(HILDA) Survey Annual Report 2012

Transcription

42710 HILDA Annual Report 2012 COVER.qxd:hilda annual report cover 17/01/13 11:33 AM Page 1
Household, Income and
Labour Dynamics in
Australia (HILDA) Survey
Annual Report 2012
Funded by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs
HILDA Survey
Annual Report 2012
HILDA Survey
Annual Report 2012
Melbourne Institute of Applied Economic and Social Research
Faculty of Business and Economics Building
Level 5, 111 Barry Street
The University of Melbourne
Victoria 3010 Australia
T: +61 3 8344 2100
F: +61 3 8344 2111
E: [email protected]
W: www.melbourneinstitute.com/hilda
© 2013 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research.
COPYRIGHT: All rights reserved. Apart from fair dealing for the purposes of research or private study, or criticism or
review, as permitted under the Copyright Act 1968, no part of this publication may be reproduced, stored or transmitted
in any form or by any means without the prior permission in writing of the Publisher.
ISSN 1447-476X (Print)
ISSN 1447-5812 (Online)
Photos by the University of Melbourne, Roy Morgan Research, and the Australian Government Department of Families,
Housing, Community Services and Indigenous Affairs.
Printed and bound by UniPrint Pty Ltd.
Contents
Foreword from the Minister for Families, Community Services
and Indigenous Affairs
4
Director’s Report
5
Personnel, 2012
7
Wave 11
10
Presentations and Publications about the HILDA Survey and Data
20
2013 HILDA Survey Research Conference
21
The HILDA Data User Community
22
Publications by HILDA Data Users, 2012 and Forthcoming
25
Accessing the Data
29
HILDA project team meeting: (from left) Nicole Watson, Michelle Summerfield, Mark Wooden, Ninette Macalalad, Simon Freidin
Page 3 – HILDA Survey Annual Report 2012
Foreword from the Minister for
Families, Community Services
The Hon Jenny Macklin, MP
With an ageing population, continuing migration and a low fertility rate, Australia will undergo many changes
in the coming decades, presenting opportunities and challenges for all of us.
The HILDA Survey is an invaluable resource in helping researchers and policy makers understand the causes
and effects of these changes. For 12 years, HILDA has taught us about ourselves—how we juggle work and
family life, how we manage our finances, and how we adapt to a changing world.
Wave 11 of the HILDA Survey has a strong focus on people’s plans for and their experiences of retirement,
fertility and family formation. This new information allows policy makers to understand how people make
decisions about starting a family or retiring from the workforce.
The rich diversity of our migrant population can be seen in the variety of ancestries and birthplaces reported
in the 2012 survey. In 2011, the HILDA Survey interviewed 2,153 new households on top of its continuing
sample, allowing it to include new migrants and other Australians, and ensuring the HILDA Survey continues
to reflect the great depth and diversity of our people.
The HILDA Survey helps policy makers plan ahead to ensure the right policies and programs are in place to
support modern families. The Government is ensuring Australia will continue to be a great place to live, work and
raise a family, with recent reforms including Australia’s first National Paid Parental Leave scheme, more support
for families with children in school, and the National Disability Insurance Scheme, DisabilityCare Australia.
With the continuing support of around 13,000 people, the HILDA Survey has collected data about households,
income and labour dynamics every year since 2001.
I want to acknowledge everyone involved in this year’s HILDA Survey and welcome those households who
are responding to the survey for the first time. I especially thank the individuals who have participated in the
HILDA Survey every year, giving us important insights into our changing society. Your contribution allows us
to build on the strengths of the Australian community and take active steps to shape our future.
The Hon Jenny Macklin, MP
Minister for Families, Community Services and Indigenous Affairs
Director’s Report
“Going forward … it will be very much business as
usual for the HILDA Survey, just with a sample
that is almost 30 per cent larger.”
Professor Mark Wooden
Overview
Yet again the HILDA Survey can point to another year of outstanding achievements.
