ECONOMICS

THE AGE-HAPPINESS PUZZLE: THE ROLE OF ECONOMIC CIRCUMSTANCES AND FINANCIAL

SATISFACTION

by

Ingebjørg Kristoffersen

Business School University of Western Australia

DISCUSSION PAPER 10.15

THE AGE-HAPPINESS PUZZLE: THE ROLE OF ECONOMIC CIRCUMSTANCES AND FINANCIAL SATISFACTION*

Ingebjørg Kristoffersen Business School (Economics), The University of Western Australia, Western Australia,

Australia

DISCUSSION PAPER 10.15

ABSTRACT:

Happiness and satisfaction is often found to be U-shaped in age. Using panel data from the

Household Income and Labour Dynamics in Australia (HILDA) survey, this paper finds a

significant age effect in life satisfaction data which appears to be robust and to reflect a

genuine lifecycle effect. About half of this observed age effect is accounted for by variation

in financial satisfaction. Finally, associations between income and financial satisfaction,

between wealth and financial satisfaction, and between financial satisfaction and life

satisfaction peak in midlife and decline thereafter. This provides strong support for the

hypothesis that material concerns are key drivers of lifecycle effects in happiness and

satisfaction.

* I am grateful for thoughtful comments made by Paul Gerrans, Peter Robertson and David Butler during the preparation of this paper. I am also grateful for valuable input provided by various conference attendees at the 2013 HILDA conference, though particularly from Richard Lucas. The study uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) survey. The HILDA project was initiated and 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, however, are those of the author and should not be attributed to either FaHCSIA or MIAESR.

1

I Introduction and Background

The apparent U-shape in happiness and satisfaction across the lifecycle has generated much interest

since its discovery. This pattern has been declared to be robust across samples from several nations,

also when holding important variables, such as health, constant (Oswald 1997; Blanchflower and

Oswald 2004; Blanchflower and Oswald 2008). Such studies report a low-point in happiness occurring

in midlife, somewhere in the mid-forties, and a maximum around age seventy (a slight dip is often

reported after seventy). However, this pattern is not necessarily universal: Easterlin (2006) finds the

exact opposite pattern in a U.S. sample; and Frijters and Beatton (2012) find a U-shape in Australian

and British data, but that German data exhibit a decline in happiness toward midlife, followed by a

small improvement around the age of 60-75, after which happiness declines further. Frijters and

Beatton also report that observed age effects are largely accounted for by individual fixed effects and,

to a lesser extent, survivor bias.

An observed U-shaped in happiness and satisfaction across the lifecycle may be explained, at least

partly, by changes in expectations and aspirations (Blanchflower and Oswald 2008). This hypothesis

emerges from evidence that the years following midlife brings diminishing expectations (Stoebe and

Stoebe 1987), narrowing goal-achievement gaps (Campbell et al. 1976), and improvements in our

ability to adjust to life situations (Argyle 1989) and cope with adversity (Carstensen 1995; Lawton

1996). Materialism (or a strong focus on materialistic goals in life), which has been demonstrated to

have a negative effect on subjective wellbeing in several separate studies, has also been found to be at

its highest in midlife and at its lowest in old age (Belk 1985).

Under this hypothesis one would expect a significant part of the variation observed in subjective

wellbeing across the lifecycle is accounted for by concerns over economic circumstances. Evidence

that financial satisfaction improves after midlife (Plagnol 2011) lends some support to this hypothesis.

2

One would also expect to see that economic circumstances, and particularly relative economic

circumstances, matter more in midlife than later in life, either in the way economic circumstances

translate into financial satisfaction, or in the way financial satisfaction translates into life satisfaction,

or both. That is, the mechanism by which economic circumstances translate into subjective wellbeing

may change across the lifecycle. Brown et al. (2014) use latent class modelling to identify stages of the

life cycle where the determinants of financial satisfaction are distinct, and find some evidence that

income matters more early in life than later in life.

This paper extends previous research by examining whether these hypotheses bear out in Australian

data. Specifically, the analysis uses panel data from the Household Income and Labour Dynamics in

Australia (HILDA) survey to determine whether associations between economic circumstances and

financial satisfaction, and between financial satisfaction and life satisfaction, vary across the lifecycle.

The observed U-shape in subjective wellbeing across the lifecycle is an essential motivation for such

an investigation. In light of recent evidence which appear to challenge the nature of this pattern, the

core analysis is preceded by an investigation of whether observed age effects are robust and reflect

genuine lifecycle effects, in as far as is possible with the available data. The paper proceeds as follows:

section II presents the models and method employed, section III presents the data, section IV presents

the results of the analysis, and section V provides a summary and conclusion.

II Models and Method

(i) Capturing Age Effects in Life Satisfaction Data

The association between age and life satisfaction is determined by estimating a standard subjective

wellbeing model, represented by equation (1).

3

itij

jitjititAitAitAit euXYageageageLS +++++++= ∑ agδβββ )()()()()( 33

22 (1)

A measure of subjective wellbeing, which in this case is life satisfaction (LS), is a function of age,

economic circumstances (Y, transformed as appropriate to accommodate an assumed linear

association with subjective wellbeing), and a set of other variables known to be associated with

subjective wellbeing (X). Since this analysis considers life satisfaction across the lifecycle, with a focus

on the second half of the lifecycle, the model accommodates a wave-shaped association between age

and subjective wellbeing, captured by linear, squared and cubed age-terms.

The availability of panel data allows for the separation of the composite error term into an individual

fixed-effects component (ui) and a time-varying random error component (eit). Individual fixed effects

have been found to account for a significant part of observed age effects in subjective wellbeing

(Frijters and Beatton 2012). However, such effects may absorb genuine age effects unless the panel is

sufficiently long (i.e. covers a long period of time). If individual fixed effects account for much (or all)

of the observed changes in wellbeing across the lifecycle, this could be explained by people sampled

in midlife having lower base-line life satisfaction scores than people sampled earlier or later in the

lifecycle. Consequently, age effects may not be appropriately captured in fixed-effects panel models

unless panels are sufficiently long.1 Furthermore, the need to account for individual fixed effects is

potentially significantly diminished if one has access to variables which capture personal

characteristics (Clark et al. 2008; Boyce 2010).

It is possible that observed age effects in subjective wellbeing data result from cohort effects rather

than from genuine lifecycle effects. Blanchflower and Oswald (2008) investigate this possibility using

1 In order to separate unique individual fixed effects from genuine life effects the panel must cover a period which is long enough to capture these long-term changes in wellbeing, which may require more than 20 years of data. The panel data used here cover an eleven-year period, which falls well short of this. A typical person sampled between the ages of 35 and 46 will exhibit a low base-line satisfaction through the entire eleven-year period, while a typical person sampled between ages of 65 and 76 will exhibit a high base-line satisfaction.

4

U.S. and European panel data covering 34 years, finding that cohort effects at best explain only a

modest portion of observed age effects in subjective wellbeing.2 A standard such approach implies

including dummy identifiers for different birth cohorts. However, again, unless one has access to very

wide panels of data, as Blanchflower and Oswald do, such identifiers will simply absorb age effects

which may reflect either genuine lifecycle effects or cohort effects. The reliable identification of cohort

effects therefore requires longitudinal (panel) data covering a long enough time-frame to be able to

distinguish between different generations, and it is otherwise difficult to distinguish between genuine

lifecycle effects and cohort effects. An eleven-year panel seems too short to be able to distinguish

genuine cohort effects from genuine lifecycle effects in the usual way. An informative alternative

approach used here is a visual representation of changes in life satisfaction within cohorts as

respondents age.

(ii) Capturing Age Effects in Associations between Economic Circumstances, Financial

Satisfaction and Life Satisfaction

In the second and main part of the analysis, the association between economic circumstances and life

satisfaction is assumed to occur via financial satisfaction, similarly to Frijters (1999).3 Specifically:

),,( XFSAGEfLS = (2)

),,( ZYAGEgFS = (3)

Life satisfaction (LS) is a function of age, financial satisfaction (FS) and a vector of other determinants

(X) which includes economic circumstances; and financial satisfaction is a function of age, economic

2 Unfortunately, they do not report fixed-effects model estimates. 3 This is not a very common approach, though financial satisfaction models are occasionally presented alongside life satisfaction models (see for example Ferrer-i-Carbonell and Frijters 2004; Headey and Wooden 2004; Clark et al. 2005).

5

circumstances (Y) and a vector of other determinants (Z).4 The model specifically allows for the

associations between economic circumstances, financial satisfaction and life satisfaction to vary across

the lifecycle. Interaction terms between these variables are therefore included, as specified in

equations (4) and (5).

itij

jitj

ititFSAititFSAitFSitAitAitAit

euXageFSageFSFSageageageLS

++++

+++++=

∑ ag

ββββββ

)())(())(()()()()( 2

23

32

2 (4)

itim

itmm

ititYititYAitYitAitAitAit

euZageYageYYageageageFS

++++

+++++=

∑ aλ

φφφφφφ

)())(())(()()()()( 2

23

32

2 (5)

The coefficients for the age-interaction terms, which are of particular interest here, are captured by the

β and φ-parameters in (4) and (5). This approach is potentially limited by treating age as a continuous

variable and the satisfaction models as continuous and differentiable in the manner implied by (4)

and (5). It is possible that the associations between economic circumstances, financial satisfaction and

life satisfaction do not follow patterns easily captured by this type of specification, and that are better

captured by comparing associations across age groups. Alternative estimates based this type of

approach are obtained as part of a set of robustness tests.

