Australian Social Policy Journal No. 9 2010 - Department of Social ...

Australian Social Policy Journal No. 9 2010

Improving the lives of Australians

The Australian Social Policy Journal publishes current research and analysis on a broad range of issues topical to Australia’s social policy and its administration. Regular features include major articles, social policy notes and book reviews. Content is compiled by the Research and Analysis Branch of the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA). Australian Social Policy Journal supercedes Australian Social Policy, published by FaHCSIA, and the Social Security Journal published by the former Department of Social Security. Refereed publication Australian Social Policy Journal is a fully refereed academic journal; all submissions of major articles and social policy notes to the journal are subject to an external blind peer review. The journal is recognised by the Australian Research Council’s (ARC) Excellence in Research for Australia (ERA) Ranked Journal List of refereed journals. Submissions Submissions are accepted from academic researchers, government employees and relevant practitioners. Submissions that contribute to current social policy research issues and debates are particularly encouraged. Submissions can be forwarded by email to . Submission guidelines are available at the back of the journal or online at . Copyright This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the Commonwealth available from the Commonwealth Copyright Administration, Attorney-General’s Department. Requests and inquiries concerning reproduction and rights should be addressed to the Commonwealth Copyright Administration, Attorney-General’s Department, Robert Garran Offices, National Circuit, Barton, ACT 2600 or posted at . Disclaimer The opinions, comments and/or analysis expressed in this document are those of the authors and do not necessarily represent the views of the Minister for Families, Housing, Community Services and Indigenous Affairs, or the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs, and cannot be taken in any way as expressions of Government policy. For more information on FaHCSIA research publications and subscription, please contact: Research Publications Unit Research and Analysis Branch Australian Government Department of Families, Housing, Community Services and Indigenous Affairs PO Box 7576 Canberra Business Centre ACT 2610 Phone: (02) 6244 5458 Fax: (02) 6133 8387 Email: Web:

© Commonwealth of Australia 2010 ISSN 1442-6331 (PRINT) ISBN 978-1- 921647-37-9

Australian Social Policy Journal No. 9

Major articles Deborah A Cobb-Clark & Vincent A Hildebrand The asset portfolios of older Australian households Jason D Brandrup & Paula L Mance Changes in household expenditure associated with the arrival of newborn children Liana Leach, Peter Butterworth, Bryan Rodgers and Lyndall Strazdins Deriving an evidence-based measure of job quality from the HILDA survey Peng Yu Sequence matters: understanding the relationship between parental income support receipt and child mortality Samara McPhedran Regional living and community participation: are people with disability at a disadvantage?

Social policy note Ibolya Losoncz and Benjamin Graham Work–life tension and its impact on the workforce participation of Australian mothers

Book review Utopias and revolutions Gornick, J & Meyers, M (eds), Gender equality: transforming family divisions of labor and Epsing-Anderson, G, The incomplete revolution: adapting welfare states to women’s new roles (Reviewer: Julie Connolly)

Australian Social Policy journal No. 9

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Contents Major articles Deborah A Cobb-Clark & Vincent A Hildebrand The asset portfolios of older Australian households

1

Jason D Brandrup & Paula L Mance Changes in household expenditure associated with the arrival of newborn children

41

Liana Leach, Peter Butterworth, Bryan Rodgers and Lyndall Strazdins Deriving an evidence-based measure of job quality from the HILDA survey

67

Peng Yu Sequence matters: understanding the relationship between parental income support receipt and child mortality

87

Samara McPhedran Regional living and community participation: are people with disability at a disadvantage? 111

Social policy note Ibolya Losoncz and Benjamin Graham Work–life tension and its impact on the workforce participation of Australian mothers

139

Book review Utopias and revolutions 159 Gornick, J & Meyers, M (eds), Gender equality: transforming family divisions of labor and Epsing-Anderson, G, The incomplete revolution: adapting welfare states to women’s new roles (Reviewer: Julie Connolly) Guidelines for contributors

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Subscription form

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Major articles Australian Social Policy Journal No. 9

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The asset portfolios of older Australian households Deborah A Cobb-Clark1 & Vincent A Hildebrand2 Economics Program, Research School of Social Sciences, The Australian National University and Institute for the Study of Labor (IZA) Bonn 1

Department of Economics, Glendon College, York University and CEPS/INSTEAD, Luxembourg

2

This paper uses confidentialised unit record file data from the Household, Income and Labour Dynamics in Australia (HILDA) survey. The HILDA project was initiated and is funded by the Australian Government Department of Families, Housing, Community Services, and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). The findings and views reported in this paper, however, are those of the authors and should not be attributed to FaHCSIA or MIAESR.

Abstract This paper investigates whether there is evidence that households adjust their asset portfolios just prior to retirement in order to maximise their eligibility for a means-tested public pension. To this end, we take advantage of recently available, detailed micro data for a nationally‑representative sample of Australian households to estimate a system of asset equations that are constrained to add up to net worth. Our results provide little evidence that in 2006 healthy households or couples were responding to the incentives embedded in the asset and income tests used to determine Australian Age Pension eligibility by reallocating their assets. While there are some significant differences in asset portfolios associated with having an income near the income threshold, being of pensionable age and being in poor health, these differences are often only marginally significant, are not robust across time, and are not clearly consistent with the incentives inherent in the Australian Age Pension eligibility rules. Any behavioral response to the incentives inherent in the Age Pension means test in 2006 appears to be predominately concentrated among single pensioners who are in poor health. In 2002 there is also evidence that healthy households above pension age held significantly more wealth in their homes than did otherwise similar younger households, perhaps suggesting some reduction in the incentives to reallocate assets over time. Keywords: asset portfolios; means testing; public pension; household wealth

1


1 Introduction Economists are increasingly using detailed comparative studies of wealth levels across different groups as a way of gaining a deeper understanding of the processes through which households accumulate and manage their wealth (Cobb-Clark & Hildebrand 2006a, 2006b, 2009). As eligibility for the Age Pension is asset and income tested, policy makers are particularly interested in understanding how households allocate their wealth across asset types in the lead‑up to retirement. This report takes advantage of recently available Household, Income and Labour Dynamics in Australia (HILDA) data on household wealth in Australia to seek answers to two questions: How

do the portfolio choices of pre and post-retirement period households differ?

Are

these differences consistent with households managing their wealth in a way that maximises access to the Age Pension?

Sections 2 and 3 briefly present some key features of the conceptual framework and the means tests underlying the Age Pension. Section 4 presents the data and discusses descriptive statistics. The empirical strategy and regression results are presented and discussed in Section 5, and concluding remarks are in Section 6.

2 Conceptual framework Households make decisions about how to allocate their wealth across asset types by comparing the relative risks and relative returns of various assets. Government policy—such as tax policy, means testing or regulation—can influence those decisions by affecting both the risk and return associated with holding a particular asset. Certain assets, such as housing, typically require minimum investment, which implies that the ability to hold particular assets may depend, in part, on a household’s overall wealth level. It is therefore reasonable to expect that the mix of assets a household maintains will depend on its overall wealth level. Economists typically rely on the lifecycle hypothesis to understand the way in which a household’s consumption and savings decisions—and ultimately wealth accumulation—evolve as that household ages. If households had perfect foresight and faced no credit constraints, they would be able to borrow and save so as to smooth their consumption levels across the lifecycle, despite often substantially fluctuating income. In reality, however, imperfect information and credit constraints imply that a household’s current situation (for example, stage of lifecycle, composition and financial situation) is likely to be important in understanding both its net worth and asset portfolio. This conceptual framework is a useful tool with which to assess the potential effect of the Age Pension means test on the portfolio allocations of Australian households. In such an assessment it would be important to consider the incentives inherent in the means tests associated with the Age Pension. Moreover, we need to make comparisons between households that are at the same approximate lifecycle stage, that is, immediately before and after retirement age, and are equally wealthy. 2

The asset portfolios of older Australian households

3 Australian Age Pension The Age Pension is the first tier program of the Australian pension system, which covered about 80 per cent of the elderly population in 2004–05. Eligibility for the Age Pension is contingent on a claimant being an Australian resident, residing in Australia at the time his or her claim is lodged, and being a resident for a total of at least 10 years, including five consecutive years.1 Male claimants become eligible at age 65 years while female claimants’ eligibility is being gradually increased from age 60 years in 1995 to age 65 years by 2014.2 In June 2006, single recipients of the Age Pension received $499 every two weeks. Partnered recipients received $834 combined.3 The benefit level for individuals is explicitly set at 25 per cent of gross male average total earnings. Benefit levels (and means test thresholds) are adjusted every six months in line with changes in the consumer price index or average male earnings—whichever is greater. Age Pension coverage is not universal. Simply meeting age and residency requirements does not guarantee benefit payments. The Age Pension targets the poorest elderly by subjecting the level of benefit payments to a broad means test based on income and assets. The level of pension benefits is determined by the test that results in the lowest payment, making (in this context) the arbitrage between the optimal levels of income and assets very complex. Furthermore, age pensioners also receive subsidies for health care, pharmaceuticals, public transport, utilities and rent. As a result, a real incentive exists at the margin to qualify for a small pension in order to take advantage of the various additional, lump-sum benefits derived from these subsidies. In 2006, the income test implied that couples experienced a reduction in pension benefit payments of 40 cents in every dollar earned in excess of $228 per fortnight; for singles it was 20 cents in every dollar earned in excess of $128 per fortnight. Relevant sources of income include all incomes received, derived and/or earned. The most common sources of income are salaries and wages; the monetary value of non-income benefits; annuities and pensions, including superannuation and overseas pensions; real estate, estates and life interests; profits and distributions from private trusts and businesses; and deemed income from financial investments. In deeming income from financial investments it is assumed that these investments are earning a specific, fixed rate of interest, regardless of the rate they are actually earning. The first $63,800 of financial investments for a couple or the first $38,400 of financial investments for a single person is deemed to be earning a rate of 3 per cent. Remaining financial investments are deemed to be earning 5 per cent. This particular aspect of the income test may give households an incentive to reallocate their financial wealth towards more volatile (riskier) financial assets that are expected to yield returns exceeding the deemed rate set by government rather than safer financial assets that yield returns lower than the deemed rate. Thus, it is reasonable to expect that the way in which households hold their financial wealth may be affected by the deeming rules. It is less clear how the deeming rules might affect the incentives to hold financial wealth in general. Home ownership status is central to the asset test. However, a claimant’s principal place of residence is exempt from the asset test. 4 As a result, the asset test is a function of home owner 3


status, but is independent of the value of the principal residence. More precisely, in 2006, single home owners could have up to $161,500 in assets and non-home owners could have up to $278,500 before their pension rate would be affected. Couples could have up to $229,000 if they owned their home and up to $346,000 if they did not. Assets exceeding these exemption amounts reduced pension rates by $3 per fortnight for every $1,000.5 Given this favourable treatment of housing, one might expect households to allocate more wealth towards their principal residence upon reaching pension age. This might imply increasing equity in the family home by decreasing some or all other types of assets. In particular, there may be an incentive to decrease holdings in risky (often liquid) assets with high yields. In the context of the Age Pension, the benefits associated with reducing positions in these assets are twofold. First, it might help households achieve a level of assets below the exemption amount set by the asset test. Second, it would decrease the amount of income received from their financial wealth, which factors into the income test calculation. The resulting combined effect would be to increase the probability that a claimant qualifies for the Age Pension under both rules. It is important to note that claimants only qualify for the lowest benefits level as determined by either the income test or the asset test. As a result, households could be expected to decrease holdings in riskier assets, such as stocks and mutual funds, and to increase assets in their principal residence. More generally, households qualifying for Age Pension benefits under the income test may have an incentive to shift investments towards either less risky, non-financial assets with low returns or towards assets that do not generate additional income (such as cars, recreational vehicles and so on), hence reducing the amount of income subjected to the income test. In such cases, an increase in lifestyle assets, such as holiday homes or recreational vehicles might be observed. Finally, if shifting assets into the family home is not possible some incentives remain to reduce overall wealth by simply purchasing expensive consumer goods; for example, cashing out superannuation to finance expensive holidays. It seems clear that targeting Age Pension benefits towards poorer households creates incentives for Australian households to reallocate their portfolios in order to maximise the likelihood of qualifying for Age Pension benefits under the combined income and asset tests. Using cross‑sectional data on the wealth levels and asset portfolios of Australian households in 2002 and 2006, we attempt to determine if there is evidence of behavioural response consistent with the various possible scenarios discussed earlier.