Most notable was the completion of the first refreshment or top-up sample, with an
additional 2,153 households recruited into the HILDA Survey. Further, the achieved
household response rate for this top-up was almost 70 per cent, noticeably higher
than what was achieved when the HILDA Survey commenced back in 2001.
Meanwhile the re-interview rate for the main sample was maintained yet again at a
level in excess of 96 per cent.
The size of the data user community also continues to grow, with over 520 orders
for Data Release 10, the highest yet for any annual data release.
It was thus against this background that the Melbourne Institute was successful in
securing the contract (following a competitive tender process) from the Department
of Families, Housing, Community Services and Indigenous Affairs to manage the
HILDA Survey project for Waves 13 to 16 (with an option to extend for a further
two years). In turn, the University of Melbourne offered Roy Morgan Research an
extension of its sub-contract to continue to provide fieldwork services for the
project, which it accepted.
Going forward, therefore, it will be very much business as usual for the HILDA
Survey, just with a sample that is almost 30 per cent larger.
Wave 11
Data collection for Wave 11 was completed in February 2012. As already mentioned,
Wave 11 is notable for the introduction for the first time of a top-up sample.
Among members of the ‘original’ or ‘main’ sample a re-interview rate of 96.5 per
cent was achieved, and it is the third year in succession we have exceeded 96 per
cent. A total of 13,603 persons were successfully interviewed in Wave 11 as part of
the main sample, which compares with 13,526 in Wave 10.
For the top-up sample, interviews were obtained at 2,153 households out of a total
of 3,117 selected households identified as in-scope, giving a household response
rate of 69.1 per cent. Within these participating households there were 4,280 persons
eligible for interview, 93.7 per cent of whom (4,009) were successfully interviewed.
This represents a considerable improvement on the household response rate
recorded in Wave 1 (65.7 per cent). Given the accumulated experience of both the
survey managers and interviewers, this perhaps should not be surprising.
Nevertheless, it contrasts with the international experience from both repeated crosssections and other household panel studies, where the accepted wisdom is that it is
increasingly difficult to persuade populations to participate in voluntary surveys.
Possibly the only disappointing outcome from Wave 11 was the response rate for
the self-completion questionnaire, which in the main sample was 87.8 per cent of
interviewees, and in the top-up sample just 85.2 per cent.
Wave 12
2012 also saw the commencement of fieldwork for our 12th annual survey wave.
Key features of the survey content for Wave 12 include:
(i) the re-introduction of the sequences on non-co-residential family relationships
(with parents, adult children and siblings) previously included in Wave 8; and
(ii) the inclusion of new question sequences with a focus on education and skills
formation and abilities.
Of particular interest, as part of the latter we have included three short ‘tests’ of
cognitive ability.
Wave 13
Work also began on the development of the interview scripts and questionnaires for
Wave 13. This wave will see the re-inclusion of a module of additional healthrelated questions previously included in Wave 9. We are also testing new questions
designed to measure: (i) waist circumference; (ii) quantity and quality of sleep; and
(iii) the extent of illicit drug use.
Data users
As noted earlier, the size of the HILDA Survey data user community continues to
grow, with over 520 orders for Release 10, the highest in the history of the study to
date, and with 175 of these orders from first time users.
It is also very pleasing to see both the large number and wide variety of outputs
that have made use of the data. Every year the number of published articles, papers
and reports continues to grow and I am sure 2013 will be no exception.
Personnel
The HILDA Survey today employs, directly or indirectly, over 200 persons spread
across three organisations. The majority of these are employed at Roy Morgan
Research, which conducts the fieldwork. Since 2009, when Roy Morgan Research
assumed responsibility for the HILDA Survey fieldwork, this small army of people
has been managed by Athina Katiforis. During 2012 this came to an end, with Athina
moving to the Executive team at Roy Morgan Research. She has been replaced by
Tania Sperti, who for two years has effectively been working as Athina’s deputy.
