composition, distribution and correlates of the wealth holdings of Australian ..... smaller correlation than typically found in the United States, a result that probably ...
Version: Submission, October 2004 Published: Journal of Sociology 41(1), March 2005, pp. 47-68. [http://dx.doi.org/10.1177/1440783305050963]
HOUSEHOLD WEALTH IN AUSTRALIA: ITS COMPONENTS, DISTRIBUTION AND CORRELATES Gary N. Marks1,2 Bruce Headey1 Mark Wooden1 1. Melbourne Institute for Applied Economic and Social Research, University of Melbourne 2. Australian Council for Educational Research
Word Count: 6,274 (not including Tables and Notes), 7,242 (with notes). Acknowledgements This paper reports on research being conducted as part of the research program, “The Dynamics of Economic and Social Change: An Analysis of the Household, Income and Labour Dynamics in Australia Survey”. It is supported by an Australian Research Council Discovery Grant (DP0342970). The paper uses the data in the confidentialised unit record file from the Department of Family and Community Services’ (FaCS) Household, Income and Labour Dynamics in Australia Survey, which is managed by the Melbourne Institute of Applied Economic and Social Research. The authors thank all of the aforementioned organizations. We also are indebted to the tremendous efforts of Nicole Watson and Simon Freidin in their careful preparation of the data used in this paper. We wish to thank them for supervising the data collection, data cleaning, variable construction and the development of the weights. In addition, we wish to acknowledge the work of the Reserve Bank of Australia for assistance in the design of the wealth module and in data imputation. The authors take full responsibility for any errors or omissions. Further, the findings and views reported in the paper, however, are those of the authors and should not be attributed to FaCS, the Reserve Bank of Australia or the Melbourne Institute.
HOUSEHOLD WEALTH IN AUSTRALIA: ITS COMPONENTS, DISTRIBUTION AND CORRELATES
Abstract Using data from the second wave of the Household, Income and Labour Dynamics in Australia (HILDA) Survey, conducted in 2002, this article provides information on the composition, distribution and correlates of the wealth holdings of Australian households. The survey results indicate that Australian households have an average net worth (or wealth) of just over $400,000, comprising assets of $473,000 and debts of $68,000. The largest component of wealth is home equity. The degree of inequality across households in wealth inequality is found to be much larger than the inequality in income and varies substantially with age and, to a lesser extent, with household type and education. Age, socioeconomic background, educational attainment, marital status and the number of children can account for about 30 per cent of the variation across households in (logged) wealth.
Keywords: Australia, correlates of wealth, education, HILDA Survey, inequality, marital status, wealth
HOUSEHOLD WEALTH IN AUSTRALIA: ITS COMPONENTS, DISTRIBUTION AND CORRELATES Introduction Wealth and its distribution are important in understanding modern societies. Wealth is an obvious indicator of position in the social structure, and almost certainly a superior indicator to income-based measures (Podder and Kakwani 1976), but is not usually incorporated in existing concepts or measures of social location (Adair 2001; Sorensen 2000). It is almost certainly closely associated with financial security, poverty, and consumption behaviour, and is an important dimension of social inequality. Indeed, research has consistently demonstrated that the distribution of wealth is far more unequal than the distributions of earnings or income (Juster and Smith 1997; Keister 2003b; Rodríguez, et al. 2002). Furthermore, there are substantial inequalities in wealth that relate to gender, race and ethnicity (Keister 2000; Straight 2001; Warren and Britton 2003; Warren, et al. 2001). These inequalities are likely to have consequences for a range of social outcomes including wellbeing, marital formation and dissolution, retirement and health (Dahl, et al. 2003; Huie, et al. 2003; Mullis 1992; White and Rogers 2000). Finally, wealth may be important in the reproduction of social inequality; it may contribute substantially to children’s educational attainment (Conley 2001; Orr 2003) or be used directly to provide employment and business opportunities for the next generation (Robinson 1984). Data on the wealth of Australian households, however, is limited, and as a result, relatively little research had been conducted on wealth in Australia. While aggregate statistics on wealth holdings have been compiled for some time and are now regularly reported as part of the National Accounts, recent estimates of the distribution of wealth across households have generally been imputed from survey data on income flows. Wealth data collected directly from households is rare. In 2002, however, the Department of Family and Community Services, in association with the Reserve Bank of Australia, funded the inclusion of a wealth module in wave 2 of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. This paper uses these data to describe the components, distribution and correlates of wealth in Australia. More specifically, this paper presents information on: the assets, debts and net worth (wealth) of Australian households; the contribution of housing and other property,
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business and farm assets, investments, bank accounts, vehicles and other assets to total assets; and the proportions of debt attributable to housing, businesses and farms, the Higher Education Contribution Scheme (HECS), credit cards and other forms of debt. This paper also provides estimates of the amount of realizable wealth of Australian households; that is, wealth not held in the form of home equity or superannuation. Finally, and like previous Australian studies of household wealth, this paper presents information on the size of correlations between household wealth and a range of demographic and socio-economic characteristics. The analysis presented here, however, both considers a wider range of correlates of wealth and estimates the size of these relationships with wealth after taking into account associations with other correlates.
