3. Income poverty

3.1 Bivariate relationships
3.2 Effects on before-housing income poverty
3.3 Effects on after-housing income poverty

Table 1 presents estimates of the percentage of individuals in income poverty on the before and after-housing measures. In both measures, the poverty lines are drawn at 50 per cent of median household income after taxes and government transfers. The modified OECD equivalence scale was used to adjust for household size. About 13 per cent are defined as being in income poverty on the before-housing measure and nearly 17 per cent on the after-housing measure. These HILDA Wave 1 and 2 estimates are higher than comparable estimates for 2000: 10 per cent on the before-housing measure and 15 per cent after-housing (Harding, Lloyd & Greenwell 2001, pp. 35-6). These differences of 2 to 3 percentage points are probably due to differences in sampling, data processing or other technical aspects rather than reflecting a trend of increasing income poverty. The most important technical difference is that the HILDA estimates are based on annual income, whereas the Australian Bureau of Statistics (ABS) survey estimates are based on weekly income. Weekly income measures are less likely to include income from all sources.

Table 1: Estimates of the percentage of individuals in income poverty
Measure	Definition of poverty line	Wave 1	Wave 2
Before-housing income poverty	50 per cent or below of median equivalised disposable household income not adjusting for housing costs	12.8	12.0
After-housing income poverty	50 per cent or below of median equivalised disposable household income after deducting housing costs	16.6	16.4

Note: The unit of analysis is the individual. Weighted by enumerated person weights.

3.1 Bivariate relationships

Tables 2 through 5 report the relationships between income poverty and a range of demographic, sociological and economic factors. Tables 2 and 3 present the relationships with categorical variables, and Tables 4 and 5 focus on continuous variables.

The percentage of households defined as in income poverty on the before-housing measure was slightly higher at 15.5 per cent in Wave 1 and 14.4 per cent in Wave 2 than the percentage for individuals (12.8 per cent in Wave 1 and 12.0 per cent in Wave 2). This is because larger households tend to have higher incomes than smaller households. About 7 per cent were categorised on this measure as in poverty in both waves (Table 2). On the after-housing measure, the percentage of households in poverty was 18.1 per cent in Wave 1 and 17.4 per cent in Wave 2; 8.9 per cent were defined as in poverty on the after-housing measure in both waves (Table 3). Again, the percentages for household levels of poverty are higher than for individuals.

Tables 2 and 3 present the percentages in income poverty for each and both waves by sex, age cohort, household type, marital status, highest education level and labour force status. For estimates of income poverty in both years, the characteristics were measured from the second wave data. It is important to note that these percentages are of the group in income poverty, not the percentage of those in poverty that belong to that group.

On the before-housing measure, poverty is higher among women than men. In Wave 1 the poverty rate among women was nearly 6 percentage points higher, and in Wave 2, 4 percentage points. About 8 per cent of women were defined as in poverty in both waves, compared to about 5 per cent of men.

Before-housing poverty is higher in the youngest cohort (18 to 24 year-olds) and two oldest age cohorts (65 to 70, and 70 and older). In Wave 1, among those aged between 25 and 54, about 10 per cent were in poverty, compared to around 20 per cent of 18 to 24 year-olds and over 30 per cent of those older than 70. Wave 2 showed much the same pattern, with a slightly higher level of poverty among the youngest cohort, and a slightly lower level among the oldest cohort. Less than 4 per cent of 25 to 54 year-olds were in poverty in both waves.