I would thus like to thank Athina for her wonderful dedication and service to the
HILDA Survey project during four very critical years, and to congratulate Tania on
her promotion.
Project Director
Personnel, 2012
Melbourne Institute
survey management
team
Director
Deputy Director,
Survey Management
Ms Michelle Summerfield
External
Reference Group
Technical
Reference Group
Professor Janeen Baxter (Chair)
University of Queensland
Professor Robert Breunig
Australian National University
Professor Garry Barrett
University of New South Wales
Dr John Henstridge
Data Analysis Australia
Dr Bruce Bradbury
University of New South Wales
Database Manager
Mr Simon Freidin
Professor David de Vaus
University of Queensland
Mr Stephen Horn
Department of Families,
Housing, Community Services
Database Support Officers
Mr Peter Ittak
Ms Ninette Macalalad
Dr Ann Evans
Mr Ross Watmuff
Australian Bureau of Statistics
Deputy Director, Research
Associate Professor Roger Wilkins
Professorial Research Fellow
Professor Richard Burkhauser
Research Officer
Mr Markus Hahn
Deputy Director,
Survey Methodology
Ms Nicole Watson
Survey Methodologist
Dr Ning Li
Professor Bryan Rodgers
Professor Steven Stillman
University of Otago
Ms Ruth Weston
Australian Institute of Family Studies
HILDA
FaHCSIA team
Dr Annette Neuendorf
(Section Manager)
Ms Joanne Harrison
Ms Deborah Kikkawa
Ms Gae Major
Ms Jenefer Tan
Administrative Assistant
Ms Victoria Lane
Participants in the External Reference Group Meeting, 5 December 2012.
Back row (from left): Michelle Summerfield, David de Vaus, Ruth Weston, Garry Barrett, Bryan Rodgers,
Nicole Watson
Front row (from left): Roger Wilkins, Janeen Baxter, Annette Neuendorf, Mark Wooden
Absent: Steven Stillman, Ann Evans, Bruce Bradbury
Roy Morgan Research
HILDA Project team
Isabelle Bertoli
Pam Bowtell
Michael Bradford
(Project Manager Systems)
Rinata Buccheri
Chris Brennan
Neil Cabatingan
Lauren Busch
Joshua Button
(Systems Team Leader)
Fiona Crockett (Supervisor)
Linda Buttel
Shannon Carter
(Sample Support Officer)
Kerrie Dinneen
Frances Carr-Boyd
Nico Disseldorp
Jay Clark
Gary Dunstan (Data Manager)
Joanna El-Masri (Supervisor)
Lisa Coleman
Stephen Gibson
(Chief Operations Officer)
Alex Findlay
Andrew Craker
Jemimah Gray
Sean Cranny
Davina Heng
(Administration Officer)
Ariel Gross
Beryl Cuff
Roopa Kamath
Monique Hameed
Anne Dedman
Christine Harding
Lea Densem
William Hollingsworth (Supervisor)
Delwyn Dix
Joelle Horan
Paul Dodds
David Kenshole
(Team 1800 Coordinator)
Brendan Howe
Bev Edwards
Ximena Ilich
Sandra Essex
Troy Kohut (General Manager
– Customised Research)
Louise La Sala (Supervisor)
Jennifer Eurell
Deborah Louwen
(Materials Coordinator)
Tim Macpherson (Supervisor)
Gary Feder
Jasmine Miu
Henry Ferst
Vivek Malpani
(Confirmit Programmer)
Jodi Norton (Supervisor)
Anthony Foley
Joshua Osei-Duro
Portia-Ann Forrest
Sigmund Marques
Brad Paez
Peter George
Mary-Ann Patterson (Interviewer
Team Leader and Trainer)
Anna Panipucci
Shaaron Glynn
Paul Pugliese
Kerry Green