Previous Research Many readers might be surprised to learn that the first serious attempt at collecting wealth data from Australian households occurred as long ago as 1915. Moreover, this collection was based on data collected not from a population sample but from a census – the 1915 War Census conducted by the Commonwealth Bureau of Census and Statistics (see Soltow 1972). Since that time there has been only one other significant attempt at collecting wealth data directly from Australian households – a university-based survey conducted over the period 1966-1968 (see Podder and Kakwani 1976). The relatively small sample size – just 2757 households – combined with high rates of non-response, however, has meant that relatively few researchers have attached much weight to the results from this survey. 1 Since that time we are unaware of any other survey-based study that has, with the exception of housing, attempted to directly measure the wealth holdings of individual Australian households. Instead, the dominant approach has been to estimate the size of different wealth components based on the size of the income flows generated by different assets. 2 Dilnot (1990), for example, used information from the 1986 Income Distribution Survey (IDS) conducted by the Australian Bureau of Statistics (ABS) to derive estimates for households of investment income. Combined with estimates of net housing wealth collected directly from households in the IDS, he was then able to derive an estimate of the distribution of household wealth. This approach has been extended by researchers at the National Centre for Social and Economic Modelling (NATSEM) who have provided more recent estimates of the distribution of wealth using both a more complete list of assets (notably the addition of superannuation) and more sophisticated methods for deriving the rate of return to different
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assets (see Bækgaard 1998a; Bækgaard 1998b; Kelly 2001). Most recently, the ABS, using a variety of data sources, have developed household-level estimates for wealth for the period 1994-2000 which seemingly cover all forms of assets and debts held by households, rather than just a subset (ABS 2002; Northwood, et al. 2002). There are thus two groups of recent studies on the distribution of wealth in Australia; the NATSEM studies covering data collected in 1986, 1993 and 1998, and the ABS 1994-2000 series. Estimates within each series are comparable but there are significant definitional and measurement differences between the two groups of studies. The main conclusions from these previous studies are threefold. First, the major component of household wealth is housing, with previous research indicating that approximately 40 to 55 per cent of either wealth or assets is held in the form of property. The significance of housing in total wealth, however, does appear to be declining. Kelly (2001), for example, estimated that owner-occupied housing constituted 43 per of wealth in 1998, compared to 49 per cent in 1986. The ABS studies, on the other hand, suggest a smaller decline. Dwellings — both owner-occupied and rental investments — were estimated to account for 48 per cent of total assets in 1994 and 46 per cent in 2000 (Northwood et al. 2002: 27). Other major components of wealth are superannuation, businesses and farms, bank deposits, and shares and other investments. According to Kelly (2001), 22 per cent of total household wealth in 1998 was held in superannuation funds, 12 per cent in business and farms, 9 per cent in interest bearing deposits, 8 per cent in shares and other investments, and 6 per cent in rental properties. The most significant change since 1986 was the increase in the proportion of wealth held in superannuation, which increased from 14 to 22 per cent. Second, the distribution of wealth in Australia is very unequal. The richest 5 per cent own about 30 per cent of wealth (Bækgaard 1998b; Kelly 2001), while the top decile’s share is around 45 per cent (Bækgaard 1998b; Kelly 2001; Northwood et al. 2002: 39). In contrast, the bottom three deciles have no wealth at all. Indeed, the bottom decile often shows negative wealth — that is their debts exceed their assets (Bækgaard 1998a; Northwood et al. 2002:122). The Gini coefficient 3 — a standard measure of income inequality — is around 0.64 for wealth (Kelly 2001: 16) compared to between 0.30 and 0.45 for income. 4 While inequality may be high, both the NATSEM and ABS studies suggest no increase in wealth inequality since the mid-1980s. Further, wealth inequality has probably declined since 1915 when the wealthiest 5 per cent of households owned two-thirds of total wealth and the wealthiest 20 per cent of households owned 90 per cent (Kelly 2001: 2; Soltow 1972). The