4 Data The HILDA survey The data used in this paper come from the Household, Income and Labour Dynamics in Australia (HILDA) survey, which is a longitudinal survey of Australian households encompassing approximately 13,000 individual respondents living in more than 7,000 households. The analysis in this paper relies on the 2002 and 2006 releases of HILDA (Waves 2 and 6), which provide the 4


only micro-level, longitudinal data on household wealth holding in Australia (see Headey, Marks & Wooden 2005; Wooden, Freidin & Watson 2002 for a detailed presentation of these data).6 We have necessarily made a number of sample restrictions. Because household wealth can be difficult to measure and conceptualise in households with multiple families, a small number of multi-family households, all group households, and all related family households have been excluded, as have all single or couple-headed households in which the respondent (or his or her partner) did not provide an interview. Finally, in order to maintain a sufficiently large sample of households around retirement age, the sample used was restricted to all households in which the reference person was aged between 55 years and 74 years. These restrictions resulted in a total analysis sample of 927 couple-headed households and 582 single-headed households in 2002, and 867 couple-headed households and 602 single-headed households in 2006.7 Most of HILDA’s wealth components are collected at the household level.8 In this paper, we consider the way in which wealth is distributed across five broad asset types—net financial wealth, net business equity, net equity in own home, lifestyle assets and total value of superannuation assets. These asset types are defined so as to capture possible incentives to reallocate assets embedded in the pre-2007 asset/income test rules for qualifying for the Age Pension. Net financial wealth is calculated as the total value of interest-bearing assets held in banks and other institutions, stocks and mutual funds, life insurance funds, trust funds and collectibles minus the total value of unsecured debts (which also include car loans). The net value (equity) of own home captures households’ equity in their principal residence. Net business equity includes the net value of all business shares owned by all household members. Lifestyle assets include all non-liquid assets that do not necessarily generate a steady income stream and include all transport and recreational vehicles (such as boats or caravans) and all other real estate assets (such as holiday homes and other properties) owned by household members.9 The superannuation component of net wealth includes the total amount of superannuation capital owned by all household members. While HILDA does not use the concept of a reference person (or household head), in this report we define the head of household to be the oldest partner in couple-headed households. We then separately account for the age of household heads and their spouses in the estimation model. Moreover, the analysis considers single and couple-headed households separately as each group faces different incentives given the asset and income test rules in place.

Retirement status of older Australians As this study’s objective was to explore whether the incentives embedded in the asset and income tests used to determine eligibility for the Age Pension induce older Australian households to revise their portfolio allocation, the analysis explicitly considered two subpopulations. The first included all households in which the reference person (or household head) was aged between 55 and 64 years. Given that the reference person was defined as the oldest partner in a couple, few household members from this group were entitled to claim the Age Pension (about 3 per cent 5


of all couple-headed households in 2006). The second subpopulation included all households in which the reference person was aged between 65 and 74 years. This implies that in the second group at least one household member was eligible to receive Age Pension benefits. We began by considering the retirement status of individuals in these two groups of households. Information on relevant demographic characteristics and place of residence for individuals in our estimation sample is reported in Table 1 for couple-headed households and in Table 2 for single individuals. While most household members in younger couple-headed households (that is, those in which the head is aged between 55 and 64 years) are not eligible to claim Age Pension benefits, in about 17 per cent (22 per cent) of couples both partners nonetheless reported being retired in 2006 (2002). In contrast, approximately 40 per cent of single-headed households in this younger age group had already left the labour force during the same period. Not surprisingly, the proportion of retirees rises substantially after the age of 64 years. For instance, in 2006 (2002), at least 80 (83) per cent of all couple-headed households reported at least one household member being retired while up to 87 (88) per cent of single individuals were no longer in the labour force in 2006 (2002).

Health status and wealth Recipients of the Age Pension are eligible to receive subsidies for health care and pharmaceuticals. As a result, the incentive to qualify for the Age Pension might also be affected by the health status of future claimants. Individuals in poor health may have greater incentives to reallocate their assets in order to qualify for the Age Pension. This report examines the impact of health using a measure of self-assessed (non-fatal) health commonly used in the health literature. Specifically, HILDA respondents are asked to rate their health on a five-point scale labelled: ‘excellent’, ‘very good’, ‘good’, ‘fair’ and ‘poor’. We have created an indicator variable for poor health equal to 1 whenever a respondent rated their health as either ‘fair’ or ‘poor’ and 0 otherwise. Tables 1 and 2 reveal that the incidence of poor health does not differ substantially across household types, with about 30 per cent of reference persons reporting being in poor health. Surprisingly, being older is also not associated with significant differences in self-reported health status. For instance, approximately 27 (30) per cent of married heads of household aged 55 to 64 years report being in poor health compared to 33 (27) per cent of married household heads in the 65 to 74 years age group in 2006 (2002) respectively. These differences in self‑reported health status across age groups are not statistically significant.10 Information about the relationship between net wealth, asset portfolios and self-reported health status is reported in Table 3 for couple-headed households and in Table 4 for single-headed households. Being in good health is associated with a higher incidence of owning each asset type as well as with holding more money in all asset types.11 For instance, couple-headed households in which both partners report being in good health hold over $300,000 more wealth at the median

6


(and the mean) than couple-headed households in which at least one spouse reports being in poor health (Table 3). These results align with findings from earlier US studies that demonstrate the close link between health and wealth. Given these differences in the level of net worth—and the potential incentives inherent in the Age Pension eligibility rules—it is reasonable to expect that health status may affect the portfolio choices of older households.

Wealth levels and asset portfolios of households Descriptive statistics for household net wealth, asset portfolios and income are presented in Table 5 for couple-headed households and in Table 6 for single-headed households. These results are presented separately for the 55 to 64 year-old and 65 to 74 year-old age groups. Cross‑sectional comparisons across age groups should be interpreted carefully, however, because such a comparison involves comparing individuals belonging to different birth cohorts. For example, a cross-sectional comparison of the level of assets held by younger and older households reveals that in 2002 (2006) couple-headed households aged 55 to 64 years had on average about $200,000 ($90,000) more net wealth than couple-headed households aged 65 to 74 years. This suggests that older households might be depleting their wealth as they grow older. It is difficult, however, to attribute this disparity in wealth across these two groups of households to the result of lifecycle changes. It is not possible to know whether any difference in the net wealth or asset portfolios of younger and older households stems from the fact that older household heads are approximately 10 years older (see Table 1); that is, a lifecycle change, or because they were born in an earlier decade (for example, the 1930s versus the 1940s). The median net wealth of younger Australian households grew substantially between 2002 and 2006. In 2006, couple-headed (single-headed) households aged 55 to 64 years had a median wealth of about $137,000 ($115,000) more than the same age group in 2002.12 This is not surprising given the exceptional boom in both the equity and the real estate markets over this period. What is surprising is that the financial boom did not extend to older households. Single‑headed households aged 65 to 74 years saw only a slight increase (less than $7,000) in median wealth between 2002 and 2006, while the median net wealth of couple-headed households in this age group increased substantially over the same period. These patterns of growth are consistent with the existence of a positive cohort effect, but are difficult to reconcile with simple ageing or period effects.

7

8

Note:

2.80 5.56 2.63 2.45 0.40 0.32 0.48 0.48 0.42 0.46 0.41 0.46 0.43 0.40 0.25 0.32 0.16 0.08 0.16

59.05 54.40 11.34 11.19 0.19 0.89 0.37 0.36 0.22 0.30 0.22 0.31 0.25 0.19 0.07 0.12 0.03 0.01 0.03 548

Standard deviation

Calculations are based on Wave 2 and Wave 6 of the HILDA survey.

Demographics Age Spouse age Years of education Spouse years of education Female (proportion) Home owners (proportion) Health and retirement (proportion of ) Retired Spouse retired Both retired Poor health Spouse poor health Place of residence (proportion) New South Wales Victoria Queensland South Australia Western Australia Tasmania Northern Territory Australian Capital Territory n

55–64 years

2002

0.36 0.27 0.14 0.10 0.11 0.03 0.00 0.00 379

0.83 0.78 0.70 0.27 0.25

69.13 64.90 10.71 10.50 0.21 0.91

65–74 years

Table 1: Descriptive statistics by age group (couple-headed households)

0.48 0.44 0.34 0.29 0.31 0.17 0.00 0.06

0.38 0.41 0.46 0.44 0.43

2.92 5.00 2.65 2.60 0.41 0.29

0.32 0.26 0.19 0.07 0.12 0.02 0.01 0.01 511

0.32 0.26 0.17 0.27 0.19

59.34 54.94 11.75 11.64 0.22 0.91

Age of reference person Standard 55–64 deviation years

0.47 0.44 0.39 0.26 0.33 0.14 0.11 0.09

0.47 0.44 0.37 0.44 0.40

2.80 5.12 2.49 2.45 0.41 0.29

Standard deviation

2006

0.38 0.24 0.16 0.09 0.11 0.01 0.00 0.01 356

0.80 0.75 0.67 0.33 0.24

69.13 64.47 11.02 10.82 0.20 0.91

65–74 years

0.48 0.43 0.36 0.29 0.32 0.12 0.00 0.11

0.40 0.43 0.47 0.47 0.43

2.77 5.27 2.77 2.49 0.40 0.29

Standard deviation


Note:

0.50 0.46 0.47 0.42 0.42 0.27 0.30 0.17 0.11 0.09

0.42 0.31 0.32 0.22 0.23 0.08 0.10 0.03 0.01 0.01 306

Calculations are based on Wave 2 and Wave 6 of the HILDA survey.

2.99 2.70 0.49 0.47 0.36 0.42 0.49

Standard deviation

59.16 11.03 0.62 0.66 0.15 0.23 0.62

55–64 years

2002

0.42 0.21 0.17 0.08 0.09 0.03 0.00 0.01 276

0.88 0.34

69.71 10.36 0.70 0.77 0.11 0.59 0.30

65–74 years

Descriptive statistics by age group (single-headed households)

Demographics Age Years of education Female (proportion) Home owners (proportion) Never married (proportion) Widowed (proportion) Divorced (proportion) Health and retirement (proportion of ) Retired Poor health Place of residence (proportion) New South Wales Victoria Queensland South Australia Western Australia Tasmania Northern Territory Australian Capital Territory n

Table 2:

0.49 0.41 0.37 0.27 0.29 0.16 0.00 0.10

0.32 0.47

2.83 2.60 0.46 0.42 0.31 0.49 0.46

0.32 0.28 0.18 0.09 0.10 0.02 0.01 0.01 336

0.40 0.32

59.52 11.14 0.62 0.69 0.16 0.25 0.60


0.47 0.45 0.39 0.29 0.30 0.14 0.10 0.07

0.49 0.47

2.78 2.63 0.49 0.46 0.36 0.43 0.49

Standard deviation

2006

0.33 0.22 0.22 0.08 0.12 0.01 0.01 0.01 266

0.87 0.35

69.33 10.88 0.67 0.73 0.10 0.55 0.35

65–74 years

0.47 0.42 0.41 0.27 0.33 0.12 0.10 0.08

0.34 0.48

2.92 2.51 0.47 0.45 0.30 0.50 0.48

Standard deviation


9

10

Notes:

786,576 157,000 283,515 140,993 40,418 183,610 110,470 37,332 76,396 318,254 221,608 186,834 61,269 257,142 62,622 0.050 0.238 0.340 0.210 0.483

612,931 375,404 115,224 45,326 5,527 59,990 12,981 8,600 11,496 284,567 75,132 49,353 25,779 126,513 52,792 0.997 0.060 0.867 0.954 0.632 369

Standard deviation

0.999 0.176 0.915 0.974 0.780 558

185,508 54,734 6,312 96,055 40,799 12,393 79,606 329,192 134,980 100,568 34,411 218,398 75,498

947,683 688,543

Good

0.033 0.381 0.279 0.160 0.415

402,832 118,993 40,230 241,129 228,211 51,189 384,698 279,938 264,944 253,882 64,150 303,930 62,323

970,511 233,219

1.000 0.077 0.864 0.967 0.685 332

113,203 46,307 1,395 65,378 6,479 6,356 17,374 346,320 122,772 96,585 26,187 162,209 57,265

761,878 498,280

Subjective health status Standard Poor/fair deviation

0.000 0.267 0.343 0.180 0.465

326,342 93,293 10,399 291,329 31,641 17,634 98,928 307,547 309,191 287,072 48,339 293,031 64,474

885,893 199,350

Standard deviation

2006

0.995 0.164 0.932 0.994 0.863 535

169,881 52,270 3,725 112,447 16,328 14,889 49,640 434,013 172,453 139,288 33,166 298,732 83,684

1,124,719 837,482

Good

0.068 0.371 0.251 0.078 0.344

357,838 105,198 48,572 312,259 98,404 64,816 217,258 307,410 345,876 337,827 45,230 386,381 67,909

940,733 266,700

Standard deviation

Authors’ calculation based on Waves 2 and 6 of HILDA data. Poor/fair health status if at least one partner rated their health as ‘poor’ or ‘fair’. All figures are reported in constant 2006 Australian dollars.