Kirk Pereira (Sample Team Leader)
Vaughan Quinn
Sandy Grieve
Shane Pickard
Lia Sharard
Elizabeth Griffiths
Billy Sheldon
Garry Grooms
Tania Sperti
(Project Director, from July 2012)
Daniel Stojkovich (Supervisor)
Tim Haddad
Sean Walton
Pat Timmins (Project Manager
Field and Respondent)
Dzemal Hadzismajlovic
Tim Williams
Penelope Hamilton-Smith
Athina Katiforis
(Project Director, to July 2012)
Matthew Williamson
(General Manager – Operations)
Marie Hammond
HILDA face-to-face field
interviewing team
Philip Hands
Jan Alcock
Linda Hill
Cathy Andrew
Louise Hill
HILDA team 1800
Zoe Arslan
Margaret Hocking
Abdul Abdullahi-Mohamed
Farah Aslankoohi
Josie Holland
Eugene Baiden Assan
Francene Beattie
Jan Houghton
Tarun Bajaj
Merril Bennett
Ben Huisman
Rowena York
(Sample Team Leader)
Donna Hickey
Frances Husher
Narelle Nocevski
Rici Tandy
Patricia Huynh
Vicky Nowak
Lynda Taylor
Dylan Hyde
Marta Ogilvie
Ewan Tolhurst
Paul Inglis
Marilyn Paul
Julia Tolj
Kim Jackson
Faye Payne
Fay Tomholt
Andrew James
Shelley Pearce
Suzie Torok
Christine Jolly
Gabriella Pendergast
Ingrid Tozer
Trudy Jones
Cheryl Perrett
Robin Trotter
Richard Joyce
Tara Perrett
Suzanne Turner
Deborah Kairn
Zoe Perrett
Irina Vasilenko
Margaretha Kassanis
David Plant
Tim Walker
Pat Kempster
Sandra Potter
Julia Ward
John Kenney
Beverley Price
Elizabeth Waymark
Ray Kerkhove
Yvette Pryor
Jayne Wiche
Jennie Knight
Ananda Ranasinghe
Marlene Wills
Jenny Komyshan
David Reed
Jilanne Wilson
Paola Kumlin
Margaret Reid
Laurie Wilson
Dianne Lavender
Marg Reynolds
Greg Lay
Lynndal Richards
Christine Leece
Aaron Rinder
Murray Legro
Gillian Rivers
Karen Leslie
Oriana Roinich
Dorothy Lucks
Pat Roy
Additional support
Sue Lukies
Lorna Russell
Murray Bishop
Cindy Macdonald
Roxanne Russell
Rachel Brown
John Macdonald-Hill
Ramiz Sargious
Ken Ferns
Paul McIntyre
Rodney Scherer
Mike Fitzgerald
Lyn McKeaveney
Robyn Schulz
Mark Hall
Jo McKee
George Shaw
Lindsay Lucas
Catherine McMahon
Michelle Short
Asif Malik
Rita Maio
Barry Simpfendorfer
Peter Newby
Dayne Matthews
Beryl Sinapius
Marcus Nguyen
Nabil Mohammad
Barbara Slattery
Deb Perriman
Peter Mulholland
Muriel Smith
Nick Petroulias
Virginia Murphy
Karen Steele
Jared Pool
Don Murray
Phillip Stock
Paul Roach
Rob Neal
Margaret Stubbs
Suvinder Sawhney
Ann Newbery
Helen Szuty
Marcus Tarrant
Marija Nikolic-Paterson
Bridgitte Tadrosse
Sabrina Verhage
Bev Worrall
Jayne Wymer
Jeffrey Yap
Wave 11
Top-up sample
As highlighted in the Director’s Report, Wave 11 is notable for the inclusion of a
new expansion, or top-up, sample. The process for selecting the top-up sample for
Wave 11 was very similar to that used for the main sample in Wave 1. Thus
sampling began with a random selection of Census Collection Districts (CDs), with
125 CDs selected (compared with 488 in Wave 1). A total of 3,250 new addresses
were issued to field. After the removal of addresses determined to be out of scope
and adjusting for addresses with multiple households, the initial top-up sample
numbered 3,117 households.
