (0.26) 0.91 (0.30) 0.94 (0.25) q63: What hand do you write with? 6 0.13 (0.33) 0.11 (0.31) 0.15 (0.36) q64: Which finger is longer? 7 1.84 (0.88) 1.95 (0.88) 0.15 (0.36)

1 Scale: 1 = poorer, 2 = the same, 3 = wealthier 2 yes = 1 3 Likert scale: definitely not true = 1, probably not true, don’t know, probably true, definitely true = 5 4 more than once a week = 5, once a week, once a month, only on special holydays/Christmas/Easter etc., other specific holy days, once a year, less often = 1 5 no = 1 6 left hand = 1 7 1 = ring finger is longer, the same, 3 = index finger longer

139

4.4 Methodology and analyses

4.4.1 Model of childhood happiness

The model of childhood happiness (4.1) takes the form:

(4.1)

where,

LSit Individual life satisfaction (happiness)

C Constant

Xit Time-varying individual demographics

Sit ‘School environment’ domain factor

Fit ‘Interaction with friends’ domain factor

Nit ‘Natural environment’ domain factor

Zi personality εit error term

Childhood happiness (LSit ) is a function (4.1) of a constant (C), time-variant socio-

economic variables specific to the individual (Xit) and time-invariant individual fixed

effects (Zi). The child’s happiness at any time is subject to changes in their school

environment (Sit), interaction with friends (Fit) and natural environment life domains

(Nit), with unobservables manifest in the error term (εit).

1 2 3 4it it it it it i itLS C X S F N Zβ β β β ε= + + + + + +

140

4.5 Analysis, results and discussion

4.5.1 Extending our view of happiness over a lifetime

Before analysing the ‘Smart Train’ data with our model of childhood happiness, let

us first extend our view of lifetime happiness by appending73 the average happiness

of the 9 to 14 year-olds from our ‘Smart Train’ dataset to the 15 to 93 year-old

Australians in the HILDA dataset (Figure 4.35).

6

6.5

7

7.5

8

8.5

9

9.5

10

Life

Sat

isfa

citio

n

AGE


Australia (HILDA) Australia (Smart Train)

7

7.5

8

8.5

9

9.5

10

Life

Sat

isfa

citio

n

AGE


Australia (HILDA 15 to 23 years) Australia (Smart Train)

Figures 4.35(a) & (b): Average Life Satisfaction for 9 to 14 year old Australian children in the

‘Smart Train’ data and 15 to 90 year-old Australians in the 2002-2008 HILDA panel data

73 There is a structural break between in the view of average happiness shown in Figure 4.35a & b. The (1-5) scale for the Smart Train happiness question is different to the (0-10) scale for the happiness question in the HILDA. The ‘Smart Train data were rescaled (0-10).

141

The first thing we see from the above figures is that the steep fall in 15 to 23 year-

olds continues back to 9 year-old Australians74. To the 7.2% (-0.73 unit) decline in

happiness we noted in 15 to 23 year-old Australians in the HILDA75 (solid line in

Figure 4.35 a & b), we see a further 9.3% decline (9.44 to 8.56) in the happiness of

Australians aged of 9 and 14 years76 (dotted line in Figure 4.35a & b). The total fall

in the happiness of 9 and 23 year old for young Australians aged is 16.5% (- 1.61

units). This 16.5% fall in the average happiness of 9 to 23 year-olds is much larger

than the 7.8% decline we see in older Australians (aged 85 to 93 years) whose

happiness we expect to decline due to failing health and the onset of death. So, why

does the happiness of 9 to 23 year-old Australians fall by 16.5%? To answer that

question I begin by seeking to explain why the childhood happiness of the 9 to 14

year-old Australians falls by the - 0.88 units we noted above.

4.5.2 Analysis of the 9 to 14 year-old cohort in the ‘Smart Train’ data

The “Smart Train’ data was analysed with the model of childhood happiness (4.1).

The regression results for each model specification are in Tables 4.40 & 4.41 in

Appendix B at the end of this chapter. To begin our analysis, specification 1(a)

includes the demographic variables we collected with the ‘Happiness Survey’. As

expected, we see that girls enjoy a level of happiness 0.7 higher than boys do.

Attending religious services more often is related to an increase (+0.1) in the

happiness for both boys and girls. None of these variables account for the - 0.88

decrease in childhood happiness. The only variable that did have a significant

negative effect on childhood happiness is school grade; a proxy for age. Children get

unhappier as they progress from grade 4 to grade 9 at school. However, in Chapter 3

we found that the negative effect of age on the happiness of aging Australians,

British, and Germans was explained by unobserved fixed effects. The most obvious

74 It is possible that the very high level of life satisfaction we see in young children is due to social desirability bias; the idea that nice kids are happy. The downward trend in happiness with age could also partly be due to diminution of this bias as the children get older. In addition, a volunteer sample (which this sample is not because it was compulsory for the school children to go to the Smart Train) might have a higher mean level of happiness than a strictly representative sample but, results showing a downward trend in happiness with age could not be due to volunteering. 75 Average life satisfaction for 15-year-olds in the HILDA data are: females, 8.35, and; males, 8.52. 76 Average life satisfaction for 9-year-olds in the Smart Train data are: females, 9.24, and; males, 9.53.

142

fixed effect is what many psychologists now consider to be stable over a lifetime,

personality (McCrae, Costa, Mroczek, & Little, 2006).

Personality has previously been considered in economic models of individual life

satisfaction (see Clark, et al., 2008 for a review). When we include personality in

our model of childhood happiness, we see some expected, and some unexpected

results. As expected, extraverted individuals are happier and neurotics (those low on

emotional stability) are less happy. The unexpected results are for conscientiousness.

If we regress conscientiousness on the overall happiness of all Australians in the

HILDA, we get a significant negative effect77. However, we get an opposite effect

for the 9 to 14 year olds in the ‘Smart Train’ data. Unlike their adult Australian

counterparts in the HILDA, Australian children who exhibit conscientious

behaviours (orderly, systematic, efficient, neat, organised, and efficient) are happier.

There could be many reasons why conscientious has the opposite relationship with

the happiness of Australian children than for Australian adults. The most obvious

reason is that conscientious, hardworking children would be more likely to complete

their work and get better grades. Academic achievement has been found to make

children happier (Huebner, 1991). Another reason could be the regimented and

procedural nature of the Australian grade school system. At lower grade levels (1 to

9), the school curriculum dictates the children’s behaviour; the children have little

scope for planning what they do at school. The children are told to be tidy (orderly,

neat) and the teacher sets the deadlines (goals) for work completion (systematic,

efficient). To encourage the above-bracketed conscientious behaviours in grade

school children, teachers reward the children (e.g. a gold star to attach to their good

work). This positive reward recognition would make the child happier. Such an

ordered and rewarding life could relieve the children from the stress of planning the

pursuit and attainment of their life goals (McKnight, Huebner, & Suldo, 2002), at

least at school. In their 30-year review of subjective well-being (happiness), Diener,

Suh, Lucas, & Smith (1999) listed stress as a major component of happiness. In

77 In example, the next chapter looks at the effect of personality on the happiness of all individuals in HILDA panel; the effect is significant and negative (-1.40, t-value = 2.22).

143

discussing adult happiness, Diener stated “ that believing one’s goals are important

has rewards but can also increase stress because of the increased pressure to

achieve those goals” (Diener, et al., 1999, p.285) and increased stress can make us

unhappy78. The planned and rewarding nature of the grade school system rewards

children who complete their work; it is reasonable to expect that those children

should get better school grades, be less stressed and happier in a rewarding school

environment.

However, what would happen to childhood happiness if the school environment

changed as children moved up in grades? Recall that school environment was one of

the four life satisfaction domain factors that Gilman (2003 proposed as having a

major effect on childhood happiness. Our ‘Happiness Survey’ collected data on three

of Gilman’s four childhood life satisfaction domain factors, school environment,

natural environment and interaction with friends. Regression results for

specifications 2 (a) to 2 (e) that include combinations of these factors are shown in

Tables 4.40 & 4.41 (Chapter 4 Appendix B). Looking at the results for specification

2 (c), the largest effect on childhood happiness comes from the interaction with

friends domain factor; a 45% larger positive effect than the positive effect from

‘school environment’ and 55 times larger than the (non-significant79) positive effect

from the natural environment domain factor. To gain a clearer understanding of what

this means, we need to look back at the behaviours relating back to each life domain

factor (Table 4.40). Children with positive ‘interaction with friends’ behaviours such

as sharing, being kind to others, and forgiving of their friends, are happier than

children with positive ‘school environment’ behaviours such as understanding the

teacher, helping classmates, concentrating on class work or learning easily.

78 The effect of stress on happiness is explored in the next chapter (Chapter 5). 79 If we look at the effect from the individual ‘natural environment’ questions in Table 37, we see that only one environment question (q17) had a significant effect on happiness. Children who perceived their family as wealthier than their friend’s families are more likely to discuss environmental issues within their family (q17 was strongly positively correlated with wealth).

144

While the regressions results provide a holistic view across the six school years of

our 9 to 14 year olds, a decomposition of the prediction for each domain factor

facilitates an internal view of the happiness changes arising from each domain factor

as the children move up from lower school grade 4 to grade 9 in high school. The

decomposition takes the form:

and a constant (c) of unexplained changes in childhood happiness arising from

unobservables. Based on predictions arising from the decomposition, Figure 4.36

provides a revealing view of predicted changes in childhood happiness as children

move through the state school system (grades 4 to 7) then transfer to high school

(grades 8 & 9).

grade 4 grade 9

1( ) ( )*

where the childhood life satisfaction domain factors are: School environment ( )Interaction with friends ( )Natural environment ( )

and the school gra

ku lk k k

k

it

it

it

y y x x c

Sk F

N

β=

− = − +

⎧⎪⎨⎪⎩

∑

de transition is from one year of schooling ( ) to the next ( ):

to 4 to 55 to 6

,6 to 77 to 88 to 9

l u

l u

l u

⎧ ⎫⎪ ⎪⎪ ⎪⎪ ⎪⎨ ⎬⎪ ⎪⎪ ⎪⎪ ⎪⎩ ⎭

145

‐0.2

‐0.15

‐0.1

‐0.05

0

0.05

0.1

0.15

4 to 5 5 to 6 6 to 7 7 to 8 8 to 9

School Environment

NaturalEnvironment

Interaction withFriends

Pred

icted life Satisfaction

Cha

nge

School Grade

Figure 4.36: The predicted changes in childhood happiness from each domain factor as the children move up in school grade

From the results of the decomposition in Figure 4.36, we see that predictions for the

school environment, interaction with friends, and, natural environment factors can

account for 44% (-0.39) of the -0.88 unit fall in childhood happiness for 9 to 14 year-

old Australians we saw in the raw data depicted in Figure 4.35. The total change in

childhood happiness arising from the natural environment factor is flat between

grades 4 & 9 is a very small -0.001, just 0.1% of the -0.88 unit fall in childhood

happiness for 9 to 14 year-old Australians we saw in the raw data depicted in Figure

4.35. Childhood happiness is little affected by the children’s natural environment.

Additional evidence of the non-significant effect of the natural environment factor

on childhood happiness can be seen by reviewing the regression results in Table 4.44

of Appendix B at the end of this chapter; the natural environment factor has a non-

significant effect on childhood happiness. In addition, looking at the significance

(Table 4.42) of the individual items that make up the natural environment factor,

only one question is significant, q18: Have you ever started a conversation about

nature or the environment, and only at the 10% level.

146

However, the predicted grade 4 to 9 changes in childhood happiness arising from the

school environment factor is statistically significant80 and much larger. Between

grades 4 and 9 an accumulated -0.31 unit drop in childhood happiness can be

attributed to the school environment factor, which accounts for 35% of the total fall

(-0.88) in childhood happiness we saw in the raw data (Figure 4.35). If we look back

at the decomposition predictions in Figure 4.36, we can see that the decline in

childhood happiness is not linear. Between grades 4 and 7, while the children are still

in the lower grades, the decline in the happiness of the children is reducing. By the

end of their time in the lower grades (grade 7), the happiness of the children is

actually increasing (+0.1) by a small amount; perhaps the children have positive

expectations of their forthcoming transition to high school. Alas, when the children

do transition from the lower grade school to the high school, their happiness falls

steeply.

In the first year of high school the happiness of the children is predicted to fall by

-0.06 and by grade 9 the predicted fall is -0.2 units. The children progressively

become more miserable the longer they are in high school; perhaps because the

schoolwork becomes more difficult, they have to allocate more time to study work

and less leisure time is available to interact with friends.

The predicted net change in childhood happiness arising from the interaction with

friends factor between grades 4 to 9 is -0.08, much smaller than the -0.31 fall from

the school environment factor. The interaction with friends factor accounts for just

8.8% of the -0.88 unit fall in childhood happiness for 9 to 14 year old Australians we

saw in the raw data depicted in Figure 4.35. Like the school environment factor, the

predicted changes arising from the interaction with friends factor are also nonlinear.

Looking again at Figure 4.36, we can see that the changes in childhood happiness

arising from the interaction with friends factor mirrors that of the changes from the

school environment factor, they have a medium positive correlation (r = +0.59). In

Figure 4.36, we can see that the decline in happiness arising from the interaction

80 See the regression results for specification 2(c), Table 4.41 in Appendix B of Chapter 4.

147

with friends factor reduces as the children move up in grades towards their transition

to high school. In their last two years in the lower grades (grade 6 & 7), the

happiness of the children rises due to their long-term interaction with friends. Alas,

after the children transition from lower school to high school the change in childhood

happiness arising from the interaction with friends factor is again negative.

We could expect such a change. The children build friendships in the lower grades

until, by the time they are in the highest grade of the lower school they are enjoying

considerable satisfaction (utility) from the regular interactions they are having with

friends they have known for the past seven years. At the end of year seven those

friendships are fragmented, the children are sent (most probably due to parental high

school choices) to one of the many high schools, schools they no longer share with

their old friends; childhood happiness declines (-0.15). After a year at high school,

the children make new friends, and, their interaction with friends again contributes to

their happiness (+0.05). However, while their interaction with friends makes them

happy, the school environment continues to make the children unhappy.

4.5.2.1 Summary of the analysis of the ‘Smart Train’ data

To summarize the findings from the analysis of the “Smart Train” data, the natural

environment factor contributed minimally (0.1%) to the decline in the children’s

happiness. The interaction with friends factor accounted for 8.8% of the -0.88 unit

drop in childhood happiness for 9 to 14 year olds. By far the largest contributory

factor to the decline in happiness of 9 to 14 year olds was their school environment.

The school environment accounted for 35% of the decline in the happiness of 9 to 14

year olds. Together, the school environment and interaction with friends factors

accounted for nearly half, 44% (-0.39), of the -0.88 decline in the happiness of 9 to

14 year old children we saw in the raw data depicted in Figures 4.35 (a) & (b).

However, we still need to explain the (-0.73) decline in the happiness of young of

Australians aged 15 and 23 years.

148

4.5.3 Analysis of the 15 to 23 year-old cohort in the HILDA

To explain the steep (-0.73) drop in the happiness of young of Australians aged 15

and 23 years we change datasets to focus on the 15 to 23 year-old cohort in the

Australian HILDA panel data. In focusing on this adolescent and young adult cohort,

our model of childhood happiness (4.1) is modified to exclude the child specific life

domain factors but add the self-reported life event shocks included in the HILDA

panel data. Our modified model (4.2) emerges as the model of individual happiness

generally seen in the economics literature:

4.2

where,

LSit Life satisfaction (individual happiness)

C Constant (what some define as ‘baseline happiness’)

Xit Time-variant socio-economic variables

Leit Life events (changing circumstances in an individual’s life)

Zi Individual fixed effects εit error term

The life satisfaction of an individual in the 15 to 23 year-old cohort emerges from a

constant (c), the effect of time-variant socio-economic variables (Xit),the effect from

the self-reported life events shocks (Leit) that affect an individual in the year prior to

HILDA survey completion, and, unobservables manifest in the usual error term εi.

Our model of individual happiness is applied to the 15 to 23-year-old subsample and

all (15 to 93 year-olds) in the HILDA; regression results for the different

specifications are in Table 4.46 in Appendix B at the end of Chapter 4. I focus the

analysis on the preferred ‘Usual suspects + Health’ specification from the U-shape of

happiness in age analysis in Chapter 3. Recall, this specification included the health

and wealth variables as well as the socio-economic variables commonly found in the

happiness regressions. These variables are log-income, gender, education in years,

the number of children, a marriage dummy, and indicators of work-status (employed

and unemployed).

1 2it it it i itLS C X Le Zβ β δ ε= + + + +

149

The results from the ‘Usual Suspects + Health’ specification with the 15 to 23 year-

old cohort are mostly as expected, income, marriage, home ownership, and

employment all have a significant positive effect on the happiness. Results that are

not expected are the positive effects from a greater number of children in a family

and the positive, but non-significant, effect from more years of education. We get a

significant negative effect for these variables in the same regression with the entire

‘All’ HILDA sample (Table 4.46). For each additional sibling, the happiness of 15 to

23 year-olds household increases by 0.12 (1.9%) while it decreases (-0.06 for each

additional child) in the entire sample. Children are happier when they have siblings,

it just makes their parents less happy.

Another regression result difference between the 15 to 23 year-old cohort and the

entire HILDA sample is the size of the effect from unemployment and maleness,

both are much smaller. The maleness effect is one third the size and non-significant.

The negative effect from unemployment for 15 to 23 year -olds is half the size as it is

for the entire HILDA sample even though more of them self-report as unemployed.

A count of unemployment by age would seem to support this proposition.

Unemployment for 15 to 23 ranges from 12% for 15 year olds to 5% for 23 year olds

with an average of 8.8%. The average self-reported unemployment rate for 15 to 65

year olds is much lower, just 3.6%, probably because 15 to 23 year-olds expect to be

unemployed because they are still engaged in education and not yet seeking

permanent employment. However, none of the effects from the variables in the

‘Usual Suspects + Health ‘ specification explains the steep drop (-0.77) in the

happiness of Australians aged 15 to 23 years, except one: age.

The age coefficient (-0.332) for the 15 to 23 year old cohort is seven times larger

than it is (-0.046) for the entire HILDA sample , and it remains so, even when we

add life event shocks (see Chapter 4 Appendix B Table 4.46 specification 2(a) &

(b)). Negative life events like losing a job or a death in the family do not explain why

15 to 23 year olds progressively become less happy as they age. Other than age

effects, none of the explanatory variables in our regression can explain away the

steep -0.77 drop in the happiness of 15 to 23 year-old Australians. Adding fixed

effects to the ‘Kitchen sink’ specification does not explain away age effects.

150

Something is happening over time to 15 to 23 year-old Australians that cannot be

explained by the ‘Kitchen sink’ specification. Perhaps the steep -0.77 drop in the

happiness of 15 to 23 year-old Australians can be explained by one or more of the

additional 14 life event shocks that are in the HILDA and not the other socio-

economic panel data sets.

Perhaps, as we proposed for 9 to 14 year-old cohort in the ‘Smart Train’ dataset,

changes in the environment in which young Australians live makes them unhappy.

After all, an individual’s personality (Diener, et al., 1999, p.214) and socialization

can affect their happiness. Happiness is affected not just by economic circumstances

but also by life events. Easterlin (2002, p. 214) stated that ‘the degree of positive or

negative change in our happiness is a function of an individual’s consideration of

their happiness expectations (their aspirations) with the changing economic

circumstances and the life events that affect them’. In Chapter 5, I intend to find out

if the inclusion of all life events in the HILDA can explain changes in happiness over

a lifetime.


This study has a number of limitations: sample representativeness; the ability of

children to respond to survey questions, and; the interpretation of the results given

the differences in the school systems across Australian states. It could be argued that

the ‘Smart Train’ data are not representative of Australian children. The data were

collected from four-hundred children residing in the rural and urban regions of one

Australian state, Queensland; a state that constitutes 20% of the Australian

population of 22.4 million. In addition, not all Queensland children had the

opportunity to visit the “Smart Train’. The only Queensland children who had the

opportunity to visit the ‘Smart Train’ were those who went to school near one of the

twenty-eight stations where the train stopped. Even then, it was probably the

children’s teacher more than the children themselves who chose to visit the Smart

Train because the visit was part of the school curriculum and was held during school

hours.