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Gini coefficient in 1915, for example, was 0.86, which compares to about 0.64 in 1996 and 1998 (Kelly 2001: 2; Soltow 1972). It is important to note, however, that unlike the 1915 data, the more recent estimates come from sample surveys, and in most cases the sample sizes are insufficient to adequately represent the most wealthy (see Juster and Smith 1997). In other words, the true Gini is likely to be greater than that calculated from most sample surveys. Third, the level of household wealth is associated with various demographic and household characteristics. Arguably the strongest relationships found have been with age. This no doubt reflects lifecycle processes wherein households accumulate wealth as individuals and couples enter the housing market and subsequently increase their home equity. Furthermore, the value of superannuation and other investments generally increase with time. Thus net worth has been found to be relatively low among 15 to 24 year olds, but increases substantially with each successive age cohort before peaking in the 55 to 64 year old cohort, after which it declines (Bækgaard 1998a: 28; Northwood et al. 2002: 30). This latter finding reflects the impact of retirement – retirees are both less able to accumulate wealth and more likely to use their assets to fund consumption. Bækgaard (1998a) also presented evidence to indicate that the association between wealth and age strengthened between 1986 and 1993 as the incidence of home ownership among younger cohorts declined. Wealth has also been shown to vary according to household type, with couples with children aged 15 to 24 showing the highest average net worth, and lone parents with young children showing the lowest levels of wealth (Bækgaard 1998a: 28; Northwood et al. 2002: 30). Single males accumulate more wealth than single females, although at most ages the difference is not large (Kelly 2001). Kelly (2001: 22) showed that the higher wealth of couples compared to other family types occurs across all ages. Couples generally accumulate more wealth than single people since they have two incomes to service mortgages and contribute to superannuation (Juster, et al. 1999). They also may have assets prior to partnering. Surprisingly, across the lifecycle, couples with children accumulate similar amounts of wealth to couples without children (Kelly 2001: 23). This suggests that children do not substantially reduce wealth, implying that families adjust their income and expenditure patterns to overcome the costs of children. Not unexpectedly, wealth has also been found to be correlated with income. After all, the amount of money than can be saved or invested is largely determined by household income. (Bækgaard 1998a), for example, found that in the early 1990s the average wealth of the top
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income decile was five to six times that of the bottom three deciles. The distributions of income and wealth, however, are quite different, with only small increases in wealth across the bottom five income deciles (Northwood et al. 2002: 33). The association between income and wealth is further reduced by substantial numbers of asset-rich but income-poor households which Bækgaard (1998a) suggests comprise the self-employed and retirees. Overall, the correlation between income and wealth is far from perfect. In the United States the correlation between wealth and income is about 0.5, which is augmented by the inclusion of asset income (generated by wealth) in the measure of total income (Keister and Moller 2000). This correlation could even be lower in Australia, since more wealth is held in the form of owner-occupied housing. Finally, level of education has also been found to be associated with wealth, especially in the United States (Juster et al. 1999; Keister 2003a, 2003b). In Australia, less work has been conducted on the relationship between wealth and education. Kelly (2001: 24-25), however, reported that degrees and diplomas were associated with greater wealth, while vocational qualifications appeared to make little difference among most age groups. Differences in wealth according to education were generally small among younger cohorts but substantial among older age groups.