Fieldwork
Given the additional fieldwork required to administer the top-up sample, fieldwork
in Wave 11 commenced earlier than in previous waves. Specifically, the Wave 11
interviews commenced towards the end of July 2011 and were completed by
mid-February 2012.
Table 1 compares the distribution of the interviews over the fieldwork period for
each wave. By the end of September, 77 per cent of interviews had been completed
for the main sample and 70 per cent for the top-up sample. By the end of
December, this figure had risen to 97 per cent for the main sample and 92 per cent
for the top-up sample.
A total of 167 interviewers were employed on Wave 11. Despite the marked
increase in the total sample size, the size of the interviewer workforce was
increased only slightly in comparison to Wave 10 (see Table 2). Further, most of this
increase was due to an expansion in the size of the Team 1800 telephone
interviewers. The number of face-to-face interviewers changed very little, with the
greater workload spread among the existing interviewer workforce.
Table 1: Percentage of individual interviews conducted each month, Waves 1 to 11
July
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Total
Wave 1
–
1.1
40.2
36.9
14.0
7.4
0.4
–
–
100
Wave 2
–
5.7
55.8
24.8
10.6
0.9
–
2.1
0.2
100
Wave 3
–
7.7
57.9
22.9
8.3
1.2
0.2
1.6
–
100
Wave 4
–
12.4
60.1
18.0
6.1
1.1
0.2
2.1
–
100
Wave 5
–
3.2
53.3
30.5
7.4
1.6
1.5
2.4
–
100
Wave 6
–
4.1
57.3
28.0
7.0
1.4
1.1
1.1
–
100
Wave 7
–
4.4
55.7
29.3
7.2
0.8
1.3
1.3
–
100
Wave 8
–
7.7
57.6
23.1
7.7
1.0
1.4
1.6
–
100
Wave 9
–
4.9
54.1
29.1
6.1
1.7
2.1
2.0
–
100
Wave 10
–
12.7
50.8
24.6
6.7
1.5
2.3
1.4
–
100
Wave 11 (MS)
0.3
36.9
40.2
12.3
5.9
1.0
1.9
1.5
0.0
100
Wave 11 (TU)
4.8
47.9
16.8
6.4
13.7
2.0
5.1
3.4
–
100
Note: MS denotes main sample; TU denotes top-up sample.
Wave 11 response:
main sample
Table 3 describes the progression of the responding individual sample over the first
11 waves. Of those initially interviewed in Wave 1, 8,780 were re-interviewed in
Wave 11 (which is 68.9 per cent of those who are still in-scope). The number
interviewed in all 11 waves is 7,229.
The wave-on-wave response rates for all sample groups are provided in Table 4.
The second set of response rates reported in this table are for those individuals
belonging to or joining households that responded in the previous wave and thus
arguably provides a more realistic comparison of response rates over time, since
these people have a recent connection to the study.
The response rate for previous wave respondents was, at 96.5 per cent, slightly
higher than in the previous wave, and is the highest recorded in the survey’s
history. The response rates for previous wave children and new sample entrants to
existing households also remain at levels that are reasonably high compared to the
survey’s history.
Wave 11 response:
top-up sample
For the top-up sample, interviews were obtained at 2,153 households out of the
3,117 selected households identified as in-scope, giving a household response rate
of 69.1 per cent. Within these participating households there were 4,280 persons
eligible for interview, 93.7 per cent of whom (4,009) were successfully interviewed.
This 69 per cent response rate is noticeably higher than the household response
rate achieved in Wave 1 (66 per cent) (see Table 5). Given the accumulated
experience of both the survey managers and interviewers, this perhaps should not
be surprising. Nevertheless, it contrasts with the international experience from both
repeated cross-sections and other household panel studies.