151

Independent of whether the children or the teacher chose to visit the ‘Smart Train’,

the children’s level of English comprehension could have biased the data. Depending

upon their age, children differ in how they respond to survey questions. Borgers, de

Leeuw, & Joop (2000) are of the opinion that children 8 years old and onwards can

be surveyed. However, the comprehension abilities of a 9-year-old respondent to our

‘Happy Survey’ could be different to a 14-year-old respondent. While the survey

questions were pretested (on 6, 10 & 14 year olds), the questions were not pretested

on all ages and the children we pretested could have had higher (or lower)

comprehension abilities than the average child of their age.

In addition to sample representativeness and the comprehension abilities of the

children, differences in the school systems across Australian states could change how

we interpret the decomposition graphic (Figure 4.36). Recall, we saw an increase in

the happiness decline arising from the school environment and interaction with

friends life satisfaction domain factors as the children transitioned from grade 7 (the

final grade in lower school) to grade 8 (the first grade of high school). In other

Australian states, children go to high school a year earlier, in grade 7. It would be an

interesting natural experiment to collect data from Queensland children in 2015 after

Queensland changes to the same school system as the other eastern Australian states.

As a robustness test of the results of this study, the pre and post data could be

compared to see if changes in childhood happiness still correlated with the grade

when the children transition from the lower grade school to high school.

152


Chapter 4 was an exploratory study that began by revisiting the steep decline in the

happiness of young 18 to 23 year old Germans that we saw in the Chapter 3 study.

Chapter 4 progressed by focussing on the Australian population and sought to clarify

confusion in the literature as to whether there was a similar steep decline, or increase,

in the happiness of young Australians. The Chapter contributed to our understanding

of lifetime happiness by extending our view of lifetime happiness back to childhood.

After developing scales to measure individual characteristics, personality and life

domain factors proposed by school psychologists to explain childhood happiness, an

Internet-based survey was developed to collect data from 9 to 14-year-old children

on their overall life satisfaction and on factors considered to affect childhood

happiness.

Childhood happiness is a research path little trodden by economists. This chapter

offered an econometric model of childhood happiness not previously seen in the

economics literature81. Analysis revealed that the natural environment life domain

factor contributed minimally (0.1%) to the decline in the children’s happiness. The

interaction with friends factor accounted for 8.8% of the -0.88 unit drop in childhood

happiness for 9 to 14 year olds. By far the largest contributory factor to the decline in

happiness of 9 to 14 year olds was the school environment. The school environment

accounted for 35% of the decline in the happiness of 9 to 14 year old Australian

children. Together, the school environment and interaction with friends factors

accounted for nearly half, 44% (-0.39), of the -0.88 decline in the happiness of 9 to

14 year old children we saw in the raw data depicted in Figures 4.35 (a) & (b). Like

adult Australians, we saw that extroverted Australian children are happier. Unlike

their conscientious adult Australian counterparts who are unhappier, we saw that

conscientious Australian children are happier. However, we still needed to explain

the (-0.73) decline we saw in the happiness of young of Australians aged 15 and 23

years.

81 Domain models of wellbeing have been used for a considerable time in the scientific discipline of psychology.

153

Seeking to further contribute to our understanding of happiness over a lifetime, I

sought to explain the (-0.73) drop in the happiness of young of Australians aged 15

and 23 years. The data for the 15 to 23 year-old cohort in the Australian HILDA

panel data was applied to the model of individual happiness. While the negative

effect from unemployment on 15 to 23 year olds is much smaller than it is for the

general Australian population, none of the demographic or life event variables could,

individually or collectively, completely explain the steep decline in the happiness of

adolescent and young adult Australians. We were left to propose that there were

important explanatory variables absent from the regression specifications. Chapter 5

pursues this proposition by including an additional fourteen life events from the

HILDA data and asks the question; do changes in the lives of our peers make us

unhappy?

154

155

Chapter 4 - Appendix A: The Smart Train Survey Questions

Figure 4.37a: The online ‘Happiness Survey’: initial screen and question q1

156

Figure 4.33b: The online ‘Happiness Survey’: questions q2 to q6

157

Figure 4.33c: The online ‘Happiness Survey’: questions q7 to q12

158

Figure 4.33d: The online ‘Happiness Survey’: questions q13 to q18

159

Figure 4.33e: The online ‘Happiness Survey’: questions q19 to q24

160

Figure 4.33f: The online ‘Happiness Survey’: questions q2782 to q29

82 Question numbers q25 and q26 were not in the survey.

161

Figure 4.33g: The online ‘Happiness Survey’: questions q3083 to q36

83 Question number q31 was not in the survey.

162

Figure 4.33h: The online ‘Happiness Survey’: questions q37 to q42

163

Figure 4.33i: The online ‘Happiness Survey’: questions q43 to q48

164

Figure 4.33j: The online ‘Happiness Survey’: questions q49 to q54

165

Figure 4.33k: The online ‘Happiness Survey’: questions q5584 to q62

84 Question numbers q57 and q58 were not in the survey.

166

Figure 4.33l: The online ‘Happiness Survey’: concluding screen and questions q63and q64

167

Chapter 4 - Appendix B: Regression results

Table 4.43: The determinants of Life Satisfaction for children aged 9 to 14 years in the Smart Train dataset; OLS regression, N = 389

1(a)

Demographics

1 (b)Demographics

Personality

2 (a) School environment

2 (b)School environment

Interaction with Friends

Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value

q1: Where did you visit the Smart Train? -0.023 1.72 -0.014 1.05

q2: female =1 0.695 3.44 0.709 3.51

q4: school grade (age proxy) -0.178 1.84 -0.137 1.52

q5: relative wealth 0.057 0.26 -0.066 1.69

q9: religious service attendance 0.102 2.72 0.055 1.50

Personality factors

extraversion, 0.142 4.54

agreeableness -0.053 1.50

conscientiousness 0.072 2.85

emotional stability -0.099 4.17

openness to experience 0.004 0.14

Life Domain Factors

School environment factor (schoolenv) 0.732 5.56 0.429 2.66

Interaction with friends factor (friends) 0.617 3.17

Natural environment factor (natenv)

constant 8.906 10.54 6.981 6.39 6.286 12.63 4.903 7.45

R2 0.063 0.1969 0.0739 0.0974

Adjusted R2 0.1757 0.0927

168

Table 4.44: The determinants of Life Satisfaction for children aged 9 to 14 years in the Smart Train dataset; OLS regression, N = 389

2 (c) School environment

Interaction with Friends Natural environment

2 (d) Natural environment

2 (e)Demographics

School environment Interaction with Friends

Natural environment

3 (a)Demographics Personality

School environment Interaction with Friends

Natural environment Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value

q1: Where did you visit the Smart Train? 0.011 0.79 0.013 0.95

q2: female =1 0.532 2.66 0.701 3.46

q4: school grade (age proxy) -0.149 1.59 -0.129 1.43

q5: relative wealth 0.023 0.11 -0.088 0.41

q9: religious service attendance 0.057 1.50 0.052 1.40

Personality factors

extraversion, 0.105 2.08

agreeableness -0.123 1.44

conscientiousness 0.033 0.61

emotional stability -0.097 4.02

openness to experience -0.027 0.63

Life Domain Factors

School environment factor (schoolenv) 0.421 2.54 0.415 2.46 0.482 0.84

Interaction with friends factor (friends) 0.612 3.11 0.463 2.22 0.514 0.82

Natural environment factor (natenv) 0.011 0.23 0.111 2.45 -0.003 0.07 0.035 0.75

constant 4.877 7.30 8.172 23.24 5.867 5.71 6.884 6.26

R2 0.0975 0.0153 0.1251 0.2001

Adjusted R2 0.0905 0.0127 0.1067 0.1724

169

Table 4.45: Other determinant variables of Life Satisfaction for the children aged 9 to 14 years in the cross-sectional Smart Train dataset; OLS regression, N = 389

Variable: coefficient t-value

q1: Where did you visit the Smart Train? -0.018 1.31

q2: female =1 0.733 3.40

q4: school year (age proxy) -0.166 1.63

q5: relative wealth 0.111 0.48

q9: religious service attendance 0.091 2.30

Magic:

q6: good luck charms do bring good luck (1 definitely not true to 5 definitely true) -0.107 0.83

q7: Do you have a lucky charm such as a mascot or a talisman? (yes = 1) 0.129 0.52

q8: Do you believe that a lucky charm can protect or help you? (1 definitely not true to 5 definitely true)

-0.032 0.25

q10: Some fortune tellers really can foresee the future (1 definitely not true to 5 definitely true) 0.053 0.53

q11: Is there someone who cannot be seen by others watching over you? (yes = 1) 0.032 0.12

Environment:

q15: Are animals an important part of your life? (yes = 1) 0.534 1.23

q16: Are plants an important part of your life? (yes = 1) -0.089 0.30

q17: Does your family talk about the environment much? (yes = 1) 0.396 1.78

q18: Have you ever started a conversation about nature or the environment? (yes = 1) 0.034 0.14

q22: Let's say that in your neighbourhood everyone throws their garbage in the river; would that be all right? (no = 1)

-0.579 0.68

q23: Let's say that in New South Wales, a whole neighbourhood throws its garbage in the river. Do you think it is all right or not all right for them to throw their garbage in the river? (no = 1)

1.029 0.92

q24: Do you think that throwing garbage in the river is harmful to the birds that live around the river? (yes = 1)

-0.467 1.13

Handedness:

q63: What hand do you write with? (left = 1)

-0.177 0.56

q64: Which finger is longer? (1 my ring finger is longer; 2 my ring and index fingers are the same length; 3 my index finger is longer)

-0.032 0.27

constant 6.723 2.75

R2 0.089

Adjusted R2 0.042

170

Table 4.46: The determinants of Life Satisfaction; Pooled OLS regression results for 15 to 23 year-old cohort and All (15 to 92 year-olds) in the HILDA; N = 12,330

1 (a) 15 to 23 year-olds

Usual Suspects+Health 1 (b) All

Usual Suspects+Health 2 (a) 15 to 23 year-olds

Kitchen Sink 2 (b) All

Kitchen Sink

Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value

age -0.322 -4.31 -0.046 -28.73 -0.315 -4.2 -0.038 -21.8 age*age 0.007 3.29 0.001 35.61 0.006 3.22 0.001 29.38 ln household income 0.035 3.3 0.033 6.37 0.034 3.27 0.032 6.09 male -0.031 -1.35 -0.092 -9.34 -0.032 -1.38 -0.088 -8.89 education 0.011 0.76 -0.061 -21.16 0.008 0.55 -0.064 -22.06 number of children 0.113 2.71 -0.063 -12.27 0.105 2.04 -0.061 -11.43 married 0.419 6.06 0.362 30.57 0.324 3.41 0.259 15.41 employed 0.076 2.58 -0.126 -9.26 0.084 2.84 -0.112 -8.27 unemployed -0.190 -4.27 -0.371 -12.74 -0.172 -3.85 -0.315 -10.76 regional income 0.000 4.27 0.000 6.65 0.000 4.23 0.000 6.01 home owner 0.093 3.47 0.148 12.15 0.086 3.2 0.135 11.09 imputed rent 0.000 -0.09 0.000 -0.53 0.000 -0.1 0.000 -0.55 health -0.528 -39.43 -0.527 -91.72 -0.522 -38.97 -0.524 -91.63 invalid -0.187 -4.92 -0.108 -8.18 -0.185 -4.88 -0.105 -8.03 family death -0.027 -0.72 0.016 1.03 divorced -0.445 -0.94 -0.063 -2.89 partner dead (dropped) 0.023 0.75 just married 0.103 0.83 0.101 3.21 just divorced -0.825 -1.41 -0.192 -2.84 just separated -0.267 -5.48 -0.419 -15.71 spouse just died -0.266 -1.4 -0.249 -4.47 just had a baby -0.070 -0.68 0.120 3.6 pregnant 0.145 2.09 0.125 4.51 just fired from job -0.171 -3.08 -0.288 -9.89 constant 12.322 16.99 10.246 149.430 12.282 16.9 10.189 147.13

R2 0.1704 0.1672 0.1795 0.1750

Adjusted R2 0.1687 0.1670 0.1769 0.1748

171

Chapter Five

Do changes in the lives of our peers make us unhappy?

In Chapter 5 I account for declines in overall life satisfaction by considering the

effects from life event shocks over time and seek to gauge if their stressful effect

explains the changes in aggregate happiness over the life cycle85. An advantage of

looking at the aggregate level of happiness is that it solves the problems of missing

peer effects and measurement error that plague models of individual level happiness,

though the disadvantage is a dramatic loss of degrees of freedom. I use panel data

from the Household Income and Labour Dynamics for Australia (HILDA), which

allows us to construct an index of the severity of life changes for each age. This

single-variable Stress Index is able to explain over 80% of the variation in happiness

over time. Unexpectedly, aggregate ‘positive stress’ (such as marriage rates by age

or levels of job promotion) has a greater negative effect on aggregate life satisfaction

than negative stress (such as negative financial events or deaths of spouses). This

result is interpreted as a strong indication that what is deemed a positive event by the

person involved is a highly negative event for his or her peers. I find some evidence

that extraverted individuals are affected less negatively by stress. The happiness

maximising policy is to reduce stress-inducing changes over the life cycle to the bare

minimum needed to sustain a dynamic economy and to sustain procreation.

85 This chapter is a peer-reviewed paper (Beatton & Frijters, 2009) presented at the 2009 HILDA Conference at the University of Melbourne, Australia (HILDA, 2009). In 2011, this chapter was submitted, as the joint paper with my supervisor Professor Paul Frijters, to the Journal of Happiness Studies. The paper is titled ‘Do changes in the lives of our peers make us unhappy?” and is currently with the reviewers. We would like to thank conference attendees, anonymous referees, and, seminar participants for useful comments and suggestions.

172

5.1 Introduction

Whilst we have now had over 30 years of experience in running regressions on self-

stated happiness, our ability to predict happiness has so far been rather poor. Recent

economics papers usually manage to explain about 15% of the cross-sectional

variance. For instance, Di Tella, et al. (2001, p.340), using over 250,000 observations

from twelve Western European countries, found that age together with demographic

variables like gender, education, employment status, income, marital status and

number of children explained 17% of the variance. Blanchflower & Oswald (2004),

using sixteen socio-demographic variables, explain only 9% of the happiness of

individuals in the US General Social Survey. Frijters & Beatton (2008), using a

‘kitchen sink” set of nineteen socio-demographic variables and eight life event

dummies, explain just 8% of the happiness of individuals in the German

Socioeconomic Panel (GSOEP).

Only when one includes other subjective variables does the percentage of variance

really go up. Ferrer-i-Carbonell & Frijters (2004) thus add subjective health which,

added to the usual socio-economic variables, explained 26% of West German

happiness. Personality factors and mood are able to increase this to about 60%. Yet,

for economists, the explanation of one fairly subjective question by another is rather

disappointing and raises the spectre of endogeneity (Powdthavee, 2007). We would

prefer to be able to explain happiness by variables we can interpret as prices,

constraints and consumption. It is remarkable that, after more than a decade of

intense economic research in this area (see Clark, et al. 2008) for a meta-analysis),

we have come no further than explaining 15% with socio-economic characteristics,

just as Argyle, et al. (1999) reported that psychologists and demographers managed

to explain in the decades prior.

In this chapter, I hypothesise that there are two main problems with analysing

happiness at the individual level: unmeasured peer effects and measurement error.

Almost no dataset is able to track all the peers of an individual and all the subtle

interactions between them. As a result, we do not measure all the influences that

friends, family, and neighbours exert on us daily. A well-known example of missing

173

peer effects arises when considering income. Results from happiness regressions

consistently show a positive and significant coefficient for income; an increase in

income makes us happier (Clark & Oswald, 1996; Di Tella, et al., 2001; Winkelmann

& Winkelmann, 1998). However, is the magnitude of the regression coefficient and

the level explanation truly reflective of the effect of the income increase? Easterlin

(2001) reminds us of the importance of peer influences. Whilst we get happier if our

own income goes up, we get unhappier if the income of our peers goes up. We

adjudge our happiness relative to the peers we compare ourselves with (Falk &

Knell, 2004); all our friends, family and acquaintances. For a model of individual

level happiness to truly reflect the effect of the change in income on happiness, we

need happiness and income data for an individual and all that individual’s peers. Of

course, this is not possible because panel surveys like HILDA and GSOEP follow

families, not individuals and all their peers; we have missing variables. This problem

may also hold for other variables like marriage events and children. Whilst the

individual who marries is happier during the wedding, those attending may feel

jealous and be unhappier. Childless individuals might be more miserable when their

friends have many children. Regression results for models of individual level

happiness that lack peer variables will suffer from bias if the observed characteristics

correlate with the unobserved peer effects.

As to measurement error, no dataset that we know of is capable of perfectly

measuring all the consumption variables economists think of as being important to

the utility of individuals. Indeed, no variable we usually put on the right-hand side

can be unequivocally interpreted as a certain unit of consumption of something. For

example, income is nearly always included in the list of explanatory variables but is

known to be measured with a great degree of error (due to recall bias, missing

compensation wage variation, contingent in-kind welfare, etc.) Even if it were

perfectly measured, it would still only be a proxy for what economists theoretically

think is really important, consumption. Another such example is marriage.

Researchers routinely add a marriage indicator in regressions, but not all marriages

are the same. Some marriages ‘work well’ and ‘produce’ lots of unmeasured

household goods, whilst others can be virtual prisons with negative production. Yet

all that remains of this heterogeneity in actual married life is a single marriage

dummy that is implicitly hypothesised to have exactly the same effect on everyone.

174

What holds for marriage could be argued to hold for every variable we routinely

include on the right-hand side: we ignore the measurement error involved in our

variables. Ignoring measurement error is almost unavoidable in any applied empirical

work using many variables, but it may be one of the key reasons for our inability to

explain more of the variation in happiness.

The approach, which I believe to be entirely novel in the economic happiness

literature, is to focus not on explaining individual happiness but on the aggregate

happiness of individuals of approximately the same age. We call this an aggregate

model of happiness. The advantage of this aggregation is two-fold. Firstly, when one

uses averages, measurement errors are dampened because the signal to noise ratio

increases. Secondly, and perhaps much more importantly, the average characteristics

by age are likely to coincide with the average characteristics of the peers. Hence,

peer effects that are almost impossible to identify at the individual level, because of

the inability to include all the relevant peers, come within reach when one averages.

Note that this does not mean we assume that the peers of an individual are others of

the same age. Rather, it means that the average peer of the average individual is of a

similar age86.

Drawing on the psychological literature, I then test the possibility that nearly all the

cross-sectional variance in happiness is due to stress. I measure stress not by using

subjective questions, but rather by measuring the believed cause(s) of stress: a

weighted average of the frequency of life events that psychologists have argued are a

major cause of stress. I test this hypothesis on the Household Income Dynamics for

Australia, progressively expanding our aggregate model of happiness. By comparing

the effect of stress at the aggregate level with the effect of the same life events at the

individual level, we can also say something about the likely direction of peer effects

and hence something about the data we are missing at the individual level. After

testing the basic idea, which holds remarkably well (over 80% of variance is

explained by a simple weighted average of life events), I expand the basic model to

allow the effect of stress to differ by personality, but first let us look at the data.

86 The definition of a peer is explained in section 5.3.1.

175

5.2 The Data

In this chapter I use the first six waves of the ‘Household, Income and Labour

Dynamics in Australia’ (HILDA) Survey. This is a household–based panel study

which began in 2001 (HILDA, 2008b). It has the following key features:

• It collects information about economic and subjective wellbeing, labour

market dynamics and family dynamics.

• Special questionnaire modules are included each wave including personality

questions in wave 5.

• The initial Wave 1 panel consisted of 7682 households and 19,914

individuals.

• Interviews are conducted annually with all adult members of each household.

• Wave 6 (2006) tracks 12,905 individuals with 95% retention from Wave 5.