Table 2: Percentage in before-housing income poverty by demographic and other factors
Characteristic	Wave 1	Wave 2	Waves 1 and 2
All	15.5	14.4	6.7
Sex
Male	12.3	12.3	5.3
Female	18.0	16.1	7.8
Age cohort
18–24	20.8	25.7	9.1
25–34	8.9	9.7	3.4
35–44	11.2	8.4	3.8
45–54	11.0	9.5	3.7
55–64	17.5	16.9	9.1
65–70	23.2	21.8	10.5
>70	32.0	28.2	16.1
Household type
Couple without children	11.3	10.0	3.1
Couple with children < 15	9.3	8.0	3.3
Couple with children > 15	5.8	6.8	1.5
Lone parent	17.8	15.9	5.7
Single person	29.5	27.4	16.2
Other	11.5	14.7	4.1
Marital status
Legally married	10.0	9.2	3.0
De facto	7.4	8.1	2.4
Separated	22.2	21.0	8.4
Divorced	23.9	19.8	12.9
Widowed	38.1	33.0	21.2
Never married and not de facto	19.6	20.4	9.7
Highest education level
< Year 12	23.3	21.4	11.3
Year 12	15.5	16.3	5.4
Certificate	14.5	16.2	6.9
Advanced certificate	13.2	10.9	4.9
Diploma/advanced diploma	11.5	8.6	4.3
Bachelor degree	7.9	7.2	2.7
Postgraduate qualification	4.0	5.7	1.8
Labour force status
Working full-time	4.4	3.8	0.9
Working part-time	9.2	11.1	3.3
Unemployed, looking for full-time work	32.5	31.9	17.3
Unemployed, looking for part-time work	31.4	36.0	11.4
Not in the labour force, marginally attached	26.6	29.6	14.1
Not in the labour force, not marginally attached	30.2	26.1	14.3

Table 3: Percentage in after-housing income poverty by demographic and other factors
Characteristic	Wave 1	Wave 2	Waves 1 and 2
All	18.1	17.4	8.9
Sex
Male	14.7	15.6	7.3
Female	20.8	18.9	10.2
Age cohort
18–24	28.6	36.3	17.1
25–34	16.8	18.4	9.2
35–44	16.9	15.9	8.5
45–54	13.9	12.0	5.5
55–64	17.8	17.2	9.4
65–70	18.6	16.0	10.2
>70	24.0	18.6	9.6
Household type
Couple without children	12.6	10.7	4.7
Couple with children < 15	14.3	14.3	6.7
Couple with children > 15	6.3	7.7	2.7
Lone parent	30.3	31.0	16.8
Single person	27.9	26.0	14.7
Other	17.2	18.9	10.6
Marital status
Legally married	12.0	12.0	5.2
De facto	12.9	11.1	6.1
Separated	32.3	31.8	16.3
Divorced	25.0	23.9	14.8
Widowed	28.4	19.1	12.7
Never married and not de facto	27.4	29.7	15.2
Highest education level
<Year 12	24.0	22.1	12.7
Year 12	19.9	22.0	8.9
Certificate	20.3	21.5	11.8
Advanced certificate	15.5	15.1	7.1
Diploma/advanced diploma	15.5	11.0	5.3
Bachelor degree	12.1	11.1	5.4
Postgraduate qualification	7.1	8.5	3.6
Labour force status
Working full-time	7.4	7.7	2.4
Working part-time	14.6	18.1	8.1
Unemployed, looking for full-time work	44.2	43.2	27.0
Unemployed, looking for part-time work	47.0	47.6	19.4
Not in the labour force, marginally attached	34.9	38.8	24.6
Not in the labour force, not marginally attached	28.2	23.1	13.4

Of household types, lone-parent and single-person households had the highest before-housing income poverty levels. On this measure, nearly 30 per cent of single-person households, which included widow and widower households, were in poverty in each year. Sixteen per cent were in poverty in both years. Lone-parent households show the next highest level of poverty, at around 16 per cent in each year, although only 6 per cent were in poverty in both years. The incidence of poverty among couples is low, at about 10 per cent among couples without children, less among couples with children younger than 15, and even less among couples with older children.

Before-housing income poverty levels differ considerably by marital status. Married couples enjoyed the lowest poverty rates at around 10 per cent in each year (with only 3 per cent in poverty in both years). This compares to over 20 per cent of widows and widowers, and 13 per cent of divorcees. De facto couples had levels of poverty similar to (or, in Wave 2, slightly lower than) the levels of poverty for married couples.

Income poverty has a generally ordinal relationship with education: the higher the educational qualification, the lower the percentage in poverty. Among those whose highest qualification was less than Year 12, over 20 per cent were in poverty in each year. Over 10 per cent were in poverty in both years. This contrasts with less than 3 per cent of those with bachelor degrees or postgraduate qualifications. The only exception to the ordinal pattern was the slightly higher levels of poverty among certificate holders than among those whose highest qualification was Year 12 or school completion.