Table 2: Number of interviewers and percentage of new interviewers, Waves 1 to 11
Face-to-face
interviewers
Telephone
interviewers
All interviewers
Percentage
new
Number
Percentage
new
0
–
133
100.0
33.8
9
100.0
142
38.0
118
18.6
10
60.0
128
21.9
Wave 4
117
12.8
9
44.4
126
15.1
Wave 5
122
14.8
10
80.0
132
19.7
Wave 6
127
28.3
13
53.8
140
30.7
Wave 7
126
0.6
15
53.3
141
24.1
Wave 8
123
11.4
15
46.7
138
15.2
Wave 9
135
34.1
24
95.8
159
43.4
Wave 10
132
11.4
23
69.6
155
20.0
Wave 11
135
15.6
32
56.3
167
23.4
Number
Percentage
new
Wave 1
133
100.0
Wave 2
133
Wave 3
Number
Table 3: Individual interviews completed, Waves 1 to 11
Wave first
interviewed
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Wave
9
Wave
10
Wave
11
Wave 1
13,969
11,993
11,190
10,565
10,392
10,085
9,628
9,354
9,245
9,002
8,780
Wave 2
–
1,048
705
594
572
542
512
483
488
475
461
Wave 3
–
–
833
543
482
429
403
376
383
365
354
Wave 4
–
–
–
706
494
426
408
369
374
362
348
Wave 5
–
–
–
–
819
578
511
462
459
441
407
Wave 6
–
–
–
–
–
845
641
545
525
499
468
Wave 7
–
–
–
–
–
–
686
509
448
427
409
Wave 8
–
–
–
–
–
–
–
687
526
491
445
Wave 9
–
–
–
–
–
–
–
–
853
640
583
Wave 10
–
–
–
–
–
–
–
–
–
824
599
Wave 11
–
–
–
–
–
–
–
–
–
–
4,758
13,969
13,041
12,728
12,408
12,759
12,905
12,789
12,785
13,301
13,526
17,612
Total
Table 4: Individual response rates, Waves 2 to 11
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Wave
9
Wave
10
Wave
11
(MS)
Previous wave
respondent
86.8
90.4
91.6
94.4
94.9
94.7
95.2
96.3
96.3
96.5
Previous wave
non-respondent
19.7
17.6
12.7
14.7
8.4
5.6
5.7
8.5
4.5
3.8
Previous wave child
80.4
71.3
70.7
74.6
75.4
70.8
73.7
73.4
72.0
70.0
New entrant
this wave
73.3
76.1
70.4
81.7
81.1
79.7
79.5
81.3
82.9
80.7
Previous wave
respondent
86.8
90.4
91.6
94.4
94.9
94.7
95.2
96.3
96.3
96.5
Previous wave
non-respondent
19.7
19.8
18.1
25.3
18.3
13.2
15.0
25.9
16.2
15.4
Previous wave child
80.4
81.8
81.2
87.3
89.5
90.5
90.9
93.0
92.3
93.0
New entrant
this wave
73.3
78.5
71.8
85.4
81.0
80.2
81.2
81.4
83.5
82.0
All people
People attached to
responding household
in previous wave
Note: MS denotes main sample.
SCQ response
Of the 13,603 Wave 11 respondents in the main sample, 11,946 returned a selfcompletion questionnaire (SCQ), resulting in an SCQ response rate of 87.8 per cent.
Of the 4,009 Wave 11 top-up respondents 3,415 returned an SCQ, resulting in an
SCQ response rate of 85.2 per cent. (See Table 6.)
Both these rates are somewhat disappointing, possibly suggesting a return to the
longer run downward trend in SCQ responses that had been arrested (and indeed
reversed) in Wave 10.
