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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).
10
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
13
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
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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: [email protected] 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