Labour force status has an even stronger relationship with income poverty than educational qualification . Less than 5 per cent of full-time workers were in poverty each year, and only 1 per cent were in poverty in both years. Part-time workers showed higher poverty rates at around 10 per cent each year, but only 3 per cent were in poverty in both years. Poverty rates are substantially higher among those who were not working. Of the unemployed, about 30 per cent were in poverty in a single year and about 15 per cent in both years. The group not in the labour force shows similarly high levels of before-housing income poverty. There is little difference between the poverty rates of the marginally attached and not marginally attached groups.⁷

Although the overall levels of after-housing poverty are higher, the pattern of its relationship with sex and education are similar to that found with the before-housing measure (Table 3). In each wave, after-housing poverty was substantially higher among women than men, and 10 per cent of women were in poverty in both years compared to 7 per cent of men. After-housing poverty shows the familiar inverse relationship with education: over 10 per cent of those without post-secondary education were in poverty in both years compared to about 5 per cent or less among those with diplomas, bachelor degrees or postgraduate qualifications.

However, the relationships between income poverty and the other variables are quite different with the after-housing measure. The after-housing poverty rate among 55 to 70 year-olds is similar to that among 25 to 44 year-olds, whereas it is substantially higher on the before-housing measure. Single-person households do not show substantially higher levels of after-housing poverty than other household types. On the before-housing measure they have the highest rates. Lone parents had much higher levels of poverty on the after-housing measure compared to the before-housing measure. Couple households with older children clearly have much lower levels of poverty than those with children under 15 on the after-housing measure. The differences between these groups are less significant on the before-housing measure.

Similarly, the relationship between income poverty and marital status is different on the after-housing measure. Widowers had the highest level of poverty on the before-housing measure, but had similar levels of poverty to the separated, divorced and single groups on the after-housing measure. Differences in poverty levels between the married, de facto and other marital status groups are larger on the after-housing measure.

After-housing income poverty is less than 10 per cent among those working full-time and 15 to 20 per cent among those working part-time. However, it is over 40 per cent among the two unemployed groups. Of those not in the labour force, the group marginally attached to the labour market has higher proportions in poverty than those unattached to the labour market. This is because the second group is more often older, retired and home owners. This difference is not apparent on the before-housing measure.

Table 4 and Table 5 present the means and medians of those in income poverty and not in income poverty for a range of continuous variables. The medians are included because mean values may be misleading, as they are more sensitive to very high and very low incomes. The before-housing measure was used in Table 4 and the after-housing measure in Table 5. The comparison variables were measured in the same year, except that data for wealth, assets and debt were collected only in Wave 2. The tables are arranged in three groups of four columns; the first two groups of columns compare those in poverty and not in poverty in Waves 1 and 2, and the third group of columns compares those in poverty in both waves with those not in poverty in both waves.

On the before-housing poverty measure, those in poverty tend to be older, with average median ages in the low to mid 50s. These summary statistics hide the cohort differences presented in Table 2, which show higher levels of before-housing income poverty in the youngest and two oldest age cohorts. On average, adults from households in poverty had more children, although the differences were small. There was no difference in the median number of children.

The average occupational status of the in-poverty group's present or previous jobs is about 10 score points lower than for the comparison groups. Thus, those in poverty worked, or more accurately had worked, in lower status jobs than those not in poverty. However, the difference is relatively small considering that this measure of occupational status ranges from zero to 100. There were even smaller differences in socioeconomic origins. The socioeconomic backgrounds of those in poverty were only 3 to 4 score points lower than for the comparison groups not in poverty. There was almost no difference in the medians. These results suggest that poverty is not closely associated with socioeconomic background.