(iii) Model Estimation Choices

In this context we are ultimately interested in associations in variation over time, which again may

imply a preference for fixed-effects models. However, as explained earlier, the changes we mean to

4 The purpose of equation (4) is to measure the pure effect of financial satisfaction on life satisfaction. If economic circumstances are not controlled for the financial satisfaction-coefficients in this model will also capture effects of economic circumstances, which are captured separately in the financial satisfaction model. By including economic circumstances in the vector X of this model these coefficients will measure how a one-point change in financial satisfaction affects life satisfaction, holding economic circumstances (and everything else) constant. In this case, vector Z is therefore a subset of vector X. Also, if the error terms in the two equations are correlated, SUR estimation procedure will produce more efficient coefficients. For now, this pair of equations is assumed to be recursive, with uncorrelated errors. These assumptions are subsequently relaxed as part of a set of robustness tests.

6

capture are subtle long-term dynamics, as opposed to short-term reactions to changes in

circumstances, and these may be better captured in cross-sectional variation unless the panel covers a

very long time-period (Kennedy 2008). Also, wealth is considered a key indicator of economic

circumstances, and in included here in addition to income. However, to date, information on wealth

is only available in waves 2, 6 and 10 of the HILDA survey. Consequently, much of the analysis is

based on an intermittent panel, using data collected across three different points in time, each four

years apart. The fact that only three time-periods are used means fixed-effects model estimates are

likely to be imprecise. More importantly, a nine-year period may not be long enough to pick up the

long-term dynamics which this analysis intends to capture. Consequently, this limitation implies

pooled models are preferred unless symptoms of bias or other problems are identified.

The benchmark method of model estimation used here is standard linear regression. This method

implies that satisfaction scores are treated as though they are continuous and unlimited variables

which are normally distributed and cardinally comparable across individuals, and by implication also

within individuals over time. All of these assumptions are clearly challenged: These data are discrete

and also bounded, which implies it is possible they ought to be treated as censored, as suggested by

Ng (2008). Subjective wellbeing data tend to be non-normally distributed, with measures of central

tendency around 7 or 8 on a 0-10 scale.5 Finally, the assumption of cardinal comparability cannot be

assured even if it may be considered intuitively sound (Kristoffersen 2010). Alternative results, based

on estimation methods which accommodate these characteristics, are therefore obtained in order to

evaluate the robustness of key results. Specifically, model estimators for data which are ordered

(ordered probit), censored (censored Tobit) and non-normally distributed (the Poisson model for

count data) are used.

5 See, for example, Frey and Stutzer (2002). The life satisfaction data used here exhibits a mean of 7.88 and median and mode of 8. Financial satisfaction data are slightly less skewed, with a mean of 6.45 and median and mode of 7.

7

Reliable model estimates also require all model regressors to be exogenous. In the context of

subjective wellbeing models, income is commonly suspected of being endogenous, and wealth is

therefore also indicated. Since these are key variables in this analysis the problem of endogeneity bias

must be considered. Income is usually considered to be potentially endogenous due to omitted

variable bias (Clark et al. 2008), though if key relevant information is omitted from the model several

other variables are likely to be affected also. Since this information is likely to be linked to personality,

the inclusion of personality variables is likely to help alleviate this problem. If not, fixed-effects panel

models present a viable solution so long as the relevant omitted information is time-invariant. If

endogeneity results from time-varying omitted information which is not captured by personality

variables, or from reverse causality, reliable (unbiased) estimates require an instrumental variable

approach. This requires the identification of a variable which is both correlated with income and

uncorrelated with subjective wellbeing, which is a challenge.

Problems with endogenous regressors would manifest in inconsistencies between random-effects and

fixed-effects panel models, which means that they may be identified and potentially circumvented. In

this context, the point of the analysis is to evaluate the extent to which associations between economic

variables and satisfaction change across the lifecycle, and unless endogeneity bias also changes across

the lifecycle, which seems unlikely, the key results will not be compromised.

III The Data

The analyses presented here use Australian panel data from the HILDA survey. Table 1 presents a list

of the variables used here. In this context, life satisfaction, financial satisfaction, economic

circumstances and age are key variables.

[Table 1 about here]

8

Economic circumstances is usually measured by income, though wealth has been demonstrated to be

at least as important in explaining variation in subjective wellbeing (Headey and Wooden 2004).

Consequently, wealth is considered an important variable here. As mentioned, this information is

available in waves 2, 6 and 10, and the analysis is therefore predominantly conducted based on an

intermittent panel, using data from these waves.

Income and wealth are both measured at the household level. Due to resource sharing across

household members, household income is equivalised to account for differences in household

composition using the new (modified) OECD equivalence scale.6 Wealth is adjusted similarly, though

only with respect to the number of adults in the household, since wealth is assumed to fund future

consumption of household heads but not of children. Here, financial and life satisfaction is specified

as linearly associated with the percentile position in the distributions of income and wealth.

Alternative model estimates based on the more common lin-log specification, where satisfaction is

assumed linearly associated with log transformations of income and wealth, are also obtained in

order to evaluate the robustness of key results.7

Other known correlates of subjective wellbeing, which are included here as controls, include gender,

marital status, the presence of children in the household, health, labour market participation and

6 Information on this and other commonly used equivalence scales can be found online: http://www.oecd.org/eco/growth/OECD-Note-EquivalenceScales.pdf 7 No consensus exists on the specific association between income and wealth and subjective wellbeing, though the lin-log specification, where wellbeing is assumed linearly associated with log-transformations of income (and wealth), is relatively common. This approach is convenient, but implies some limitations (Layard et al. 2008). First, it assumes the elasticity marginal effects or utilities with respect to income (and wealth) are constant, which may or may not hold true. Second, estimated coefficients are highly sensitive to whether or not low levels of income and wealth are included, imposing a very sharp fall in marginal utilities early in the income and wealth distributions and very flat utility over the rest of the distributions. A convenient alternative is the log-normal specification, where subjective wellbeing is assumed linearly associated with the position in the distribution of income (and wealth) (Van Herwaarden and Kapteyn 1981). Though this specification is not identified elsewhere in the literature, it is preferred here: This specification yields coefficients which are not sensitive to the inclusion or omission of low values, fitted models match the data well across the entire income and wealth distributions, this specification treats satisfaction as bounded rather than unbounded and income and wealth as relative rather than absolute, and it is intuitive in interpretation. These observations are based on prior unpublished work.

9

personal characteristics. Education appears to have some relevance also, though the relationship

between education and subjective wellbeing is not well established.8

The inclusion of personal characteristics is of particular importance in this context. Personal

characteristics are generally known to explain a significant portion of variation in subjective

wellbeing across individuals (Diener and Lucas 1999). As discussed, the inclusion of personality

indicators is likely to substantially reduce the likelihood of omitted variable bias (Clark et al. 2008),

and personality has also been demonstrated to account for a substantial portion of individual fixed

effects (Boyce 2010). This information can therefore be highly valuable, especially where fixed-effects

panel model estimation is problematic, as is the case here.

In the HILDA survey, information on personal characteristics is available in the form of the Big5

Personality Inventory (McCrae and John 1992), which covers extroversion, agreeableness,

conscientiousness, emotional stability and openness to experience. This information is available only

in waves 5 and 9 to date. As personal characteristics are largely determined by genetics and early

childhood experiences they tend to be reasonably stable over time, and it is therefore considered

permissible to extrapolate the information available in waves 5 and 9 into the intermittent panel

which consists of waves 2, 6 and 10, which is used here. Specifically, personality data from wave 5 are

used to complement data from waves 2 and 6, while personality variables from wave 9 are used to

complement data from wave 10 (this approach yields the largest sample size).

8 A survey of known correlates of subjective wellbeing is provided by Dolan et al. (2008).

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IV Results

(i) Lifecycle effects in Australian Panel Data

Figure 1 illustrates observed changes in life satisfaction, financial satisfaction and mental health scores

across the lifecycle, without controls.9 Unadjusted life satisfaction reaches a minimum around age 40,

and a maximum around age 80. Mental health is measured by the specific mental health component

(MH5) of the SF36 general health survey instrument.10 This index, which here is adjusted to fit a 0-10

scale, captures information about persistent mood. This figure demonstrates the wave-shape in

unadjusted life satisfaction data, which is also observed in mental health, though the increase in

mental health scores observed after midlife is less pronounced. On the other hand, financial

satisfaction scores are U-shaped in age, also with a minimum around age 40, and with a more

pronounced surge over the second half of the lifecycle. The age effect observed in raw life satisfaction

data thereby reflect genuine changes in mental health: People report feeling less happy, calm and

peaceful; and more nervous, down and depressed; in midlife than later in life. However, there is

clearly more to this effect than merely variation in persistent mood. Also, it seems likely that the

increase in life satisfaction observed after midlife to some extent reflect improvements in financial

satisfaction.

[Figure 1 about here]

9 These graphs represent fitted data using linear regression models with linear, squared and cubic age-terms and no other controls. They represent unadjusted data in the sense that no other variables are controlled for. 10 The MH5 index is generated by responses to the question ‘How much of the time during the past 4 weeks (a) have you been a nervous person, (b) have you felt so down in the dumps that nothing could cheer you up, (c) have you felt calm and peaceful, (d) have you felt down, and (e) have you been a happy person’. Responses are coded to a six-point scale of (1) all of the time, (2) most of the time, (3) a good bit of the time, (4) some of the time, (5) a little of the time, and (6) none of the time. The MH5 score is calculated by first reversing the scores where appropriate such that higher values indicate better mental health, then adding the score for each question, and finally standardising this sum to a 0-100 index, in accordance with the procedure outlined in Ware et al. (2000).

11

Table 2 presents estimates the standard life satisfaction model (equation 1) using the intermittent

panel consisting of waves 2, 6 and 10, which allows for the inclusions of wealth. All age coefficients

are significant in all models, indicating a wave-shaped pattern with a minimum observed at age 40

and a maximum at age 90, when all other included variables are held constant. Age coefficients differ

slightly across the random and fixed-effects models. This could indicate that age effects observed in

pooled data are unreliable, or that individual fixed effects absorb genuine age effects and that the

fixed-effects panel model fails to capture age effects in the intended way. The latter explanation is

promoted here.