Net wealth ($) Mean total net wealth Median total net wealth Mean asset portfolio ($) Total financial wealth Interest-earning assets (banks) Interest-earning assets (other) Equity in stocks Other assets Unsecured debts Business Own home Total lifestyle Other real estate Vehicles Superannuation Current income ($) Proportion owning Financial wealth Business Own home Lifestyle Superannuation n

Poor/fair

2002

Table 3: Wealth holding by subjective health status (couple-headed households)


Notes:

162,328 41,321 3,436 117,680 60,763 17,220 199,301 181,925 78,250 74,893 17,428 117,530 20,914 0.194 0.218 0.488 0.416 0.471

60,737 19,977 384 32,385 11,533 3,542 17,889 149,071 28,534 17,543 10,991 40,308 21,103 0.961 0.050 0.616 0.778 0.328 184

0.987 0.050 0.758 0.830 0.513 398

112,585 29,923 5,050 70,660 10,116 3,165 25,131 215,087 53,205 40,159 13,047 65,527 33,160

471,534 275,085

Good

0.114 0.219 0.429 0.376 0.500

267,428 69,866 37,521 237,067 43,594 20,064 171,658 228,406 243,940 242,946 18,538 159,605 32,634

668,703 161,859

0.978 0.016 0.610 0.792 0.404 196

61,685 20,366 2,694 35,487 5,761 2,622 352 182,411 42,581 32,276 10,305 49,491 23,548

336,520 223,153

Subjective health status Standard Poor/fair deviation

0.148 0.124 0.489 0.407 0.492

179,552 50,582 17,667 148,659 56,845 7,206 4,061 205,847 133,313 129,564 13,441 137,321 30,987

455,053 194,680

Standard deviation

2006

Authors’ calculation based on Waves 2 and 6 of HILDA data. All figures are reported in constant 2006 Australian dollars.

455,591 124,616

Standard deviation

296,539 151,987

Poor/fair

2002

Wealth holding by subjective health status (single-headed households)


Table 4:

0.987 0.067 0.752 0.882 0.621 406

123,648 36,759 3,339 80,972 8,267 5,689 16,506 283,087 77,498 61,895 15,603 106,879 41,007

607,618 379,919

Good

0.114 0.251 0.433 0.323 0.486

263,716 86,222 24,550 212,972 40,187 24,760 96,686 306,547 239,989 232,505 22,197 231,479 55,626

727,055 239,600

Standard deviation


11

12

Notes:

969,935 229,359 406,518 137,935 38,551 214,804 240,082 55,078 364,181 300,309 229,057 195,016 78,443 302,608 71,526 0.033 0.371 0.315 0.176 0.355

891,434 617,918 161,339 53,564 4,211 77,903 41,668 16,007 71,172 318,443 120,828 84,056 36,771 219,652 78,289 0.999 0.165 0.889 0.968 0.852 548

Standard deviation

0.997 0.071 0.906 0.961 0.513 379

149,394 46,694 8,751 86,452 10,304 2,808 21,341 299,128 94,250 72,547 21,703 120,181 47,376

684,294 422,896

65–74 years

0.051 0.258 0.292 0.193 0.500

273,507 112,314 42,750 227,940 39,261 24,407 155,847 291,566 278,274 275,862 21,843 255,551 41,780

805,118 178,500

0.997 0.179 0.904 0.985 0.908 511

120,593 44,198 1,825 74,824 15,233 15,486 49,530 393,808 176,139 141,971 34,169 286,247 90,259

1,026,318 755,250


0.056 0.384 0.295 0.122 0.289

284,004 78,730 34,063 232,509 95,934 56,996 205,701 304,463 339,666 326,038 49,616 381,804 77,347

930,768 256,955

Standard deviation

2006



55–64 years

2002

Table 5: Wealth holding by age (couple-headed households)

0.998 0.059 0.907 0.980 0.627 356

184,631 57,907 4,154 120,015 8,319 5,763 18,482 405,807 119,021 94,047 24,974 183,906 48,739

911,846 647,800

65–74 years

0.048 0.235 0.291 0.139 0.484

416,800 124,818 43,279 383,242 44,349 41,982 133,638 318,631 319,151 306,831 41,443 312,122 40,269

939,738 259,527

Standard deviation


Notes:

259,754 61,338 41,076 222,024 54,215 25,595 181,251 219,285 96,923 92,413 20,370 182,061 34,615 0.167 0.268 0.473 0.340 0.488

96,808 22,239 5,200 61,627 12,976 5,233 31,582 176,314 38,107 24,239 13,869 85,659 34,952 0.971 0.078 0.664 0.867 0.611 306

0.987 0.017 0.769 0.750 0.269 276

94,910 32,005 1,616 54,542 7,746 999 12,495 214,531 53,712 43,066 10,645 24,264 22,635

399,912 240,818

65–74 years

0.113 0.131 0.422 0.434 0.444

214,618 63,078 10,473 188,312 43,755 4,711 180,173 212,019 285,198 284,888 15,114 81,163 21,399

611,573 146,295

0.980 0.067 0.687 0.866 0.696 336

108,532 32,236 3,923 72,412 6,962 7,000 16,449 260,537 79,137 62,099 17,038 119,994 42,133

584,648 339,925


0.140 0.250 0.465 0.341 0.461

265,658 89,585 27,526 213,282 49,778 26,982 101,385 325,014 200,783 197,479 22,171 237,464 58,974

753,879 247,200

Standard deviation

2006


615,859 145,700

Standard deviation

428,472 224,619

55–64 years

2002

Wealth holding by age (single-headed households)


Table 6:

0.989 0.029 0.729 0.834 0.360 266

96,441 30,244 2,090 57,695 8,071 1,658 4,352 236,260 48,932 39,208 9,723 46,347 26,325

432,331 313,775

65–74 years

0.105 0.168 0.445 0.373 0.481

204,636 55,785 13,387 168,921 41,402 5,249 33,617 211,160 223,894 213,709 15,565 149,230 31,530

507,972 194,750

Standard deviation


13


Younger households (2002) versus older households (2006) Given the longitudinal nature of the HILDA survey, some respondents aged 55 to 64 years in 2002 would be in the older age group in 2006. In order to infer more accurately the changes in wealth components due strictly to ageing, it is best to compare the wealth and asset portfolios of households aged 55 to 64 years in 2002 with those of households aged 65 to 74 years in 2006. Such a comparison seems to suggest that overall older Australian households are not depleting their wealth as they age. For instance, couples aged 65 to 74 years in 2006 hold on average about $30,000 more net wealth at the median than couples aged 55 to 64 years in 2002. At the same time, this relatively small growth in net wealth may stem from changes in the equity and housing markets over this period. Comparison of the asset portfolios of those households aged 65 to 74 years in 2006 with those of households aged 55 to 64 years in 2002 shows that on average couples aged 65 to 74 years in 2006 held approximately $23,000 more financial wealth than couples aged 55 to 64 years in 2002. Surprisingly, couples aged 65 to 74 years in 2006 held, on average, significantly more wealth in stocks and mutual funds (about $120,000) than their counterparts aged 55 to 64 years did in 2002 (about $78,000). At the same time, they also held significantly less wealth in life insurance, trust funds or collectables. This pattern was surprising because we typically expect households to reduce their exposure to risky assets as they age.

Changes in asset portfolios over time Asset portfolios have changed over time. Specifically, couple-headed households aged 55 to 64 years in 2006 held less financial wealth than their counterparts in 2002 ($120,593 versus $161,339) but held more net wealth on average (and at the median). From this observation, one could speculate that the lower level of financial wealth the younger cohort held in 2006 could have reflected a change in allocation across asset types. In particular, younger couple and single-headed households held substantially more assets in superannuation in 2006 than their counterparts did in 2002. For example, couple-headed (single-headed) households aged 55 to 64 years in 2006 held approximately $67,000 ($34,000) more wealth in superannuation than the same age group did in 2002. Similar growth over time is observed in the superannuation wealth of older households. As a result, the substantial gap in superannuation wealth between those aged 55 to 64 years and those aged 65 to 74 years in 2002 (and 2006) is most likely a combination of a pure age effect (superannuation is used to finance consumption in retirement) and a cohort effect (the younger cohort tends to have reallocated more assets towards superannuation). A cross-sectional comparison suggests little difference in the average home equity of the two age groups, regardless of household type. There was a small drop in housing equity between the two age groups (approximately $19,000) in 2002 and a small increase (less than $13,000 in 2006). Over time, younger households appear to be holding more wealth in their homes. For example, couple-headed households aged 55 to 64 years in 2006 held $75,000 more wealth in the form of equity in their principal residence than did their counterparts in 2002. Moreover, both singles and couples aged 65 to 74 years in 2006 held significantly more wealth in their own home than did corresponding households aged 55 to 64 years in 2002.

14


These patterns provide some evidence that Australian households hold more wealth in their homes as they age, which is consistent with incentives created by the asset test to become eligible for or increase payment of Age Pension benefits. However, it could also merely reflect the boom in the housing market over the period covered by these data. Furthermore, home ownership rates are consistently high, regardless of age, among couple-headed households (at about 90 per cent) and increasing over time among single-headed households.

Summary Figures 1 to 8 depict the way in which households of different ages distribute their net wealth across the five major asset categories. Figures 1 and 2 present the asset portfolios of couple-headed households aged 55 to 64 years in 2006 and 2002 respectively. The most striking difference in the asset portfolios of younger couples over time is the disparity in financial wealth. Younger couples held 18 per cent of total net wealth in the form of financial wealth in 2002 compared to 12 per cent in financial wealth in 2006. This suggests a switch in preferences for other assets, in particular superannuation, lifestyle assets and, to some degree, housing. Figures 3 and 4 present the asset allocation of older couple-headed households in 2006 and 2002 respectively. The asset portfolios of older couples do not seem to have changed much across survey years.13 A comparison of Figures 2 and 3—which potentially controls to some degree for the existence of cohort effects—seems to support the view that households increase the portfolio share devoted to equity in their own home as they age. The fact that the 65 to 74 years age group had, on average, more total net wealth in 2006 than did the 55 to 64 years age group in 2002 (see Table 5) provides additional evidence that households hold a higher share of their wealth in their homes as they age.14 These patterns are even more striking among single-headed households (see Figures 5, 6, 7 and 8). In particular, these figures reveal that the share of wealth held in one’s own home was 14 percentage points higher among singles aged 65 to 74 years in 2006 than among those aged 55 to 64 years in 2002 with approximately 55 per cent of total net wealth allocated to the family home. Furthermore, single-headed households appear to have allocated a smaller proportion of wealth to superannuation than did couples.

15


Figure 1: Couple-headed households, head aged 55 to 64 years, 2006

Superannuation

Financial wealth 12% Business 5%

28%

Lifestyle

Own home 38%

17%

Note:

Authors’ calculation based on Wave 6 of HILDA data.