Table 4: Characteristics of households in and not in poverty (before-housing income poverty measure)
Factor	Wave 1				Wave 2				Waves 1 and 2
	Means		Medians		Means		Medians		Means		Medians
	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty
Age	53.9	46.4	54.0	44.0	52.5	46.7	54.0	45.0	56.6	47.2	60.0	45.0
Number of children	2.4	1.9	2.0	2.0	2.1	1.8	2.0	2.0	2.3	1.8	2.0	2.0
Occupational status	34.2	45.1	31.8	39.5	34.7	45.1	32.2	39.9	32.8	44.4	30.1	39.5
Parental occupational status	38.7	42.0	39.9	40.6	39.7	42.5	40.4	40.6	38.2	42.4	39.9	40.6
Personal income ($)	7.6	34.6	8.2	28.0	8.7	36.6	9.4	30.0	9.5	34.3	10.1	27.0
Personal disposable income ($)	7.0	26.7	8.2	23.0	7.9	28.1	9.4	24.3	8.8	26.4	10.0	22.4
Household income ($)	11.1	65.4	10.3	54.3	11.8	66.9	11.0	55.8	12.1	62.0	10.8	50.5
Equivalised household income ($)	10.1	50.5	10.2	43.5	10.7	51.4	10.9	44.1	11.1	47.8	10.8	40.8
Equivalised disposable household income ($)	6.6	29.8	8.6	25.5	7.2	30.9	9.4	26.7	8.2	28.8	9.9	25.1
Equivalised disposable household income after housing costs ($)	3.8	25.7	5.9	21.6	4.5	26.8	6.7	22.9	6.0	24.8	7.7	21.2
Household wealth $ (Wave 2 only )	239.3	458.4	120.9	266.7	243.6	432.4	100.2	244.2	190.4	425.0	80.2	239.0
Household assets $ (Wave 2 only)	269.2	535.3	130.2	344.7	269.9	508.2	114.6	322.9	202.7	498.1	90.2	311.0
Household debt $ (Wave 2 only)	30.5	77.2	0.0	15.5	27.3	75.6	0.0	15.5	15.7	72.5	0.0	12.7

Note: All dollar amounts are in thousands of dollars.

Table 5: Characteristics of households in and not in poverty (after-housing income poverty measure)
Factor	Wave 1				Wave 2				Waves 1 and 2
	Means		Medians		Means		Medians		Means		Medians
	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty	Poverty	Not in poverty
Age	48.2	47.5	45.0	46.0	45.6	48.0	43.0	46.0	46.9	47.9	43.0	46.0
Number of children	2.2	1.9	2.0	2.0	1.9	1.8	2.0	2.0	2.2	1.9	2.0	2.0
Occupational status	35.3	45.2	32.2	39.5	36.1	45.2	32.8	39.9	33.6	44.6	31.3	39.5
Parental occupational status	39.6	41.9	39.5	40.6	41.2	42.3	40.5	40.6	40.0	42.3	39.6	40.6
Personal income ($)	10.2	34.9	8.9	28.4	12.3	36.9	10.5	30.0	12.4	34.6	10.7	27.6
Personal disposable income ($)	9.0	26.9	8.8	23.1	10.7	28.2	10.4	24.5	10.9	26.6	10.7	22.5
Household income ($)	15.4	66.2	12.5	55.5	17.5	67.7	14.3	57.0	17.0	62.7	13.5	51.7
Equivalised household income ($)	13.4	51.1	12.1	44.4	15.1	52.0	14.0	45.1	14.8	48.3	13.4	41.6
Equivalised disposable household income ($)	8.3	30.2	9.3	26.0	9.4	31.3	10.4	27.3	9.5	29.2	10.5	25.6
Equivalised disposable household income after housing costs ($)	3.6	26.5	5.9	22.1	4.1	27.6	6.4	23.5	4.6	25.3	6.6	21.7
Household wealth $ (Wave 2 only )	225.4	468.3	64.0	278.1	202.6	448.0	32.3	261.1	144.9	435.3	13.0	249.9
Household assets $ (Wave 2 only)	273.2	542.7	81.2	351.0	248.0	521.5	48.4	332.6	178.3	507.7	15.4	321.0
Household debt $ (Wave 2 only)	45.8	75.4	0.6	14.5	44.5	73.7	1.0	14.0	33.9	72.1	0.1	12.0

Note: All dollar amounts are in thousands of dollars.

Because poverty is measured by income, the mean and median personal and household incomes are much lower for the in-poverty group. The equivalised annual disposable incomes of the in-poverty groups are very low: $6,000 to $8,000 before housing and $3,800 to $6,000 after deducting housing costs. They are about 20 to 25 per cent of the incomes of the comparison groups.