Data and Measures The HILDA Survey The data used in this article come from the second wave of the Household, Income and Labour Dynamics in Australia (HILDA) Survey, a longitudinal survey of households focusing on the interactions between the labour market, families and social welfare. The survey commenced, in 2001, with a two-stage probability sample. In the first stage 488 Census Collection Districts (CDs), based on 1996 Census boundaries, were randomly selected. Within each CD, all households (approximately 200 to 250) were enumerated and 22 to 34 dwellings randomly selected. Personal interviews were attempted with all household members aged 15 years and over. In wave 1, interviews were obtained at 7,682 households, which represented 66 per cent of all households identified as in-scope. This in turn generated a sample of 15,127 persons eligible for interview, 13,969 of whom were successfully interviewed. 5
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In 2002 all responding households from wave 1 were re-contacted. Sixty-eight households were out of scope due to death or moves overseas and there were an additional 712 households arising from changes in household composition. A total of 8,326 households were, therefore, in-scope for wave 2. Interviews were obtained from members of 7,245 of these households, giving a household response rate of 87 per cent. Interviews were again sought with all household members aged 15 or over, including persons who did not respond in wave 1, as well as any new household members. In total, interviews were completed with 13,041 persons. Of this group, almost 12,000 were respondents from wave 1, which represented almost 87 per cent of the wave 1 individual sample. 6 The data include weights to adjust for the probability of selection and differences in the distributions of benchmark variables between the sample and population (Watson and Fry 2002). Weights were also developed to adjust for differential attrition by respondent characteristics. A model of response/non-response was estimated and the inverse of the probabilities of response were included in the weights so that respondents who responded with characteristics associated with non-response received larger weights (Watson 2004b). All estimates presented in this paper are weighted estimates. The Measurement of Wealth A special feature of the wave 2 survey instruments was the inclusion of a large number of questions seeking information on household wealth. The intent was to derive a comprehensive measure of the total net worth position of all households as well as measures of all major wealth components. During testing, however, it was determined that it was not feasible, at least within the time available, to obtain a reliable measure of home contents. Vehicles are thus the only type of consumer durable covered. Questions covering housing, unincorporated businesses, equity-type investments (e.g., shares and managed funds), cash-type investments (e.g., bonds and debentures), life insurance policies, vehicles and valuables (e.g., jewellery, art works) were asked at the household level and answered by one adult on behalf of the entire household. Questions about superannuation, bank accounts, credit cards, HECS debt and other personal debt, however, were asked directly of individuals. For most questions, respondents were asked to provide exact dollar amounts. However, bands were offered to those who could not provide an exact estimate of the value of their superannuation. 7
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Wealth is difficult to measure in surveys and, when it has been attempted overseas, has been associated with high item non-response rates (Ripahn and Serfing 2002). High levels of item non-response reflect both the perceived sensitivity of wealth information and the difficulty many respondents have in providing estimates of some components, especially in a survey where there is limited time to refer to records. The HILDA Survey was not immune to this difficulty. Specifically, despite item response rates on most wealth components of over 90 per cent, total household wealth could only be directly computed for 61 per cent of wave 2 households (4,399 households). This is potentially a serious problem given the strong likelihood that omitting cases with missing data would introduce a bias against larger and wealthier households. For example, the likelihood of having complete wealth data from a two-adult household is clearly lower than that from a single-adult household. The chances of obtaining complete wealth data are further reduced in households with three or more adult members. Furthermore, wealthy individuals are more likely to provide incomplete wealth data since they have more diverse asset portfolios. Missing wealth data for households were thus imputed. The imputation procedure involved modelling wealth and its components using the data collected about respondents to identify a nearest neighbour (i.e., a household with characteristics most similar to the household with missing values). The responses provided by this nearest neighbour were then used to replace the missing values. 8 In total, the data set analysed here contains complete wealth data for all 7,245 households interviewed at wave two, but with data imputed to some extent for 2,846 of these households. 9
Results Wealth and its Components As reported in Table 1, according to the HILDA Survey data the average net worth, or wealth, of Australian households in 2002 was about $404,000. Median wealth, however, was, at $218,000, close to half this amount. The wealth of the richest quartile was greater than half a million dollars and the bottom quartile’s wealth was below $55,000. Wealth comprised, on average, $473,000 in assets and $68,000 in debts. Median assets and debts were $288,000 and $10,000, respectively. Twenty-five per cent of households had assets exceeding $583,000 and the bottom quartile had assets of less than $83,000. Twenty-five per cent of households had debts greater than $87,000 and at least 25 per cent had no debt. 10