[Table 2 about here]

The other model parameters are generally consistent with reports made elsewhere in the literature

(Dolan et al. 2008). These are not of particular interest in this context, though some brief comments are

warranted. First, as reported by Headey and Wearing (2004), wealth is at least as important as income

in explaining life satisfaction, in terms of the size of the coefficients. However, these measure the

effects of percentile movements in the income and wealth distributions, which are different, and the

marginal effect per dollar is in fact greater for income than for wealth across nearly all of these

distributions.11

Females are generally found to be marginally happier than males, though this appears to be explained

mostly by females scoring higher on agreeableness and reporting fewer working hours, and when

these variables are not controlled for the usual gender effect is observed also in these data. The

11 Specifically, the 25, 50 and 75 percentile group means are about $23,300, $38,600 and $56,300 for income; and $46,000, $260,000 and $540,000 for wealth. Clearly, the marginal effect per dollar is much greater for income than for wealth. The more commonly used lin-log model specification assumes constant elasticity of marginal effects. This specification (using natural log transformations of income and wealth) yields income and wealth coefficients of 0.028*** and 0.041*** (when all positive observations are included), respectively, which implies the effect of a proportional change in wealth is greater than that of a proportionate change in income. However, since wealth tends to be so much greater than income, a proportionate increase in wealth tends to dwarf the proportional change in income in absolute terms.

12

negative association between education and life satisfaction found here is consistent with results

reported elsewhere in the literature (Headey and Wooden 2004; Hickson and Dockery 2008; Dockery

2010), and the relationship between education and subjective wellbeing is not as yet well understood.

In terms of the explanatory power, health is the most important variable in the model, followed by

personal characteristics.

Table 3 presents estimates of age-coefficients across various different versions of the standard life

satisfaction models. This table demonstrates that the observed wave-shape in life satisfaction across

the lifecycle persists across various types of models. Age-coefficients are not sensitive to whether

economic or family circumstances are controlled for, but they are sensitive to whether health and

labour market participation are controlled for: when health is omitted the age effect strengthens

somewhat, as one would expect; and omitting labour market characteristics weakens the age effect

slightly, also as expected. The fixed-effects model produces age-coefficients which may appear to be

similar to the pooled model coefficients, though they do in fact produce a different pattern which is

more consistent with Frijters and Beatton’s (2012) results. However, as argued earlier, individual fixed

effects are here considered likely to absorb genuine age effects, and fixed-effects model estimates are

therefore not considered reliable in this sense.

[Table 3 about here]

Figure 2 illustrates differences in estimated age effects across key model specifications and samples.

Compared to the standard model (model b), a panel consisting only of people who drop out of the

panel at some point (model i) yields an age effect which is somewhat diminished, and this group

appears to be less happy later in life compared to people who remain within the panel for the entire

time period in question. This is consistent with prior results reported by Frijters and Beatton (2012),

implying that survivorship bias causes the positive effect of old age to be exaggerated, though a

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substantial age effect remains. This figure also illustrates the effect of only controlling for financial

satisfaction, which provides a measure of the amount of variation in life satisfaction accounted for by

variation in financial satisfaction and therefore economic concerns. If the magnitude of the age effect

is measured by the difference between the observed minimum and maximum points, controlling for

financial satisfaction halves the estimated age effect (specifically, this difference is reduced by 58

percent).

The last three sets of age-effect estimates presented in Table 3 demonstrate that the observed age

effect is robust also with respect to alternative assumptions about life satisfaction scores.12 The

Poisson model for count data produces age effects which are exactly consistent with the standard

model estimates, and though the ordered probit and censored Tobit models produce slightly different

coefficients they do imply a similar pattern. The ordered probit model coefficients may appear to

imply a weaker age effect, though these reflect changes in probabilities and are not directly

comparable. However, they confirm the general shape of life satisfaction across the lifecycle and the

position of a minimum and maximum point. Age effects are markedly stronger when estimated using

censored Tobit, implying much higher life satisfaction later in life. This reflects the fact that the life

satisfaction scale is treated as bounded (consequently, different assumptions are made about people

who score at the ends of the scale). Consequently, the observed age effect in life satisfaction data is

confirmed by these models, though the exact strength of this effect will be stronger than what

standard linear regression model estimates imply if the life satisfaction scale is treated as though it is

censored.

As with fixed-effects models, the identification of genuine cohort effects requires panel data which

cover a long enough time-frame to be able to distinguish between different generations. As discussed,

it is otherwise difficult to distinguish between genuine lifecycle effects and cohort effects. The

12 These estimates are not sensitive to whether the intermittent panel or full eleven-wave panel is used.

14

necessary information will eventually emerge, but in the meantime it is useful to investigate the

possible presence of cohort effects in life satisfaction data visually. Figure 3 depicts a diagram of

average raw life satisfaction at each age level, across cohorts, using data collected between 2001 (wave

1) and 2011 (wave 11).13 This window of time may be sufficient for picking up some cohort effects.

Cohorts are defined here as the decade during which a person is born (from before the 1920s to the

1970s). In a perfect diagram of a panel with no cohort effects, the curves for each cohort would line up

perfectly to form one smooth curve, like the smooth continuous curve superimposed upon the

diagram. By contrast, a perfect diagram of a panel where life satisfaction is not determined by age but

rather by cohorts, the curve for each cohort would resemble a horizontal line, and there would be a

clear vertical gap between each cohort curve.

[Figure 3 about here]

There are some vertical gaps in the diagram: people born in the 1940s appear happier in their fifties

than people who were born in the 1950s, but they appear less happy in their sixties than people who

were born in the 1930s. Nonetheless, falls in life satisfaction are observed as people near their mid-

forties, and increases are observed as people move through their fifties and sixties, after which life

satisfaction again falls. The cohort curves follow the smooth trend-line very closely, suggesting the U-

shape is genuine and not dominated by cohort effects. The data used to generate this picture are not

adjusted for changes in macroeconomic conditions, and what appears to indicate cohort effects may

instead reflect changing macroeconomic conditions. Any such effects would presumably be absorbed

in survey-wave controls included in the standard set of control variables. Estimated age coefficients

do not appear to be sensitive to the inclusion or omission of such controls (see Table 3, model c),

which suggests the observed age effect is not accounted for by changes in macroeconomic conditions.

13 Note that within each cohort there are more observations in the middle of the age range covered than toward the edges of that age range. This causes the observations at the edges to be less reliable, and results in unnecessary ‘noise’ in the graph. The age range of each cohort is therefore cut at each end, such that two observations at either end are removed.

15

(ii) Associations between economic circumstances, financial circumstances and life

satisfaction

The core purpose of this analysis is to determine whether the associations between economic

circumstances and financial satisfaction, and between financial satisfaction and life satisfaction, vary

across the lifecycle. This information is captured by the age-interaction terms in the extended life

satisfaction and financial satisfaction models represented by equations (4) and (5), estimated using the

intermittent panel. Alternative estimates using the full panel, omitting wealth, are considered later as

part of a set of robustness tests. Key estimates are presented in Table 4. These suggest people are

more sensitive to financial satisfaction in their evaluations of life satisfaction in midlife than in old

age, and that financial concerns matter more in midlife than in old age. The association between

financial satisfaction and life satisfaction across the lifecycle is illustrated in Figure 4. This association

peaks at age 45, and drops thereafter.

[Table 4 about here]

[Figure 4 about here]

When the financial satisfaction model is estimated as specified in equation (5), the linear and squared-

age-income interaction term coefficients are non-significant, and imply that the association between

income and financial satisfaction decreases across the lifecycle, with a slope that diminishes very

slightly. Dropping the squared-age-income interaction term produces a significant coefficient for the

linear age-income interaction term which implies a very similar relationship, and the results of this

simplified specification is reported in Table 4. The association between income and financial

satisfaction ( ))(/ percentileIncomeFinSat ∂∂ therefore appears to fall with age (with no significant

nonlinearities). The association between wealth and financial satisfaction ( ))(/ percentileWealthFinSat ∂∂

is inverted U-shaped in age, with a maximum observed at age 46. Hence, wealth matters increasingly

as we approach midlife and less thereafter. The sizes of these coefficients imply that income and

16

wealth are similarly important in determining financial satisfaction early in adult life, but that wealth

soon becomes more important. Combined, the effect of improvements in economic circumstances on

financial satisfaction diminishes with age. Figure 5 illustrates the estimated change in financial

satisfaction from quartile movements in the income and wealth distributions across the lifecycle.

[Table 5 about here]

[Figure 5 about here]

Figure 6 illustrates the total effect on life satisfaction of a quartile upward movement in the

distributions of both income and wealth, via financial satisfaction (based on the estimates presented

in Table 4). The figure shows that the importance of economic circumstances for determining life

satisfaction peaks in midlife, falls thereafter, and appears to become negligible in old age.

[Figure 6 about here]

In order to evaluate the robustness of the results presented above, alternative estimates are obtained

and presented in the appendix. Specifically, these include estimates using the full eleven-wave panel,

omitting wealth (Table 5); fixed-effects panel models (Table 6); an alternative lin-log specification of

the associations between income and wealth and satisfaction scores (Table 7); an alternative model

specification using discrete age groups (Table 8); and alternative models based on different

assumptions about life satisfaction scores (specifically, ordered probit, censored Tobit, and Poisson

model estimates; presented in Tables 9 and 10). These alternative estimates confirm that the

associations between income and wealth and financial satisfaction, and between financial satisfaction

and life satisfaction, fall after midlife. Results are slightly weaker for the probit, Tobit and Poisson

model estimates. Alternatively, these models are not powerful enough to pick up this pattern in the

17

data. The association between financial satisfaction and life satisfaction is found to be age-dependent

when using the censored Tobit model, but not when using the ordered probit and Poisson model for

count data. This finding may therefore be sensitive to assumptions about satisfaction scores. Because

the life satisfaction and financial satisfaction models are obtained using the same sample the error

terms in these models may be correlated, though no evidence of this is identified (SUR estimation

does not improve on the results reported here).