In both waves, the groups defined as in poverty had considerable amounts of wealth, on average between $200,000 and $250,000. However, they were only about half as wealthy as the not-in-poverty groups. The group that was in poverty in both years had substantially less wealth (at around $190,000), suggesting some running down of assets. Similarly, the in-poverty groups had fewer assets than the comparison groups, but the differences are not as large as they are for income. Incomes differ by a factor of around five, whereas wealth and assets differ by a factor of two. A surprising result is that the in-poverty groups have less debt than the comparison groups. In each of Waves 1 and 2, the mean debt of those in poverty was around $30,000, compared to over $70,000 in the comparison groups. The group in poverty in both waves had even less debt at around $16,000. The median household in poverty had no debt.

For the after-housing measure of income poverty, the age differences are minimal. In Wave 2 the in-poverty group tended to be slightly younger. This was also true of the smaller group that was in poverty in both waves. As was the case for the before-housing measure, the groups in poverty had, on average, slightly more children. Similarly, the results for the occupational status of present or previous job and parental occupational status are the same as for the before-housing measure. There are no striking differences between before and after-housing measures on the mean and median incomes of the two groups. The results for wealth, assets and debts are almost identical between the two poverty measures.

3.2 Effects on before-housing income poverty

Table 6 presents the results from regression analyses of the influences on before-housing income poverty. There are significant differences between the sexes.

In the initial model, sex, age, number of siblings, Indigenous status, language background and school type accounted for about 7 per cent of the variation in before-housing income poverty. This rises to about 9 per cent with the addition of education, and it increases more substantially to 16 per cent with the addition of marital status and the number of children. Labour market experiences and occupation increase the R square value to 19 per cent, and the addition of wealth increases it very slightly to just over 20 per cent.

The odds for women being in poverty rather than not in poverty are about 1.4 times the odds for men.⁸ This effect is net of other factors in the initial model (Table 6). Differences between the sexes decrease when controlling for education and marital status. This is because the average education level of men is still higher than that of women, and the bulk of lone parents and widowers are women. When taking into account labour market history, the situation is reversed: the odds of men being in poverty are about 1.3 times the odds for women. So, among men and women with the same labour market experiences, men are more likely to be in poverty than women. The addition of wealth slightly decreases the difference, but men are still more likely to be in poverty than women when labour market experiences and wealth are taken into account.

A 10-year age difference increases the odds of being in before-housing income poverty by about 1.2 times. Controlling for education, marital status and other factors makes little difference to the age effect.

In the initial model, an increase in the number of siblings is associated with increased odds of being in poverty. Compared to having no siblings, one sibling increases the odds of being in poverty by a factor of 1.04, two siblings 1.08, and three siblings 1.12. This is a weak effect and disappears when controlling for educational qualifications.

'Not living with both parents at age 15' is not associated with before-housing income poverty in Wave 2. Analyses of income poverty in Wave 1 show significant effects for 'not living with both parents at age 15', suggesting that there is some effect.

Table 6: Effects on income poverty (before-housing), Wave 2
Variable	Background		+Education		+Marital status		+Work		+Wealth
Intercept	-2.01	***	-1.81	***	-1.10	***	-1.53	***	-1.60	***
Male	-0.31	***	-0.20	**	-0.13		0.23	*	0.21	*
Age	0.22	***	0.17	***	0.17	***	0.20	***	0.21	***
Number of siblings	0.04	*	0.02		0.01		0.00		0.00
Not living with both parents at age 15	0.04		0.03		-0.04		-0.07		-0.07
First language not English	0.61	***	0.65	***	0.84	***	0.65	***	0.63	***
Indigenous	0.96	***	0.91	***	0.65	**	0.43		0.42
Parental occupational status (10s)	-0.05	**	0.00		0.00		0.00		0.01
Catholic school	-0.15		-0.09		-0.12		-0.10		-0.08
Independent school	0.13		0.25	*	0.27	*	0.23		0.26
Postgraduate qualification	-		-1.14	***	-1.06	***	-0.57	*	-0.53	*
Bachelor degree	-		-0.88	***	-0.90	***	-0.51	**	-0.51	**
Diploma	-		-0.66	***	-0.68	***	-0.50	**	-0.48	*
Advanced certificate	-		-0.40	**	-0.36	*	-0.23		-0.22
Certificate	-		0.05		0.08		0.05		0.06
<Year 12	-		0.17		0.15		-0.06		-0.06
Married	-		-		-1.56	***	-1.23	***	-1.13	***
De facto	-		-		-1.29	***	-1.02	***	-0.97	***
Separated	-		-		-0.43	**	-0.08		-0.08
Divorced	-		-		-0.52	***	-0.18		-0.18
Widowed	-		-		-0.51	**	-0.40	*	-0.37	*
Number of children	-		-		0.10	***	0.05		0.04
Occupational status (10s)	-		-		-		-0.09	***	-0.08	***
% time in work (10s)	-		-		-		-0.16	***	-0.15	***
% time unemployed (1s)	-		-		-		0.01	***	0.01	**
Wealth ($100,000)	-		-		-		-		-0.05	***
Rescaled R square	0.06		0.09		0.15		0.20		0.20