Superannuation 25%

Financial wealth 18%

Business 8% Lifestyle 14% Own home 36%

Note:

16




Superannuation

Financial wealth

20%

20% Business 2%

Lifestyle 13%

Own home 45%

Note:



Superannuation 18%

Financial wealth

Lifestyle 14%

22%

Business 3%

Own home 44%

Note:


17


Figure 5: Single-headed households, head aged 55 to 64 years, 2006

Superannuation

Financial wealth

21%

19% 3% Business

Lifestyle 14% Own home 45%

Note:



Superannuation 20%


Lifestyle 9%

Business 7%

Own home 41%

Note:

18




Superannuation 11%


Lifestyle 11%

Business 1%

Own home 55%

Note:


Figure 8: Single-headed households, head aged 65 to 74 years, 2002 Superannuation 6% Lifestyle 13%


Business 3%

Own home 54%

Note:


19


5 Regression results The descriptive results discussed above highlight the broad differences in asset portfolios across household type, age and time. However, it is often difficult to interpret these differences because the level of household wealth varies with household type, age and time. Consequently, we are often comparing households that are not equally wealthy. This is problematic because the nature of credit markets and financial institutions implies a link between total wealth and asset portfolios. We would like to know whether the changes over time in asset portfolios represent a structural change in the way Australian households allocate their wealth or are merely the result of Australians becoming wealthier. Similarly, we would like to understand whether changes in portfolios as households age can be attributed to the incentives inherent in the Age Pension eligibility rules or are merely the result of households spending down their wealth to finance consumption in retirement. To gain a deeper understanding of these issues, a model is needed that will help estimate the effect of access to a public pension (the Age Pension) on households’ portfolios. Such an estimation strategy must recognise that the propensity to invest in a specific asset will depend on the types (and amounts) of other assets held; compare households with the same level of net wealth; and allow control for other confounding factors, such as poor health. Therefore, we need to estimate a system of regression equations with an adding up constraint imposed to account for total net wealth (see Blau & Graham 1990). Consequently, we estimated the following reduced-form model of asset composition: sinh –1(Aik)=a0k+Yib1k+Xib2k+AgePensionib3k+Wib4k+µik Aik where is the dollar value of asset k that household i holds. We consider the five major asset categories of financial wealth, business equity, equity in own home, lifestyle assets, and superannuation funds where: Yi includes both total family gross income and a dummy variable capturing whether household income is within the range of being able to collect Age Pension15 and Xi includes a measure of poor health as well as a vector of those demographic characteristics reflecting a household’s lifecycle stage. This specification allows households’ asset portfolios to depend on net wealth in order to account for any capital market imperfections (such as credit constraints), which might vary across households and be related to the decision to hold a particular asset. The variable ‘Age Pension’ captures the impact of meeting the age requirement for claiming the Age Pension. For couple-headed households, we account separately for the effect of each partner being over the pension age. An inverse hyperbolic sine transformation (sinh –1) of assets and income has been adopted to account for the potentially non-positive and highly skewed nature of the distributions of these variables (see Cobb-Clark & Hildebrand 2006a, for further discussion). Finally, equation (eq1) 20


is estimated as a system of equations and a set of cross-equation restrictions are imposed in order to satisfy the adding-up requirement that the sum of assets across asset types equals net wealth.16 We considered two model specifications. Our baseline model does not capture explicitly the effect a household’s lifecycle stage on asset allocation other than by controlling for whether its members have become eligible to claim Age Pension. Our second specification adds a quadratic term in the age of the reference person (the oldest partner in a couple) in order to better disentangle the effects due to ageing from the effect of becoming eligible to claim pension benefits. The model for single individuals is defined analogously with an added control for whether singles are divorced or never married. Being widowed constitutes the reference group. Marginal effects and t-statistics from this estimation are presented in Tables 7 to 10.17 We have estimated our models using both the 2002 and 2006 HILDA data. In this section, the results from the 2006 data are discussed (see Tables 7 to 10) and the estimation results based on the 2002 data are reported in the appendix (see Tables A1 to A4).

Determinants of asset portfolios Given the estimation framework described above, the potential impact of the Age Pension on asset portfolios is captured in two ways: first, through a measure of income eligibility and second, through measures of age eligibility. Total wealth levels are held constant through the inclusion of our measure of net wealth. In effect, the results are calculated for households with average levels of wealth.18

Income and income eligibility By considering the effects of income first, we found that asset allocation is, unsurprisingly, related to households’ current income levels. Comparing households that are equally wealthy, but that have different incomes, we find that at higher levels of household income couples and single individuals held significantly more of their net wealth in superannuation and less in their own homes. In addition, couples allocate more wealth to lifestyle assets, a finding which is robust to model specifications. Finally, both single and couple-headed households allocate more wealth to business assets when they have higher incomes. When considering the effect of income eligibility for the Age Pension, we found that, among couples, there were no significant effects of having household income in the range of income eligibility on asset portfolios (see Tables 7 and 9). However, among singles, we found that being within the income eligibility range was associated with holding significantly less wealth in one’s own home and more in financial wealth. This effect is robust across specifications and is net of the (linear) effect of current income on asset allocation generally. In particular, although there was no overall relationship between income levels and the amount of financial wealth that single individuals held, there was a sharp increase in the holding of financial wealth in the income range associated with eligibility for the Age Pension.

21

22

414,953.00

–169,924.81

Eligible for Age Pension x poor health

Spouse eligible x poor health

–0.90

0.05

R2

0.33

0.56

2.45

–1.11

1.09

0.06

2.84

0.67

–1.14

861

0.00

15,283.87

–28,542.13

–10,985.54 –0.63

15,118.51

–9,860.09 –0.81

–23,241.27

–9,894.26 –0.50

659.13

13,145.73

0.27

t-stat

0.22

0.81

0.21

2.08

0.42

0.25

861

0.53 20.44

99,017.37

–213,699.94 –0.90

232,189.88

–10,479.37 –0.11

76,929.34

33,155.30

–13,296.45 –0.08

4,624.86

–12,981.48 –0.10

–11.62 –4.83

dy/dx

Own home

1.04

1.05

–1.43

–1.00

0.92

4.26

t-stat

0.83

–1.73

0.18

861

0.72

1.37

–73,729.78 –0.80

72,849.75

–60,701.48

–569.20 –0.02

28,347.90

64,402.85

–81,987.10

–6,417.91

33,705.19

2.37

dy/dx

Lifestyle

0.06

2.10

–0.44

–2.32

–1.02

0.83

1.31

5.99

t-stat

0.35

861

0.12

129,353.34

5.54

0.48

–245,560.67 –1.07

4,967.30

174,556.38

–32,900.13

–391,782.16

–147,294.12

12,074.67

107,240.62

6.88

dy/dx

Superannuation

Eligible for Age Pension if at least one partner is eligible. Poor health if one member reports being in poor health (see text for precise definition). All figures are reported in constant 2006 Australian dollars.

861

n

Notes:

1.97

–1.37

–2.02

2.22

–0.38 –0.69

–165,470.17

Poor health

Net worth

–178,626.31

–62,517.02 –0.71

Female head

Previously married

317,465.28

Spouse eligible for Age Pension

1.81

252,471.95

Head eligible for Age Pension

–1.11

1.46

–10,940.74 –0.53

–141,110.05

2.09

Education

Demographics

Eligibility range

Total income

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

(Average) determinants of asset portfolios: couple-headed households (marginal effects and t-statistics), Wave 6

Income ($)

Table 7:


0.91

t-stat

19.91

2.14

–1.02

2.48

0.22

1.33

0.53

–1.35

0.34

595

0.48

325,834.13

–120,983.87

199,377.11

26,563.90

107,974.28

46,892.15

–19,005.58

–423,493.31 –3.74

–9.07 –2.10

dy/dx

Own home

1.28

1.71

0.56

t-stat

–1.71

1.32

0.26

595

0.84

3,883.98

5.38

0.08

–25,174.48 –0.78

–45,775.47 –2.17

–56,463.84

30,684.03

–86,083.52 –3.38

5,296.71

49,585.87

0.66

dy/dx

Lifestyle

0.02

0.17

5.42

t-stat

1.12

0.54

1.69

0.26

0.08

0.58

–1.66

595

0.03

50,166.76

–103,035.92

47,385.87

35,893.67

80,808.96

–254,625.19 –5.12

126.45

13,417.91

5.36

dy/dx

Superannuation


0.07

0.08

R2

Notes:

595

0.00

595

–0.35 –0.86

1.09

–1.91

–1.87

n

Net worth

4,059.91

Eligible for Age –383,944.78 –2.71 Pension x poor health

–6,084.76 –5,680.73

254,875.02

Poor health

0.92

–1.02

0.08

1.05

1.77

–2,315.62 –0.70

2,270.78

–3,427.01

37.68

12,093.36

0.24

2.28

–194,902.73 –2.55

–0.04

Female

–3,678.12

–221,738.05 –2.94

Divorced

Never married

3.44

297,243.56

Eligible for Age Pension

3.64

0.92

1.08

348,396.19

2.80

13,544.75

Education

Demographics

Eligibility range

Total income

Income ($)

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

Table 8: (Average) determinants of asset portfolios: single-headed households (marginal effects and t-statistics), Wave 6


23

24

216,208.08

–59,098.09 –0.67

–122,233.80

–157,176.11

381,069.34

–145,315.11


Female head

Previously married

Poor health



2

Notes:

R

n

0.82

0.07

0.06

3.00

0.64

–1.12

–0.63

861

0.00

14,743.49

–28,100.32

–11,013.81

11,517.49

–10,100.45 –0.82

861

–0.40 –0.74

–0.77

1.81

–1.32

–1.36

0.66

0.13

–1.73

0.60

2.22

–18,783.26 –0.85

16,460.19

267.35

–3,420.74

13,962.05

0.25

–4.81

t-stat

0.80

–0.01

0.20

0.16

2.13

0.47

0.25

861

0.53 20.49

110,446.47

–232,626.09 –0.98

236,160.92

–5,468.03 –0.06

76,878.42

–1,008.12

36,777.55

3,170.34

–5,313.79 –0.30

–8,123.26 –0.06

–11.61

dy/dx

Own home

0.91

4.15

t-stat

–1.06

1.01

1.60

1.05

–1.83

0.19

861

0.75

1.43

–83,636.55 –0.94

87,465.66

–63,342.79

–12,189.96 –0.42

27,762.31

97,921.09

–53,897.23 –0.75

–6,528.64

–4,272.77 –0.97

32,513.59

2.31

dy/dx

Lifestyle

1.37

5.82

t-stat

0.43

0.63

1.54

–0.47

0.35

861

0.12

103,761.71

5.61

0.38

–207,808.58 –0.89

–4,628.23 –0.05

128,374.30

–35,442.19

–294,337.78 –1.65

72,324.81

9,544.66

–29,898.90 –2.34

110,706.01

6.58

dy/dx

Superannuation


Net worth

–71,665.33 –0.42

Head eligible for Age Pension 1.43

–6,453.72 –0.33

Education

2.80

–1.19

1.71

42,906.20

–149,058.39

2.46

Age

Demographics

Eligibility range

Total income

Income ($)

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

Table 9: (Average) determinants of asset portfolios: couple-headed households (marginal effects and t-statistics), Wave 6


2

Notes:

R

n

–6,187.17

0.07

0.09

t-stat

2.17

–1.04

2.30

0.12

1.21

0.54

–1.31

0.34

595

0.49 20.48

330,662.91

–125,320.01

195,830.06

14,543.77

99,228.78

81,550.64

–18,496.23

–3,810.37 –0.24

–441,710.00 –3.77

–9.39 –2.08

dy/dx

Own home

–1.84

1.14

–0.66

1.20

–1.78

1.79

0.53

t-stat

0.26

5.59

0.04

–0.70

595

0.87

1,926.34

–22,739.51

–51,723.57 –2.42

–62,955.89

27,090.13

–28,404.18

4,872.95

–6,258.96

53,527.20

0.58

dy/dx

Lifestyle

5.64

t-stat

0.01

–3.35

1.07 0.55

0.29

0.44

0.61

–1.64

595

0.15

53,014.71

–103,863.94

23,676.75

–4,404.22 –0.07

51,650.26

–31,301.39 –0.39

108.28

–24,255.51

–7,014.76 –0.09

5.12

dy/dx

Superannuation


595

0.96

1.10

–1.89

–1.88

595

0.00

4,116.17

–1.18

0.82

–2,540.05 –0.77

2,100.05

Eligible for Age –389,720.16 –2.77 Pension x poor health

–0.50

0.09

–2,455.92 –0.49

44.00

–5,728.56

Net worth

1.04

1.70

–106.43 –0.29

11,885.50

0.24

2.25

257,652.02

Poor health

0.51

–161,596.08 –2.06

55,356.40

–180,069.22 –2.33

–0.12

1.07

2.32

3.92

1.01

Female

Never married

Divorced

–19,389.14

13,471.00

Education


34,431.27

383,312.06

3.45

Age

Demographics

Eligibility range

Total income

Income ($)

