48656 0 HILDA SR COVER v3.qxp_Layout 2 12/06/2015 7:34 am Page 1

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

The Household, Incomeand Labour Dynamics
in Australia Survey:
Selected Findings
from Waves 1 to 12

The Household, Income and Labour Dynamics in Australia (HILDA) Survey
is funded by the Australian Government Department of Social Services

The Household, Income and Labour
Dynamics in Australia Survey:
Selected Findings from Waves 1 to 12
Roger Wilkins
Melbourne Institute of Applied Economic and Social Research
The University of Melbourne

The Household, Income and Labour Dynamics in Australia(HILDA) Survey is funded by the Australian Government
Department of Social Services

48656 1 HILDA SR 1_5.qxp_Layout 1 16/06/2015 10:04 am Page 2

Melbourne Institute of Applied Economic and Social Research
Faculty of Business and Economics
Level 5, 111 Barry Street
FBE Building
The University of Melbourne
Victoria 3010 Australia
Tel: +61 3 8344 2100
Fax: +61 3 8344 2111
Web: www.melbourneinstitute.com/hilda© Commonwealth of Australia 2015
ISSN 2205-0558 (Print)
ISSN 2205-0566 (Online)
All material presented in this publication is provided under a Creative Commons CC-BY Attribution 3.0 Australia<http://creativecommons.org/licenses/by/3.0/au/deed.en> licence. For the avoidance of doubt, this means this licence only
applies to material as set out in this document.

The opinions, comments and analysis expressed in this document are those of the author and do not necessarily represent theviews of the Minister for Social Services or the Australian Government Department of Social Services and cannot be taken in any
way as expressions of government policy.
Photo credits: photo on page 94 by Cliff Howard (© The University of Melbourne); photo on page 95 from Roy Morgan Research;
all other photos ©iStock.com
Designed and typeset by UniPrint Pty Ltd.
Printed and bound by UniPrint Pty Ltd.

Contents
1.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

2.

Family life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

3.

Household economic wellbeing . . . . . . . . . . . .23

4.

The labour market . . . . . . . . . . . . . . . . . . . . . . . . . . . .35

5.

Health and subjective wellbeing . . . . . . . . . . .51

6.

Cognitive activity and cognitive ability . . . .59

7.

Education and labour
market outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67

8.

Family background andeconomic wellbeing . . . . . . . . . . . . . . . . . . . . . . . . . . .77

9.

Household expenditure on food . . . . . . . . . . . .81

10. Sexual identity and economic,
health and social outcomes . . . . . . . . . . . . . . . .85
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .90
Technical Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .91
HILDA Survey personnel . . . . . . . . . . . . . . . . . . . . . . . . . . .95
Acknowledgements
Thanks to Deborah Kikkawa, Peng Yu and Anastasia Sartbayeva from the National Centre for Longitudinal Data in the
Commonwealth Department of Social Services for comments on drafts of this report, Gerda Lenaghan for subediting,and Nellie Lentini for performing consistency checks and overseeing subediting, typesetting and printing of the report.

Contents3

1

Introduction
The HILDA Project
Commenced in 2001, theHousehold, Income and Labour
Dynamics in Australia (HILDA) Survey
is a nationally representative
longitudinal study of Australianhouseholds. The study is funded by
the Australian Government
Department of Social Services (DSS;
previously Department of Families,
Housing, Community Services andIndigenous Affairs) and is managed
by the Melbourne Institute of Applied
Economic and Social Research at
the University of Melbourne. Roy
Morgan Research has conducted thefieldwork since Wave 9 (2009), prior
to which The Nielsen Company was
the fieldwork provider.
The HILDA Survey seeks to provide
longitudinal data on the lives ofAustralian residents. It annually
collects information on a wide
range of aspects of life in Australia,
including household and family
relationships, child care,
employment, education, income,expenditure, health and wellbeing,
attitudes and values on a variety ofsubjects, and various life eventsand experiences. Information is
also collected at less frequentintervals on various topics,
including household wealth, fertilityrelated behaviour and plans,
relationships with non-resident
family members and non-resident
partners, health care utilisation,eating habits and retirement.
The important distinguishing featureof the HILDA Survey is that thesame households and individuals
are interviewed every year, allowing
us to see how their lives arechanging over time. By design, thestudy can be infinitely lived,
following not only the initial sample

4

members for the remainder of theirlives, but also the lives of theirchildren and grandchildren, andindeed all subsequent descendants.
Household longitudinal data, known
as panel data, provide a much more
complete picture than crosssectional data because they
document the life-course each
person takes. Panel data tell us
about dynamics—family, income andlabour dynamics—rather thanstatics. They tell us about
persistence and recurrence, forexample, of poverty, unemployment
or welfare reliance. Perhaps mostimportantly, panel data can tell us
about the antecedents andconsequences of life outcomes,
such as poverty, unemployment,
marital breakdown and poor health,because we can see the paths that
individuals take to arrive at theseoutcomes and the paths they takesubsequently. Indeed, one of thevaluable attributes of the HILDA
panel is the wealth of information ona variety of life domains that itbrings together in one dataset. This
allows us to understand the many
linkages between these life
domains; to give but one example,
we can examine the implications ofpoor health for risk of poor
economic outcomes.
Panel data are also importantbecause they allow causal
inferences in many cases that are
more credible than what othertypes of data permit. In particular,
statistical methods known as ‘fixed
effects’ regression models can be
employed to examine the effects ofvarious factors on life outcomessuch as earnings, unemployment,
income and life satisfaction. Thesemodels can control for the effectsof stable characteristics of

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

individuals that are typically notobserved, such as innate ability
and motivation, and which
confound estimates of causal
effects in cross-sectional settings.

This report
This report presents brief
statistical analyses of the first
12 waves of the study, which wereconducted between 2001 and2012. It examines nine topics:
family life; economic wellbeing;labour market outcomes; healthand subjective wellbeing; cognitive
activity and cognitive ability;
education and labour market
outcomes; family background andeconomic wellbeing; expenditure on
food; and sexual identity. As wideranging as these topics are, thisreport should be viewed as
containing only ‘selected findings’.
Each of the topics is covered in a
cursory fashion, and there aremany other topics that can be
examined with the data. The HILDA
Survey is an extremely rich data
source, and testament to this is
the large number of publications
on a diverse range of topics.
Further details on thesepublications are available on the
HILDA Survey website at <http://
www.melbourneinstitute.com/hilda/
biblio/> and on the DSS website at<http://flosse.fahcsia.gov.au/>.

Most of the analysis presented inthis report consists of graphs andtables of descriptive statistics thatare reasonably easy to interpret.
However, several tables in thisreport contain estimates from
regression models. These are lesseasily interpreted than tables ofdescriptive statistics, but areincluded because they are valuable
for better understanding the various
topics examined in the report. In
particular, a regression model
provides a clear description of thestatistical relationship between two
factors, holding other factors
constant. For example, a regression
model of the determinants ofearnings can show the average
difference in earnings between
disabled and non-disabled
employees, holding constant otherfactors such as age, education,
hours of work, and so on (i.e., theaverage difference in earnings
when people do not differ in othercharacteristics). Moreover, undercertain conditions, this statisticalassociation can be interpreted as a
causal relationship, showing theeffects of the ‘explanatory variable’on the ‘dependent variable’.
Various types of regression models
have been estimated for thisreport, and while these models are
not explained in depth, brief
outlines of the intuition for thesemodels and how to interpret theestimates are provided in theTechnical Appendix.
The Technical Appendix also
provides details on the HILDA
Survey sample and the population
weights supplied in the data tocorrect for non-response andattrition. These weights are used in
all our analysis so that all statistics
represent estimates for theAustralian population. Note also thatthe estimates based on the HILDA
Survey, like all sample survey
estimates, are subject to sampling
error. As explained in more detail in
the Technical Appendix, fortabulated results of descriptive
statistics, we have adopted an
Australian Bureau of Statistics (ABS)convention and marked with anasterisk (*) estimates which have arelative standard error—thestandard error relative to the size ofthe estimate itself—of more than25%. Note that a relative standard
error of less than 25% implies there
is a greater than 95% probability
that the true quantity lies within
50% of the estimated value. For
regression model parameter
estimates presented in this report,
estimates that are not statisticallysignificantly different from 0 at the
10% level are marked with a ‘plus’
superscript (+). Estimates that are
statistically significant at the 10%
level have a probability of not being
0 that is greater than 90%.

Introduction

5

2

Family life1

The HILDA Survey is uniquely placed to provide us with an understanding of family
life in Australia. Every year, a considerable amount of information is collected on
various aspects of family life, including household structures, how parents copewith parenting responsibilities, and perceptions of and attitudes to the roles ofhousehold members. The collection of this information from the same individuals
each year allows us to investigate how and why family circumstances changeover time—partnering and marriage, separation and divorce, childbirth, adultchildren leaving the family home, and indeed any other change to the composition
or nature of family circumstances.
In this chapter, analyses are presented for the 2001 to 2012 period on threefamily-life topics: partnering and separation; families with dependent children;
and adult children living with their parents.

Partnering andseparation
While the marital status profile of
the Australian population, and how
it has changed over time, isreasonably well understood, the
HILDA Survey data provide theopportunity to examine theexperiences at the individual levelof changes to marital status overtime. In this section, we present a
brief analysis of the patterns ofpartnering and separation
experienced by individuals.
Before turning to longitudinal
analysis, Table 2.1 provides crosssectional ‘snapshots’ of the marital
status of the population in 2001,
2006 and 2012, disaggregated by
sex and age group. Legal marriages(‘married’) are distinguished fromde facto marriages (‘de facto’),
where the latter category refers topeople living with a partner but notlegally married. A trend decline in
the proportion of the population
who are married is evident, to asignificant extent mirrored by
growth in de facto marriages. For
men, the proportion married
declined from 56.2% in 2001 to1

6

53.2% in 2012, while theproportion de facto married rosefrom 9.2% to 11.8%. For women,
the proportion married declined
from 54.4% in 2001 to 51.4% in
2012, while the proportion de facto
married rose from 9.1% to 11.5%.
Significantly, for men, the decline inmarriage is predominately among
those aged 40 and over, whereas
for women the decline is evident in
all age ranges. Growth in de facto
marriages is evident for all age
groups for both men and women.

Individuals’ experiences ofpartnering and separation
Taking a longitudinal perspective, inTable 2.2 we examine partnering bysingle people, and marriage byunmarried people, over a 5-year
period. The first two columns
present the proportions of single
people who moved in with a partner,
disaggregated by sex and by age
group at the start of the 5-year
window. The last two columns
present the proportions of unmarriedpeople who got married, likewise
disaggregated by sex and age group.
Two pooled periods are examined:
the 5-year periods beginning in

Thanks to Markus Hahn for undertaking the statistical analysis for this chapter.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 2.1: Marital status by sex and age group (%)
2001
Married2006
De facto

Married2012
De facto

MarriedDe facto

Men
18–24

3.6

9.0

2.4

8.9

1.8

9.6

25–29

25.9

20.6

24.9

23.5

27.4

24.6

30–34

50.0

14.6

49.5

20.0

50.5

18.6

35–39

58.8

12.4

59.8

13.5

59.2

16.8

40–49

69.3

8.8

62.8

11.3

63.3

12.3

50 and over

76.4

4.1

75.6

4.8

71.4

6.5

Total

56.2

9.2

55.1

10.6

53.2

11.8

Women
18–24

8.2

15.4

8.2

15.8

5.6

18.4

25–29

36.5

19.1

35.8

24.5

35.2

23.7

30–34

60.7

14.5

55.3

17.1

54.0

15.6

35–39

67.2

11.8

65.6

12.4

64.0

15.6

40–49

67.0

8.0

65.3

9.7

65.7

11.4

50 and over

61.8

2.9

61.5

3.6

58.9

5.0

Total

54.4

9.1

53.5

10.2

51.4

11.5

2001, 2002 and 2003; and the
5-year periods beginning in 2005,
2006 and 2007. Thus, for the firstpooled period, everyone who was
single in 2001 is examined overthe period to 2006, everyone who
was single in 2002 is examinedover the period to 2007 andeveryone who was single in 2003
is examined over the period to
2008. Note, therefore, that aperson who was single in 2001,
2002 and 2003 would be
represented three times in the data
used to produce the estimates forthe 2001 to 2003 pooled period.
For both men and women, rates ofpartnering and marriage arestrongly ordered by age. The peakage group for both partnering andmarriage is 25–29, with rates thendeclining with age (with the minor
exception that, in the 2005 to2007 period, the partnering rate forwomen was similar across the 18–24, 25–29 and 30–34 age groups).
Comparing the two pooled periods,
there are indications of declines inpartnering and marriage among
those aged 18–29, and increases
among those aged 30–34.
Table 2.3 presents the counterpart
to Table 2.2, showing the
proportion of de facto marriedpeople becoming single, and the
proportion of legally married people
becoming single, over a 5-year
period. Most commonly, a person
will become single due toseparating, but some people,
particularly in the older age groups,

will become single because theirpartner died. Also note that aperson who is partnered in one
wave and in the next wave ispartnered with a different person isdeemed to have ‘become single’,although it is of course possible

Table 2.2: Percentage of unpartnered people partnering within
5 years, and percentage of unmarried people marrying within 5 years,
by age group
A. Initially not partnered:
Moved in with a partner

B. Initially not legally married:
Got married

2001–2003

2005–2007

2001–2003

2005–2007

18–24

42.2

41.8

18.6

16.1

25–29

49.5

43.9

32.6

30.6

30–34

32.3

35.8

20.7

26.7

35–39

30.3

24.5

16.2

14.3

Men

40–49

24.2

23.2

13.3

13.8

50 and over

14.2

11.6

9.8

6.8

Total

33.2

31.1

18.4

17.0

18–24

50.9

48.6

23.9

22.8

25–29

53.8

48.2

36.2

38.8

30–34

35.6

48.5

22.9

26.4

35–39

28.6

30.7

16.2

12.0

40–49

19.8

21.4

13.1

9.6

50 and over

3.6

7.3

2.2

4.3

Total

25.6

28.0

15.9

16.2

Women

Notes: Persons not initially partnered (Panel A) includes married people separated from their spouse.
Persons not legally married (Panel B) includes people living with a partner (i.e., de facto married).

Family life7Perhaps not unexpectedly, de facto
couples are more likely to separate
than married couples. In contrast to
the findings for partnering andmarriage, there is not a strong
association between age andlikelihood of separation. De facto
couples aged 18–24 areconsiderably more likely toseparate than older de facto
couples, but otherwise rates ofseparation are not substantiallydifferent across the age groups.
Figure 2.1 shows more precisely
how long de facto and legal
marriages survive, presenting‘empirical survival functions’ forlegal marriages and de facto
marriages commencing between
2001 and 2011. Each line
represents the proportion still living
together at each year of duration of
marriage—that is, the survival rate,
where a marriage is deemed to‘die’ once the first separation has
occurred. Note that de facto
marriages are treated as continuing(‘surviving’) if the partners become
legally married.
Consistent with Table 2.3, de facto
marriages are less likely to survive
to each duration. For example, atfour years duration, 74% of de facto
couples are still together,compared with over 90% of married
couples. At 11 years duration, 57%
of de facto couples are still
together, compared with 80% of
married couples. In general, thelonger the relationship, the lower
the likelihood it will end in any
given year. This is reflected in theslope of the survival function
tending to decrease as relationshipduration increases.

Factors affecting marital
breakdown
In Table 2.4, the characteristics
and events associated with marital
breakdown are investigated. The
table presents ‘hazard ratio’estimates from hazard models of
the probability of separation of the8

Table 2.3: Percentage of partnered people becoming single within 5 years,
by age group
Initially de facto marriedInitial years:

Initially legally married

2001–2003

2005–2007

2001–2003

2005–2007

18–24

39.2

37.1

*26.1

*11.7

25–29

29.8

17.0

13.1

11.4

30–34

23.6

17.7

12.2

9.8

35–39

27.2

32.1

10.8

8.7

40–49

23.4

19.9

8.2

7.9

50 and over

19.9

13.5

5.7

4.6

Total

26.4

21.4

8.1

6.7

18–24

38.4

34.3

12.3

13.5

25–29

26.8

16.8

11.0

11.7

30–34

20.8

22.0

10.5

9.2

35–39

20.3

25.6

7.4

11.3

40–49

21.6

16.9

9.9

7.0

50 and over

21.9

13.3

11.3

9.5

Total

25.7

21.4

10.4

9.3

Men

Women

Note: * Estimate not reliable.

indicates a positive association
between the explanatory factor andthe likelihood of marital breakdown,
while an estimate of less than 1
indicates a negative association.

partners. The models essentiallyshow the effects of factors on theprobability that the marriage ends
in a given year. A hazard ratio
estimate of greater than 1

Figure 2.1: Marriage survival rates
100
95
90

Percentage still living with partnerthat at no stage was the person
actually single.

85
80
75
70
65
60
55
50
1

2

3

4

5

6

7

8

Duration of marriage (years)
MarriedDe facto

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

9

10

11

(See the Technical Appendix for
more information on hazard
models.) Two models arepresented, one for legal marriages
and the other for de facto
marriages. The ‘unit of analysis’ isthe marriage, and the respective
samples of the two models are alllegal marriages observed in the2001 to 2011 period, and all defacto marriages observed in the2001 to 2011 period.
The estimated models include a setof factors separately measured forthe male and female members of
the couple, as well as factors
defined for the marriage. For both
legal and de facto marriages, nostatistically significant effects areevident for previous marriage orlabour force status of either the
male or female member of thecouple. Measures of health (seeBox 5.1, page 51) included in themodels show that poor general
health of either member of thecouple is not a significant factor,
but poor mental health of either
member of the couple is a strong

Box 2.1: Personality measures in the HILDA Survey
Waves 5 and 9 of the HILDA Survey included a short version of Saucier’s (1994) ‘Big 5’
personality test, from which personality scores are derived for extroversion, agreeableness,
conscientiousness, emotional stability, and openness to experience. Administered in the selfcompletion questionnaire, the personality test involved respondents indicating the extent towhich each of 36 words described them. The scores were derived using a process called
principal components analysis. See Summerfield et al. (2013) for more information on thederivation of the scores. In this report, the personality scores are assumed to be fixed for eachindividual at the average values over Waves 5 and 9.

predictor of marital breakdown, in
all cases approximately doubling
the probability of separation in any
given year.
Personality measures (see Box 2.1,
above) show statistically significant
effects of female conscientiousness
and emotional stability in legal
marriages, but not in de facto
marriages, and statisticallysignificant effects of maleconscientiousness and emotional
stability in de facto marriages, but
not in legal marriages. In both cases,
greater conscientiousness increases
the probability of separation, while
greater emotional stability decreases
the probability of separation. Male
openness to experience is also
associated with a higher probability

Box 2.2: Importance of religion
In Waves 4, 7 and 10, the self-completion questionnaire contained a sequence of questions on
religious affiliation, frequency of attendance at religious services, and the importance ofreligion in one’s life. The importance of religion was ascertained by the question: On a scalefrom 0 to 10, how important is religion in your life? In this report, for Waves 4 to 12,
individuals are assigned their most recently reported value, while in Waves 1 to 3, they are
assigned the value reported in Wave 4.

of separation in legal marriages, but
not de facto marriages.
Each year, the self-completion
questionnaire contains a life events‘inventory’, asking whether eachof 21 events (22 since Wave 9)
have occurred in the last 12 months.
Variables for the following threelife events are included in
the models: victim of physicalviolence (e.g., assault); a major
improvement in financial situation(e.g., won lottery, received aninheritance); and, a majorworsening in financial situation(e.g., went bankrupt). The variable
in respect of physical violence isdefined separately for the two
members of the couple and is anindicator for experience of physicalviolence within the last two years.
This is a very strong predictor of
separation for females, particularly
in legal marriages, and maleexperience of physical violence isalso a predictor in legal marriages.
The strong effect evident for thisfactor may be because theperpetrator of the violence was,in many cases, the partner fromwhom the individual separated.
The model for legal marriage
includes an indicator variable forwhether the couple lived together
before they got married—that is,were in a de facto marriage. The
estimate indicates a positive
association with marital breakdown,
but it is not statistically significant.
The duration of the relationship is
not a significant factor for de facto
marriages, but legal marriages aresubstantially less likely to end
within the first three years than atlonger durations. The presence ofa dependent child significantly
increases the likelihood ofseparation for legal marriages.

Family life9

Table 2.4: Factors impacting on marital breakdown—Hazard ratios
Legal marriage

De facto marriage

Male characteristics

Female characteristics

Male characteristics

Female characteristics

1.01+

1.19+

1.10+

1.06+

Employed full-time in previous wave

0.71+

1.14+

0.67+

1.24+

Employed part-time in previous wave

0.67+

0.86+

0.83+

0.95+

Unemployed in previous wave

1.83+

1.39+

1.64+

1.24+

Poor general health (< 40)

1.02+

1.24+

0.90+

1.18+

Poor mental health (≤ 50)

2.30+

1.79+

2.67+

2.38+

Extroversion

1.01+

1.00+

0.96+

1.04+

Agreeableness

1.04+

1.15+

0.89+

1.02+

Conscientiousness

1.08+

1.15+

1.20+

1.02+

Emotional stability

1.00+

0.88+

0.85+

0.95+

Openness to experience

1.23+

0.90+

0.96+

1.04+

Victim of physical violence in last 2 years

1.84+

6.42+

1.24+

2.05+

Previously married
Labour force status (Reference category: NILF)

Personality

Couple characteristics

Couple characteristics

1.24+

–

3–5 years

2.14+

1.08+

6–9 years

2.99+

1.46+

10–19 years

2.52+

1.22+

20 or more years

2.66+

1.13+

30–39

0.56+

0.70+

40–49

0.46+

0.49+

50–59

0.29+

0.32+

60 and over

0.23+

0.30+

Have a dependent child

1.35+

1.29+

Major improvement in financial situation 1–2 years ago

0.72+

2.15+

Major worsening of financial situation 1–2 years ago

0.66+

1.10+

Born in different countries

1.06+

1.03+

Male is 5 or more years older

1.12+

1.25+

Female is 5 or more years older

1.31+

1.02+

Male has higher educational attainment

1.16+

1.28+

Female has higher educational attainment

1.22+

0.92+

Religion important to both partners

1.08+

1.06+

Religion important to only one partner

1.17+

2.72+

Lived together before got married
Relationship duration (Reference category: < 3 years)

Mean age of couple (Reference category: < 30)

Extent to which couple has traditional views on marriage and children
Mean of couple

0.95+

0.97+

Absolute difference between partners

1.03+

1.01+

Extent to which male has more traditional views on
parenting and work than female

1.00+

1.00+

Absolute difference in personality

1.01+

1.02+

One partner (only) smoker in previous wave

1.61+

1.34+

One partner (only) regular drinker in previous wave

1.09+

0.98+

Number of couples in sample

3,167

1,207

269+

196+

Number of break-ups

Notes: Table reports hazard ratios from Cox proportional hazards regression models. See the Technical Appendix for details. Samples comprise all legal/de facto
marriages observed in the HILDA Survey period (2001–2012). NILF—Not in the labour force. + indicates estimate is not significantly different from 1 at the 10% level.

10

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

The dependent child estimate issimilar in magnitude for de facto
couples, but is not statisticallysignificant.
The indicator variables included for
major changes in financial situation
relate to the 12 months leading up
to the previous-wave interview—that is, one to two years prior to thecurrent wave—to ensure that the
major change was not itself a resultof the marital breakdown. Eachvariable is equal to 1 if either
member of the couple reported the
major change (and 0 otherwise).
No significant effects are evident.
The remaining explanatory
variables capture similarities andBox 2.3: Attitudes to marriage and children, and to parenting and paid work
Attitudes to marriage and children, and to parenting and paid work, have been measured inWaves 5, 8 and 11. Views on marriage and children are ascertained by asking respondents theextent to which they agree, on a 7-point Likert scale, with each of the following ninestatements: a. It is alright for an unmarried couple to live together even if they have nointention of marrying; b. Marriage is a lifetime relationship and should never be ended; c.
Marriage is an outdated institution; d. It is alright for a couple with an unhappy marriage to get
a divorce even if they have children; e. A woman has to have children in order to be fulfilled; f.
Children will usually grow up happier if they have a home with both a father and a mother; g. It
is alright for a woman to have a child as a single parent even if she doesn’t want to have astable relationship with a man; h. When children turn about 18–20 years old they should start
to live independently; and i. Homosexual couples should have the same rights as heterosexual
couples do. Assigning a score ranging from 1 to 7 to each response based on the extent ofagreement with the statement, the measure of the extent to which views about marriage andchildren are ‘traditional’ that is used in this report is calculated as (8 – a) + b + (8 – c) + (8 –d) + e + f + (8 – g) + h + (8 – i), potentially ranging from 8 to 56.
Views on parenting and paid work are ascertained by asking respondents the extent to which
they agree, on a 7-point Likert scale, with each of the following 14 statements: a. Many
working mothers seem to care more about being successful at work than meeting the needs oftheir children; b. If both partners in a couple work, they should share equally in the houseworkand care of children; c. Whatever career a woman may have, her most important role in life isstill that of being a mother; d. Mothers who don’t really need the money shouldn’t work; e.
Children do just as well if the mother earns the money and the father cares for the home andchildren; f. It is better for everyone involved if the man earns the money and the woman takes
care of the home and children; g. As long as the care is good, it is fine for children under 3
years of age to be placed in child care all day for 5 days a week; h. A working mother canestablish just as good a relationship with her children as a mother who does not work for pay;i. A father should be as heavily involved in the care of his children as the mother; j. It is notgood for a relationship if the woman earns more than the man; k. On the whole, men makebetter political leaders than women do; l. A pre-school child is likely to suffer if his/her motherworks full-time; m. Children often suffer because their fathers concentrate too much on theirwork; and n. If parents divorce it is usually better for the child to stay with the mother than withthe father. In this report, a measure of the extent to which views about parenting and work are‘traditional’ is calculated as a + (8 – b) + c + d + (8 – e) + f + (8 – g) + (8 – h) + (8 – i) + j + k +l + (8 – m) + n, potentially ranging from 14 to 98.
For both of the above measures, for Waves 5 to 12, individuals are assigned the value in themost recent wave, while in Waves 1 to 4, they are assigned the Wave 5 value.

differences between the partners
in terms of country of birth, age,educational attainment, theimportance of religion, attitudes
to marriage and children and toparenting and paid work,
personality, smoking and drinking.
No significant effects are evident
for differences in country of birth,age and educational attainment.
Religion being important to both
members of the couple (see Box
2.2, page 9) has no significant
effects on the likelihood ofseparation, but if religion isimportant to only one member ofa de facto couple, they are much
more likely to separate.
Traditional views on marriage andchildren (see Box 2.3, at left) areassociated with a lower probabilityof separation, while a greater
difference in views on marriage
and children is associated with ahigher probability of separation inlegal marriages, but not de facto
marriages. No significant effects
of differences in attitudes toparenting and paid work are found.
A measure of personality
differences between the partners—simply the sum of the absolutedifferences in scores for each of
the five dimensions—indicatespersonality differences do not
impact on marital breakdown. The
impacts of differences in smoking
and drinking behaviour are captured
by indicator variables for whether

Family life11

Table 2.5 presents the mean
ratings for men and women in
2001, 2006 and 2012,
distinguishing de facto married andlegally married couples. Two clear
patterns are evident. First, men are
on average more satisfied with theirpartners than women. Second, men
and women in de facto marriages
are on average more satisfied with
their partners. That said, averageratings are very high, in all cases
lying somewhere between 8 and 9.

(only) one partner was a smoker
and whether (only) one partner wasa regular drinker (three or moretimes per week), both measured in
the previous wave to ensure thatthe smoking or drinking behaviour
took place while the couple wastogether. The estimates indicatethat differences in smoking
behaviour matter, but differences indrinking behaviour do not.

Relationship satisfactionof partners

Results of regression models of
the determinants of relationshipsatisfaction (measured on the
0–10 scale), estimated for males
and females separately, arepresented in Table 2.6.
Unsurprisingly, the table shows thatthe differences between de facto

Each year, the self-completion
questionnaire has contained aquestion asking respondents torate, on a scale from 0 (completely
dissatisfied) to 10 (completely
satisfied), the extent to which they
are satisfied with their partner.

and legal marriages in relationshipsatisfaction evident in Table 2.5
derive from differences in otherfactors (such as duration of therelationship) rather than fromthe relationship types themselves.
That is, holding other factors
constant, there is no significant
difference between de facto andlegally married couples inrelationship satisfaction for men
or women. However, a number ofthe other factors considered inTable 2.6 are found to significantly
impact on the relationship.
The length of the relationship
impacts one’s satisfaction with it,
although the effects differ slightly
between de facto and legal
marriages. For both men andwomen, the longer the duration of

Table 2.5: Mean satisfaction with relationship with partner (0–10 scale)
2001

2006

2012

Men

Women

Men

Women

Men

Women

De facto

8.9

8.6

8.6

8.4

8.6

8.3

Married

8.5

8.3

8.4

8.3

8.5

8.2

12

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

the relationship, the lower issatisfaction, with the exception forlegal marriages that satisfaction isslightly higher for those in thelongest-duration category (20 ormore years) compared with thesecond-longest duration category(10–19 years). The presence ofdependent children also decreases
relationship satisfaction, by 0.32
for men and 0.37 for women.

Table 2.6: Factors affecting relationship satisfaction

Age is not a strong predictive factor
for men, with the exception that
men aged 40–49 are, all else
equal, significantly less satisfied
with their partner. For women,
relationship satisfaction isdecreasing in age up to the 50–59
age category. An age difference offive or more years does not impact
on men’s satisfaction, but womenare, holding other factors constant,
0.23 less satisfied if their partner
is five or more years older.
Compared with holding no postschool qualifications, a partner
holding a university qualificationacts to decrease satisfaction by
0.17 for men and 0.16 for women,
while the partner holding any otherpost-school qualification also
decreases men’s satisfaction by
0.21. The labour force status ofboth oneself and one’s partner
also impact on relationshipsatisfaction, with unemployment of
the man negatively impacting
satisfaction of both members of thecouple, and unemployment of thewoman negatively impacting on her
own relationship satisfaction (butnot her partner’s satisfaction).
Interestingly, employment—
particularly of the female
partner—is also a negative factor(compared with being out of thelabour force) for relationshipsatisfaction of both men andwomen. However, given
employment, there is no evidenceof additional adverse effects oflong (50 or more) hours of work. It
also does not seem to matter how
much one’s partner earns.
The health, and particularly mental
health, of one’s partner is an
important determinant of one’s

Men

Women

0.02+

–0.04+

3–5 years–0.33+–0.43+

6–9 years–0.52+–0.57+

10–19 years–0.66+–0.85+

20 or more years–0.95+–0.90+

3–5 years–0.33+–0.34+

6–9 years–0.44+–0.52+

10–19 years–0.61+–0.62+

20 or more years–0.56+–0.50+Have a dependent child–0.32+–0.37+

30–39–0.05+–0.13+

40–49–0.12+–0.40+

50–59–0.10+–0.48+

60 and over

0.02+

–0.34+

Partner is 5 years or more older–0.13+–0.23+

Partner is 5 years or more younger–0.02+

0.01+

Degree–0.17+–0.16+

Other post-school–0.21+–0.07+

Year 12–0.09+–0.04+

Legally married
De facto relationship duration (Reference category: < 3 years)

Marriage duration (Reference category: < 3 years)

Age (Reference category: 18–29)

Partner’s education (Reference category: Less than Year 12)

Labour force status (Reference category: Not in the labour force)
Employed full-time–0.09+–0.15+

Employed part-time–0.07+–0.11+

Unemployed–0.18+–0.19+

Partner’s labour force status (Reference category: Not in the labour force)
Employed full-time–0.08+

0.03+

Employed part-time–0.06+–0.01+–0.18+

Unemployed–0.09+

Works 50 or more hours per week

–0.01+

0.01+

Partner works 50 or more hours per week

–0.01+–0.01+

Partner’s annual personal labour earnings($’000, December 2012 prices)

0.00+

0.00+

Partner in poor general health (< 40)–0.10+–0.07+

Partner in poor mental health (≤ 50)–0.38+–0.45+

Extroversion

0.00+–0.01+

Agreeableness

0.07+

0.14+

Conscientiousness

0.03+

0.06+

Emotional stability

0.10+

0.10+

Openness to experience–0.04+–0.02+

Extroversion

0.00+–0.01+

Agreeableness–0.05+

0.06+

Conscientiousness–0.02+–0.01+

Emotional stability

0.00+–0.04+

Openness to experience–0.03+–0.09+

Non-smoker and partner is a smoker–0.34+–0.20+

Non-regular drinker and partner is regular drinker–0.05+–0.03+

Major improvement in financial situation–0.07+

0.07+

Major worsening of financial situation–0.35+–0.32+

Partner’s personality

Absolute difference in…

Constant
Number of observations

8.62+

8.07+

30,80830,973

Notes: Table reports coefficient estimates from linear random-effects regression models. See theTechnical Appendix for details. Sample comprises all partnered people and estimation is on all 12
waves (2001–2012). + indicates estimate is not significantly different from 0 at the 10% level.

Family life

13

relationship satisfaction. The
effects are similar for men andwomen, with poor general health ofthe partner decreasing relationshipsatisfaction by approximately 0.1,
and poor mental health decreasing
it by approximately 0.4.
The personality of one’s partner
also appears to matter, and tosome extent so do differences
between the partners inpersonality. Greater agreeablenessand emotional stability of one’s
partner increases satisfaction,
while greater conscientiousnessof the male partner increases
women’s satisfaction, andgreater openness to experienceof the female partner decreases
men’s satisfaction.
The effects of differences inpersonality are quite different formen and women. For men, greater
difference in agreeableness acts todecrease relationship satisfaction,
while differences in the other traits
do not significantly affect theirsatisfaction. For women, greater
differences in emotional stability
and openness to experience act todecrease relationship satisfaction,
but greater difference inagreeableness acts to increasesatisfaction. Collectively, theeffects of partner’s agreeablenessand differences from partner inagreeableness imply women’ssatisfaction is increasing in
partner’s agreeableness, andthe gain is higher the lessagreeable the woman herself.
For emotional stability, by contrast,
the estimates imply women’s

proportion with the youngest childaged under 18 declined, and the
proportion with the youngest
dependent child aged 18–24
increased, rising from 1.5% in2001 to 3.0% in 2012. Also
evident is that the average age ofparents over this period has
increased. For example, in 2001,
42.7% of those aged 25–34 wereparents with dependent children
under 18, but in 2012 this had
fallen to 35.6%. Over the same
period, the proportion of those
aged 45–54 who were parents withdependent children aged under 18
increased from 35.7% to 41.5%.

satisfaction is highest when both
she and her partner score highlyfor emotional stability.
Similar to the findings with respect
to marital breakdown (Table 2.4),
being a non-smoker and having apartner who smokes lowers
relationship satisfaction, by 0.34
for men and 0.20 for women, while
differences in drinking behaviour donot seem to matter. Finally, a major
improvement in financial situation
does not affect relationshipsatisfaction, but a major worseningof financial situation has large
negative effects, decreasing
satisfaction by approximately 0.3
for both men and women.