Finally, symptoms of endogeneity bias are evaluated by comparing random and fixed-effects model

coefficients. Estimates presented in Table 2 show consistent income and wealth coefficients in the life

satisfaction model. However, the key variable in the life satisfaction model is financial satisfaction, and

although income and wealth coefficients may be consistent in life satisfaction models the same may not

be true in financial satisfaction models. Key estimates in these models are therefore provided in Tables

11 and 12. These estimates are based on the full eleven-year panel, in order to produce more reliable

fixed-effects model estimates, though this means wealth is omitted. These estimates do not show

symptoms of financial satisfaction being endogenous in the life satisfaction model, though some

inconsistency is detected for income in the financial satisfaction model, which is likely caused by the

omission of wealth. Under the assumption that fixed-effect model estimates are reliable, key results are

robust in the sense that the association between income and financial satisfaction weakens with age.

V Summary, Discussion and Conclusion

Happiness and satisfaction is often found to be U-shaped over the lifecycle, and this pattern is also

identified in data from the HILDA survey. Elsewhere, such patterns have been demonstrated to be

accounted for by individual fixed effects, and, to a lesser extent, by survivorship bias and cohort

effects. It is difficult to separate genuine individual fixed effects and cohort effects from genuine

lifecycle effects without access to very wide panels which covering an extensive period of time. If so,

18

fixed-effects models and models which control for birth cohorts yield estimated age effects which

cannot be interpreted in the usual way. A graphical representation of movements in life satisfaction

scores across different cohorts based on decade of birth does not provide any convincing indication

that observed age effects reflect cohort effects. Consequently, age effects observed in life satisfaction

data appear to reflect genuine lifecycle effects.

Suggested explanations for observed age effects in subjective wellbeing include changes in aspirations

and expectations, and specifically that have-want gaps and status anxiety may peak in midlife and

abate in old age. Earlier research has demonstrated that a focus on materialistic goals has a negative

impact on subjective wellbeing, and also that materialism is high in midlife and at its lowest in old

age. The implication of this hypothesis is that the associations between economic circumstances,

financial satisfaction and life satisfaction, change over the lifecycle. The analysis presented here

provides strong and robust support for this hypothesis. Variation in financial satisfaction explains

more than half of the observed wave-shape in life satisfaction across the lifecycle. More specifically,

associations between income and wealth and financial satisfaction, and between financial satisfaction

and life satisfaction, peak in midlife and diminish thereafter to become seemingly negligible in old

age.

A recent study on great apes provide an indication that observed lifecycle effects in wellbeing are

genuine, with a possible basis in biology: Like us, apes appear to exhibit a U-shaped wellbeing curve

over their life spans, complete with a midlife crisis (Weiss et al. 2012). Great apes live in groups with

complex social structures, and so do humans. Perhaps a strong focus on achievement and status in

midlife once served humans (and continue to serve apes) an evolutionary purpose by creating the

best possible survival opportunities for offspring, while being contented is a better strategy earlier

and later in life. Contented individuals may be more likely to attract mates when younger, and

perhaps less likely to be rejected or ostracised from the group or tribe when older and less productive.

19

These ideas are difficult to test directly. However, there are other lines of research which could help

shed further light on this puzzle. For example, lifecycle effects may also be observed in individualist

and collectivist attitudes. It is perhaps possible to measure upward comparison and investigate

whether this abates in old age. It is also possible to look at the question slightly differently and

identify the happy and unhappy middle-aged and find out who they are and what sets these groups

apart, beside their subjective wellbeing.

In sum, the evidence presented here supports the general finding that subjective wellbeing is U-

shaped across the lifecycle, and that this observation is at least partly accounted for by changes in

attitudes to material welfare and status, which become less important as we age. This informs policy-

makers that the effectiveness of financial incentives is likely to be age-dependent, being greater in

mid-life and abating thereafter.

20

VI References Argyle, M. (1989). The social psychology of leisure. London, Penguin. Belk, R. W. (1985). "Materialism: Trait aspects of living in the material world." Journal of

Consumer Research 12(December): 265-280. Blanchflower, D. G. and A. J. Oswald (2004). "Well-being over time in Britain and the

USA." Journal of Public Economics 88: 1359-1386. Blanchflower, D. G. and A. J. Oswald (2008). "Is well-being U-shaped over the life-cycle?"

Social Science & Medicine 66: 1733-1749. Boyce, C. J. (2010). "Understanding fixed effects in human well-being." Journal of

Economic Psychology 31: 1-16. Brown, S., R. B. Durand, M. N. Harris and T. Weterings (2014). "Modelling financial

satisfaction across life stages: A latent class approach." Journal of Economic Psychology 45: 117-127.

Campbell, A., P. E. Converse and W. L. Rodgers (1976). The quality of American life: Perceptions, evaluations, and satisfaction. New York, Russell Sage Foundation.

Carstensen, L. L. (1995). "Evidence for a life-span theory of socioemotional selectivity." Current Directions in Psychological Science 4(5): 151-155.

Clark, A. E., F. Etile, F. Postel-Vinay, C. Senik and K. Van der Straeten (2005). "Heterogeneity in reported well-being: Evidence from twelve European countries." Economic Journal 115(502): C118-132.

Clark, A. E., P. Frijters and M. A. Shields (2008). "Relative income, happiness, and utility: an explanation for the Easterlin Paradox and other puzzles." Journal of Economic Literature 46(1): 95-144.

Diener, E. and R. E. Lucas (1999). Personality and subjective well-being. Well-being: The foundations of hedonic psychology. D. Kahneman, E. Diener and N. Schwartz. New York, Russell Sage Foundation: 213-229.

Dockery, A. M. (2010). Education and happiness in the school-to-work transition. Adelaide, South Australia, The National Centre for Vocational Education Research (NCVER).

Dolan, P., P. T. and M. White (2008). "Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being." Journal of Economic Psychology 29(1): 94-122.

Easterlin, R. A. (2006). "Life cycle happines and its sources: Intersections of psychology, economics and demography." Journal of Economic Psychology 27: 463-482.

Ferrer-i-Carbonell, A. and P. Frijters (2004). "How important is methodology for the estimates of the determinants of happiness?" The Economic Journal 114(July): 641-659.

Frey, B. S. and A. Stutzer (2002). Happiness and Economics, Princeton University Press. Frijters, P. (1999). Explorations of welfare and well-being. PhD Thesis, University of

Amsterdam. Frijters, P. and T. Beatton (2012). "The mystery of the U-shaped relationship between

happiness an age." Journal of Economic Behaviour & Organization 82: 525-542. Headey, B. and M. Wooden (2004). "The effects of wealth and income on subjective well-

being and ill-being." Economic Record 80(Special Issue): S24-S33. Hickson, H. and A. M. Dockery (2008). Is ignorance bliss: Exploring the links between

education, expectations and happiness. 37th Australian Conference of Economists, 30 September - 3 October. Gold Coast, Australia.

Kennedy, P. (2008). A guide to econometrics, Wiley-Blackwell.

21

Kristoffersen, I. (2010). "The Metrics of Subjective Wellbeing: cardinality, neutrality and additivity." The Economic Record 86(272): 98-123.

Lawton, M. P. (1996). Quality of life and affect in later life. Handbook of emotions, adult development and aging. C. Magai and S. H. McFadden. San Diego California, Academic Press: 327-348.

Layard, R., G. Mayraz and S. Nickell (2008). "The marginal utility of income." Journal of Public Economics 92: 1846-1857.

McCrae, R. R. and O. P. John (1992). "An introduction to the five-factor model and its applications." Journal of Personality 60(2): 175–215.

Ng, Y.-K. (2008). "Happiness studies: Ways to improve comparability and some public policy implications." The Economic Record 84(265): 253-266.

Oswald, A. J. (1997). "Happiness and economic performance." The Economic Journal 107: 1815-1831.

Plagnol, A. C. (2011). "Financial satisfaction over the life course: The influences of assets and liabilities." Journal of Economic Psychology 32: 45-64.

Stoebe, W. and M. S. Stoebe (1987). Bereavement and health: The psychological and physical consequences of partner loss. New York, Cambridge University Press.

Van Herwaarden, F. G. and A. Kapteyn (1981). "Empirical comparisons of the shape of welfare functions." European Economic Review 15: 261-286.

Ware, J. E., K. K. Snow, M. Kosinski and B. Gandek (2000). SF-36 Health Survey: Manual and Interpretation Guide Lincoln, RI, QualityMetric Inc.

Weiss, A., J. E. King, M. Inoue-Murayama, T. Matsuzawa and A. Oswald (2012). "Evidence for a midlife crisis in great apes consistent with the U-shape in human well-being." Proceedings Of The National Academy Of Sciences Of The USA 109: 19949-19952.

22

Appendix: Extra tables [Insert Tables 5-12]

All Tables and Figures

TABLE 1 Included Variables

Type of variable Measures Description

Subjective wellbeing

Life satisfaction scores (LS)

Answer to the question: “how satisfied are you with your life in general?” where 0 is completely unsatisfied, 10 is completely satisfied, and 5 is neither satisfied nor unsatisfied.

Financial satisfaction scores (FS)

Answer to the question: “how satisfied are you with your financial situation?” where 0 is completely unsatisfied, 10 is completely satisfied, and 5 is neither satisfied nor unsatisfied.

Economic circumstances (Y)

Income Total disposable annual household income (net of tax), adjusted for household composition using the modified OECD equivalence scale. These data are ranked and sorted into household percentiles (1-100).