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

Table 10: (Average) determinants of asset portfolios: single-headed households (marginal effects and t-statistics), Wave 6


25


Demographics Couple-headed households in which the head of household is female (those in which the female partner is older) allocate their wealth across asset types in the same way as couple-headed households in which the head of the household is male. This symmetry is robust to model specifications. Single women, however, allocate significantly more wealth than comparable single men to their homes, while holding significantly less financial wealth and lifestyle assets. A relationship clearly exists between previous marital status and asset portfolios, even after accounting for any potential differences in wealth levels associated with marital history. Single individuals who are divorced (rather than widowed) hold substantially less financial wealth and somewhat more superannuation wealth and lifestyle assets. Singles who have never married allocate their wealth across asset types in much the same way as equally wealthy widowers who have not remarried, except that they hold relatively fewer lifestyle assets. Couple-headed households in which the reference person has been previously married hold less financial wealth and more superannuation wealth. However, once detailed controls for age are added this effect is no longer statistically significant (compare Tables 7 and 9). The discussion in Section 2 suggests that individuals’ health status might also affect the way in which they allocate wealth. Regardless of model specification, we found that couple-headed households in which at least one member is in poor health have significantly more equity in their homes and less in lifestyle assets than similar couples in which both partners are in good health. It is possible that these differences stem from the incentives for household members with poor health to become eligible for the Age Pension in order to access the associated health care benefits. At the same time, these differences may simply reflect the effects of poor health on households’ optimal asset allocation. This seems to indicate that those in poor health may spend more time at home (and hence allocate more wealth to their homes) and have less use for lifestyle assets. Singles in poor health held more financial wealth and less business assets than singles in good health. The model also includes an interaction term that separately identifies those singles who are both in poor health and who have reached pension eligibility age. In the case of couples, we interacted poor health status (specifically, at least one partner reporting poor health) with the pension eligibility indicator for each partner. These interactions allowed us to distinguish the asset portfolios of those who had reached pension age in good health from those who had reached pension age in poor health. This distinction provides some evidence on whether the health care benefits associated with the Age Pension seem to be leading people in poor health (and who presumably most value these additional health care benefits) to hold their wealth differently to similar people in good health. The results indicate that couples in which at least one partner is in poor health and in which the head of the household has reached pensionable age hold significantly more financial wealth than equally wealthy couples in which both partners are in good health. Singles who are over pension age and in poor health hold significantly less financial wealth and more equity in their own homes than similar single individuals who are in good health. This latter effect is consistent with the rules that exclude home equity from the asset test when determining Age Pension eligibility. It is less clear whether the income and asset tests underlying the Age Pension give couples in poor health an incentive to hold more financial wealth. 26


Age and age eligibility All income and demographic results discussed above are remarkably robust to model specification. Our substantive conclusions remain the same, even if they include detailed controls for age in the model. Despite this, we found that allocation of wealth across asset types is nonetheless clearly related to age. This relationship is, however, difficult to interpret. Given the cross-sectional nature of the analysis, we cannot explicitly control for birth cohorts. As a result, the estimated age effect on the level of any particular asset captures both differences across birth cohorts in allocation of assets as well as any effect due to ageing (lifecycle stages). Consider first the baseline specification that simply controls the effects of having reached pension eligibility age (see Tables 7 and 8). Among couples, the indicator for head of household eligibility captures the effect of the oldest partner being over pension age, while the spouse eligibility indicator measures the additional effect of the spouse also being over pension age. Using this specification we found some evidence that couples may hold more financial wealth and less superannuation wealth once both partners become eligible to claim the Age Pension. Singles who have reached pension age hold more financial wealth, but less wealth in superannuation or lifestyle assets. However, these findings are not robust to model specification (see Tables 8 and 10 for singles and Tables 7 and 9 for couples). Once we controlled for the overall effects of ageing,19 we found that, not surprisingly, there was a relationship between a household’s age and the way in which it allocates its assets.20 However, the effect of reaching pension eligibility age completely disappears. Thus, there is no additional effect of reaching pension age on portfolio allocations. These results—based on Wave 6 of HILDA—suggest that the disparity in the asset portfolios of younger and older households stem from lifecycle changes (that is, ageing) rather than from changes associated specifically with reaching pension eligibility age. However, results based on Wave 2 of HILDA (see Tables A3 and A4) do indicate some independent effect of reaching pension age on portfolio allocations. In particular, couples in which both partners are eligible for the Age Pension hold significantly more financial wealth and home equity and significantly less in all other assets than do couples in which only the head has reached pension age. Singles who have reached pension age hold significantly more wealth in their own homes than do other singles. This is consistent with the Age Pension having had some effect on asset portfolios in 2002.

Longitudinal evidence All results discussed so far have been derived using cross-sectional variation in the data. The HILDA data are, however, longitudinal, which opens up the possibility of assessing changes in asset portfolios for the same households over time. The main limitation of such analysis is the small sample size available. There were 539 couple-headed households that did not change household type and reported wealth information in both Waves 2 and 6 of HILDA. There were 334 single-headed households that did not change household type and provided wealth data in both waves. Unfortunately, these sample sizes are too small for the type of simultaneous

27


regression analysis conducted above. However, for each household type, we separately identify those households in which at least one member had become eligible for the Age Pension and those in which there was no change in eligibility between the two waves. Comparison of those households that had become eligible with those that had not provides a crude estimate of the potential effect of reaching pension age on asset allocation. Table 11 presents the average change in asset levels between 2006 and 2002 for those households present in both HILDA waves. Among couples, we found a real increase in all assets except business equity, irrespective of pension eligibility status. However, we did not find any statistically significant differences in the magnitude of these changes between those households that had become eligible for the Age Pension and those that had not (see p-values, Table 11). The same result held for singles, with the exception that levels of financial wealth appear to have increased more among households that had become eligible for the Age Pension. Table 11: Changes in assets holding by change in eligibility to Age Pension

Business Financial wealth Lifestyle Own home Superannuation Wealth n Notes:

Couples Change in eligibility Yes ($) No ($) p-value –21,718 –12,507 0.615 28,231 19,369 0.836 38,135 34,237 0.886 70,088 94,646 0.471 39,918 38,079 0.943 154,653 173,824 0.708 133 406

Singles Change in eligibility Yes ($) No ($) p-value –8,412 1,031 0.569 35,859 –10,095 0.028 32,712 11,912 0.557 61,264 56,864 0.723 16,675 17,370 0.968 138,098 77,083 0.146 90 254


Finally, Figures 9, 10, 11 and 12 present the portfolio allocations for the subset of couple‑headed and single-headed households that had become eligible to receive the Age Pension (between 2002 and 2006) both before and after becoming eligible. These figures do not reveal any significant changes in asset shares after becoming eligible to claim the Age Pension. In particular, the share in own home remains unchanged—40 per cent among couples and 43 per cent among singles. Taken together, these crude longitudinal comparisons seem to corroborate the main findings from the cross-sectional analysis of 2006 HILDA data that the asset and income tests underlying the Age Pension do not seem to trigger substantial changes in the portfolio choice of Australian households.

28


Figure 9: Couple-headed households, before eligibility, 2002

Financial wealth Superannuation 25%

18% Business 5%


Note:


Figure 10: Couple-headed households, after eligibility, 2006

Financial wealth Superannuation 25%

Lifestyle 15%

Note:

18% 2% Business

Own home 40%


29


Figure 11: Single-headed households, before eligibility, 2002

Superannuation 24%


Business

4%


Note:


Figure 12: Single-headed households, after eligibility, 2006

Superannuation 21%

Financial wealth 21% Business

1%


Note:

30



6 Conclusions This paper has examined the wealth levels and asset portfolios of Australian households in an effort to determine whether there is evidence that the income and asset tests underlying eligibility for the Age Pension lead households to reallocate their assets. The question has been examined using descriptive analysis (both cross-sectional and longitudinal) as well as simultaneous-equation regression analysis. The simple descriptive statistics presented in this paper suggest that, irrespective of year, younger Australian households (that is, those in which the reference person is aged 55 to 64 years) have more wealth on average than those households that are approximately 10 years older. This is consistent with households spending-down wealth in order to finance post‑retirement consumption. There was a substantial increase in the amount of wealth both younger and older households held in superannuation assets and owner-occupied housing between 2002 and 2006. The result is that assets in the form of owner-occupied housing remained high even among households over the pension age. This finding is not surprising given the preferential tax treatment of owner-occupied housing in Australia and its pivotal role in establishing eligibility to receive the Age Pension under the asset test. Moreover, we would expect that the incentives to qualify for the Age Pension benefits (and its associated health care benefits) would result in households either decreasing their holdings of non-housing assets or reallocating wealth towards assets that do not generate future income streams. As Barrett and Tseng (2008) point out, the fact that many households over the pension age still hold substantial superannuation assets—instead of converting them into a secure income stream—might be crude evidence that some behavioural responses to the incentives to qualify for the Age Pension are taking place. The regression analysis results indicate that having an income that is within 10 per cent of the relevant income eligibility threshold for the Age Pension has no effect on the asset allocation of couples. However, single-headed households that are within the income eligibility range hold significantly less wealth in their homes and more in lifestyle assets. While the latter is consistent with the incentives inherent in the Age Pension eligibility rules, the former is not. We also found that single-headed households that are over pension age and in poor health hold significantly less financial wealth and more equity in their own homes than similar single individuals who are in good health. This is consistent with the rules that exclude home equity from the asset test when determining Age Pension eligibility. At the same time, using Wave 6 HILDA data, no evidence was found that reaching pension age is associated with reallocation of household assets once the effects of ageing are taken into account. In other words, the disparity in the asset portfolios of younger and older households appears to stem from lifecycle changes (that is, ageing) rather than from changes associated specifically with reaching pension eligibility age. However, parallel results based on Wave 2 of HILDA data do indicate some independent effect of reaching pension age on portfolio allocations. Specifically, couples in which both partners are eligible for the Age Pension hold significantly more financial wealth and home equity and significantly less of all other assets than do couples in

31


which only the head has reached pension age. Single individuals who have reached pension age hold significantly more wealth in their own homes than do other singles. Again, the increase in home equity is consistent with the incentive to qualify for the Age Pension, while the disparity in other assets is less clear-cut. Finally, simple longitudinal comparisons between 2002 and 2006 do not provide any evidence that the asset and income tests underlying the Age Pension result in significant changes in the portfolio choices of Australian households. Taken together, these results do not provide compelling evidence that on average households respond to the incentives embedded in the asset and income tests used to determine Age Pension eligibility by reallocating their assets. While there are some significant differences in asset portfolios associated with having an income near the income threshold, being of pensionable age, and being in poor health, these differences are often only marginally significant, not robust across HILDA waves, and not clearly consistent with the incentives inherent in the Age Pension eligibility rules. It is important to note several caveats. First, some households may adjust their financial situation in order to ensure they are eligible for the Age Pension. By its very nature, however, this analysis was concerned with the behaviour of households in the aggregate (or on average). We did not find evidence showing large disparities in the asset portfolios of large numbers of Australia households that are consistent with Age Pension eligibility. Second, while we believe that the HILDA data provide the best opportunity to address the research question of interest, it is not ideal. In particular, small sample sizes make it impossible to estimate a model of asset allocation using longitudinal variation in asset portfolios. Estimation of asset determination is complex and, in this case, involved simultaneous estimation of five separate asset equations imposing cross‑equation restrictions in order to ensure the sum of assets equals total net wealth. Estimating such a model in a longitudinal context would require far more data than are likely to ever be available in a panel survey like HILDA, which is representative of the entire population. More progress would be made using data, such as that from the US Health and Retirement Survey, which specifically sample older cohorts.