Table 2.8 presents descriptive
information on dependent children.
Consistent with the estimatespresented in Table 2.7, thepercentage of those aged 18–24
who are dependent children rose
substantially between 2001 and2012, from 15.8% to 26.6%. The
table also shows the proportion ofchildren aged under 18 living inlone-parent families. Perhaps
contrary to popular perception, thishas fallen between 2001 and 2012,
from 20.4% to 18.6%. The averageage of children living in lone-parent
families has also risen: theproportion of children aged under 6living in lone-parent families has
decreased substantially, from
18.3% to 13.8%, while theFamilies withdependent children
Table 2.7 presents basic
descriptive information for 2001,
2006 and 2012 on the proportionof people who are parents livingwith dependent children (see Box
2.4, below), disaggregated by age
group and by whether the youngest
dependent child is under 18 oraged 18–24. Just over 30% of
people aged 15 and over areparents living with dependent
children. This proportion remained
relatively steady over the 2001 to2012 period, although theBox 2.4: Dependent children

The definition of a dependent child used in this report follows the Australian Bureau of
Statistics approach (see Australian Bureau of Statistics, 1995). According to this definition,
a dependent child is: (1) any child under 15 years of age; or (2) a child aged 15–24 who isengaged in full-time study, not employed full-time, living with one or both parents, not livingwith a partner, and who does not have a resident child of their own.

Table 2.7: Percentage of people who are parents with dependent children, by own age and age of youngest child
2001

2006

2012

Age of youngest childAge of youngest childAge of youngest child

< 18

18–24

Total

< 18

18–24

Total

< 18

18–24

Total

15–24

5.5

*0.0

5.5

5.2

*0.0

5.2

4.2

*0.0

4.2

25–34

42.7

*0.0

42.7

39.0

*0.0

39.0

35.6

*0.0

35.6

35–44

68.9

*0.8

69.7

67.4

*0.5

67.9

68.9

1.3

70.2

45–54

35.7

5.5

41.2

38.4

8.9

47.3

41.5

9.1

50.6

55 and over

2.4

1.5

3.9

2.7

2.4

5.1

3.7

4.0

7.7

Total

29.3

1.5

30.8

27.8

2.4

30.2

27.3

3.0

30.3

Note: * Estimate not reliable.

14

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 2.8: Dependent children
2001

2006

2012

15.8

22.8

26.6

Aged under 6

18.3

14.7

13.8

Aged 6–12

22.4

21.5

20.2

Aged 13–17

20.1

23.9

22.8

All aged under 18

20.4

19.9

18.6

Percentage of people aged 18–24 who are dependent children
Percentage of dependent children under 18 living in a lone-parent family

proportion of children aged 6–12
living in lone-parent families has
decreased from 22.4% to 20.2%,
and the proportion of children aged
13–17 living in these families has
actually risen from 20.1% to 22.8%.

Figure 2.2: Proportion of children for whom work-related child care isused, by family type and age of child55

50

Child care use for children
aged under 13

Figure 2.2 shows work-related childcare (see Box 2.5, at right) is used
for approximately 40–45% ofchildren aged 6–12, with rates ofuse similar for couple families andlone-parent families. For children
aged under 6, use of work-relatedchild care is considerably higher incouple families than in lone-parent
families. There is no clear trend inchild care use over the 2002 to2012 period for children aged 6–12
or for children aged under 6 living
in lone-parent families. However,there is a clear trend rise in the
proportion of couple families using
work-related child care for children
under 6, rising from approximately
40% in 2003 to approximately 50%
in 2012.

%Child care has been a significant
public policy issue for some years
now, largely because of the steady
growth in female employment
participation since the 1970s.
While government subsidies forchild care are significant, there islittle doubt that access toaffordable and high quality childcare looms large in the minds ofmany parents with young children.
Figure 2.2 and Tables 2.9 and 2.10attest to this, providing crosssectional information on child careused for children aged under 13
and the difficulties faced byparents in obtaining child care.

45

40

35

30

25
2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Couple family—aged under 6

Lone-parent family—aged under 6

Couple family—aged 6–12

Lone-parent family—aged 6–12

Box 2.5: Types of child care
In this report, distinctions are drawn between work-related and non-work-related child care, andbetween formal and informal child care. Work-related child care is child care which is used while
the parents are engaged in paid employment. Non-work-related child care refers to all other childcare. Formal care refers to regulated care away from the child’s home, such as before or after
school care, long day care, family day care, and occasional care. Informal child care refers to
non-regulated care, either in the child’s home or elsewhere. It includes (paid or unpaid) care bysiblings, grandparents, other relatives, friends, neighbours, nannies and babysitters.

Table 2.9 shows the types of workrelated child care used in 2012 forchildren not yet at school, children
at school aged less than 10, andchildren at school aged 10–12. For
children not yet at school for whom
work-related child care is used,
informal child care, most commonlycomprising a parent, a partner of aparent, or a grandparent, is used for
68.1% of children, while formal childcare, most commonly a private orcommunity long day centre, is used
for 69.1% of children. For children atschool but aged less than 10 (forwhom work-related child care isused), informal care is used for
83.2% during term time and 92.9%
during school holidays. As withchildren not yet at school, informalcare mostly consists of a parent,
parent’s partner or a grandparent,
although significant numbers ofchildren at school aged under 10
also receive informal care fromothers, including a relative living
elsewhere, a friend or neighbour, asibling or the child himself orFamily life

15

Table 2.9: Types of care used for children for whom work-related child care is used, 2012 (%)
Children at school
aged less than 10Children at school
aged 10–12

Children not
yet at school

Duringterm time

Duringschool holidays

Duringterm time

Duringschool holidays

Me or my partner

37.6

41.3

69.6

39.1

58.4

Child’s brother or sister

*0.3

7.2

5.2

19.7

14.5

Child looks after self

–

8.0

4.3

25.2

17.5

Child comes to my workplace

–

3.5

3.8

*4.0

*4.3

Child’s grandparent who lives with us

7.2

*2.9

*3.3

*2.5

*1.7

Child’s grandparent who lives elsewhere

26.7

26.4

36.0

27.3

37.9

Other relative who lives with us

*0.2

*1.6

*1.0

*1.1

*0.6

Other relative who lives elsewhere

9.3

9.8

11.0

8.0

12.6

Informal child care

A friend or neighbour coming to our home

2.5

5.7

*1.0

*4.0

*1.0

A friend or neighbour in their home

3.7

13.5

12.0

7.1

13.4

A paid sitter or nanny

6.0

5.0

3.4

*3.0

*3.1

Any informal care

68.1

83.2

92.9

90.6

94.3

15.9

*1.9

*2.1

*1.9

*1.6

Formal outside of school hours care

–

32.1

–

20.0

–

Vacation care

–

–

25.5

–

18.9

Long day care at workplace

7.3

–

–

–

–

Private or community long day care centre

39.3

–

–

–

–

Formal child care
Family day care

Kindergarten/pre-school

15.0

–

–

–

–

Any formal care

69.1

33.7

26.8

21.2

20.5

Notes: Multiple-response question; columns therefore do not add to 100. * Estimate not reliable.

16

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 2.10: Difficulties with child care experienced by families who used, or thought about using, child care
Proportion of families experiencing difficulties with child care (%)
2001

2004

2007

2010

2012

Couple family
Availability

59.4

65.0

59.7

59.2

65.6

Quality

38.5

39.0

36.9

33.3

36.9

Cost

41.8

48.0

48.4

43.7

50.1

Any difficulty

67.2

72.0

70.0

65.9

73.3

68.9

70.0

67.7

68.2

68.5

Quality

48.7

43.9

35.6

40.8

35.4

Cost

40.2

34.2

39.9

43.5

50.5

Any difficulty

75.9

72.3

72.6

74.4

78.1

2001

2004

2007

2010

2011

Availability

74.7

80.6

77.7

74.6

80.0

Quality

49.8

61.0

57.9

50.8

54.5

Cost

60.4

73.5

71.1

66.2

68.8

Any difficulty

78.9

84.8

82.6

81.5

84.7

Lone-parent family
Availability

One-year persistence of difficulties with child care (%)

Couple family

Lone-parent family
Availability

*85.8

*94.1

*87.9

*88.9

78.7

Quality

64.6

*74.0

*75.1

*72.8

45.1

Cost

49.5

*60.0

*85.4

*74.8

*72.2

Any difficulty

*85.1

*93.7

*91.2

*91.9

*85.1

Notes: The unit is the family where a family is defined by using one reference person. This allows tracking the ‘family’ over time. If a family is a couple family, thereference person is the female. If a family is a lone-parent family, the reference person is the lone parent. * Estimate not reliable.

herself. Formal care is used for
33.7% of these children during term
time (mostly comprising formal careoutside of school hours) and by
26.8% during school holidays(mostly comprising vacation care).
For children at school aged 10–12
for whom work-related child care isused, 90.6% receive informal careduring term time and 94.3% receive
informal care during school holidays.
The informal care used is more
evenly distributed across thedifferent types, and for substantial
numbers—25.2% during term timeand 17.5% during school holidays—
informal care used includes thechild looking after himself or herself.
Formal care is less commonly used
for children aged 10–12, with it
being used for 21.2% of the children
in this age range who receive workrelated child care during term time,and 20.5% of these children during
school holidays.

In every wave of the HILDA Survey,
parents who have used or thought
about using child care in the last
12 months are asked to rate on ascale of 0 to 10 how much difficulty
they have had in the past 12
months with each of 12 aspects ofobtaining child care: (1) finding
good quality care; (2) finding theright person to take care of yourchild; (3) getting care for the hours
you need; (4) finding care for a sickchild; (5) finding care during school
holidays; (6) the cost of child care;(7) juggling multiple child carearrangements; (8) finding care for adifficult or special needs child; (9)finding a place at the child care
centre of your choice; (10) finding achild care centre in the rightlocation; (11) finding care yourchildren are happy with; and (12)finding care at short notice.
In Table 2.10, these aspects areaggregated into three categories:

availability (3–5, 7–10, 12); quality(1, 2, 11); and cost (6). The
proportion of children inhouseholds experiencing difficulties
with each aspect is reported in thetable, where a household isclassified as experiencing difficultywith an aspect if they rate theextent of difficulty as 5 or more forany component of the aspect. For
example, if a household scores
5 or more for any of aspects 3–5,
7–10, or 12, then the household isdefined as having difficulty with theavailability of child care.
The upper panel of the tablereveals a consistent pattern ofavailability difficulties being themost common, affecting thehouseholds of at least half thechildren in households where theparents had used or thought
about using child care. This isperhaps unsurprising, sinceavailability difficulties account for

Family life17

8 of the 12 types of difficulties
households could report. The next
most common difficulty is cost.
In 2012, this affected thehouseholds of half of children forwhom parents had used or thought
about using child care. Difficulties
with child care reached a peak in
2012, when difficulties withavailability, quality and/or cost
applied to 78.1% of children inlone-parent families and 73.3% ofchildren in couple families.
Difficulties with child care are acause for greater concern if they
are not able to be resolved byfamilies and therefore tend to
persist over time. Persistence ofdifficulties from one year to thenext, and how this has changed
over the HILDA Survey period, isexamined in the lower panel ofTable 2.10. The estimatespresented in the table are theproportion of children living in ahousehold experiencing difficulties
with an aspect of child care(availability, quality or cost) in theinitial year (2001, 2004, 2007,2010 and 2011) who were still
living in a household experiencing
difficulties with that aspect ofchild care in the next year. The
population examined comprises
children who were living inhouseholds in which the parents
had used or thought about using
child care in the last 12 months
in both of the two years overwhich persistence is evaluated,
and who were under the age of 13
in both years.
A relatively high degree of
persistence of child caredifficulties from one year to thenext is evident. Persistence ishighest for availability difficulties,
but is nonetheless also very highfor quality and cost difficulties,
exceeding 50% in almost allcases examined in the table.
Lone-parent families havesomewhat higher persistence inavailability difficulties than couple
families, and generally havegreater persistence in quality
difficulties. No clear trends in18

Box 2.6: Socio-Economic Index for Areas (SEIFA)
Constructed by the Australian Bureau of Statistics (ABS) using Census data, SEIFA is a suite offour indexes that can be used to explore different aspects of socio-economic conditions bygeographic areas. For each index, every geographic area in Australia is given a SEIFA number
which shows how disadvantaged that area is compared with other areas in Australia. In
analysis presented in this report, the SEIFA index used is the Index of Relative Socio-Economic
Advantage and Disadvantage, which is derived from Census variables such as low income, low
educational attainment, unemployment, and dwellings without motor vehicles. For more
information, see ABS (2009).

persistence are evident over the
HILDA Survey period.

Children’s education in 2012
Wave 12 of the HILDA Survey had
a special focus on ‘human capital’,included as part of which was aquestion sequence in respect ofeach child in the householdattending school in 2012. Thissequence of questions collected
information on the type of school,
the amount of school fees, andsubjective assessments of school
experiences and prospects forattending university.
Table 2.11 draws on theinformation collected on school
type and fees, presenting
comparisons of enrolment, school
fees and family characteristics
across the three main school types
in Australian education systems:
government schools, Catholicschools, and other non-government
schools. The table shows that
67.9% of primary-school children
are enrolled in government schools,
while 62.9% of high-school children
are enrolled in government schools.
Catholic schools account for 18.9%
of primary-school enrolments and21.3% of high-school enrolments,
while other non-government schools
account for 12.0% of primaryschool enrolments and 13.7% of
high-school enrolments.
Unsurprisingly, school fees are paid
for most students in non-government

schools. The mean annual school
fee in 2012 was $1,856 forprimary-school students at Catholicschools, $6,204 for primary-school
students at other non-government
schools, $5,371 for high-school
students at Catholic schools, and$10,925 for high-school students
at other non-government schools.
Substantial differences in students’family characteristics are evident
across the three school types. The
mean SEIFA decile (see Box 2.6,
above), mean equivalised income(see Box 3.2, page 25) andproportions with parents holding
university degrees are all highest for
students of other non-government
schools and lowest for students ofgovernment schools. The proportion
living in a lone-parent household isalso lowest for children in other nongovernment schools and highest forchildren in government schools. In
all cases, differences across school
types are more pronounced forhigh-school students than primaryschool students.
Students in non-government highschools are also more likely thanstudents in government schools
to live in major urban areas (seeBox 2.7, below), as are students inother non-government primaryschools. However, primary-school
children in Catholic schools are
actually less likely to live in majorurban areas than primary-school
children in government schools.

Box 2.7: Classification of region of residence
There are various ways of characterising the region of residence of sample members. In thisreport, we primarily characterise regions by population density, classifying households into
three categories: major urban (cities with populations of 100,000 or more); other urban (towns
and cities with populations of 1,000 to 99,999); and other regions (towns with populations
less than 1,000, and rural and remote areas). Note that some analysis in this report combines‘other urban’ and ‘other regions’ into a single category.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 2.11: School fees and family characteristics by type of school—Children enrolled at school, 2012
Primary school (Year 6 and below)

Percentage enrolled at school typea

Government
schoolCatholicschoolOther nongovernment
school

67.9

18.9

12.0

High school (Year 7 and above)
Government
schoolCatholicschoolOther nongovernment
school

62.9

21.3

13.7

School fees
Percentage for whom school fees paid

–

98.6

98.9

–

98.3

99.7

Mean fee ($)

–

1,856

6,204

–

5,371

10,925

59.7

55.1

71.9

58.6

65.2

65.0

Family characteristics
Live in major urban area (%)
Mean SEIFA decile

5.2

5.9

6.5

4.8

6.2

6.9

Mean household equivalised income ($)

42,17649,47459,27639,303

51,83158,721

Live in lone-parent household (%)

19.8

17.5

11.2

23.1

18.0

13.1

Mean number of school children in household

2.2

2.2

2.1

2.2

2.0

2.0

Mother has a university degree (%)

31.3

41.0

58.0

20.8

32.9

53.0

Father has a university degree (%)

27.4

40.8

60.8

19.5

34.1

55.1

One or more parents NESB immigrant (%)

19.3

23.3

25.0

19.2

25.5

20.2

One or more parents ESB immigrant (%)

13.8

9.9

22.1

19.1

20.0

17.6

Notes: In 2012, high school did not start in Queensland until Year 8, but Queensland children in Year 7 are nonetheless classified as in high school for the
purposes of this table. a An ‘other’ school type was reported for 1.1% of primary-school children and 2.1% of high-school children.

The mean number of children in thehousehold is fractionally lower for
students of non-government
schools. There is no strong patternin the prevalence of immigrantparents across the school types,
although children in nongovernment schools tend to be
more likely to have an NESB
immigrant parent (see Box 2.8,
page 22).
Table 2.12 compares subjective
assessments of school
experiences, and outcomes andprospects for going to university,
across school types and acrossboys and girls. The upper twopanels are based on parent or
guardian responses in respect of
the school children in thehousehold, while the bottom panel

is for school students aged 15 andover and is based on responses by
the students themselves.
As in Table 2.11, clear differences
across school types are evident.
Subjective assessments of qualityof education, school achievement,
and likelihood of going to university
are all highest for students inother non-government schools
and lowest for students ingovernment schools. Reported
rates of experience of bullying andbeing contacted by the school
because of poor behaviour are alsoordered by school type, with the
exception that parents of primaryschool children in othernon-government schools are more
likely to have been contacted by theschool because of poor behaviour

than parents of primary-school
children in Catholic schools. Amongstudents aged 15 and over,reported rates of skipping schooland suspension or expulsion are
higher for those in government
schools, and similar for Catholicand other non-government school
students. Worry about bullying is
also more prevalent among
students in government schools.
Examining differences between
boys and girls, there are indications
that boys do not fare as well as
girls at school. Parents andguardians on average report worse
educational outcomes andprospects for boys—the notable
exception being the experience ofbullying in high school, whichappears to be worse for girls than

Family life

19

Table 2.12: Education outcomes of children at school, 2012
By school type
By sexGovernment

Catholic

Other nongovernment

Boys

Girls

Primary school (Year 6 and below): Parent or guardian responses for each child at school
Mean satisfaction with education

7.8

8.0

8.3

7.9

7.9

School achievement excellent or above average (%)

52.1

57.4

67.4

49.9

60.3

Child will definitely or probably go to university (%)

57.8

74.8

81.3

58.2

70.0

Bullied at school (%)

27.4

23.8

19.1

26.6

24.8

Contacted by school because of poor behaviour (%)

12.7

7.6

10.1

17.3

5.3

High school (Year 7 and above): Parent or guardian responses for each child at school
Mean satisfaction with education

7.3

7.9

8.2

7.4

7.6

School achievement excellent or above average (%)

45.3

59.6

67.7

45.3

58.4

Child will definitely or probably go to university (%)

49.1

74.3

77.3

54.4

63.2

Bullied at school (%)

21.7

15.2

11.1

16.4

21.5

Contacted by school because of poor behaviour (%)

24.3

11.8

11.5

25.9

13.1

Responses by those aged 15 and over attending school
Skipped school without an excuse (%)

24.0

*9.2

*8.1

21.4

15.8

Suspended or expelled (%)

10.5

*1.4

*1.5

10.0

*3.8

Worry about bullying (%)

13.4

*9.7

*7.8

10.3

13.4

Note: * Estimate not reliable.

Table 2.13: Percentage of adult children living at home, by sex and age
2001

2004

2007

2010

2012

74.8

78.7

81.1

82.1

81.7

Men
18–21
22–25

39.8

47.9

53.8

51.2

52.1

26–29

20.1

27.5

26.7

27.8

24.2

Total

45.0

51.9

53.3

53.5

51.4

18–21

59.1

70.5

71.1

71.8

72.5

22–25

29.8

33.7

39.3

38.1

41.9

Women

26–29

14.6

14.2

19.7

17.1

14.7

Total

34.0

39.4

43.2

41.8

41.3

Table 2.14: Adult children (aged 18–24) moving out of home and back home, by sex and age
Men
Over 1 year
2001–2003

Women
Over 5 years

2009–2011

Over 1 year

Over 5 years

2001–2003

2001–2003

2009–2011

2001–2003

Proportion of those living at home who move out of home (%)
18–21

17.9

12.3

62.3

18.4

17.7

67.1

22–25

26.0

19.8

73.5

21.1

22.3

65.0

26–29

25.2

16.3

67.8

25.1

32.6

62.9

Total

21.5

15.5

66.7

20.0

21.3

65.9

8.1

17.6

6.4

8.0

10.1

Proportion of those living away from home who move back home (%)
18–21

7.2

22–25

5.9

3.4

9.6

*2.5

3.0

5.5

26–29

*2.0

*1.5

5.6

*1.0

*1.3

3.3

Total

4.0

2.9

8.7

2.5

2.9

5.3

Note: * Estimate not reliable.

20

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

boys. Consistent with the parent
and guardian reports, boys aged
15 and over at school are also more
likely to report skipping school andbeing suspended or expelled, while
girls are more likely to report being
worried about bullying.

Adult children livingwith their parents
Table 2.8 showed that a significant,and increasing, proportion of those
aged 18–24 years are dependent
children, almost all of whom livewith one or both parents. It
therefore seems likely that the
proportion of adults living in theparental home has likewise been
growing over time. Table 2.13
shows the proportion of young
adults—people aged 18–29—
living in the parental home over the
2001 to 2012 period (where, in thecase of separated parents, theparental home could be either
parent’s home).
The table shows there has indeed
been growth in the proportion ofyoung adults living with theirparents, although all of the growthappears to have occurred byaround 2007, since when there has
been a slight decline. In 2001, 45%of men aged 18–29 and 34% of
women aged 18–29 were living with
their parents, while in 2012, 51.4%
of men and 41.3% of women in thisage range were living with theirparents. That women seem to leave
the nest earlier than men probably
reflects their propensity to partnerat younger ages than men. As
expected, the proportion living with
their parents is highest for the
youngest of the three age groups
examined in Table 2.13, and lowest
for the oldest age group.

clear whether young adults tend tolive with their parents until they
move out, never to return, or
whether they tend to move backand forth between the parental
home and other places ofresidence. To investigate thisquestion, in Table 2.14 we examine
movements into and out of theparental home. The upper panel inthe table shows, for those living athome, the proportion moving out
over 1-year and 5-year time-frames,
while the lower panel shows, forthose living away from home, theproportion moving back home overthe same time-frames.
Overall, it appears that most people
move out once, never to return, withcomparatively few people moving
back home over a 1-year or 5-yeartime-frame. Nonetheless, theproportion moving back home is not
insignificant: over a 5-year period,
8.7% of men and 5.3% of women
aged 18–29 living away from theparental home move back in with
their parents. Rates of return to theparental home are particularly
sizeable for those aged 18–21,
with 17.6% of men and 10.1% of
women in this age group moving

back in with their parents over a
5-year period. Interestingly, over the
2001 to 2003 period, men had asimilar propensity to women to
move out of the parental home (beit over a 1-year or 5-year time-frame).
However, they had a higher rate of
return to the parental home, which
explains why the proportion livingwith their parents in a given year
was higher for men than women.

Characteristics
associated with livingin the parental home
The factors affecting whether young
adults live with their parents areinvestigated in Table 2.15, which
presents Probit regression model
estimates of the probability an
individual lives with his or her
parents. Each estimate can beinterpreted as the effect on theprobability an individual lives in theparental home of a 1-unit increasein the value of the explanatory
variable. In the case of an indicator(or dummy) variable, this is simply
the effect of changing the variablefrom 0 to 1.
As expected, given the resultspresented in Table 2.13, age is an

While it is clear that a highproportion of young adults live with
their parents, what is not clear fromthe cross-sectional snapshotspresented in Table 2.13 is theextent of ‘fluidity’ between theparental home and other places ofresidence. For example, it is not

Family life21

important factor. For example, amale aged 28 or 29 is, all else
equal, predicted to have a 0.38
lower probability of living with hisparents than a male aged 18 or 19.
A female aged 28 or 29 is similarly
predicted to have a 0.37 lower
probability of living with her parents

than a female aged 18 or 19.
Living in a major urban area acts
to decrease the probability of livingwith one’s parents. Indigenous
women are, all else equal, lesslikely to live with their parents thanother native-born women, while
NESB immigrant men and women

Table 2.15: Factors affecting whether one lives at home—Persons aged
18–29
Men

Women

20–21

–0.090–0.12722–23–0.166–0.20524–25–0.229–0.25726–27–0.319–0.29928–29–0.381–0.371

Major urban area

–0.025–0.016+Age (Reference category: 18–19)

Place of birth and Indigenous status (Reference category: Other Australian-born)
Indigenous–0.046+ESB–0.038+0.002+

NESB

0.064

0.052

Both parents live together

0.096

0.097

One or more parents has a university degree–0.022+0.002+Disabled with moderate or worse work restriction

0.024+

0.017+

Extroversion–0.012+–0.011

Agreeableness0.000+

0.007+

Conscientiousness–0.014–0.010

Emotional stability

0.000+

0.001+–0.079Personality

Openness to experience

0.003+

0.005+

Full-time student–0.0520.005+

Labour force status (Reference category: Not employed)
Employed full-time–0.0290.082

Employed part-time–0.017+0.051

Personal income ($’000, December 2012 prices)–0.001–0.001

Partnered–0.443–0.383

Has a dependent child

0.309

0.264

Number of observations

11,10012,106

Number of persons

2,134

2,311

Notes: Estimates are mean marginal effects obtained from random-effects Probit models. See theTechnical Appendix for details. Sample comprises all persons aged 18–29 and estimation is on all
12 waves (2001–2012). + indicates estimate is not significantly different from 0 at the 10% level.

Box 2.8: Classification of place of birth and Indigenous status
An English-speaking background (ESB) immigrant is a person born in one of the ‘main’ Englishspeaking countries, which comprise the United Kingdom, United States, Canada, Ireland, New
Zealand and South Africa. A non-English-speaking background (NESB) immigrant is a foreignborn person born in any other country.
Among people born in Australia, in some analysis in this report a distinction is drawn between
people who self-identify as Aboriginal or Torres Strait Islander (Indigenous) and other people
born in Australia.

22(see Box 2.8 below for thedefinition of NESB immigrants) areconsiderably more likely to live with
their parents.
The educational attainment of theparents does not appear to affectthe likelihood of living with one’s
parents, but an individual isconsiderably more likely to live inthe parental home if the parents
themselves live together. The
presence of a disability (see Box
5.2 on page 54 for information on
the disability measure used in thisreport) does not significantly affectthe probability of living with one’s
parents (although the estimates
are positive for both men andwomen). The only personality trait
that appears to impact onlikelihood of living in the parental
home is conscientiousness, which
is negatively associated with thelikelihood of living at home—perhaps reflecting greater
willingness of more conscientious
people to take on the
responsibilities that go with living
away from one’s parents.
Being a full-time student decreases
the probability of living in theparental home for men, but not
women. Curiously, while full-time
employment reduces the probabilityof living in the parental home by
0.029 (compared with nonemployment) for men, for women it
actually increases this probability
by 0.08. Part-time employment also
increases the probability a woman
lives with her parents, by 0.05
compared with non-employment.
Higher personal income acts todecrease the probability of livingwith one’s parents, which is likely tosimply reflect the greater capacity ofhigher-income individuals to moveout on their own.
Living with a partner substantiallyreduces the probability of living with
one’s parents, by 0.44 for men andby 0.38 for women, but having adependent child has a large
positive impact on the probability ofliving in the parental home for both
men and women.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

3Household economic
wellbeing
Study of the distribution of income, and how an individual’s income changes overtime, is integral to understanding the economic fortunes of the Australian
population. The HILDA Survey is the only nationally representative data source inAustralia that has the capacity to provide information on both the distribution ofincome at a point in time and how incomes of individuals change over time.
The HILDA Survey also regularly collects other information relevant to assessmentof economic wellbeing, most notably collecting information on household
expenditure and wealth. Moreover, in addition to objective financial data,
information is regularly collected on the experience of financial stress, the ability
to raise funds at short notice, perceived adequacy of household income, savings
habits, saving horizon, attitudes to financial risk and satisfaction with one’s
financial situation.
This chapter contains three sections that focus on the income data, respectively
examining the distribution and dynamics of household income, the prevalenceand dynamics of income poverty, and the extent of welfare reliance.

Income levels andincome inequality
Annual income
Cross-sectional estimates of mean
and median household annual
disposable income (defined inBox 3.1, page 24) are presented
in Table 3.1. For this table,
the household is the unit ofobservation, meaning thateach household contributes
one ‘observation’ to the calculationof the mean and the median.
Mean household disposable
incomes have grown quite strongly
for the in-scope population over the
HILDA Survey period, increasing by$18,997, or $1,727 per year,
expressed at December 2012
prices. The median has likewisegrown, increasing by $16,532.
Growth was particularly strong
between 2003 and 2009, when themean increased by $18,032, or
28.6%, and the median increased
by $17,164, or 31.4%. In theabsence of substantial changes tohousehold composition over theperiod—and the last two columnsof Table 3.1 would indicate there
has been little change—thistranslates to a significant increasein average material living standards
over this period.
Table 3.2 considers the distributionof household income, taking into
account potential changes tohousehold composition by
examining ‘equivalised’ income perperson. (See Box 3.2 on page 25
for an explanation of how
equivalised income is calculated
and Box 3.3 on page 26 for an
explanation of the income
distribution measures.) The
individual is the unit of observation,
meaning the statistics presented
are for the distribution ofhousehold equivalised incomes
across all individuals in the
population, including children.
As expected, growth in the averagelevel of incomes since 2003 isrobust to the move to equivalised
incomes and the individual as theunit of analysis, as there have been
only modest changes in householdcomposition of the population overthis period. Broadly speaking, overthe 2001 to 2012 period as aHousehold economic wellbeing

23

Box 3.1: Measurement of household income in the HILDA Survey
The main household income measure examined in this report is ‘real household annual
disposable income’. Household annual disposable income is the combined income of allhousehold members after receipt of government pensions and benefits and deduction of income
taxes in the financial year ended 30 June of the year of the wave (e.g., 2001 in Wave 1). This isthen adjusted for inflation—the rise in the general price level in the economy—using the Australian
Bureau of Statistics Consumer Price Index, so that income in all waves is expressed at December
2012 prices, to give real income. Since prices tend to rise over time, real incomes are higher than
the nominal incomes reported by sample members.
HILDA Survey respondents do not actually report their disposable income; rather, each respondentis asked how much income they received from each of a number of sources, including
employment, government benefits, investments and any businesses they own. Total gross incomeof each individual is equal to the sum of these income components. The disposable income ofeach respondent is then calculated by estimating the income tax payable by the individual andsubtracting this from the individual’s total gross income. Disposable incomes of all household
members are added together to obtain household disposable income. See Wilkins (2014) for
details on the construction of gross income and the methods used to calculate disposable
income. Note that, consistent with the Canberra Group’s recommendations (see United Nations,2011), large irregular payments received by individuals are excluded from income for the analysis
presented in this report—that is, it is regular disposable income that is examined.

whole, income growth appears tohave been something of a ‘risingtide lifting all boats’, with the threemeasures of inequality presented
in Table 3.2 showing little netchange between 2001 and 2012;that is, income growth has applied
equally to low-, middle- and highincome persons.
Figure 3.1 compares medianincomes across family types(defined in Box 3.4, page 27). A
reasonably consistent ordering of
median incomes by type of family isevident across the 12 waves of thesurvey, ranging from single elderly
persons at the bottom to nonelderly couples without dependent
children at the top. It also appears
that there are three broad‘clusters’ of family types: nonelderly couples without dependent
children, who have the highest
incomes; couples with children andnon-elderly single persons, whohave middle-level incomes; andlone-parent families and elderly
people, who have low incomes. All
family types have experienced
growth in median incomes over thefull period, although the extent ofgrowth varies somewhat.

Long-term income
Friedman’s (1957) permanent
income hypothesis implies thatwhat is important to an individual’s
living standard is not current
income, but rather ‘permanent’ or(anticipated) lifetime income.
Current income is affected by
lifecycle stage and by transitory
fluctuations and therefore is often
not a good measure or reflection of
permanent income. Of course, inpractice, the stage of life at which
income is received also matters,
particularly since there is always
uncertainty about future income
streams. But the permanent income
concept is nonetheless relevant andimplies that even income measured
over a 1-year interval may provide a
misleading picture because of
short-term fluctuations.
The longitudinal structure of the
HILDA Survey data allows us to24

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 3.1: Household annual disposable incomes (December 2012 prices)

address the limitations of current
income by constructing measuresof income over longer intervals oftime than 1 year. In Table 3.3, thedistribution of 5-year equivalised
income is presented. Income iscalculated for each individual as
the sum of annual equivalised
income (adjusted for inflation) overthe 5-year period—that is,
equivalised income is obtained foreach of the 5 years and these fivevalues are then added together.
This has the effect of allowing forchanges to household composition
over time—for example, if total
household income over the 5-year
period was divided by theequivalence scale that prevailed in
the first year, it could be misleading
if the individual’s householdcomposition changed during theperiod examined.
Consistent with the presence oftemporary fluctuations and lifecycle
trends in incomes, the inequality
measures in Table 3.3 indicate thereis less inequality in the distributionof our measure of permanent
income than in the distribution of
1-year income. The last column ofTable 3.4 provides a summary
measure of the relationship between
inequality of permanent income andinequality of 1-year income.
Specifically, it presents the ratio ofthe value of the Gini coefficient for
5-year income to the average value of
the Gini coefficient for 1-year income
over that 5-year period. Known as‘Shorrocks’ R’ (Shorrocks, 1978),this in fact provides a measure ofincome mobility. The closer thisMean ($)

Median ($)

Number ofhouseholds

Number of
persons

2001

63,44854,7007,425,697

18,986,818

2002

63,231

54,8637,535,509

19,218,072

2003

62,96354,7377,630,313

19,454,807

2004

65,21056,473

7,696,203

19,684,566

2005

68,27759,6507,792,815

19,955,825

2006

72,33562,095

7,917,587

20,265,863

2007

75,85365,3788,049,252

20,634,376

2008

78,52467,471

8,184,394

21,069,248

2009

80,99571,901

8,342,004

21,495,651

2010

81,21469,5308,459,863

21,799,276

2011

81,55069,0488,588,771

22,140,094

2012

82,445

71,2328,771,960

22,513,334

Box 3.2: Equivalised income
Equivalised income is a measure of material living standards, obtained by adjusting household
disposable income for the household’s ‘needs’. Most obviously, a household of four persons
will require a higher household income than a lone-person household for each household
member to achieve the same living standard as the lone-person household. There are,however, many factors other than household size that could be taken into account indetermining need. These include the age and sex of household members, health and disabilityof household members (since poor health and/or disability increase the costs of achieving agiven standard of living), region of residence (since living costs differ across regions) andhome-ownership status (since the income measure does not usually include imputed rent forowner–occupiers).
In practice, it is common for adjustment of income to be based only on the number of adult andchild household members, achieved by an equivalence scale. In this report, we have used the‘modified OECD’ scale (Hagenaars et al., 1994), which divides household income by 1 for the
first household member plus 0.5 for each other household member aged 15 or over, plus 0.3 foreach child under 15. A family comprising two adults and two children under 15 years of age
would therefore have an equivalence scale of 2.1 (1 + 0.5 + 0.3 + 0.3), meaning that the family
would need to have an income 2.1 times that of a lone-person household in order to achieve thesame standard of living. This scale recognises that larger households require more income, but it
also recognises that there are economies of scale in ‘household production’ (e.g., the rent on a
2-bedroom flat is typically less than twice the rent on an otherwise comparable 1-bedroom flat)and that children require less than adults. Each member of a household is assigned the sameequivalised income, the implicit assumption being that all household income is pooled and thenshared equally.

