RDP 2007-05: Labour Force Participation and Household Debt 4. The Data

Data are sourced from the first five waves of the HILDA Survey (Release 5.0).[7] Along with detailed information on employment, income, housing and housing wealth available in each wave, Wave 2 contains a detailed module on households' holdings of assets and outstanding debts. The survey is broadly representative of the Australian population and population weights are available to correct for the most obvious differences. See Goode and Watson (2007) for information on sampling, response rates and attrition.

In this paper, the LFP of women and men are analysed separately due to their distinct labour supply patterns.[8] Full-time students and the self-employed are excluded as their labour market attachment is likely to be influenced by different factors from those which affect the general population. For the cross-section, the sample is restricted to those aged between 25 and 50 years; this excludes those approaching retirement age, whose participation decision might be influenced by additional factors such as asset accumulation for retirement, health and so forth. This leaves a sample of 2,999 women and 2,568 men, after removing those with missing data.

For the panel analysis, an unbalanced panel of individuals who responded to the survey in at least two waves is used. To match the selection of those aged between 25 and 50 years in the cross-section, the panel sample includes individuals aged between 24 and 49 years in wave 1 (in 2001) and progresses through to those aged between 28 and 53 years in wave 5 (in 2005). A similar selection criterion was used in Booth and Wood (2006). This leaves a sample of 3,350 women and 2,822 men, after removing those with missing data. Approximately 86 per cent of women in the sample were present in at least three waves, and 50 per cent were in all five waves. For men, the figures are 85 per cent and 48 per cent respectively.

4.1 Description of Variables

Demographic variables relevant to life-cycle considerations and human-capital are likely to be important influences on LFP decisions; these are described in Appendix A. Labour income – earned through wages, salaries or business – is not included as an explanator since the wage offer is not observed for those who are not working. However, each individual's potential wage can be captured through the set of individual characteristics in the model (such as education and labour force history).

While the non-labour income variables are also outlined in Appendix A, the family tax benefit variables (FTB A and FTB B) warrant clarification. For each individual, the family tax benefit that would be due to the household if they did not work is imputed.[9] The rationale for constructing these potential benefits is to account for an individual's basic reservation wage – that is, the income that they could expect to receive from the government given their family characteristics if they were not working.[10]

For the panel, only owner-occupied mortgage debt is available. Detailed data on debt (and assets) were only collected in one year of the survey, 2002, and are used for the cross-section analysis. For the cross-section, the vector of Debts includes the owner-occupied mortgage debt and other debt of that individual's household. Other debt combines debts on investment properties, credit card debts, HECS, car loans, overdrafts and other personal loans. Statistical tests show that these variables can be combined.

Debts are included in three ways. First, each debt variable is specified in levels. Second, because the ability to pay is likely to be important for LFP, the debt-to-income ratio is also included for both the owner-occupied mortgage and other debt. Finally, since data on yearly repayments on the owner-occupied mortgage are available, these are included as a ratio of household income (excluding the labour income of the individual). This variable is described as the debt-servicing ratio.

It is important to note that household income used in the denominator of both the debt-to-income ratios and the debt-servicing ratio excludes the labour income of the individual but includes the partner's labour income. Intuitively, these ratios provide a guide as to whether the household can or cannot service their debt under the scenario that the individual does not work. For those with no debt, these ratios are set to zero. For those with no household income (exclusive of the labour income of the individual), the ratio is set equal to the numerator (which is debt or repayments depending on the ratio in question).[11] Throughout the paper, when we refer to household income we are referring to this measure, that is, household income excluding the labour income of the individual but including the partner's labour income.

The square-root of the debt-servicing ratio is also included to account for this variable's non-linearity. The non-linearity is the result of large ratios for those individuals where household income is very small or zero.[12] Repayment information is not available for non-mortgage debts.

Two measures of household assets are included separately in the cross-section model: financial and non-financial assets. Financial assets are the sum of equity and cash investments, trust funds and household bank accounts. These should be relatively liquid and thus may provide readily available funds in the case of an adverse shock. Superannuation assets are excluded because they are illiquid, particularly for the age group under consideration. Non-financial assets include the home, other property values, vehicles and collectibles.[13] In the panel, only the value of the owner-occupied home is available.[14]

4.2 Descriptive Statistics

Tables A2 and A3 in Appendix A present the summary statistics for the cross–section and panel samples. Men have a higher attachment to the labour force, with 92 per cent participating compared to around 73 per cent of women.

More detailed summary statistics for assets and debt are shown in Table 2. In Wave 2, median owner-occupied mortgage debt is approximately $10,000. This rises to $100,000 among those with a mortgage (not shown). The proportion with an owner-occupied mortgage and the median outstanding owner-occupied mortgage debt are each slightly higher in the panel sample. This likely reflects the increase in indebtedness and in the number of indebted home owners over the first half of this decade.