32

2

Notes:

R

n 0.06

0.09

0.00

1.41

0.67

2.00

1.83

0.58

–3.90

921

5.31

62,895.45

20,308.47

–82,845.14

28,140.56

12,614.93

921

0.20

0.11

0.05

–1.90

–1.53

–1.36

2.50

0.85

–1.23

–65,816.23 –2.24

–40,417.05

6,219.63

31,314.63

–0.28

10.70

t-stat

0.99

2.64

2.50

5.91

15.34 0.24

921

0.64

–417,140.75 –2.16

–7,892.80 –0.05

614,628.25

–195,282.77 –2.32

98,035.05

356,209.91

299,604.06

–62,646.32 –5.35

–66,311.56 –0.59

11.19

dy/dx

Own home

0.34

2.28

–3.41

t-stat

5.40

2.10

–1.01

–1.72

0.49

1.03

–1.97

0.05

921

0.06

124,794.23

–58,161.18

–65,527.38

12,052.84

31,827.83

–68,632.11

–14,470.28 –0.45

1,600.19

66,101.94

–1.18

dy/dx

Lifestyle

4.00

–1.20

–2.76

t-stat

0.25

5.54

1.06

0.24

–4.45

4.68

–0.11

921

0.09

209,660.23

38,092.49

–281,458.91

311,242.66

–7,148.71

–517,598.09 –4.25

–478,104.69 –4.83

39,793.69

–114,612.77

–2.13

dy/dx

Superannuation


Net worth

19,790.82

–184,796.83

Poor health


–156,153.28 –2.22

Previously married

7,653.02

–135,329.09

Female head


295,836.50


3.48

2.84

233,387.95


0.78 1.19

83,507.76

–7.59 –6.25

15,032.82

Education

Demographics

Eligibility range

Total income

Income ($)

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

Table A1: (Average) determinants of asset portfolios: couple-headed households (marginal effects and t-statistics), Wave 2

Appendix


33

34

–105,873.15

Female

All figures are reported in constant 2006 Australian dollars.

0.09

0.17

R2

0.00 577

Note:

0.55

–2.91

–1.63

2.72

–4,306.57 –0.35

6,698.70

–18,712.71

–13,507.88

–3,824.95 –0.62

–15,949.57 –2.09

577

0.38 16.46

0.52

1.26

–873.44 –0.76

9,522.74

1.11

n

Net worth

0.32

–1.78

–0.30

–63,091.35 –0.60

–24,487.14

Never married

–1.04


–57,423.04

Divorced

0.90

28,610.62

53,554.44


1.61

–0.20

Poor health

14,296.42

–20,371.17

–7.26 –8.73

Education

Demographics

Eligibility range

Total income

Income ($)

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

2.13

t-stat

3.91

–0.20

0.50

1.92

1.45

0.34

577

0.46 27.43

–21,445.01

46,363.15

119,063.05

130,200.73

–14,413.67 –0.28

227,413.19

–21,447.46 –2.31

–61,562.60 –0.61

2.69

dy/dx

Own home

1.05

–1.66

0.63

1.85

1.15

t-stat

0.97

–0.95

0.17

577

0.09

9.23

–22,075.73 –0.70

24,004.32

–15,352.01

–54,702.34 –2.05

17,024.04

–29,681.78

1,852.11

49,323.90

0.45

dy/dx

Lifestyle

Table A2: (Average) determinants of asset portfolios: single-headed households (marginal effects and t-statistics), Wave 2

1.24

0.38

2.85

t-stat

2.02 0.74

0.29

577

0.07

110,918.66

7.44

2.00

–105,676.79 –2.53

20,874.81

–37,503.37 –0.93

58,637.61

–235,336.28 –7.21

6,172.37

23,087.13

3.01

dy/dx

Superannuation


–181,211.92

Poor health

2

Notes:

R

n 0.06

0.13

0.00

2.18

1.92

0.53

–3.90

1.10

0.66

–1.75

–0.49

2.41

–0.97

0.83

–1.30

921

5.29

65,367.16

18,646.59

–82,837.37

22,477.15

12,383.11

–55,968.68

–18,541.36

5,990.60

–2,732.47

30,370.63

–0.30

921

0.20

–0.05

0.17

–1.86

–1.00

–1.57

1.93

–1.31

1.36

4.11

0.92

–5.41

0.03

5.87

–1.81

1.03

2.02

1.12

–5.25

1.20

–0.54

10.79

t-stat

15.65 0.24

921

0.63

–435,673.06 –2.23

5,720.67

613,403.69

–159,669.81

102,187.85

289,287.38

169,365.30

–60,710.17

15,896.34

–60,899.69

11.04

dy/dx

Own home t-stat

0.05

5.51

2.10

–1.03

–1.69

0.18

1.04

–1.63

0.65

0.28

–1.25

2.21

–3.59

921

0.06

125,279.58

–60,000.70

–64,593.87

4,802.41

32,444.28

–63,909.54

32,562.81

1,319.31

–5,645.64

64,871.25

–1.27

dy/dx

Lifestyle

–0.23

3.70

–5.76

–1.34

–3.59

t-stat

2.97

–0.17

0.25

921

0.10

253,297.34

7,585.88

6.06

1.29

0.05

–284,760.50 –4.53

206,687.05

–10,610.35

–353,384.56 –2.72

–29,620.29

36,240.99

–55,743.30

–129,723.10

–2.83

dy/dx

Superannuation

Eligible to Age Pension if at least one partner is eligible. Poor health if one member reports being in poor health (see text for precise definition). All figures are reported in constant 2006 Australian dollars.

Net worth

–8,271.02

–74,296.80

Previously married


–136,404.89

Female head

28,047.56

183,975.41



–153,766.47

17,159.27

Education


48,225.06

95,380.91

–6.64

Age

Demographics

Eligibility range

Total income

Income ($)

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

Table A3: (Average) determinants of asset portfolios: couple-headed households (marginal effects and t-statistics), Wave 2


35

36

–61,220.58


2

Note:

R

n

All figures reported in constant 2006 Australian dollars.

0.11

0.18

0.00

–6,611.65

8,815.50

–22,310.79

–15,543.31

–5,592.91

7,507.86

–786.10

–2,112.07

9,617.73

0.90

577

16.39

–0.58

0.23

–1.41

–0.11

–0.51

–1.25

1.78

2.36

–0.14

–8.76

577

0.38

20,699.88

Poor health

Net worth

–84,323.42

Female

–9,183.29

–28,376.68

Divorced

Never married

–126,801.80

15,653.64

Education


18,394.52

–13,890.16

–7.12

Age

Demographics

Eligibility range

Total income

Income ($)

2.76

–0.55

0.75

–3.00

–1.81

–0.82

0.56

–0.68

–1.69

0.50

1.06

t-stat

dy/dx

dy/dx

t-stat

Business assets

Financial wealth

2.47

t-stat

27.30

–0.21

0.51

1.94

1.46

–0.23

2.16

–2.31

0.16

–0.62

0.34

577

0.46

–22,368.69

47,458.00

120,543.96

131,608.27

–11,861.32

217,518.66

–21,529.81

1,269.83

–62,651.11

3.06

dy/dx

Own home

0.18

577

0.09

–20,468.89

23,612.10

–16,828.92

–56,217.93

12,549.09

–12,641.27

1,545.43

–2,011.10

48,272.58

0.45

dy/dx

Lifestyle

9.26

–0.64

0.93

–0.99

–2.12

0.75

–0.39

0.52

–0.76

1.78

1.14

t-stat

Table A4: (Average) determinants of asset portfolios: single-headed households (marginal effects and t-statistics), Wave 2

0.31

577

0.07

110,669.81

–100,585.48

2,919.17

–50,663.73

33,281.83

–85,583.45

5,116.84

–15,541.19

18,650.95

2.70

dy/dx

7.49

2.01

–2.42

0.10

–1.22

1.10

–1.47

1.07

–3.31

0.29

2.58

t-stat

Superannuation



Endnotes 1

There are exceptions to this general rule for refugees, newcomers under special programs, or those widowed in Australia not meeting the 10-year residency requirement.

2

At the time of collection of the data used in the regression analysis discussed in this report, all females aged 63 years and over were eligible to claim the Age Pension.

3

A partnered recipient may be eligible to receive the single rate if the couple is separated because of illness (in this case, the combined assets of the couple are still used to calculate rates) or if his or her partner, who does not receive a pension, is jailed or in a psychiatric hospital.

4

If the title to the claimant’s principal residence includes less than two hectares of land, the land must be used primarily for private and domestic purposes in order for it to be included in the assets test exemption. If the title includes more than two hectares of land, the claimant must have an attachment to the land of 20 consecutive years and be using the land (if productive) to the best of their abilities so as to generate an income. Income generated from productive land is assessed under the income test if it accrues directly to the pensioner. It is not assessed if it accrues to a family member working the land.

5

Major changes to the asset test rules were introduced in September 2007. In particular, the level of pension benefits were reduced by $1.50 per fortnight for every $1,000 assets above the disregard levels.

6

Alternative recently available micro-level data on wealth include the Household Expenditure Survey and the Survey of Income and Housing. These data are not, however, longitudinal.

7

Couple-headed households include both married and cohabiting couples.

8

See Headey (2003) for a detailed discussion of wealth measurement in HILDA.

9

This paper considers the total value of all vehicles, not vehicle equity because the amount of any car loans is combined with other debts (such as other loans, hire purchase or overdraft) in the HILDA survey making it impossible to derive a measure of vehicle equity.

10

Test results are not reported in the tables but are available upon request.

11

These differences across health status are both economically meaningful (that is, relatively large) and statistically significant. Test results are not reported in the tables but are available upon request.

12

These differences are statistically significant. All 2002 figures are expressed in 2006 dollars. The ABS CPI quarterly number for September was used as deflator.

13

However, it is likely to change when all members in the 55 to 64 year-old cohort in 2006 reach pension age.

37


14

As previously mentioned, this variation could also be driven by the strong house price growth that occurred between 2002 and 2006.

15

The reported specification assumes that a household is in the range of eligibility when total household gross income is +/– 10 per cent of the relevant eligibility threshold.

16

Specifically, the estimated marginal effect of an additional dollar of wealth are required to sum to one across asset types, while the marginal effect of a change in any other independent variable is restricted to sum to zero. Note that while these constraints hold on average, they may not hold for any particular couple.

17

Marginal effects are calculated for each individual and then averaged over the relevant sub‑sample using the sample weights (see Greene 1997, p. 876). Bootstrapped standard errors (with 500 replications) are used to calculate the reported t-statistics. Following standard practice, on each bootstrap sample, we first generated our measure of permanent/transitory income, and then estimated our model of net wealth.

18

The following discussion concentrates those results, which are statistically significant, that is, those which we can be reasonably confident do not result from random chance. Results are significant at the 5 per cent level (in a two-tailed test) when the t-statistic (reported in all regression tables) exceeds 1.96. Results are significant at the 10 per cent level (in a two-tailed test) when the t-statistic exceeds 1.65.

19

This is done through a quadratic in age. The marginal effect of age reported in Tables 9 and 10 accounts for both terms in the quadratic. Accounting for age through a cubic and quartic resulted in substantially the same results.

20 The exception is that couples in which both partners are eligible for the Age Pension hold less superannuation wealth than couples in which only the head is eligible. This effect is, however, only marginally significant at 10 per cent.

References Barrett, G & Tseng, YP 2008, ‘Retirement saving in Australia’, Canadian Public Policy, vol. 34, supplement 1, pp. S177–93. Blau, FD & Graham, JW 1990, ‘Black–white differences in wealth and asset composition’, Quarterly Journal of Economics, vol. 105, no. 2, pp. 321–39. Cobb-Clark, DA & Hildebrand, VA 2006a, ‘The wealth and asset holdings of US-born and foreign‑born households: evidence from SIPP data’, Review of Income and Wealth, vol. 52, no. 1, pp. 17–42. ——2006b, ‘The portfolio choices of Hispanic couples’, Social Science Quarterly, vol. 87, no. 5, pp. 1344–63.