The happiness question is based on the Fordyce (1973) Global Happiness Scale87. It

asks ‘All things considered, how satisfied are you with your life?’ with the ordinal

responses ranging from 0 (very unhappy) to 10 (very happy). It seeks to measure the

aggregate utility from all the good and bad things that occur throughout our lives

(Fordyce, 1988). Table 5.47 shows the sample averages for the 55,177 person-year

observations we have available. Average life satisfaction is 7.94, which is relatively

high compared to other Western countries (see Clark et al. 2008).

87 The HILDA user manual (HILDA, 2008a; p.144) notes that the HILDA questions relating to life satisfaction domains is based upon the work of (Cummins (1996). Fordyce (1998, p.357) notes that the Global Happiness Question arises from the early work of Wessman & Ricks (1966).

176

Table 5.47: Sample averages for individuals in the HILDA; N = 55,177 Mean s.d. Min Max Variable:

Individuals in the HILDA waves 2 to

6 (2002 to 2006)

11,035 155.16 10,869 11,255

happiness 7.94 1.49 0 10

age 43.61 17.67 15 93

age*age 2214 1672 225 8649

time* time 2858.5 2048.03 225 7056

Ln (weekly household income) 5.188 3.145 0 9.195

weekly household income ($) 1054.29 1050.01 1 9845

pension Income ($) 97.28 162.67 0 3000

female .530 .499 0 1

education years 12.68 1.785 9 18

married .520 .500 0 1

separated .035 .183 0 1

never married .231 .421 0 1

divorced .087 .282 0 1

widowed .048 .214 0 1

employed .646 .478 0 1

unemployed .034 .180 0 1

disability .234 .423 0 1

health 3.391 .959 1 5

health a year ago 3.075 .688 1 5

Tables 5.44 & 5.45 show the sample averages of the life-events used to construct the

measure of stress. As one can see, the HILDA includes more self-reported life events

(21, versus 7 in the GSOEP). In addition, there are many recorded life events by

category. For instance, per person-year observation, 0.059 change jobs. That is

almost 3200 job changes over the 6 years of the sample. Similar high numbers of life

events hold for all the other categories.

177

Table 5.48: Sample averages for life events affecting individuals in the HILDA; N = 55,177 Mean s.d. Min Max Variable:

spouse/child death .007 .078 0 .87

death of a relative .086 .246 0 .79

personal injury .067 .218 0 .78

jailing of self .002 .034 0 .76

injury to a family member .121 .296 0 .72

property crime victim .039 .160 0 .70

victim of violence .012 .091 0 69

just separated .027 .131 0 .66

just reconciled .007 .070 0 .66

fired from job .019 .108 0 .64

worsening finances .018 .104 0 .62

death of friend .066 .189 0 .61

friend jailed .007 .064 0 .56

just married .011 .068 0 .43

start new job .059 .148 0 .43

just pregnant .021 .089 0 .41

moving house .061 .133 0 .35

improved finances .011 .058 0 .33

promoted at work .021 .081 0 .33

birth of child .011 .060 0 .33

just retired .006 .041 0 .28

178

5.3 Methodology analyses and results

5.3.1 Theoretical framework

Suppose the true model of the happiness (GSit) of individual i in period t is given by:

(5.1a)

Here, GSit is affected by an individual’s own circumstances (Xit) and those of the

peers (Xpeerit), as well as random errors (uit). Suppose now that what is usually

estimated in empirical happiness regressions is the following:

(5.1b)

Here, Zit is now a noisy measure of (Xit) that includes random measurement error

(eit). In the absence of peer effects, it is well known that the estimate of β will be a

downward biased estimate of the true βs because of the presence of measurement

error. In our case though, there is both measurement error and missing variables.

What we then get as the asymptotic estimate of β is (neglecting errors that go to zero

as i and t go to infinity):

which is biased in two directions: biased towards zero because of measurement error

and biased in an unknown direction (because we have no a priori expectation of the

sign of λ) due to the correlation between individual characteristics and the peer

characteristics.

it s it it itGS X Xpeer uβ λ= + +

ititit vZGS += β

ititit eXZ +=

2

2 2 2 2 2

cov( , ) cov( , )it it X s it it

Z X e X e

Z GS X Xpeerσ β λβσ σ σ σ σ

⎡ ⎤= = +⎢ ⎥ + +⎣ ⎦

179

What we propose to estimate is:

(5.1c)

Where S(t) is the set of individual-year combinations of approximately the same age t

and Nt is the number of observations on age t. If we now presume that:

then both the measurement error problem and the peer effects issue get ‘solved’ in

the sense that the asymptotic estimate of the parameter now becomes:

which occurs because averaging gets rid (asymptotically) of the measurement error,

and the assumption that the average peer is the same as the population average of the

same age means we obtain a coefficient whose estimate we can interpret as the sum

of the individual and peer effect. It is important to point out that this procedure gives

very different results to simply including the average X by age in equation (1a)

because the correlation between the characteristics of the actual peers of each

individual and our artificial ‘aggregate peer’ may be very small88. It is only for the

aggregate of individuals of the same age that we assume the aggregate peer has the

same characteristics as themselves.

88 The obvious disadvantage of averaging is that we have far fewer observations than before: from 55,000 person-year observations, the data is reduced to a mere 70 different age-happiness points. This means we should apply extreme care when choosing which variables we wish to include and hence we wish immediately to choose a variable that can be argued to be responsible for a lot of variation in happiness.

1 1

1 1N N

it iti it t

X XpeerN N= =

=∑ ∑

( ) ( )

1 1it it t

i S t i S tt t

GS Z wN N

β∈ ∈

= +∑ ∑

λββ += s

180

Typically, our peers are those with whom we have something in common. Those

referent few with whom we compare ourselves; our friends, family, neighbours and

work colleagues. In models of individual of happiness, peers are often identified as

those who have a similar income (Becchetti, Trovato, & Bedoya, 2011), education

level (Mora & Oreopoulos, 2011; Ng, 2002) or age (Glaeser, Laibson, & Sacerdote,

2002) to ourselves. However, the model in this study is an aggregate model of

happiness and the peer effects emerge from the aggregate societal peer. This study

takes the position that the aggregate peer is someone from the same age-band. We

grow up in age-bands, armies are organised in age-bands, old-age care is roughly

organised in age-bands, and many laws are age-specific (e.g. when we need to start

school). Changes in the socio-economic situation of peers in our age-band have an

effect on our happiness throughout our lives.

Let us look at some examples of how change in the socio-economic variables that are

known to affect happiness can occur in age bands. To begin, we have laws that

dictate when we have to start school; in Australia, this is age five. During our school

years, our happiness can be affected by what happens to us and our classmates; who

must be in the same age-band as ourselves. Our peer in our age-band gets high

marks, they are happy, we are less happy. Let us look at what happens as we age.

According to The Australian Council for Educational Research (Marks, 2007), young

adults leave high school, enter then complete university or tertiary training within the

first three years after leaving high school; in an age-band from 20 to 23 years of age.

As a person in the 20 to 23-year age-band you and your peer look for your first job.

As an average graduate with average ability, your peer gets an average job with an

average income, but you get a higher paying job. You are happy but your peer in the

same age-band is less happy.

We move through the working period of our lives in age-bands. Most people get

work promotions and income increases in a certain age-band. For Australians in the

HILDA, on average, income increases occur in the age-band of 38 and 43 year of

age. We also retire with in age-bands. The Australian Bureau of Statistics reports that

on average Australians retire at age 58 (ABS, 2008). Your wealthy work peer, who

on average is most likely to be in our age-band because you both started school at the

same time, retires early but you have to and continue to work because you don’t have

181

the resources to retire; she is happy and you are not. Peers in our age-band continue

to have an effect on our happiness throughout our lives.

It is not just work peers in the same age-band that affect our happiness. Our peers are

often our neighbours, and, ‘neighbourhoods often include people in the same life

stage who are of similar ages’ (Feld, 1984, p.641). We saw in (Figure 4.32, p. 119)

that Australian women have babies in the age-band of 27 and 36 years. If a

neighbour (a peer that is most probably in our age-band) were to become pregnant

that would make them happier but it could make you unhappy if you could not get

pregnant. Similarly, our happiness is affected by failing health within particular age-

band(s) because health conditions such as cardiovascular disease, diabetes and

arthritis or osteoporosis particularly are associated with people at particular ages

(ABS, 2008). On average, changes in happiness affecting socio-economic variables

like education, income, job status, retirement or our health situation among others are

more likely to occur in age bands across our lives.

To capture this age-band, happiness and independent variable data have been

smoothed about age. The happiness and independent variable(s) at each age is an

equally weighted smoothed average of the variable two years prior to the age, the

age, and three years after the age; a six year equally weighted smoothed average. The

obvious disadvantage of averaging is that we have far fewer observations than

before: from 55,000 person-year observations, the data gets reduced to a mere 70

different age-happiness points. This means we should apply extreme care when

choosing which variables we wish to include and hence we wish immediately to

choose a ‘big variable’ that can be argued to be responsible for a lot of variation in

happiness.

The proposed ‘big variable’ that is constructed to explain happiness change over the

lifetime is the stress arising from life events. Richard Easterlin (ed., 2002; 2006)

argues that life is a succession of little mishaps and triumphs that determine how we

feel in the short-run, and big events that determine how we feel in the medium term

(say, a year). Imagine the difference in our response on an average day versus the

response on a rainy day where we had missed our train and walked to work sans

umbrella. We may have been perfectly happy before all this happened and self-rated

182

a happiness level of seven. However, having been asked the happiness question after

getting wet and missing our train, and because we now feel miserable, we record a

happiness level of five. Similarly, how we feel about a whole year will depend on the

various positive and negative life events our peers and we have experienced.

Therefore,

Hypothesis #1: An Aggregate Model of Happiness based on the average stress of

life events explains happiness over a lifetime.

How do we measure stress based on life events? One option is to include each life

event in the regressions, but, given that there are twenty-one of them in this sample

which are quite highly correlated, this is not statistically feasible. Yet, we can do this

at the individual level, and Table 5.55 (in Appendix C at the end of chapter 5) shows

the results from a standard regression of the type in equation (1a) that thus ignores

the peer effects and the measurement error problem. I do not discuss those results at

this time, but will come back to them later.

Since we cannot accurately gauge the stress of a life event from individual happiness

responses in our sample, I adopt the expert judgment by psychologists as to the

believed importance of individual events. Our measure of stress is then based on

Social Readjustment Rating Scale Theory (Hobson et al., 1998). Developed by

Holmes & Rahe in 1967, the Social Readjustment Rating Scale (SRRS)89 has been

one of the most widely used and cited assessment instruments in the literature on

stress and stress management. Published research since 1967 in psychology,

medicine and business indicates over 4000 citations (Hobson, et al., 1998). The basis

for SRRS theory is that sociologists and psychologists believe all life events bring

about change in our lives and because on average individuals are change averse, we

resist change (Lewin, 1951), change creates stress and greater levels of change make

us unhappy (Chamberlain & Zika, 1992) and even unhealthy (Wolff, Wolf, & Hare,

89 The SRRS levels emerged from a US study of 3,122 individuals and the review of the results by a panel of 30 professionals from the behavioural, medical, and social sciences (Hobson, et al., 1998). The professional review panel was representative of the gender and ethnic diversity of present day U.S. society: 15 (50%) panel members were female; 15 (50%) male; 3 (10%) were African American; 3 (10%) Hispanic; 2 (7%) Asian, and; 22 (73%) white.

183

1950). The more salient, and unexpected, the life event the greater the level of stress

from that event (Hobson et al., 1998). Also, stress is believed to be cumulative so the

more events affecting us at a particular time in our lives, the greater our aggregate

level of stress (Carlopio, Andrewartha, Armstrong, & Whetten, 2001) and the less

happy we are. For now, we take the SRRS weights as given, though we will return to

the issue of whether these weights are really reasonable later. Tables 5.46 & 5.47

show all the life events considered by SRRS theory, highlighting those life events

available in our HILDA data set.

Table 5.49: Stress levels defined by the Social Readjustment Rating Scale90

Stress Level Life Event

.87 Death of a spouse

.79 Death of a close family member

.78 Major injury or illness to self

.76 Detention in gaol or other institution

.72 Major injury or illness to close family member

.71 Foreclosure on a loan/mortgage

.71 Divorce

.70 Victim of crime

.69 Victim of police brutality

.69 Infidelity

.69 Experiencing domestic violence/sexual abuse

.66 Separation with spouse/mate

.66 reconciliation with spouse/mate

.64 Being fired/laid-off/unemployed

.62 Experiencing financial problems/difficulties

.61 Death of a close friend

.59 Surviving a disaster

.59 Becoming a single parent

.56 Assuming responsibility for a sick or elderly loved one

.56 Loss or major reduction in health insurance/benefits

.56 Self/close family member being arrested for violating the law

90 The highlighted and italicised life events are in the HILDA panel data waves 2 to 6.

184

Table 5.50: Stress levels defined by the Social Readjustment Rating Scale (continued)

Stress Level Life Event

.53 Experiencing/involved in a car accident

.53 Being disciplined at work/demoted

.51 Dealing with an unwanted pregnancy

.50 Adult Child moving in with parent/parent moving in with adult child

.48 Experiencing employment discrimination/sexual harassment

.47 Attempting to modify addictive behaviour of self

.46 Discover/attempt to modify addictive behaviour of close family membe

.45 Employer reorganising/downsizing

.44 Dealing with infertility/miscarriage

.43 Getting married/remarried

.43 Changing employers/careers

.42 Failure to obtain/qualify for a mortgage

.41 Pregnancy of self/spouse

.39 Experiencing discrimination/harassment outside the workplace

.39 Release from gaol

.38 Spouse/mate begins/ceases work outside home

.37 Major disagreement with boss or co-worker

.35 Change in residence

.34 Finding appropriate child care/day care

Our measure of Stress by age is the smoothed simple average of the life events, weighted by the SRRS-based ‘stress level’ impact estimates (Table 5.49 & 5.50):

1*t s stsStress SRRS Le −= ∑ 0< SRRS <1

Which defines Stress as the sum of, the life events (Le) occurring in the previous

period (year91) weighted by the stress level (SRRS) for each type (s) of life event92.

91 While stress is calculated at each age, the resultant time series data (Figure 5.39) are smoothed with a simple moving average that equally weights t-2 to t+3; therefore, a peer is defined as someone two years younger to three years older than self. 92 Tables 5.46 & 5.47 highlight the stress level for each life event based on SRRS theory and the highlighted life events are the twenty-one life events used from the HILDA.

185

5.3.2 Analysis and results

I begin testing Hypothesis #1 by using HILDA data to initiate the development of

our aggregate model (2) of average happiness (GS) with 15 to 84 year olds as the

time (t) reference:

ttt StressCGS εδ ++= )( (5.2) 8.67 -1.18 (215.43) (17.21) R2 = .81

C is the underlying ‘stress-free’ level of happiness that is subject to changes arising

from the Stress from life event shocks at a particular time in our lives.

Relative to models of happiness based on the individual, the aggregate model (5.2) of

happiness explains considerably more (R2 = 0.81) of the variance in happiness (Table

5.53 in Appendix B at the end of this chapter). Stress is strongly negatively related

(r = -0.90) to happiness. Figures 5.38 and 5.39 reiterate the remarkably good

empirical fit between happiness and stress by showing happiness by age and stress by

age; stress over a lifetime looks like the inverse of lifetime happiness. At age fifteen,

we have a higher level of happiness because we have been exposed to less stress. A

steep increase in stress between the ages of 15 to 19 years negatively correlates with

the steep decline in happiness we saw in 15 to 23 year olds in Chapter 4. Perhaps

stress really is explaining some of the change in happiness over a lifetime. As we age

further into our mid-years, we are exposed to more stress-creating life events and

subsequently become less happy. As we grow older, we are subject to less stress and

this leads to an increase in our happiness. Interestingly, over a lifetime, stress and

raw happiness appear as a U-shaped inverse of one another (Figures 5.38 & 5.39).

186

7.6

7.8

88.

28.

48.

6A

vera

ge L

ife S

atis

fact

ion

20 40 60 80Age

Figure 5.38: Average happiness for Australians aged 15 to 84

0.2

.4.6

.8S

tress

20 40 60 80Age

Figure 5.39: Average stress level for Australians aged 15 to 84

187

These two figures are also informative in the sense of the time series properties of the

two variables. It is known that if one regresses two lines with strong trends on each

other that one gets a high spurious relationship. This is clearly not the case with our

data , life satisfaction is much the same at age 18 as it is at age 80. Stress goes up,

plateaus, and then almost linearly reduces93.

A peculiar, so far implicit, aspect of the regression results for equation (5.2) is that

all life events affect aggregate life satisfaction negatively. This view is consistent

with Social Readjustment Rating Scale (SRRS) theory from psychology, but differs

considerably from what we see from economic models of individual happiness. In

economics, negative life events like unemployment or declining health have been

shown to decrease our happiness (Clark & Oswald, 1994; Wilson, 1967) while

positive life events like marriage or the birth of a child lead to increased happiness

(Frey & Stutzer, 2005). Essentially, equation (5.2) presumes all those events that

seem to be positive at the individual level are in fact still negative at the aggregate

level due to missing peer effects. Even though Table 5.54 (Chapter 3 Appendix B)

shows that marriage, promotion and financial improvements increase life satisfaction

at the individual level, equation (5.2) presumes they decrease life satisfaction at the

age level because of the negative effect of these events on peers.

I test if this really holds at the aggregate level by applying model (5.3) and splitting

the Stress variable into Positive_Stress and Negative_Stress where positive stress is

made up of those life events with a positive effect on the individual (see fixed effect

regression results in Table 5.53 in Chapter 5 Appendix B). Figure 5.40 shows the

evolution by age of this positive stress and negative stress.

93 The impact of life-events is not cumulative: there is adaptation to each life vent, implying that new shocks are needed to sustain a low level of happiness. When we get older, the lack of new shocks allows us to retain high levels of happiness.

188

0.1

.2.3

.4.5

20 40 60 80Age

Negative Life Events Positive Life Events

Figure 5.40: Average stress from positive and negative life events; Australians aged 15 to 84

Looking at Figure 5.40, we see that positive events happen more in mid-life

(promotions, marriages, income increases), whilst negative events are more

concentrated earlier on (injuries to family members, crime).

When we look at how positive stress and negative stress affect happiness, we get:

tttt StressNegativeStressPositiveCGS εδδ +++= )_()_( 21 (5.3)

8.73 -1.76 -0.57 (177.83) (5.13) (1.58)

R2 = .82

When disaggregated into positive and negative events we find that both positive and

negative life events reduce happiness, but that the effect of positive life events is

stronger and more significant. This is quite revealing. Why do life events that are

positive at the individual level, suddenly become negative in the aggregate? Within

the context of equation (5.1), the reason is the peer effects: what makes us happier at

the individual level can increase the jealousy, frustration, and hence stress levels of

189

our peers. On aggregate, it is clear that the peer effect dominates the individual

effect. More unhappiness is created by promotions, marriages, births, etc., via our

peers than we gain personally. This obviously has very strong policy ramifications

since it would mean nearly all life events not essential for our continued survival

should be reduced to a minimum, ceteris paribus.

So far, I have relied on ‘objective’ variables to explain life satisfaction. These life

events do not suffer from endogeneity problems to the same extent that, for instance,

health or mood does: it is not our unobserved individual proclivity to be happy that

causes our friends to marry and get promotions. We now introduce more subjective

variables and turn to the hypothesis that stress may not be equally bad for everyone

and that the importance of personality for life satisfaction is mainly in terms of how

personality allows us to cope with stress.

Psychologists have long argued that the level of stress is not only affected by the

number of life events, but is directly affected by personality (Mroczek & Kolarz,

1998). I test this in steps. Firstly, I look to see if there is any residual effect of

personality on happiness, after which I test the mediating effect of personality on

stress. The ‘direct effect of personality’ is usually argued to hold mainly for

extraversion and emotional stability. Costa & McCrae (1980) and Headey (2008)

identified an increased variance in the happiness of extraverts (talkative, outgoing,

lively) and neurotics (moody, touchy, jealous, temperamental) who exhibit lower

levels of emotional stability. In (5.4), we look at the direct effects of personality

traits on happiness (GS):

(5.4)

9.35 -1.54 -0.21 3.84 -0.74 -1.40 -1.53 (2.54) (10.91) (0.29) (5.62) (1.03) (2.22) (2.43)

R2 = .88

tttttttt PoPcPemPexPaStressCGS ελλλλλδ +++++++= 54321)(

190

The personality traits are the average at each age for the individuals measured in the

HILDA on a scale of 1 (lowest) to 7 (highest), using Goldberg’s Big-Five personality

factors:

Pa agreeableness

Pex extraversion

Pem emotional stability 94

Pc conscientiousness

Po openness

The thirty-six items tapping personality in the HILDA are based on Saucier's (1994)

edited version of Goldberg's (1990) Big-Five personality factors.