Note: ***p<0.001, **0.001<p<0.01, *0.01<p<0.05.

Those whose first language is not English are more likely to be in income poverty. The effects are reasonably large: according to the initial model their odds of being in poverty (rather than not in poverty) are 1.8 times the odds for the comparison group. This effect is net of parental occupational status, type of school attended and other variables in the initial model. The difference remains the same when controlling for other factors. This is a remarkable finding—even when education, marital status, children, labour market history and wealth are taken into account, this group is still nearly twice as likely to be in poverty (rather than not in poverty) than those with English-speaking backgrounds.

In the initial model, the effect of being Indigenous is even larger than for having a non-English speaking background. The odds for the Indigenous group being in poverty (rather than not in poverty) are 2.6 times the odds for the non-Indigenous group. This effect is much the same when controlling for educational qualifications, decreases more substantially when marital status and children are taken into account, and is no longer significant when labour market history is taken into account.

In the initial model, parental occupational status is significantly associated with being in income poverty. However, the effect is rather weak. A 10-unit increase in parental occupational status decreases the odds of being in poverty by 1.05 times. Comparing the highest possible occupational background (medical practitioners, with a ANU4 score of 100) with the lowest (agricultural labourers, with a ANU4 score of zero) also shows that poverty is only weakly associated with occupational background. The odds ratio for this extreme comparison is 1.7, comparable to that for a non-English speaking background and much less than that for a bachelor degree or marriage. The effect of occupational background is not statistically significant when differences in educational qualifications are taken into account, so it appears to be mediated through educational attainment.

There was no significant relationship between type of school attended and income poverty in Wave 2. In Wave 1, on the other hand, respondents who had attended a catholic school were less likely to be in after-housing income poverty than those who had attended a government school (Table A13). However, this effect is barely significant and is no longer significant when controlling for educational attainment.

Educational qualifications have a strong relationship with income poverty. In the model that includes the variables in the initial model and educational qualifications, postgraduate qualifications reduce the odds of being in poverty compared to school completion by 3.1 times, bachelor degrees by 2.4 times, diplomas by 1.9 times and advanced certificates by 1.5 times. These effects are slightly reduced when marital status is taken into account, and apart from the advanced certificate, the effects are significant when controlling for occupational status, labour market experiences and wealth. In the final model, postgraduate qualifications and bachelor degrees reduce the odds of income poverty by a factor of 1.7. These are strong effects, and indicate that the reduced chances of being in poverty from higher educational qualifications can be only partially attributed to the associations between education and labour market experiences, occupational status and wealth.

A certificate does not appear to affect the chances of being in income poverty compared to school completion. In Wave 2, not completing school was not associated with an increased likelihood of being in poverty, although in Wave 1 the coefficient was significant at the p<0.05 level. However, its magnitude (0.24) is not much larger than that estimated for Wave 2, and it is no longer statistically significant when controlling for labour market experiences.

Marital status has an even stronger relationship with income poverty than educational qualifications. The odds of married couples being in poverty, net of the variables in the initial model and educational qualifications, are 4.8 times less than the odds for single people. This effect remains large after controlling for labour market experiences and wealth. According to the final model, compared to being single, marriage reduces the odds of being in poverty by 3.1 times, net of differences in social background, education level, occupational status, labour market experiences and household wealth. De facto relationships also reduce the chances of being in poverty. In the final model, being in a de facto relationship reduces the odds of being in poverty by 2.6 times.