Wealth Total household assets less total household debts, adjusted for household composition using the modified OECD equivalence scale (but does not account for children). These data are ranked and sorted into household percentiles (i.e. 100 groups).

Age Age Age when interviewed.

Control variables (Set X)

Gender Dummy variable (1=female).

Marital status Dummy variables identifying people who are married or de-facto; separated, divorced and widowed. The reference group is ‘never married’.

Children Dummy variable identifying people living in households where children under the age of 15 are present.

Employment status Dummy variables for people who are unemployed and not in the labour force. There is no overlap between these variables. Hours worked is also included.

Education Dummy variables identifying people whose highest formal educational achievements are high-school completion, certificate, diploma, bachelor or honours degree, graduate diploma, and masters or PhD degree. The reference group is people with no high-school completion or any other formally recognised qualification.

Physical health The physical health component of the SF36 Health Index. Measured on a scale between 0 (poorest health) and 100 (best health).

Personal characteristics

The Big 5 Personality Inventory, including measures of extraversion, agreeableness, conscientiousness, emotional stability and openness to experience. Scores on each characteristic are aggregated from a set of specific factors, and fall within values of 1 and 7.

23

TABLE 2 Standard Life Satisfaction Models

Key variables: Pooled panel model Random-effects

panel model Fixed-effects panel model

Age -0.17*** (0.0225) -0.17*** (0.0237) -0.20*** (0.0456) Age2 0.0031*** (0.0004) 0.0031*** (0.0005) 0.0039*** (0.0009) Age3 -0.000016*** (0.000003) -0.000016**** (0.000003) -0.000023*** (0.000006)

Income 0.0016*** (0.0005) 0.0016*** (0.0005) 0.0017** (0.0007) Wealth 0.0030*** (0.0005) 0.0035*** (0.0005) 0.0046*** (0.0010)

Controls: Female -0.04* (0.0228) -0.02 (0.0270) - Children -0.01 (0.0269) 0.00 (0.0277) 0.02 (0.0431) Partnered 0.32*** (0.0316) 0.26*** (0.0322) 0.09* (0.0490) Separated -0.52*** (0.0626) -0.47*** (0.0627) -0.35*** (0.0905) Divorced -0.14*** (0.0473) -0.12** (0.0497) -0.04 (0.0791) Widowed 0.14** (0.0576) 0.09 (0.0606) -0.09 (0.1029) Health 0.0184*** (0.0005) 0.0178*** (0.0005) 0.0141*** (0.0009) Unemployed -0.57*** (0.0744) -0.53*** (0.0708) -0.42*** (0.0940) Not in L.F. -0.06 (0.0406) -0.09** (0.0406) -0.13** (0.0569) Hours worked -0.0051*** (0.0009) -0.0047*** (0.0009) -0.0037*** (0.0013)

Education: High-school -0.20*** (0.0362) -0.20*** (0.0430) -0.29 (0.1932) Certificate -0.12*** (0.0285) -0.11*** (0.0337) 0.01 (0.1238) Diploma -0.21*** (0.0367) -0.20*** (0.0442) -0.17 (0.2095) Bach./Hon. -0.25*** (0.0347) -0.25*** (0.0410) -0.66*** (0.2139) Grad.Dip. -0.24*** (0.0450) -0.23*** (0.0533) -0.42* (0.2375) Masters/PhD -0.29*** (0.0539) -0.30*** (0.0630) -0.64** (0.2551)

Personality: Extraverted 0.09*** (0.0096) 0.09*** (0.0110) 0.03 (0.0256) Agreeable 0.17*** (0.0124) 0.14*** (0.0136) -0.02 (0.0261) Conscientious 0.03*** (0.0107) 0.04*** (0.0120) 0.01 (0.0261) Emot. Stab. 0.14*** (0.0106) 0.13*** (0.0118) 0.04* (0.0234) Openness -0.04*** (0.0107) -0.04*** (0.0121) 0.06** (0.0265)

Wave 2 0.08*** (0.0265) 0.09*** (0.0227) - Wave 6 0.03 (0.0228) 0.04** (0.0188) -

Intercept 6.89*** (0.3810) 7.13*** (0.4010) 9.49*** (0.7751)

N =17,065 N =17,065 N =17,065 (9,072)

Adj. R2 = 0.1971 R2 : Within = 0.0372 Between = 0.2305 Overall = 0.1976

R2 : Within = 0.0466 Between = 0.1116 Overall = 0.1094

F = 150.58*** χ2 = 3005.82*** F = 15.58***

Corr (ui, Xb) = 0

(assumed) Corr (ui, Xb) = 0 (assumed)

Corr (ui, Xb) = 0.0315

ρ = 0.4324 (fraction of variance due to ui)

ρ = 0.6195 (fraction of variance due to ui)

Test that all ui = 0: F = 2.34***

Note: These models are estimated using data from waves 2, 6 and 10 of the HILDA survey. The sample is restricted to individuals aged 26 and above. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

24

TABLE 3 Age-Coefficients in Life Satisfaction Models

Models:

Age coefficients

Age Age2 Age3 Model information

Model a: Full Panel; pooled data; No controls

-0.20*** (0.0086)

0.0039*** (0.0002)

-0.000022*** (0.000001)

N = 116,635 Adj R2 = 0.0245

Model b: Intermittent Panel; pooled data; Standard model with full set of controls

-0.17*** (0.0225)

0.0031*** (0.0004)

-0.000016*** (0.000003)

N = 17,065 Adj R2 = 0.1971

Model c: Intermittent Panel; pooled data; Standard controls but no wave dummies

-0.17*** (0.0225)

0.0031*** (0.0004)

-0.000016*** (0.000003)

N = 17,065 Adj R2 = 0.1967

Model d: Full Panel; pooled data; Standard controls but no wealth

-0.17*** (0.0102)

0.0033*** (0.0002)

-0.000017*** (0.000001)

N = 86,088 Adj R2 = 0.1921

Model e: Full Panel; pooled data; Standard controls but no wealth or income

-0.17*** (0.0102)

0.0031*** (0.0002)

-0.000016*** (0.000001)

N = 86,088 Adj R2 = 0.1899

Model f: Full Panel; pooled data; Standard controls but no wealth, income, health

-0.22*** (0.0102)

0.0042*** (0.0002)

-0.000023*** (0.000001)

N = 91,305 Adj R2 = 0.1184

Model g: Full Panel; pooled data; Standard controls but no wealth, income, health, marital status, children

-0.22*** (0.0099)

0.0041*** (0.0002)

-0.000022*** (0.000001)

N = 91,305 Adj R2 = 0.0946

Model h: Full Panel; pooled data; balanced (complete); Standard controls but no wealth

-0.17*** (0.0147)

0.0031*** (0.0003)

-0.000016*** (0.000002)

N = 63,270 Adj R2 = 0.2004

Model i: Full Panel; pooled data; dropouts only Standard controls but no wealth

-0.20*** (0.0299)

0.0037*** (0.0006)

-0.000020*** (0.000003)

N = 9,151 Adj R2 = 0.1812

Model j: Full panel; fixed-effects model; Standard controls

-0.20*** (0.0432)

0.0039*** (0.0009)

-0.000024*** (0.000006)

N = 10,561 R2 = 0.0476

Model k: Intermittent panel; fixed-effects model; Standard controls

-0.16*** (0.0154)

0.0030*** (0.0003)

-0.000019*** (0.000002)

N = 18,431 R2 = 0.0273

Model l: Full panel; pooled model; Only controlling for financial satisfaction

-0.17*** (0.0078)

0.0033*** (0.0001)

-0.000019*** (0.000001)

N = 116,334 R2 = 0.2139

Model m: Intermittent panel; ordered probit; Standard controls

-0.14*** (0.0183)

0.0025*** (0.0004)

-0.000013*** (0.000002)

N = 17,065 Pseudo R2 = 0.0656

Model n: Intermittent panel; censored Tobit; Standard controls

-0.18*** (0.0252)

0.0034*** (0.0005)

-0.000017*** (0.000003)

N = 17,065 R2 = 0.0598

Model o: Intermittent panel; Poisson model for count data; Standard controls

-0.17*** (0.0259)

0.0031*** (0.0005)

-0.000016*** (0.000003)

N = 17,065 R2 = 0.0553

Note: The intermittent panel consists of waves 2, 6 and 10 of the HILDA survey, whereas the full panel consists of waves 1 to 11. The sample is restricted to individuals aged 26 and above. The standard set of control variables include household income and wealth (percentiles), health, marital status, the presence of children in the household, labour market participation (including hours worked), gender, education, and personal characteristics. Controls for survey waves are included in all pooled models (except model c).