38


——2009, ‘The asset portfolios of native born and foreign born Australian households’, Economic Record, vol. 85, no. 268, pp. 46–59. Greene, WH 1997, Econometric analysis, 3rd edn, Prentice-Hall Inc., Upper Saddle. Headey, B 2003, Income and wealth—facilitating multiple approaches to measurement and permitting different levels of aggregation, HILDA Project Discussion Paper Series, 3/03. Headey, B, Marks, G & Wooden, M 2005, ‘The structure and distribution of household wealth in Australia’, Australian Economic Review, vol. 38, no. 2, pp. 159–75. Wooden, M, Freidin, S & Watson, N 2002, ‘The Household, Income and Labour Dynamics in Australia (HILDA) survey: Wave 1’, Australian Economic Review, vol. 35, no 3, pp. 339–48.

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40

Changes in household expenditure associated with the arrival of newborn children Jason D Brandrup & Paula L Mance Research and Analysis Branch, Department of Families, Housing, Community Services and Indigenous Affairs Acknowledgements This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) survey. The HILDA project was initiated and is funded by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). The opinions, comments and/or analysis expressed in this document are those of the authors and do not necessarily represent the views of the Minister for Families, Housing, Community Services and Indigenous Affairs or the MIAESR and cannot be taken in any way as expressions of Government policy. We thank our Departmental colleagues for their comments and/or assistance and Dr Tue Gørgens (Australian National University) for his statistical advice during the early stages of this study.

Abstract An understanding of the changed financial circumstances of families with newborn children is important to a range of current policy debates, including those surrounding the provision of family assistance, women’s attachment to the labour force and paid parental leave. Although there is a body of Australian research on the costs of raising children, in most cases this has been undertaken to enable the calculation of child support entitlement or to evaluate the effects of policy designed to reverse the effects of an ageing demographic. These studies do not report specifically on expenses associated with the arrival of newborn children. To address this gap in the evidence base, the current study investigates changes in household expenditure associated with the arrival of newborn children for three groups of families—those experiencing the arrival of their first, second, or third and subsequent-born children. Household spending items in Waves 6 and 7 (2006 and 2007) of the Household, Income and Labour Dynamics in Australia (HILDA) survey are used to estimate whether different categories of expenditure typically increase or decrease for couple families with the arrival of newborn children. This study shows that a range of expenditure categories are influenced by

41


the arrival of a new baby. Parents of first-born children increase expenditure on health care and clothing. Parents of second-born children increase expenditure on health care, and on meals eaten out and takeaway; however, they decrease expenditure on child care. Parents of third and subsequent-born children increase expenditure on health care. Keywords: household expenditure; child costs; newborn children, family policy, HILDA

1 Introduction Explicit in many family policies is the recognition of additional expenses incurred by families raising children. Many family assistance and income support programs have child-specific payments and in a number of cases higher income thresholds apply to these payments to cater for the presence of one or more children. Some payments also provide different rates of assistance for children of younger and older ages, acknowledging that children place different demands on the family budget as they progress through each life stage. There is a body of Australian and international research that has provided broad evidence to underpin policy formulation and evaluation in this field. The existing studies have mainly focused on estimating the costs of raising children in broad age ranges (that is, under school age, school age and adolescence) and examining how families allocate their household budget when children are present. In the Australian context, research has largely centred on examining the policy issues of child support and demographic ageing. For instance, studies conducted in the period leading up to the reforms of the Child Support Scheme in 2005 concentrated on estimating the costs of children to enable the calculation of child support entitlement (Gray 2007; Henman 2007; Percival & Harding 2007). Studies concerned with compensating for the effects of demographic ageing generally focused on evaluating the effects of government payments—such as the Baby Bonus1—on increasing Australia’s fertility rate (for example, see Drago et al. 2009). However, to our knowledge, no Australian studies specifically focus on analysing changes to family expenditure patterns associated with the birth of a child. The current study exploits the rich panel data in the Household, Income and Labour Dynamics in Australia (HILDA) survey to examine family expenditure patterns for three types of couple families who experienced the birth of a child between 2006 and 2007—those experiencing the birth of a first child, second child or third and subsequent child. In the first group, the research compares expenditure for the same families before and after they had children. In the other two groups, the effect of additional children can be examined. The strength of this study lies in the methodology applied and the longitudinal nature of information collected in the HILDA survey. Fixed effects linear regressions are used to analyse the impact of the arrival of newborn children, while controlling for a range of time-varying independent variables. In using this methodology we are able to exploit the potential of the longitudinal data set to reduce omitted variable bias and generate estimates that are closer to the true underlying effect. Increasing our knowledge of the demands on, and choices made by, parents of newborns is particularly important in the current policy climate. In May 2009 the government announced that 42

Changes in household expenditure associated with the arrival of newborn children

it will introduce a Paid Parental Leave scheme for new parents who are the primary carers of a child born or adopted on or after 1 January 2011.2 The scheme will prove financial assistance to parents of newborns with the dual aims of encouraging women to maintain their connection with the workforce to help prepare Australia for the challenges of an ageing population, and enabling more parents to stay at home to care for their baby full-time during the vital early months of social, cognitive and physical development.3 Although this study will not provide an estimate of total costs associated with the birth of a child, and consequently the level of financial assistance required by new parents, it will contribute empirical evidence on changes in parental expenditure behaviour occurring in a discrete window of time around the birth of a child.

2 What influences spending patterns for families with children? It is reasonable to assume that when households increase in size, there will be increased costs associated with additional consumers. However, prior studies suggest that the magnitude of these costs are not objectively based but vary according to the tastes, preferences and the amount of money that parents have available to spend on their children (McDonald 1990). In most cases, expenditure on children will need to be offset by reductions in expenditure on other household items. As such, we expect that when adults become parents, they will reallocate their financial resources to provide adequate food, clothing, shelter and education to their children. However, it is likely that the relationship between expenditure and income pre and post-children may not be as straightforward as reallocating resources. For instance, there may be changes in total household income coinciding with the birth of a child and, as a result, the amount of money available to spend. On one hand, income may decrease temporarily or permanently as mothers withdraw from the workforce to care for the new baby. On the other hand, family income may increase due to rises in fathers’ earnings due to career advancement or longer working hours, or eligibility for government payments, particularly family assistance. 4 Parents may also meet additional demands for expenditure through consumer credit. A number of studies have also found sociodemographic factors that influence how families allocate their resources, including the ages of children, marital status, geographic location, income level of parents and number of other children and adults in the family (Exter 1992; Percival & Harding 2007; Valenzuela 1999). For example, a couple whose oldest child is aged less than 6 years has been estimated to spend over 10 per cent more in annual expenditures than the average couple without children, while a couple whose oldest child is aged 6 to 17 years spends 24 per cent more (Exter 1992). While the total costs of raising children increase as the number of children increases, the average cost per child falls, noting that the relationship between family size and expenditure on children is not linear and varies according to family income (Percival & Harding 2007). When considered together, these studies indicate that, despite economies of scale being evident as the number of children increase, for most families, income necessarily constrains expenditure on children by determining the upper limit on the amount the family has available to spend (McDonald 1990; Percival & Harding 2007).

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From these studies it follows that when the patterns of expenditure for individual items are examined, expenditure is likely to vary depending on a range of demographic and contextual factors. For example, expenditure on housing may increase prior to the birth of a first child as a formerly childless couple purchase a family home, but transport costs may increase with the addition of subsequent children due to upsizing the family car and as older children start school. Although Australian studies have not specifically focused on demographic factors that influence families’ expenditure on particular items in the period following the birth of a child, a number of Australian and overseas studies contribute to our understanding of this issue more broadly. These studies are reviewed below, with particular attention to the relationship between demographic factors and resource allocation, and how these factors might influence expenditure on items collected in the HILDA survey for families with newborn children. Groceries: Australian and overseas studies consistently report that food expenditure comprises the largest share of total expenditure, followed by housing and transport (Percival et al. 2007; Valenzuela 1999). Together, these three expenditures have been estimated to make up between 60 and 73 per cent of subsistence, or basic, expenditures of a typical Australian household (Valenzuela 1999). The contribution of each expenditure item depends on family composition and sociodemographic factors. For instance, housing expenditure has been reported to exceed food expenditure for Australian couple families with young children (ABS 2006), while for low‑income American families, work status predicted whether families expended a greater proportion of income on food and housing as against transportation, personal insurance and retirement pensions (Passero 1996).5 Prevailing evidence suggests that children are ‘food intensive’ and economies of scale do not apply to food consumption in the same way as they might apply to other essential items. For example, on average, it has been estimated that one child will increase food requirements by 22 per cent, while two children will increase food expenditure by 44 per cent, when compared to food expenditure by families with no children (Valenzuela 1999). Existing studies also find evidence of income constraints, with food expenditure consuming a larger proportion of family income for lower-income families than high-income families (ABS 2006; Valenzuela 1999). These studies suggest that as most food expenditure is a necessity, rather than a luxury, it is difficult for families to make significant savings below a threshold level. Meals eaten out and takeaway: Expenditure on takeaway or food consumed away from home has been examined in several overseas studies, with a number of socioeconomic factors and income constraints evident in determining expenditure. Studies from the United States (US) cite the relevance of maternal labour force participation, marital status and education, the presence of children and ethnicity (Fan & Zuiker 1998; Kaushal, Gao & Waldfogel 2007), while in a Korean study, lifecycle stage and cultural values influenced families to spend more on education, which constrained their budget and limited expenditure on food away from home (Lee & Huh 2004). In the Australian context, Australian Bureau of Statistics (ABS) (2006) statistics show increasing takeaway expenditure associated with increasing income quintile. Clothing: Previous studies observe increased spending on clothing as a proportion of total household expenditure, as household expenditure increases overall (Percival & Harding 2007). Although a basic level of clothing may be considered essential, many clothing expenditures 44


are purchases of ‘comfort’ goods, rather than necessities (Carlucci & Zelli 1998). It follows that wealthy families have more scope to increase their expenditure on clothing over and above what could be considered as a necessity when compared to basics such as groceries, where there is an upper limit on the amount of grocery items a household can consume (Percival & Harding 2007). Expenditure on adult and children’s clothing may also be influenced by factors apart from income. For example, increasing expenditure on adult clothing has been associated with increasing labour force participation (Kaushal, Gao & Waldfogel 2007), while decreasing expenditure on children’s clothing has been associated with increasing family size as clothing is passed down from older to younger children (Valenzuela 1999). Child care: Expenditure on child care is influenced by a range of contextual factors, including mother’s employment hours, the number of children in care, service accessibility, the availability of relatives and partners, and cost (ABS 2008; Doiron & Kalb 2005; Walker & Reschke 2004). The cost of child care also largely depends on the child care setting. Most formal care6 involves a cost to parents whereas informal care, provided predominantly by relatives and friends, is mostly provided at low or zero cost. Expenditure on child care is also higher for children under school age and high-income families, generally reflecting longer average hours in care (ABS 2008). Although overseas researchers have examined factors influencing child care expenditure, it is difficult to compare studies across jurisdictions due to differing levels of government support and parental preferences for informal and formal care. For example, in contrast to expectations, Kaushal, Gao and Waldfogel (2007) found there was no corresponding increase in child care costs associated with increasing labour force attachment. This was most likely to be due to the expansion of child care subsidies in the US during the 1990s, when their study was undertaken, and reliance on relatives or other informal sources of child care. Education: Intuitively it might be expected that there would be no increased education expenditure associated with the arrival of a newborn child, although parents may reduce their own participation in education due to time pressures associated with caring for a new child. In support of this proposition, in a related study, US researchers did not find significant differences in education expenditure for couples with and without children (Exter 1992). However, expenditure differences associated with older children, family income, marital status and type of education have been observed (ABS 2006; Exter 1992). Health: In Australia the Medicare system meets a large proportion of basic health care costs7 and as such it is not surprising that studies find health expenditure comprising a small proportion of total family expenditure overall (Valenzuela 1999). Health expenditure has been reported to increase with rising income quintile (ABS 2006) and, contrary to expectations, decrease with the presence of children (Exter 1992). However, many studies compare couples with and without children and thus the higher health expenditure for childless couples reflects their older average ages. When households with children are compared, rising health expenditures are associated with increasing income, family size and couple’s marital status (ABS 2008; Valenzuela 1999). Transport: The costs of transport may impose a significant burden on the household budget. Transportation has been estimated as the second-largest spending category for American families (Exter 1992), and the third-largest spending category for Australian families (Valenzuela