Table 3.2: Distribution of individuals’ equivalised household disposable
income (December 2012 prices)
Mean ($)

Median ($)

p90/p50

p50/p10Gini coefficient2001

37,75632,978

1.95

2.10

0.308

2002

37,671

32,934

1.93

2.05

0.305

2003

37,73033,445

1.89

2.07

0.303

2004

38,692

34,388

1.89

2.10

0.302

2005

40,64735,513

1.92

2.05

0.303

2006

42,89436,996

1.96

2.02

0.305

2007

45,35339,135

1.96

2.14

0.316

2008

46,40640,206

1.96

2.16

0.312

2009

47,99643,084

1.84

2.18

0.297

2010

47,90042,021

1.92

2.12

0.309

2011

48,29042,092

1.96

2.12

0.313

2012

48,935

43,210

1.89

2.05

0.301

Household economic wellbeing25

Box 3.3: Income distribution statistics
A variety of inequality measures are used in income distribution studies. In this report,
estimates are presented for several commonly used measures. Average income levels aredescribed by the mean and median, while inequality in the income distribution is described by
the ratio of the 90th percentile to the median (p90/p50), the ratio of the median to the 10th
percentile (p50/p10) and the Gini coefficient. The 90th percentile is the income of theindividual who has 10% of individuals with higher incomes and 90% with lower incomes. The
10th percentile is the income of the individual who has 90% of individuals with higher incomesand 10% with lower incomes. The Gini coefficient is an overall measure of inequality that
ranges from 0, where everyone has the same income, to 1, where one individual has all the
income. See the Technical Appendix for further explanation of these measures.

Figure 3.1: Median equivalised income by family type60,00055,000$ (December 2012 prices)

50,00045,00040,00035,00030,00025,00020,00015,0002001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 201226

Non-elderly couple

Couple with dependent children

Sole parent
Single non-elderly female
Single elderly maleSingle non-elderly male
Elderly couple
Single elderly female

value is to 1, the lower is income
mobility; conversely, the closer it isto 0, the greater is income mobility.
For example, if no-one’s incomechanged from year to year, the Gini
coefficient for 1-year income would
be equal to the Gini coefficient for 5year income, and Shorrocks’ R
would be equal to 1—and there
would indeed be no income mobility,
since no-one moves up or down theincome distribution from one year to
the next. At the other extreme, if
everyone had different incomes inany given year (such that, forexample, the average Gini coefficientfor 1-year income was 0.3), but all
had the same total income over 5
years (implying the Gini coefficientfor 5-year income would be 0),
Shorrocks’ R would equal 0—asituation of perfect income mobility.
The estimates of Shorrocks’ R
presented in Table 3.3 indicate that
inequality of 5-year income isapproximately 90% of inequality of
1-year income. Thus, some degreeof income mobility is evident over
5 years, but it is relatively limited.
It follows that there are many
persistently high-income persons
and many persistently low-income
persons. Study of the characteristicsof those with low income over the
5-year period would in particularreveal important information about
the identities of the entrenched poor.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 3.3: Distribution of long-term (5-year) equivalised income (December 2012 prices)
Mean ($)

Median ($)

p90/p50

p50/p10

Gini

Shorrocks’ R

2001–2005

193,808

174,715

1.78

1.93

0.273

0.897

2002–2006

196,892

177,246

1.79

1.92

0.273

0.899

2003–2007

204,485

182,165

1.81

1.88

0.276

0.903

2004–2008

214,197

191,927

1.82

1.92

0.279

0.907

2005–2009

222,318

198,733

1.83

1.92

0.280

0.913

2006–2010

228,698

202,990

1.83

1.93

0.279

0.906

2007–2011

234,742

209,709

1.83

1.99

0.279

0.902

2008–2012

240,099

215,554

1.80

1.98

0.276

0.901

A wide variety of definitions ormeasures of poverty, or material
deprivation, have been employed byeconomic and social researchers.
While recognising this diversity ofpotential measures, in this chapter
we focus on the most commonly
employed definition applied to thestudy of poverty in developed
countries, which conceives ofpoverty as relative deprivation orsocio-economic disadvantage, andwhich measures deprivation interms of inadequacy of income.
Consistent with the approach of theOrganisation for Economic Cooperation and Development (OECD)and other international bodies, wedefine relative income poverty as
having a household income below
50% of median income. While
based on a degree of public andresearcher consensus, it should
nonetheless be acknowledged that
there is an element of arbitrarinessto this—or any other—definition ofrelative poverty.

Cross-sectional poverty rates
Figure 3.2 presents relative andabsolute poverty rates in each year
covered by the HILDA Survey. The
absolute poverty line is the 2001
relative poverty line, adjusted for
inflation to maintain its purchasing
power over the 2001 to 2012 period(see Box 3.5, at right). Our income
measure is equivalised income;thus, the poverty lines presented
at the bottom of Figure 3.2 can beinterpreted as the annual income

Box 3.4: Family types
The following eight family types are distinguished in this chapter: (1) non-elderly couples, defined
to be couples (married or de facto) without dependent children with at least one member of thecouple under 60 years of age; (2) couples with at least one dependent child living with them; (3)lone parents living with at least one dependent child; (4) non-elderly single males (under 60 yearsof age); (5) non-elderly single females; (6) elderly couples, where both persons are over 60 yearsof age; (7) elderly single males (aged 60 and over); and (8) elderly single females. Note thatsome households will contain multiple ‘families’. For example, a household containing a nonelderly couple living with a non-dependent son will contain a non-elderly couple family and a
non-elderly single male. Both of these families will, of course, have the same equivalised income.

Figure 3.2: Percentage of the population in income poverty, 2001 to 2012
14

12

10

8

%

Income poverty

6

4

2

0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012$16,510 $16,473 $16,683 $17,123 $17,748 $18,518 $19,593 $20,124 $21,589 $21,048 $21,043 $21,688

Relative poverty

Absolute poverty

Note: Dollar values at the base of the figure are the relative poverty lines in each of the financial years,
expressed at December 2012 prices.

Box 3.5: Relative and absolute income poverty
A person is in relative income poverty if they are unable to afford the goods and services
needed to enjoy a normal or mainstream lifestyle in the country in which they live. In thisreport, we define a person to be in relative income poverty if household equivalised income isless than 50% of the median household equivalised income.
An absolute (or anchored) poverty line is an income poverty threshold which has its real value
held constant over time rather than adjusted for changes in average living standards. It is‘absolute’ in the sense that the purchasing power of the poverty line—the basket of goods andservices that it can purchase—remains fixed over time. The level at which an absolute poverty
line is set may nonetheless be based on the level of a relative poverty line obtained at aparticular point in time, for example the beginning of the time period under study.

Household economic wellbeing27

proportion of the population below
this poverty line has fluctuated overtime, rising substantially between
2006 and 2008, since when it has
steadily declined from 13.2% to11.4% in 2012. A key reason for thisfluctuation is that many welfare
recipients in Australia have incomesquite close to 50% of medianincome, so that relatively small
movements in government benefits
or the median can bring about
sizeable changes in the poverty rate.

after taxes and government
benefits that a single-personhousehold would require to avoid
relative poverty. Poverty rates referto the proportion of people (nothouseholds) living in poverty.
Reflecting the high rate of householdincome growth that has occurred
over much of the 2001 to 2012
period, the relative poverty line has
increased substantially, from
$16,510 to $21,688 expressed at
December 2012 prices. The

While the limited progress in
reducing relative income poverty
between 2001 and 2012 may be
regarded as undesirable, concern
may be tempered by the poverty
estimates obtained when the realvalue of the poverty line ismaintained at its 2001 level of$16,510 (at December 2012
prices). For this absolute poverty
line, the proportion of the
population below the poverty line
drops from 12.5% in 2001 to 4.4%

Table 3.4: Poverty rates by family type (%)
2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Non-elderly couple

10.5

8.4

7.6

7.9

7.0

6.9

8.6

8.3

8.8

7.4

7.3

7.6

Couple with children

7.1

6.4

7.2

8.0

6.3

5.3

7.6

7.3

5.8

6.4

6.2

5.4

Lone parent

18.5

17.1

18.1

18.7

17.1

20.3

23.0

22.8

19.8

24.3

21.9

18.5

Non-elderly single male

12.3

12.3

12.6

12.4

12.4

9.6

11.7

13.6

12.7

12.3

13.1

14.2

Non-elderly single female

14.9

13.7

15.3

14.5

13.9

12.5

15.4

15.8

15.5

13.7

14.2

11.7

Elderly couple

20.7

18.1

14.6

18.5

23.2

24.7

25.4

29.4

31.9

29.6

28.0

25.6

Elderly single male

39.3

38.5

39.9

39.1

34.6

32.2

34.8

37.2

42.2

39.5

33.8

33.8

Elderly single female

44.8

37.9

41.0

37.2

37.6

37.9

39.3

42.5

43.6

39.2

39.1

38.7

Table 3.5: Rates of child poverty—Children under 18 years of age (%)
2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

7.3

6.1

7.3

8.3

6.2

5.3

8.2

7.9

6.3

7.1

6.5

5.2

Live with 1 parent

19.8

17.7

18.3

19.5

17.8

22.1

24.3

24.3

21.5

25.4

23.1

19.8

All children

10.1

9.0

10.0

11.1

9.3

8.8

11.6

11.9

9.6

10.6

9.7

8.1

All persons

12.4

11.0

11.3

11.9

11.0

10.5

12.6

13.0

12.4

12.1

11.9

11.2

Live with both parents

28

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 3.6: Long-term income poverty (%)
2001–2005 2002–2006 2003–2007 2004–2008 2005–2009 2006–2010 2007–2011 2008–2012
All persons

8.6

8.3

7.6

8.7

9.0

8.7

9.9

9.7

Non-elderly couple

5.4

5.2

5.0

5.7

5.9

5.5

6.5

7.0

Couple with children

4.0

4.3

3.0

4.3

4.4

4.0

4.6

4.6

Lone parent

12.6

11.2

11.0

12.8

13.1

10.7

13.1

15.4

Single non-elderly male

7.7

6.7

6.3

7.3

7.0

7.4

10.0

8.7

Predominant family type

Single non-elderly female

11.6

10.4

9.3

9.4

9.6

10.3

11.0

9.7

Elderly couple

20.9

19.9

21.7

23.2

24.6

25.9

29.1

27.4

Single elderly male

34.3

32.1

29.2

32.9

35.2

36.7

39.0

35.7

Single elderly female

39.5

39.1

37.4

36.1

40.3

39.0

39.0

37.3

1 parent every wave

16.4

11.3

10.6

13.2

13.1

11.4

17.0

19.8

1 parent majority of waves

11.1

8.7

9.7

12.9

11.5

6.8

9.4

21.1

2 parents majority of waves

5.4

4.9

4.3

6.1

5.5

7.5

7.8

9.7

2 parents all waves

4.0

4.5

2.8

3.8

3.6

3.7

5.2

4.9

All children

6.2

5.7

4.4

5.7

5.4

5.0

7.0

8.0

ChildrenNote: Children are aged 0–13 in the initial year of each 5-year period (and are therefore aged 4–17 in the final year of each 5-year period).

in 2012. It is therefore clear that,even among the poor, average living
standards have increased over thefull 12-year period.

Poverty by family type
Table 3.4 shows that poverty rates
vary substantially by family type.
Rates are consistently high among
the elderly, particularly elderly single
people. Note, however, that elderly
people are more likely to own theirown house than are younger
people, and our income poverty
measure does not account forin-kind income provided by owneroccupied housing—that is, the rent
that home owners would have to
pay for their housing if they did not
own it. The income poverty rates forthe elderly are therefore likely tooverstate the extent of their relative
deprivation. Moreover, we can seethat the poverty rates for elderly
single males and females
decreased substantially between
2009 and 2011, from 42.2% to33.8% for males, and from 43.6%
to 39.1% for females. This is likely
to be attributable to the increase in
the payment rate for single Age
Pensioners from September 2009.
Poverty rates are also high for loneparent families, typically lying

between 18% and 20%. In 2012,
18.5% of people living in loneparent families were in poverty. By
contrast, non-elderly couples(married or de facto), whether withor without dependent children, haveconsistently low poverty rates.

Child poverty
Child poverty is a particular concern
for policy-makers because of thedamage poverty may do to children’s
future productive capacity and life
prospects more generally. The bottom
two rows of Table 3.5 show that thechild poverty rate is consistently
below the community-wide poverty
rate. However, as the second row of
the table shows, there is still muchroom for improvement among loneparent families. That said, after
peaking at 25.4% in 2010, thepoverty rate for children in loneparent families fell in each of thenext two years, to be 19.8% in 2012.

Long-term income poverty
In the same way that the distributionof ‘permanent’ income can be
examined, it is possible to use thelongitudinal structure of the HILDA
Survey data to examine ‘permanent’
income poverty. Table 3.6 shows the
proportion of people in relative

income poverty for a 5-year measureof income, where 5-year income ismeasured in the same way as
earlier in this chapter, and where thepoverty line is equal to 50% of
median 5-year equivalised income.
Consistent with some people inincome poverty in a given year being
only temporarily poor, the overall
income poverty rate is lower for
5-year income than for 1-year
income. The 5-year income poverty
rate ranges from 7.6% (2003–2007)
to 9.9% (2007–2011), comparedwith a range for 1-year poverty(shown in Figure 3.2) of 10.5% to13.0%. Somewhat concerning,
however, is that the highest 5-year
poverty rates have occurred in themost recent periods, with the 5-year
poverty rate 9.9% in the second-lastperiod (2007–2011) and 9.7% in
the last period (2008–2012). This isalmost certainly connected to theless favourable macroeconomic
conditions prevailing since the
Global Financial Crisis (GFC) in 2008.
The second panel of Table 3.6
shows the 5-year poverty rates
broken down by family type, whereindividuals are assigned to theirmost common (predominant) family
type over the 5-year period. Longterm poverty is clearly moreHousehold economic wellbeing

29The bottom panel of Table 3.6
focuses on child poverty. It
examines, for each 5-year period,
children aged under 18 for theentire period (and therefore aged
under 14 in the first year and aged
4–17 in the final year). Prevalenceof permanent income poverty islower among children than among
the general population. There has,
however, been a sharp rise in childpermanent income poverty between
the 2006 to 2010 and 2008 to2012 periods, rising from 5.0% to
8.0%. It is also clear that long-termchild poverty is strongly connectedto the presence of only one parent
in the household. Children livingwith only one parent in all or the
majority of the 5-year period havethe highest long-term income
poverty rates, while children livingwith both parents in all 5 years
have the lowest long-term income
poverty rates.

receipt. Specifically, it is possible toidentify entrenched welfare relianceand the factors associated with it.
In this section, we present a brief
overview of the extent of welfare
reliance in Australia, examining both1-year and 5-year time-frames.

Welfare reliance over a
1-year time-frame
Figures 3.3 and 3.4 respectively
present cross-sectional estimates of
welfare receipt and welfare reliancefor ‘workforce age’ persons, defined
here as people aged 18–64. In
2012, 31.1% of individuals aged 18–
64 were living in a household thatreceived income support at somestage of the 2011–12 financial year.
Significantly, there was a substantial
decline in the rate of receipt ofincome support payments between
2001 and 2009. For example, theproportion of working-age people inhouseholds that received income
support payments declined from

Box 3.6: Welfare payments
Welfare payments in Australia are known as income support payments, which are benefits paid toAustralian residents that are intended to represent the primary source of income of recipients.
Studies of welfare reliance in Australia correspondingly focus on receipt of income support
payments, although supplementary government benefits, known as non-income support payments,
are typically included by studies when determining the extent of welfare reliance of those who havereceived income support payments. Income support payments include the Age Pension, Disability
Support Pension, Carer Payment, Parenting Payment (Single and Partnered), Newstart Allowance,
Youth Allowance and Department of Veterans’ Affairs Service Pension, as well as several othersmaller payment types. Non-income support payments include Family Tax Benefit (Parts A and B),
the Baby Bonus and Carer Allowance.

Figure 3.3: Receipt of welfare by persons aged 18–64
40

35

30

25

%

prevalent among the elderly thanamong people in other family types.
It is nonetheless also relatively highamong lone-parent families,
particularly in the most recent 5year period (2008–2012), when
15.4% of people in lone-parent
families had 5-year income below
the poverty line. As with 1-year
poverty (Table 3.4), non-elderly
couples, with or without children,
have relatively low rates of 5-year
income poverty.

20

15

Welfare reliance
Dependence on welfare remains asignificant concern for policy-makers
in Australia because it is associatedwith significant demands on
government budgets and reduced
economy-wide market output.
Moreover, individuals’ reliance on
welfare is often associated withlong-term poverty, social exclusion
and other adverse outcomes forthem and their children. The HILDA
Survey is an important data source
for understanding welfare reliance,since the longitudinal nature of thedata enables the study of theduration and dynamics of welfare

30

10

5

0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Personally received welfareHousehold received welfare

Box 3.7: Definitions of welfare reliance
While a person may be regarded as to some extent reliant on welfare if any welfare payments
are received by that person’s household, welfare reliance is usually conceived as a situation inwhich welfare represents the primary or main source of income. In this report, two alternative
specific definitions of welfare reliance are adopted:
1.

The household receives income support payments and more than 50% of householdincome comes from income support and non-income support payments.

2.

The household receives income support payments and more than 90% of householdincome comes from income support and non-income support payments.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

As would be expected, theproportion of the population
classified as welfare reliant
depends on whether the 50% or
90% threshold is employed, withreliance lower adopting the 90%
threshold. However, the two
measures show similar trends,
both declining between 2002 and2008, and both remaining relatively
constant thereafter, atapproximately 10% for the 50%
threshold, and at approximately 5%
for the 90% threshold.

Figure 3.4: Reliance on welfare among persons aged 18–64
14

12

10

8

%

37.7% in 2001 to 30.6% in 2009.
Since 2009, welfare receipt has
increased only slightly.

6

4

Figure 3.5 shows that welfare
reliance among working-age people
is very much associated with living
in lone-parent families. For eachyear from 2001 to 2012, the figure
presents the proportion ofindividuals in each family typeobtaining more than 50% ofhousehold income from welfare
benefits. Lone parents haveconsiderably higher rates of welfare
dependence than people in otherfamily types, although there wassome decline in lone-parent welfare
reliance between 2002 and 2008,
falling from a peak of 44.9% in2002 to a low of 32.5% in 2008.

Welfare receipt over a
5-year time-frame
Table 3.7 presents 5-year rates of
welfare receipt and welfare reliance(50% threshold) among people02001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
More than 50% of household income from welfare
More than 90% of household income from welfare

Figure 3.5: Welfare reliance of persons aged 18–64, by family type
50
45
40
35
30

%

Couples, with or without dependent
children, have the lowest rates of
welfare dependence, and have also
experienced a decline in welfare
dependence, falling from 8.6% in2002 to 5.6% in 2012 for couples
with dependent children, and fallingfrom 10.9% in 2002 to 6.2% in2012 for other couples. Single men
and women have welfare
dependence rates slightly higher
than couples, and haveexperienced no trend decline inwelfare reliance. The gap between
couples and single people has
therefore risen over the HILDA
Survey period.

2

25
20
15
10
5
0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
CoupleCouple with dependent children

Lone parent

Single maleSingle female

Note: A person is defined to be welfare reliant if more than 50% of household annual income comesfrom welfare.

Household economic wellbeing

31

Table 3.7: Welfare reliance over a 5-year period, by initial age group and by predominant family type—Persons
aged 18–60 at the start of the 5-year period (%)
2001–2005

2003–2007

2005–20092007–20112008–2012

55.1

52.5

51.7

50.5

49.9

18–24

69.4

65.6

65.4

64.7

62.4

25–34

50.0

46.4

43.9

42.0

42.6

35–44

51.5

47.9

47.7

45.7

44.1

45–54

54.3

53.5

51.3

51.0

50.8

55–60

58.9

57.3

59.1

57.5

57.1

Couple

46.6

42.6

44.5

41.8

42.0

Couple with dependent children

50.7

46.8

45.5

43.2

42.3

Lone parent

88.0

88.3

83.5

84.6

81.9

Single male

64.3

62.0

61.9

61.7

62.2

Single female

64.6

65.9

63.8

64.8

62.7

7.9

7.3

7.9

8.3

8.1

18–24

5.4

4.0

4.3

5.2

5.2

25–34

5.2

5.3

6.6

5.8

6.4

35–44

7.3

6.4

6.9

7.8

6.8

45–54

9.2

8.5

8.3

8.3

8.2

55–60

16.3

15.2

16.1

17.5

17.4

Couple

6.4

6.5

6.7

6.3

6.6

Couple with dependent children

3.8

3.5

4.8

4.2

4.0

Lone parent

31.3

25.9

32.0

29.4

28.0

Single male

9.3

8.9

8.4

11.1

11.2

Single female

12.5

11.0

10.5

12.8

12.2

Household received welfare at some stage of the 5-year period
All aged 18–60 at start of period
Initial age group

Predominant family type

More than 50% of 5-year household income from welfare
All aged 18–60 at start of period
Initial age group

Predominant family type32

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 3.8: Persons commencing welfare receipt: Personal welfare reliance over the 5-year period fromcommencement, total and by initial payment type—Persons aged 18–60 at the start of the 5-year period
Personal receipt of income support (IS)Mean number of years on IS

Proportion on IS in all 5 years (%)

Spells commenced
2001–02 to 2004–05

Spells commenced
2005–06 to 2007–08

Spells commenced
2001–02 to 2004–05

Spells commenced
2005–06 to 2007–08

2.84

2.65

22.7

18.9

Unemployment benefits

2.72

2.82

20.9

22.7

Parenting Payments

2.56

2.39

18.6

15.5

Disability Support Pension

4.31

4.21

68.5

60.1

Other income support

2.92

2.57

20.6

15.0

Total
Initial main payment type

Personal income from welfare
Mean proportion of personal
income from welfare (%)

Total

Proportion on average personallyreliant on welfare over the 5 yearsa (%)

Spells commenced
2001–02 to 2004–05

Spells commenced
2005–06 to 2007–08

Spells commenced
2001–02 to 2004–05

Spells commenced
2005–06 to 2007–08

34.0

30.9

29.4

25.3

Initial main payment type
Unemployment benefits

26.7

29.0

23.6

22.7

Parenting Payments

32.2

29.0

25.7

23.8

Disability Support Pension

67.3

61.7

71.0

70.4

Other income support

36.0

30.3

30.5

23.4

Note: a To determine if an individual is ‘on average personally reliant over the 5 years’, the proportion of income from welfare is calculated for each year in the
5-year period and the average value of this proportion across the 5 years is obtained. If this average value is 50% or higher, the individual is deemed to be ‘onaverage personally reliant over the 5 years’.

aged 18–64 for the entire 5-year
period, in total and disaggregatedby (initial) age group and by
predominant family type. A
strikingly high proportion ofworking-age people come into
contact with the welfare systemover a 5-year period, although it isalso significant that this proportion
has steadily declined over the
HILDA Survey period. In the 2001
to 2005 period, 55.1% of
individuals aged 18–60 in 2001
were at some stage in a householdthat received income support, while
the corresponding figure for the2008 to 2012 period was 49.9%.
Rates of receipt over a 5-year
window are high for all age groups
in all periods examined in the table,
the lowest rate being 42.0% overthe 2007 to 2011 period for those
aged 25–34 in 2007. Nonetheless,substantial differences across theage groups are evident. Rates ofreceipt are highest for the 18–24
age group, but drop sharply for the
25–34 age group, which has thelowest rates of receipt of all theage groups. Rates of 5-year receipt
are then increasing in age group,with those in the 55–60 age grouphaving only slightly lower rates ofreceipt than those in the 18–24
age group.
Declines in rates of 5-year welfare
receipt between the 2001 to 2005
and 2008 to 2012 periods havebeen similar among the three
youngest age groups shown in
Table 3.7, falling approximately
7 percentage points. The twooldest age groups experienced
less decline in rates of 5-year
receipt, at 3.5 percentage points
among those aged 45–54 and only
1.8 percentage points for those
aged 55–60.
Couples without dependent
children have the lowest rates ofincome support receipt over a 5year window, closely followed bycouples with dependent children.
Both single men and single women
have high rates of income support
receipt, with more than 60%
receiving income support in all

Household economic wellbeing33

5-year periods examined inTable 3.7. Single parents havethe highest rates of receipt, withmore than 80% receiving income
support over any given 5-year
period. Couples with dependent
children have experienced thebiggest decline in 5-year welfare
receipt, followed by lone parents
and then couples without
dependent children. Single men
and women have experienced
only slight declines in rates of
welfare receipt.
While contact with the welfare
system is high, relatively few
working-age people are reliant on
welfare for more than half theirincome over a 5-year period.
Overall rates of 5-year reliance
range from 7.3% in the 2003 to2007 period to 8.3% in the 2007
to 2011 period. However, in an
important contrast to the findings
for rates of receipt, no trend
decline in 5-year welfare reliance isevident over the 2001 to 2012
period. Indeed, reliance washighest in the 2007 to 2011 and2008 to 2012 periods.
Disaggregation by age group and by

family type indicates this haslargely arisen from increases inreliance for the 25–34 and 55–60
age groups (with the increase forthe 25–34 age group actuallyoccurring in the 2005 to 2009
period, but sustained thereafter),and for single men.
In Table 3.8, we focus on personalrather than household income
support receipt and restrict theanalysis to people who commenced
a spell on income support,
examining how subsequent 5-year
receipt and reliance on income
support differs by initial benefit
payment type. For example, forpeople who commenced a spell on
income support in 2007–08, welfare
receipt and reliance is examined in
2007–08, 2008–09, 2009–10,
2010–11 and 2011–12. For the
purposes of the table, a spell on
income support is deemed tocommence if the individual received
income support in the current
financial year and did not receive
income support in the previous
financial year. The initial payment
type is the main payment type in thefirst year of the spell, defined as thebenefit from which the individual
derived the most income in that year.
The table shows that, amongindividuals aged 18–60 who
commenced an income support
spell in the 2001–02 to 2004–05
period, income support wasreceived, on average, in 2.84 of the
5 years from commencement of thespell; the corresponding figure forspells commenced in the 2005–06
to 2007–08 period is 2.65 years.
The proportion who were on income
support in all 5 years was 22.7% forspells commenced 2001–02 to
2004–05, and 18.9% for spells
commenced 2005–06 to 2007–08.
Consistent with these trends inreceipt among those whocommenced an income support spell,the mean proportion of personal
income from welfare declined from
34.0% for spells commenced 2001–
02 to 2004–05 to 30.9% for spells
commenced 2005–06 to 2007–08.
Likewise, the proportion who wereon average personally reliant on
welfare over the 5-year period (i.e.,
the proportion of income fromwelfare each year averaged over50%) fell from 29.4% to 25.3%.
Differences across initial payment
types show that subsequentreliance is highest for those whocommence on the Disability
Support Pension, which isconsistent with the tendency fordisabilities to persist over time.
Perhaps not so easily anticipated isthat people who commence on
Parenting Payment on averagespend less time on welfare in the
5 years following commencement
than people who commence on
unemployment benefits. Also
significant is that, while 5-year
reliance of those who commenced
on Parenting Payments, theDisability Support Pension andother income support payments
declined over the HILDA Survey
period, for those who commenced
on unemployment benefits, thetime spent on income support andthe mean proportion of income
from welfare over the subsequent
5 years actually increased.

34

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

4

The labour market
A primary focus of the HILDA Survey is the labour market activity of household
members. In each wave, detailed information is obtained from respondents toascertain their labour force status, earnings, hours worked, the type of work
undertaken, employer characteristics and a host of other work-related aspects.
Perceptions and attitudes on a range of labour market issues, such as satisfaction
with the current main job, likelihood of retaining the current job and preferred
hours of work, are also collected every year. Periodically, additional information isgathered on retirement intentions, attitudes to work and, more recently, workrelated training and experience of job-related discrimination.
Such an emphasis on the labour market reflects the pivotal role employment plays
in determining economic and social wellbeing. Not only is it the key determinant of
the majority of households’ incomes, it is key to participation in society both
economically and socially. Understanding individuals’ labour market outcomes,
and the causes and consequences of those outcomes, is correspondingly core to
the purpose of the HILDA Survey.

Labour force statustransitions
Standard statistical summaries ofthe labour force, such as producedby the Australian Bureau of
Statistics (ABS) for its monthly
publication, Labour Force, Australia(ABS, 2013), divide the population
aged 15 and over into ‘employed’,‘unemployed’ and ‘not in the labour
force’ (see Box 4.1, page 36). The
HILDA Survey collects informationfrom respondents each year
enabling classification of all
respondents into one of these three
categories. This allows us to
produce cross-sectional labour
statistics of the same kind as
produced by the ABS, but more
importantly, it facilitates longitudinal
analysis of many aspects of labour
force status mobility—that is,movements over time acrossdifferent labour force states.
Table 4.1 presents cross-sectional
HILDA Survey estimates of thelabour force status of the population
aged 15 and over for each year overthe 2001 to 2012 period. They

show, consistent with ABS Labour
Force Survey data, that the Global
Financial Crisis (GFC) marked
something of a turning point for theAustralian labour market. From2001 until 2008, employment
participation had been rising andunemployment had been falling; in2009, employment participation felland unemployment rose, with only apartial recovery occurring in 2010
and 2011, before further
deterioration in 2012. What is notclear from Table 4.1 is how thissoftening of the labour market has
translated into the rates at which
various transitions in labour force
status occur. For example, weaker
employment growth could arise fromfewer transitions fromunemployment to employment, fewertransitions from not in the labour
force to employment, increasedtransitions from employment tounemployment, and/or increasedtransitions from employment to notin the labour force.
Table 4.2 examines this issue bycomparing 1-year transitions inlabour force status over the 2003
to 2007 period with transitions overThe labour market

35the 2008 to 2012 period, showing,for each initial labour force state,
the proportion in each labour force
state one year later. The upper
panel of the table pools all 1-year
transitions occurring between 2003
and 2007 (i.e., 2003 to 2004,
2004 to 2005, 2005 to 2006 and2006 to 2007), while the lower
panel pools all 1-year transitions
occurring between 2008 and 2012
(i.e., 2008 to 2009, 2009 to 2010,2010 to 2011 and 2011 to 2012).

Box 4.1: Labour force status
In this report, insofar as is possible, we follow international and Australian Bureau of Statistics(ABS) conventions in determining an individual’s labour force status. In particular:

The table shows that men
experienced an increase in the rateof transitions from employment tounemployment (from 1.4% to 1.9%)
and a decrease in the rate oftransitions from unemployment toemployment (from 51.8% to44.8%). Women also experienced
an increase in the rate oftransitions from employment tounemployment (from 1.5% to 1.8%)
and a decrease in the rate oftransitions from unemployment toemployment (from 52.8% to43.9%), but also experienced anincrease in the rate of transitions
from employment to not in thelabour force (from 6.7% to 7.3%)
and a reduction in transitions fromnot in the labour force toemployment (from 17.9% to15.8%). Thus, weakening of thelabour market has primarily
affected transitions between
employment and unemployment for1.

A person is classified as employed if that person had a job, business or farm in the weekleading up to the interview, and had either worked in the last four weeks or had notworked but: had been in paid work for any part of the last four weeks; or had been on
worker’s compensation and expected to return to work for the same employer; or had notworked because of a strike or lock-out.

2.

An employed person is classified as employed part-time if usual weekly hours of work in
all jobs total less than 35. Otherwise, an employed person is classified as employed fulltime. (The definition of part-time employment adopted in this report differs from thedefinition the ABS uses in its Labour Force Survey. The ABS definition requires both usual
and current actual weekly hours to be less than 35.)

3.

A non-employed person is classified as unemployed if that person had actively looked forwork at any time in the four weeks preceding the interview and was available to start workin the week preceding the interview; or if that person was waiting to start a new job withinfour weeks from the date of interview and could have started in the week preceding theinterview if the job had been available.

4.

A non-employed person who is not unemployed is classified as not in the labour force(NILF). Among people not in the labour force, several distinctions are often made based on
the degree of ‘attachment’ to the labour market. This includes identifying the marginallyattached—people who want to work and are either available to start work but are notcurrently looking, or are looking for work but are not currently available.

Several key statistics are commonly produced based on these definitions of labour force
status, including the participation rate (the proportion of the population in the labour force)and the unemployment rate (the proportion of those in the labour force who are unemployed).

men, but for women has affectedboth transitions between
employment and unemployment
and transitions between
employment and non-participation(not in the labour force).