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In line with previous estimates, the major component of wealth is the home, or principal place of residence. Home equity is estimated to represent 41 per cent of total net worth, while the value of the home comprises 44 per cent of total assets. If ownership of other property is included, then 54 per cent of total assets are in real estate. The proportion of assets in superannuation was around 16 per cent, businesses and farms 9 per cent, shares and other investments 7 per cent, bank accounts 5 per cent, and vehicles 4 per cent. ‘Other’ assets make up only 5 per cent of total assets. 11 [Table 1 about here] Table 1 also provides the first hint of how unequally household wealth is distributed. In particular, the only assets held by most households are the home, superannuation, a car and bank accounts, and apart from the home, the value of these asset holdings is generally quite small. The median value of superannuation, for instance, is just $18,000. Further, only a relatively small proportion of households (less than 25 per cent) hold any wealth in the form of investment properties, shares and other forms of investment, or assets held in unincorporated businesses. These tend to be types of assets that only the wealthiest households hold. Although an average household wealth of $400,000 appears high, most wealth is not realizable. That is, the high proportion of assets tied up in the family home cannot be transformed into substantial amounts of cash unless the household is downsizing or moves to a much less expensive location. Similarly, superannuation cannot be accessed until at least 55 years of age and is intended to cover living costs from retirement until death, which may be a period lasting anywhere up to 50 years. To provide an indication of how much realizable wealth Australians hold, liquid assets was calculated as net worth minus home equity and superannuation. On average, Australian households hold only $160,000 in liquid assets. This mean is inflated by the small number of households with very substantial liquid assets. The median household holds only $32,000 in liquid assets and 25 per cent of households have less than $7,500 in liquid assets. The largest component (57 per cent) of debt is debt on the home or principal place of residence. Furthermore, about three-quarters of all debt is property debt. The median household has no home or property debt and 25 per cent of households have more the $50,000 in debt on the principal place of residence. The other components of debt are relatively minor; 10 per cent is business or farm debts, 2 per cent is HECS debt, between 1
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and 2 per cent is credit card debt and ‘other’ debt contributes about 12 per cent of all debt. However, most households have no business or farm, HECS, credit card or ‘other’ debt. The Distributions of Assets, Debts and Wealth The distribution of assets, debts, wealth and liquid assets across the corresponding deciles is very unequal (see Table 2). The 10 per cent of households with the most assets own 42 per cent of all assets and the top three deciles own 73 per cent of assets. For debts, the distribution is even more unequal, with 10 per cent of households owing 55 per cent of total household debt. The 20 per cent of households with the most debt owe three quarters of all debt. The Gini coefficients reflect the much greater inequality in debts than assets. [Table 2 about here] In terms of net worth, Table 2 indicates that the top decile owns about 45 per cent of total wealth. Remarkably, this estimate is the same as that calculated from both the ABS and NATSEM studies. Moreover, the average wealth of these households is $1.8 million. Going slightly further down the wealth distribution we see that the top two deciles own about 63 per cent of total wealth and the top three deciles just over 75 per cent. Consistent with previous studies, the wealth holdings among the 30 per cent of households that are the least wealthy is close to negligible. The Gini coefficient is 0.62, very close to previous estimates, thus providing further evidence that wealth inequality in Australia has not been increasing over time. Liquid assets are more unequally distributed than wealth. The top decile owns two-thirds of liquid assets and these households have, on average, over one million dollars in liquid assets. The average level of liquid assets held by households in the second decile is only a quarter as much. Together the top two deciles hold 80 per cent of all liquid assets and the top three deciles 90 per cent. Consequently, the Gini coefficient is very high, at 0.80. As noted earlier, survey-based estimates of wealth inequality are likely to be underestimates since relatively few of the extremely wealthy, who own a vastly disproportionate share of total wealth, are selected in most sample surveys. Juster et al. (1999) compared wealth estimates from the Panel Survey of Income Dynamics (PSID), a survey similar to the HILDA Survey, with the Survey of Consumer Finances (SCF), which specifically oversampled the top one per cent of income earners. They found that the wealth of the top percentile was ten times lower in the PSID than in the SCF. In order to provide an estimate of wealth inequality assuming that HILDA has underestimated the wealth of the top one per cent by a factor of