Table 2: Summary Statistics – Assets and Debts
Percentile Per cent with positive debt or assets Percentile Per cent with positive debt or assets
25 Median 75 25 Median 75
Wave 2; 2,999 observations Panel; 13,672 observations
Owner-occupied mortgage debt outstanding ($'000) 0 8.0 98.0 50.9 0 12.0 110.0 51.6
Owner-occupied mortgage debt-to-income ratio 0 0.2 2.0 50.9 0 0.2 2.2 51.6
Debt-servicing ratio 0 0 0.2 48.2 0 0 0.2 49.0
Other debt ($'000) 0 3.6 18.5 67.7 na na na na
Other debt-to-income ratio 0 0.1 0.5 67.7 na na na na
Value of owner-occupied home ($'000)(a) 0 200.0 350.0 69.4
Non-financial assets ($'000) 38.0 213.5 378.0 95.8 na na na na
Financial assets ($'000) 1.2 7.0 27.4 98.3 na na na na
Wave 2; 2,568 observations Panel; 11,374 observations
Owner-occupied mortgage debt outstanding ($'000) 0 10.0 100.0 51.4 0 19.0 115.0 52.5
Owner-occupied mortgage debt-to-income ratio 0 0.3 4.1 51.4 0 0.5 4.5 52.5
Debt-servicing ratio 0 0 0.4 48.5 0 0 0.5 49.6
Other debt ($'000) 0 4.6 19.0 68.3 na na na na
Other debt-to-income ratio 0 0.2 1.1 68.3 na na na na
Value of owner-occupied home ($'000)(a) 0 200.0 350.0 68.5
Non-financial assets ($'000) 30.0 203.8 361.5 96.5 na na na na
Financial assets ($'000) 1.3 7.0 29.2 97.7 na na na na
Notes: Full descriptions of all variables are available in Appendix A, Table A1.
(a) Summary statistics for the value of the owner-occupied home are not reported for the cross-section as it is captured in non-financial assets.

The median ratio of owner-occupied mortgage debt to household income is 20 per cent for women overall and 210 per cent for women with a mortgage, using the panel data. For men, the equivalent ratios are 50 per cent and 430 per cent respectively. The differences between men and women for the debt-to-income and debt-servicing ratios at the median (and also at the 75th percentile) reflect that men generally have a lower household income. This is because men often earn more than their partners, and our measure of household income excludes the individual's own labour income.

Around half of the sample does not make any mortgage repayments. Among those with mortgage repayments, the median debt-servicing ratio (where the denominator is household income, excluding individual labour income) is 0.23 for women and 0.46 for men in the panel sample (not reported in the table). For women, around 5 per cent of the sample has a debt-servicing ratio equal to or greater than 1, that is, their household income is less than the amount of annual housing debt repayments paid. In contrast, for men, around 13 per cent have a debt-servicing ratio equal to or greater than 1, reflecting their higher labour income relative to their partner.

Higher rates of LFP are associated with a higher debt-servicing ratio for both men and women (Figure 1). As might be expected, this relationship is generally stronger for women than for men. LFP is also associated with higher levels of owner-occupied mortgage debt and debt-to-income ratios.

Figure 1: LFP versus Various Measures of Owner-occupied Mortgage Debt

Table 2 also shows that the value of non-housing-related debt is relatively small. Other debt outstanding and other debt as a ratio to household income are also positively correlated with increases in LFP. The vast majority of respondents have some assets, with median values of less than $10,000 for financial assets and around $200,000 for non-financial assets in the cross-section. In the panel, 70 per cent of the sample own, or are purchasing, their own home; the median value of homes is approximately $200,000.


The in-confidence unit record data are used. [7]

A considerably larger proportion of prime-age males participate in the labour force compared to women. Empirical studies have found that men's LFP is relatively wage inelastic (Pencavel 1986) whereas women are generally found to have a more flexible attachment to the labour force (Killingsworth and Heckman 1986; Birch 2005). [8]

The counterfactual family tax benefit was constructed by adapting code supplied by the Melbourne Institute of Applied Economic and Social Research, which applies the historical benefit rules as published by the Department of Families, Community Services and Indigenous Affairs (<http://www.facs.gov.au/guides_acts/fag/faguide-3/faguide-3.6.html>). [9]

Other potential government benefits, such as unemployment benefits, are not imputed in a similar manner due to the complexity associated with such a task. Most government payments are means or asset tested and are strongly related to other demographic factors. As a result, their effects should be captured elsewhere in the model. [10]

This is equivalent to assigning those individuals with negative or zero household income (exclusive of the labour income of the individual) with one dollar of household income. Drago et al (2006) adopt a similar approach. [11]

The effects of this non-linearity can be observed in the average ratios which are very large (presented in Tables A2 and A3 in Appendix A). [12]

Net business wealth was considered. However, its inclusion made no qualitative difference. [13]

Note that in the cross-section model, imputed wealth data are used. [14]