Labour force
transitions of youth
and young adults
The early years in the labour market
following completion of full-time
study are generally thought to be

critical to longer-term labour market
outcomes. Extended periods of
unemployment in these formative
years can potentially have long-term‘scarring’ effects that further
undermine future prospects for
employment and wage growth. Theage at which entry to the labour
market occurs in a ‘substantive’fashion (i.e., ignoring part-time andholiday employment while a student)of course depends on the amount ofeducation an individual undertakes,
but will typically occur in the 15–29
age range. In Table 4.3, weTable 4.1: Labour force status of the population aged 15 and over (%)
Males

Females

Employed

Unemployed

Not in thelabour force

2001

68.0

5.3

26.8

100.0

53.2

3.5

43.3

100.0

2002

68.8

4.5

26.8

100.0

53.5

3.4

43.2

100.0

2003

68.7

4.1

27.2

100.0

54.1

3.0

42.9

100.0

2004

69.6

3.4

27.0

100.0

54.7

3.2

42.2

100.0

2005

69.6

3.5

27.0

100.0

56.2

2.9

40.9

100.0

2006

70.2

3.1

26.7

100.0

56.9

2.7

40.4

100.0

2007

70.2

2.9

26.9

100.0

58.2

2.6

39.2

100.0

2008

70.5

2.9

26.6

100.0

58.8

2.8

38.5

100.0

2009

69.4

4.2

26.4

100.0

57.8

2.9

39.3

100.0

2010

70.1

3.6

26.3

100.0

58.1

3.0

39.0

100.0

2011

70.3

3.4

26.3

100.0

57.0

3.3

39.6

100.0

2012

69.7

4.0

26.3

100.0

57.0

3.0

40.0

100.0

36

Total

Employed

Unemployed

Not in thelabour force

Total

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 4.2: One-year labour force status transitions before and after the GFC—Persons aged 18–64 (%)
Labour force status in year t + 1

Proportion in labour
force state in year t

Employed

Unemployed

82.7

95.5

1.4

3.1

Unemployed in year t

3.4

51.8

27.8

20.5

100.0

NILF in year t

13.9

17.8

6.9

75.4

100.0

83.6

95.0

1.9

3.1

100.0

NILF

Total

Males
2003–2006
Employed in year t

100.0

2008–2011
Employed in year t
Unemployed in year t

3.6

44.8

33.1

22.2

100.0

NILF in year t

12.8

17.7

7.7

74.6

100.0

Employed in year t

67.3

91.8

1.5

6.7

100.0

Unemployed in year t

3.1

52.8

17.3

29.9

100.0

NILF in year t

29.7

17.9

4.3

77.9

100.0

Employed in year t

70.6

90.9

1.8

7.3

100.0

Unemployed in year t

3.3

43.9

28.1

28.0

100.0

NILF in year t

26.1

15.8

4.1

80.2

100.0

Females
2003–2006

2008–2011

Note: NILF—Not in the labour force.

therefore examine labour force
status outcomes of individuals inthis age range.
The table presents participation andunemployment rates (defined in Box
4.1, page 36), as well as rates oftransition from unemployment toemployment from one year to thenext, comparing across three agegroups and across three time
periods: 2001 to 2003, 2004 to
2007, and 2008 to 2011.
For males, both the participation
rate and unemployment rate arestrongly ordered by age group.
Just over 60% of males aged 15–19
are in the labour force, comparedwith nearly 90% of males aged
20–24 and over 90% of males
aged 25–29. In all three periods
examined in the table, theunemployment rate is over 16%
for males aged 15–19, at least
6.6% for males aged 20–24 and 5%
or lower for males aged 25–29. For
females, those aged 20–24 havethe highest participation rates; thelower rate for those aged 25–29
most likely reflects withdrawal fromthe labour force to have children.

The labour market

37

2004 to 2007 period and the 2008
to 2011 period.

Unemployment rates of females are,however, similar (although certainly
not identical) to male unemployment
rates in the corresponding age
groups. Transitions fromunemployment to employment are
not clearly ordered by age, with only
about half of unemployed males andfemales in the 15–29 age range
being employed one year later.

Unemployment
duration
While unemployment is never agood experience, of most concern
to policy-makers and others areextended periods of unemployment,
which often cause substantial
economic hardship and potentiallyresult in a host of other adverse
outcomes such as deterioration inskills, health and sense of selfworth. Being a longitudinal study,
the HILDA Survey is well suited toReflecting macroeconomic
conditions, participation,
unemployment and transitions fromunemployment to employment
improved in almost all cases
between the 2001 to 2003 period
and the 2004 to 2007 period, butthen deteriorated between the

Table 4.3: Labour force status and transitions from unemployment—
Persons aged 15–29 (%)
2001–2003

2004–2007

2008–2011

Participation rate

63.4

63.5

60.1

Unemployment rate

17.8

16.8

16.2

Unemployed in year t:
Percentage employed in year t + 1

47.7

55.1

44.7

Participation rate

64.9

65.1

62.8

Unemployment rate

16.4

13.6

13.8

Unemployed in year t:
Percentage employed in year t + 1

50.2

60.5

46.4

Participation rate

89.2

88.3

87.5

Unemployment rate

11.4

6.6

7.8

Unemployed in year t:
Percentage employed in year t + 1

55.6

63.2

41.6

Participation rate

80.0

82.6

79.2

Unemployment rate

7.8

6.2

7.5

Unemployed in year t:
Percentage employed in year t + 1

50.6

69.0

50.7

Participation rate

92.3

93.7

92.9

Unemployment rate

5.0

4.1

5.0

Unemployed in year t:
Percentage employed in year t + 1

57.5

48.4

45.7

Participation rate

74.1

76.2

77.9

Unemployment rate

5.5

4.6

5.4

Unemployed in year t:
Percentage employed in year t + 1

46.5

42.8

47.3

Aged 15–19
Males

Femalesstudying the duration ofunemployment, including who ismost vulnerable to long-term
unemployment, the factors that
impact on unemployment duration,and the consequences of extended
periods of unemployment. In thissection, a brief analysis ofunemployment duration and thefactors affecting unemployment
duration is presented, but
considerably more detailed analysis
could, and should, be undertaken.
Table 4.4 draws on the informationcollected on labour force status at
the time of interview to examine
unemployment durations. The upper
panel examines, for those observed
to commence an unemployment
spell (defined as unemployed at thetime of interview in the current
wave and not unemployed at thetime of interview in the previouswave), the number of consecutive
waves individuals are unemployedat the time of interview. The lower
panel examines the number ofconsecutive waves individuals were
not employed at the time ofinterview for individuals who wereAged 20–24
Males

Females

Aged 25–29
Males

Females38

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 4.4: Duration of spells commencing in unemployment—Persons aged 16–59 at the time ofspell commencement
Males
Spells commencing
2002–2005

Females
Spells commencing
2006–2009

Spells commencing
2002–2005

Spells commencing
2006–2009

Number of consecutive waves observed to be unemployed at time of interview
1

64.5

70.1

68.4

68.4

2

20.9

16.0

19.0

17.8

3

9.3

7.2

6.9

5.5

4 or more
Total

5.3

6.7

5.7

8.3

100.0

100.0

100.0

100.0

Number of consecutive waves observed to be not employed at time of interview
1

49.6

52.7

41.2

38.5

2

19.2

15.3

17.2

13.9

3

10.0

8.4

9.0

7.8

4 or more

21.2

23.6

32.6

39.8

Total

100.0

100.0

100.0

100.0

initially observed to be unemployed,
which captures people who havegiven up trying to find employment.
Considering first the upper panel,
the table shows that the majority ofpeople observed to commence an
unemployment spell in one wave
are not unemployed in the nextwave, with between 64.5% and70.1% of people commencing an
unemployment spell in thiscategory. That said, of those who
are observed to enter
unemployment, a significant
proportion—around 30%—areobserved in the state in two or
more consecutive waves.
Notwithstanding the potential for
people to temporarily exit
unemployment between waves, thistranslates to a large number ofpeople experiencing unemployment
for one or more years. Even morestriking, the lower panel shows thatabout half of males and 60% of
females who commence an
unemployment spell are observed
to be not employed for two or moreconsecutive waves.
Females who commence an
unemployment spell appear to beslightly less vulnerable than males tolong-term unemployment in the 2002
to 2005 spell-commencement
period, but slightly more vulnerable
to long-term unemployment in the2006 to 2009 spell-commencement

Box 4.2: The HILDA Survey employment and education calendar
In each wave, respondents are asked to recall their employment and education activity in eachthird of each month from the beginning of the last financial year to the time of interview. It is
potentially difficult for respondents to recall such detail—particularly if one or more jobs werecommenced and/or ended in the period—and so some degree of caution is warranted ininterpreting the data. Moreover, the unemployment definition does not exactly equate to thedefinition used for ascertaining labour force status at the time of interview: an individual isclassified as unemployed in a given period if they report being ‘not employed and looking for
work’. Nonetheless, the calendar is useful for providing an indication of the extent to which theannual interview misses unemployment spells, job spells and periods out of the labour force.

period. However, in both periods,
females were considerably more
vulnerable than males to long-term
non-employment. For example,
among those observed to commence
an unemployment spell in the 2006
to 2009 period, 39.8% of females
were not employed at the time ofinterview in four or more consecutive
waves, compared with 23.6% ofmales. Comparing spells
commencing in the 2002 to 2005
period with spells commencing in the2006 to 2009 period, an increase inspells of four or more waves isevident for both unemployment andnon-employment, largely arising fromdeclines in spells of two or threewaves. The increases in spells offour or more waves are greater for
females than males, rising from
5.7% to 8.3% of unemployment
spells and from 32.6% to 39.8% of
non-employment spells, comparedwith increases for males from 5.3%
to 6.7% for unemployment and from
21.2% to 23.6% for non-employment.

The analysis presented in Table 4.4
provides coarse measures of
unemployment duration, notdistinguishing durations at a finerlevel than the number of years.
Moreover, it is not technically ananalysis of unemployment
durations, since an individual
unemployed at the time of interview
in two consecutive waves may havebeen employed or not in the labour
force at some stage between
interviews. The HILDA Survey datado, however, permit more detailed
analysis of unemployment duration,
drawing on the ‘employment andeducation calendar’ collectedeach wave (see Box 4.2, above).
Table 4.5 presents information on
unemployment durations based on
this calendar.
The table provides a sense of the
distribution of spell durations
among spells lasting less than
one year, showing that most are
between 1 month and 6 months.
It also presents somewhat

The labour market

39

Table 4.5: Duration of unemployment spells based on the employment calendar—Persons aged 16–59 at the timeof spell commencement
Males
Spellscommencing
2001–2004

Females

Spellscommencing
2005–2008

Spellscommencing
2009–2010

Spellscommencing
2001–2004

Spellscommencing
2005–2008

Spellscommencing
2009–2010< 1 month

7.9

8.3

10.4

6.1

7.3

7.8

1 month to < 3 months

35.5

40.4

37.0

33.8

40.1

36.5

3 months to < 6 months

26.6

20.7

20.5

26.4

23.8

23.3

6 months to < 1 year

17.6

17.2

16.2

18.6

15.4

18.3

1 year to < 2 years

7.9

10.1

10.6

12.4

11.3

11.7

2 years to < 3 years

2.9

2.1

3.1

1.5

1.0

2.1

3 years to < 4 years

1.2

0.5

4 or more years

0.2

0.6

100.0

100.0

0.8

0.4

0.3

0.6

100.0

100.0

}2.1
Total

100.0

}0.4

contrasting information from Table
4.4 in terms of the proportion ofspells lasting one year or more. For
example, in Table 4.5, 12.2% of
unemployment spells of males in
the 2001 to 2004 period were one
year or longer, whereas in Table
4.4, 35.5% of male spells in the2002 to 2005 period were one year
or longer. In part, this may reflect
differences in the definition ofunemployment. Unemployment at
the time of interview is measured
in essentially the same way asmeasured by the ABS for its Labour
Force Survey (see Box 4.1, page36), whereas a simpler
approximation, ‘not employed andlooking for work’, is used for theemployment calendar. However, themore important source ofdifference is likely to be that many
people unemployed at the time ofinterview in two consecutive waves
were not unemployed for the entire
interval between interviews
because they temporarily obtained
employment or temporarily
withdrew from the labour force.

What factors influence
unemployment duration?
Table 4.6 presents hazard ratiosfrom models of unemployment
duration, using labour force status at
the time of interview in each wave.
Two models are estimated. In both,an unemployment spell commences
if a person is observed to be
unemployed or marginally attached40

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

100.0

Table 4.6: Factors affecting the likelihood of exiting unemployment—Persons aged 16–59 at the time ofcommencement of the unemployment spell
Excluding people who move from unemployment
to out of the extended labour force

Including all people who commence
an unemployment spell

Males

Females

Males

Females

2 waves

0.83

0.77

0.77

0.61

3 waves

0.76

0.58

0.56

0.42

4 or more waves

0.71

0.48

0.27

0.31

0.90+

1.23

0.93+

1.19

Spell duration (Reference category: 1 wave)

Current year (Reference category: 2002–2004)
2005–2006
2007–2008

0.78

0.96+

0.82

0.95+

2009–2011

0.62

0.77

0.72

0.95+

1.32

1.04+

1.41

1.14+

Age group (Reference category: 16–19)
20–24
25–29

1.27

0.85+

1.43

0.78

30–34

1.17+

1.03+

1.13+

1.10+

35–39

1.05+

1.02+

1.04+

0.94+

40–44

1.47

0.78

1.09+

0.89+

45–49

1.07+

0.77

0.89+

0.76

50 and over

1.15+

0.87+

0.51

0.41

0.87+

0.78

Place of birth and Indigenous status (Reference category: Non-Indigenous native-born)
0.86+

0.75

NESB immigrant

0.85+

0.79

0.87+

0.64

ESB immigrant

0.92+

0.89+

1.09+

0.83

Couple

1.07+

1.02+

0.93+

0.87+

Couple with dependent children

1.03+

0.73

1.02+

0.62

Lone parent

1.12+

0.82

1.06+

0.77

Partner employed

1.33

1.13+

1.55

1.08+

Indigenous

Family type (Reference category: Single person)

Educational attainment (Reference category: Less than high-school completion)
Degree or higher

1.44

1.38

1.68

1.63

Other post-school qualification

1.32

1.17

1.23

1.26

Completed high school

1.41

1.38

1.29

1.37

Other urban

1.19

0.90+

1.20

0.92

Other region

1.04+

0.81

1.07+

0.82

Region (Reference category: Major urban)

Housing tenure type (Reference category: Home owner)
Private rental

0.98+

0.78

0.99+

0.83

Public rental

0.68

0.46

0.60

0.42

Moderate disability

0.66

0.80

0.52

0.63

Severe disability

0.59

0.50

0.27

0.29

Number of observations

1,685

2,253

2,792

5,191

Notes: Estimates are hazard ratios obtained from Cox proportional hazards regression models. See the Technical Appendix for details. Sample comprises all
unemployment spells observed at the time of interview and commenced in Waves 2 to 11 (2002–2011). + indicates estimate is not significantly different from
1 at the 10% level.

The labour market

41

to the labour force (an expandeddefinition of unemployment). In thefirst model, only incomplete spells
and spells that exit to employment
are included in the sample. That is,
if a person moves fromunemployment to out of the labour
force (and not marginally attached),
the unemployment spell is excluded
from the sample. The second model
retains people who move fromunemployment to out of the labour
force, but treats the unemployment
spell as continuing when a person
moves from unemployment to not in
the labour force. Thus, this model
estimates the determinants of exitfrom non-employment toemployment of people who initiallybecome unemployed.1
As described in Chapter 2 inrespect of Table 2.4, a hazard
ratio of greater than 1 implies apositive association between the
explanatory variable and theprobability of exiting unemployment
or non-employment, while a hazard
ratio of less than 1 implies anegative association.
Considering first the effects of spell
duration on the likelihood of exiting
unemployment, the estimates showa pattern of negative ‘duration
dependence’: the longer the spell
duration, the less likely is exit. Thisis particularly the case for themodel of non-employment duration.
Significant differences are evident
across years that are broadly
consistent with movements in theoverall unemployment rate, withhazard ratios highest in the earlyyears of the HILDA Survey period
and lowest in the years at the endof the survey period.
No consistent patterns across age
groups are evident, with theimportant exception for the model ofnon-employment, which shows those
aged 50 and over who commence an
unemployment spell are much lesslikely to exit non-employment. No
significant differences by Indigenous1

and immigrant status are evident formales, but Indigenous females and
NESB immigrant females are, allelse equal, less likely than otherfemales to exit unemployment or
non-employment. Females withdependent children are also
considerably less likely to exit toemployment than other females.
There are also no significant
differences by family type for males,
but having a partner who is
employed acts to increase thelikelihood of moving into employment
for males—an effect that is notevident for females.
Educational attainment (see Box
7.1 on page 68 for details on theclassification of educational
attainment) is an important factor,
those with university degrees
having the highest probability ofmoving into employment and those
who have not completed highschool (and have no post-school
qualifications) having the lowest
probability. Effects of region ofresidence differ for males andfemales. Holding other factors
constant, males living in ‘otherurban’ regions are more likely tomove into employment than males
living in other regions, whilefemales are most likely to moveinto employment if living in majorurban areas, and least likely tomove into employment if living in
non-urban regions. Housing tenure
type is also associated withsignificant differences in likelihoodof moving into employment. Homeowners are, all else equal, themost likely to move into
employment, while public housing
tenants are the least likely. Finally,
the presence of a disability, andparticularly a severe disability,
substantially reduces the prospectsof moving into employment.

Labour market
earnings
Earnings levels anddistribution
Earnings represent a key dimensionof labour market outcomes.
A worker’s earnings per hourmeasures the rate at which his or
her labour is rewarded in the labour
market, and thus provides ameasure of the value of thatworker’s labour. Earnings are also
an important contributor to anindividual’s economic wellbeing,
being the main income source formost working-age people. The
HILDA Survey data allow us to not
only examine workers’ earnings at
a point in time, and track
movements in overall earnings
levels, but also to track individuals’
earnings progression over time.
We begin by describing the earnings
distribution in each year, presenting
cross-sectional snapshots in orderto provide an overall picture of
earnings outcomes and changes
over the period spanned by the
HILDA Survey. Figures 4.1 and 4.2

Box 4.3: HILDA Survey measures of labour market earnings
The HILDA Survey does not ask respondents to report their hourly wage; rather, usual weekly(typically gross) earnings and usual weekly hours of work are obtained from everyone who is
employed. Hourly rates of pay can then be calculated from this information. The hourly rate ofpay so obtained is ‘current usual earnings per hour worked’. While the hourly wage rate is theappropriate focus when interest is in the rate at which labour is rewarded, one concern thatarises in hourly wage rate analysis is that additional measurement error is introduced by
dividing reported weekly earnings by reported weekly hours of work. This provides one rationale
for examining weekly earnings, at least as an augmentation to the study of hourly earnings.
Another reason for examining weekly earnings is that, for full-time employees who are paid asalary, the notion of an hourly wage is less relevant. For example, a full-time employee mayreport working more than 40 hours per week, but is implicitly only paid for 40 hours. Possibly,
the longer hours of work reflect a preference of the worker to work longer hours at a lower
intensity per hour.

An alternative to these two models is to estimate a more complicated model, known as a competing risks model, which could
simultaneously examine the determinants of both exits to employment and exits to out of the labour force. Estimates from thesemodels are more difficult to interpret and so are not presented in this report.

42

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

present graphs of summary
measures of the male and female
earnings distributions over the
2001 to 2012 period, plotting themean, median, 10th percentile,
90th percentile and Gini coefficient.
Figure 4.1 examines weekly
earnings of full-time employees,
while Figure 4.2 examines hourly
earnings of part-time employees.2
Over the full 2001 to 2012
period, the graphs show mean
2

weekly earnings of full-time
employees increased by 21.4% formales and 19.6% for females,
while the Gini coefficient increased
by 5.4% for males and 7.4% forfemales. For hourly earnings ofpart-time employees, the mean
increased by 20.2% for males andby only 2.4% for females, while
the Gini coefficient increased by
2.3% for males and decreased by
24% for females—however, theGini coefficient for hourly
earnings of part-time employees
exhibits considerable year-to-year
fluctuation for both males andfemales, so it is difficult todiscern the underlying trend.

Earnings progression
The cross-sectional earnings
information presented in Figures
4.1 and 4.2 do not tell us how
individual workers have fared, andNote that Figures 4.1 and 4.2 are for earnings of employees and therefore exclude earnings of the self-employed and employers, whose
earnings are often confounded with returns on capital invested in the business, either because reported earnings include a return on
capital, or because reported capital income includes a component that is actually a return on labour. Full-time employment is defined to
be a situation in which usual weekly hours of work are 35 or more. In the case where a respondent holds more than one job, we restrict
analysis to earnings and hours worked in the respondent’s main job.

The labour market

43

Figure 4.1: Weekly earnings of full-time employees

Mean
10th percentile
Gini coefficient

2012

2001

2012

Mean
10th percentile
Gini coefficientMedian90th percentile

2011

0.20
2010

500
2009

0.20

2008

500

2007

0.22

2006

1,000

2005

0.22

2004

1,000

2003

0.24

2002

1,500

$ (December 2012 prices)

0.24

Gini coefficient

1,500

2011

0.26

2010

2,000

2009

0.26

2008

2,000

2007

0.28

2006

2,500

2005

0.28

2004

2,500

2003

0.30

2002

3,000

Gini coefficient

Females0.30

2001$ (December 2012 prices)

Males
3,000

Median90th percentile

Note: Weekly earnings less than $100 at December 2012 prices have been excluded.

Figure 4.2: Hourly earnings of part-time employees
Males

Females
0.50

50

0.50

50

45

45

0.45

0.45
40

0.35
25
0.30

20

0.40

35
30

0.35
25
0.30

20
15

15

0.25

0.25
10

Median90th percentile

Mean
10th percentile
Gini coefficientMedian90th percentile

Note: Hourly wages less than $2 and more than $500 at December 2012 prices have been excluded.

44

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

Mean
10th percentile
Gini coefficient

0.20

5
2002

0.20

5

2001

10

Gini coefficient30$ (December 2012 prices)

0.40

35

Gini coefficient$ (December 2012 prices)40

Table 4.7: Change in weekly earnings of full-time employees
Base year
2001–2003

2004–2007

2008–20092010–2011

Change over 1 year among those who were full-time employees in both years
Males
Mean percentage wage change

8.8

9.2

7.6

8.6

Median percentage wage change

2.3

3.1

2.2

2.4

90th percentile percentage wage change

37.6

40.4

34.3

36.6

Wage declined (%)

41.7

40.5

42.4

42.1

7.6

8.3

7.0

8.5

Females
Mean percentage wage change
Median percentage wage change

2.4

3.1

2.4

2.3

90th percentile percentage wage change

32.7

36.2

32.2

34.5

Wage declined (%)

40.9

38.9

40.7

41.3

Change over 5 years among those who were full-time employees in all 6 years
Males
Mean percentage wage change

30.3

26.6

–

–

Median percentage wage change

14.9

14.3

–

–

90th percentile percentage wage change

85.0

75.4

–

–

Wage declined (%)

25.5

27.3

–

–

Mean percentage wage change

26.2

24.6

–

–

Median percentage wage change

16.1

16.8

–

–

Females

90th percentile percentage wage change

72.1

67.6

–

–

Wage declined (%)

22.5

22.3

–

–

in particular whether some workers
have experienced more rapid
earnings progression than others.
There are many ways of examining
earnings progression over time,and some of these ways have beenpresented in previous volumes ofthis report. In this year’s report,a brief analysis is presented ofthe distribution of earnings
changes of full-time employees over
1-year and 5-year intervals.
Table 4.7 shows that, be it over
1 year or 5 years, the meanearnings change of those who are

full-time employees in all years in
the interval is greater than thechange in mean earnings of all fulltime employees. For example, themean 1-year change in realearnings of full-time employees isapproximately 7% to 9%, whereas
Figure 4.1 shows annual increases
in mean earnings of full-time
employees of approximately 2%.
At first glance, these differences
appear contradictory; however, they
are explained simply by the fact thateach year some people startworking full-time (typically at

relatively low wages), while somepeople cease working full-time (often
at relatively high wages). Thus, forthose who remain in full-time
employment, earnings growth is on
average considerably higher thanwhat you might be led to believe
simply by examining changes overtime in the cross-sectional means.
That said, it is also clear that there
is considerable variation in earnings
changes among those who remain
full-time employment. Indeed, fromone year to the next, approximately
40% of full-time employees

Table 4.8: Median percentage change in weekly earnings of full-time employees, by whether changed jobs
Males

Females

2001–2003

2004–2007

2008–20092010–2011

2001–2003

2004–2007

2008–20092010–2011

Changed jobs

5.9

9.4

5.1

10.2

5.0

6.6

4.3

8.0

Did not change jobs

2.1

2.7

2.1

1.9

2.2

2.6

2.3

1.7

Changed jobs

25.2

23.1

–

–

23.0

24.6

–

–

Did not change jobs

11.7

11.6

–

–

13.1

14.8

–

–

One-year change

Five-year change

The labour market

45

experience a decrease in realearnings, and even over a 5-year
period, approximately one-quarter offull-time employees experience areal decline in earnings.

Box 4.4: HILDA Survey measures of perceived employment prospects
Each year, employee respondents are asked the following three questions:

Table 4.8 considers whether
earnings changes of full-time
employees systematically differ
depending on whether theemployee changed employers. The
clear pattern for both males andfemales is that median earnings
growth is substantially higher for
those who change jobs. Thus, itwould seem that, on average,
achieving high earnings growthrequires one to change jobs.

1.

I would like you to think about your employment prospects over the next 12 months. Whatdo you think is the per cent chance that you will leave your job voluntarily (that is, quit orretire) during the next 12 months?

2.

What do you think is the per cent chance that you will lose your job during the next 12months? (That is, get retrenched or fired or not have your contract renewed.)3.

If you were to lose your job during the next 12 months, what is the per cent chance thatthe job you eventually find and accept would be at least as good as your current job, interms of wages and benefits?

Employees’
perceptions of
employment
prospects

for another job, and indeed isprobably more likely to quit that job
than an employee who feels moresecure in his current job. Each year,
respondents who are employees
are asked three key questions
about their employment prospects:
the likelihood of quitting theircurrent job; the likelihood of being
dismissed from their current job;
and the likelihood of being able tofind another equally good job in theevent of losing their current job(see Box 4.4, above).

Employees’ perceptions of their job
security and employment prospects
are likely to be important factors intheir labour market decisions. For
example, an employee who
believes he is likely to bedismissed from his current job ismore likely to be actively searching

Figure 4.3 shows the mean
responses of employees to thesethree questions in each wave of the
HILDA Survey. Largely reflecting
broader macroeconomic conditions,for both males and females there isa slight downward trend in perceived
likelihood of job dismissal, and aslight upward trend in perceived
prospects of finding another job,
between 2001 and 2007, after
which there is a slight reversal of
the trends. Particularly notable isthe uptick in the mean probabilityof dismissal in 2012, which
increased 1.6 percentage points
for males and 1.8 percentage
points for females, and the dropin the mean probability of finding
another job as good, which declined
0.7 percentage points for males
and 1.9 percentage points forfemales. These changes seem tohave anticipated the rise in
unemployment, from 5.4% to 5.9%,that occurred in 2013 (see ABS,
2013). The mean reported
likelihood of resigning, however,shows no clear trends over the
2001 to 2012 period.

Figure 4.3: Employees’ perceptions of job prospects—Mean percentage chance of each event

70

60

60

Dismissed

Find another job

ResignDismissed

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Find another job

2012

2011

2010

2009

2008

2007

2006

2012

2011

2010

2009

2008

2007

0
2006

0
2005

10

2004

10

2003

20

2002

20

2005

30

2004

30

40

2003

40

50

2002

50

2001

Mean percentage chance

70

Resign46

Females80

2001

Mean percentage chance

Males
80

Determinants of perceptionsof job prospects
While Figure 4.3 provides strong
indications that macroeconomic
conditions are an important
influence on employees’ perceived
job prospects, it is also interesting
to look at the factors that determine
differences in perceptions acrossemployees. Table 4.9 presents
results of regression models
investigating these factors,
examining the roles played by both
employee characteristics and job
characteristics. Specifically,
regression models are estimated ofthe perceived percentage chancethe employee will be dismissed fromthe current job, and the perceived
percentage chance that, in theevent of job loss, the employee
would be able to obtain another job
as good as the current job.
Holding other factors constant,perceived chances of dismissal
do not significantly differ for males
and females, but males rate theirchances of finding another job
1 percentage point higher thanfemales. There are significant
differences in perceived job
prospects across age groups,
with older employees reporting
higher chances of dismissal andlower chances of finding anotherjob. Employees aged 55 and overhave particularly low assessedchances of finding another job in
the event of job loss. More highly
educated employees have higher
perceived risks of dismissal, allelse equal, but they also haveconsiderably higher assessedchances of getting equally goodalternative employment.
No significant differences indismissal probability by region ofresidence are evident. However,consistent with there being more
job opportunities in major urban
areas, employees living in theseareas have a higher assessedchance of finding another job.
Other factors held constant, fulltime and part-time employees do
not have different assessed risks

Table 4.9: Factors affecting employees’ perceptions of job prospects
Male

Dismissed

Find another job

0.56+

1.07

Age group (Reference category: 15–24)
25–34

0.31+

–1.81

35–44

1.72

–2.42

45–54

2.47

–4.03

55 and over

2.21

–12.36

Educational attainment (Reference category: No post-school qualifications)
Degree or higher

1.24

6.85

Other post-school qualification

0.78

4.23

Major urban–0.07+

3.91

Other urban

0.56+

0.27+

Employed full-time

0.51+

1.38

Casual employee

5.20

0.81+

Fixed-term contract

6.55

0.08+

Union member

0.04+–0.49+

Work weekends

–1.70

0.77+

Work nights or irregular hours

0.38+

0.23+

Job tenure (years)

–0.24

–0.57

Hourly wage in main job ($, December 2012 prices)

0.02

–0.02

Region (Reference category: Other region)

Occupation (Reference category: Labourers)
Managers

–1.71

0.18+

Professionals

–1.28

3.27

Technicians and trades workers

–1.15

1.38+

Community and personal service workers

–0.96

0.41+

Clerical and administrative workers–0.98+–1.87

Sales workers

–1.52

3.66

Machinery operators and drivers

0.50+

0.95+

Industry (Reference category: Other services)
Agriculture, forestry and fishing

3.10–2.34+

Mining2.88–2.79+

Manufacturing

2.23

–4.75

Electricity, gas, water and waste services

0.94+

–6.95

Construction

4.25–0.13+

Wholesale trade

2.07

–4.85

Retail trade

0.07+–0.86+

Accommodation and food services–0.94+

3.03

Transport, postal and warehousing

0.08+

–3.26

Information media and telecommunications

2.60

–5.74

Financial and insurance services

2.52

–4.12

Rental, hiring and real estate services–0.91+

0.40+

Professional, scientific and technical services

0.72+–2.27+

Administrative and support services

0.62+–1.65+

Public administration and safety–0.06+–6.47

Education and training

0.13+–0.61+

Health care and social assistance

–2.33

5.01

Arts and recreation services–1.02+–5.54

Private for-profit sector–0.26+

4.59–0.74+

Firm size (Reference category: 100 or more employees)
Fewer than 20 employees–0.40+

20–99 employees–0.12+

0.38+

Number of observations

27,222

26,300

Number of persons

10,016

9,861

Notes: Table presents coefficient estimates from linear random-effects models. See the Technical Appendix
for details. Sample comprises all employees in all waves (2001–2012). Models also contain controls forsurvey year (not reported). + indicates estimate is not significantly different from 0 at the 10% level.

The labour market

47

Box 4.5: Casual employment and fixed-term employment
Casual employment is a form of employment unique to Australia. It is characterised byflexibility for employers and employees in the number and timing of hours worked from week toweek (including the ability for employers to very readily reduce hours to 0). Typically, casual
employees are not entitled to paid annual and sick leave.
Fixed-term employees are employed on contracts that specify an end date for the employment
relationship. Note that many employees on fixed-term contracts nonetheless remain employed in
the same job after the expiration of the contract, either because a new fixed-term contract iscommenced or because they convert to continuing or permanent employees.

of dismissal, but full-time
employees are more confident offinding another job in the event of
dismissal. Unsurprisingly, casualand fixed-term employees (definedin Box 4.5, above) have higher
perceived risks of dismissal thancontinuing/permanent employees,
although they have no lessconfidence than other employeesthat they will find another equally
good job.
Somewhat unexpectedly, membersof trade unions are no more
confident of avoiding dismissal

than other employees. Increasing
job tenure acts to increase
perceived job security, but also acts
to decrease perceived ability to findanother equally good job. Higher
hourly earnings reduce both
perceived job security andperceived chances of finding
another equally good job.
Variables for occupation of
employment (see Box 4.6, below)
show labourers, and machinery
operators and drivers, have thelowest perceived job security andmanagers and sales workers haveBox 4.6: Occupation classification
Occupation variables in this report are based on the first (2006) edition of the Australian
Bureau of Statistics (ABS) ANZSCO classification system. ANZSCO stands for the Australianand New Zealand Standard Classification of Occupations. It is based on a conception of typesof tasks and skill-level requirements. It has six ‘levels’, with 10 occupation groups
distinguished at the highest level of aggregation, known as the 1-digit level, 54 groups
distinguished at the next (2-digit) level of aggregation, and so on. See ABS (2006) for details.
In this report, only the 1-digit level classification is used.

48

the highest perceived job security.
Professionals and sales workers
have the most confidence in being
able to obtain another equally goodjob, while clerical andadministrative workers have theleast confidence.
Variables for industry of
employment (see Box 4.7, page 49)
show seven industries haverelatively low perceived job security:
construction; agriculture, forestry
and fishing; mining; information
media and telecommunications;financial and insurance services;manufacturing; and wholesaletrade. Health care and social
services stands out as an industry
having high job security. Employees
in the health care and social
assistance industry and theaccommodation and food services
industry have the highest perceived
chances of finding equally goodjobs in the event of job loss, while
employees in electricity, gas, water
and waste services, public
administration and safety,
information media andtelecommunications, and arts andrecreation services have the lowest
perceived prospects for equally
good alternative employment.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Are perceptions justified?
While there are systematicdifferences in employees’
perceptions of their employment
prospects depending on theirpersonal and job characteristics,
a natural question is whether
these perceptions actually reflect
the reality faced by employees.
Table 4.10 takes a step towards
answering this question. The
upper panel examines whether
the perceived chances of
dismissal differ for employees
who lost their job over thesubsequent year compared with
employees who did not lose theirjob. The lower panel examines
whether the perceived chancesof obtaining a job as good as
the current job differ between
dismissed employees who did
get another job as good as their
original job and dismissed
employees who did not. For the
lower panel, an employee isdeemed to have got a job ‘asgood’ if they were employed (inanother job) at the time of thenext wave’s interview and thereal wage was at least equal to
the wage in the job from which
they were dismissed.

Table 4.10: Perceptions of job prospects and subsequent job outcomes,
2001–2012 (%)
Mean perceived chance of dismissal, by whether actually dismissed or not
Not dismissed

Dismissed

Males

10.2

27.7

Females8.8

28.6

Persons dismissed from their job: Mean perceived chance of getting another job as good as
current job, by whether actually got another job as good
Did not get anotherjob as good

Got anotherjob as good

Males

63.1

70.8

Females

63.6

71.0

Box 4.7: Industry classification
Industry variables in this report are based on the Australian Bureau of Statistics (ABS) ANZSIC
classification system. ANZSIC is the Australia and New Zealand Standard Industry
Classification. It classifies the economic activity of firms and other employers, and has astructure comprising categories at four levels: ‘divisions’ (the broadest level); ‘subdivisions’;‘groups’; and ‘classes’ (the finest level). These levels are commonly referred to as ‘1-digit’, ‘2digit’, ‘3-digit’ and ‘4-digit’, reflecting the number of digits used in the code to describe eachcategory. At the 1-digit level, which is used in this report, 17 industry categories are
distinguished. See ABS (2008) for details.

chance of getting as good a job
would be greater if a longer timeframe (such as two or three years)
was adopted. That is, some of theemployees who did not have as

good a job in the next wave, but
had assessed their chances of
eventually getting such a job ashigh, may in fact have eventually
done so.