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ten, sensitivity analysis indicated that the proportion owned by the wealthiest decile would be 74 per cent and the Gini 0.82. If HILDA has underestimated the wealth of the top percentile by a factor of five — more plausible since the top percentile in Australian is unlikely to be as wealthy relative to the rest of the population as the top percentile in the United States 12 — the proportion owned by the wealthiest decile would be 63 per cent and the Gini 0.75. Correlates of Wealth The first step in investigating the correlates of wealth is to examine the simple bivariate correlations of wealth and liquid assets with age, education, income, occupational status and father’s and mother’s occupational status. 13 A reference person was selected to link personal characteristics with the wealth and liquid assets of the household. Generally, the highest income earner was selected and in cases where couples had the same incomes (for example, pensioners and small business people), the oldest member of the couple was selected. These correlations should be understood as only providing a guide to the relative strength of association of these factors with wealth. Analyses presented later indicate that for some factors, such as age and education, there is not a simple correspondence. Table 3 presents the Pearson correlations. [Table 3 about here] Not surprisingly, the strongest correlate with wealth is household income at 0.43. This is a smaller correlation than typically found in the United States, a result that probably reflects the larger contribution of housing and smaller contribution of investments to wealth in Australia. The correlations of wealth with individual income and labour earnings are substantially lower. Similarly, occupational status and education have only moderate to low correlations with wealth. 14 Age is also correlated with income, especially when the population limited to those in paid employment. The relationship with both father’s and mother’s occupational status is relatively weak, each with correlations of less than 0.10. Note that this does not necessarily mean that that there is very little intergenerational transfer of wealth — this issue cannot be explored with the HILDA data at present. 15 Table 3 also shows that the correlations of liquid assets with these same characteristics are usually much weaker than those for wealth. The next step in the investigation of the correlates of wealth is to examine the bivariate relationships of wealth and liquid assets with age cohort, household type and education using categorical rather than continuous measures.
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Consistent with previous analyses, wealth rises substantially with age and then tapers off with retirement (Table 4). This is the same pattern as observed in previous studies of wealth in Australia. The youngest cohort between 18 and 24 years old have a mean wealth of $41,000 and a median wealth of only $8,000. In contrast, the comparable figures for 55 to 65 yearolds are $685,000 and $444,000. Liquid assets show much the same pattern across age groups; the 55 to 64 year-old cohort has the highest level of liquid assets and 18 to 24 yearolds the lowest. [Tables 4 to 6 about here] Wealth also varies according to household type. Couples with children aged 15 years or older show the highest levels of wealth (Table 5). Next are couples without children followed by couples with children younger than 15. Lone parents and single people have substantially lower amounts of wealth than couples with or without children. Differences in wealth according to household type are smaller than differences between age cohorts. These findings are consistent with previous analyses of wealth by household type (e.g., Kelly 2001). The level of liquid assets is lower across all household types and there is greater inequality. The median amount of liquid assets among couples with older children is five times that of single people and nearly nine times that of lone parents. This finding is further indication of even greater inequality in liquid assets than in wealth. Wealth and liquid asset holdings are also closely related to the level of education of the household reference person. Those with post-graduate qualifications have a mean wealth of $693,000 and a median wealth of $460,000 (Table 6). In contrast, the mean and median wealth of those who did not complete school was only $316,000 and $159,000. The final analysis presented in this paper examines the independent relationships of wealth with age, socioeconomic background, educational background and household type. Identical analyses were performed on liquid assets in which the pattern of relationships were similar but weaker (as was the case for the correlations) so are not reported here. Current characteristics such as income, job earnings and occupational status were not included since they are contemporaneous with the measure of wealth. Separate analyses were performed for men and women since there may be gender differences in the relationships between wealth and education and marital status. The man or woman with the highest income in the household who was not living with a parent was selected to link individual characteristics with household wealth. Households with zero or negative wealth