It should be noted that the effects of marriage and being in a de facto relationship are sensitive to the equivalence scale used. The older OECD scale assigned a weight of 0.7 to the second adult instead of 0.5. Using the older scale results in a weaker effect. However, the relevant poverty lines would increase by only about $2,000, which would not change the general conclusion that marriage and de facto relationships strongly reduce the odds of poverty. On the other hand, if the international equivalence scale were used, the protective effects of marriage and de facto relationships would appear even stronger, since this scale gives less weight to the second adult.

Being separated, divorced or widowed also reduces the odds of being in income poverty compared to being single, although not nearly to the same extent as being married or in a de facto relationship. Separation, divorce and widowhood reduce the odds of being in poverty by between 1.5 and 1.7 times, a larger effect than that for sex or occupational background. The effects for the separated and divorced are substantially lower when labour market experiences are taken into account. However, widowhood is associated with a reduced risk of poverty even when controlling for wealth.

In the third model, comprising of variables in the initial model plus education, marital status and number of children, one child increases the odds of being in income poverty by 1.1 times. By extension, two children increase the odds by 1.2 times and three children by 1.3 times. Thus, having a small number of children does not substantially increase the risk of being in poverty, although having many children does. Of course, these effects are sensitive to the equivalence scale employed. This result did not survive further controls; the number of children has little or no effect on the odds of being in poverty once labour market experiences are taken into account.

A respondent's occupational status, either present occupation or, if not working, previous occupation, is not unexpected—it is inversely related to the odds of being in poverty. However, the relationship is not particularly strong. A 10-unit increase in occupational status reduces the odds of being in poverty by 1.09 times. A 40-unit difference reduces the odds by about 1.4 times.

Labour market history had a moderate effect on the odds of being in income poverty. A 10 percentage point increase in the time spent working since leaving full-time education decreases the odds of being in poverty by 1.2 times. A 30 percentage point difference decreases the odds by 1.6 times and a 50 percentage point difference—for example, contrasting those who have worked the entire time since leaving school and those who have worked only half the time—decreases the odds by 2.2 times. Experience of unemployment increases the odds of being in poverty, although the effect is also not large. A 10 percentage point difference in the time spent unemployed since leaving school increases the odds of being in poverty by 1.2 times. The effects of labour market history remain significant when controlling for household wealth.

A $100,000 increase in wealth reduces the odds of income poverty by only 1.05 times. The odds of households with average levels of wealth (at around $400,000) being in poverty are 1.2 times less than for households with no wealth. A $1 million difference in wealth is associated with a change of odds by a factor of 1.6. This is less than the effects for marriage, de facto relationships, postgraduate qualifications and bachelor degrees. Therefore, the effect of wealth on before-housing income poverty is not particularly strong. The relatively weak effects for wealth reflect the moderate correlation between income and wealth.

3.3 Effects on after-housing income poverty

Table 7 presents the results on after-housing income from regression analyses identical to those performed on the before-housing measure. Generally the pattern of effects is very similar, although the effects are a little weaker for many variables, which accounts for the lower rescaled R square values.

Differences between the sexes in after-housing income poverty follow the same pattern as for the before-housing measure. Women are more likely to be in poverty in the initial model and when controlling for other labour market experiences, men are more likely to be poverty than women. The odds ratio of women being in poverty (rather than not in poverty) is about 1.2 times that for men. However, once experience in the labour market is taken into account, men are more likely to be poverty than women. This effect is not large.

In contrast to the before-housing measure, age is negatively associated with after-housing income poverty. A 10-year increase in age decreases the odds of being in income poverty by about 1.1 times. For a 30-year difference in age, the odds of being in poverty are reduced by between 1.2 and 1.3 times. These are not large effects and, as was concluded from the bivariate analyses, they reflect the generally lower housing costs of older households.