Clearly evident is that perceptions
have predictive power, particularly
in respect of chances of job loss.
The mean perceived chance ofdismissal was approximately 10%
for employees who were notdismissed, and approximately
28% for employees who weredismissed. For the chances ofgetting another job as good as thecurrent job, the predictive power of
expectations is lower, with themean perceived chance of getting
as good a job approximately 63%
for those who did not in fact get
another job as good, compared with
71% for those who did get anotherjob as good. Note, however, thatwhile the question on dismissal
specified ‘the next 12 months’,
the question on getting anotherjob simply specified ‘eventually’,so that it is possible that thedifference in the mean predicted

The labour market

49

50

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

5

Health andsubjective wellbeing
While much of the HILDA Survey is concerned with the economic wellbeing ofpeople, extensive information is also collected on the health, lifestyle behaviours,social activity and education participation of respondents. In addition, views andperceptions on a variety of life domains are elicited, including levels of satisfaction
with these life domains. In this section, we make use of some of this information
to present cursory analyses of the ‘subjective wellbeing’ and physical and mental
health of the Australian community.

Population healthand disability
General healthA measure of general health isprovided by the SF–36 survey
instrument (see Box 5.1, below),which has been administered by
the HILDA Survey in every wave aspart of the self-completion
questionnaire. The SF–36 general
health measure is based on
responses to five items whichobtain respondent assessments ofoverall health and expectations
about overall health. It has 21
distinct possible values which aretransformed to range between 0
and 100.1 In the population as awhole, the median score is 72.

Table 5.1 presents summary
information, disaggregated by sex
and age group, on the cross-sectional
distribution of the SF–36 general
health measure and individual-levelchanges in the measure over 5-year
and 10-year time-frames. All 12
waves of the HILDA Survey(2001–2012) are used to produce
the estimates, although of course
the last wave from which a 5-yearchange can be calculated is Wave 7(2007) and the last wave fromwhich a 10-year change can becalculated is Wave 2 (2002).
Self-reported general health has aclear pattern of declining with age.
The mean score declines from 74.8
among males aged 15–29 to 59.0
among males aged 70 and over,and from 71.6 among females

Box 5.1: SF–36 measures of health
The SF–36 Health Survey is a 36-item questionnaire that is intended to measure healthoutcomes (functioning and wellbeing) from a patient point of view. It was specifically developed
as an instrument to be completed by patients or the general public rather than by medical
practitioners, and is widely regarded as one of the most valid instruments of its type. See<http://www.sf-36.org/> for further details.
The SF–36 measures of general health and mental health are used in this report. The scoresfor both measures potentially range from 0 to 100. Where SF–36 health measures are used as
explanatory factors in analysis in other chapters in this report, indicator variables are createdfor poor general health and poor mental health. There are no universally accepted thresholdscores for defining poor general and mental health, but for the purposes of this report, poor
general health is defined as a score less than or equal to 37, on the basis that approximately
10% of the population are at or below this threshold. Similarly, poor mental health is defined
as a score less than or equal to 52, on the basis that approximately 10% of the population areat or below this threshold.

1

The actual number of distinct values of the SF–36 general health measure in the HILDA
Survey data is more than 21 because of procedures, as per the SF–36 guidelines, fordealing with item non-response.

Health and subjective wellbeing

51

aged 15–29 to 59.1 amongfemales aged 70 and over.
Consistent with this cross-sectional
pattern, the mean change in
general health of individuals over a
5-year period is –2.3 for males and–1.7 for females, while the meanchange over a 10-year period is –5.0
for males and –4.1 for females.

Nonetheless, the table shows there
is considerable variation in changes
at the individual level. Over boththe 5-year and 10-year time-frames,
only about half the population
experiences a decline in general
health, and there is evenconsiderable movement out of the‘poor’ general health category, withapproximately 4% of the 10% of the
population in poor health in one
year no longer in poor health 5 or
10 years later.

Mental health
The SF–36 instrument also
produces a measure of mental
health, which is based on

Table 5.1: Levels and changes in SF–36 measure of general health, 2001–2012 (pooled)
15–29

30–39

40–49

50–59

60–69

70 and over

Total

Mean health (0–100 scale)

74.8

72.0

68.5

65.5

61.3

59.0

68.5

Below-median health (< 72) (%)

34.5

38.9

44.7

49.3

56.2

61.8

44.7

Poor health (≤ 37) (%)

4.0

5.6

9.0

13.1

19.0

20.4

10.0

Mean change in health

–1.9

–1.7

–2.4

–2.3

–2.8

–4.8

–2.3

Health decreased (%)

48.0

47.6

48.5

48.2

48.8

55.1

48.6

Entered below-median health (%)

15.8

15.0

13.9

13.2

13.8

17.7

14.7

Exited below-median health (%)

12.1

10.8

9.1

8.5

8.0

7.6

9.8

Entered poor health (%)

3.6

3.5

5.7

6.8

6.7

10.3

5.4

Exited poor health (%)

2.6

3.7

4.1

4.0

5.1

5.1

3.8

Males
LevelsChange over 5 years

Change over 10 years
Mean change in health

–3.4

–4.7

–5.4

–5.0

–7.0

–9.6

–5.0

Health decreased (%)

52.7

56.5

55.2

53.9

60.8

66.1

55.6

Entered below-median health (%)

18.0

19.8

19.9

19.2

22.8

26.2

19.8

Exited below-median health (%)

10.6

9.1

7.9

6.6

8.8

8.1

8.7

Entered poor health (%)

4.7

5.7

7.9

8.5

10.6

9.6

7.0

Exited poor health (%)

3.1

4.1

3.0

4.0

4.4

3.9

3.6

Mean health (0–100 scale)

71.6

72.9

69.6

65.7

63.8

59.1

68.3

Below-median health (< 72) (%)

39.5

35.4

41.3

48.3

51.3

62.2

44.3

Poor health (≤ 37) (%)

6.6

6.9

10.4

14.3

15.9

19.5

11.1

FemalesLevelsChange over 5 years
Mean change in health

0.4

–1.5

–2.3

–2.1

–2.8

–4.7

–1.7

Health decreased (%)

41.9

45.8

48.4

46.6

50.2

53.5

46.7

Entered below-median health (%)

13.6

13.0

13.8

12.9

14.6

16.0

13.7

Exited below-median health (%)

14.6

10.8

9.8

9.9

8.1

7.4

10.7

Entered poor health (%)

3.9

4.5

5.7

6.9

6.7

10.4

5.8

Exited poor health (%)

4.6

3.0

4.3

4.8

5.0

4.4

4.3

Mean change in health

–1.2

–3.6

–5.1

–4.4

–7.3

–7.9

–4.1

Health decreased (%)

45.0

50.3

54.1

53.8

61.4

59.9

52.1

Entered below-median health (%)

17.6

17.9

19.6

18.2

22.2

24.2

19.1

Exited below-median health (%)

14.1

11.5

9.7

9.7

7.3

7.5

10.8

Entered poor health (%)

3.9

5.5

9.1

7.6

10.8

11.7

7.1

Exited poor health (%)

5.1

3.0

4.3

5.2

3.9

4.7

4.4

Change over 10 years

Note: The age groups for the estimates of changes in health refer to age at the beginning of the period over which the change is evaluated.

52

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

responses to five items asking
respondents how much of the time
during the last four weeks they had
been feeling certain ways, such as‘calm and peaceful’ and ‘so downin the dumps nothing could cheer
you up’. It has 26 distinct possible
values, which are transformed torange between 0 and 100. In the
population as a whole, the medianscore is 76.
Table 5.2 presents, for the SF–36
mental health measure, analogous
information to Table 5.1. Unlike
general health, mental health does
not diminish with age, and indeed aslight positive association isevident in the 40–49, 50–59 and
60–69 age ranges. Correspondingly,
the average change in mental
health score at the individual levelis close to 0 in most age groups forboth males and females, andsimilar proportions move into andTable 5.2: Levels and changes in SF–36 measure of mental health, 2001–2012 (pooled)
15–29

30–39

40–49

50–59

60–69

70 and over

Total

Mean health (0–100 scale)

74.4

74.7

74.1

75.7

77.1

77.2

75.2

Below-median health (< 76) (%)

40.8

39.3

41.4

37.2

34.5

35.8

38.9

Poor health (≤ 52) (%)

12.5

11.9

13.2

11.9

10.8

10.1

12.0

Males
LevelsChange over 5 years
Mean change in health

–0.5

0.0

0.2

1.1

0.2

–2.2

0.0

Health decreased (%)

43.8

41.7

41.9

36.8

40.0

46.9

41.6

Entered below-median health (%)

15.3

13.5

13.3

9.8

11.2

15.6

13.1

Exited below-median health (%)

14.6

14.1

14.0

11.7

11.1

9.0

13.1

Entered poor health (%)

8.3

7.9

6.6

6.0

4.7

6.5

6.9

Exited poor health (%)

7.5

6.7

7.3

6.5

5.2

5.2

6.7

Mean change in health

0.2

0.9

–0.1

1.9

–0.5

–4.3

0.3

Health decreased (%)

42.5

41.7

39.9

36.4

42.1

50.1

41.0

Entered below-median health (%)

14.9

14.5

16.0

9.6

13.7

17.8

14.1

Exited below-median health (%)

15.7

18.2

13.8

14.0

10.9

8.2

14.7

Entered poor health (%)

7.5

8.7

7.4

5.7

6.2

9.7

7.4

Exited poor health (%)

8.4

8.2

8.2

5.8

6.1

3.5

7.4

Mean health (0–100 scale)

71.3

72.3

72.5

73.1

75.2

75.1

72.9

Below-median health (< 76) (%)

48.4

44.7

44.4

44.0

38.9

42.1

44.5

Poor health (≤ 52) (%)

15.9

15.3

15.9

15.4

12.6

12.1

14.9

Mean change in health

1.1

0.7

0.4

0.6

0.2

–1.3

0.5

Health decreased (%)

40.7

41.0

41.1

39.7

39.8

44.7

40.9

Entered below-median health (%)

14.5

13.8

13.8

12.6

12.2

13.9

13.6

Exited below-median health (%)

17.9

15.1

14.7

14.6

13.1

11.3

15.0

Entered poor health (%)

9.0

7.8

9.1

7.5

6.2

7.9

8.1

Exited poor health (%)

10.7

9.2

8.6

7.5

6.4

5.7

8.5

Change over 10 years

FemalesLevelsChange over 5 years

Change over 10 years
Mean change in health

1.9

1.2

0.8

2.4

–1.4

–1.8

1.0

Health decreased (%)

39.2

39.5

40.4

35.0

43.9

47.1

39.8

Entered below-median health (%)

15.4

12.4

15.2

10.5

14.4

15.4

13.8

Exited below-median health (%)

20.6

16.8

16.2

18.5

11.9

12.1

17.1

Entered poor health (%)

10.0

8.0

8.2

6.9

7.9

8.0

8.3

Exited poor health (%)

10.5

8.1

9.4

9.8

5.3

7.2

8.9

Note: The age groups for the estimates of changes in mental health refer to age at the beginning of the period over which the change is evaluated.

Health and subjective wellbeing53

Box 5.2: Defining disability
The International Classification of Functioning, Disability and Health (ICF), produced by theWorld Health Organisation, defines disability as an umbrella term for impairments, activity
limitations and participation restrictions. It denotes the negative aspects of the interaction
between an individual’s health conditions and the various contextual (environmental andpersonal) factors of that individual. In this report, a person is defined as having a disability ifthey have ‘any long-term health condition, impairment or disability that restricts the individualin everyday activities and which has lasted, or is likely to last, for 6 months or more’. This isan ‘operational’ definition of disability which is very similar to that used in many household
surveys, such as the Australian Bureau of Statistics Survey of Disability, Ageing and Carers.
Disability severity is typically conceived in terms of restrictions in the core activities of selfcare, communication and mobility. The HILDA Survey does not collect information each wave
on core activity restrictions, but does collect information on the extent to which healthconditions limit the amount of work an individual can do (on a 0 to 10 scale, where 0 equals‘not at all’ and 10 equals ‘unable to do any work’). In this report, we use a measure of
disability severity based on this information, defining three levels of severity: no work
restriction (0); moderate work restriction (1–7); and severe work restriction (8–10).

out of poor mental health over both5-year and 10-year time-frames.
A notable exception is that mental
health among men and women
aged 70 and over on averagedeteriorates considerably, andparticularly striking is that, over a
10-year period, 9.7% of men aged
70 and over move into the poor
health category, while only 3.5% of
men in this age group move out of
the category.

Disability
Information on the presence ofdisabilities has been collected inevery wave of the HILDA Survey.
Each year, respondents are asked
whether they have each of 17
long-term health conditions or
impairments that restrict everyday
activities and have lasted, or are
likely to last, for 6 months or more.
Those with one or more conditions
are then asked to indicate, on ascale from 0 (not at all) to 10(unable to do any work), the extent
to which the conditions limit theamount of work they can do. It istherefore possible to construct ameasure of disability and disability
severity using this information (seeBox 5.2, above).
Table 5.3 presents, for our measureof disability, information of thesame nature as presented in Tables
5.1 and 5.2 for general health andmental health. A ‘restricting’disability is one that limits theamount of work that an individual
can do (a rating of 1 or more out of10), while a ‘severe’ disability is
one that severely limits the amountof work the individual can do (arating of 8 or more out of 10).

Box 5.3: Indicators of financial stressIn each wave, HILDA Survey respondents have been asked if, since the beginning of that year,
because of a shortage of money they:1.

Could not pay electricity, gas or telephone bills on time.

2.

Could not pay the mortgage or rent on time.

3.

Pawned or sold something.

4.

Went without meals.

5.

Were unable to heat the home.

6.

Asked for financial help from friends or family.

7.

Asked for help from welfare/community organisations.

In this report, two levels of financial stress are distinguished based on responses to thisquestion: 1 or 2 of the above events occurred; and 3 or more of the above events occurred.

54

The table shows that disability
prevalence, and the severity ofdisability, is very strongly related toage. Approximately 60% of people
aged 70 and over have a disability
according to our definition of
disability, and 13.0% of women and14.5% of men in this age range
have a severely restricting
disability. At the other end of theage spectrum, 11.1% of males
aged 15–29 and 12.3% of females

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 5.3: Prevalence and changes in disability by age group, 2001–2012 (pooled, %)
15–29

30–39

40–49

50–59

60–69

70 and over

Total

11.1

16.4

22.8

32.3

48.3

59.9

26.3

Have a restricting disability

5.9

10.0

14.2

22.6

35.8

43.9

17.7

Have a severe disability

1.0

2.1

3.6

8.1

11.2

14.5

5.2

Acquired disability

6.7

9.8

10.9

14.4

15.5

24.4

11.7

Ceased being disabled

6.3

8.0

7.0

7.2

10.4

9.0

7.6

Acquired restricting disability

4.3

5.5

6.8

9.6

12.7

23.3

8.2

Males
Have a disability

Changes over 5 years

Ceased having a restricting disability

3.6

5.2

4.4

5.0

10.6

9.2

5.4

Acquired a severe disability

1.0

1.5

3.1

5.8

6.1

13.8

3.8

Ceased having a severe disability

0.8

1.2

2.0

3.5

6.0

5.0

2.4

Acquired disability

8.4

10.9

18.4

20.1

23.5

39.9

15.8

Ceased being disabled

6.6

6.2

5.7

5.9

7.7

5.3

6.3

Acquired restricting disability

5.0

7.1

10.2

16.1

19.2

32.7

10.9

Ceased having a restricting disability

4.0

4.0

3.5

5.1

8.5

5.9

4.6

Acquired a severe disability

1.7

2.5

5.2

7.6

9.4

17.7

5.0

Ceased having a severe disability

0.5

1.0

1.4

4.1

5.0

2.8

1.9

12.3

15.7

21.6

32.9

43.8

60.8

26.8

Changes over 10 years

FemalesHave a disability
Have a restricting disability

6.9

9.9

15.4

24.4

32.4

46.3

19.0

Have a severe disability

1.0

1.7

3.6

6.2

7.2

13.0

4.5

Acquired disability

8.1

9.1

12.8

17.2

19.7

21.2

13.2

Ceased being disabled

6.2

6.1

6.9

7.1

10.1

7.9

7.0

Acquired restricting disability

5.2

6.2

9.0

12.8

16.1

20.3

10.0

Changes over 5 years

Ceased having a restricting disability

3.8

3.6

5.8

5.8

8.5

7.9

5.4

Acquired a severe disability

1.1

2.1

3.5

4.9

6.4

14.5

4.3

Ceased having a severe disability

0.7

1.2

1.7

2.7

3.8

4.0

2.0

Acquired disability

10.9

13.7

20.5

23.8

33.7

35.2

19.5

Ceased being disabled

6.1

4.7

4.1

6.4

6.4

3.8

5.3

Acquired restricting disability

6.8

9.3

15.2

17.7

26.3

29.1

14.3

Ceased having a restricting disability

3.6

3.0

5.1

5.2

6.5

4.6

4.4

Acquired a severe disability

1.3

3.5

5.8

6.7

11.6

17.9

5.7

Ceased having a severe disability

0.6

0.8

1.8

3.0

2.0

0.6

1.4

Changes over 10 years

Health and subjective wellbeing

55

aged 15–29 have a disability,
and only 1.0% have a severely
restricting disability.
Reflecting its long-term nature, thereis less fluidity in disability statusevident over 5-year and 10-yeartime-frames than was evident for
general health and mental health.
In particular, there is relatively little
movement out of disability, andespecially severe disability. For
example, over a 10-year period, over
5% of the population acquires asevere disability, while less than 2%ceases to have a severe disability.

The determinantsof general health
Table 5.1 identified age as animportant factor in determining
general health. In this section,
we look to more precisely quantify
the effects of ageing, as well as
investigate the roles played by avariety of other factors, including
demographic characteristics,
income, employment activity,
health behaviours and body fat.
Table 5.4 presents estimates frompanel models of the determinantsof an individual’s SF–36 general
health score.
Confirming the differences acrossage groups in Table 5.1, a strong
negative relationship between
general health and age is evident inTable 5.4. The strength of therelationship is stronger for males
than females, although this largely
arises from the absence ofsignificant differences in health forfemales across the 15–24, 25–29,
30–34, 35–39 and 40–44 age
groups. The ‘age gradient’ is similar
for males and females at ages
above the 40–44 age group, withthe predicted general health score
decreasing by approximately 10 inmoving from the 40–44 age groupto the 85 and over age group.
Holding other factors constant, selfreported general health is higher inmore socio-economically advantaged
regions, with a move from thebottom SEIFA decile to the top SEIFA

56

Table 5.4: Determinants of general health
Males

Females

Current-wave variables
Age group (Reference category: 15–24)
25–29
–1.3
–0.2+
30–34
–3.0
0.9+
35–39
–3.9
–0.2+
40–44
–4.9
–0.7+
45–49
–7.0
–2.0
50–54
–8.1
–4.3
55–59
–9.2
–4.9
60–64
–10.4
–4.6
65–69
–10.5
–4.9
70–74
–12.4
–6.0
75–79
–13.7
–6.8
80–84
–13.8
–9.9
85 and over
–15.0
–11.3
Region (Reference category: Other region)
Major urban
–0.9
–1.1
Other urban
–0.7+
–0.2+
SEIFA decile
0.4
0.4
Partnered
0.7
0.2+
Family type (Reference category: Not a parent with dependent children)
Partnered, with dependent children
–0.2+
1.1
Lone parent
0.5+
0.0+
Equivalised income ($’0,000, December 2012 prices)
0.1
0.1
Financial stress (Reference category: No financial stress)
1 or 2 indicators of financial stress
–1.4
–1.7
3 or more indicators of financial stress
–3.2
–4.3
Previous-wave variables
Hours of paid work per week (Reference category: Not in the labour force and not retired)
1–15
1.4
1.3
16–30
2.8
1.5
31–40
3.2
1.9
41–50
3.5
2.0
51 and over
3.6
3.0
Unemployed
2.0
1.1
Retired–2.1–1.6
Total hours of paid and unpaid work per week (Reference category: Less than 20)
20–40
0.3+
0.5+
41–60
0.3+
1.0
61–80
0.2+
1.5
81 and over
0.0+
1.4
Smoking status (Reference category: Non-smoker)
Smoke daily
–3.8
–3.6
Smoke occasionally
–2.3
–2.1
Usual alcohol consumption per week (Reference category: Never or rarely drink alcohol)
1–14 drinks
0.6
1.2
15–28 drinks
0.3+
1.6
29–42 drinks
–0.7+
–0.1+
42 or more drinks
–1.9
–3.8
Exercise: Number of days per week (Reference category: 3–6)
Never
–4.0
–4.9
Less than once
–2.4
–2.6
1–2
–1.4
–1.2
7
0.6
1.8
Body Mass Index: Extent to which below
normal weight (0 if not underweight)
–1.9
–0.2+
Body Mass Index: Extent to which above
normal weight (0 if not overweight or obese)
–0.4
–0.4
Constant
72.6
68.9
Number of observations
21,85022,831
Number of persons
6,737
7,269
Notes: Estimates are coefficient estimates from linear random-effects panel models. See the TechnicalAppendix for more information on these models. The models are estimated on Waves 6 to 12 (2006–2012) to allow inclusion of Body Mass Index, which is only available in those waves. + indicatesestimate is not significantly different from 0 at the 10% level.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

decile increasing the healthmeasure by 4 for both males andfemales. (See Box 2.6 on page 18
for an explanation of SEIFA.) This
may reflect differences across SEIFA
deciles in individuals’
characteristics, such as educational
attainment. It could also in partreflect better access to healthservices in higher SEIFA deciles;
however, seemingly at odds with thishypothesis is that, holding otherfactors constant, living in a majorurban area lowers health comparedwith living in a non-urban area.
Living with a partner increases
men’s general health, but not
women’s; yet living with a partner
and a dependent child increases
women’s general health, but not
men’s (relative to not having adependent child or being a loneparent). Household income
positively impacts on health—each$1,000 of equivalised income (atDecember 2012 prices) acts toincrease the general health scoreby 0.1. While this may seem asmall effect, consider the example
of a household of two adults andtwo children moving from ahousehold income of $30,000 to ahousehold income of $250,000.
The estimated coefficient onequivalised income implies thiswould increase the health score ofeach of the household members by
10, a very large change indeed.
Further evidence that thehousehold financial situation
impacts on health is found for themeasures of financial stress (seeBox 5.4: Body Mass Index (BMI)
BMI is a crude measure of body fat. It is calculated by dividing weight (in kilograms) by height(in metres) squared (which have been collected by the HILDA Survey since Wave 6). A person
is classified as ‘underweight’ if BMI is less than 18.5, ‘normal weight’ if BMI is at least 18.5
but less than 25, ‘overweight’ if BMI is at least 25 but less than 30 and ‘obese’ if BMI is 30
or higher. BMI takes no account of body composition (e.g., muscle mass), and is therefore
not regarded as a reliable measure of body fat for individuals, but it is regarded as a usefulmeasure for population groups.

Box 5.3, page 54), which show
experience of financial stress haslarge and statistically significant
adverse effects on health.
The remaining factors examined inTable 5.4 relate to behaviours
which could change in response tohealth changes, rather than be thecause of changes in health. To
reduce the potential for this‘endogeneity’, variables for thesefactors are all measured in the
wave previous to the wave in which
health is measured.
Variables for hours of paid work andhours of total (paid and unpaid)
work indicate work acts to improvegeneral health, with no evidence
that long hours of work (51 or more
hours per week of paid work, and81 or more hours of total work) are
detrimental to health. However,even unemployment is associatedwith better health compared with
non-participation in the labour
force, suggesting other factors are
driving the apparent relationshipbetween labour force status andhealth. For example, movements
out of the labour force may be
precipitated by onset of disability
which, for reasons that are notclear, does not immediatelytranslate to deterioration inmeasured health.
Unsurprisingly, smoking reduces
measured health, but regularconsumption of alcohol does notappear to adversely affect health
until quite high levels of
consumption (42 or more drinks
per week) are reached. Note,however, that long-term adverse
health effects of drinking are not
identified by the model, which only
identifies whether drinking
behaviour 1 year ago has
implications for current health.
Substantial health benefits ofregular exercise are apparent, andit seems that it is optimal toexercise daily.
Two variables are included for Body
Mass Index (BMI), a measure ofbody fat (see Box 5.4, above). The
first variable measures the extent to

Health and subjective wellbeing

57

which a person is underweight—
equal to 18.5 minus BMI if BMI isless than 18.5, and equal to 0otherwise. The second variablemeasures the extent to which aperson is overweight—equal to BMI
minus 25 if BMI is greater than 25,
and equal to 0 otherwise. The
estimates for the overweight variableindicate that, for both males andfemales, the more overweight anindividual, the lower is general
health. However, the estimates forthe underweight variable indicatethat only for males does being
underweight adversely affect health.

Life satisfaction:
How is it affectedby where you live?
Table 5.5 provides a brief analysisof the effects on life satisfaction ofwhere you live. The table presents
panel regression model estimatesof the effects on life satisfaction oflocation of residence and thecharacteristics of the location ofresidence. Note that life
satisfaction is measured on a 0(completely dissatisfied) to 10(completely satisfied) scale.
No significant differences acrossthe states and territories areevident, with the exception that asignificant positive effect of livingin Queensland is evident for
females. Population density, bycontrast, is an important factor
for both males and females.
(See Box 2.7 on page 18 for
information on the region variables.)Towns smaller than 1,000 people
and non-urban areas increase life
satisfaction the most, closely
followed by urban areas outside themajor cities. Thus, other things
being equal, the major cities are theleast desirable places to live. Thisis somewhat counteracted by theeffects of socio-economic
disadvantage of the area in which
an individual lives, since the mostadvantaged locations tend to belocated in the major cities. Movingfrom the lowest SEIFA decile to the58

highest SEIFA decile acts toincrease life satisfaction by 0.6 formales and 1.0 for females.
In Waves 1 to 4, 6, 8, 10 and 12,
information was collected fromeach respondent on thecharacteristics of theneighbourhood in which they live.
Respondents were asked toindicate how common each of
10 phenomena are in theirneighbourhood, with the response
options being ‘never happens’,‘very rare’, ‘not common’, ‘fairly
common’ and ‘very common’.
Variables for each of these 10
items are included in the model
reported in Table 5.5, where eachvariable takes an integer value
potentially ranging from 0 (never
happens) to 4 (very common).

Characteristics of the local
neighbourhood appear to be very
important factors in life
satisfaction, for both males andfemales. Neighbours helping out
and doing things together havelarge positive effects on life
satisfaction, while noise fromaeroplanes, trains or industry,homes and gardens in bad
condition, people being hostile andaggressive, and burglary and theft
all significantly decrease life
satisfaction. Loud traffic noise alsosignificantly negatively impacts on
females’ life satisfaction. No
significant effects are, however,evident for rubbish and litter lying
around, teenagers hanging aroundon the streets, and vandalism anddeliberate damage to property.

Table 5.5: Effects of location of residence and neighbourhood attributes
on life satisfaction
Males

Females

Victoria

0.031+

0.026+

Queensland–0.002+0.067

Western Australia–0.039+–0.008+South Australia–0.009+0.057+

Tasmania

0.086+

0.050+

Northern Territory

0.039+–0.008+Australian Capital Territory

0.087+–0.007+

Other urban

0.077

0.108

Other region

0.108

0.127

SEIFA decile

0.006

0.010

Neighbours helping each other out

0.079

0.091

Neighbours doing things together

0.037

0.039

Loud traffic noise–0.010+–0.013Noise from aeroplanes, trains or industry–0.016–0.019Homes and gardens in bad condition–0.049–0.052

Rubbish and litter lying around–0.010+–0.009+Teenagers hanging around on the streets–0.009+–0.007+People being hostile and aggressive–0.071–0.074

Vandalism and deliberate damage to property

0.003+

0.006+

Burglary and theft–0.026–0.039Number of observations

37,86540,759Number of persons

10,517

11,278

State or territory (Reference category: New South Wales)

Region (Reference category: Major urban)

Local neighbourhood traits—How common is…

Notes: Controls are included for year of interview and age of respondent. Estimates are derived fromlinear random-effects models estimated on data from Waves 1 to 4, 6, 8, 10 and 12. + indicatesestimate is not significantly different from 0 at the 10% level.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

6

Cognitive activity andcognitive ability
Skills and abilities are key drivers of individuals’ economic and social wellbeing,
and it is important to understand their determinants and the mechanisms by which
they impact on economic and social outcomes. The HILDA Survey has had astrong focus on skills since its inception, each year collecting detailed information
on educational attainment, work experience and—since Wave 5—on-the-job
training. Nonetheless, considerable gaps in information on skills and abilities
motivated a special focus on ‘human capital’ in Wave 12, which collected new
information on educational attainment, cognitive ability, cognitive activities,
computer use and proficiency in languages other than English. In this chapter,a brief analysis of the information collected on cognitive activities and cognitive
ability is presented, while in Chapter 7, analysis of the additional education datacollected is presented.

Cognitive activities
The self-completion questionnaireadministered in Wave 12 contained
a question about the frequency
respondents engage in eightspecific cognitive activities: readingbooks; reading magazines ornewspapers; doing puzzles (likecrosswords or Sudoku) or playing
word games (such as Scrabble);playing other games, such as board
games or computer games; writing(e.g., reports, letters, stories orjournal entries); attending
educational lectures or courses;
engaging in arts or crafts or otherartistic activities (e.g., playing
musical instruments); and going tomuseums or art galleries. For eachactivity, respondents were asked to
select from the options: every day
or most days; several times a
week; about once a week; two or
three times a month; about once amonth; less than once a month; or
not at all.
These activities are of course notexhaustive of the range of cognitive
activities in which an individual
potentially engages. They areweighted towards activities
undertaken outside of employment,
exclude many computer-related

activities (e.g., programming) andindeed exclude many cognitively
demanding hobbies. Nonetheless,they are likely to provide indicative
information of the level of cognitive
activity of an individual, particularly
when examined in conjunction with
employment activity.
Figure 6.1 summarises the
responses to this question,
showing how the frequency of eachactivity varies by sex and age
group. Four levels of frequency are
distinguished in the figure: three ormore times per week (combining
the two highest-frequency
categories); weekly or monthly(combining the three middlefrequency categories); less thanmonthly; and not at all.
Substantial differences are evident
across the sex-by-age groups.
Frequent reading of books is morecommon for older age groups,
while, within each age group,
females are more likely than males
to read books frequently. For
example, approximately 20% ofmales aged 15–24 report readingbooks three or more times per
week, compared with 33% offemales aged 15–24. At the otherend of the age spectrum, 43% of
men aged 65 and over, and 61% of

Cognitive activity and cognitive ability

59

Figure 6.1: Frequency of various cognitive activities, by sex and age group, 2012
Read books

Read magazines or newspapers

80

80

80

60

60

60

Males

Females

80

80

80

60

60

60

Females

Males

Arts or crafts or otherartistic activities

Females

Males

80

60

60

0

0

Females

Males

45–64

65 and over

25–44

15–24

65 and over

45–64

25–44

Not at all
15–24

65 and overLess than monthly

45–64

20

25–44

20

15–24

Weekly or monthly

45–64

40

65 and over

40

25–44

65 and over

45–64

25–44

15–24

FemalesThe Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

65 and over

45–64

25–44

3 or more times per week

%

80

%

100

15–24

Females

Visit museums or art galleries

100

Males

15–24

45–64

65 and over

25–44

45–64

65 and over

25–44

15–24

65 and over

45–64

25–44

15–24

0

65 and over

0
45–64

0
25–44

20

15–24

20

45–64

20

65 and over

40

25–44

40

15–24

40

15–24

%

100

%

100

%

Females

Attend educational
lectures or courses

100

Males

45–64

Males

Play other games (e.g., board
games or computer games)

Do puzzles or play word games

65 and over

15–24

45–64

65 and over

25–44

15–24

65 and over

45–64

15–24

Females

25–44

0

65 and over

0
45–64

0
25–44

20

15–24

20

45–64

20

65 and over

40

25–44

40

15–24

40

25–44

%

%

100

%

100

Males

60

Write

100

women aged 65 and over, reportreading books three or more times
per week. Reading of newspapers
and magazines is even morestrongly associated with older age
groups, although, for this activity,
differences between males andfemales are quite small.

Box 6.1: What is cognitive ability?

Females are somewhat more likely
than males to write at least monthly,
but frequent writing—three or moretimes per week—is equally common
among males and females. For bothmales and females, the frequency ofwriting tends to be lower for older
age groups, particularly those aged
65 and over: 50% of men and 45%
of women aged 65 and over do notwrite at all.

three or more times per week issimilar across all four age groups
examined in Figure 6.1.

Playing ‘other games’, which
include board games and computer
games, is the next most frequent
activity. Males aged 15–24 are themost likely to engage in this activity
frequently, which probably reflects
the popularity of computer games
among this demographic group.
Nearly 50% of males aged 15–24
engage in this activity three or moretimes per week, compared withfewer than 20% of females in thisage range. Consistent withcomputer games being the main
component of this cognitive activity,
people in the older age groups tend
to engage in this activity lessfrequently than those in theyounger age groups. Notable,
however, is that the proportion of
females playing ‘other games’

Cognitive ability, or cognitive function, refers to the ability to process thoughts—in simple
terms, the ‘ability to think’. Cognitive ability is generally regarded as multidimensional,
comprising a number of distinct abilities. More concretely, it is the set of all mental abilities,
including attention, memory, judgement and evaluation, reasoning, problem solving, andcomprehension. Cognitive ability tests will typically not attempt to measure all dimensions ofcognitive ability, although these dimensions are generally found to be positively correlated:people who are highly able in one cognitive skill tend to be more able in other cognitive skills.

Younger people are more likely toattend educational lectures orcourses, which is unsurprising
given that many young people
have yet to complete theireducation. Perhaps less anticipatedis that the likelihood of doing
artistic activities decreases withage. Also evident with respect toboth educational attendance andartistic activities is that females
are more likely to engage in these
activities than males.

Cognitive ability
Three cognitive ability tasks were
included in the interview
component of Wave 12 of the
HILDA Survey. The first task, knownas ‘Backward Digits Span’ (BDS),
is a test of working memory span;
the second task, known as‘Symbol Digits Modalities’ (SDM),
is a test of divided attention, visualscanning and motor speed; while
the third test is a shortened(25-item) version of the NationalBox 6.2: Cognitive ability tasks in the HILDA Survey
Backward Digits Span (BDS): The interviewer reads out successively longer strings of singledigit numbers which the respondent is asked to repeat back in reverse order. The string length
starts at 2, and with each correct answer it increases by 1, up to a maximum of 8. Respondents
are given two chances at each string length, with the task ending if both responses at the same
string-length are incorrect. The BDS score ranges from 0 (no correct responses) to 7 (a correct
response at all seven string lengths).
Symbol Digits Modalities (SDM): Respondents are given a sheet of paper with a printed key
matching symbols to single-digit numbers, below which is a grid of symbols. Each symbol has ablank space next to it, and the respondent is given 90 seconds to enter the corresponding
number for as many of the symbols as possible, starting with the symbol in the upper left cornerand completing each row in sequence. The SDM score is equal to the number of correct entries.
NART–25: Respondents are asked to pronounce 25 irregularly spelled words printed on ashow card. The NART–25 score ranges from 0 (no correct responses) to 25 (all 25 words
correctly pronounced).