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were excluded since preliminary analyses indicated that households with high levels of negative wealth were not too dissimilar from households with substantial amounts of wealth. Their inclusion often produces inexplicable relationships. A total of 339 households (about 5 per cent) were excluded. Wealth was logged to overcome its very skewed distribution. Therefore, the coefficients can be interpreted as percentage effects. 16 Age is specified by both linear and square terms to take into account the decline in wealth among older age groups. It was centred at 45 years old and divided by 10. Thus 45-year-olds were recoded to zero, 55 year olds to 1, and 35 year olds to -1. Socioeconomic background was measured by a combination of father’s and mother’s occupational status at age 15. 17 It was centred at its mean and divided by 10. Highest educational qualification was entered as a categorical variable since it does not have an entirely ordinal relationship with wealth. Household type was split into two variables, marital status and number of children ever had, so that the independent effects of each can be assessed. Since the relationship between wealth and the number of children is not expected to be linear (for example, the first and second child should reduce wealth more than a third, fourth or subsequent child), the square root of the number of children was used. 18 The model comprising age, socioeconomic background, educational qualifications, marital status and number of children accounts for over 30 per cent of the variation in logged wealth. 19 All have statistically significant relationships with wealth. Focusing on the intercepts, the estimated average wealth of single 45-year old men, whose highest educational qualification was school completion, was $139,000. For comparable women, their estimated wealth was substantially lower, at $82,000 (Table 7). [Table 7 about here] According to this model, the wealth of 55-year-old men is about 50 per cent higher than that of 45-year-old men. Because of the curvilinear relationship between wealth and age, the wealth of 65-year-old men is only about 55 per cent greater than that of 45-year-old men. The estimated wealth of 35-year-old men is about 52 per cent less that of 45-year-old men. The estimated wealth of 25-year-old men is approximately 83 per cent lower than that of 45-yearold men. Very similar age differences were found for women. Thus, there are substantial age differences in wealth independent of education and martial status. Socioeconomic background had only small effects on wealth. A ten point difference in parental occupational status — on a zero to one hundred scale — was associated with a 4 per
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cent difference in wealth. This result confirms the analyses presented in Table 3 that there is not a strong association between wealth and socioeconomic background. Larger effects on wealth were found for educational qualifications. Relative to school completion, post-graduate qualifications increased wealth by nearly two-thirds. The returns to bachelor degrees were less but also substantial. Bachelor degrees were associated with 43 per cent greater wealth for men and nearly 60 per cent greater wealth for women relative to Year 12 completion. The returns in wealth to diplomas were comparable to that for degrees. Certificates and advanced certificates did not increase wealth compared to school completion. Indeed, among men, certificates were associated with about 25 per cent less wealth. Not completing school was associated with nearly 40 per cent less wealth. Therefore, differences in wealth according to education are substantial when taking into account, age and marital status. Furthermore, there are gender differences in the relationship between highest educational level and wealth. Marriage was associated with 200 per cent more wealth among men and over 600 per cent more wealth among women. The gender difference here can be attributed to the relatively higher earnings and longer attachment to the labour force of women’s spouses; that is, men. De facto relationships are also associated with increased wealth, again to a greater extent among women than men. Marital separation had no effect, presumably because assets had yet to be divided up. Divorce, on the other hand, was associated with lower wealth among men but higher wealth among women. 20 Divorced men had, on average, 27 per cent less wealth than single men, and divorced women 50 per cent more wealth than single women. Widows and widowers tended to have substantially greater wealth than single people, even when taking into account age. Although, not apparent from analyses of household type, children were associated with less wealth. Among men, one child decreased wealth by about 20 per cent, 2 children by about 30 per cent and 3 children by about 40 per cent. Among women, children had a larger impact, decreasing wealth by 32 per cent for one child and by over 40 per cent for two children. Although age, socioeconomic background, educational qualifications, marital status and children all have significant relations with wealth, these and other factors account for little of the inequality in wealth. After taking into account age differences in wealth, the Gini coefficient declines, but only slightly, from 0.62 to 0.60. Adding educational qualifications further reduces remaining inequalities in wealth a little more, the Gini declining to 0.59. A further decline (to 0.57) occurs when marital status and children are taken into account. After
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controlling for a range of factors including household income, the Gini coefficient remains very high at around 0.54. 21 Therefore, most of the inequality in wealth cannot be explained by these factors and thus implying there there is substantial variation in wealth within age, educational, marital, income and other groups. 22 In turn, such variation implies that other factors are important; for example, work and marital histories, saving, spending and investment behaviour, and to some extent luck.