Wave 5 of the HILDA (2008b, p. 10) measured personality traits on a seven-point

scale and the five trait factors are composed by taking the average of the items

(Losoncz, 2007). The higher the score from the items in Table 5.51, the better that

personality trait describes the respondent. The Big 5 personality traits and related

behaviours are:

• Extroversion – talkative, bashful (reversed), quiet (reversed), shy (reversed), lively, and extroverted. • Agreeableness - sympathetic, kind, cooperative, and warm. • Conscientiousness - orderly, systematic, inefficient (reversed), sloppy (reversed), disorganised (reversed), and efficient. • Emotional stability - envious (reversed), moody (reversed), touchy (reversed), jealous (reversed), temperamental (reversed), and fretful (reversed). • Openness to experience - deep, philosophical, creative, intellectual, complex, imaginative.

94 Neuroticism is the inverse of emotional stability.

191

Table 5.51: The HILDA personality questionnaire (HILDA, 2008a)

192

Explaining each personality trait in more detail, openness refers to the extent to

which people are sensitive, flexible, creative or curious. Low scored individuals tend

to be more resistant to change and less open to new ideas, they are more fixed in

their ways. Agreeableness refers to traits where we are courteous, good-natured, kind

and considerate of others. This personality trait develops trust between individuals.

People with low agreeableness tend to be uncooperative, short-tempered and

irritable; they are hard to deal with. Conscientiousness refers to people who are

careful, dependable and self-disciplined; they have a will to achieve. Low

conscientiousness tends to predict carelessness, disorganisation and sloppy work.

Emotional stability and extraversion are the two traits that are most considered to

impact on happiness (Costa & McCrae, 1980; Diener, Sandvik, Pavot, & Fujita,

1992; William Pavot, Diener, & Fujita, 1990; Sahoo, Sahoo, & Harichandan, 2005).

Individuals exhibiting a low level of emotional stability (high in neuroticism) suffer

from negative affect and dissatisfaction while those high in extraversion exhibit

positive affect, satisfaction, and higher levels of happiness (Costa & McCrae, 1980;

Furnham & Petrides, 2003).

Contrary to early opinion of those like Freud who stated that our personality is fully

formed by adolescence, others have argued that our personality traits, particularly in

younger individuals, can change over time (see McCrae, Costa, Mroczek, & Little

,2006 for a review). Such a change manifests in the HILDA (Figures 3a to 3e), albeit

very small personality trait changes. On average95, Australians become more

agreeable (+2.4%), less extraverted (-1.6%), more emotionally stable (+7.9%), more

conscientious (+5.9%), and less open (-5.2%) to changes over their lifetime96.

One needs to be careful when interpreting this evidence of changes in personality

traits over time. There is a large body of opinion in the psychology literature that the

changes in the personality traits over time arise from measurement error (Ehrhardt,

Saris, & Veenhoven, 2000) and thus, personality is thought to be stable over age and

gender (McCrae, et al., 2002), others disagree (Rantanen, Metsapelto, Feldt, 95 While there is no such thing as an average person, we are individuals; Table 5.49 in Chapter 5 Appendix A provides personality trait averages, by age, for Australians in the HILDA. 96 Assuming the five pooled years of the 2002-2006 HILDA panel data are indicative of a typical lifetime.

193

Pulkkinen, & Kokko, 2007). Over time, subjects respond differently when asked to

respond to the same Big-5 personality questionnaire (this could not be the case in our

dataset because personality was measured in just one year of the six waves in our

panel dataset (wave 5, 2005). Other psychologists still assert that an individual’s

personality traits do change over time. In adults aged 33 to 42, Rantanen, Metsapelto,

Feldt, Pulkkinen, & Kokko (2007) found that neuroticism decreased over time and

extraversion, openness to experience, agreeableness, and conscientiousness increased

from age 33 to 42 (we can see some of these finding in Figures 5.41a to 5.41e). The

jury is still out on whether personality traits change over time, are subject to cohort,

or peer effects97. Given that the personality trait data in the HILDA are cross-

sectional (wave 5 only); researchers could better pursue the question of personality

change over time if data were available tracking individuals over a lifetime. To do

this, the socio-economic panel surveys would need to include the personality

questions in every annual survey.

97 It is possible that the personality of a particular age cohort could have been formed by events particular to that cohort; in example, children exposed to food scarcity or traumatic events during world war two ( today’s 70 year olds).

194

Figures 5.41a to e: Change in personality factors over time for Australians aged 15 to 84; scale is 1 to 7

4.22

4.24

4.26

4.28

4.3

Per

sona

lity

Trai

t - A

gree

able

ness

20 40 60 80Age

Figure 41a: Agreeableness by Age

4.05

4.1

4.15

Per

sona

lity

Trai

t - E

xtra

vers

ion

20 40 60 80Age

Figure 41b: Extraversion by Age

4.15

4.2

4.25

4.3

4.35

4.4

Per

sona

lity

Trai

t - E

mot

iona

l Sta

bilit

y

20 40 60 80Age

Figure 41c: Emotional Stability by Age

4.1

4.15

4.2

4.25

4.3

4.35

Per

sona

lity

Trai

t - C

onsc

ient

ious

ness

20 40 60 80Age

Figure 41d: Conscientiousness by Age

195

Figure 5.41a to e (continued): Change in personality factors over time for Australians aged 15 to 84; scale is 1 to 7

3.9

3.95

44.

054.

1Pe

rson

ality

Tra

it - O

penn

ess

20 40 60 80Age

Figure 41e: Openness by Age

196

Looking back at our regression results in Table 5.53 (Chapter 5 Appendix B),

personality has a direct effect on happiness and increases the level of explanation

from R2 = .82 to .88 whilst the effect of Stress reduces by thirteen-per cent. Stress

remains by far the most important variable, which, from an economist’s point of

view, is heartening because it suggests real events can trump subjective perceptions

in the ability to explain life satisfaction.

Looking at each trait, extraversion has a significant positive direct effect on

happiness (Table 5.53). Extraversion and neuroticism impact life satisfaction through

daily emotional experiences (Howell, 2006). Extraversion is associated with a

positive outlook on life, better health, higher levels of success in marriage, work and

other aspects of our lives; this positive reinforcement makes us happier

(Lyubomirsky, et al., 2005). Conscientiousness and openness make us unhappier.

Conscientiousness impacts happiness through daily behavioural choices (Howell,

2006). We can imagine the systematic, procedural organised person getting unhappy

when life event changes upset the equilibrium of their orderly environment. The

openness items in the personality measure tap the notion of intelligence (creative,

intellectual, imaginative). Cognitive level (intelligence) is highly positively

correlated with education level (Rindermann, 2008) and higher levels of education

translates to reduced happiness (Clark & Oswald, 1996). We can imagine a creative

intellectual worrying about the problems of the world (like the environment) and

seeking answers to improve the situation, at the expense of their happiness. The

personality variables that have an insignificant direct effect on happiness are

emotional stability and agreeableness.

Agreeableness relates to those who exhibit sympathy and are warm, kind and

cooperative towards others. The coefficient is non-significant but negative, probably

because agreeableness acts indirectly through daily behavioural choices (Howell,

2006). The non-significance and negative coefficient for emotional stability defies

current literature. Costa & McCrae (1980) and Furnham & Petrides (2003) found that

emotional stability positively affects happiness. Perhaps this is because emotional

stability impacts happiness through daily emotional experiences (Howell, 2006). We

need to examine the indirect effect of personality on the life events that impact our

lives and lead to the stress that affects our happiness.

197

We next turn to the role of personality as an intermediary of stress. The longitudinal

study of McCrae & Costa (1995) found that personality traits affect how we react to

situations that confront us throughout our lives. Happiness pursuing persons behave

differently and have a more positive notion of happiness (Rojas, 2007). Headey &

Wearing (1989) found that stable personality traits of emotional stability,

extraversion and openness to experience predispose people to experience moderately

stable levels of favourable and adverse reaction to life events; personality plays a role

in how we react to the life events that confront us throughout our lives. In finalising

our Aggregate Model of Happiness (5.5), we evolve the model (5.4) by adding the

indirect effect of each personality trait and the Stress from life events on happiness

(see Model 5 regression results are shown in Table 5.53 in Chapter 3 Appendix B).

tttttt PStressPStressCGS εηλδ ++++= )'*()'(1 (5.5)

where

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

=

54321

λλλλλ

λ

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

=

54321

ηηηηη

η

and

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

=

PoPc

PemPexPa

P

.

198

The first striking aspect is that happiness is almost completely explained (R2 = .95)

by this set of variables98. Since we are now up to 11 variables explaining 70 data

points, a high R2 was to be expected, but 0.95 is simply a novelty in this literature.

The inclusion of the indirect effects of personality (P) and Stress has reduced the

direct effect of Stress by 56%, which suggests strong intermediate effects of

personality on the experience of stress. In addition, the direct effect of the personality

traits has changed; openness and conscientiousness have both become insignificant.

Only extraversion remains as a mildly significant personality factor directly affecting

happiness. Otherwise, the effect of personality is entirely through the life events

leading to stress, with the main interactions being for the direct positive effects of

extraversion and the negative effects from stress (Figure 5.42).

Stress

Emotional Stability

Conscientiousness

Life Events

Openness/Intellect

Big 5 Personality

Traits

Extraversion

Agreeableness

Happiness

+

‐

‐

+

‐

‐

Figures 5.42: The role of the direct and indirect effects from personality on life events and

the stress arising from those life events

98 Personality traits should be viewed as causally antecedent to life events, they are partly genetic. Traits predispose people towards experiencing specific patterns of events. In example, those who are high in the neuroticism trait experience more negative events than those low in neuroticism. As supported by the regression results (Table 5.50, p.195), the total effects (both direct & indirect via life events) are strong;, and explain much of the variance in happiness.

199

Interestingly, openness worsens stress. It appears that creative intellectuals (= open)

worry when they are confronted with the problems (life event shocks) of the world

and in seeking answers do so at the expense of their happiness. Extraverts are the

opposite. In their daily reactions to life event shocks, the positive outlook of

extraverts makes them experience their own life shocks and that of their peers as,

overall, positive events out of which they get enjoyment. In this final model, on

average, a one standard deviation increase in stress translates to a .17 unit decrease in

happiness (holding the personality variables at their mean). A one standard deviation

increase in extraversion has a minimal direct effect on happiness of less than 0.05

units. The effect from a one standard deviation in openness*stress decreases

happiness by 0.865 units but the indirect effect of a one standard deviation increase

from extraversion*stress has the largest effect with a 1.043 unit increase in

happiness. Thus, extraverts react positively to events and benefit from situations that

would make those with a high degree of openness less happy. Conditional on the

other factors, conscientiousness no longer significantly affects happiness, neither

directly nor indirectly. Similarly, emotional stability has no conditional effect on

happiness.

200

5.3.2.1 Importance of the SRRS weights

In order to see whether the main results are highly dependent on the SRRS scales, we

ran equation (5.2) in Table 5.54 with the 11 life events that made the biggest

contribution to aggregate stress, adding them in sequentially. When we include all 21

life events (not shown), standard deviations become very large and all significance is

lost. Table 5.54 shows that there is general non-robustness of the effects of individual

life events. Personal injury for instance has a strongly negative effect in the first four

specifications, with a coefficient of -0.014 if it is included as the only life event.

When eleven life events are included, personal injury has a coefficient of -0.001 and

is non-significant. Similar parameter instability holds for being a victim of violence

(which has a positive coefficient!) and financial stress, which we attribute to the

strong multi-collinearity between the frequencies of these life events.

Despite the multi-collinearity problem, which makes it difficult to take the relative

magnitudes at face value, Table 5.54 does confirm that positive life events can have

strong negative effects on aggregate happiness. Having just married and an

improvement of finances all have significant negative aggregate effects, whereas they

are strongly positive at the individual level. Indeed, the negative effect of

improvement in finances is the single highest coefficient in the final specification.

Interestingly, the other significant negative variables are separated and worsening of

finances. It is tempting to think of this group of variables as highly visible variables

that are likely to affect friends and families. Given that we do not want to put too

much emphasis on these results due to the multi-collinearity problem, we do not

want to overplay this interpretation and merely note that the main thrust of the

analyses based on a particular weighting of the life events is also evident if we use

unweighted aggregate life events.

201


The first limitation of the Chapter 5 study applies to all studies in this thesis. The

studies use panel data from western countries. There are cultural differences

between Western and Asian countries (Hofstede, 1983). While an extraverted

person may be more desirable and therefore happier in an individualistic western

culture like Australia, an extraverted person in a collective culture (e.g. China)

may be ostracised and therefore unhappier because they are not complying with

the cultural norm of their (Chinese) group; generally introverted. It would be

interesting to analyse how cross-cultural personality differences affect happiness.

Research that considers happiness, adaption, as well as the cultural norms may

help to explain if and why some immigrants contribute more slowly (faster) to

the economic growth of their adopted country.

The second limitation of the Chapter 5 study arises from a claim that the dependent

variable of the study is not necessarily life satisfaction but the age profile of life

satisfaction. The aggregate happiness variable was constructed from the individual

happiness question data. This constructed aggregate variable is the dependent

variable in the regressions that seek to explain happiness change over a lifetime,

happiness change as we age. The left-hand side variable is the age profile of

happiness and abstracts from the vast majority of variation in happiness. As such, all

the statements in the study apply to this aggregate variable, not interpersonal

difference.

The next limitation concerns whether the age-profile in happiness would be well

explained by anything that looks U-shaped or inverted-U shaped. The selection of

stress as the independent variable did not emerge from a process of test and selection.

The decision to use stress as the independent variable is theory-based and its

construction is consistent with the application of the well-accepted Social

Readjustment Rating Scale Theory from psychology (Holmes & Rahe, 1967; Hobson

et al., 1998). The stress variable was not selectively chosen because it looked U-

shaped or inverted-U shaped, its choice was founded upon a large existing

psychological literature.

202

Another limitation is the acceptance of the scaling of the life events with the SRRS-

theory-based weights to form the aggregate stress variable. Interacting personality

directly with non-SRRS weighted life event dummies produces a very different stress

profile in age. Ideally, econometricians would prefer that weights come from the data

and not be imported from the theory of another scientific discipline. Frijters,

Johnston & Shields (2009) used this approach when they sought to contribute to our

understanding of how to weight the effect of each life event type on the overall

wellbeing of individuals. They used quarterly life event data to model anticipation

and adaption to a small number of life events that included changes in financial

situation, marital status, death of a close relative, and becoming a victim of crime.

Speaking with the authors, the reason they chose a small number of life events from

the 21 available in the HILDA was because some excluded events replicated the

included events (e.g. pregnancy, death of other close relative). The ten events they

did use were the only ones that occurred often enough to provide significant

regression results. The authors were not only concerned about the small number of

observations per life event type; they also voiced their concern about measurement

error in the self-reported quarterly life event data.

HILDA subjects respond to the survey annually but are asked to recall whether a life

event occurred one, two, three or four quarters ago. Can subjects really recall how

many quarters ago a life event occurred? Certainly, we could recall a major event

like the death of a spouse. However, as we see in Figures 5.43a to 5.43k, the total

stress at each age arises from the accumulation of stress from a large number of

minor individual life events. Can we truly remember when minor individual life

events occurred? Was it three or four quarters ago that my finances worsened, or did

I acquire that injury on the 28th of June (Quarter 2) or on the 3rd of July (Quarter 3)?

Measurement error in quarterly life event data is a major limitation to a happiness

study. Looking at the effects of a partner’s life events on individual happiness,

Mervin & Frijters (2011) uncovered so much noise in the life event data that the

causal effects were very difficult to identify. Measurement error in life event

reporting is of particular concern to this economics of happiness researcher because I

need to know when an event occurred if I am to accurately gauge the happiness

change from each life event category. As to there not being enough of each life event

203

category in the data to identify their effect on happiness, we will just have to wait for

additional waves of the HILDA panel data. In the meantime, researchers might

consider using the aggregate approach from this study.


This chapter sought to account for life event shocks by gauging if their stressful

effect explained changes in aggregate happiness over the life cycle. The advantage of

looking at the aggregate level of happiness was that it solved the problems of missing

peer effects and measurement error that plague models of individual level happiness.

The key assumption under which this aggregation allowed us to say something about

peer effects is the assumption that the aggregate peer of the aggregate individual is

someone of approximately the same aggregate age.

It was hypothesised that happiness is almost entirely explained by the direct effects

of the stress from the life event shocks, mediated by personality. We found that the

use of the stress variable could indeed explain over 90% of the variation in aggregate

happiness. Of particular interest is the finding that both negative and positive life

events bring about aggregate unhappiness. It might not be surprising that negative

events stress us. Aristotle already said that we humans focus our energies in pursuit

of virtuous happiness and hence become unhappy at negative events: we plan, set

expectations, and are delightedly happy when our plans are achieved and our

expectation met; yet we are disappointedly unhappy when they are not (Aristotle,

1819, p. 234, 254, 257). However, the finding that positive events (the ‘fruits of our

planning’, if you like) bring aggregate unhappiness makes no sense at the individual

level. Our interpretation is that the positive feelings we individually get from

promotions, pay rises, births, etc., are swamped by the stress this causes amongst our

family, friends and workmates.

The happiness maximising policy recommendation, ceteris paribus, is that we should

minimise life event shocks on society. All changes that are not essential to

procreation and minimum needs appear to lead to net loss of life satisfaction. At face

value, this would mean that divorcees should be taxed because their actions have

204

negative effects on their peers; people who go to jail or move house should also be

taxed to compensate the misery they are causing their neighbours and friends, etc.

These fairly radical conclusions require deeper examination99. Replication of our

results to other countries would generate more variation and would allow richer

specifications to be run. In addition, should these results turn out to be robust, we

might have to reconsider whether happiness is such a good measure of social utility.

If, in order to be happy, we have to force everyone to lead exceedingly dull lives,

perhaps happiness is not everything after all.

99 The application of a tax is a typical economic policy response to a negative externality, but, not necessarily the only effective policy response. The consideration of incentives that lead to lower rates of divorce or jailing etc. is more worthy of future research.