1

Adult Reading Test (NART–25),
which provides a measure ofpre-morbid intelligence. The
BDS and SDM tests can becharacterised as providing
measures of ‘fluid’ general
intelligence—the ability to thinklogically and solve problems innovel situations, independent ofacquired knowledge—while the
NART provides a measure of‘crystallised’ general intelligence—the ability to use skills,
knowledge, and experience.1
Table 6.1 presents summary
statistics for the scores on thethree cognitive ability measures.
The upper panel presents
information on the distribution ofscores for males and females,
while the lower panel presentsmean scores by sex and age group.
For all three measures, thedistributions of scores are very
similar for males and females,
although females tend to perform
slightly better than males on the
SDM and NART–25 tests.
Comparing across age groups, themean BDS score peaks in the
30–39 age group and thereafter
declines with age. The mean SDM
score is even more strongly related
to age, tending to be highest for
the youngest age groups andthereafter declining with age. The
mean NART–25 score, by contrast,
is increasing with age up to the
60–69 age group, after which it
declines slightly.
The cognitive ability tasks provide
objective measures of cognitive
ability, but the HILDA Survey alsocollected subjective measures ofability in Wave 12. Respondents

See Wooden (2013) for an explanation of the cognitive ability tasks administered in the HILDA Survey and for an examination of thequality of the measures of cognitive ability produced by the tasks.

Cognitive activity and cognitive ability

61

Table 6.1: Cognitive ability task scores, 2012
Backward digits span

Symbol digits modalities

NART–25

Males

Females

Males

Females

Males

Females

Mean

4.9

4.9

47.6

50.3

13.0

13.2

Median

5.0

5.0

48.0

51.0

13.0

13.0

10th percentile

3.0

3.0

31.0

33.0

5.0

5.0

90th percentile

7.0

7.0

63.0

66.0

20.0

20.0

15–19

4.9

4.9

53.9

56.7

10.4

10.6

20–29

5.0

5.0

53.5

57.4

11.6

11.4

30–39

5.2

5.1

53.0

55.7

12.7

13.3

40–49

5.0

5.0

49.4

52.2

13.6

13.6

50–59

4.8

4.9

45.1

49.0

13.8

14.2

60–69

4.7

4.8

40.6

42.8

14.6

14.7

70–79

4.5

4.5

32.6

35.2

14.5

14.2

80 and over

4.1

4.1

26.3

27.4

13.4

13.5

Distribution of scores

Mean scores by age groupwere asked to rate their own(English) reading and maths skills
compared with ‘the average ortypical Australian adult’. The rating
was on a 0–10 scale, withrespondents instructed to interpreta rating of 5 as ‘about average’.2
Table 6.2 presents the mean selfassessed ratings by sex and age
group. It is immediately obvious
that, on average, people think they
are above average. This provides
some hint of the problems withsubjective measures of ability andhence the motivation for theobjective measures introduced in
Wave 12. The absence of a strong
relationship between rating and age
Table 6.2: Mean self-assessed reading skills and maths skills, by sex andage group (0–10 scale), 2012
Self-assessed reading skills

Self-assessed maths skills

Males

Females

Males

Females

15–19

7.7

8.1

7.2

6.8

20–29

7.7

8.0

7.2

6.6

30–39

7.7

8.2

7.3

6.7

40–49

7.7

8.3

7.4

6.8

50–59

7.6

8.2

7.3

6.9

60–69

7.6

7.9

7.3

6.6

70–79

7.7

7.9

7.2

6.5

80 and over

7.5

7.8

7.0

6.5

2

also perhaps provides an indicationof the unreliability of subjective
ability measures. Notable is that,on average, females rate their own
reading skills more highly thanmales rate their own reading skills,
while males rate their own maths
skills more highly than females rate
their own maths skills.
Correlations between the fivemeasures of cognitive ability are
examined in Table 6.3. All correlation
coefficients are positive, including
for the subjective measures, whichperhaps to some extent validates
them as ability measures. Indeed,
for both males and females, thehighest correlation coefficient for
the NART–25 is with self-assessedreading skills. That said, none of
the measures are even close tobeing perfectly correlated, although
this is consistent with eachmeasure containing distinct
information about the ability of theindividual. Comparing males andfemales, it is interesting that all
correlation coefficients are
somewhat higher for males,meaning that there is a greater
tendency for a male with a highscore on one measure to also score
highly on the other measures.

Self-assessed reading and maths skills were obtained from respondents prior to undertaking the cognitive ability tasks, reducing thepotential for assessments to be affected by perceived performance on the tasks.

62

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 6.3: Correlations between measures of cognitive ability, 2012
Backward
digits span

Symboldigits modalities

NART–25

Self-assessedreading skills

Self-assessedmaths skills

1.000

–

–

–

–

Males
Backward digits span
Symbol digits modalities

0.375

1.000

–

–

–

NART–25

0.373

0.223

1.000

–

–

Self-assessed reading skills

0.259

0.210

0.497

1.000

–

Self-assessed maths skills

0.274

0.265

0.273

0.529

1.000

1.000

–

–

–

–

Females
Backward digits span
Symbol digits modalities

0.344

1.000

–

–

–

NART–25

0.356

0.189

1.000

–

–

Self-assessed reading skills

0.225

0.182

0.478

1.000

–

Self-assessed maths skills

0.238

0.216

0.175

0.453

1.000

Associations
betweencharacteristics andcognitive ability
In Table 6.4, the associations
between cognitive ability testscores and characteristics are
explored. The table presents
coefficient estimates fromordinary least squares models
for each test. The models containvariables for age, self-assessed
English language proficiency of
non-native speakers, educational
attainment, cognitive activities(as presented in Figure 6.1),
disability, labour force status andoccupation, computer use and,for persons aged 65 and over,participation in employment, childminding, voluntary work and regularexercise. The sample is restricted
to people aged 25 and over, since
many people under 25 have yet toleave full-time education (for the
first time).
The models also contain controlsfor ‘achievement motivation’—fear of failure and hope forsuccess—on the basis that they
will impact on the degree of effort
exerted in the tests. The two
measures are derived from therespondent’s attitudes to new ordifficult tasks and situations (seeBox 6.3, at right).

Significant differences between
men and women are evident for all
three cognitive ability tests. Holding
other factors constant, men on
average score more highly than
women on the BDS and NART–25
tests, while women score more
highly on the SDM test. The lower
mean score for women for the
NART–25 is at odds with thehigher subjectively assessedreading ability of women, and isperhaps surprising. However, it isimportant to note that interviewers
are required to assess whether
the respondent has pronounced
each word correctly, and this may
bias the results. It is also possiblethat women are less likely thanmen to attempt to pronounce
unfamiliar words.

In line with the simple crosstabulations presented in Table 6.1,
age is an important determinant ofscore for the SDM and NART–25
tests, but is not a significant factor
for the BDS test until the post-65
age ranges are reached. For theSDM test, the score decreases
with age from the 30–34 age range
onwards. This is consistent with
the SDM providing a measure of‘fluid intelligence’ which peaks inthe early 30s and thereafter
declines. For the NART–25 test,
the score increases with age
across the entire age range, sothat people in the 85 and over age
group have the highest expectedscore, all else equal. This isconsistent with the NART–25
providing a measure of ‘crystallised

Box 6.3: Measures of ‘achievement motivation’To measure achievement motivation, respondents were asked to indicate the extent to which
they agreed with each of the following statements:
1.

In difficult situations where a lot depends on me, I am afraid of failing2.

I like situations where I can find out how capable I am

3.

I feel uneasy about undertaking a task if I am unsure of succeeding

4.

When confronted by a difficult problem, I prefer to start working on it straight away

5.

I am afraid of tasks that I cannot work out or solve

6.

I enjoy situations that make use of my abilities

7.

Even when nobody is watching, I feel anxious in new situations

8.

I am attracted to tasks that allow me to test my abilities

9.

I start feeling anxious if I do not understand a problem immediately

Responses were on a 7-point Likert scale, ranging from strongly disagree (1) to strongly agree(7). The fear of failure measure is equal to the sum of the individual’s responses to items 1, 3,
5, 7 and 9, while the hope for success measure is equal to the sum of the individual’s
responses to items 2, 4, 6 and 8. Scores for the fear of failure measure range from 5 to 35(with a sample mean of 18.2) and scores for the hope for success measure range from 4 to
28 (with a sample mean of 21.1).

Cognitive activity and cognitive ability

63

Table 6.4: Factors associated with scores on cognitive ability tasks—Persons aged 25 and over, 2012
Backward
digits span

Symboldigits modalities

0.114–2.241

0.561

Male

NART–25

Age group (Reference category: 25–29)
30–34

0.057+

0.058+

0.647

35–39

0.040+–1.712

1.550

40–44

–0.039+–2.565

1.553

45–49

–0.026+–4.964

2.282

50–54

–0.116+–5.950

2.275

55–59

–0.177–7.497

2.577

60–64

–0.006+–9.138

3.384

65–69

–0.224–12.494

3.418

70–74

–0.223–15.942

3.422

75–79

–0.261–18.525

3.900

80–84

–0.479–21.032

3.824

85 and over

–0.482–23.979

4.042

Educational attainment (Reference category: Less than high-school completion)
Degree or higher

0.513

3.551

3.139

Diploma

0.219

2.288

1.796

Certificate Level 3 or 4

0.005+

0.464+

0.654

Completed high school

0.256

2.164

2.085

Labour force status and occupation group (Reference category: Not in the labour force)
Unemployed–0.044+–2.145+–1.007

Managers

0.216

3.117

0.517

Professionals

0.229

3.116

1.168

Technicians and trades workers–0.054+1.847–0.184+

Community and personal service workers–0.024+1.051–0.372+

Clerical and administrative workers

0.306

3.869

0.948

Sales workers

0.151

2.063

0.040+

Machinery operators and drivers

0.096+

1.169–0.812

Labourers–0.078+0.214+–0.801

Use a computer in day-to-day activities

0.303

3.966

1.880

0.694

Frequency of cognitive activities (1–4 scale)
Read books

0.072

0.456

Read magazines or newspapers–0.043–0.3580.007+

Do puzzles or play word games

0.127

1.118

0.530

Play other games

0.028

0.478

–0.156

Write

0.008+

0.264

0.310

Attend educational lectures or courses–0.032+–0.154+–0.147

Artistic activities

0.035

0.362

0.213

Visit museums or art galleries–0.017+–0.586

0.602

Aged 65 and over: In paid employment–0.071+–1.571–0.039+Aged 65 and over: Look after others’ children

0.166

0.438+

0.170+

Aged 65 and over: Do voluntary work

0.081+

1.317

0.733

Aged 65 and over: Exercise regularly

0.035+

1.206–0.002+Disability (Reference category: No moderate or severe disability)
Disability with moderate work restriction–0.014+–1.645–0.136+

Disability with severe work restriction–0.237–4.704–1.018English proficiency (Reference category: Native English speaker, or English good or better)
Poor English–0.771–3.767–9.311

Okay English–0.590–4.418–6.285

Hope for success

0.008

0.026+–0.016+

Fear of failure–0.004+–0.058–0.008+Constant

2.933

46.403

4.475

Number of persons

10,80910,796

10,789Notes: Table presents coefficient estimates from ordinary least squares regression models. + indicates estimate is not significantly different from 0 at the
10% level.

64

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

associated with higher test scores,
while there is a positive association
between all three test scores andfrequency of reading books, doing
puzzles or word games and engaging
in artistic activities. Greater
frequency of writing is associatedwith higher scores for the SDM
and—unsurprisingly—the NART–25,
but has no significant association
with score on the BDS test. Playing‘other’ games (including computer
games) is associated with higherscores on the BDS and SDM tests,
but lower scores on the NART–25.
Attendance at lectures or courses islikewise associated with lower
NART–25 scores, but has no
significant relationship with BDS andSDM scores. Visiting museums andgalleries is associated with higher
NART–25 scores, but lower scoreson the SDM test. Curiously, readingof newspapers and magazines is
associated with lower performanceon the BDS and SDM tests, and noeffect on performance on the
NART–25 test.
intelligence’. Nonetheless, it isnotable that, on average, there isno evidence of cognitive decline in
the older ages for this dimensionof crystallised intelligence.
Scores on the BDS test do notsignificantly differ by age among
those aged 25–64, but thereafter
decline with age, consistent withthis being the age range when‘working memory’ declines.
Unsurprisingly, university-level
educational qualifications areassociated with higher test scores(see Box 7.1 on page 68 for
information on the classificationof educational attainment). Highschool completion (with nopost-school qualifications at
Certificate Level 3 or above) is
also associated with relatively
high scores: holding other factors
constant, those who havecompleted high school have
similar scores to those holding
diplomas, and higher scores thanthose in the Certificate Level 3
or 4 category. Indeed, all else

constant, holders of Level 3 or 4
Certificates do not score any betterin the BDS and SDM tests thanthose in the ‘less than high-school
completion category’.
All else equal, unemployed people
do not score significantly lower
than other people on the BDS andSDM tests, but they do score lower
on the NART–25 test. Among
employed people, managers andprofessionals score more highly on
all three tests. Clerical andadministrative workers, and sales
workers, also score relatively highlyon the BDS and SDM tests, while
technicians and trades workers,
and community and personal
service workers, score relatively
highly on the SDM test, but not on
the other two tests.
The estimates indicate that a
number of cognitive activities areassociated with higher measured
cognitive ability. Holding otherfactors constant, regular use of acomputer in day-to-day activities is

The additional variables included
for people aged 65 and over
explore the potential for various
activities (additional to the above
cognitive activities) to protect
against cognitive decline. There issome evidence supporting such aneffect for voluntary work, looking
after children and regular exercise,although none of these activities ispositively associated with all threecognitive measures. Looking afterchildren is associated with apositive impact on the BDS score,
voluntary work is associated with apositive impact on SDM and NART–
25 scores, and regular exercise(three or more times per week) is
associated with a positive impact
on the SDM score. Of course, it
must be acknowledged thatcognitive decline could affectparticipation in these activities,
rather than the activities slowing
decline. Surprisingly, no positiveeffects are found for employment,
and indeed there is a negative
association between employment
and the SDM score.

Cognitive activity and cognitive ability

65

The variables for English language
proficiency, disability andachievement motivation are included
primarily to serve as ‘controls’ so as
to accurately ascertain theassociations between othercharacteristics and cognitive ability.
Achievement motivation turns out tohave little apparent effect on thescores achieved. It might be
expected that people with greater
hope for success and/or fear of
failure would, all else equal, performbetter on the tests, if the tests did infact capture effort exerted incarrying out the cognitive ability
tasks. However, hope for successonly has a significant positive impact
for the BDS test, while the only
significant effect of fear of failure isin the opposite direction, withgreater fear of failure associatedwith a lower score on the SDM test.
As expected given the nature of thetasks, which are administered in
English, non-native-English
speakers who are not completely
proficient in English on averagescore considerably lower. Disability
that moderately restricts the ability
to work does not significantly affectperformance, but severely
restricting disability is associatedwith lower scores. This is likely toat least in part reflect the effects
of impairments on the ability toactually carry out the tasks.

66

Table 6.5: Partner and parent–child correlations in scores on cognitive
ability tasks, 2012

Scores of partners

Backward
digits span

Symboldigits modalities

NART–25

0.203

0.436

0.550

0.252

0.413

Age-adjusted scores of parents and children
Mother and son

0.230

Father and son

0.261

0.275

0.458

Mother and daughter

0.221

0.289

0.440

Father and daughter

0.270

0.210

0.427

Notes: The mean age of the children in the parent–child comparisons is 23 (with a minimum of 15).
The mean age of the mothers is 52 (with a minimum of 32 and maximum of 91) and the mean age ofthe fathers is 54 (with a minimum of 33 and maximum of 90).

Partner andparent–childcorrelations in
cognitive ability
scoresWithin-family correlations in
cognitive ability scores are
examined in Table 6.5. The tableshows the correlation coefficientsfor partners’ test scores and forparent–child test scores. Consistent
with the notion of ‘assortative
mating’, the partner correlation
coefficients are all positive, ranging
from 0.2 for the BDS test to 0.55
for the NART–25 test. If partnering
was essentially random with respect
to cognitive ability, we would expect
a correlation coefficient of 0, whilea tendency for high-ability people
to partner with low-ability people
would result in a negative
correlation coefficient. Note thatthe positive correlation coefficientsfor the SDM and NART–25 arelikely to derive in part from theirassociations with age and the factthat partners tend to be of similarages. Nonetheless, the HILDA
Survey data show a tendency forpeople to partner with people ofsimilar cognitive ability.
The lower panel of Table 6.5
presents parent–child correlation
coefficients, separately examining
mother–son, father–son, mother–daughter and father–daughtercorrelations. To attempt to control
for the effects of age differences
between parents and children,the correlation coefficients arecalculated for ‘age-adjusted’scores, obtained by calculating thedeviation of an individual’s scorefrom the score predicted for theirage group (based on the estimatespresented in Table 6.4). Parent–child correlation coefficients are
all positive. They are higher thanthe partner correlation coefficientfor the BDS test, but—despite
controlling for age effects—lower
for the SDM and NART–25 tests.
Nonetheless, the parent–childcorrelation coefficients are higher
for NART–25 than the other two
measures, suggesting that parents
have a greater influence on theirchildren’s vocabulary than on the
cognitive abilities measured by the
BDS and SDM.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

7

Education and labour
market outcomes
While the educational attainment of respondents has been collected by the HILDA
Survey since its inception, in Wave 12 some additional information was collected.
People with post-school qualifications were asked about the type of educational
institution they attended, their main field of study and, for those with university
qualifications, the name of the institution they attended. As reported in Chapter 2,
additional information was also collected on school education of those still inschool. This chapter provides a brief statistical overview of the information on
educational qualifications collected in Wave 12, followed by an exploration of therelationship between the educational qualifications of individuals and their labour
market outcomes. In addition, new information on individuals’ use of computers
and its implications for earnings is briefly examined. For all of the analysis
presented in this chapter, adults aged 25 and over are examined on the basis thatmost people have completed formal education by the age of 25 (although it
should be acknowledged that many people undertake further education after theage of 25).

Educational
qualifications ofAustralians
Table 7.1 shows the educational
attainment of the population aged
25 and over, disaggregated by sex
and age group. In 2012, thehighest qualification was amaster’s degree or doctorate for7.1% of men and 5.3% of women,a graduate diploma or graduate
certificate for 5.4% of men and6.4% of women, a bachelor’s
degree for 15.6% of men and17.3% of women, a diploma or
advanced diploma for 9.3% of men
and 10.8% of women, a Certificate
Level 3 or 4 for 28.9% of men and15.8% of women, Year 12
completion for 11.4% of men and12.6% of women, and less thanYear 12 for 21.9% of men and31.4% of women.
There are substantial differences
evident in the educational
attainment profile by age. Eachsuccessively older age group has asmaller proportion with universitylevel qualifications (bachelor’s

degree, graduate diploma, graduate
certificate, master’s degree or
doctorate), and a higher proportion
with a highest qualification of Year
11 or lower. For example, theproportion of men with university
qualifications in 2012 is 38.6%
in the 25–34 age group, 32.3% in
the 35–44 age group, 27.1% in the
45–54 age group, 24.9% in the
55–64 age group and 15.2% in
the 65 and over age group; while
the proportion of men with ahighest qualification of Year 11
or less is 12.8% in the 25–34
age group, 13.7% in the 35–44 age
group, 21.5% in the 45–54 age
group, 26.1% in the 55–64
age group and 38.3% in the 65 andover age group. The differencesby age group are even larger for
women, reflecting a societal
shift over time towards greater
education participation by women.
For example, in 2012, 62.8% of
women aged 65 and over had ahighest qualification of Year 11
or lower, compared with only 9.5%of women aged 25–34. We thus
see that women in the 55 and overage groups are on average less

Education and labour market outcomes

67

educated than men in these agegroups, while women in the 25–34
and 35–44 age groups are on
average more educated thansimilarly-aged men.

Box 7.1: Classification of educational attainment and field of study
The classification of educational qualifications adopted by the HILDA Survey is based on theAustralian Standard Classification of Education (ASCED) (Australian Bureau of Statistics (ABS),
2001), which classifies formal educational qualifications by level and by field of study.
The level of highest educational attainment is derived from information on highest year of
school completed and level of highest non-school qualification. In this report (e.g., Table 7.1),
up to seven levels of attainment are distinguished, ranging from ‘Masters or Doctorate’ down
to ‘Year 11 and below’. Note in particular that, as explained in ABS (2014), Year 12 is defined
to be a higher qualification than a Certificate Level 1 or 2, so that the category ‘Year 11 andbelow’ includes people who hold a Certificate Level 1 or 2.

Table 7.2 summarises theinformation collected on institutionof highest qualification that was
obtained in Wave 12 for all
respondents holding a university
qualification. It presents, by sex
and age group, the proportion who
obtained their highest qualificationfrom each of six groups ofuniversities (described in Box 7.2,
page 69). The table shows that, of
the 28.1% of men and 29% of
women aged 25 and over holding auniversity qualification in 2012,
27.7% of men and 23.8% of womenobtained their highest qualificationfrom a ‘Group of 8’ (Go8) university.
Significantly, overseas universities
are an important source ofqualifications for people living inAustralia, representing the secondmost common source of highest
qualification for men and the thirdmost common source for women.

ASCED classifies field of study into 12 broad groups which form the basis for the HILDA Survey
classification of main field of study. The HILDA Survey classification of field of study
nonetheless deviates from the ASCED broad groupings in three ways: (1) medicine and nursing
are each distinguished from other ‘health-related’ fields; (2) law is distinguished from other‘society and culture’ fields; and (3) the ‘mixed fields’ category is removed (i.e., respondents
are required to identify a single main field).

Consistent with their more recent
establishment, the non-Go8Australian universities represent ahigher proportion of qualificationsin the younger age groups. For men
with university qualifications, 22.5%of those in the 25–34 age groupobtained their highest qualificationfrom a Go8 university, comparedwith 45.0% of those in the 65 andover age group. Similarly, 20.5% of
women aged 25–34 with university
qualifications obtained their highest
qualification from a Go8 university,

compared with 35.6% of universityqualified women age 65 and over.
Field of study of highest
qualification is examined in Table
7.3 for all respondents aged 25
and over with non-school
qualifications (including Certificate
Levels 1 and 2). For men, the mostcommon field is engineering andrelated, followed by management
and commerce and thenarchitecture and building. For
women, the most common field ismanagement and commerce,

Table 7.1: Educational attainment by sex and age group, 2012 (%)
25–34

35–44

45–54

55–64

65 and overAll aged
25 and over

Master’s degree or doctorate

9.4

8.4

6.5

6.5

4.1

7.1

Graduate diploma or certificate

4.9

6.5

5.3

6.5

4.0

5.4

Bachelor’s degree

24.3

17.4

15.3

11.9

7.1

15.6

Diploma or advanced diploma

5.7

9.9

11.0

10.8

9.9

9.3

Males

Certificate Level 3 or 4

23.7

31.5

32.2

28.9

28.3

28.9

Year 12

19.2

12.6

8.1

8.9

6.8

11.4

Year 11 and below

12.8

13.7

21.5

26.1

38.3

21.9

Undetermined

*0.1

*0.0

*0.1

*0.4

1.6

0.4

Total

100.0

100.0

100.0

100.0

100.0

100.0

Master’s degree or doctorate

9.0

6.6

5.2

3.5

1.6

5.3

Graduate diploma or certificate

5.1

7.6

8.7

7.2

3.5

6.4

Bachelor’s degree

29.9

23.0

14.4

10.4

7.4

17.3

Diploma or advanced diploma

12.1

13.8

10.6

10.0

7.4

10.8

FemalesCertificate Level 3 or 4

17.6

15.5

19.7

17.6

8.9

15.8

Year 12

16.8

14.5

12.2

11.0

8.1

12.6

Year 11 and below

9.5

18.9

29.2

38.7

62.8

31.4

Undetermined

*0.0

*0.1

*0.0

1.5

*0.3

0.4

Total

100.0

100.0

100.0

100.0

100.0

100.0

Note: * Estimate not reliable.

68

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

followed by education, nursing, andsociety and culture. The fields ofstudy differ substantially across theage groups examined in the table,with younger age groups more likely
to have studied management andcommerce, information technology,
food, hospitality and personal
services, creative arts and law, andless likely to have studied
agriculture, environment andrelated studies, and education.
Men in the younger age groups arealso less likely to have studied
engineering and related
technologies, and society andcultures; while women in younger
age groups are also more likely tohave studied natural and physicalsciences, engineering and related
technologies, agriculture,Box 7.2: University groupings
In total, there are 39 Australian universities. There are several ways in which these universities
could be classified into groups, but in this report we classify universities according to the fourmain formal groupings:
The Group of 8 (Go8) markets itself as the group of ‘Australia’s Leading Universities’,
comprising The University of Adelaide, The Australian National University, The University of
Melbourne, Monash University, The University of New South Wales, The University of
Queensland, The University of Sydney and The University of Western Australia.
The Australian Technology Network (ATN) is a coalition of five Australian universities (Curtin
University of Technology, University of South Australia, RMIT University, University of Technology
Sydney and Queensland University of Technology) that share a common focus on the practicalapplication of tertiary studies and research.
Innovative Research Universities (IRU), is a group of seven universities (Flinders University,
Griffith University, La Trobe University, Murdoch University, University of Newcastle, James Cook
University and Charles Darwin University) that ‘share a common mode of operation … and acommon background, having been founded in the 1960s and 1970s as research universities’.
The Regional Universities Network (RUN) comprises six universities (Central Queensland
University, Southern Cross University, University of Ballarat, University of New England,
University of Southern Queensland and University of the Sunshine Coast), it was formed in2011 to take advantage of the regional focus of the Gillard Labor Government.
The remaining 13 universities are classified as ‘other Australian’ universities. (A further three
foreign universities have, or have had, campuses in Australia taking Australian students.
However, none of the HILDA Survey respondents reported obtaining their highest qualificationfrom one of these universities.)

Education and labour market outcomes69

Table 7.2: University grouping of highest educational qualification, by sex and age group—Persons with university
qualifications, 2012 (%)
25–34

35–44

45–54

55–64

65 and overAll aged
25 and overGroup of 8 (Go8)

22.5

23.3

25.1

35.9

45.0

27.7

Australian Technology Network (ATN)

13.4

18.2

12.2

11.4

*5.7

13.1

Innovation Research Universities (IRU)

8.1

9.5

7.2

7.8

*5.8

7.9

Males

Regional Universities Network (RUN)

12.9

5.3

7.8

*5.5

*3.5

7.9

Other Australian university

21.4

21.4

20.3

16.9

11.6

19.4

Overseas university

21.8

22.3

27.4

22.5

28.4

23.9

Total

100.0

100.0

100.0

100.0

100.0

100.0

20.5

25.5

21.9

24.0

35.6

23.8

Females
Group of 8 (Go8)Australian Technology Network (ATN)

17.1

15.1

10.4

15.9

10.0

14.6

Innovation Research Universities (IRU)

12.8

12.2

12.5

9.9

8.6

11.9

Regional Universities Network (RUN)

4.8

4.9

7.8

9.8

*4.8

6.0

Other Australian university

25.0

23.6

29.7

24.6

19.5

25.0

Overseas university

19.9

18.7

17.7

15.8

21.5

18.8

Total

100.0

100.0

100.0

100.0

100.0

100.0

Note: * Estimate not reliable.

environment and related studies,
medicine and law, and areconsiderably less likely to havestudied nursing and education
than older women with nonschool qualifications.

Education andlabour market
outcomes
Economists have long beeninterested in the labour market
benefits of education, and there
has correspondingly been a great
deal of research attempting toquantify these benefits (see, forexample, Ashenfelter et al., 1999;
and Card, 1999). Irrefutableevidence on the benefits ofeducation has, however, proved
somewhat elusive, reflecting thefundamental ‘identification’ problem
that we do not observe what an
educated person would haveexperienced had they not obtained
their education.1 In this report we

1

do not attempt to estimate the‘returns’ to education, but rather
simply describe the empiricalassociation between education andlabour market outcomes.
Table 7.4 presents regression
results from models of the effects
associated with level of educational
attainment and university grouping
on three outcomes: the probabilityof employment; the probability offull-time employment; and weekly
earnings. The models are
estimated separately for men andwomen, and restricted to persons
aged 25–59. The earnings models
are further restricted to full-time
employees. All models include
controls for ‘confounding factors’,
comprising variables for age, placeof birth and Indigenous status,state of residence, population
density of region of residence,
disability and English language
proficiency. The employment
models additionally include controls
for family type and the age of theyoungest child. The table alsopresents estimates with theaddition of controls for cognitive
ability, as measured by the scoreson the three cognitive ability tests
described in Chapter 6. The
addition of these controls arguably
provides a stronger basis forinterpreting estimates for education
variables as ‘causal’, on the grounds
that this controls for the higher
innate ability of the more-educatedAs with many economic and social phenomena, the problem of identification of the causal effects of education is not easily overcome.
For example, ‘double-blind’ randomised controlled trials, the gold standard for evaluation of treatment effects in medical research, arenot viable for a ‘treatment’ such as education.

70

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 7.3: Main field of study of persons with non-school qualifications, 2012 (%)
25–34

35–44

45–54

55–64

65 and overAll aged
25 and over

2.6

4.3

4.3

3.4

4.3

3.7

Males
Natural and physical sciences
Information technology

8.8

5.6

5.3

4.6

*2.3

5.7

Engineering and related technologies

22.1

26.2

30.9

31.4

34.3

28.2

Architecture and building

10.0

8.1

10.8

10.6

9.7

9.8

Agriculture, environment and related

3.4

4.0

4.7

4.6

5.2

4.3

Medicine

*1.4

*1.7

*1.3

*0.8

2.5

1.5

Nursing

*1.3

*1.9

*1.7

*1.3

*0.1

1.3

Other health-related

3.1

2.8

3.3

*1.4

*2.4

2.7

Education

3.0

3.7

6.3

8.7

7.5

5.4

Management and commerce

26.0

22.9

16.7

17.4

17.1

20.6

Law

3.6

3.2

1.6

*2.0

*1.7

2.5

Society and culture

4.7

5.7

5.8

8.6

8.0

6.3

Creative arts

4.0

3.0

2.6

*1.8

*2.0

2.8

Food, hospitality and personal services

5.7

6.2

4.7

3.4

2.9

4.8

Other

*0.5

*0.8

*0.1

*0.0

*0.2

*0.4

Total

100.0

100.0

100.0

100.0

100.0

100.0

FemalesNatural and physical sciences

4.5

3.6

2.2

*1.3

*2.4

3.0

Information technology

5.2

*1.9

2.2

*2.1

*1.8

2.8

Engineering and related technologies

3.4

*1.6

*1.2

*1.1

*1.3

1.9

Architecture and building

*1.2

*0.8

*0.9

*1.2

*0.5

0.9

Agriculture, environment and related

*1.7

*1.9

*0.8

*0.8

*0.4

1.3

Medicine

*1.5

*1.8

*1.2

*0.9

*1.2

1.4

Nursing

8.2

8.3

15.6

18.3

19.9

12.8

Other health-related

7.5

7.9

8.9

6.4

5.7

7.5

Education

11.3

15.8

20.1

20.7

19.0

16.7

Management and commerce

26.0

26.5

22.8

23.2

23.1

24.6

Law

2.1

2.3

*1.4

*0.6

*0.7

1.6

Society and culture

12.5

11.8

11.7

14.0

12.6

12.4

Creative arts

7.1

5.8

3.6

*3.2

5.4

5.2

Food, hospitality and personal services

7.9

10.1

7.3

5.3

5.9

7.7

Other

*0.1

*0.1

*0.2

*0.8

*0.4

*0.3

Total

100.0

100.0

100.0

100.0

100.0

100.0

Note: * Estimate not reliable.

that would suggest they would havebetter labour market outcomeseven without the additional
education. Nonetheless, theestimates should at best be
regarded as tentative evidence ofthe causal effects of education.
The estimates indicate that
education has little effect on
employment participation of men,be it in any (part-time or full-time)
job. For women, by contrast,

employment participation—
especially full-time employment
participation—does appear to besignificantly higher for the more
highly educated. In particular, a
diploma or higher educational
qualification is associated with anapproximate 10 percentage
points or greater increase in
the probability of full-time
employment compared withmaximum educational attainmentof Year 11 and below. For neither
men nor women is there asignificant association between
university grouping andemployment participation.
Earnings of full-time employees areclearly more strongly related tolevel of educational attainment anduniversity type. For example, basedon the estimates in the lower panelof Table 7.4, compared withattainment of Year 11 and below, a

Education and labour market outcomes71

Table 7.4: Effects of education on employment and earnings, 2012
Employed
Males

Employed full-timeFemales

Log weekly earnings
of full-time employees

Males

Females

Males

FemalesBase models
Educational attainment (Reference category: Year 11 and below)
Master’s degree or doctorate

0.068

0.100+

0.120+

0.156

0.521

0.446

Graduate diploma or certificate

0.011+

0.130

0.061+

0.122

0.492

0.367

Bachelor’s degree

0.019+

0.134

0.040+

0.107

0.454

0.335

Diploma or advanced diploma

0.022+

0.082

0.064

0.115

0.316

0.103

Certificate Level 3 or 4

0.032

0.119

0.057

0.045

0.202

0.014+

Year 120.008+

0.075

0.015+

0.054

0.217

0.159

Type of university (Reference category: Group of 8 (Go8))Australian Technology Network (ATN)–0.012+0.074–0.009+0.053+

0.109

0.122

Innovation Research Universities (IRU)

0.073

0.084

0.043+

0.058+

0.147

0.155

Regional Universities Network (RUN)

0.042+

0.062+

0.014+

0.027+

0.025+

0.019+

Other Australian university

0.011+

0.026+

0.015+

0.048+–0.192

0.143

0.067

0.090

–0.012+0.043+

Overseas university

0.080

0.101

Controlling for cognitive ability
Educational attainment (Reference category: Year 11 and below)
Master’s degree or doctorate

0.053

0.080+

0.112

0.145

0.471

0.421

Graduate diploma or certificate–0.001+0.107

0.056+

0.113

0.445

0.348

Bachelor’s degree

0.009+

0.111

0.034+

0.094

0.407

0.319

Diploma or advanced diploma

0.015+

0.065

0.061

0.104

0.289

0.084

Certificate Level 3 or 4

0.030

0.109

0.057

0.040

0.193

0.009+

Year 120.002+

0.064

0.013+

0.047

0.186

0.145

Type of university (Reference category: Group of 8 (Go8))Australian Technology Network (ATN)–0.018+0.054+–0.008+0.036+

0.103

0.094

Innovation Research Universities (IRU)

0.077

0.070

0.048+

0.049+

0.146

0.132

Regional Universities Network (RUN)

0.040+

0.054+

0.019+

0.020+

0.021+

0.006+

Other Australian university

0.015+

0.017+

0.017+

0.041+–0.1860.129
0.025+

Overseas university

0.079

0.092

0.071

0.082

–0.014+

BDS score–0.003+0.015

0.003+

0.015

0.026

0.014
0.000+

SDM score

0.002

0.002

0.002

0.002

0.001+

NART–25 score0.001+

0.003+–0.002+0.001+

0.004

0.007

Did not do 1 or more of BDS,
SDM and NART–25

0.061

0.097

0.030+

0.122

0.094

0.093+

Constant

.–

.–

.–

.–

6.640

6.568

Number of persons

4,673

5,154

4,673

5,154

3,031

1,899

Notes: The employment models are estimated by Probit regressions and reported estimates are mean marginal effects. The earnings model is estimated byordinary least squares regression and the estimates are the regression coefficients. The sample for the employment models is all persons aged 25–59. The
sample for the earnings model is full-time employees aged 25–59. All models contain controls for age, place of birth and Indigenous status, state of residence,
population density of region of residence, disability and English proficiency. The two employment models additionally contain controls for family type and age ofyoungest dependent child. + indicates estimate is not significantly different from 0 at the 10% level.