Conclusions The average wealth of Australian households is substantial. However, most of this wealth is tied up in owner-occupied housing and superannuation, and the very unequal distribution of wealth means that most households have little in the way of realizable assets. On a more positive note, age and education are more strongly associated with wealth than socioeconomic background. Furthermore, comparison of these findings with estimates from surveys conducted in the 1980s and 1990s suggests that wealth inequality in Australia is not increasing. However, there are reasons to be concerned that wealth inequalities may increase in the future given younger cohorts are delaying, or not participating in, marriage and home ownership. If they are saving and investing their money then they should accumulate wealth, but if they are spending most of their earnings they will be considerably less wealthy than older cohorts. The wealth data from the HILDA Survey provide opportunities for further sociological research. For example, it is not known how inequalities in wealth in Australia compare to that in other industrialized countries. Wealth inequality in Australia may be substantially greater than in countries with more generous welfare provisions. Alternatively, the high level of home ownership may be responsible for lower inequality in Australia compared to some other countries. The relationship between wealth and poverty is another understudied area. Wealth adds an important component to the study of poverty, which generally relies on measures of relative household income. It may be the case that the level of poverty in Australia is over-estimated if a substantial proportion of low-income households have substantial assets. Alternatively, there may be substantial numbers of households with incomes just above the poverty line but with negative or very little wealth. Future research could also examine how wealth inequalities vary with gender, race and ethnicity. Detailed examination of gender differences in wealth may show where the gender differences are greatest, the main factors responsible and whether or not gender differences in wealth are
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decreasing. Similarly, research could examine ethnic differences in wealth and the extent to which such differences can be attributed to educational qualifications, labour force experience and other factors. The strong association between wealth and marital status found in this investigation could be the basis for investigating the role of relationship formation and dissolution on wealth accumulation. Finally, Australian research on wealth could also investigate the consequences of wealth and its role in social reproduction.
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TABLES Table 1: Means, Medians and Quartiles of Wealth and Its Components
Mean
1st Quartile
Median
3rd Quartile
Total assets Total debts
472,840 68,499
288,000 10,000
83,000 0
583,000 87,000
Wealth (net worth)
404,341
218,500
54,600
498,928
Home equity
166,861
105,000
0
250,000
Liquid assets
160,426
32,426
7,500
132,500
Property value (All) Home value
256,191 205,773
180,000 160,000
0 0
350,000 300,000
Other property value
50,418
0
0
0
Superannuation
77,054
18,000
300
74,500
Business/farm value
44,895
0
0
0
Shares and other investments
31,382
0
0
8,500
Bank accounts
21,548
4,500
835
16,500
Vehicles value
18,958
12,000
3,800
25,000
Other assets
22,812
0
0
6,000
Property debt (All) Home property debt
51,402 38,912
0 0
0 0
70,000 50,000
Other property debt
12,490
0
0
0
Business debt HECS debt
6,808 1,310
0 0
0 0
0 0
Credit card debt
1,004
0
0
500
Other debt
7,975
0
0
4,000
See Note 10.
17
Table 2: Distribution of Assets, Debts Wealth and Liquid Assets
Assets Decile Bottom 2
Mean
Debts Share
Mean
Net worth Share
Mean
Liquid assets
Share
Mean
Share
3,316 24,662
0.1 0.5
0 0
0.0 0.0
-5,689 14,604
-0.1 0.4
-18,337 1,510
-1.1 0.1
3 4
82,954 164,317
1.8 3.5
0 596
0.0 0.1
54,703 114,296
1.4 2.8
7,478 15,348
0.5 1.0
5
245,041
5.2
5,608
0.8
181,035
4.5
26,067
1.6
6
334,742
7.1
17,498
2.6
263,233
6.5
42,516
2.7
7 8
442,007 587,641
9.3 12.4
44,273 87,886
6.5 12.8
365,321 503,273
9.0 12.4
72,678 134,325
4.5 8.4
9
852,137
18.0
149,541
21.8
739,415
18.3
262,535
16.4
Top
1,991,577
42.1
379,587
55.4
1,813,216
44.8
1,060,134
66.1
Gini
0.59
0.76
0.62
0.80
Table 3: Correlations of Age and Socioeconomic Characteristics with Household Wealth and Liquid Assets
Correlation with:
Characteristic of household reference person
Wealth
Liquid assets
Age Age (workers only)
0.17 0.32
0.09 0.18
Education Household income
0.14 0.43
0.07 0.39
Income (financial year)
0.36
0.31
Disposable income (financial year)
0.37
0.33
Earnings (main job)
0.18
0.10
Occupational status (ANU 4)
0.17
0.08
Father’s occupational status
0.07
0.05
Mother’s occupational status
0.07
0.05
Note: All correlations statistically significant, P