25

FIGURE 1 Life Satisfaction, Financial Satisfaction and Mental Health across the Lifecycle (unadjusted)

FIGURE 2 Life Satisfaction Across the Lifecycle: Different controls and samples

26

FIGURE 3 Cohort-Effects in Life Satisfaction across the Lifecycle

TABLE 4 Life Satisfaction and Financial Satisfaction models: Pooled data

Key variables: Life Satisfaction Model Key variables Financial Satisfaction Model

Age -0.14*** (0.0233) Age -0.23*** (0.0350) Age2 0.0025*** (0.0004) Age2 0.0034*** (0.0007) Age3 -0.000012*** (0.000003) Age3 -0.000011** (0.000004)

FS 0.17*** (0.0485) Income 0.0174*** (0.0024) FS*Age 0.0044** (0.0020) Income*Age -0.00012** (0.00005) FS*Age2 -0.000052*** (0.00002) Income*Age2 -

Wealth 0.0098 (0.0068) Wealth*Age 0.00059** (0.00026) Wealth*Age2 -0.0000065*** (0.0000024)

N = 17,065 N =17,067 Adj. R2 = 0.3118 Adj. R2 = 0.2614 F = 250.44*** F = 195.80*** Both models include intercepts and full sets of controls Note: The sample is restricted to people aged 26 and above, and to waves 2, 6 and 10 of the HILDA survey. The models include intercepts and the standard set of explanatory variables, including gender, family circumstances, health, education, labour market participation and personal characteristics, wave dummies. The life satisfaction model also includes income and wealth as controls. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

Decade of birth

27

FIGURE 4 The Association between Financial Satisfaction and Life Satisfaction across the Lifecycle

FIGURE 5 The Effect of Quartile Movements in the Income and Wealth Distributions

28

FIGURE 6 The Total Effect on Life Satisfaction of a Quartile movement in Income and Wealth, via Financial Satisfaction

TABLE 5

Life Satisfaction and Financial Satisfaction models: Full Panel Models, Pooled Data

Key variables: Life Satisfaction Model Key variables Financial Satisfaction Model

Age -0.16*** (0.0106) Age -0.16*** (0.0151) Age2 0.0026*** (0.0002) Age2 0.0030*** (0.0003) Age3 -0.000011*** (0.000001) Age3 -0.000013*** (0.000002)

FS 0.14*** (0.0225) Income 0.0244*** (0.0010) FS*Age 0.0064*** (0.0009) Income*Age -0.000084*** (0.000018) FS*Age2 -0.000075*** (0.000008) Income*Age2 -

N = 86,073 N = 86,895 Adj. R2 = 0.3098 Adj. R2 = 0.2190 F = 1289.08*** F = 871.10*** Both models include intercepts and full sets of controls Note: The sample is restricted to people aged 26 and above, and to waves 2, 6 and 10 of the HILDA survey. The models include intercepts and the standard set of explanatory variables, including gender, family circumstances, health, education, labour market participation and personal characteristics, wave dummies. The life satisfaction model also includes income and wealth as controls. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

29

TABLE 6 Life Satisfaction and Financial Satisfaction models: Fixed-effects models (Full Panel)

Key variables: Life Satisfaction Model Key variables Financial Satisfaction Model

Age -0.16*** (0.0159) Age -0.06*** (0.0216) Age2 0.0027*** (0.0003) Age2 0.0026*** (0.0004) Age3 -0.000015*** (0.000002) Age3 -0.000016*** (0.000003)

FS 0.16*** (0.0245) Income 0.0204*** (0.0012) FS*Age 0.0027*** (0.0010) Income*Age 0.000218*** (0.000022) FS*Age2 -0.00004*** (0.00001) Income*Age2 -

N =104,267 (18,425) N =105,814 (18,537)

R2: Within = 0.1005 Between = 0.1648

Overall = 0.1593

R2: Within = 0.0450 Between = 0.1468 Overall = 0.1259

F = 436.01***

F = 205.70***

Corr (ui, Xb) = 0.0101 Corr (ui, Xb) = -0.0410

ρ = 0.6093 (fraction of variance due to ui)

ρ = 0.6260 (fraction of variance due to ui)

Both models include intercepts and full sets of controls Note: The sample is restricted to people aged 26 and above, and contains data from waves 1 to 11 of the HILDA survey. The models include intercepts and the standard set of explanatory variables, including gender, family circumstances, health, education, labour market participation and personal characteristics, wave dummies. The life satisfaction model also includes income as a control. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

30

TABLE 7 Alternative Models Estimates using Log-Transformation of Income and Wealth

Key variables: Life Satisfaction Model Key variables Financial Satisfaction Model

Age -0.15*** (0.0247) Age -0.55*** (0.0779) Age2 0.0027*** (0.0004) Age2 0.0071*** (0.0009) Age3 -0.000013*** (0.000003) Age3 -0.000012*** (0.000004)

FS 0.17*** (0.0525) Income 0.88*** (0.1303) FS*Age 0.0043** (0.0021) Income*Age -0.0052** (0.0024) FS*Age2 -0.00005** (0.00002) Income*Age2 -

Wealth -0.26* (0.1498) Wealth*Age 0.0305*** (0.0059) Wealth*Age2 -0.00030*** (0.00006)

N =15,495 N =15,497

Adj. R2 = 0.3095 Adj. R2 = 0.2331

F = 224.98*** F = 152.94*** Both models include intercepts and full sets of controls

Note: The sample is restricted to people aged 26 and above, and to waves 2, 6 and 10 of the HILDA survey. The models include intercepts and the standard set of explanatory variables, including gender, family circumstances, health, education, labour market participation and personal characteristics, wave dummies. The life satisfaction model also includes income and wealth as controls. Income and wealth are transformed by natural log. Individuals with incomes and wealth below $10,000 are omitted from the sample. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

31

TABLE 8 Alternative Model Estimates using Discrete Age Groups

Key variables: Life Satisfaction Model Key variables: Financial Satisfaction Model

(Control: 75+) 26-35 -1.08*** (0.1928)

(Control: 75+) 26-35 -2.07*** (0.1903)

36-45 -1.26*** (0.1887) 36-45 -2.35*** (0.1858) 46-55 -1.08*** (0.1897) 46-55 -2.29*** (0.1869) 56-65 -0.77*** (0.1918) 56-65 -1.99*** (0.1858) 66-75 -0.66*** (0.2141) 66-75 -1.14*** (0.2021)

FS(Control: 76+) 0.18*** (0.0200) Income (Control: 76+) 0.0105*** (0.0033) FS*(26-35) 0.07*** (0.0222) Income*26-35 0.0052 (0.0036) FS*(36-45) 0.09*** (0.0217) Income*36-45 0.0020 (0.0036) FS*(46-55) 0.08*** (0.0219) Income*46-55 0.0005 (0.0036) FS*(56-65) 0.06*** (0.0222) Income*56-65 -0.0016 (0.0036) FS*(66-75) 0.06*** (0.0248) Income*66-75 0.0002 (0.0040)

Wealth (Control: 76+) 0.0098*** (0.0027) Wealth*26-35 0.0098*** (0.0031) Wealth*36-45 0.0134*** (0.0030) Wealth*46-55 0.0132*** (0.0030) Wealth*56-65 0.0144*** (0.0031) Wealth*66-75 0.0092*** (0.0035)

N= 17,065 N= 17,067

Adj. R2 = 0.3112 Adj. R2 = 0.2595 F = 215.13*** F = 150.51*** Both models include intercepts and full sets of controls

Note: The sample is restricted to people aged 26 and above, and to waves 2, 6 and 10 of the HILDA survey. The models include intercepts and the standard set of explanatory variables, including gender, family circumstances, health, education, labour market participation and personal characteristics, wave dummies. The life satisfaction model also includes income and wealth as controls. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

TABLE 9 Alternative Estimates of the Life Satisfaction Models (Pooled data)

Key Variables

Standard linear pooled regression

Ordered probit Censored tobit Poisson model for count-data (marginal effects)

Age -0.14*** (0.0233) -0.11*** (0.0206) -0.14*** (0.0261) -0.12*** (0.0278) Age2 0.0025*** (0.0004) 0.0021*** (0.0004) 0.0026*** (0.0005) 0.0024*** (0.0005) Age3 -0.000012*** (0.000003) -0.000012*** (0.000002) -0.000013*** (0.00003) -0.000014*** (0.000003)

Fin. Sat. 0.17*** (0.0485) 0.20*** (0.0427) 0.21*** (0.0543) 0.23*** (0.0553) Fin.Sat*Age 0.0044** (0.0020) 0.0010 (0.0017) 0.0033 (0.0022) -0.0006 (0.0022) FinSat*Age2 -0.000052*** (0.00002) -0.000001 (0.000017) -0.00003* (0.00002) 0.000012 (0.000022)

N =17,065 N =17,065 N =17,065 N =17,065 Adjusted R2 = 0.3118 Pseudo R2 = 0.1102 Pseudo R2 = 0.1016 Pseudo R2 = 0.0854 F = 250.44*** χ2 = 6311.15*** χ2 = 6247.56*** χ2 = 4980.41*** All models include intercepts and full sets of controls

Note: These models are estimated using data from waves 2, 6 and 10 of the HILDA survey. The sample is restricted to individuals aged 26 and above. The models include an intercept term and full set of control variables, including gender, family circumstances, labour market participation, health and personality characteristics, as well as income and wealth. Wave dummies are also included. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

32

TABLE 10 Alternative Estimates of the Financial Satisfaction Models (Pooled data)

Key Variables

Standard linear pooled regression

Ordered probit Tobit Poisson

Age -0.23*** (0.0350) -0.10*** (0.0226) -0.23*** (0.0379) -0.08** (0.0430) Age2 0.0034*** (0.0007) 0.0013*** (0.0004) 0.0033*** (0.0007) 0.0008 (0.0008) Age3 -0.000011** (0.000004) -0.000003 (0.000002) -0.000009*** (0.000005) -0.000002 (0.000005)

Income 0.0174*** (0.0024) 0.0118*** (0.0039) 0.0165*** (0.0026) 0.0280*** (0.0075) Income*Age -0.00012** (0.00005) -0.00020 (0.00016) -0.00008 (0.00005) -0.00075** (0.00031) Income*Age2 - 0.000002 (0.000002) - 0.000008*** (0.000003)

Wealth 0.0098 (0.0068) 0.0054 (0.0039) 0.0130* (0.0073) 0.0200*** (0.0076) Wealth*Age 0.00059** (0.00026) 0.00028* (0.00015) 0.00049* (0.00028) -0.000015 (0.00030) Wealth*Age2 -0.0000065*** (0.0000024) -0.000003** (0.000001) -0.000005** (0.000003) 0.00000006 (0.0000003)