Attrition bias
The fact that not all sample members agree to participate each year raises concerns
about the possibility that those who choose to respond are systematically different
from those who drop out. Table 7 reports the percentage of Wave 1 respondents
who have been re-interviewed, both in all waves (the balanced panel) and in
Wave 11, by various respondent characteristics measured at Wave 1. People who
have died or moved overseas are excluded from these figures.
Table 5: Wave 1 and Wave 11 top-up sample household outcomes compared
Wave 1
Wave 11 top-up
Sample outcome
Number
Per cent
Number
Per cent
Addresses issued
12,252
–
3,250
–
Less out-of-scope (vacant, non-residential,
foreign)
804
–
212
–
Plus multi-households additional to sample
245
–
79
–
Total households
11,693
100.0
3,117
100.0
Refusals to interviewer
2,670
22.8
885
28.4
Refusals to fieldwork company
(via 1800 number or email)
431
3.7
16
0.5
Non-response with contact
469
4.0
16
0.5
Non-contact
441
3.8
47
1.5
Fully responding households
6,872
58.8
1,963
63.0
Partially responding households
810
6.9
190
6.1
Table 6: SCQ response rates, Waves 1 to 11
Wave
1
Wave
2
Wave
3
Wave
4
Wave
5
Wave
6
Wave
7
Wave
8
Wave
9
Wave
10
Wave
11
(MS)
Wave
11
(TU)
Face-to-face
interviews
93.7
93.9
93.5
93.3
91.8
92.7
91.5
90.7
89.3
91.6
90.5
85.7
Phone
interviews
52.7
63.3
68.1
68.2
62.3
64.1
62.2
59.7
63.0
62.4
59.6
56.2
Overall
93.5
93.0
92.3
91.9
89.9
90.8
89.0
87.6
86.9
89.0
87.8
85.2
Percentage
of phone
interviews
0.5
3.0
4.6
5.6
6.5
6.9
8.4
10.1
9.1
8.4
8.6
1.8
Note: MS denotes main sample; TU denotes top-up sample.
Table 7: Percentage of Wave 1 respondents re-interviewed by selected sample characteristics
Wave 1 characteristics
In all
waves
In
Wave 11
Area
In all
waves
In
Wave 11
Indigenous status
Sydney
54.3
66.4
Indigenous
44.7
68.9
Rest of New South Wales
60.2
70.4
Non-Indigenous
58.4
68.0
Melbourne
56.1
68.2
Education attainment
Rest of Victoria
55.3
64.8
Year 11 or below
53.2
64.4
Brisbane
62.6
71.1
Year 12
55.8
67.4
Rest of Queensland
60.3
70.0
Certificate
57.5
68.4
Adelaide
61.8
71.6
Diploma
65.1
74.7
Rest of South Australia
57.7
71.5
Degree or higher
68.7
77.9
Perth
59.1
67.4
Dwelling type
Rest of Western Australia
54.6
68.1
House
58.4
69.3
Tasmania
58.6
69.3
Semi-detached
59.8
69.8
Northern Territory
73.4
87.9
Flat, unit, apartment
53.3
63.7
Australian Capital Territory
60.8
73.0
Other
51.8
64.3
Sex
Labour force status
Male
56.4
67.5
Employed full-time
58.6
69.6
Female
59.6
70.0
Employed part-time
61.0
72.3
Unemployed
46.7
58.6
57.1
67.0
Age group (years)
15–19
42.3
60.5
Not in the labour force
20–24
46.2
62.5
Employment status in main joba
25–34
55.4
67.8
Employee
59.3
70.6
35–44
60.7
70.8
Employer
57.3
67.7
45–54
62.9
71.8
Own account worker
61.3
70.6
55–64
67.5
75.8
Contributing family worker
57.1
74.2
65–74
67.1
73.2
Occupationa
75+
41.0
47.7
Managers/administrators
63.4
74.6
Professionals
68.2
77.9
Marital status
Married
61.6
70.7
Associate professionals
59.1
70.0
De facto
55.3
68.1
Tradespersons
53.8
66.0
Separated
59.1
69.7
Advanced clerical/service
58.7
69.3
Divorced
66.5
75.9
Intermediate clerical/sales/service
59.6
70.8
Widowed
60.0
65.5
Intermediate production/transport
53.6
62.8
Single
48.3
63.7
Elementary clerical/sales/service
55.9
68.8
Labourers
50.7
63.3
59.7
70.4
58.1
68.9
7,229
8,780
Country of birth
Australia
Overseas
Total
Main English-speaking
60.5
69.2
Other
47.3
60.3
Note: (a) Employed sub-sample only.