The effect of the number of siblings on the after-housing poverty measure is also small, and is reduced further when controlling for education and marital status. The effects for 'first language not English' and 'Indigenous status' are weaker on the after-housing measure. This implies that these two groups have, on average, lower housing costs. The effects for language background are notable. Compared to native English speakers, the odds of being in after-housing poverty for those with non-English speaking backgrounds increased by 1.4 times, net of differences in education, labour market experiences and wealth.

The weaker effect for educational qualifications on after-housing compared to before-housing poverty suggests that education has a stronger effect on income than on housing costs. However, the effect is still substantial. Compared to school completion, postgraduate qualifications, bachelor degrees and diplomas reduce the odds of being in after-housing poverty by between 1.5 and 1.6 times, net of labour market experiences, occupational status and household wealth.

Marriage substantially reduces the odds of being in post-housing income poverty by 3.9 times. The effect for being in a de facto relationship is even stronger. The effect for marriage is stronger on the before-housing measure, but for de facto relationships the effect is stronger on the after-housing measure.

The effect of widowhood on poverty is stronger on the post-housing measure. The odds of widows and widowers being in income poverty on the post-housing measure are less than half the odds for single people.

Table 7: Effects on income poverty (after-housing), Wave 2
Variable	Background		+Education		+Marital Status		+Work		+Wealth
Intercept	-1.70	***	-1.60	***	-0.98	***	-1.30	***	-1.42	***
Male	-0.23	***	-0.15	*	-0.13		0.21	*	0.19	*
Age	-0.08	***	-0.12	***	-0.11	***	-0.09	**	-0.05
Number of siblings	0.05	***	0.03	*	0.02	*	0.01		0.00
Not living with both parents at age 15	0.14		0.14		0.08		0.03		0.02
First language not English	0.40	***	0.45	***	0.60	***	0.36	***	0.34	***
Indigenous	0.79	***	0.73	***	0.51	*	0.29		0.27
Parental occupational status (10s)	-0.03		0.02		0.02		0.02		0.03
Catholic school	-0.11		-0.03		-0.07		-0.04		-0.02
Independent school	0.07		0.18		0.22		0.21		0.25	*
Postgraduate qualification	-		-0.92	***	-0.85	***	-0.50	*	-0.44	*
Bachelor degree	-		-0.68	***	-0.69	***	-0.40	*	-0.40	*
Diploma	-		-0.60	***	-0.65	***	-0.49	**	-0.47	**
Advanced certificate	-		-0.25	*	-0.25		-0.15		-0.14
Certificate	-		0.18		0.19		0.14		0.16
<Year 12	-		0.27	*	0.24		0.02		0.01
Married	-		-		-1.37	***	-1.05	***	-0.89	***
De facto	-		-		-1.41	***	-1.20	***	-1.14	***
Separated	-		-		-0.15		0.18		0.17
Divorced	-		-		-0.45	***	-0.14		-0.15
Widowed	-		-		-0.83	***	-0.76	***	-0.75	***
Number of children	-		-		0.14	***	0.08	***	0.08	***
Occupational status (10s)	-		-		-		-0.09	***	-0.07	***
% time in work (10s)	-		-		-		-0.15	***	-0.14	***
% time unemployed (1s)	-		-		-		0.01	**	0.01	**
Wealth ($100,000)	-		-		-		-		-0.07	***
Rescaled R square	0.02		0.05		0.12		0.16		0.17

Note: ***p<0.001, **0.001<p<0.01, *0.01<p<0.05.

In contrast to the analyses for the before-housing measure, the effect of number of children on after-housing income poverty is stronger and remains significant in the final two models. The odds of being in poverty increase by about 1.2 times for each additional child. Thus, on the after-housing measure, having more children increases the odds of being in poverty, reflecting the costs associated with raising children. The discrepancy in the findings for the before and after-housing measures probably arises because, on average, households with larger numbers of children have higher housing costs. However, with the exception of households with very large numbers of children, the effects for the number of children are smaller than for marriage, suggesting that the reason that sole parents are more often in poverty has more to do with not being in a couple than with having children.

Wealth has a stronger influence on after-housing than on before-housing poverty. This reflects the fact that about half of household wealth in Australia is tied up in housing. As was the case for the before-housing measure, the effects of wealth on poverty are only comparable with the effects of educational qualifications and marital status when there are large differences in wealth.

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