Perceptions and Expenditure Patterns - Social Policy Research ...

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Table 2 The association between financial satisfaction and prosperity with ageMen and womenCross-section OLSFixed effectsEffect size t Effect size tPerceived prosperityAge 55 minus age 45 0.13 2.8 0.09 1.6Age 65 minus age 55 0.01 0.5 0.10 1.7Age 75 minus age 65 -0.01 -0.7 0.08 1.3Age 85 minus age 75 0.06 1.6 0.04 0.5Financial satisfactionAge 55 minus age 45 0.13 3.3 0.15 2.9Age 65 minus age 55 0.17 9.4 0.23 5.3Age 75 minus age 65 0.20 9.1 0.19 4.2Age 85 minus age 75 0.22 7.1 0.05 0.8Women onlyCross-section OLSFixed effectsEffect size t Effect size tPerceived prosperityAge 55 minus age 45 0.10 2.1 0.15 2.4Age 65 minus age 55 -0.01 -0.3 0.11 1.9Age 75 minus age 65 -0.02 -1.0 0.08 1.1Age 85 minus age 75 0.04 0.9 0.04 0.4Financial satisfactionAge 55 minus age 45 0.13 2.8 0.07 0.9Age 65 minus age 55 0.19 8.5 0.17 2.6Age 75 minus age 65 0.21 7.8 0.13 1.7Age 85 minus age 75 0.19 6.2 -0.05 -0.6Notes: Calculated from the HILDA survey, waves 1 to 6. The dependent variables are financial satisfactionand perceived prosperity scores, normalised to have standard deviation of one. The parameter estimatesare thus effect sizes, showing the number of standard deviation units increase in the dependent variablewhen increasing age by 10 years. The left-hand panel shows a pooled OLS estimation, including acubic function of age and dummy variables for each survey wave. The right-hand panel shows a fixedeffects estimation, controlling for waves 3 to 6 (vs waves 1 or 2). The ‘t’ columns show the estimatedivided by the standard error, calculated using the cross-section replication weights provided in theHILDA survey (absolute values greater than 1.96 are significant at the 5% level).The top-left number in Table 2 shows that as people increase from age 45 to age 55, their expressedfinancial satisfaction increases by 0.13 standard deviations – a small, but statistically significant,increase. Subsequent decades see no increase, though there is a small (not significant) increase afterage 75. This mirrors the pattern shown in Figure 3, as does the steady increase in financialsatisfaction, shown in the second panel.These estimates also control for the survey wave in which the data was collected. These parameters(not shown) indicate that perceived prosperity and satisfaction increased significantly after thesecond wave, possibly due to changes in the survey methodology. The fixed effects estimates on theright-hand side of the figure also control for interview wave, but only by distinguishing betweenwaves 1 or 2 vs the remaining waves. It is not possible to control for each wave separately, as thechange in wave is strongly associated with the change in age.12
Table 3Association between financial hardship and ageIncrease inincidence (%)Could not pay electricity, gas or telephone bills on timeAge 55 minus age 45 -5.5 -3.6Age 65 minus age 55 -3.4 -5.3Age 75 minus age 65 -1.2 -2.4Age 85 minus age 75 0.4 0.6Asked for financial help from friends or familyAge 55 minus age 45 -4.8 -4.5Age 65 minus age 55 -2.8 -6.1Age 75 minus age 65 -1.8 -4.8Age 85 minus age 75 -0.8 -2.3Could not pay the mortgage or rent on timeAge 55 minus age 45 -3.2 -3.5Age 65 minus age 55 -1.8 -5.4Age 75 minus age 65 -0.3 -1.2Age 85 minus age 75 0.5 1.0Pawned or sold somethingAge 55 minus age 45 -4.0 -3.5Age 65 minus age 55 -0.8 -2.4Age 75 minus age 65 -1.3 -3.2Age 85 minus age 75 -0.7 -4.0Asked for help from welfare/community organisationAge 55 minus age 45 -1.7 -2.2Age 65 minus age 55 -0.7 -2.5Age 75 minus age 65 0.0 -0.1Age 85 minus age 75 0.7 1.1Went without mealsAge 55 minus age 45 -1.1 -1.3Age 65 minus age 55 -1.1 -3.3Age 75 minus age 65 -0.8 -3.5Age 85 minus age 75 -0.3 -1.9Was unable to heat homeAge 55 minus age 45 -0.2 -0.3Age 65 minus age 55 -0.5 -1.6Age 75 minus age 65 -0.1 -0.4Age 85 minus age 75 -0.1 -0.2Notes: Calculated from the HILDA survey, waves 1 to 6. Estimated via a logistic regression across the pooledcross-section. The regression includes a quartic function of age, sex and flags for each wave of thesurvey. The estimates show the predicted increase in incidence when increasing age by 10 years asindicated. The ‘t’ column shows the estimate divided by the standard error calculated using the crosssectionreplication weights provided in the HILDA survey (absolute values greater than 1.96 aresignificant at the 5% level).tThe fixed-effects regression model shows the impact of changes in age on changes in expressedsatisfaction or prosperity. This is equivalent to a regression that includes a dummy variable for each13
Page 1: Living Standards after Retirement:
Page 4 and 5: Summary ...........................
Page 6 and 7: There is thus a disjuncture between
Page 8: 2 BackgroundThough there is substan
Page 11 and 12: People who had already retired were
Page 13 and 14: cent of men and 57 per cent of wome
Page 18 and 19: person. If, for example, the patter
Page 20 and 21: 4 Expenditures in retirement4.1 The
Page 22 and 23: Table 4 Expenditure categories dire
Page 24 and 25: Table 6 Sample size by retirement s
Page 26: Table 8 Mean expenditures and budge
Page 29 and 30: How high would the non-housing expe
Page 31 and 32: The results of this regression are
Page 33 and 34: Bradbury, Bruce (1997), 'The living

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