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1999). Among Australian households, car ownership is a major influencing factor for transport expenditure (Saunders et al. 1998), with expenditure costs reflecting the purchase price of the car, fuel, insurance, registration, servicing and repairs. Average spending on transport tends to increase as children age; however, on average, expenditure is less for families with children than for young childless couples (ABS 2006), possibly reflecting lower workforce participation by mothers (Kaushal, Gao & Waldfogel 2007; Passero 1996). Other demographic factors have also been found to be relevant including income (ABS 2006) and lifecycle stage (Lee & Huh 2004). Housing: Demands for housing are likely to place a major strain on the family budget as a family grows in size. In fact, housing expenditure may constitute almost one-third of the before-tax income of couple families with children aged less than 6 years (Exter 1992). Although it has been estimated that a family of three needs a housing budget 38 per cent higher than that required by a two–adult childless household, researchers have observed economies of scale with increasing numbers of children, with no significant differences found for two-parent families with two and three children (Valenzuela 1999). Economies could be due to children sharing rooms or foresight by parents; the home being purchased or rented at the time of the birth of the first child had sufficient rooms to accommodate extra occupants (Valenzuela 1999). Similar to other essential expenditures, demographic factors have been observed to influence housing expenditure. For example, unemployed families apportion greater expenditure to housing when compared to employed families (Passero 1996), while expenditure tends to diminish over the life cycle as housing increases in value and capital is paid off (ABS 2006; Carlucci & Zelli 1998; Percival & Harding 2007). However, studies have also identified characteristics that set housing expenditure apart from other essential expenditures. Housing expenditure can differ markedly between households that are similar in every other aspect apart from tenure type (Carlucci & Zelli 1998). Furniture and appliances: Few studies have undertaken detailed examination of expenditure items that comprise smaller shares of total expenditure, such as furnishing and appliances. Of exception are two Australian studies. Valenzuela (1999) observed economies of scale for household furnishings with increasing numbers of children, while Percival and Harding (2007) found increased expenditure recorded in the ‘other’ category of the Household Expenditure Survey for families with young children and speculated that this might be due to expenditure on furnishings for babies. General insurance, telephone and internet: Similar to other items that comprise a small share of total household expenditure, studies tend to group the items of insurance, telephone and internet expenditure, or not treat them as the subject of the research focus (for example, see Valenzuela 1999). Conversely, research findings are difficult to apply to the Australian context given differences in cultural or policy settings between countries. For example, in a US study, increases in spending on insurance associated with labour force participation reflected US retirement savings policy, which requires working people to contribute to their own retirement pension (Kaushal, Gao & Waldfogel 2007)

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Electronic goods: Of particular interest in our study is expenditure on electronic goods. Although there are no Australian studies specifically examining family expenditure on electronic goods, the ABS (2006) reports differences associated with increasing income quintile and geographic location. However, various media reports suggest that some families spend the government’s baby bonus on electronic goods8 rather than child-related expenses incurred by the family. These reports are based on anecdotes and personal observation and have not been the subject of quantitative analysis. Data from the HILDA survey used in this study allows us to examine this spending item separately. Holidays: The discretionary nature of spending on recreation may lead to a differential pattern of holiday taking behaviour through various stages of the family life cycle that is distinct from expenditure on essential items. In an Australian study by Yusuf and Naseri (2005), the authors found income, ethnicity and family life cycle (rather than family size) influenced holiday taking behaviour. Ethnicity and the presence of an older child were positively associated with overseas travel, while the presence of a pre-school child was negatively associated with travel overseas, resulting in low overall combined expenditure on holidays for families with pre-school children when compared to other household types. Alcohol and cigarettes: The presence of children appears to move expenditure towards child‑focused goods and, as a consequence, away from adult-focused goods such as alcohol and tobacco. These behavioural changes may reflect decreasing social opportunities associated with increasing demands of childrearing, reductions in consumption for health reasons relating to pregnancy or budget restraints. Few studies have examined the reasons for such reductions; however, demographic differences in expenditure on these items have been found. For example, when compared to married parent families, cohabiting parent families tend to spend more on adult goods such as alcohol and tobacco and less on potentially child-related goods such as education (DeLeire & Kalil 2005), while declining consumption of alcohol and tobacco is associated with increasing family size (Valenzuela 1999). However, separate examination of these items using the 2004–05 ABS Household Expenditure Survey (HES) (ABS 2006) indicates that expenditure on tobacco products remains relatively constant across couple families with and without dependent children, while alcohol expenditure is markedly less for couples with dependent children when compared to childless couples.

3 Theoretical framework A review of literature formed the basis for the following hypotheses for changes to family expenditure patterns associated with the birth of a child. Firstly, we expect increased expenditure on groceries, clothing, health, housing, furniture and appliances, and transport. Expenditure on groceries and transport is likely to increase by a similar amount for families experiencing a first, second or higher order birth due to the addition of an extra consumer to the household. However, for grocery expenditure, families have some discretion in the choice of individual items that may offset some expenditure demands if families adjust their consumption in favour of baby-specific items.

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For items of children’s clothing and furniture and appliances, we expect increases in expenditure with the birth of the first child, with smaller increases for subsequent children because they are often able to use items originally purchased for their older siblings. Although we might also expect decreases in adult clothing expenditure due to women’s labour force withdrawal, overall we anticipate increases as mothers purchase maternity clothes. Consistent and sizeable increases in health expenditure associated with pregnancy, childbirth and an increasing number of health service consumers in the household are predicted. However, some health expenditure is likely to be discretionary given the choice between low-cost public and higher-cost private health systems in Australia, and thus, on average, we may observe lower increases than expected. Secondly, we expect decreases in expenditure on meals eaten out and takeaway food, housing, child care, and alcohol and tobacco. Expenditure on meals eaten out is likely to be limited by reduced opportunities for social engagement outside the home and budgetary constraints on consumer spending. However, some of these decreases might be offset by increases in expenditure on takeaway food consumed at home. A decrease in housing expenditure associated with the birth of the first child is anticipated as home purchases, relocations to larger premises and renovations are likely to have been made in the period before the birth of the first child. Further, families are likely to decrease higher mortgage payments, which may have been affordable for a childless couple, to offset demands for expenditure on child-related goods. For second and subsequent births, we expect no change in housing expenditure. Decreased expenditure on child care is anticipated due to reductions in mothers’ employment hours, while decreased spending on alcohol and tobacco is expected due to behavioural change. However, it is likely that some of the behavioural change associated with having children may have already occurred prior to the time of data collection, given that approximately three‑quarters of the mothers in the study were already pregnant at the time of pre-birth survey. Thirdly, we expect expenditure on education, insurance, telephone, internet, electronic goods and holidays to remain constant. It is possible that overall expenditure on education will decrease if parents place on hold or reduce their own participation in education with the arrival of a new child, but we expect, on average, these changes to fall short of statistical significance. Further, although intuitively we expect expenditure on holidays to decrease when a new child enters the household, expenditure may increase if families time a holiday to introduce a new baby to family and friends. We do not expect to observe evidence to support press reports that imply families with newborns spend government lump-sum payments on consumer items.

4 Method Data The data used in this study were drawn from Waves 6 (2006) and 7 (2007) of the HILDA survey, a household-based panel survey described in detail by Wooden and Watson (2007). The HILDA

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survey is a broad social and economic survey established to support research in: household and family dynamics; income and welfare dynamics; and labour market dynamics. The survey began in 2001 with a large probability sample of Australian households occupying private dwellings (ed. Watson 2009). All members of the households providing at least one interview in 2001 form the basis of the panel that was pursued in each subsequent wave. In this study, data from female HILDA survey respondents were selected for analysis if the respondents were: interviewed in both years; aged between 15 and 45 years in Wave 7; living in a couple household in both years without any other persons besides their own children; were in a married or cohabiting relationship with the same person in both years;9 and were not in a same‑sex relationship (to avoid double-counting some households). The selection process therefore excluded women who were not living with the same male partner10 in both waves and resulted in a sample of 1,621 women. The study did not use expenditure data collected in earlier surveys because only 10 of the 25 household spending categories used in 2006 and 2007 were common across all surveys. Household, respondent and partner data were incorporated into each respondent’s record. This enabled the household to be the unit of analysis because the selection process ensured that the household each respondent lived in at the time of the 2007 survey was essentially the same one in which they had lived in a year earlier. Eighty-one households in 2006 and 97 households in 2007 had missing data for women’s partners in relation to education, employment, income support and health status. These households were dropped from the models when these variables were used. Missing data for household disposable income data was imputed from income data provided in the HILDA survey datasets (Starick & Watson 2007). A total of 248 households who did not provide any household spending data in at least one of the two waves and six households with top-coded income in either 2006 or 2007 were also dropped from the study sample. The final sample was therefore 1,367 households, in which 307 newborns arrived in the twelve months leading up to either of the 2006 or 2007 surveys. One hundred and twenty-six of the newborns had no older siblings in the household (first-born), 118 had one older sibling in the household (second-born) and 63 had more than one older sibling in the household (third or subsequent-born).

Dependent variables The household spending section of the 2006 and 2007 HILDA self-completion questionnaires listed 25 types of expenses on which Australians regularly spend money. This study used the household spending items contained in the HILDA survey household file, which averaged the household spending responses if more than one person in a household provided responses (ed. Watson 2009). The 25 items were aggregated into 13 spending categories as per Table A1, with each of those spending categories being the dependent variable in 13 fixed effects linear regression models. Two other spending categories were created from household spending items found in the HILDA household questionnaire, thereby resulting in 15 dependent variables in total.11 The household spending responses in 2006 were converted to 2007 dollars by adjusting for changes in the Consumer Price Index (CPI), using CPI figures broken down into the group of goods and services most closely corresponding to the expenditure item. Table 1 presents

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descriptive statistics on the 15 dependent variables used in this study, for the households included in this study and for other HILDA survey households in 2006 and 2007. Table 1:

Average household expenditure (2007 dollars), study sample compared with other HILDA survey households(a)

Expenditure category (annual)

2006

2007

Study sample Other HILDA (n=1,367) households (n=4,742)

Study sample Other HILDA (n=1,367) households

(n=4,543)

Groceries

10,113.48*

7,732.00*

10,469.90*

7,819.10*

Meals eaten out and takeaway

2,660.41*

2,091.81*

2,807.92*

2,120.51*

Adults’ clothing and footwear

1,525.13*

1,176.84*

1,699.56*

1,246.42*

798.61*

282.06*

884.87*

255.28*

16.38*

1.79*

21.39*

1.54*

Education

1,314.89*

786.31*

1,507.81*

687.74*

Health

2,234.69

2,048.68

2,428.12*

2,058.50*

Transport

8,999.14*

6,682.58*

9,631.62*

6,137.82*

Housing

20,474.01*

10,230.11*

21,549.37*

10,082.32*

Furniture and appliances

1,614.87*

1,015.75*

1,558.54*

1,021.78*

General insurance

1,346.13*

1,077.11*

1,462.27*

1,099.17*

Electronic goods

1,069.20*

762.05*

1,334.40*

870.50*

Telephone and internet

1,901.60

1,724.50

2,140.06*

1,735.09*

Holidays and holiday travel

2,781.64*

2,384.70*

2,731.06

2,591.50

Alcohol and cigarettes/tobacco

2,300.00*

2,030.66*

2,371.76*

1,988.71*

Children’s clothing and footwear Child care (typical week)

(b)

(a) (b) Note: Source:

Excluding households that did not provide household spending responses. Not annualised as data on the number of typical weeks was not available. * Study sample different from other HILDA survey households at p