205

Chapter 5 - Appendix A: Descriptive Statistics

Table 5.52: Descriptive statistics for aggregate variables used in models (1) to (6); N = 70

Variable Mean s.d. Min Max

Average overall life satisfaction by age

(self-assessed on a scale of 0 to 10)

8.041 .317 7.646 8.556

Stress/1000 (sum of life events at each

age)

.531 .248 .076 .836

Positive_Stress/1000 (average sum of

positive life events at each age) .315 .130 .049 .495

Negative_Stress/1000 (average sum of

negative life events at each age)

.216 .123 .026 .417

Average of Personality Traits by Age

(self-assessed on a scale of 1 to 7)

agreeableness 4.275 .037 4.199 4.402

extraversion 4.082 .030 4.025 4.164

emotional stability 4.254 .082 4.132 4.519

conscientiousness 4.228 .059 4.084 4.366

openness 4.030 .050 3.842 4.099

Indirect effect of Personality Traits &

Stress/1000

agreeableness * stress 2.310 1.024 .396 3.555

extraversion * stress 2.221 .997 .373 3.419

emotional stability * stress 2.284 .1 .402 3.491

conscientiousness * stress 2.277 1.001 .398 3.506

openness * stress 2.197 .987 .361 3.391

206

207

Chapter 5 - Appendix B: Regression Results for the Aggregate Model of Happiness

Table 5.53: OLS regressions results for nested Aggregate Models of Happiness (5) for Australians aged 15 to 84; N = 70

(2) Stress

(3) Stress Valency

(4) Stress + Direct Personality

(5) Stress + Direct Personality

+ Indirect Stress*Personality Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value Stress/1000 -1.18 (17.21) -1.54 (10.91) -0.68 (2.54) Positive_Stress/1000 -1.76 (5.15) Negative_Stress/1000 -.572 (1.58) Average Personality Agreeableness -0.21 (0.29) -0.55 (1.07) Extraversion 3.84 (5.62) 1.62 (2.64) Emotional Stability -.074 (1.03) -0.17 (0.34) Conscientiousness -1.40 (2.22) 0.004 (0.01) Openness/Intellect -1.53 (2.43) -0.39 (0.73) Stress * Personality Agreeableness * Stress 4.15 (0.95) Extraversion* Stress 10.43 (5.71) Emotional Stability * Stress 0.44 (0.22) Conscientiousness * Stress 1.83 (0.79) Openness/Intellect * Stress -8.65 (5.17) constant 8.67 (215.43) 8.73 (177.83) 9.35 (2.54) 6.63 (2.42)

R2 0.81 0.82 0.88 0.95

208

Table 5.54: OLS regressions for the Aggregate Model of Happiness (5.2) with the eleven most important life events; N = 70

Model (2) Model (2) Model (2)

Variable: coefficient t-value coefficient t-value coefficient t-value personal injury -0.014 (6.16) -0.011 (6.96) -0.010 (6.87)just separated -0.012 (9.39) -0.008 (2.56)just reconciled -0.011 (1.07)victim of violence worsening finances constant 8.76 (73.15) 8.38 (110.87) 8.83 (110.14)R2 0.36 0.72 0.73

Model (2) Model (2) Model (2)

Variable: coefficient t-value coefficient t-value coefficient t-value

personal injury -0.011 (8.92) -0.005 (3.75) -0.001 (0.61)

just separated -0.017 (5.97) -0.012 (4.44) -0.008 (2.46)

just reconciled -0.007 (0.88) -0.004 (0.57) -0.001 (0.08)

victim of violence 0.018 (6.58) 0.014 (5.49) 0.009 (3.77)

worsening finances -0.015 (5.53) -0.009 (3.36)

improved finances -0.034 (5.02)

fired from job 0.001 (1.18)

death of a spouse/child 0.007 (1.20)

just married -0.007 (2.03)

just pregnant -0.003 (0.69)

birth of child 0.005 (0.81)

constant 8.83 (141.10) 8.67 (145.70) 8.55 (147.7)

R2 0.84 0.88 0.94

209

Figure 5.43a to k: Graphics of the stress at each age arising from the eleven most important life events

2040

6080

mul

eins

rsrr

s

20 40 60 80age

Figure 43a: Personal injury

020

4060

mul

esep

rsrrs

20 40 60 80age

Figure 43b: Just separated

05

1015

20m

uler

clrs

rrs

20 40 60 80age

Figure 43c: Just reconciled

010

2030

40m

ulev

iors

rrs

20 40 60 80age

Figure 43d: Victim of violence

210

Figure 5.43a to k (continued): Graphics of the stress at each age arising from the eleven most important life events

010

2030

40m

ulef

nwrs

rrs

20 40 60 80age

Figure 43e: Worsening finances

05

1015

mul

efni

rsrr

s

20 40 60 80age

Figure 43f: Improved finances

010

2030

40m

ulef

rdrs

rrs

20 40 60 80age

Figure 43g: Fired from job

05

10m

uled

scrs

rrs

20 40 60 80age

Figure 43h: Death of spouse/child

211

Figure 5.43a to k (continued): Graphics of the stress at each age arising from the eleven most important life events

010

2030

40m

ulem

arrs

rrs

20 40 60 80age

Figure 43i: Just married

020

4060

80m

ulep

rgrs

rrs

20 40 60 80age

Figure 43j: Just pregnant

010

2030

4050

mul

ebth

rsrr

s

20 40 60 80age

Figure 43k: Birth of child

212

213

Chapter 5 - Appendix C: Results for the Model of Individual Level of Happiness

Table 5.55: The determinants of Life Satisfaction for Australians; Pooled OLS regression results for individuals in the HILDA; N = 55,177 100 Age Age + Age2 + Demographics + Life Events Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value age .0080 22.24 -.0451 26.01 -.0478 22.81 -.0450 21.33age*age .0005 31.29 .0007 31.05 .0006 29.32 ln (weekly household income) .0158 5.70 . 0114 4.13

pension Income ($) -.0001 2.30 -.0001 1.72female . 0853 7.16 . 0857 7.24education years -.0622 17.99 -.0616 17.85married . 1620 6.76 .1148 4.73separated -.6360 16.59 -.4891 12.50never married -.2031 8.36 -.1701 6.91divorced -.2288 7.56 -.2128 7.06widowed -.1641 4.24 -.1687 4.35employed -.1323 7.45 -.0881 5.20unemployed -.3826 11.12 -.2706 7.85disability -.0798 5.07 -.0591 3.77health . 5140 72.02 .4923 68.89health a year ago . 1252 13.98 .1233 13.85spouse/child death - .2995 4.01death of a relative .0184 0.77personal injury -. 1268 4.62jailing of self . 0379 0.23injury to a family member -. 0876 4.03property crime victim -. 2560 7.08

100 These pooled OLS regression results are for Australians aged 15 to 84 in the HILDA panel data waves 2 to 6 for the period 2002 to 2006.

214

Age Age + Age2 + Demographics + Life Events Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value victim of violence - .5945 9.20just separated - .5987 12.40just reconciled - .1893 2.20fired from job - .2474 4.46worsening finances - 1.2115 21.54death of friend .0938 3.04friend jailed .0880 0.97just married .3015 3.48start new job - .1504 3.55just pregnant .3192 3.93moving house .0411 0.89improved finances .5446 5.47promoted at work - .0497 0.68birth of child .2479 2.09just retired .7762 5.49constant 7.5938 451.84 8.6336 232.26 7.2077 102.42 7.2998 102.49

R2 0.01 0.03 0.17 0.19

215

Table 5.56: The determinants of Life Satisfaction for Australians; Fixed-effect regression results for individuals in the balanced HILDA panel; N = 55,177 101

Age Age + Age2 + Demographics + Life Events Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value age -.0233 7.16 -.0488 5.30 -.0501 5.09 -.0450 4.55age*age .0003 2.96 .0004 4.18 .0004 3.63ln (weekly household income) .0115 3.13 . 0094 2.58

pension Income -.0001 1.45 -.0001 1.17female education years -.0310 2.01 -.0362 2.35married -. 0501 1.06 -.1217 2.37separated -.6000 9.02 -.4815 6.96never married -.2228 5.68 -.1304 3.30divorced -.3025 4.36 -.2922 4.17widowed -.6463 7.19 -.6206 6.68employed -.0095 0.42 -.0027 0.12unemployed -.1840 5.30 -.1571 4.52disability -.0255 1.55 -.0182 1.11health . 2479 25.84 ..2394 24.99health a year ago . 0890 10.36 .0844 9.81spouse/child death - .1721 2.45death of a relative -.0205 0.97personal injury -. 0937 3.72jailing of self . 0092 0.05injury to a family member -. 0424 2.12property crime victim -. 1507 4.59victim of violence - .2574 4.10

101 These fixed-effect regression results are for Australians aged 15 to 84 in the HILDA panel data waves 2 to 6 for the period 2002 to 2006.

216

Age Age + Age2 + Demographics + Life Events Variable: coefficient t-value coefficient t-value coefficient t-value coefficient t-value just separated - .4554 9.98just reconciled - .0156 0.20fired from job .0276 0.55worsening finances - .6187 11.75death of friend .0366 1.29friend jailed .0138 0.15just married .2884 3.36start new job .0417 1.06just pregnant .2944 4.07moving house .2533 5.87improved finances .3299 3.74promoted at work .0497 0.728birth of child .3624 3.70just retired .0364 0.28 constant 7.5938 451.84 9.443 45.53 8.605 33.72 8.624 33.84

R2 (overall) 0.01 0.00 0.04 0.04

217

Chapter Six

Summary of Findings

Chapters 3 to 5 of this thesis have taken us on a journey of discovery along the road of

lifetime happiness. The three empirical studies sought to inform current econometric

models of happiness by considering happiness from multi-disciplinary perspectives. The

research questions addressed in each study emerged from gaps between the economic

and other scientific literatures.

The gap considered in the first study (Chapter 3) analysed a puzzle in the relationship

between age and happiness. A review of the literature revealed a U-shape finding of

happiness in age in the economic literature that is not shared by other scientific

disciplines. To begin resolving this difference in theoretical opinion, the U-shape of

happiness in age was initially replicated for 18 to 93 year-olds using three oft-used panel

data sets; the German Socio-Economic Panel, the Household, Income and Labour

Dynamics in Australia, and, the British Household Panel Survey. The U-shape arising

from age effects (the age & age2 variables) was evident in all three data sets and

remained so even when socio-demographic and life event variables were included in the

regression specifications. The findings were typical of other scholars in the economics

literature and confirmed the presence of a U-shape of happiness in age in all three data

sets.

After confirming the presence of a U-shape of happiness in age in all three data sets, the

study sought to explain it. Several explanations were offered for the U-shape of

happiness in age. The initial explanation pursued a line of enquiry that considered

average happiness changes at young and old ages; there is a sharp decline in raw average

happiness from 18 years to 22 years and in those close to death (where there are fewer

individuals). Excluding the young and old (22 to 80 year olds) from all three data sets,

the regressions were repeated. There was no clear qualitative difference between the

previous results and those excluding the very old and the very young. The U-shape could

218

not be explained by the extremities of the age range. It was proposed that the U-shape of

happiness in age must be due to relationships in large parts of the age range.

With the age and age2 coefficients remaining strongly significant across all

specifications with all three data sets, the search refocussed back on the full sample and

considered omitted variables and reverse causality. This line of enquiry was based upon

important findings in the literature, that happiness is strongly affected by stable

personality traits. In an econometric model of individual happiness, these fixed

individual traits are usually part of the error term. The line of enquiry pursued a stylised

finding from both the economic and the psychological literature that accounting for

fixed traits has a very strong impact on the coefficients found for socio-economic

variables. A leading explanation for this is the possibility of reverse causality arising

from unobserved heterogeneity.

Pursuing the line of enquiry that reverse causality caused by unobserved fixed traits

explained the U-shape, the same OLS regressions were rerun but this time with fixed

effects. With all three data sets the age effects disappeared, the U-shape was indeed due

to reverse causality. In seeking robustness in this result, we considered more deeply how

the unobserved heterogeneity could bias the pooled regression results. Consistent with

the findings from other studies, the coefficients of most of the socio-economic variables

became much smaller when fixed effects were included. Next, we sought to identify if

changes in the coefficients of these non-age variables lead to a difference in the

predicted age-profile. The OLS prediction showed a clear inverted u-shape for all three

data sets and, with fixed effects added, we saw that the inverted U-shape was much less

pronounced. The inclusion of fixed-effects reduces the coefficients of variables that

themselves systematically vary by age (incomes and marriage peak in middle age) and

that this in turn reduces the predicted inverted U-profile of their effects.

219

Robustness checks provided weight of evidence that the U-shape of happiness in age did

indeed emerge from reverse causality caused by unobserved fixed traits. When the

analysis was repeated with multiple latent-variable techniques, the results were

consistent with the OLS findings. The highly significant and positive effect on age-

squared found in the cross-section disappeared with the inclusion of fixed-effects.

Robustness checks went further. Instead of including self-reported health as a

continuous variable, the 5 possible health states (from very bad to very good) were

included as separate dummy variables (as recommended by Terza, 1987). Again, this

made almost no difference to the age-squared effects. Adding an additional robustness

check using age-bands (Clark, 2006) supported the initial finding; the U-shape of

happiness in age did indeed emerge from reverse causality caused by unobserved fixed

traits.

In the process of using the German Socio-Economic Panel to explain the U-shape of

happiness in age, a new puzzle emerged. For some reason, even when we use controls in

our regressions, the happiness of Germans declines with age (but not to the same extent

for British and Australians). Tests for time or cohort effects did explain a small amount

of the decline in German happiness, but the predicted size of the effects over a lifetime

left much unexplained. Perhaps there was something wrong with the German panel data.

Regressions including a ‘time in panel’ variable revealed there was a large decline in

reported satisfaction the longer an individual remained in the panel. Various reasons

were discussed for why the amount of time in the German panel would bias happiness

results. Perhaps it is an artefact of the GSOEP, perhaps it is in the nature of the

respondents, perhaps we need a country specific variable to account for the bias. Either

way, further research into these time and cohort effects is required otherwise findings

from GSOEP-based cross-sectional or panel analyses may become highly suspect.

After beginning our journey into happiness over a lifetime in Chapter 3, Chapter 4

pursued a cohort little visited by economists, children. Examining childhood happiness

was considered study-worthy because there is some evidence from other scientific

disciplines that the happiness of individuals in their childhood can affect the happiness

220

of those same individuals in later life. After extending our view of lifetime happiness to

15 to 93 year olds in the Household Income & Labour Dynamics in Australia panel data

set, we saw a steep 7.2% decline in the happiness of 15 to 23 year-old Australians. This

steep decline in the happiness of young Australian was twice the size of the 3.6%

happiness decline we see in 75 to 86 year old Australians who we expect to have

declining happiness due to their falling incomes, failing health and the onset of death.

We questioned when this happiness decline began in young Australians.

To reveal when this happiness decline began in young Australians, we collected data

from 9 to 14 year-old Australian children. Drawing on happiness-life domain theory

from the school psychology scientific discipline, measurement scales were developed

and data were collected with an Internet-based ‘Happiness’ survey that questioned the

children on three life satisfaction domain factors (the child’s natural environment, their

school environment, and, their interaction with friends). This additional ‘Smart Train’

data initially revealed a further 9.3% decline in the happiness of 9 to 14 year-old

Australians. Subsequent analysis of the ‘Smart Train’ data with a model of childhood

happiness and a subsequent decomposition of the prediction for each domain factor

revealed that two of the life domain factors explained almost half of this steep decline in

the happiness of 9 to 14 year-old children.

Children are much affected by their school environment, it accounts for 35% of the

happiness decline and much of that happiness decline occurs when the children

transition from lower school grades to high school. This transition would also seem to

affect the children’s school friendships. We find that the children’s interaction with

friends positively contributes to their happiness right up until they transition to high

school where its effect goes negative then recovers somewhat as the children make new

friends at high school. However, the overall effect of the interaction with friends life

domain factor is negative, accounting for 8.8% of the decline in happiness of Australian

children aged 9 to 14 years.

221

The childhood happiness not only revealed the steep decline in childhood happiness, the

study revealed one expected and one unexpected finding concerning personality and

happiness. The expected finding was that extraverted, talkative, gregarious, outgoing,

children are happier. Extraversion has the same effect in adulthood. The unexpected

finding was the effect of conscientiousness on happiness. Conscientious adults are

unhappier. This is not the case with children. Conscientious children are happier.

Perhaps conscientious children are happier because they do better at school. Perhaps

conscientious children enjoy more attention from peers who seek out the conscientious

students so they can benefit from their greater knowledge. These propositions are worthy

of future research. In the mean time, let us review the findings from the 15 to 23 year old

cohort in the Australian HILDA data.

With much of the decline in the happiness of young children explained, Chapter 4

returned our focus to explaining steep 7.2% decline in the happiness of 15 to 23 year-old

Australians we noted earlier in this findings summary. The 15 to 23 year old cohort from

the HILDA panel data were analysed with a model of individual happiness and the

results were compared from those from the entire HILDA panel. The most obvious

finding was that age effects in regressions on the 15 to 23 year old cohort were seven

times larger than for the same regression with the entire HILDA sample. Other than this

overly large negative effect from 15 to 23 year-old unemployment, none of the socio-

demographic or life event variables that are usually included in happiness regressions

nor the inclusion of fixed effects adequately explained away the steep decline in the

happiness of 15 to 23 year old Australians. We were left to ponder as to why this steep

decline in happiness continued through adolescence and into young adulthood.

A review of the literature provided some direction into explaining why happiness

continues to decline for adolescents and young adults. The degree of positive or

negative change in our happiness can be a function of our happiness expectations and

how those expectations are affected not only by the changing economic circumstances

that were included in the Chapter 3 & 4 analysis, our happiness is also affected by the

stressful life events that change our everyday lives. Chapter 5 pursued this path of

222

reasoning by taking advantage of the additional fourteen life events in the HILDA panel

data and we asked if changes in our lives and the lives of our peers make us (un)happy.

Chapter 5 accounted for changes in overall life satisfaction by considering the effects

from life event shocks over time and sought to gauge if their stressful effects explained

the changes in aggregate happiness over the life cycle. Panel data from the Household

Income and Labour Dynamics for Australia was used to construct an index of the

severity of the stress from life changes at each age. This single-variable Stress Index

explained over 94% of the variation in happiness over time. Unexpectedly, aggregate

‘positive stress’ (such as marriage rates by age or levels of job promotion) has a greater

negative effect on aggregate life satisfaction than negative stress (such as negative

financial events or deaths of spouses), We interpreted this as a strong indication that

what is deemed a positive event by the person involved is a highly negative event for his

or her peers. We saw some evidence that extraverted individuals are affected less

negatively by stress.

223

Chapter Seven

Discussion, Policy Conclusions and Future Research

This thesis pursued a multidisciplinary line of inquiry into happiness over a lifetime.

Recent advances in the content of socio-economic data sets created research

opportunities for economists to consider happiness from multidisciplinary perspectives.

These opportunities emerged from socio-economic panel data surveys that now include

the psychology-derived scales that allow us to econometrically consider and test how

behavioural factors contribute to the theme of this study, lifetime happiness.

Of course, one could ask why economists should be concerning themselves with

happiness. Shouldn’t we leave happiness research to other sociological sciences that

have decades of experience in the research of overall wellbeing. Some political leaders

would disagree. President Sarkozy of France and David Cameron, the Prime Minister of

Great Britain, are of the opinion there is more to maximising the overall wellbeing of

society than GDP growth. Certainly, money is important, and its absence definitely

makes us unhappy. However, if western societies are to become better places in which to

live, the people of those societies expect not only health and wealth, they expect

happiness as well.

If economists are to provide political leaders with advice on how to best allocate scarce

resources and thereby maximise the overall wellbeing of individuals and society, we

need to consider happiness from all theoretical perspectives. We need to include in our

econometric models of happiness all the variables that could affect individual happiness,

including those variables considered important by other scientific disciplines. Clearly,

this has not happened to date because the vast majority of the variance associated with

individual happiness remains unexplained. At the onset, this thesis did not purport to

include all relevant variables that could affect individual happiness, but it did consider

some variables that have not been considered before and one that warranted

reconsideration, age.

224

The first study in this thesis sought to explain happiness change over time by

reconsidering what was becoming a stylised fact in the economics literature, the U-shape

of happiness in age; individuals get happier as they age. The empirical analysis began by

seeking to confirm the presence of the U-shape of happiness in age. Regression results

provided strong evidence that the U-shape clearly existed in the German data and was

evident in the British and Australian socio-economic panel data. However, No U-shape

can be seen when we look at an age-happiness prediction or a scatter plot of the age

happiness relationship. There was no U-shape to be seen in the graphical depiction of

the raw average happiness data. There was no visually discernable U-shape for the

Germans, or for the Australians, or for the British. Looking at age cohorts, we could see

no strong age-happiness relationship for the age range 22 to 60 years. We did see a steep

decline in raw average happiness for 18 years to 23 year-olds (perhaps the left-hand side

of a u-shape), but, we also saw a steep decline in the happiness of those in old age (more

like an inverted U-shape). For all three data sets, no U-shape could be seen in the age-

happiness relationship.

However, for some countries like Australia it could be argued that the raw average

happiness across age cohorts does look somewhat like a U-shape. There is a steep

decline in the happiness of 15 to 23 year old Australians, a midlife low period of

happiness before happiness increases from about 50 to 60 years of age. Removing the

effects of the steep happiness decline of the young and the old could have eliminated the

U-shape of happiness in age finding; it persisted. In addition, the U-shape finding

continued to persist even when we tested for cohort effects that could emerge from

newcomers to the panel data sets. The results were robust across all three data sets; it

would appear that we do get happier as we age.