N =17,067 N =17,067 N =17,067 N =17,067 Adjusted R2 = 0.2614 Pseudo R2 = 0.0696 Pseudo R2 = 0.0674 Pseudo R2 = 0.0812 195.80*** χ2 = 5061.55*** χ2 = 5084.42*** χ2 = 5997.62*** All models include intercepts and full sets of controls

Note: These models are estimated using data from waves 2, 6 and 10 of the HILDA survey. The sample is restricted to individuals aged 26 and above. The models include an intercept term and full set of control variables, including gender, family circumstances, labour market participation, health and personality characteristics. Wave dummies are also included. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

TABLE 11 Extended Life Satisfaction Models: Random and Fixed-effects Models (Full Panel)

Key variables:

Random-effects panel model

Fixed-effects panel model

Hausman test of differences

Age -0.15*** (0.0130) -0.16*** (0.0163) -0.0047 (0.0098) Age2 0.0027*** (0.0002) 0.0025*** (0.0003) -0.0002 (0.0002) Age3 -0.000013*** (0.000001) -0.000013*** (0.000002) -0.0000003 (0.0000010)

FS 0.14*** (0.0236) 0.12*** (0.0162) -0.0145 (0.0115) FS*Age 0.0043*** (0.0009) 0.0039*** (0.0010) -0.0004 (0.0004) FS*Age2 -0.000052*** (0.000009) -0.000047*** (0.000010) 0.0000052 (0.0000041)

N = 86,073 (9918) N = 86,073 (9918)

R2: Within = 0.1027

Between = 0.4428 Overall = 0.3069

R2: Within = 0.1029 Between = 0.1889 Overall = 0.1653

χ2 = 16,745.60*** F = 396.83***

Corr (ui, Xb) = 0 (assumed) Corr (ui, Xb) = 0.0160

ρ = 0.3625 (fraction of variance due to ui)

ρ = 0.5218 (fraction of variance due to ui)

All models include intercepts and full sets of controls Note: The sample is restricted to people aged 26 and above, and contains data from waves 1 to 11 of the HILDA survey. The models include intercepts and the standard set of explanatory variables, including gender, family circumstances, health, education, labour market participation and personal characteristics, wave dummies. The life satisfaction model also includes income as a control. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

33

TABLE 12 Extended Financial Satisfaction Models: Random and Fixed-effects Models (Full Panel)

Key variables: Random-effects panel model

Fixed-effects panel model

Hausman test of differences

Age -0.14*** (0.0188) -0.04* (0.0228) 0.0938*** (0.0129) Age2 0.0031*** (0.0004) 0.0023*** (0.0004) -0.0007*** (0.0002)

Age3 -0.000015*** (0.000002) -0.000015*** (0.000003) -0.0000006 (0.0000015) Income 0.0216*** (0.0011) 0.0210*** (0.0012) -0.0005 (0.0006) Income*Age -0.00018*** (0.00002) -0.00023*** (0.00002) -0.00005*** (0.00001)

N = 86,895 (9955) N = 86,895 (9955)

R2: Within = 0.0450 Between = 0.2984 Overall = 0.2013

R2: Within = 0.0451 Between = 0.1632 Overall = 0.1248

χ2 = 7851.23*** F = 181.48***

Corr (ui, Xb) = 0 (assumed) Corr (ui, Xb) = -0.0442

ρ = 0.4552 (fraction of variance due to ui)

ρ = 0.5622 (fraction of variance due to ui)

All models include intercepts and full sets of controls Note: The sample is restricted to people aged 26 and above, and contains data from waves 1 to 11 of the HILDA survey. The models include intercepts and the standard set of explanatory variables, including gender, family circumstances, health, education, labour market participation and personal characteristics, wave dummies. The life satisfaction model also includes income as a control. Statistical significance at the 90, 95 and 99 per cent level of confidence is indicated by *, **, and ***, respectively. Standard errors are provided in brackets.

34

Editor, UWA Economics Discussion Papers: Sam Hak Kan Tang University of Western Australia 35 Sterling Hwy Crawley WA 6009 Australia Email: ecoadmin@biz.uwa.edu.au The Economics Discussion Papers are available at: 1980 – 2002: http://ecompapers.biz.uwa.edu.au/paper/PDF%20of%20Discussion%20Papers/ Since 2001: http://ideas.repec.org/s/uwa/wpaper1.html Since 2004: http://www.business.uwa.edu.au/school/disciplines/economics

ECONOMICS DISCUSSION PAPERS 2013

DP NUMBER AUTHORS TITLE

13.01 Chen, M., Clements, K.W. and Gao, G.

THREE FACTS ABOUT WORLD METAL PRICES

13.02 Collins, J. and Richards, O. EVOLUTION, FERTILITY AND THE AGEING POPULATION

13.03 Clements, K., Genberg, H., Harberger, A., Lothian, J., Mundell, R., Sonnenschein, H. and Tolley, G.

LARRY SJAASTAD, 1934-2012

13.04 Robitaille, M.C. and Chatterjee, I. MOTHERS-IN-LAW AND SON PREFERENCE IN INDIA

13.05 Clements, K.W. and Izan, I.H.Y. REPORT ON THE 25TH PHD CONFERENCE IN ECONOMICS AND BUSINESS

13.06 Walker, A. and Tyers, R. QUANTIFYING AUSTRALIA’S “THREE SPEED” BOOM

13.07 Yu, F. and Wu, Y. PATENT EXAMINATION AND DISGUISED PROTECTION

13.08 Yu, F. and Wu, Y. PATENT CITATIONS AND KNOWLEDGE SPILLOVERS: AN ANALYSIS OF CHINESE PATENTS REGISTER IN THE US

13.09 Chatterjee, I. and Saha, B. BARGAINING DELEGATION IN MONOPOLY

13.10 Cheong, T.S. and Wu, Y. GLOBALIZATION AND REGIONAL INEQUALITY IN CHINA

13.11 Cheong, T.S. and Wu, Y. INEQUALITY AND CRIME RATES IN CHINA

13.12 Robertson, P.E. and Ye, L. ON THE EXISTENCE OF A MIDDLE INCOME TRAP

13.13 Robertson, P.E. THE GLOBAL IMPACT OF CHINA’S GROWTH

13.14 Hanaki, N., Jacquemet, N., Luchini, S., and Zylbersztejn, A.

BOUNDED RATIONALITY AND STRATEGIC UNCERTAINTY IN A SIMPLE DOMINANCE SOLVABLE GAME

13.15 Okatch, Z., Siddique, A. and Rammohan, A.

DETERMINANTS OF INCOME INEQUALITY IN BOTSWANA

13.16 Clements, K.W. and Gao, G. A MULTI-MARKET APPROACH TO MEASURING THE CYCLE

35

13.17 Chatterjee, I. and Ray, R. THE ROLE OF INSTITUTIONS IN THE INCIDENCE OF CRIME AND CORRUPTION

13.18 Fu, D. and Wu, Y. EXPORT SURVIVAL PATTERN AND DETERMINANTS OF CHINESE MANUFACTURING FIRMS

13.19 Shi, X., Wu, Y. and Zhao, D. KNOWLEDGE INTENSIVE BUSINESS SERVICES AND THEIR IMPACT ON INNOVATION IN CHINA

13.20 Tyers, R., Zhang, Y. and Cheong, T.S.

CHINA’S SAVING AND GLOBAL ECONOMIC PERFORMANCE

13.21 Collins, J., Baer, B. and Weber, E.J. POPULATION, TECHNOLOGICAL PROGRESS AND THE EVOLUTION OF INNOVATIVE POTENTIAL

13.22 Hartley, P.R. THE FUTURE OF LONG-TERM LNG CONTRACTS

13.23 Tyers, R. A SIMPLE MODEL TO STUDY GLOBAL MACROECONOMIC INTERDEPENDENCE

13.24 McLure, M. REFLECTIONS ON THE QUANTITY THEORY: PIGOU IN 1917 AND PARETO IN 1920-21

13.25 Chen, A. and Groenewold, N. REGIONAL EFFECTS OF AN EMISSIONS-REDUCTION POLICY IN CHINA: THE IMPORTANCE OF THE GOVERNMENT FINANCING METHOD

13.26 Siddique, M.A.B. TRADE RELATIONS BETWEEN AUSTRALIA AND THAILAND: 1990 TO 2011

13.27 Li, B. and Zhang, J. GOVERNMENT DEBT IN AN INTERGENERATIONAL MODEL OF ECONOMIC GROWTH, ENDOGENOUS FERTILITY, AND ELASTIC LABOR WITH AN APPLICATION TO JAPAN

13.28 Robitaille, M. and Chatterjee, I. SEX-SELECTIVE ABORTIONS AND INFANT MORTALITY IN INDIA: THE ROLE OF PARENTS’ STATED SON PREFERENCE

13.29 Ezzati, P. ANALYSIS OF VOLATILITY SPILLOVER EFFECTS: TWO-STAGE PROCEDURE BASED ON A MODIFIED GARCH-M

13.30 Robertson, P. E. DOES A FREE MARKET ECONOMY MAKE AUSTRALIA MORE OR LESS SECURE IN A GLOBALISED WORLD?

13.31 Das, S., Ghate, C. and Robertson, P. E.

REMOTENESS AND UNBALANCED GROWTH: UNDERSTANDING DIVERGENCE ACROSS INDIAN DISTRICTS

13.32 Robertson, P.E. and Sin, A. MEASURING HARD POWER: CHINA’S ECONOMIC GROWTH AND MILITARY CAPACITY

13.33 Wu, Y. TRENDS AND PROSPECTS FOR THE RENEWABLE ENERGY SECTOR IN THE EAS REGION

13.34 Yang, S., Zhao, D., Wu, Y. and Fan, J.

REGIONAL VARIATION IN CARBON EMISSION AND ITS DRIVING FORCES IN CHINA: AN INDEX DECOMPOSITION ANALYSIS

36

ECONOMICS DISCUSSION PAPERS 2014

DP NUMBER AUTHORS TITLE

14.01 Boediono, Vice President of the Republic of Indonesia

THE CHALLENGES OF POLICY MAKING IN A YOUNG DEMOCRACY: THE CASE OF INDONESIA (52ND SHANN MEMORIAL LECTURE, 2013)