72

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

master’s degree or doctorate
increases earnings by 47.1% for
men and 42.1% for women, a
graduate diploma or certificate
increases earnings by 44.5% for
men and 34.8% for women, abachelor’s degree increases
earnings by 40.7% for men and31.9% for women, and a diploma or
advanced diploma increases
earnings by 28.9% for men and8.4% for women. A Certificate Level
3 or 4 is also associated with a
19.3% increase in earnings formen, but no significant increase inearnings for women, while
completion of high school is
associated with an 18.6% increasein earnings for men and a 14.5%
increase in earnings for women.
The estimates also suggest
there is an earnings premium
attached to Australian Technology
Network (ATN) and Innovation
Research University (IRU)
universities. For example, theestimates from the models which
control for cognitive ability show
that, for both men and women,
there is an approximate 10%
premium to obtaining the highest
qualification from an ATN university
as opposed to a Go8 university,and an approximate 15% premium
to obtaining the highest
qualification from an IRU university

as opposed to a Go8 university. No
significant earnings differences areevident between Go8, Regional
Universities Network (RUN) andoverseas universities for men or
women, but relative to the ‘other’
Australian universities, all three ofthese groups have an earnings
premium of approximately 20% formen, and an earnings penalty of
approximately 13% for women.2

Box 7.3: HILDA Survey measure of computer skills
To measure computer skills, respondents were asked to indicate the extent to which theyagreed with each of the following statements:
1.

My level of computer skills meets my present needs

2.

I feel comfortable installing or upgrading computer software

3.

Computers have made it possible for me to get more done in less time

4.

Computers have made it easier for me to get useful information

5.

Computers have helped me learn new skills other than computer skills

6.

Computers have helped me communicate with people

7.

Computers have helped me reach my occupational (career) goals

Computer use

Responses were on a 7-point Likert scale, ranging from strongly disagree (1) to strongly agree(7). The measure used in this report is simply the sum of the responses to each statement, andtherefore has a potential range from 7 to 49. This is, of course, far from a perfect measure ofcomputer skills, in part because several of the above statements relate to the usefulness ofcomputers to the respondent rather than the capacity of the respondent to use computers.
Nonetheless, it seems highly likely that people who find computers more useful tend to havehigher computer skills.

2

It is perhaps surprising that
graduates of the Go8 universities
do not have the highest (conditional)
earnings, and indeed havesignificantly lower earnings than
ATN, IRU and, for women, RUN
universities. This may in part reflect
differences in the field of study
composition of graduates, withperhaps a greater focus on
vocational fields in the ATN and IRU
universities. It should also be notedthat the earnings regressions
restrict to full-time employees, andit may be, for example, that
graduates of Go8 universities are
more likely to become (high-earning)self-employed or employers.

As part of the ‘human capital’focus, a limited amount of
information was collected oncomputer use and computer skills
in Wave 12. Computer use wasassessed using a simple ‘yes–no’question on whether the respondent

In further analysis not presented in this report, earnings models that include a separate dummy variable for each Australian university were
estimated. These models showed that the specific universities with the highest graduate earnings (controlling for other factors) are: Australian
Catholic University; Charles Darwin University; Curtin University of Technology; Murdoch University; Queensland University of Technology; The
University of New South Wales; The University of Newcastle; The University of Sydney; and The University of Western Australia.

Education and labour market outcomes

73

uses a computer to assist in day-today activities, while skills were
measured by ascertaining theextent of agreement with each ofseven statements related tocompetency using computers andthe benefits derived from using
them (see Box 7.3, page 73).
In Table 7.5, differences incomputer use and computer skills
by sex, age and educational
attainment are explored using
regression methods. Model A
presents mean marginal effects
estimates from a Probit model ofthe effects of sex, age andeducational attainment on theprobability an individual uses a
computer to assist in day-to-day
activities. Model B presents
ordinary least squares coefficientestimates of the effects of thesesame characteristics on our
measure of computer skills.

Table 7.5: Differences in computer use and computer skills by sex, age
and educational attainment, 2012
Use computer

Computer skills–0.0140.6

30–34–0.021+

–1.4

35–39

–0.056

–2.8

40–44

–0.059

–4.4

45–49

–0.057

–5.1

50–54

–0.112

–6.7

55–59

–0.140

–7.8

60–64

–0.168–8.4

65 and over

–0.312–14.2

Male
Age group (Reference category: 25–29)

Educational attainment (Reference category: Year 11 and below)
Master’s degree or doctorate

0.444

12.1

Graduate diploma or certificate

0.352

9.6

Bachelor’s degree

0.330

9.7

Diploma or advanced diploma

0.254

8.1

Certificate Level 3 or 4

0.123

3.8

Year 12

0.151

5.2

Constant
Number of persons

–

33.4

14,067

11,898Notes: The first data column presents mean marginal effects estimates obtained from a Probit model ofthe probability an individual uses a computer to assist in day-to-day activities. The second data column
presents coefficient estimates from an ordinary least squares model of the individual’s computer skills.
The sample comprises all persons aged 25 and over. + indicates estimate is not significantly different
from 0 at the 10% level.

74

Model A shows that, holding
constant age and education, men
are less likely than women to use
a computer to assist in day-to-day
activities. Not unexpected is thestrong ordering by age of thelikelihood an individual uses a
computer for day-to-day activities.
Those aged 65 and over have aparticularly low probability of using
computers for day-to-day activities,
having a 31.2 percentage point
lower probability of such use of
a computer than a person aged
25–29. A clear ordering ofcomputer use by educational
attainment is also evident, with thenotable deviation being that thosewith a highest qualification of Year12 have a slightly higher probabilityof using a computer to assist with
day-to-day activities than thoseholding a Certificate Level 3 or 4.
The estimation results for computer
skills show a very similar patternwith respect to age and education,
but not with respect to sex. Holding
constant age and educational
attainment, the measure ofcomputer skill is on average 0.6
higher for men than women. Thus,
while men are less likely to use acomputer to assist in day-to-day

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

activities, they nonetheless on
average report being more skilledwith computers than women.
Are computer skills and regularcomputer use associated withhigher earnings? The resultspresented in Table 7.6 suggest they
are indeed. The table presents
coefficient estimates from ordinary
least squares regression of log
weekly earnings of full-time
employees, including as explanatory
variables the measures of computeruse and computer skills. Use ofcomputers to assist in day-to-day
activities is associated with a 0.1%
increase in earnings, a very small
but still statistically significant
effect. Effects of computer skills
are larger, each 1-point increase in
the skills measure associated with
a 0.6% increase in earnings for men
and a 0.3% increase in earnings for
women. Given the potential range ofthe computer skills measure is from
7 to 49, a movement from thelowest possible score to the highest
possible score acts to increase
earnings by 25.2% for men (42times 0.6) and by 12.6% for women(42 times 0.3).

Table 7.6: Effects of computer use and computer skills on earnings offull-time employees, 2012
Males

Females

Use computer

0.001

0.001

Computer skills

0.006

0.003

Educational attainment (Reference category: Year 11 and below)
Master’s degree or doctorate

0.421

0.450

Graduate diploma or certificate

0.398

0.441

Bachelor’s degree

0.390

0.339

Diploma or advanced diploma

0.229

0.143

Certificate Level 3 or 4

0.157

0.060

Year 12

0.155

0.120

Number of persons

2,842

1,809

Notes: Table presents ordinary least squares coefficient estimates from models of the determinants of log
weekly earnings. The sample comprises full-time employees aged 25 and over not enrolled in full-time study.
The models additionally contain controls for age, place of birth and Indigenous status, region of residence,
disability and English proficiency. All estimates are significantly different from 0 at the 10% level.

Education and labour market outcomes

75

76

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

8

Family background andeconomic wellbeing
The role played by family background—essentially the circumstances in which anindividual grew up—is at the heart of studies of ‘intergenerational mobility’ and theintergenerational transmission of socio-economic advantage and disadvantage.
The nationally representative nature of the HILDA Survey, combined with its
longitudinal structure, makes it better poised than any other Australian data source
to study these intergenerational issues, and its capacity to improve ourunderstanding of these issues will only grow as the length of the panel grows. This
chapter briefly considers how family background is associated with economic
wellbeing. First, we examine the association between individuals’ current income
and wealth, and the retrospectively collected information on their family
backgrounds; and second, we examine economic outcomes at age 25 of people
who were in the HILDA Survey sample when aged 17.

Associations
between family
background andmaterial wellbeing
The HILDA Survey collects a
considerable amount of ‘one-off’ or‘historical’ information from
respondents, including: family
background, such as each parent’s
place of birth, educational
attainment and occupation; family
circumstances at age 14 (inparticular, which parents the
respondent lived with); the numberof siblings; and the age at which the
respondent moved out of theparental home. Here we examine
the associations between thesefamily background characteristics
and household income and wealth.
We restrict the analysis to people
aged 35–54, on the basis thatpeople in this age range are unlikely
to (directly) depend on their parents,
and are also relatively unlikely to be
partly or completely retired.
Table 8.1 presents estimation
results from ordinary least squares
regression models of theassociations between family
background and household
equivalised income (see Box 3.2,

page 25) and between family
background and household wealth.
Household wealth has beenmeasured in Waves 2, 6 and 10(2002, 2006 and 2012) and so
only those three waves are used toestimate the association between
family background and wealth. All
12 waves are used to examine theassociation between family
background and income. Income
and wealth are both converted to
December 2012 prices to remove
the effects of inflation.
The family backgroundcharacteristics examined by themodels include: the individual’s
immigrant status and age at
migration; family living situation atage 14; whether the individual’s
parents separated before theindividual was 18 years of age;
number of siblings; age moved outof the parental home; father’s andmother’s immigrant status andeducational attainment; father’sand mother’s employment statuswhen the individual was aged 14;
father’s and mother’s occupation
when the individual was growing up;
and whether the individual’s father
was unemployed for 6 months ormore while the individual was
growing up.

Family background and economic wellbeing

77

Table 8.1: Association between family background and economic wellbeing—Persons aged 35–54
Equivalised income
Men

Net wealth

Women

Men

Women

Immigrant status (Reference category: Native-born)
ESB

1,193+

3,979–110,124+

136,234+

NESB—Europe–8,353+–4,989+–382,512+–327,611

NESB—other–7,822+–8,594+50,204+–59,540+80+

34+–4,803–4,396+

Age at migration
Family living situation at age 14 (Reference category: Lived with both parents)

1,190+

2,205+

101,648–64,406+439+–2,730+163,857+–64,496+–1,604+–4,345+–9,561+–66,258+–475+–1,235+–124,162+–45,291+1–2,800+–4,438+–240,371+–4,826+2–2,490+–6,367+–196,843+–11,093+

3 or more–6,410+–7,554+–314,010+–130,967+

Eldest child

2,036

1,248+

81,196

11,326+

Under 18

–3,396+–2,779+–184,877+–106,977

18–20

–747+

–891+

–99,834–25,768+–5,280+–2,830+–20,779+–95,676+

Lived with 1 parent only
Lived with 1 parent and a step-parent
Lived with neither parent
Parents separated before age 18
Number of siblings (Reference category: 0)

Age moved out of home (Reference category: 21–24)

25 and overFather’s immigrant status (Reference category: Native-born)
ESB

1,525+

609+

132,379

57,547+

NESB—Europe

6,400–1,595++231,256+

100,577+770+

1,624+–119,931+–56,511+

ESB

226+

–1,315+–52,138+–123,933+

NESB—Europe–720+

2,391+

121,384+

169,088+

NESB—other

521+

563+

32,226+

77,747+

NESB—other
Mother’s immigrant status (Reference category: Native-born)

Father’s educational attainment (Reference category: Some high school only)
University degree

1,968+

3,578

169,398

60,441+

Completed high school

5,464

1,337+

6,651+–25,136+

Primary school only

–1,324+–2,096+–42,399+–65,537+

Don’t know educational attainment–2,593+–5,201+–41,943+–162,134

University degree

1,789+

3,481

3,035+

27,751+

Completed high school–1,472+438+

47,960+122,789

Primary school only

–1,876+–407+–22,846+

46,161+

Don’t know educational attainment–4,566+–865+–48,919+–1,474+

Mother’s educational attainment (Reference category: Some high school only)

Father’s employment status when aged 14 (Reference category: Not employed)
Employed

3,266

2,964

184,178–32,707+

Don’t know if employed

–398+

871+

248,709

4,397+

Mother’s employment status when aged 14 (Reference category: Not employed)
–391+

2,226

–48,725+–72,849+–4,920+–383+–156,481+

171,611+

Manager

1,717+

3,936

163,296

170,787

Professional

5,802

5,124

129,759+

329,755
84,369+

Employed
Don’t know if employed
Father’s occupation (Reference category: Technical or trades worker)

852+

876+

12,868+

Labourer or machine operator or driver–2,019+–655+–108,898+

42,672+

Don’t know occupation

1,176+–6,211

266,320–16,701+

Manager

1,514+–2,492+

142,864+

85,769+Professional

3,065

–650+

42,457+

128,091+
123,035

Community, personal service, clerical, administrative or sales worker

Mother’s occupation (Reference category: Technical or trades worker)

Community, personal service, clerical, administrative or sales worker

2,283

1,566+

109,517

Labourer or machine operator or driver–1,020+–3,271–27,526+

2,9826+

Don’t know occupation–2,110+–2,340+119,160+–51,267+

Father unemployed 6 or more months–1,054+–486+–97,602–136,500

Don’t know if father unemployed 6 or more months

1,239+–366+–27,801+–118,430

Number of persons

27,97031,310

6,765

7,528

Notes: Table presents coefficient estimates from ordinary least squares regressions. The earnings mode is estimated on all 12 waves (2001–2012) and thewealth model is estimated on Waves 2, 6 and 10. + indicates estimate is not significantly different from 0 at the 10% level.

78

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

The estimates for the immigrant
status variables indicate that being
an immigrant from continental
Europe is associated with significant
negative effects on both income andwealth of both men and women. For
example, compared with native-born
men, men born in continental
Europe have a mean equivalised
income that is $8,353 lower,holding other factors constant. For
women, the income penalty issmaller, but still a statisticallysignificant $4,989. Wealth effects
associated with being born incontinental Europe appear to beeven more dramatic: all else equalmen born in continental Europe
have $382,512 less in household
wealth than native-born men, andwomen born in continental Europe
have $327,611 less in household
wealth than native-born women.
Negative effects on income, but not
wealth, are also evident for NESB
immigrants from countries outside
Europe. Age at migration to Australia

is only a statistically significant
factor for wealth of men, with each
additional year of age before arrivalin Australia acting to reduce
household wealth by $4,803.

particularly large. Relative tomoving out at age 21–24, there arealso negative effects of moving out
at age 18–20 or age 25 and overfor income and/or wealth.

Variables for living situation at age
14 show, curiously, a positive
wealth impact for males of livingwith only one parent (comparedwith having lived with both parents).
There is, however, an even larger
opposite (negative) wealth impact
for men of having had their parents
separate before age 18, implyingthat in fact there is little net effectof living with only one parent at age
14, but there is a large negative
effect if the parents separated
when aged 15–17.

Income and wealth when aged 35–
54 show a tendency to be
negatively associated with thenumber of siblings, although theestimated effects on wealth offemales are not statisticallysignificant. Significant positiveeffects of being the eldest child areevident for men, but not women.
The immigrant status of one’s
parents has few statisticallysignificant effects on income andwealth, but the educational
attainment of the parents appears
to matter, with higher attainment
mostly translating to higher income
and wealth. There is one exception
to this pattern, which is that, allelse constant, equivalised incomeof men is highest if the father
completed high school but did not
obtain a university degree.

The 21–24 age range seems to be
the optimal age to move out of theparental home in terms of income
and wealth in later adulthood.
Negative effects on income andwealth associated with moving outof home before the age of 18 are

Family background and economic wellbeing79

A person’s father being employedwhen that person was aged 14 is
associated with positive effects on
income for both men and women, andpositive effects on wealth for men,but not women. It may also be
significant that a mother’s
employment is associated withpositive effects on incomes ofwomen, but not men. There are
significant effects of both father’s andmother’s occupation when growing
up, for both men and women.
Professional and managerial
occupations of fathers, andprofessional and community, personal
service, clerical, administrative andsales worker occupations of mothers
tend to be associated with positiveeffects on income and/or wealth. The
indicator variable for the father
experiencing 6 or more months of
unemployment while growing up isalso associated with large negative
effects on household wealth, but has
no significant effects on income.

Economic outcomesat age 25 by levelof family income
at age 17
As the length of the HILDA Survey
panel grows, it will increasingly
be possible to examine how
outcomes and experiences
measured when individuals areyoung affect their outcomes inlater life. With 12 years of data
as of Release 12, there is still only
a limited capacity to undertakesuch analysis, since the maximum
lifespan that can be examined is
12 years. Nonetheless, this iscertainly sufficient to gain
important insights into how
circumstances when growing up
affect outcomes in adulthood.
Here we undertake a very brief
analysis of how family income when

Table 8.2: Outcomes at age 25 by household income quintile at age 17
Obtained adegree (%)

Employed(%)

Employedfull-time (%)

Weekly earnings($, December
2012 prices)

Bottom quintile

23.7

70.2

54.4

1.606.82

2nd quintile

17.0

82.3

62.0

1.749.66

Middle quintile

29.1

78.9

64.2

1.735.23

Income quintile

4th quintile

29.8

89.4

74.6

1.918.04

Top quintile

36.5

94.7

77.9

1,015.23

80

growing up affects economic
outcomes in early adulthood,following individuals over eight years
from the age of 17. Specifically, inTable 8.2, education and labour
market outcomes at age 25 arecompared across individuals
classified by their household’s
quintile in the income distribution
when they were 17 years of age.
The table shows clear differencesin economic outcomes at 25 yearsof age by level of family income
when aged 17. In general,
educational attainment,
employment and earnings at age
25 are higher the further up theincome distribution was one’s
family. There is, however, an
important deviation from thispattern in respect of those from the
2nd income quintile. Only 17% of
individuals from this quintile had
obtained a university degree, which
is lower than any other income
quintile, including the bottom
quintile. Individuals from the 2nd
quintile did, however, have arelatively high employment rate andrelatively high mean earnings at
age 25, both of which are greaterthan for those from the middle
income quintile. The full-time
employment rate of those from the
2nd quintile was, nonetheless, stillslightly lower than the full-time
employment rate of those from the
middle quintile.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

9Household expenditure
on food
The HILDA Survey has collected information on household expenditure in mostwaves, although the information collected has varied from wave to wave. An
expenditure component that interests many researchers, particularly in relation to
low-income households, is expenditure on food. The HILDA Survey has collected
information on household food expenditure in all waves other than Wave 2(2002), but it is only possible to separately identify food expenditure in Waves 1,
3, 4, 5, 11 and 12, when the information was collected in the household
questionnaire. (In other waves, expenditure on food eaten at home was part of thebroader category ‘grocery expenditure’.)Food expenditurelevelsTable 9.1 presents summary
cross-sectional information on
household weekly expenditure on
food (including meals preparedoutside the home) for the years inwhich food expenditure isseparately identified. Meanhousehold expenditure, atDecember 2012 prices, was$213.04 in 2001 and $235.85 in2012, a real increase of 10.7%.
Overall, couples with children havethe highest mean expenditure on
food, on average spending$279.24 per week. However,elderly couples have experienced
the greatest growth in foodexpenditure between 2001 and2012; in real terms theirexpenditure increased by 22.9%.
Expenditure on food is clearly
dependent on income, rising from amean in 2012 of $187.56 per
week in the bottom income quintile
to a mean of $283.28 per week inthe top income quintile. However,the differences in food expenditure
by income quintile are considerably
smaller than the differences inmean income by income quintile.
For example, the mean income of
the top quintile is approximately
four times the mean income of thebottom quintile, whereas themean food expenditure of the top
income quintile is only 1.5 times
the mean food expenditure of thebottom income quintile. Thisimplies that food expenditure is ahigher share of income for low
income households.
Table 9.2 confirms the fact thatfood expenditure is a much larger
share of household income in
low-income households. It presents
the mean share of income spent
on food by household type andincome quintile, pooling all six
waves for which the foodexpenditure data are available. The
bottom row of the table shows thathouseholds in the bottom income
quintile on average spend 26.6% ofhousehold income on food,compared with 18.0% for the 2nd
quintile, 14.7% for the 3rd quintile,
12.4% for the 4th quintile and only
9.2% for the top quintile.
Disaggregation by household typewithin each income quintile showsthat lone-person households in thebottom quintile have the highest
mean food expenditure share,
while elderly single people in the
top income quintile have thelowest mean food expenditure
share. However, within each
income quintile, differences infood expenditure shares acrosshouseholds are not large. Most of
the variation in food expenditure

Household expenditure on food81

Table 9.1: Household weekly expenditure on food ($, December 2012 prices)
2001

2003

2004

2005

2011

2012

Change 2001
to 2012 (%)

213.04214.15218.79224.92240.55235.85

10.7

Couple189.02187.84195.01

196.24209.98205.47

8.7

Couple with dependent children

252.23253.60260.03267.00281.67279.24

10.7

Lone parent

179.45

182.55

197.41203.95210.28205.60

14.6

Lone person

122.57117.47123.08125.32

137.32140.50

14.6

Elderly couple

148.39155.66

157.46159.95

183.59182.4022.9

Elderly single person

87.83

86.13

88.17

87.63

100.09

98.25

11.9

Other

245.17260.23217.15237.80275.47247.66

1.0

Bottom quintile

163.78169.37167.12169.73185.29187.5614.5

2nd quintile

200.48200.64203.00208.32223.46217.808.6

All households
Household type

Equivalised income quintile

Middle quintile

215.21221.77225.19229.86242.58231.50

7.6

4th quintile

231.55229.92238.04253.14256.71

259.16

11.9

Top quintile

254.22249.08260.73263.58294.74283.28

11.4

shares is across income quintiles,
reflecting the fact that foodexpenditure is considerably lessvariable across households thanis income.
Table 9.3 focuses on people in thebottom two quintiles of thehousehold income distribution,
presenting, in total and byhousehold type, median foodexpenditure shares and the
proportion of individuals inhouseholds spending more than
30% of income on food, which canloosely be interpreted as anindicator of financial hardship andpossibly ‘food insecurity’. The tableshows the median food expenditure
share of the bottom two income

quintiles has declined for all
household types between 2001
and 2012. Couples without
dependent children and lone-parent
households have experienced thebiggest declines in foodexpenditure shares, respectively
declining from 31.8% to 22.1%
and from 30.1% to 23.5%.
Reflecting the trend in medianfood expenditure shares, theproportion of individuals inhouseholds spending more than
30% of income on food has fallen
substantially for all household
types. In 2001, 46.3% of people inthe bottom two quintiles of thehousehold income distribution werespending more than 30% of theTable 9.2: Mean share of household income spent on food, by household type and income quintile
Income quintile
Bottom

2nd

Middle

4th

Top

28.8

17.5

14.5

12.1

9.2

Household type
CoupleCouple with dependent children

26.9

18.1

14.8

12.6

9.2

Lone parent

26.5

18.2

14.9

12.1

10.0

Lone person

30.5

18.5

15.3

13.2

10.5

Elderly couple

26.4

19.2

14.6

12.3

8.6

Elderly single person

24.1

16.9

12.8

12.3

7.5

Other

24.5

15.4

14.0

11.3

8.9

All households

26.6

18.0

14.7

12.4

9.2

Note: Sample comprises all individuals in Waves 1, 3, 4, 5, 11 and 12.

82

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 9.3: Food expenditure share of individuals in the bottom two quintiles of the household income
distribution (%)
2001

2003

2004

2005

2011

2012

28.9

27.3

27.3

26.8

24.7

24.1

Couple

31.8

28.4

30.6

30.7

28.5

22.1

Couple with dependent children

29.2

27.3

27.4

26.5

24.2

26.5

Lone parent

30.1

27.0

28.2

27.6

23.9

23.5

Lone person

31.7

31.5

31.8

31.3

27.4

28.5

Elderly couple

27.5

26.8

26.2

26.2

26.4

25.5

Elderly single person

27.6

24.8

25.0

22.9

22.1

22.2

Other

25.6

30.5

27.0

27.2

26.9

19.5

46.3

42.5

41.9

42.1

35.0

34.0

Couple

53.8

46.3

52.0

52.9

45.3

35.5

Couple with dependent children

46.8

42.0

41.3

43.1

32.1

38.9

Lone parent

50.8

43.6

45.2

41.4

32.0

28.3

Lone person

53.0

53.5

52.5

53.8

45.5

47.0

Elderly couple

40.7

39.2

36.6

38.1

37.6

35.6

Elderly single person

41.7

33.7

32.9

31.6

31.6

30.7

Other

37.4

50.2

41.3

43.3

39.0

19.3

Median share
All households in the bottom two quintiles
Household type

Percentage spending more than 30% of household income on foodAll households in the bottom two quintiles
Household type

household’s income on food. This
had fallen to 34.0% by 2012. The
2001 to 2012 period appears,
therefore, to have been a good one
for improving food security ofAustralian households.

Determinants ofhousehold foodexpenditure
The effects of householdcomposition, household income,region of residence, and year, on
household food expenditure are
examined in Table 9.4, which
presents coefficient estimates fromordinary least squares regression
models. The estimates for thenumber of each type of person in
the household provide a sense of
how food costs differ across thedifferent person-types.1 We see thateach man aged 18–64 on average
1

The model essentially assumes no economies of scale in food expenditure—that is, the model does not allow for the possibility that
expenditure per person is lower the larger the household—although the inclusion of a constant captures economies of scale in a very
restricted way, with the estimated constant representing a ‘fixed cost’ of food that does not depend on household size. Note also that
estimates for the ‘number of persons’ variables are sensitive to the reference categories for income quintile, region of residence and wave.

Household expenditure on food83

adds $53.71 to food expenditure,
while each woman aged 18–64 on
average adds $50.66. Men over theage of 65 each add $52.94 to thehousehold food budget, while
women over the age of 65 add
only $38.71. Children cost more
to feed as they get older: eachchild aged under 5 on averageadds $18.92 to the weekly foodbudget; each child aged 5–9 adds
$30.61; each boy aged 10–14
adds $38.62; each girl aged 10–14
adds $37.68; each boy aged
15–17 adds $57.57; and each girlaged 15–17 adds $48.39 per week.
Boys aged 15–17 therefore appear
to be the most expensive to feed of
all household members.
Consistent with the evidence inTable 9.1, food expenditure
increases with income, withhouseholds in the top income
quintile spending $74.69 more per
week on food than the bottom
quintile, all other factors held
constant. Households in the major
cities spend more on food thanpeople in other regions. The
estimates also indicate that foodexpenditure was highest in 2011,
with the estimated coefficient for2012 slightly lower than the 2011
estimated coefficient.

84

Table 9.4: Effects of household composition and other factors on foodexpenditure ($, December 2012 prices)
Explanatory variable
Number of men aged 18–64

Coefficient estimate
53.71

Number of women aged 18–64

50.66

Number of men aged 65 and over

52.94

Number of women aged 65 and over

38.71

Number of children aged under 5

18.92

Number of children aged 5–9

30.61

Number of boys aged 10–14

38.62

Number of girls aged 10–14

37.68

Number of boys aged 15–17

57.57

Number of girls aged 15–17

48.39

Income quintile (Reference category: Bottom quintile)
2nd quintile

4.83

Middle quintile

21.50

4th quintile

40.86

Top quintile

74.69

Region (Reference category: Major urban)
Other urban–16.43

Other region–18.80Year (Reference category: 2001)
2003–0.19+

2004

4.25

2005

9.44

2011

23.02

2012

20.36

Constant

41.50

Number of observations

47,514

Notes: Table presents coefficient estimates from an ordinary least squares regression model ofhousehold total food expenditure. The unit of analysis is the household and the model is estimated on
Waves 1, 3 to 5, 11 and 12. + indicates estimate is not significantly different from 0 at the 10% level.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

10

Sexual identity andeconomic, health andsocial outcomes
Individuals’ sexual identity represents an important demographic characteristic
which, at least until recently, has seldom been collected in household surveys. Thisis despite its relevance to understanding not only household and family formation,
but also labour market behaviour, and even health and wellbeing. In Wave 12, thisgap in the HILDA Survey data was filled with the inclusion of a question on sexual
identity in the self-completion questionnaire (see Box 10.1, below).

Sexual identity ofAustralians
Table 10.1 presents the distributionof responses to the sexual identity
question, disaggregated by sex andage group. It shows 92.6% of males
aged 15 and over and 92.4% offemales aged 15 and over identify
as heterosexual. Males are more
likely than females to identify as
homosexual, with 1.8% of males and1.0% of females in this category,
while females are more likely thanmales to identify as bisexual, with
1.3% of females and 0.8% of males
in this category. A further 0.8% ofmales and females identify as‘other’, and 1.0% of males and 0.8%
of females indicated they are unsure
or don’t know.
A significant minority—3.0% ofmales and 3.7% of females—chose
not to disclose their sexual identity.
While this is a relatively small
fraction of the population, it isconsiderably larger than the
proportion who report being gay,lesbian or bisexual—2.6% of males
and 2.3% of females. The
proportion identifying as gay,lesbian or bisexual could therefore
be substantially affected by theactual sexual identity of those who
preferred not to disclose it. If, forexample, all those who preferred
not to disclose their sexual identity
are in fact homosexual, then the
proportion identifying as
homosexual would be 4.8% rather
than 1.8% for males, and 4.7%
rather than 1.0% for females.
There are distinct differences inthe distribution of sexual identity
across the three age groups
examined in Table 10.1. People
aged 15–29 are the least likely toreport being heterosexual, whilepeople aged 30–49 are the most
likely to report being heterosexual.
For males aged 15–29, the lower
proportion reporting being
heterosexual is reflected in higher

Box 10.1: Measurement of sexual identity in the HILDA Survey
The following question was included in the self-completion questionnaire in Wave 12 to identify
respondents’ sexual identity:
Which of the following categories best describes how you think of yourself?
1. Heterosexual or straight
2. Gay or lesbian
3. Bisexual
4. Other
5. Unsure/don’t know
6. Prefer not to say
See Wooden (2014) for further details on the development of the question and an analysisof responses.

Sexual identity and economic, health and social outcomes85

Table 10.1: Sexual identity of persons aged 15 and over, 2012 (%)
15–29

30–49

50 and overAll ages

90.2

94.5

92.7

92.6

Males
Heterosexual or straight
Gay or lesbian

2.7

1.8

1.2

1.8

Bisexual

*0.7

0.9

0.8

0.8

Other

1.2

*0.2

0.9

0.8

Unsure/don’t know

1.3

*0.6

1.1

1.0

Prefer not to say
Total

3.9

2.0

3.3

3.0

100.0

100.0

100.0

100.0

Females
Heterosexual or straight

90.8

93.3

92.5

92.4

Gay or lesbian

1.0

1.4

0.6

1.0

Bisexual

2.6

1.2

0.6

1.3

Other

*0.6

*0.6

1.1

0.8

Unsure/don’t know

0.9

0.8

0.9

0.8

Prefer not to say
Total

4.2

2.7

4.3

3.7

100.0

100.0

100.0

100.0

Note: * Estimate not reliable.

proportions reporting being
homosexual (2.7%), ‘other’ (1.2%)
and unsure (1.3%), and also ahigher proportion preferring not todisclose (3.9%). For females aged
15–29, the lower proportion
reporting being heterosexual isreflected in a higher proportion
reporting being bisexual (2.6%) anda higher proportion preferring not todisclose (4.2%). The relatively highproportion of people aged 30–49
identifying as heterosexual mostly
reflects a greater willingness todisclose sexual identity, which
possibly suggests that a significant
fraction of those in the other two
age categories who preferred not todisclose their sexual identity are infact heterosexual.

bisexual people are grouped
together primarily because of therelatively small sample sizes
involved. Panel A examines all
people aged 15 and over, while
Panel B examines people aged
25–59. The table excludes people
who selected the ‘other’, ‘unsure/
don’t know’ and ‘prefer not to say’
response options to the sexual
identity question.