Given the predicted happiness depictions in Figures 3.20, 3.21 & 3.22, a happiness

decline in old age is more believable. Old age is that period in our life when we are

starved of resources, we no longer enjoy the high income of our middle years and our

health is failing, those we grew up with are dying around us and we see the imminent

225

onset of our own demise. With such a miserable outlook, it is difficult to believe that the

U-shape of happiness actually holds in reality. We cannot honestly believe that we just

have to wait around until we get old to be happy. Clearly, something else is happening.

Nevertheless, evidence from existing economics of happiness studies and the OLS

regressions in this study, revealed that the U-shape of happiness in age did exist in the

data, it just must be a proxy for something else.

The first study pursued this proxy notion and revealed that the U-shape of happiness in

age could be explained by time invariant unobserved fixed traits. Running the same

regressions with fixed effects eliminated the U-shape. This finding was robust across all

three data sets, the German GSOEP, the British BHPS and the Australian HILDA.

Before concluding that the U-shape emerged from fixed effects, the choice of analytical

methods was considered.

One could argue that the findings had more to do with the selection of analytical

methods than the data or any real difference in the theoretical perspective of economists

or those from other scientific disciplines. To allay this possibility, robustness tests were

completed using three additional analytical methods. The same results emerged from the

latent variable analysis (ologit) that some in the economics literature demand as the

preferred method of happiness analysis. Coding health as ordinal did little to change the

results, and, the results from the recent conditional fixed effect ordered logit method

mirrored the other results. The results were consistent across all analytical methods and

with all three data sets. The U-shape of happiness in age was robustly explained by

reverse causality arising from unobserved fixed traits.

For the economics literature, this empirical finding was new, but how did it contribute to

explaining the changes in happiness over a lifetime? From a theoretical perspective, I

argue that it should wake us from the complacency of following a single analytical

method. There is strong evidence that the U-shape emerged from fixed effects and any

econometrician could have found this if only they had pursued analytical methods

beyond the latent variable analysis that has recently pervaded the economics of

226

happiness literature. The singular selection of analytical methods can potentially

constrain the vision of happiness researchers. If we are to overcome such myopia, we

need to select a range of analytical methods that also allow us to take advantage of the

inter-temporal nature of our socio-economic survey data. Breaking the shackles of latent

variable analysis allowed us to view the happiness data from a different perspective; it

allowed us to see that the U-shape of happiness in age could be explained by reverse

causality arising from unobserved fixed traits.

Of course, the psychologists would just sit back and say, I told you so. For decades, the

psychologists have been espousing that much of the variance associated with changes in

our overall wellbeing could be explained by the most obvious of fixed traits, personality.

Long ago, psychologists accepted that individuals who have desirable personalities, who

are extraverted, agreeable, open and reliable are more desirable as partners, sought after

by employers, and, enjoy above average happiness. This study sought to bridge the

divide between the theoretical position of economics and other scientific disciplines with

empirical evidence based on the econometric models and data that economists trust. In

doing so, this study has allowed us to look at the economics of happiness research from

a new perspective.

However, from an economic policy perspective, the first study did little to maximise the

overall wellbeing of society, no policy recommendations emerged from the study.

However, two contributions did emerge, one theoretical and the other econometric.

Firstly, economics of happiness theory has been clarified; age effects in individual

models of happiness are explainable by fixed effects. If we are to increase the level of

explanation from our happiness models, we need to pursue new lines of inquiry that

include fixed effects and their interactions with other happiness affecting variables. The

second contribution was the recommendation concerning the choice of econometric

methods. This study showed that expanding economics of happiness analysis methods

beyond latent variable analysis could open new and revealing views on our rich socio-

economic data sets. The flexible application of a range of econometric methods allowed

us to see that the U-shape of happiness in age was explainable by fixed effects. This

227

finding expands the range of variables that we can now incorporate into our economic of

happiness research. We now have strong evidence of the presence of reverse causality

caused by unobserved fixed traits and therefore need to include personality and variables

affected by personality in our happiness models. The second study did just this by

including personality-affected variables in a model new to economics, a model of

childhood happiness.

Childhood happiness is little studied by economists. This is understandable, because,

while children could be considered consumers, it is the children’s parents who produce,

make choices, purchase and contribute to the economic wellbeing of society. However,

there is evidence that unhappy children make unhappy adults and unhappy adults can be

a burden on society, e.g. increasing health costs. The unhappy get sick more often, are

more likely to be absent from work, are less productive and can make those around them

unhappy. Therefore, it is in the long-run best interests of society if we study the

happiness of the children in expectation that they can become happier and more

productive adults in our societies of the future.

To begin our study into the happiness of the young, we initially saw evidence of

increasing unhappiness as Australians progressed from adolescence into adulthood.

There was a 7.2% decline in the happiness of Australians between the ages of 15 and 23

years. This steep happiness decline is twice what we saw in 75 to 86 year old Australians

who we expect to be unhappy due to declining incomes, failing health and the onset of

death. The Smart train data collected with our online ‘Happiness Survey’ uniquely

extended our lifetime view of happiness to children and revealed a happiness decline

even more startling than what we saw in 15 to 23 year olds. Between the ages of 9 and

14 years, the happiness decline in Australian children is bigger, 9.3%. Added to the

7.2% decline we saw in 15 to 23 year olds, the happiness of 9 to 23 year old Australians

declines by a very large 16.2%; five times the happiness decline we saw in 75 to 86 year

old Australians. The economics of happiness literature focuses on adults. With evidence

that the happiness decline in the young can be five times what we see in the old, it is

time we extended our study of happiness beyond adulthood to children.

228

To extend the study happiness back to childhood, the life satisfaction domain approach

of Frijters (1999c) was adopted. Frijters found that if we are happy in all the domains of

our life then overall we are happy. The review of the literatures revealed that domain

approach methods are commonly used by other scientific disciplines but not by

economics of happiness researchers. We see that economists use the raw data that

emerges from individual questions in the panel surveys. In recent times, these panel

surveys have begun to personality factor questions. This has made it easier for

economists to include personality in happiness regressions because the providers of

panel data like the HILDA have done the hard work of pre-constructing and validating

the factors. This is not to say that economists lack the skills to construct and test the

reliability and validity of measures. Kapteyn, Smith, Van Soest, & Vonkova (2011)

developed survey instruments for sleep, concentration and memory domains in their

study of the importance of vignette equivalence and response consistency when

individuals are asked to describe and rate their health. However, the domain approach

remains little used in economics of happiness studies. This is an opportunity forgone; the

breadth and depth of the questions in panel data surveys like the HILDA provide us with

rich data that lends itself to the use of a life satisfaction domain approach.

It was the use of the life satisfaction domain approach in the Chapter 4 study that

contributed to our understanding of childhood happiness. Three life satisfaction domain

factors were constructed and their reliability and validity tested before the factors were

incorporated into a unique model of childhood happiness. The three life domains were

identified by taking a multidisciplinary approach to the literatures. The school

psychology literature proposed that a child’s school environment, their child’s natural

environment, and the child’s interaction with friends were domain factors that could

affect childhood happiness. The three childhood happiness domains incorporated into

the new model of childhood happiness were constructed from a survey instrument

employed by school psychologists, the BFC-Q scale. Again, a multi-disciplinary

approach to the literatures prompted the development of reliable instruments that proved

suitable for this economics of happiness research. The instrument is considered suitable

for constructing the life satisfaction domains used in this study because the children

229

were questioned on how they behave at school and how they behave with their friends.

After construction, the three childhood happiness domain factors exhibited good internal

reliability and consistency. The BFQ-C scale and the childhood happiness domain

factors are recommended for use by other economics of happiness researchers.

The BFC-Q personality scale is considered suitable for economics of happiness research

for two additional reasons. Firstly, the adult personality scales usually included in socio-

economic panel data surveys are unsuitable for children. However, there have been

numerous studies where the BFC-Q personality scale was successfully used to measure

the personality of children (Barbaranelli at al., 2003; del Barrio Carrasco Miguel &

Holdago, 2006). To add robustness to the validity of the BFC-Q scale, these researchers

cross validated the scale by comparing the children’s responses to the BFC-Q

questionnaire with the parents’ evaluation of their child’s personality using the adult

personality scale; the two sets of responses were strongly positively correlated.

The second reason the BFQ-C scale is appropriate for economics of happiness research

comes from empirical evidence in this study. Analysis of the data collected with the

scale revealed regression results consistent with those from the adult happiness

literature; extraverted adults (children in this study) are happier and neurotic adults

(children) are unhappier. Of course, one could argue that childhood personality operates

differently from adult personality, childhood happiness research that reveals the same

personality results as adult happiness research could emerge from an unsuitable

childhood happiness scale because extraverted children are not really happier. Decades

of findings from psychologists generally do not support this reverse argument. The

consensus in the psychology literature is that personality remains stable from a very

early age and that any change in adolescent or adult happiness emerges from

measurement error. Therefore, this researcher is of the opinion that the BFC-Q scale and

the life satisfaction domain factors developed for this research are suitable instruments

for use by other economics of happiness researchers.

230

Analysis using life satisfaction domain factors provided interesting results. The school

environment factor explained 35% of the steep decline in happiness of 9 to 14 year old

Australian children. Much of this happiness decline occurs as the children transition

from lower grade school to high school. One could argue that this decline in happiness is

predominantly due to physiological and emotional changes taking place in 9 to 14 year

old children. Hormonal changes could be contributing to the steep happiness decline in

children. In the absence of hormonal change data we have to rely on the data we do

have. Analysis of the data collected with our online ‘Happiness Survey’ revealed, that

children had: difficulty understanding their teacher (or the material taught); the children

had to concentrate more in class; they have to forego leisure because they have to work

harder, and; the children find it more difficult to learn. It is no wonder that children get

unhappier as they move from lower grade school to high school. The question is; how

can we help children to be happier as they make this compulsory transition from lower

grade school to high school.

The second finding in the study provides some direction. We could help schoolchildren

with their transition from the lower grade school to high school by making them more

extraverted. Children high on extraversion are happier. Looking at the questionnaire

items used to construct the extraversion trait factor reveals the extraverted behaviours

that correlate with happier children. Extraverted children are more talkative, they are

more assertive and participative. Extraverted children make friends easier, they like to

share their thoughts and experiences with others. If we could make children more

extraverted this would make them happier. However, changing the personality of 9 to 14

year-old children is not considered feasible because personality is formed much earlier,

during the child’s formative years. While personality may not be changeable, the

findings of Lischetzke & Eid (2006) provide some direction on what we could do to

make children more extraverted and happier. Lischetzke & Eid find that extraverts have

better mood regulation; we could teach the children how to maintain a more positive

attitude in their day-to-day lives, but how.

231

One way to teach the children how to maintain a more positive attitude in their day-to-

day lives is through behaviour modification, or learning through reinforcement. Operant

and reinforcement theory takes the view that learning is dependent upon the

environment (McShane & Travaglione, 2003, pp. 45-50). With behaviour modification

theory, thinking is not considered part of the learning process but an intermediate step

between behaviour and the environment. Our experience with the environment teaches

us to alter how we behave so that we maximise positive and minimise adverse

consequences (Miltenberger, 1997). A law of cause and effect is enacted whereby an

operant behaviour will be repeated dependent upon how behaviour is reinforced.

Positive reinforcement is provided for preferred behaviours and negative reinforcement

for non-preferred behaviours (Connellan, 1978). With children, the behaviour

modification process could manifest as positive-reinforcing praise when the child

exhibits extraverted behaviours like being more assertive and participative in class or

when the children share their thoughts and experiences with others. Negative

reinforcement (sometimes called avoidance learning) would involve not criticising

children when they do not exhibit extraverted behaviours. By withholding criticism, the

children are more likely to repeat the extraverted behaviours for which they received

praise. Behaviour modification would not change the personality of the schoolchildren;

it would just provide feedback that incentivizes the schoolchildren to behave in a

happiness-maximising way.

There are examples of the successful use of behaviour modification with children. In

1978 Wolraich, Drummond, Salomon, O'Brien, & Sivage found that behaviour

modification assisted with the classroom behaviour and academic performance of 6 to 9

year olds. Behaviour modification has also been successfully applied to treat psychotic

children (Meyers & Craighead, 1979); to increase school attendance and improve

behaviour in school cafeterias (Fabiano, et al., 2008); to help disabled children learn

(Routh, 1979), and; reduce youth delinquency and violence in the classroom (Eddy,

Reid, & Fetrow, 2000). Behaviour modification has even been used by parents reduce

the amount of time their children spend viewing television (Jason & Fries, 2004).

However, the use of behavioural modification with children is controversial. As far back

232

as 1986, Boivin, Sewell, & Scott (1986) questioned whether behavioural modification

was an ethically appropriate procedure for children. In spite of this early criticism,

behavioural modification continues to be used; particularly on students with extreme

behavioural problems arising from conditions such as ADHD; Attention Deficit

Hyperactivity Disorder (Miranda, Presentación, & Soriano, 2002; Waxmonsky et al.,

2008).

Behavioural modification has also been effectively used on students by school teachers.

After being taught behavioural modification skills teachers could better cope with

keeping problem children in the classroom (Schiff & BarGil, 2004). Dua (1970)

investigated the effectiveness of behavioural orientated therapy programs used to treat

introversion and extraversion in the classroom. Dua found that a behavioural

modification program was more effective than a re-education program in inducing

attitudinal change in students. Lowenstein (1983) took shy 9 to 16 year old students and

used behavioural modification therapy to increase extraverted behaviours. In doing so,

he improved the student’s reading, spelling and math outcomes. More recently, Nelson

(2010) explored classroom participation in the presence of a token economy. He found

that undergraduate students participated more when they received bonus points and

extraverted students participated more than non-extraverted students. Behavioural

modification programs that sought to increase extraverted behaviours in school children

and young adults have been shown to improve classroom behaviour and educational

outcomes.

A cursory search of the Queensland State Government Department of Education

teaching methods revealed little mention of the general use of similar behavioural

modification programs in Queensland State schools. This is not to say that behavioural

modification is not on the agenda of the Department of Education; the 2011 School-wide

Positive Behaviour Support conference is scheduled to discuss how to apply behavioural

modification methods to children with ADHD (Riffel, 2011). One wonders whether the

ethical issues have forced behavioural modification off the general teaching agenda or

whether the method is only considered appropriate for use on children with extreme

233

behavioural problems. With evidence from this study that children with extraverted

behaviours are happier and do better at school, one wonders whether it is time to put

behavioural modification for schoolchildren back on the research agenda.

If we are to have a highly educated, productive and happy labour force in the future,

perhaps now is the time for economists to contribute to an argument that hitherto has

been the property of educationalists and school psychologists. What would happen to

long-run educational outcomes, the level and quality of human capital, and GDP growth

if schoolchildren were incentivized to engage in extraverted behaviours? On the other

hand, perhaps the effect of personality has a general equilibrium element in that the

effect would change if we change the distribution (if everyone is an extravert, it might

be less fun to be one). Such questions lend themselves to collaborative research between

economists, educationalists and school psychologists.

With the first part of the second study finding that extraverted 9 to 14 year old

schoolchildren are happier, the research refocussed on the 15 to 23 year old cohort in the

HILDA. Recall, we saw a steep 7.2% decline in happiness of 15 to 23 year-old

Australians. A happiness fall of twice what we see in older Australians who we expect

to be unhappier due to their declining incomes, failing health, and the imminent onset of

their own demise. So, what is it about young adulthood that makes us unhappier than the

threat of imminent death?

Analysis with the Australian HILDA panel data using the ‘Kitchen Sink” specification

from the first study did not provide an answer. None of the demographic variables

(health, wealth, gender, home ownership, education etc.) nor the six life event variables

(death of spouse, job loss, pregnancy, marriage, divorce etc.) could adequately explain

the steep decline in the happiness of young Australians. We did see a higher than

expected effect for unemployment. This is to be expected because the average

unemployment level for 15 to 23 year olds in the HILDA is 8.8% versus 3.6% for the

Australian population. In addition, most 15 to 23 year olds are either still in school or

undertaking technical or tertiary education and 35% of also have a part-time job at the

234

same time. A leisure reduction and increased workload from holding down a part-time

job while still studying may partly explain why young Australian adults progressively

become unhappier. However, neither the larger than expected negative effect from

unemployment nor the effect of any of the other variables in the ‘Kitchen Sink”

specification could adequately explain the steep decline in the happiness of young

Australians.

However, recall the ‘Kitchen Sink” specification only included life event variables

common to both the GSOEP and the HILDA. The HILDA has an extra fourteen life

event variables that could potentially extended the level of happiness explanation.

Unfortunately, including the extra fourteen life event dummies minimally increased the

level of happiness explanation from 17 to 19 percentage points, a mere 2%. The

additional life event variables in the HILDA panel data did not explain the steep decline

in the happiness of young Australians. A new theoretical and or analytical approach was

needed if happiness over a lifetime was to be adequately explained.

A review of the findings from the first two studies in the context of theory from another

scientific discipline revealed a new approach to explaining happiness over a lifetime.

Recall the first study found that time invariant fixed traits (like personality) explained

the U-shape happiness change over a lifetime and the second study showed that a steep

decline in the happiness of schoolchildren correlated strongly with the life event change

of the children transitioning from lower grade school to high school. What would

happen if we incorporated personality and life event changes into a model of happiness?

The literature from another scientific discipline provided direction. An individual’s

personality can affect their happiness (Diener, et al., 1999, p.214). In addition, happiness

is affected not just by economic circumstances it is also affected by life events

(Easterlin, 2002). Easterlin stated that ‘the degree of positive or negative change in our

happiness is a function of an individual’s consideration of their happiness expectations

(their aspirations) with the changing economic circumstances and the life events that

affect them’ (Easterlin, 2002, p 214). The third study leveraged from the findings of the

235

first and second studies and sought to extend those findings by proposing that the stress

arising from the interaction between personality and life events can explain the change

in happiness over a lifetime.

A review of the stress literatures from psychology and organisational behaviour revealed

that, on average, individuals are change averse and that the stress arising from

unexpected life event shocks can make us happy. Too much stress can make us feel

physically unwell, causing the onset of hypertension, heart attack and even death. In

addition, while small amounts of stress can motivate some people, too much stress

negatively affects the overall wellbeing of all individuals. The magnitude of the stress

from life events is dependent upon the personality of an individual. When confronted

with unexpected life event shocks extraverted individuals look on the bright side, but

neurotic individuals view the same change negatively and experience wide swings in

their happiness. Because personality mediates the level of stress from a life event shock,

the life event dummies were not just added to the usual model of individual happiness

(as is usual in the literature). The stress arising from the interaction between personality

and the life events was incorporated into a new model of happiness.

Of course, just including a scaled life event dummy into a happiness model just changes

the size of the regression coefficients. The Aggregate Model of Lifetime Happiness

provided a new methodological approach to overcome this impediment. This time-

series-based methodological approach drew on findings from other sociological

literatures (the Social Readjustment Rating Scale). Stress is cumulative; individuals

become more stressed and unhappier as they are confronted with additional life events.

In addition, stress arises not only from changes in our own lives, but also from changes

in the lives of our peers. For example, if our fellow worker receives a pay rise and we do

not, they are happier but we are less happy because we did not get a pay rise. This notion

of relative happiness is familiar to economists; it is the theoretical proposition at the

heart of the Easterlin paradox. Happiness is a relative self-measure, we adjudge our

happiness relative to those around us, our peers. The Aggregate Model of Lifetime

236

Happiness incorporates the cumulative effect of stress from changes in our lives as well

as the stress from the changes in the lives of our peers.

Construction of the stress variable from the interaction between the life events affecting

an individual and that individual’s personality provided a most surprising revelation. A

graph of average stress and average happiness at each age appeared as a U-shaped

inverse image of one another. The regression results from the Aggregate Model of

Lifetime Happiness supported this view. The aggregate stress coefficient was negative

and highly significant, explaining over 94% of the change in happiness over a lifetime.

Increased stress in the young and through midlife significantly explained the decrease in

happiness that occurs in the young and on into mid and later life. Stress has an inverse

U-shape in age. Perhaps this research has revealed one of the variables that lie behind

the age effects we see reported in so many happiness studies.

If we accept that the stress from life events does reduce individual happiness, then it

would be reasonable to ask; what is the cost of stress to an individual and society. A few

economic researchers have sought to price the affect of life event changes; Frijters et al.