14.02 Metaxas, P.E. and Weber, E.J. AN AUSTRALIAN CONTRIBUTION TO INTERNATIONAL TRADE THEORY: THE DEPENDENT ECONOMY MODEL

14.03 Fan, J., Zhao, D., Wu, Y. and Wei, J. CARBON PRICING AND ELECTRICITY MARKET REFORMS IN CHINA

14.04 McLure, M. A.C. PIGOU’S MEMBERSHIP OF THE ‘CHAMBERLAIN-BRADBURY’ COMMITTEE. PART I: THE HISTORICAL CONTEXT

14.05 McLure, M. A.C. PIGOU’S MEMBERSHIP OF THE ‘CHAMBERLAIN-BRADBURY’ COMMITTEE. PART II: ‘TRANSITIONAL’ AND ‘ONGOING’ ISSUES

14.06 King, J.E. and McLure, M. HISTORY OF THE CONCEPT OF VALUE

14.07 Williams, A. A GLOBAL INDEX OF INFORMATION AND POLITICAL TRANSPARENCY

14.08 Knight, K. A.C. PIGOU’S THE THEORY OF UNEMPLOYMENT AND ITS CORRIGENDA: THE LETTERS OF MAURICE ALLEN, ARTHUR L. BOWLEY, RICHARD KAHN AND DENNIS ROBERTSON

14.09

Cheong, T.S. and Wu, Y. THE IMPACTS OF STRUCTURAL RANSFORMATION AND INDUSTRIAL UPGRADING ON REGIONAL INEQUALITY IN CHINA

14.10 Chowdhury, M.H., Dewan, M.N.A., Quaddus, M., Naude, M. and Siddique, A.

GENDER EQUALITY AND SUSTAINABLE DEVELOPMENT WITH A FOCUS ON THE COASTAL FISHING COMMUNITY OF BANGLADESH

14.11 Bon, J. UWA DISCUSSION PAPERS IN ECONOMICS: THE FIRST 750

14.12 Finlay, K. and Magnusson, L.M. BOOTSTRAP METHODS FOR INFERENCE WITH CLUSTER-SAMPLE IV MODELS

14.13 Chen, A. and Groenewold, N. THE EFFECTS OF MACROECONOMIC SHOCKS ON THE DISTRIBUTION OF PROVINCIAL OUTPUT IN CHINA: ESTIMATES FROM A RESTRICTED VAR MODEL

14.14 Hartley, P.R. and Medlock III, K.B. THE VALLEY OF DEATH FOR NEW ENERGY TECHNOLOGIES

14.15 Hartley, P.R., Medlock III, K.B., Temzelides, T. and Zhang, X.

LOCAL EMPLOYMENT IMPACT FROM COMPETING ENERGY SOURCES: SHALE GAS VERSUS WIND GENERATION IN TEXAS

14.16 Tyers, R. and Zhang, Y. SHORT RUN EFFECTS OF THE ECONOMIC REFORM AGENDA

14.17 Clements, K.W., Si, J. and Simpson, T. UNDERSTANDING NEW RESOURCE PROJECTS

14.18 Tyers, R. SERVICE OLIGOPOLIES AND AUSTRALIA’S ECONOMY-WIDE PERFORMANCE

14.19 Tyers, R. and Zhang, Y. REAL EXCHANGE RATE DETERMINATION AND THE CHINA PUZZLE

37

ECONOMICS DISCUSSION PAPERS 2014

DP NUMBER AUTHORS TITLE

14.20 Ingram, S.R. COMMODITY PRICE CHANGES ARE CONCENTRATED AT THE END OF THE CYCLE

14.21 Cheong, T.S. and Wu, Y. CHINA'S INDUSTRIAL OUTPUT: A COUNTY-LEVEL STUDY USING A NEW FRAMEWORK OF DISTRIBUTION DYNAMICS ANALYSIS

14.22 Siddique, M.A.B., Wibowo, H. and Wu, Y.

FISCAL DECENTRALISATION AND INEQUALITY IN INDONESIA: 1999-2008

14.23 Tyers, R. ASYMMETRY IN BOOM-BUST SHOCKS: AUSTRALIAN PERFORMANCE WITH OLIGOPOLY

14.24 Arora, V., Tyers, R. and Zhang, Y. RECONSTRUCTING THE SAVINGS GLUT: THE GLOBAL IMPLICATIONS OF ASIAN EXCESS SAVING

14.25 Tyers, R. INTERNATIONAL EFFECTS OF CHINA’S RISE AND TRANSITION: NEOCLASSICAL AND KEYNESIAN PERSPECTIVES

14.26 Milton, S. and Siddique, M.A.B. TRADE CREATION AND DIVERSION UNDER THE THAILAND-AUSTRALIA FREE TRADE AGREEMENT (TAFTA)

14.27 Clements, K.W. and Li, L. VALUING RESOURCE INVESTMENTS

14.28 Tyers, R. PESSIMISM SHOCKS IN A MODEL OF GLOBAL MACROECONOMIC INTERDEPENDENCE

14.29 Iqbal, K. and Siddique, M.A.B. THE IMPACT OF CLIMATE CHANGE ON AGRICULTURAL PRODUCTIVITY: EVIDENCE FROM PANEL DATA OF BANGLADESH

14.30 Ezzati, P. MONETARY POLICY RESPONSES TO FOREIGN FINANCIAL MARKET SHOCKS: APPLICATION OF A MODIFIED OPEN-ECONOMY TAYLOR RULE

14.31 Tang, S.H.K. and Leung, C.K.Y. THE DEEP HISTORICAL ROOTS OF MACROECONOMIC VOLATILITY

14.32 Arthmar, R. and McLure, M. PIGOU, DEL VECCHIO AND SRAFFA: THE 1955 INTERNATIONAL ‘ANTONIO FELTRINELLI’ PRIZE FOR THE ECONOMIC AND SOCIAL SCIENCES

14.33 McLure, M. A-HISTORIAL ECONOMIC DYNAMICS: A BOOK REVIEW

14.34 Clements, K.W. and Gao, G. THE ROTTERDAM DEMAND MODEL HALF A CENTURY ON

38

ECONOMICS DISCUSSION PAPERS 2015

DP NUMBER

AUTHORS TITLE

15.01 Robertson, P.E. and Robitaille, M.C. THE GRAVITY OF RESOURCES AND THE TYRANNY OF DISTANCE

15.02 Tyers, R. FINANCIAL INTEGRATION AND CHINA’S GLOBAL IMPACT

15.03 Clements, K.W. and Si, J. MORE ON THE PRICE-RESPONSIVENESS OF FOOD CONSUMPTION

15.04 Tang, S.H.K. PARENTS, MIGRANT DOMESTIC WORKERS, AND CHILDREN’S SPEAKING OF A SECOND LANGUAGE: EVIDENCE FROM HONG KONG

15.05 Tyers, R. CHINA AND GLOBAL MACROECONOMIC INTERDEPENDENCE

15.06 Fan, J., Wu, Y., Guo, X., Zhao, D. and Marinova, D.

REGIONAL DISPARITY OF EMBEDDED CARBON FOOTPRINT AND ITS SOURCES IN CHINA: A CONSUMPTION PERSPECTIVE

15.07 Fan, J., Wang, S., Wu, Y., Li, J. and Zhao, D.

BUFFER EFFECT AND PRICE EFFECT OF A PERSONAL CARBON TRADING SCHEME

15.08 Neill, K. WESTERN AUSTRALIA’S DOMESTIC GAS RESERVATION POLICY THE ELEMENTAL ECONOMICS

15.09 Collins, J., Baer, B. and Weber, E.J. THE EVOLUTIONARY FOUNDATIONS OF ECONOMICS

15.10 Siddique, A., Selvanathan, E. A. and Selvanathan, S.

THE IMPACT OF EXTERNAL DEBT ON ECONOMIC GROWTH: EMPIRICAL EVIDENCE FROM HIGHLY INDEBTED POOR COUNTRIES

15.11 Wu, Y. LOCAL GOVERNMENT DEBT AND ECONOMIC GROWTH IN CHINA

15.12 Tyers, R. and Bain, I. THE GLOBAL ECONOMIC IMPLICATIONS OF FREER SKILLED MIGRATION

15.13 Chen, A. and Groenewold, N. AN INCREASE IN THE RETIREMENT AGE IN CHINA: THE REGIONAL ECONOMIC EFFECTS

15.14 Knight, K. PIGOU, A LOYAL MARSHALLIAN?

15.15 Kristoffersen, I. THE AGE-HAPPINESS PUZZLE: THE ROLE OF ECONOMIC CIRCUMSTANCES AND FINANCIAL SATISFACTION

15.16 Azwar, P. and Tyers, R. INDONESIAN MACRO POLICY THROUGH TWO CRISES

15.17 Asano, A. and Tyers, R. THIRD ARROW REFORMS AND JAPAN’S ECONOMIC PERFORMANCE

15.18 Arthmar, R. and McLure, M. ON BRITAIN’S RETURN TO THE GOLD STANDARD: WAS THERE A ‘PIGOU-MCKENNA SCHOOL’?

15.19 Fan, J., Li, Y., Wu, Y., Wang, S., and Zhao, D.

ALLOWANCE TRADING AND ENERGY CONSUMPTION UNDER A PERSONAL CARBON TRADING SCHEME: A DYNAMIC PROGRAMMING APPROACH

15.20 Shehabi, M. AN EXTRAORDINARY RECOVERY: KUWAIT FOLLOWING THE GULF WAR