The table shows LGB males are
more likely than heterosexual males
to live in major urban areas, which isperhaps unsurprising. LGB females,by contrast, are no more likely tolive in major urban areas thanheterosexual females. LGB females
are, however, less likely to live
outside of urban areas, being more
likely than heterosexual females tolive in ‘other urban’ areas.
Considering lifestyle behaviours,
it is particularly striking thatsmoking rates are much higher for
LGB people—33.8% of males and26.8% of females, compared with
19.8% and 14.1% for heterosexual
males and females, respectively.
Prevalence rates for regulardrinking of alcohol and regularexercise are, however, similar forthe two sexual-identity groups.
Indeed, LGB females are less likely
to regularly drink than areheterosexual females. In terms of
Body Mass Index (see Box 5.4,
page 57), LGB people are nearly
twice as likely as heterosexual
people to be underweight. LGB
males are considerably less likely
to be overweight or obese thanheterosexual males, while LGB
females are similarly likely toheterosexual females to beCharacteristics andoutcomes of the
LGB population
Table 10.2 presents somedescriptive comparisons between
heterosexual people and lesbian,gay and bisexual (LGB) people,
examining various characteristics,
lifestyle behaviours, and measuresof health and economic and social
wellbeing. Lesbian, gay and86

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 10.2: Characteristics and outcomes by sexual identity, 2012
Males

FemalesGay, lesbian
or bisexual

HeterosexualGay, lesbian
or bisexual

Heterosexual

Major urban

76.0

64.5

64.6

64.9

Other urban

11.2

20.9

25.6

20.9

A. All persons aged 15 and over
Region (%)

Other region

12.9

14.6

9.8

14.1

Total

100.0

100.0

100.0

100.0

Smoker

33.8

19.8

26.8

14.1

Regular drinker

16.9

17.3

6.5

9.6

Exercise regularly

52.0

53.5

46.6

45.4

*2.9

1.6

*5.8

3.6

Lifestyle behaviours (%)

Weight (BMI) (%)
Underweight
Normal weight

44.6

34.3

41.7

46.7

Overweight

34.6

41.6

25.1

26.6

Obese

17.9

22.6

27.4

23.1

Total

100.0

100.0

100.0

100.0

SF–36 general health (0–100 scale)

66.7

68.4

63.1

68.2

SF–36 mental health (0–100 scale)

70.4

76.0

66.5

73.6

Mean life satisfaction (0–10 scale)

7.6

8.0

7.5

8.0

Health and wellbeing

Mean satisfaction with safety (0–10 scale)

8.0

8.3

8.1

8.2

Social support (0–100 scale)

52.8

53.9

52.6

56.0

24.1

22.5

28.6

28.4

54,63153,03855,03150,399

55.3

73.8

58.7

74.1

Degree

38.1

33.2

38.9

36.2

Other post-school qualifications

30.8

39.0

32.6

29.5

IncomeReceived income support (annual) (%)
Mean equivalised income ($, December 2012 prices)
B. Persons aged 25–59
Living with a partner (%)
Educational attainment (%)

No post-school qualifications

31.1

27.8

28.5

34.3

Total

100.0

100.0

100.0

100.0

67.9

80.1

50.5

40.3

Labour force status (%)
Employed full-time
Employed part-time

17.4

8.6

24.8

32.9

Not employed

14.7

11.3

24.7

26.8

Total

100.0

100.0

100.0

100.0

IncomeReceived income support (annual) (%)

10.8

9.4

20.6

16.4

Weekly earnings ($, December 2012 prices)

1,006

1,265

778

676

Mean equivalised income ($, December 2012 prices)

58,85556,22357,09953,926

Note: * Estimate not reliable.

Sexual identity and economic, health and social outcomes87

overweight, but somewhat more
likely to be obese.
On all of the measures of health,subjective wellbeing and social
support (see Box 10.2, at right)
examined in Table 10.2, LGB people
on average fare worse thanheterosexual people. Differences in
the means of the SF–36 mental
health measure (see Table 5.2,
page 53) and the measure of life
satisfaction are particularly large.
For example, mean life satisfaction
for heterosexual people is 8.0 (on ascale from 0 to 10), versus 7.6 for
LGB males and 7.5 for LGB
females. LGB people do, however,appear to fare better in terms of
economic wellbeing, with similar
rates of income support receipt andslightly higher average incomescompared with heterosexual people.
Restricting attention to people aged
25–59, we see that heterosexual
people are considerably more likely
than LGB people to be living with apartner. In terms of educational
attainment, LGB women tend to be
more educated than heterosexual
women, while LGB men are
considerably more likely thanheterosexual men to hold abachelor’s degree, but are also
somewhat more likely to have no

Box 10.2: HILDA Survey measure of social support
The HILDA Survey measure of the level of social support an individual has is based on a 10item question in the self-completion questionnaire. Respondents are asked to indicate, on a
7-point Likert scale, the extent to which they agree with each of the following 10 statements: a.
People don’t come to visit me as often as I would like; b. I often need help from other people
but can’t get it; c. I seem to have a lot of friends; d. I don’t have anyone that I can confide in; e.
I have no one to lean on in times of trouble; f. There is someone who can always cheer me up
when I’m down; g. I often feel very lonely; h. I enjoy the time I spend with the people who areimportant to me; i. When something’s on my mind, just talking with the people I know can makeme feel better; and j. When I need someone to help me out, I can usually find someone.
Assigning a score ranging from 1 to 7 to each response based on the extent of agreement withthe statement, the measure of social support used in this report is equal to (8 – a) + (8 – b) +c + (8 – d) + (8 – e) + f + (8 – g) + h + I + j, which potentially ranges in value from 10 to 70.

post-school qualifications.
Comparisons of labour force statusshow that LGB men are less likely
than heterosexual men to be
employed full-time and more likely
to be employed part-time or not
employed at all. LGB women, bycontrast, are more likely to be
employed full-time, and slightly lesslikely to be not employed, thanheterosexual women—although
they are nonetheless much lesslikely to be employed full-time,and more likely to be not employed,than LGB men.
Rates of income support among
those aged 25–59 are higher for
women than men, irrespective of
sexual identity, but LGB men havea slightly higher rate of receipt thanheterosexual men, and LGB women

have a significantly higher rate ofreceipt than heterosexual women,with 20.6% of LGB women aged
25–59 receiving income support overthe course of the 2011–12 financial
year. The high rate of income support
receipt for LGB women is interesting
given that the higher rate of receiptof heterosexual women (comparedwith men) is largely driven by receipt
of Parenting Payment Single bysingle mothers. Of course, it ispossible that many LGB women arein fact single mothers.
Mean weekly earnings from
employment are highest forheterosexual men, followed by
LGB men, LGB women and thenheterosexual women, reflecting—to a significant extent—
differences in rates of full-time
employment, part-time employment
and non-employment. However,equivalised incomes are, on average,
highest for LGB men, followed by
LGB women, heterosexual men andheterosexual women. The relatively
higher equivalised incomes of LGB
people compared with theirearnings is largely explained by thelower number of dependent children
in the households in which LGB
people live.

Quality ofrelationship withpartner
While Table 10.2 shows thatpartnering rates are lower for LGB
people, it is nonetheless the casethat more than half of LGB people
aged 25–59 are living with a

88

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Table 10.3: Mean relationship quality and satisfaction of persons with a partner, 2012
Men
Gay, lesbian
or bisexual
Satisfaction with relationship with partner (0–10 scale)

8.2

How good relationship with partner is compared to most (1–5 scale)
How often wish had not got into this relationship (1–5 scale)

Women
HeterosexualGay, lesbian
or bisexual

Heterosexual

8.5

7.6

8.2

4.3

4.3

4.2

4.2

1.6

1.4

1.6

1.5

Extent to which relationship met original expectations (1–5 scale)

4.1

4.2

3.9

4.0

How much love your partner (1–5 scale)

4.7

4.7

4.6

4.6

How many problems in relationship (1–5 scale)

1.8

1.7

2.1

1.8

How well partner meets needs (1–5 scale)

4.2

4.2

4.0

4.0

partner. How do relationships with
one’s partner compare for LGB andheterosexual couples? The HILDA
Survey periodically collects
information on various aspects
relating to satisfaction with one’s
partner and the quality of therelationship more generally.
In the Wave 12 self-completion
questionnaire, as part of a batteryof questions ascertaining
satisfaction with various
relationships, respondents wereasked to rate their satisfaction with
their partner on a scale from 0(completely dissatisfied) to 10(completely satisfied). In a separate

multiple-item question, respondents
were also asked to assess, on a 5point scale, six aspects of therelationship with their partner: howgood the relationship is comparedto most; how often they wished they
had not got into the relationship;
the extent to which the relationshiphas met their original expectations;
how much they love their partner;
how many problems there are in therelationship; and how well theirpartner meets their needs.
In Table 10.3, responses to thesequestions are compared forheterosexual and LGB couples.
In general, the impression is that

LGB couples tend to be slightly
less happy with their relationships.
Overall satisfaction is on averageslightly lower for LGB couples.
Similarly, partnered LGB people areslightly more likely thanheterosexual partnered people toreport that they wish they had nevergotten into the relationship, that therelationship does not meet their
original expectations, and that they
have problems in their relationship.
However, the differences between
LGB and heterosexual couples
should not be overstated. The
responses are, on average, quite
similar for the two groups.

Sexual identity and economic, health and social outcomes89References
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Wooden, M. (2014) ‘The Measurementof Sexual Identity in Wave 12 of the
HILDA Survey (and Associations with
Mental Health and Earnings)’, HILDA
Project Discussion Paper Series No.
1/14, Melbourne Institute of Applied
Economic and Social Research,
University of Melbourne.

Australian Bureau of Statistics (2013)
Labour Force Australia, Catalogue
No. 6202.0, ABS, Canberra.
Australian Bureau of Statistics (2014)
Education Variables, June 2014,
Catalogue No. 1246.0, ABS, Canberra.

Friedman, M. (1957) A Theory of theConsumption Function, National Bureau
of Economic Research, Princeton,
New Jersey.

90

Watson, N. (2004a) ‘Income and Wealth
Imputation for Waves 1 and 2’, HILDA
Project Technical Paper Series No.
3/04, Melbourne Institute of Applied
Economic and Social Research,
University of Melbourne.

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Technical Appendix
A. Overview of statistical methods and terms used in the report
Balanced panel
A longitudinal household survey is known as a household panel study. A balanced panel restricts the sample to individuals who have
responded to the survey in all waves of the period under study. For example, a balanced panel for Waves 1 to 10 of the HILDA
Survey consists of individuals who have responded in all 10 waves.
Deciles and quintiles
A decile is any of the nine values that divide data that have been sorted from lowest to highest into 10 equal parts, so that eachpart represents one-tenth of the sample or population. Thus, for example, the first decile of the income distribution cuts off thelowest 10% of incomes, and people in the first (or bottom) decile have the lowest 10% of incomes. A quintile is any of the four values
that divide data that have been sorted from lowest to highest into five equal parts; for example, people in the first (or bottom)
quintile have the lowest 20% of incomes.
Dummy variable
Used in regression analysis, a dummy variable is an indicator variable equal to 1 if a particular characteristic or event is present, andequal to 0 otherwise. In ordinary least squares regression, the coefficient on a dummy variable is interpreted as the mean effect on
the dependent variable of the presence of the characteristic/event, holding all else constant.
Gini coefficient
The Gini coefficient is a measure of dispersion often used as a measure of inequality of income and wealth. It ranges between 0
and 1, a low value indicating a more equal distribution and a high value indicating a more unequal distribution. ‘Zero’ corresponds toperfect equality (everyone having exactly the same) and 1 corresponds to perfect inequality (where one person has everything andeveryone else has nothing).
Mean, median and mode
The mean, median and mode are all measures of central tendency. The mean is the statistical term used for what is more commonly
known as the average—the sum of the values of a data series divided by the number of data points. The median is the middle data
point in data sorted from lowest to highest value; 50% of the data points will lie below the median and 50% above it. The mode issimply the most frequently occurring value of a data series.
Mean marginal effects
Qualitative dependent variable models, such as Probit, are ‘non-linear’, meaning that the effects of explanatory variables on theprobability of an outcome depend upon the value of that explanatory variable at which the effects are evaluated, and indeed also
depend on the values of the other explanatory variables at which they are evaluated. For example, in the Probit models of theprobability of living with one’s parents, presented in Chapter 2, the effects of age will depend on the values of the other explanatory
variables. This makes it difficult to interpret coefficient estimates. We therefore report ‘mean marginal effects’ estimates, which
provide a straightforward way of ascertaining the effects of explanatory variables that are analogous to those obtained in linear
regression models—that is, the effect on the dependent variable of a 1-unit increase in the explanatory variable. Specifically,
continuing with the example above, the mean marginal effect estimate for the ‘age 20–21’ dummy variable is the mean effect on theprobability of living with one’s parents, evaluated over all members of the sample, of changing this variable from 0 (not aged 20–21)
to 1 (aged 20–21).
Regression models
In statistical analysis, a regression model is used to identify associations between a ‘dependent’ variable (such as earnings) and1 or more ‘independent’ or ‘explanatory’ variables (such as measures of educational attainment and work experience). In particular,
it shows how the typical value of the dependent variable changes when any one of the independent variables is varied and all otherindependent variables are held fixed. Most commonly, regression models estimate how the mean value of the dependent variable
depends on the explanatory variables—for example, mean (or ‘expected’) earnings given a particular level of education and work
experience. Different types of regression models are used depending on factors such as the nature of the variables and data, andthe ‘purpose’ of the regression model. The following types of models are estimated in this report:• Ordinary least squares models estimate linear associations between a dependent variable (such as earnings) and one or more
independent (or explanatory) variables (such as age and educational attainment). The method finds the linear combination of the
explanatory variables that minimises the sum of the squared distances between the observed values of the dependent variable
and the values predicted by the regression model.
• Probit models are used to estimate the effects of factors, such as age and educational attainment, on a ‘qualitative’ or
categorical dependent variable, such as labour force status. (The variable ‘labour force status’ is qualitative because it is notnaturally ‘quantitative’ or numerical, such as is the case with income.) The standard models examine ‘binary’ dependent variables,
which are variables with only two distinct values, and estimates obtained from these models are interpreted as the effects on theTechnical Appendix

91

probability the variable takes one of those values. For example, a model might be estimated on the probability an individual isemployed (as opposed to not employed).
• Fixed-effects models are often applied to panel data such as the HILDA Survey data. They involve accounting for the effects of all
characteristics of sample members that do not change over time. For example, if we are interested in how life events impact onlife satisfaction, a fixed-effects model is useful because we can control for (remove the effects of) fixed individual traits such as
optimism and pessimism. This is achieved by examining how the outcome of interest (e.g., life satisfaction) changes at theindividual level in response to changes in explanatory variables (e.g., income). For example, a fixed-effects model will find apositive effect of income on life satisfaction if individuals who experience increases in income from one year to the next tend to
exhibit increases in life satisfaction over the same period, and individuals who experience decreases in income from one year to
the next tend to exhibit decreases in life satisfaction over that period.
• Random-effects models are also often applied to panel data. They differ from fixed-effects models by allowing estimation of theeffects of characteristics that do not change over time. This is made possible by assumptions about the distribution and nature ofunobserved fixed individual traits, such as intrinsic motivation. The models are relatively complicated. For more information on
random-effects models, see, for example, Hsiao (2003).
• A hazard model is an approach to examining the factors affecting whether a particular ‘state’ (such as marriage) continues ornot. The hazard is the risk, or probability, of exiting the state, so that what is being explained is the probability of exiting thestate, given that the state has not already been exited. In this report, all hazard models estimated are known as Cox
proportional hazards models, in which the ‘baseline’ hazard rate comes from the estimated effects of duration in the state on
the hazard rate, and where the log of the hazard ratio (the hazard rate relative to the baseline hazard rate) is a linear functionof the explanatory factors. Hazard ratio estimates are presented in this report. A hazard ratio is greater than 1 if an increasein the explanatory variable increases the probability of exit from the state, and is less than 1 if an increase in the explanatory
variable decreases the probability of exit from the state. For more information on hazard models, see, for example,
Box-Steffensmeier and Jones (2004).
Relative standard error
The standard error of an estimate is a measure of the precision with which the estimate is estimated. For example, assuming
statistical independence of the values in the sample, the standard error of the mean of a variable (such as income) is the standard
deviation of the variable divided by the square root of the sample size, and there is a 95% probability that the true mean lies within
1.96 standard deviations of the estimated mean. The relative standard error of an estimate is the ratio of the standard error to thevalue of the estimate. In this report, we have marked with an asterisk (*) estimates which have a relative standard error greater than25%. Note that a relative standard error that is less than 25% implies there is a greater than 95% probability the true quantity lies
within 50% of the estimated value.
Standard deviation
The standard deviation is a measure of variability or ‘dispersion’ of a variable. It is equal to the square root of the mean squared
difference of a variable from its mean value.
Statistical significanceIn the context of statistical analysis of survey data, a finding is statistically significant if it is unlikely to be simply due to sampling
variability—that is, if it is unlikely to be due to random factors causing specific characteristics of the survey sample to differ fromthe characteristics of the population. A common standard is to regard a difference between two estimates as statistically significant
if the probability that they are the different is at least 95%. However, 90% and 99% standards are also commonly used. The 90%
standard is adopted for regression results presented in this report. Note that a statistically significant difference does not mean
the difference is necessarily large or significant in the common meaning of the word.

B. Population inferences from the HILDA Survey data
Non-response is an issue for all household surveys, and attrition (i.e., people dropping out due to refusal, death, or our inability tolocate them) is a further particular issue in all panel surveys. Because of attrition, and despite sample additions due to changes inhousehold composition, panels may slowly become less representative of the populations from which they are drawn, although dueto the ‘split-off’ method, this does not necessarily occur.
To overcome the effects of survey non-response (including attrition), the HILDA Survey data managers analyse the sample each year
and produce weights to adjust for differences between the characteristics of the panel sample and the characteristics of theAustralian population.1 That is, adjustments are made for non-randomness in the sample selection process that causes some groups
to be relatively under-represented and others to be relatively over-represented. For example, non-response to Wave 1 of the survey
was slightly higher in Sydney than in the rest of Australia, so that slightly greater weight needs to be given to Sydneysiders in data
analysis in order for estimates to be representative of the Australian population.
The population weights provided with the data allow us to make inferences about the Australian population from the HILDA Survey
data. A population weight for a household can be interpreted as the number of households in the Australian population that thehousehold represents. For example, one household (Household A) may have a population weight of 1,000, meaning it represents
1,000 households, while another household (Household B) may have a population weight of 1,200, thereby representing 200 morehouseholds than Household A. Consequently, in analysis that uses the population weights, Household B will be given 1.2 times(1,200/1,000) the weight of Household A. To estimate the mean (average) of, say, income of the households represented by
1

Further details on how the weights are derived are provided in Watson and Fry (2002), Watson (2004b) and Summerfield et al. (2013).

92

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

Households A and B, we would multiply Household A’s income by 1,000, multiply Household B’s income by 1,200, add the two
together, and then divide by 2,200.
The sum of the population weights is equal to the estimated population of Australia that is ‘in-scope’, by which is meant ‘they had achance of being selected into the HILDA sample’ and which therefore excludes those that HILDA explicitly has not attempted to sample—namely, some persons in very remote regions in Wave 1, persons resident in non-private dwellings in 2001 and non-resident
visitors.2 In Wave 12, the weights sum to 22.5 million.
As the length of the panel grows, the variety of weights that might be needed also grows. Most obviously, separate cross-sectional
weights are required for every wave, but more important is the range of longitudinal weights that might be required. Longitudinal(multi-year) weights are used to retain representativeness over multiple waves. In principle, a set of weights will exist for every
combination of waves that could be examined—Waves 1 and 2, Waves 5 to 9, Waves 2, 5 and 7, and so on. The longitudinal weights
supplied with the Release 12 data allow population inferences for analysis using any two waves (i.e., any pair of waves) and analysisof any ‘balanced panel’ of a contiguous set of waves, such as Waves 1 to 6 or Waves 4 to 7. In this report, cross-sectional weights
are always used when cross-sectional results are reported and the appropriate longitudinal weights are used when longitudinal
results are reported. Thus, all statistics presented in this report should be interpreted as estimates for the in-scope Australian
population. That is, all results are ‘population weighted’ to be representative of the Australian community.
A further issue that arises for population inferences is missing data for a household, which may arise because a member of ahousehold did not respond or because a respondent did not report a piece of information. This is particularly important forcomponents of financial data such as income, where failure to report a single component by a single respondent (e.g., dividend
income) will mean that a measure of household income is not available. To overcome this problem, the HILDA data managers
impute values for various data items. For individuals and households with missing data, imputations are undertaken by drawing
on responses by individuals and households with similar characteristics, and also by drawing on their own responses in waves
other than the current wave. Full details on the imputation methods are available in Watson (2004a), Hayes and Watson (2009)
and Sun (2010). In this report, imputed values are used in all cases where relevant data are missing and imputed values areavailable. This largely applies only to income, expenditure and wealth variables.
The population weights and imputations allow inferences to be made from the HILDA Survey about the characteristics and outcomesof the Australian population. However, estimates based on the HILDA Survey, like all sample survey estimates, are subject tosampling error. Because of the complex sample design of the HILDA Survey, the reliability of inferences cannot be determined byconstructing standard errors on the basis of random sampling, even allowing for differences in probability of selection into thesample reflected by the population weights. The original sample was selected via a process that involved stratification by region andgeographic ‘ordering’ and ‘clustering’ of selection into the sample within each stratum. Standard errors (measures of reliability of
estimates) need to take into account these non-random features of sample selection, which can be achieved by using replicate
weights. Replicate weights are supplied with the unit record files available to approved researchers for cross-sectional analysis andfor longitudinal analysis of all balanced panels that commence with Wave 1 (e.g., Waves 1 to 4 or Waves 1 to 8). Full details on thesampling method for the HILDA Survey are available in Watson and Wooden (2002), while details on the construction, use andinterpretation of the replicate weights are available in Hayes (2009).
In this report, standard errors of statistics are not reported. Instead, for tabulated results of descriptive statistics, estimates whichhave a relative standard error of more than 25% are marked with an asterisk (*). For regression model parameter estimates,
estimates that are not statistically significantly different from 0 at the 10% level are marked with a ‘plus’ superscript (+).

C. Fieldwork process and outcomes
SampleThe HILDA Survey commenced, in 2001, with a nationally representative sample of Australian households (residing in private dwellings).
Of the 11,693 households selected for inclusion in the sample in 2001, 7,682 households agreed to participate, resulting in ahousehold response rate of 66%. The 19,914 residents of those households form the basis of the ‘main sample’ that is interviewed ineach subsequent year (or survey wave), but with interviews only conducted with persons aged 15 years or older. Interviews are also
conducted with any other person who joins a household in which an original sample member is living. Most of these persons only remainin the sample for as long as they remain living with the original sample member. The exceptions to this are persons who have a childwith an original sample member, and recent immigrants. Persons who are known to have died are removed from the sample. We also do
not pursue interviews with persons who have moved overseas, with persons who have requested to no longer be contacted, or withpersons that we have not able been to contact for three successive survey waves. In 2011, an entirely new ‘top-up’ sample was added.
This resulted in the addition of 2,153 households, and 5,451 persons. The household response rate for the top-up sample was 69%.
Data collection
The annual interviews for the main sample commence towards the end of July each year and conclude by mid-February of the next
year. The interviewer workforce comprised 176 interviewers in Wave 12, 144 of whom undertook interviews in person, with theremaining 32 being dedicated telephone interviewers. Most interviews are undertaken in person, usually in the home of the sample
member. Some interviews, however, are undertaken by telephone, usually because the cost of sending an interviewer to the locationof that sample member was prohibitive or because the sample member had a preference for a telephone interview. In Wave 12,
1,383 interviews (or 7.9% of the total completed) were undertaken by telephone.

2

In principle, the in-scope population in Waves 2 to 10 excludes most immigrants arriving in Australia after 2001. However, due to a lack of suitable external benchmarks forthis population sub-group, these immigrants are in practice included in the in-scope population. Consequently, in all waves, the HILDA Survey weights sum to the total
Australian population inclusive of new immigrants.

Technical Appendix

93Response
Table A1 and Figure A1 summarise key aspects of the HILDA sample for the period examined in this report (Waves 1 to 12).3 Table
A1 presents the number of households, respondents and children under 15 years of age in each wave. In Wave 12, interviews wereobtained with a total of 17,476 persons; 13,537 in the main sample and 3,939 in the top-up sample. Of the original 13,969
respondents in 2001, 8,543, or 61%, were still participating at Wave 12.
Note that—the top-up aside—the total number of respondents in each wave is greater than the number of Wave 1 respondents
interviewed in that wave, for three main reasons. First, some non-respondents in Wave 1 are successfully interviewed in later waves.
Second, interviews are sought in later waves with all persons in sample households who turn 15 years of age. Third, additional
persons are added to the panel as a result of changes in household composition. For example, if a household member ‘splits off’
from his or her original household (e.g., children leave home to set up their own place, or a couple separates), the entire newhousehold joins the panel. Inclusion of ‘split-offs’ is the main way in which panel surveys, including the HILDA Survey, maintainsample representativeness over the years.
Figure A1 reports the re-interview rate (percentage of persons responding at one wave who responded at the next wave) and the
response rate among new entrants to the sample for the continuing sample. As can be seen, re-interview rates for the continuingsample are high, exceeding 95% for the first time in Wave 8, and remaining above that level ever since. In Wave 12, the continuingsample re-interview rate was 96.2%. We expect much lower response rates among new persons joining the sample. Nevertheless,response rates for this group have averaged around 80% for much of the period since Wave 4. In Wave 12, the rate was 78.8%.
Within the top-up sample, the re-interview rate in Wave 12 was 92.3%. The comparable rate within the continuing sample is the rate
recorded in Wave 2, which was 86.8%. The interview rate for new entrants to the top-up sample in Wave 12 was, at 80.5%, also
comparatively high.
All persons who are interviewed are also asked to complete a separate paper-based questionnaire. Of the 17,476 persons who wereinterviewed in Wave 12, 15,380 (88%) returned this self-completion questionnaire.
Table A1: HILDA Survey sample sizes
Childrenunder 15

Figure A1: HILDA Survey response rates, Waves 2 to 12,
continuing sample

Households

Persons
interviewed

Wave 1

7,682

13,9694,784

100

Wave 2

7,245

13,041

4,275

95

Wave 3

7,096

12,728

4,088

Wave 4

6,987

12,4083,887

Wave 5

7,125

12,7593,897

Wave 6

7,139

12,905

3,756

Wave 7

7,063

12,7893,691

Wave 8

7,066

12,785

3,574

Wave 97,234

13,301

3,621

Wave 10

7,317

13,526

3,600

Wave 11 (continuing)

7,390

13,603

3,601

60

Wave 12 (continuing)

7,421

13,537

3,607

55

Wave 11 (top-up sample)

2,153

4,009

1,179

50

Wave 12 (top-up sample)

2,117

3,939

1,088

90
85

%

80
75
70
65

2002

2003

2004

2005

Previous-wave respondents
3

2006

2007

2008

2009

New entrants

More detailed data on the sample make-up and, in particular, response rates can be found in the Summerfield et al. (2013).

94

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12

2010

2011

2012

HILDA Survey personnel
Melbourne Institute Survey
Management Team

Professor Bryan Rodgers (resigned June2013), Australian National University

Mary-Ann Patterson (Project Manager
Field and Respondent)

Director
Professor Mark Wooden

Ruth Weston (resigned June 2013),
Australian Institute of Family Studies

Shane Pickard (Sample Support Officer)

Deputy Director, Research
Associate Professor Roger Wilkins

Technical Reference Group

Deputy Director, Survey Methodology
Nicole Watson

Professor Robert Breunig, Australian
National University

Deputy Director, Survey Management
Michelle Summerfield

Dr John Henstridge, Data Analysis
Australia

Database Manager
Simon Freidin

Stephen Horn, Department ofSocial Services

Survey Methodologist
Dr Ning Li

Ross Watmuff, Australian Bureauof Statistics

Database Support Officers
Peter Ittak (resigned October 2013)
Ninette Macalalad
Laura Mundy

Department of Social Services
HILDA Team

Professorial Research Fellows
Professor Richard Burkhauser
Professor Nattavudh Powdthavee

Joanne Harrison

Senior Research Fellow
Dr Hielke Buddelmeyer
Research Officer
Markus Hahn

Dr Annette Neuendorf (Section Manager)
Deborah Kikkawa
Dr Peng Yu

Roy Morgan Research

Administrative AssistantVictoria Lane (resigned April 2015)

HILDA Project Team
Antonietta Bono (National Call Centre
Manager)

External Reference Group

Michael Bradford (Project Assistant
Director)

Professor Garry Barrett (Chair),
University of New South Wales

Joshua Button (Systems Team Leader)

Associate Professor Bruce Bradbury,
University of New South Wales

Briony Coultman-Smith (Materials
Coordinator)

Associate Professor Peter Butterworth,
Australian National University
Dr Ben Edwards, Australian Institute of
Family Studies
Dr Ann Evans, Australian National
University
Dr Belinda Hewitt, University of
Queensland
Associate Professor David Johnston,Monash University
Dr Leah Ruppanner, University of
Melbourne
Professor Steven Stillman, Universityof Otago

Shannon Carter (Sample Support Officer)

Gary Dunstan (Data Manager)
Joanna El-Masri (Materials Coordinator)
Stephen Gibson (Chief Operations
Officer)Jemimah Gray (Administration Officer)
Davina Heng (Sample Team Leader)
Roopa Kamath (Research Analyst)
Louisa Katerelos (Sample Support
Officer)David Kenshole (Team 1800 Coordinator)
Troy Kohut (General Manager –Customised Research)
Deborah Louwen (HILDA Researcher)
Vivek Malpani (Confirmit Programmer)

Professor Janeen Baxter (Chair)(resigned October 2013), Universityof Queensland

Sigmund Marques (Sample Support
Officer)

Professor David de Vaus (resigned June2013), University of Queensland

James Moriarty (Sample Support Officer)

Dinushi Mendis (Research Analyst)
Lachlan Morton (Project Support)

Tania Sperti (Project Director)
Pat Timmins (Workforce Director)
Rick Van Der Meer (Project Support)
Nicholas White (Sample Support Officer)
Rowena York (Sample Team Leader)
HILDA Team 1800
Julian Adams
Tarun Bajaj
Rinata Buccheri
Jaz Burstall
Neil Cabatingan
Bee Callaghan
Fiona Crockett (Supervisor)
Louise Dickins
Pat Edwards
Emily Eller
Alex Findlay
Tansy Fisher
Alison Flehr (Supervisor)
Adam Flett
Adrian Follacchio
Amarlie Foster
Sam GrayAriel Gross
Levi Heeringa
William Hollingsworth (Supervisor)
Caitlin Houghton
Elizabeth Ibrahim
Ximena Ilich
Ethan Jones
Kieran Kelly
Jane Kingsford
Louise La Sala (Supervisor)Skye Lee
Sara McGuigan
Tim Macpherson (Supervisor)
Brett Mahar
Lukas Meintjes
Sean Mijares
Miles Miller (Supervisor)
Simon Nathan
Jodi Norton (Supervisor)
Leisa Pratt
Paul Pugliese
Adam Quigley
Vaughan Quinn
Sydney Ruiz
Kieran Ryan (Supervisor)
Elle Savanovic
Lia Sharard
Quinho Soler
Isaac Still
Daniel Stojkovich (Supervisor)
Samantha TaitHILDA Survey personnel

95Josh Thomas
Anastasia Veno
Sean Walton
Jane Watson
Oliver Wedd
Jackson Wilde
George WoodHILDA Face-to-Face Field
Interviewing TeamJan Alcock
Cathy Andrew
Robert Armstrong
Zoe Arslan
Farah Aslankoohi
Sheryle Baldock
Francene Beattie
Julie Bennett
Merril Bennett
Barbara Bluett
Pam Bowtell
Chris Brennan
Sheila Brown
Robyn Burgess
Lauren Busch
Linda Buttel
Luz Cabrera De Mesa
Frances Carr-Boyd
Jay Clark
Susan Collins
Dianne Corthorne
Andrew Craker
Sean Cranny
Beryl Cuff
Laurie-Anne Curry
Karen Dambrauskas
Jennifer De Lacey
Lea Densem
Delwyn Dix
Paul Dodds
Bev Edwards
Sandra Essex
Jennifer Eurell
Stuart Eyers
Catherine Fernbach
Henry Ferst
Anthony Foley
Peter George
Shaaron Glynn
Jeff Grant
Kerry Green
Sandy Grieve
Elizabeth Griffiths
Garry Grooms
Dzemal Hadzismajlovic
Penelope Hamilton-SmithMarie HammondPhilip Hands
Michael Hendry
Donna Hickey
Linda Hill
Louise Hill
Loretta Ho
Josie Holland
Ian Hosking
Mick Hosking
Jan Houghton
Ben Huisman
Frances Husher
Patricia Huynh

96

Dylan Hyde
Jill Irvine
Jane Jackson
Kim Jackson
Paula Jackson
Julie-Anne Jacobs
Andrew James
Mirjana Jesensek
Christine Jolly
Trudy Jones
Richard Joyce
Penny Jurd
Deborah Kairn
Sivapalan Kanapathippillai
Margaretha Kassanis
Pat Kempster
John Kenney
Ray Kerkhove
Brian King
Jenny Komyshan
Greg Lay
Christine Leece
Murray Legro
Karen Leslie
Dorothy Lucks
Laureen Lutz
Cindy Macdonald
John Macdonald-Hill
Marilyn McDonnell
Lyn McKeaveney
Lisa Mackinnon
Catherine McMahon
Andrea McPherson
Rita Maio
Annette Major
Priscilla Martinus
Dayne Matthews
Kaleil Merren
Nabil Mohammad
Tony Moloney
Robyn Moylan-GarrickPeter Mulholland
Fiona Mullen
Virginia Murphy
Don Murray
Rob Neal
Ann Newbery
Gwen Nickolls
Marija Nikolic-Paterson
Narelle Nocevski
Vicky Nowak
Marta Ogilvie
Sally O’Neal
Jasmin Osborne
Bradford Paez
Marilyn Paul
Faye Payne
Shelley Pearce
Gabriella Pendergast
Cheryl Perrett
Tara Perrett
Zoe Perrett
David Plant
Sandra Potter
Beverley Price
Anna Prichard
Yvette Pryor
Lyle Pyers
Ananda RanasingheDavid Reed

Margaret Reid
Marg Reynolds
Lynndal Richards
Lynn Richardson
Aaron Rinder
Gillian Rivers
Nea Roberts
Sandra Robertson
Oriana Roinich
Pat Roy
Lorna Russell
Roxanne Russell
Maree Saleh
Rodney Scherer
Susan Schiller
Stephen Schubert
Robyn SchulzPeter Sexton
George Shaw
Barry Simpfendorfer
Beryl Sinapius
Barbara Slattery
Marilyn Smiley
Muriel Smith
Karen Steele
Elizabeth Stock
Phillip Stock
Tathra Street
Margaret Stubbs
Helen Szuty
Bridgitte Tadrosse
Rici Tandy
Lynda Taylor
Sandra Teagle
Beth Tilley
Fay Tomholt
Suzie Torok
Kerrie Townley-Jones
Ingrid Tozer
Robin Trotter
Andrew Van der Lugt
Irina Vasilenko
Julia Ward
Elizabeth Waymark
Elizabeth Weaver
Jayne Wiche
Marlene Wills
Jilanne Wilson
Rochelle Woodley-Baker
Bev Worrall
Pamela Wright
Jayne Wymer
Jeffrey Yap
Additional support
Murray Bishop
Rachel Brown
Chris Chippett
Ken Ferns
Mark Hall
Oscar Kong
Lindsay Lucas
Sewwandi Navarathna
Peter Newby
Deb Perriman
Nick Petroulias
Grant Rhys-JonesPaul Roach
Suvinder Sawhney
Allan Wei

The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 12