(2008) used quarterly life event data to calculate that the loss of a partner could be offset

by a windfall income gain of $US 200,000. However, they were constrained by

insufficient life events of each type and measurement error in the quarterly life event

data (probably arising from recall bias). As was argued in this study, the use of average

stress from the life event shocks overcomes these impediments and the Aggregate Model

of Happiness offers a new approach to pricing the cost of stress on the average

individual in society. As to whether the use of stress as the independent variable in the

Aggregate Model of Happiness similarly prices the loss of a partner at two-hundred

thousand dollars, we will just have to wait for the results of future research.

This thesis has taken us on a journey that sought to explain changes in happiness over a

lifetime. Along the way, this study has contributed to the economics of happiness

literature and illustrated the benefits of using multiple econometric methods. By taking

existing econometric models of happiness and complementing them with theory from

237

other scientific disciplines, this thesis has provided an explanation for the U-shape of

happiness in age. This explanation provided the theoretical foundation for developing

two new models of happiness that provided explanations to changes in the happiness of

children as well as changes in happiness over a lifetime. Both models extended the level

of happiness explanation in the economics literature. Along the way, this study revealed

opportunities for future research, the opportunity for economists to study a little-visited

cohort, childhood happiness; an opportunity to build on the stress approach of the final

study and price the impact that life event changes have on the happiness of society.

238

239

Chapter 1 Verse 1

Confucius said, “To learn and to practice what is learned time and again is pleasure, is it not? To have friends come from afar is happiness, is it not? To be unperturbed when not appreciated by others is gentlemanly, is it not?”

(Cheung, 2010)

240

REFERENCES

ABS. (2008). Health of Mature Age Workers in Australia: A Snapshot, 2004-05 Quality Declaration. Canberra, Australia: Australian Bureau of Statistics.

ABS. (2010). 4102.0 - Australian Social Trends, Jun 2010: Australian Bureau of Statistics.

Alesina, A., Di Tella, R., & MacCulloch, R. (2004). Inequality and Happiness: Are Europeans and Americans Different? Journal of Public Economics, 88(9-10), 2009-2042.

Andrews, F., & Withey, S. (Eds.). (1976). Social Indicators of Well-being: Americans' Perceptions of Life Quality. New York, USA: Plenum Press.

Argyle, M., Kahneman, D., Diener, E., & Schwarz, N. (1999). Causes and correlates of happiness Well-being: The foundations of hedonic psychology. (pp. 353-373). New York, NY US: Russell Sage Foundation.

Aristotle. (1819). A new translation: The Nichomachean Ethics. In R. Pearson (Eds.) Available from http://www.archive.org/details/newtranslationof014417mbp

Baetschmann, G., K. E. Staub, et al. (2011). Consistent Estimation of the Fixed Effects Ordered Logit Model. Discussion Paper Series. P.O. Box 7240, 53072 Bonn, Germany, The Institute for the Study of Labor (IZA).

Barbaranelli, C., Carpara, G., Rabasca, A., & Pastorelli, C. (2003). A questionnaire for measuring the Big Five in late childhood. Personality and Individual Differences, 34, 645–664.

Bassi, M., & Delle Fave, A. (2004). Adolescence and the Changing Context of Optimal Experience in Time: Italy 1986-2000. Journal of Happiness Studies, 5(2), 155-179.

Beatton, T., & Frijters, P. (2009). Do changes in the lives of our peers make us unhappy? . Working Paper Series. Queensland University of Technology: Brisbane. Australia.

BHPS. (2010). British Household Panel Survey: Institute for Social & Economic Research, University of Essex.

241

Baetschmann, G., K. E. Staub, et al. (2011). Consistent Estimation of the Fixed Effects Ordered Logit Model. Discussion Paper Series. P.O. Box 7240, 53072 Bonn, Germany, The Institute for the Study of Labor (IZA).

Becchetti, L., Trovato, G., & Bedoya, D. A. L. (2011). Income, Relational Goods and Happiness. Applied Economics, 43(1-3), 273-290.

Blanchflower, D. G. (2008). International evidence on well-being. National Bureau of Economic Rseearch.

Blanchflower, D. G., & Oswald, A. J. (1998). The Rising Well-Being of the Young.Unpublished manuscript.

Blanchflower, D. G., & Oswald, A. J. (2001). Well-Being Over Time in Britain and the USA (pp. p1359, 1328p1347 pages): University of Warwick, Department of Economics, The Warwick Economics Research Paper Series (TWERPS).

Blanchflower, D. G., & Oswald, A. J. (2004). Well-being over time in Britain and the USA. [Article]. Journal of Public Economics, 88(7/8), 1359.

Blanchflower, D. G., & Oswald, A. J. (2007). Is Well-being U-Shaped over the Life Cycle? (pp. 36 pages): University of Warwick, Department of Economics, The Warwick Economics Research Paper Series (TWERPS).

Blanchflower, D. G., & Oswald, A. J. (2008). Hypertension and happiness across nations. Journal of Health Economics, 27 218–233.

Boivin, M., Sewell, R., & Scott, K. (1986). Attitudes Toward Behavior Modification. Behavior Modification, 10(4), 435-456.

Borgers, N., de Leeuw, E., & Joop, H. (2000). Children as Respondents in Survey Research: Cognitive Development and Response Quality. Bulletin de Methodologie Sociologique, 66(April 2000), 60-75.

Bruhin, A., & Winkelmann, R. (2009). Happiness Functions with Preference Interdependence and Heterogeneity: The Case of Altruism within the Family. Journal of Population Economics, 22(4), 1063-1080.

Campbell, A. (1976). Subjective measures of well-being. American Psychologist, 31(2), 117-124.

Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The Quality of American Life. New York: Russell Sage Foundation.

242

Cantril, H. (1965). The pattern of Human Concerns. New Brunswick, NJ: Rutgers University Press.

Carlopio, J. R., Andrewartha, G., Armstrong, H., & Whetten, D. A. (2001). Developing management skills : a comprehensive guide for leaders (2nd ed.). Frenchs Forest, N.S.W: Pearson Education.

Cavana, R. Y., Sekaran, U., & Delahaye, B. L. (2001). Applied business research : qualitative and quantitative methods. Milton, Qld.: John Wiley & Sons Australia.

Chamberlain, K., & Zika, S. (1988). Religiosity, Life Meaning and Wellbeing: Some Relationships in a Sample of Women. Journal for the Scientific Study of Religion, 27(3), 411.

Chamberlain, K., & Zika, S. (1992). Stability and change in subjective well-being over short time periods. Social Indicators Research, 26(2), 101-117.

Cheng, H., & Furnham, A. (2004). Perceived Parental Rearing Style, Self-Esteem and Self-Criticism as Predictors of Happiness. Journal of Happiness Studies, 5(1), 1-21.

Cheung, W. (2010). The Lun Yu in English. Retrieved from http://www.confucius.org/lunyu/ed0101.htm

Clark, A. E. (2006). Born to be mild? Cohort effects don't explain why well-being is U-shaped in age (Vol. Working Paper N° 2006 - 35): Centre National de la Recherche Scientifique – École des Hautes Études en Sciences Sociales, École Nationale des Ponts et Chaussees – École Normale Superiure.

Clark, A. E., Frijters, P., & Shields, M. A. (2008). Relative Income, Happiness, and Utility: An Explanation for the Easterlin Paradox and Other Puzzles. Journal of Economic Literature, 46(1), 95-144.

Clark, A. E., & Oswald, A. J. (1994). Uhhappiness an unemployment. [Article]. Economic Journal, 104(424), 648-659.

Clark, A. E., & Oswald, A. J. (1996). Satisfaction and comparison income. [Article]. Journal of Public Economics, 61(3), 359.

Connellan, T. K. (1978). How to improve human performance. New York: Harper & Row.

243

Costa, P. T., & McCrae, R. R. (1980). Influence of extraversion and neuroticism on subjective well-being: Happy and unhappy people. Journal of Personality and Social Psychology, 38(4), 668-678.

Costa, P. T., & McCrae, R. R. (1992). Multiple uses for longitudinal personality data. European Journal of Personality, 6(2), 85-102.

Costa, P. T., Jr., Herbst, J. H., McCrae, R. R., & Siegler, I. C. (2000). Personality at midlife: Stability, intrinsic maturation, and response to life events. Assessment, 7(4), 365-378.

Cribier, F. (2005). Changes in the experience of life between two cohorts of Parisian pensioners born circa 1907 and 1921. Ageing and Society, 1(part 1), 51-71.

Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease 175(9), 526-536.

Csikszentmihalyi, M., & Hunter, J. (2003). Happiness in Everyday Life: The Uses of Experience Sampling. Journal of Happiness Studies, 4(2), 185-199.

Cummins, R. A. (1996). The domains of life satisfaction: An attempt to order chaos. Social Indicators Research, 38(3), 303.

Dear, K., Henderson, S., & Korten, A. (2002). Well-being in Australia. Social Psychiatry and Psychiatric Epidemiology, 37(11), 503-509.

Dehejia, R., Deleire, T., & Luttmer, E. F. P. (2007). Insuring Consumption and Happiness Through Religious Organizations. Journal of Public Economics, 91(1-2), 259-279.

del Barrio, V., Carrasco, M. Á., & Holgado, F. P. (2006). Factor structure invariance in the Children's Big Five Questionnaire. European Journal of Psychological Assessment, 22(3), 158-167.

Di Tella, R., & MacCulloch, R. (2006). Some Uses of Happiness Data in Economics. Journal of Economic Perspectives, 20(1), 25-25.

Di Tella, R., MacCulloch, R. J., & Oswald, A. J. (2001). Preferences over Inflation and Unemployment: Evidence from Surveys of Happiness. [Article]. American Economic Review, 91(1), 335-341.

Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542-575.

244

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction With Life Scale. Journal of Personality Assessment, 49(1), 71-75.

Diener, E., Lucas, R. E., & Scollon, C. N. (2006). Beyond the Hedonic Treadmill: Revising the Adaptation Theory of Well-Being. American Psychologist, 61(4), 305-314.

Diener, E., Sandvik, E., Pavot, W., & Fujita, F. (1992). Extraversion and subjective well-being in a U.S. national probability sample. Journal of Research in Personality, 26(3), 205-215.

Diener, E., Sapyta, J. J., & Suh, E. (1998). Subjective well-being is essential to well-being. Psychological Inquiry, 9(1), 33-37.

Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276-302.

Dockery, A. M. (2005). The Happiness of Young Australians: Empirical Evidence on the Role of Labour Market Experience. Economic Record, 81(255), 322-335.

Dockery, A. M. (2005). Happiness, life satisfaction and the role of work: Evidence from two Australian surveys. from http://www.melbourneinstitute.com/hilda/Biblio/cp/conf-hd05.pdf

Dua, P. S. (1970). Comparison of the Effects of Behavioral Oriented Action and Psychotherapy Reeducation on Introversion Extraversion, Emotionality, and Internal External Control: Journal of Counseling Psychology.

Duncan, E., & Grazzani-Gavazzi, I. (2004). Positive Emotional Experiences in Scottish and Italian Young Adults: A Diary Study. Journal of Happiness Studies, 5(4), 359-384.

Easterlin, R. A. (1974). Does Economic Growth Improve the Human Lot ? . Paper presented at the Nations and Households in Economic Growth: Essays in Honor of Moses Abramovitz.

Easterlin, R. A. (1995). Will raising the incomes of all increase the happiness of all? [Article]. Journal of Economic Behavior & Organization, 27(1), 35.

Easterlin, R. A. (2001). Income and Happiness: Towards a Unified Theory. The Economic Journal, 111(473), 465-484.

245

Easterlin, R. A. (2002). Income and Happiness: Towards a Unified Theory. In R. A. Easterlin (Ed.), Happiness in Economics (pp. 206-225): Elgar Reference Collection. International Library of Critical Writings in Economics, vol. 142.Cheltenham, U.K. and Northampton, Mass.

Easterlin, R. A. (2004). The economics of happiness. Daedalus, 133(2), 26-33.

Easterlin, R. A. (2005). Diminishing marginal utility of income? cavaet emptor. [Article]. Social Indicators Research, 70(3), 243-255.

Easterlin, R. A. (2006). Life cycle happiness and its sources: Intersections of psychology, economics, and demography. [Article]. Journal of Economic Psychology, 27(4), 463-482.

Easterlin, R. A., ed. (2002). Happiness in Economics: Elgar Reference Collection. International Library of Critical Writings in Economics, vol. 142. Cheltenham, U.K. and Northampton, Mass.:Elgar; distributed by American International Distribution Corporation, Williston, Vt.

Easterlin, R. A., Schaeffer, C. M., & Macunovich, D. J. (1993). Will the Baby Boomers Be Less Well Off Than Their Parents? Income, Wealth, and Family Circumstances over the Life Cycle in the United States. Population and Development Review, 19(3), 497-522.

Ebner, A. (2008). 'Nest-leaving' in Osterreich: Was beeinflusst die Wohnentscheidung junger Erwachsener? (Nest-Leaving in Austria: What Determines Young Adults' Housing Decisions? With English summary.). Wirtschaftspolitische Blatter, 55(2), 407-423.

Economist. (2011). ‘The U-bend of life Why, beyond middle age, people get happier as they get older’ The Economist, , Dec 16th 2010.

Eddy, J. M., Reid, J. B., & Fetrow, R. A. (2000). An Elementary School-Based Prevention Program Targeting Modifiable Antecedents of Youth Delinquency and Violence. Journal of Emotional and Behavioral Disorders, 8(3), 165-176.

Ehrhardt, J. J., Saris, W. E., & Veenhoven, R. (2000). Stability of Life-Satisfaction Over Time: Analysis of Change in Ranks in a National Population. Journal of Happiness Studies, 1(2), 177-205.

Fabiano, G. A., Pelham, W. E., Karmazin, K., Kreher, J., Panahon, C. J., & Carlson, C. (2008). A Group Contingency Program to Improve the Behavior of Elementary School Students in a Cafeteria. Behavior Modification, 32(1), 121-132.

246

Falk, A., & Knell, M. (2004). Choosing the Joneses: Endogenous Goals and Reference Standards: CEPR Discussion Papers: 4459.

Faul, F., Buchner, A., Erdfelder, E., Faul, F., & Lang, A.-G. (2006). G*Power version 3.08 Software. Kiel, Germany.

Ferrer-i-Carbonell, A. (2005). Income and Well-Being: An Empirical Analysis of the Comparison Income Effect. Journal of Public Economics, 89(5-6), 997-1019.

Ferrer-i-Carbonell, A., & Frijters, P. (2004). How Important Is Methodology for the Estimates of the Determinants of Happiness? Economic Journal, 114(497), 641-659.

Flouri, E. (2004). Subjective Well-Being in Midlife: The Role of Involvement of and Closeness to Parents in Childhood. Journal of Happiness Studies, 5(4), 335-358.

Fogle, L. M., Scott Huebner, E., & Laughlin, J. E. (2002). The Relationship between Temperament and Life Satisfaction in Early Adolescence: Cognitive and Behavioral Mediation Models. Journal of Happiness Studies, 3(4), 373-392.

Fordyce, M. W. (1973). Measuring happiness. Edison Community College.

Fordyce, M. W. (1988). A review of research on the happiness measures: A sixty second index of happiness and mental health. Social Indicators Research, 20(4), 355-381.

Frey, B. S., & Stutzer, A. (2000). Happiness, Economy and Institutions. Economic Journal, 110(466), 918-938.

Frey, B. S., & Stutzer, A. (2002). What Can Economists Learn from Happiness Research? Journal of Economic Literature, 40(2), 402-435.

Frey, B. S., & Stutzer, A. (2005). Happiness Research: State and Prospects. Review of Social Economy, 63(2), 207-228.

Frey, B. S., & Stutzer, A. e. (2007). Economics and Psychology: A Promising New Cross-Disciplinary Field: CESifo Seminar Series.Cambridge and London:MIT Press.

Frijters, P., & Beatton, D. (2011). The mystery of the U-shaped relationship between happiness and age. QUT Working Papers.

247

Frijters, P., Haisken-DeNew, J. P., & Shields, M. (2004). Money Does Matter! Evidence from Increasing Real Income and Life Satisfaction in East Germany Following Reunification. [Article]. American Economic Review, 94(3), 730-740.

Frijters, P., Haisken-DeNew, J. P., & Shields, M. A. (2004). Investigating the Patterns and Determinants of Life Satisfaction in Germany Following Reunification. Journal of Human Resources, 39(3), 649-674.

Frijters, P., Johnston, & Shields, M. (2008). anticipation & adaption effect of life events, shocks. QUT Working Paper.

Frijters, P., Johnston, D., & Shields, M. (2008a). What is the shadow price of crime and bereavement to well-being? QUT Working Paper.

Frijters, P., Johnston, D., & Shields, M. (2009). Life Satisfaction Dynamics with Quarterly Life Event Data. Scandinavian Economic Journal.

Frijters, P. (1999c). Explorations of welfare and well-being. Unpublished Article, University of Amsterdam, Amsterdam.

Furnham, A., & Petrides, K. V. (2003). Trait emotional intelligence and happiness. Social Behavior & Personality: An International Journal, 31(8), 815-823.

Gardner, J., & Oswald, A. J. (2006). Do Divorcing Couples Become Happier by Breaking Up? Journal of the Royal Statistical Society: Series A (Statistics in Society), 169(2), 319-336.

Gerdtham, U.-G., & Johannesson, M. (2001). The relationship between happiness, health, and social economic factors: Results based on Swedish microdata. Journal of Socio-Economics, 30(6), 553-557.

Gilman, R., & Huebner, E. S. (1997). Children's reports of their life satisfaction: Convergence across raters, time and response formats. School Psychology International, 18(3), 229-243.

Gilman, R., & Huebner, E. S. (2000). Review of life satisfaction measures for adolescents. Behaviour Change, 17(3), 178-195.

Gilman, R., & Huebner, S. (2003). A review of life satisfaction research with children and adolescents. School Psychology Quarterly, 18(2), 192-205.

Glaeser, E. L., Laibson, D., & Sacerdote, B. (2002). AN ECONOMIC APPROACH TO SOCIAL CAPITAL. The Economic Journal, 112, F437-F458.

248

Goldberg, L. R. (1990). An alternative "description of personality", The Big-Five factor structure. Journal of Personality and Social Psychology, 59, 1215-1229.

Graham, C. (2005). Insights on Development from the Economics of Happiness. World Bank Research Observer, 20(2), 201-231.

GSOEP. (2008). The German Socio-Economic Panel Study (SOEP) Retrieved December 22, 2008, from SOEP, DIW Berlin, D-10108, Berlin: http://www.diw.de/english/soep/soepoverview/27908.html

Haisken-DeNew, J. P., & Hahn, M. (2006). PanelWhiz: A Flexible Modularized Stata Interface for Accessing Large Scale Panel Data Sets [mimeo].

Haranin, E. C., Huebner, E. S., & Suldo, S. M. (2007). Predictive and incremental validity of global and domain-based adolescent life satisfaction reports. Journal of Psychoeducational Assessment, 25(2), 127-138.

Hayo, B., & Seifert, W. (2003). Subjective economic well-being in Eastern Europe. [Article]. Journal of Economic Psychology, 24(3), 329.

Headey, B., Muffels, R., & Wagner, G. G. (2010). Choices Which Change Life Satisfaction: Revising SWB Theory to Account for Change IZA Discussion Paper Series IZA DP No. 4953. from http://ftp.iza.org/dp4953.pdf

Headey, B. (2008). The Set-Point Theory of Well-Being: Negative Results and Consequent Revisions. Social Indicators Research, 85(3), 389-403.

Headey, B., & Wearing, A. (1989). Personality, life events, and subjective well-being: Toward a dynamic equilibrium model. Journal of Personality and Social Psychology, 57(4), 731-739.

Headey, B., Veenhoven, R., & Wearing, A. (1991). Top-down versus bottom-up theories of subjective well-being. Social Indicators Research, 24(1), 81-100.

Helliwell, J. F. (2003). How's life? Combining individual and national variables to explain subjective well-being. [Article]. Economic Modelling, 20(2), 331.

