RDP 2007-05: Labour Force Participation and Household Debt 6. Discussion

In addition to examining the estimated marginal effects of the debt and asset variables individually, the change in the predicted probability of participation in response to a change in a range of the household balance sheet variables is also of interest. These balance sheet variables can be expected to move together and sometimes by much larger increments than those used to calculate the marginal effects in Sections 5.1 and 5.3 above. Table 5 shows the difference between the predicted probabilities of participation for two women, where one has housing debt and the other does not, using the results from both the cross-section and the panel random-effects probits.

For example, take two ‘median’ women; one purchases a $200,000 house using $20,000 of her existing assets as a 10 per cent deposit, while the other simply holds the $20,000 in financial assets.[25] The purchaser has owner-occupied mortgage debt of $180,000 and a corresponding debt-to-income ratio of 4.5 and debt-servicing ratio of 0.36 (assuming a 25-year loan at 6.5 per cent interest[26]). Her non-financial assets (or the value of the owner-occupied home for the panel) are now higher as a result of the house purchase, totalling $200,000. Other than for the given criteria, the two women are assumed to have median characteristics.[27]

Using the cross-section estimates, the purchaser, with a partner who is in the labour force and who has a child between the ages of 0 and 4 years, has a propensity to participate that is 6.2 percentage points higher than it is for the woman without debt, a statistically significant difference. For the panel estimates, the difference is smaller, but still statistically significant, at 3.3 percentage points.

The difference in the effects across the panel and cross-section estimates may indicate that the cross-section results are biased because they ignore unobserved individual heterogeneity. However, data on other debt and non-owner-occupied housing assets are not available for the panel. These omissions may mean that the effect of owner-occupied mortgage debt on participation propensities is harder to estimate precisely.

Table 5 also shows that the difference in the propensity to participate between the two ‘median’ women is moderated if the women have a university degree or have no children, as each of these characteristics in and of themselves would make both women more likely to participate.

Table 5: Difference in Predicted Probabilities With and Without Owner-occupied Housing debt
Women, percentage points
Has children aged 0–4 years Has no children
Cross-section probit
Has a partner; spouse is in the labour force 6.2** 1.3*
– also has a university degree 5.6** 0.4
Single 6.0** 1.5*
– also has a university degree 5.9** 0.5*
Panel random-effects probit
Has a partner; spouse is in the labour force 3.3* 0.1
– also has a university degree 1.4* 0.0
Single 3.4* 0.2
– also has a university degree 1.6* 0.0
Note: ***, ** and * represent significance at the 1, 5 and 10 per cent levels respectively.

A similar analysis can be conducted for men. However, while the house purchase does imply a greater propensity to participate, a significant difference is only found for single men with no children and with only a basic level of education.

In comparison to the size of the marginal effect associated with a small change in just one of the debt variables, this analysis shows a larger net effect for a reasonable shift in a set of assets and debts associated with a house purchase. Nevertheless, for women, the positive effect of the house purchase on the propensity to participate does not offset the strong negative effect on participation of having a young child (results not reported). This is consistent with results found for the UK (Bottazzi 2004), but is in contrast to results found for the Netherlands (Aldershof et al 1999) and Canada (Fortin 1995).

The model estimates can also be used to make some ‘back-of-the-envelope-calculations’ about the effect of indebtedness on aggregate LFP. That is, a measure of the contribution of the recently observed increases in household indebtedness to the observed change in LFP can be roughly estimated. To do this, the average predicted probability of participation in the labour force, with debts and the value of the owner-occupied home set equal to their 1998/99 median (from the Household Expenditure Survey (HES)), is compared to the predicted probability of participation when debts and the value of the owner-occupied home are set equal to their 2005 median (from the HILDA Survey data). To keep the exercise relatively simple, all those with positive owner-occupied mortgage debt are assigned the non-zero median value of debts and assets.[28]

Table 6 shows that the average predicted probability of participation across all women (both with and without owner-occupied mortgage debt) is 77.2 per cent in 1998/99 and 78.6 per cent in 2005 (columns I and III, row 3), an increase of 1.4 percentage points. This is smaller than the actual increase of 4.4 percentage points in the aggregate LFP rate between 1998/99 and 2005 for women aged 25–54 (ABS 2006). That is, the model attributes around one-third of the rise in aggregate LFP rates as being due to the rise in debt.

Table 6: Change in LFP Using Debt from HES and HILDA
1998/99 2005
Average predicted percentage in the labour force
Actual percentage of the sample with and without debt
Average predicted percentage in the labour force
Actual percentage of the sample with and without debt
Has no debt 73.0 61.8 73.1 47.3
Has median debt 83.9 38.2 83.5 52.7
Total 77.2   78.6  
Has no debt 89.6 61.1 89.4 46.8
Has median debt 97.0 38.9 96.4 53.2
Total 92.5   93.1  

Table 6 also allows an investigation of the likely source of this predicted increase in the probability of participation. The analysis suggests that the increase in the level of indebtedness has had little practical effect on the predicted probability of LFP. For women, among those with debt, the predicted probability of participation actually decreased slightly from 83.9 per cent in 1998/99 to 83.5 per cent in 2005 (columns I and III, row 2).[29] Instead, the analysis suggests that the change in the probability of participation, and thereby some part of the increase in the aggregate LFP rate, is likely to be due to a compositional effect associated with the increase in the proportion of those with owner-occupied mortgage debt (from around 38 per cent of households to just over 50 per cent of households).

For men, the aggregate LFP rate has fallen by 0.4 percentage points over the same period. In contrast, the model predicts that changes in debt and asset values imply an increase in the average probability of participation of 0.6 percentage points (columns I and III, row 6). This suggests that despite an increase in the proportion of those with owner-occupied mortgage debt, other factors have dominated and have driven the participation rate down between 1998/99 and 2005.

To assess whether or not the magnitude of the predicted increase is reasonable, it can be compared to the size of the predicted increase in LFP associated with a change in the proportions of individuals with different levels of education, a change which is widely accepted to have had a strong effect on participation propensities. Between 1998/99 and 2005, the proportion of individuals with tertiary education has increased, and the predicted effect on LFP (based on the random-effects model) is estimated to be an increase of 2.3 percentage points for women and 1.0 percentage point for men in this age group.


Among those living in a capital city, the median owner-occupied home is valued at $200,000 in 2002. The median value rises to $250,000 in the panel sample; however the results are not qualitatively different when that value is applied in the scenario. [25]

The interest rate of 6.5 per cent reflects the 2001–2005 average rate paid on outstanding mortgages. [26]

These ‘median’ women are 38 years of age, have not finished Year 12, have spent 14 years in and 1 year out of the labour force, are proficient at English, Australian born, living in a capital city, do not have a health condition that adversely affects their ability to work, have zero investment income, receive no family tax benefits and have $40,000 of household income (excluding their own). For the cross-section analysis, they are assumed to have household other debt of $3,000 and an other debt-to-income ratio of 0.075. Financial assets are set equal to the median of $6,700 for the purchaser, and to $26,700 for the non-purchaser who does not use the $20,000 as the deposit for a house. [27]

The estimated coefficients from the panel random-effects probit are used to generate predicted probabilities for 1998/99 and 2005 with all demographic, family and income characteristics held constant at their 2005 values. [28]

For those without debt, the average predicted percentage in the labour force is around 73 per cent for women in 1998/99 and 2005 (columns I and III, row 1). These predicted probabilities should be quite similar by construction as only the asset value of the owner-occupied home varies. [29]