Hobson, C. J., Kamen, J., Szostek, J., Nethercut, C. M., Tiedmann, J. W., & Wojnarowicz, S. (1998). Stressful life events: A revision and update of the Social Readjustment Rating Scale.

Hofstede, G. (1983). National cultures revisited. Behavior Science Research, 18(4), 285-305.

249

Huebner, E. S. (1991). Correlates of life satisfaction in children. School Psychology Quarterly, 6(2), 103-111.

HILDA. (2008a). HILDA User Manual – Release 6. 165. Retrieved from http://www.melbourneinstitute.com/hilda/doc.html

HILDA. (2008b). The Household, Income and Labour Dynamics in Australia (HILDA) Survey , Release 6. Retrieved October 6, 2008, from http://www.melbourneinstitute.com/hilda/

HILDA. (2009). The Household, Income and Labour Dynamics in Australia (HILDA) Survey , Release 7. Retrieved February 6, 2009, from http://www.melbourneinstitute.com/hilda/

Hobson, C. J., Kamen, J., Szostek, J., Nethercut, C. M., Tiedmann, J. W., & Wojnarowicz, S. (1998). Stressful life events: A revision and update of the Social Readjustment Rating Scale. International Journal of Stress Management, 5(1), 1-23.

Howell, R. T. (2006). Models of happiness: The role of personality traits and daily experience in understanding life satisfaction. ProQuest Information & Learning, US.

Huebner, E. S. (1991). Correlates of life satisfaction in children. School Psychology Quarterly, 6(2), 103-111.

Huebner, E. S., Valois, R. F., Paxton, R. J., & Drane, J. W. (2005). Middle School Students' Perceptions of Quality of Life. Journal of Happiness Studies, 6(1), 15-24.

Jason, L. A., & Fries, M. (2004). Helping Parents Reduce Children's Television Viewing. Research on Social Work Practice, 14(2), 121-131.

Juster, F. T. (1986). Response Errors in the Measurement of Time Use. Journal of the American Statistical Association, 81(394), 390.

Kahneman, D. (2003). Experiences of collaborative research. American Psychologist, 58(9), 723-730.

Kapteyn, A., Smith, J. P., Van Soest, A., & Vonkova, H. (2011). Anchoring Vignettes and Response Consistency. RAND Center for the Study of Aging (P30AG012815) and the NICHD funded RAND Population Research Center (R24HD050906).

250

Kashdana, T. B., Biswas-Diener, R., & Laura A. King, L. A. (2008). Reconsidering happiness: the costs of distinguishing between hedonics and eudaimonia. The Journal of Positive Psychology, Vol. 3(No. 4), 219–233.

Kassenboehmera, S. C., & Haisken-DeNew, J. P. (2011). Heresy or Enlightenment? The Well-being Age U-Shape Effect is Flat. forthcoming in Economic Letters.

Kornilaki, E. N., & Chlouverakis, G. (2004). The situational antecedents of pride and happiness: Developmental and domain differences. British Journal of Developmental Psychology, 22, 605-619.

Layard, R. (1980). Human satisfactions and public policy. Economic Journal, 90, 737-750.

Lee, W.-S., & Oguzoglu, U. (2007). Income Support and Stigma Effects for Young Australians. Australian Economic Review, 40(4), 369-384.

Leiser, D., & Beth Halachmi, R. (2006). Children's Understanding of Market Forces. Journal of Economic Psychology, 27(1), 6-19.

Lewer, J. J., Gerlich, R. N., & Gretz, R. T. (2009). Maximizing and Satisficing Consumer Behavior: Model and Test. Southwestern Economic Review, 36(1), 127-139.

Lischetzke, T., & Eid, M. (2006). Why Extraverts Are Happier Than Introverts: The Role of Mood Regulation. Journal of Personality, 74(4), 1127-1162.

Lewin, K. (1951). Field theory in social science. New York: Harper & Row.

Losoncz, I. (2007). Personality Traits in HILDA. Paper presented at the HILDA SURVEY RESEARCH CONFERENCE 2007 Retrieved from http://www.melbourneinstitute.com/hilda/conf/conf2007/HILDA%20Conf%20Papers%202007/All%20Papers/Losoncz,%20Ibolya_final%20paper.pdf

Lowenstein, L. F. (1983). Treatment of extreme shyness: By implosive, counselling and conditioning approaches. AEP (Association of Educational Psychologists) Journal, 6(2), 64-69.

Lu, L., & Hu, C.-H. (2005). Personality, Leisure Experiences and Happiness. Journal of Happiness Studies, 6(3), 325-342.

251

Lucas, R. E., Clark, A. E., Georgellis, Y., & Diener, E. (2003). Reexamining adaptation and the set point model of happiness: Reactions to changes in marital status. Journal of Personality and Social Psychology, 84(3), 527-539.

Lyubomirsky, S., King, L., & Diener, E. (2005). The Benefits of Frequent Positive Affect: Does Happiness Lead to Success? Psychological Bulletin, 131(6), 803-855.

Magnus, K., Diener, E., Fujita, F., & Pavot, W. (1993). Extraversion and Neuroticism as Predictors of Objective Life Events: A Longitudinal Analysis. Journal of Personality & Social Psychology, 65(5), 1046-1053.

Marks, G. N. (2007). Completing University: Characteristics and Outcomes of Completing and Non-completing Students. Camberwell, Victoria, Australia: The Australian Council for Educational Research Ltd.

Mawson, T. (2002). Mill's Proof. Cambridge Journal, 77(3), 375-405. Retrieved from http://journals.cambridge.org

McCrae, R. R., & Costa, P. T. (1995). Positive and Negative Valence within the Five-Factor Model. Journal of Research in Personality, 29(4), 443-460.

McCrae, R. R., Costa, P. T., Jr, Terracciano, A., Parker, W. D., Mills, C. J., De Fruyt, F., et al. (2002). Personality trait development from age 12 to age 18: Longitudinal, cross-sectional and cross-cultural analyses. Journal of Personality and Social Psychology, 83(6), 1456-1468.

McCrae, R. R., Costa, P. T., Jr., Mroczek, D. K., & Little, T. D. (2006). Cross-Cultural Perspectives on Adult Personality Trait Development Handbook of personality development. (pp. 129-145). Mahwah, NJ US: Lawrence Erlbaum Associates Publishers.

McKnight, C. G., Huebner, E. S., & Suldo, S. (2002). Relationships among stressful life events, temperament, problem behavior, and global life satisfaction in adolescents. Psychology in the Schools, 39(6), 677-687.

McShane, S., & Travaglione, T. (2003). Organisational Behaviour on the Pacific Rim. Sydney, Australia: McGraw-Hill Australia Pty Limited.

Mervin, C., & Frijters, P. (2011). Shared Misery Double Misery? The Effects of the Partner's Life Events on Own Mental Health. School of Economics Discussion Paper, The University of Queensland. Australia.

252

Meyers, A., & Craighead, W. (1979). Classroom Treatment of Psychotic Children. Behavior Modification, 3(1), 73-96.

Mill, J. S. (2009). Utilitarianism. In G. Sher (Eds.) E-book available from http://www.qut.eblib.com.au.ezp01.library.qut.edu.au/patron/FullRecord.aspx?p=473615&userid=GWsjdwJbxnxVmHCp2fmMiA%3d%3d&tstamp=1305000359&id=2DA15B62860B8B6AB6CA7DAD204EC31EB011CB03

Miltenberger, R. G. (1997). Behavior modification: principles and procedures. Pacific Grove, CA: Brooks - Cole.

Miranda, A., Presentación, M. J., & Soriano, M. (2002). Effectiveness of a School-Based Multicomponent Program for the Treatment of Children with ADHD. Journal of Learning Disabilities, 35(6), 547-563.

Moore, J., Stinson, L., & Welniak, E. (2000). Income measurement error in surveys: A review. Journal of Official Statistics, 16(4).

Mora, T., & Oreopoulos, P. (2011). Peer Effects on High School Aspirations: Evidence from a Sample of Close and Not-So-Close Friends. Economics of Education Review, 30(4), 575-581.

Mroczek, D. K., & Kolarz, C. M. (1998). The effect of age on positive and negative affect: A developmental perspective on happiness. Journal of Personality and Social Psychology, 75(5), 1333-1349.

Natvig, G. K., Albrektsen, G., & Qvarnstrom, U. (2003). Associations Between Psychosocial Factors and Happiness Among School Adolescents. International Journal of Nursing Practice, 9, 166-175.

Nelson, K. G. (2010). Exploration of Classroom Participation in the Presence of a Token Economy. [Article]. Journal of Instructional Psychology, 37(1), 49-56.

Neugarten, B., Havighurst, R., & Tobin, S. (1961). The measurement of life satisfaction. .Journal of Gerontology, 16(134-143.).

Ng, Y.-K. (2002). Economic Policies in the Light of Happiness Studies with Reference to Singapore. Singapore Economic Review, 47(2), 199-212.

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.

253

Oswald, A. J. (1997). Happiness and economic performance. [Article]. Economic Journal, 107(445), 1815-1831.

Oswald, A. J., & Powdthavee, N. (2007). Obesity, Unhappiness, and The Challenge of Affluence : Theory and Evidence (pp. 21 pages): University of Warwick, Department of Economics, The Warwick Economics Research Paper Series (TWERPS).

Oswald, A. J., & Powdthavee, N. (2008). Does Happiness Adapt? A Longitudinal Study of Disability with Implications for Economists and Judges. Journal of Public Economics, 92(5-6), 1061-1077.

Palmore, E., & Luikart, C. (1972). Health and social factors related to life satisfaction. Journal of Health and Social Behavior, 13(1), 68-80.

Pavot, W., Diener, E., & Fujita, F. (1990). Extraversion and happiness. Personality and Individual Differences, 11(12), 1299-1306.

Pavot, W., Diener, E., & Suh, E. (1998). The Temporal Satisfaction With Life Scale. Journal of Personality Assessment, 70(2), 340-354.

Phelps, C. D. (2001). A clue to the paradox of happiness. Journal of Economic Behavior and Organization, 45(3), 293-300.

Plug, E. J. S., & Van Praag, B. M. S. (1998). Similarity in Response Behavior between Household Members: An Application to Income Evaluation. Journal of Economic Psychology, 19(4), 497-513.

Powdthavee, N. (2003). Unhappiness and Crime : Evidence from South Africa (pp. 36 pages): University of Warwick, Department of Economics, The Warwick Economics Research Paper Series (TWERPS).

Powdthavee, N. (2005). Unhappiness and Crime: Evidence from South Africa. Economica, 72(3), 531-547.

Powdthavee, N. (2007). Causal Analysis in Happiness Research. Institute of Education, University of London.

Powdthavee, N. (2008). Putting a Price Tag on Friends, Relatives, and Neighbours: Using Surveys of Life Satisfaction to Value Social Relationships. Journal of Socio-Economics, 37(4), 1459-1480.

254

Powdthavee, N. (2009). I Can't Smile without You: Spousal Correlation in Life Satisfaction. Journal of Economic Psychology, 30(4), 675-689.

Rindermann, H. (2008). Relevance of education and intelligence at the national level for the economic welfare of people. Intelligence, 36(2), 127-142.

Rantanen, J., Metsapelto, R.-L., Feldt, T., Pulkkinen, L., & Kokko, K. (2007). Long-term stability in the Big Five personality traits in adulthood. Scandinavian Journal of Psychology, 48(6), 511-518.

Rayo, L., & Becker, G. S. (2007). Habits, Peers, and Happiness: An Evolutionary Perspective. American Economic Review, 97(2), 487-491.

Riffel, L. (2011). ADHD, autism, oppositional defiant disorder and self esteem - strategies for classrooms. Paper to be presented at the Schoolwide Positive Behaviour Support Conference Brisbane, Australia.

Rojas, M. (2007). Heterogeneity in the Relationship between Income and Happiness: A Conceptual-Referent-Theory Explanation. Journal of Economic Psychology, 28(1), 1-14.

Routh, D. (1979). Activity, attention, and aggression in learning disabled children. Journal of Clinical Child Psychology, 8(3), 183-187.

Ryff, C. D., & Singer, B. H. (2008). Know Thyself and Become What You Are: A Eudaimonic Approach to Psychological Well-Being. Journal of Happiness Studies, 9(1), 13-39.

Sahoo, F. M., Sahoo, K., & Harichandan, S. (2005). Five Big Factors of Personality and Human Happiness. Social Science International, 21(1), 20-28.

Sarkozy, N. (2009). DISCOURS DE M. LE PRÉSIDENT DE LA RÉPUBLIQUE: Conférence internationale de présentation des conclusions du rapport de la Commission de mesure de la performance économique et du progrès social. Paper presented at the Commission on the Measurement of Economic Performance and Social Progress. Retrieved from http://www.ambafrance-au.org/france_australie/spip.php?article3605

Saucier, G. (1994). Mini-Markers: A brief version of Goldberg's unipolar Big-Five markers. Journal of Personality Assessment, 63(3), 506-516.

255

Schiff, M., & BarGil, B. (2004). Children with behavior problems: improving elementary school teachers' skills to keep these children in class. Children and Youth Services Review, 26(2), 207-234.

Seifert, W. (2003). Subjective economic well-being in Eastern Europe. Journal of Economic Psychology, 24(3), 329-348.

Seligman, M. (2011). Flourish A Visionary New Understanding of Happiness and Well-being New York: Free Press

Seligson, J., Huebner, E., & Valois, R. (2005). An Investigation Of A Brief Life Satisfaction Scale With Elementary School Children. Social Indicators Research, 73(3), 355-374.

Senik, C. (2004). When information dominates comparison: Learning from Russian subjective panel data. [Article]. Journal of Public Economics, 88(9/10), 2099.

Shields, M. A., & Price, S. W. (2005). Exploring the economic and social determinants of psychological well-being and perceived social support in England. Journal of the Royal Statistical Society: Series A (Statistics in Society), 168(3), 513-537.

Shinn, K. (2009). Confucianism: A Brief Summary of Confucius and His Teachings. Retrieved from http://www.csuchico.edu/~cheinz/syllabi/asst001/fall97/11kshinn.htm

Singer, E., von Thurn, D. R., & Miller, E. R. (1995). Confidentiality assurances and response: A quantitative review of the experimental literature. Public Opinion Quarterly, 59(1), 66-77.

Srivastava, A. K., & Misra, G. (2003). Going beyong the model of economic man: An indigenous perspective on happiness. Journal of Indian Psychology, 21(2), 12-29.

Stutzer, A., & Frey, B. S. (2006). Does Marriage Make People Happy, or Do Happy People Get Married? Journal of Socio-Economics, 35(2), 326-347.

Suldo, S., & Huebner, E. (2006). Is Extremely High Life Satisfaction During Adolescence Advantageous? Social Indicators Research, 78(2), 179-203.

Terza, J. V. (1987). "Estimating Linear Models with Ordinal Qualitative Regressors." Journal of Econometrics 34(3): 275-291.

256

Theodossiou, I. (1998). The effects of low-pay and unemployment on psychological well-being: a logistic regression approach. [Article]. Journal of Health Economics, 17(1), 85-104.

Trzcinski, E., & Holst, E. (2007). Initial Predictors of Life Satisfaction in Early Adulthood. Schmollers Jahrbuch: Zeitschrift fur Wirtschafts- und Sozialwissenschaften/Journal of Applied Social Science Studies, 127(1), 95-104.

Tsang, L. L. W. (2003). The Effects of Children, Dual Earner Status, Sex Role Traditionalism, and Marital Structure on Marital Happiness over Time. Journal of Family and Economic Issues, 24(1), 5-26.

UCL. (2009). The Collected Works of Jeremy Bentham, ed. J. H. Burns (1961-79), J. R. Dinwiddy (1977-83), F. Rosen (1983-94), F. Rosen and P. Schofield (1995-2003), P. Schofield (2003-). Retrieved from http://www.ucl.ac.uk/Bentham-Project/Publications/index.htm

Ulker, A. (2008). Mental Health and Life Satisfaction of Young Australians: The Role of Family Background. Australian Economic Papers, 47(2), 199-218.

Uppal, S. (2006). Impact of the timing, type and severity of disability on the subjective well-being of individuals with disabilities. [Article]. Social Science & Medicine Social Science & Medicine J1 - Social Science & Medicine, 63(2), 525-539.

Van Landeghem, B. G. M. (2008). Human Well-Being over the Life Cycle: Longitudinal Evidence from a 20-Year Panel. Katholieke Universiteit Leuven, LICOS Centre for Institutions and Economic.

van Praag, B. M. S. (2007). Perspectives from the Happiness Literature and the Role of New Instruments for Policy Analysis: Tinbergen Institute, Tinbergen Institute Discussion Papers: 07-049/3.

van Praag, B. M. S., Frijters, P., & Ferrer-i-Carbonell, A. (2000). A Structural Model of Well-being: with an application to German Data: Tinbergen Institute, Tinbergen Institute Discussion Papers: 00-053/3.

van Praag, B. M. S., Frijters, P., & Ferrer-i-Carbonell, A. (2003). The anatomy of subjective well-being. [Article]. Journal of Economic Behavior & Organization, 51(1), 29.

Varian, H. R. (1992). Microeconomic Analysis. New York: w. W. Norton & Company Inc.

257

Veenhoven, R. (1994). Is happiness a trait? Tests of the theory that a better society does not make people any happier. Social Indicators Research, 32(2), 10.

Veenhoven, R. (2007). Healthy happiness: effects of happiness on physical health and the consequences for preventive health care. Journal of Happiness Studies, 1-21.

Veenhoven, R. (2008). Healthy happiness: effects of happiness on physical health and the consequences for preventive health care. Journal of Happiness Studies, 9(3), 449-469.

Wagner, G. G., Burkhauser, R. V., & Behringer, F. (1993). The English language public use file of the German Socio-Economic Panel. Journal of Human Resources, 28(2), 429-433.

Ware, J. E. (2009). SF-36® Health Survey Update. Retrieved January, 21, 2010, from http://www.sf-36.org/tools/sf36.shtml

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS Scales. Journal of Personality and Social Psychology, 47, 1063–1070.

Watson, N., & Wooden, M. (2010). Data Survey: The HILDA Survey: Progress and Future Developments. Australian Economic Review, 43(3), 326-336.

Waxmonsky, J., Pelham, W. E., Gnagy, E., Cummings, M. R., O'Connor, B., Majumdar, A., et al. (2008). The Efficacy and Tolerability of Methylphenidate and Behavior Modification in Children with Attention-Deficit/Hyperactivity Disorder and Severe Mood Dysregulation. Journal of Child and Adolescent Psychopharmacology, 18(6), 573-588.

Wessman, A. E., & Ricks, D. F. (1966). Mood and personality. Oxford England: Holt, Rinehart, & Winston.

White, N. P. (2006). A brief history of happiness. Malden, MA ; Oxford: Blackwell Pub.

Wilson, W. R. (1967). Correlates of avowed happiness. Psychological Bulletin, 67(4), 294-306.

Winkelmann, L., & Winkelmann, R. (1998). Why Are the Unemployed So Unhappy? Evidence from Panel Data. [Article]. Economica, 65(257), 1-15.

Wolff, H. G., Wolf, S. G., & Hare, C. C. (1950). Life stress and bodily disease. Baltimore: Williams and Wilkins.

258

Wolpert, D. H. (2010). Why Income Comparison Is Rational. Games and Economic Behavior, 69(2), 458-474.

Wolraich, M., Drummond, T., Salomon, M. K., O'Brien, M. L., & Sivage, C. (1978). Effects of methylphenidate alone and in combination with behavior modification procedures on the behavior and academic performance of hyperactive children. Journal of Abnormal Child Psychology, 6(1), 149-161.

Wunder, C., Wiencierz, A., Schwarze, J., Küchenhoff, H., Kleyer, S., & Bleninger, P. (2009). Well-Being over the Life Span: Semiparametric Evidence from British and German Longitudinal Data. Unpublished Discussion Paper No. 4155. Institute for the Study of Labor.

Zimmermann, A. C. (2007). Adaptation, Assets, and Aspirations. Three Essays on the Economics of Subjective Well-Being. Unpublished Ph.D., University of Southern California.

Date post:	11-Oct-2020
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

New The economics of happiness: A lifetime perspective · 2012. 4. 30. · happiness studies, that...

Documents