Number responding
As we have noted in previous Annual Reports, the likelihood of having responded
in every wave is relatively low for people who in Wave 1 were:
• relatively young (aged between 15 and 24 years);
• born in a non-English-speaking country;
• of Aboriginal or Torres Strait Islander descent;
• single;
• unemployed; or
• working in low-skilled occupations.
Nevertheless, there is still a reasonably high level of engagement among most
respondents in these groups. Take Indigenous people for example, while less than
half of the Indigenous respondents have been re-interviewed every wave, 69 per
cent were interviewed in Wave 11, which is slightly higher than the proportion of
non-Indigenous sample members.
Fortunately, the extensive array of characteristics collected over time means that
many of the characteristics of attritors can be identified and hence controlled for in
analyses of the data. Users can either directly model the attrition process or use the
weights included in the dataset.
Top-up sample
characteristics
Table 8 provides a summary of selected characteristics of respondents in the top-up
sample compared to population estimates from the Australian Bureau of Statistics
(ABS) Labour Force Survey. The top-up sample, like the main sample selected in
2001, is intended to be broadly representative of the wider population. There are,
however, some differences between the top-up sample and the ABS estimates.
Specifically, and similar to what was found in the main sample in 2001:
• Sydney residents are under-represented;
• males are less likely to respond than females; and
• married people are more likely to respond than unmarried people (though this
may reflect the exclusion from the HILDA sample of people living in
institutions rather than any difference in response likelihood).
Unlike our main sample in 2001 where immigrants from a non-English-speaking
background were under-represented and Australian-born people were overrepresented, we have a different result for the top-up sample. Here people born
overseas in (the main) English-speaking countries are over-represented. This is
perhaps because the saliency of being included in a top-up sample in 2011 is
strongest for those who did not have a chance of being included in the main
sample in 2001.
The differences between the HILDA Survey estimates and the Labour Force Survey
are mostly eliminated through the use of weights which adjust for differences in the
probability of selection and response. Indeed, the HILDA Survey estimates for area of
usual residence, sex, age, marital status and employment status will match the Labour
Force Survey estimates exactly as these are used to calibrate the HILDA weights.
Table 8: Selected Wave 11 individual top-up sample characteristics compared to ABS estimates (per cent)
HILDA
top-upa
ABSb
Area
HILDA
top-upa
ABSb
Sex
Sydney
17.4
20.4
Male
47.8
49.3
Rest of New South Wales
13.5
12.0
Female
52.2
50.7
Melbourne
17.8
18.4
Marital status
Rest of Victoria
6.3
6.7
Married/De facto
63.2
58.2
Brisbane
9.8
8.9
Not married
36.8
41.8
Rest of Queensland
10.2
11.1
Indigenous
2.5
2.1
Adelaide
7.3
5.5
Birthplace
Rest of South Australia
2.2
2.0
Born in Australia
68.7
70.1
Perth
6.3
7.7
Main English-speaking country
13.0
10.8
Rest of Western Australia
3.5
2.6
Other country
18.3
19.1
Tasmania
3.7
2.2
Labour force status
Northern Territory
0.5
0.9
Employed full-time
41.4
44.3
Australian Capital Territory
1.5
1.6
Employed part-time
20.9
18.3
Unemployed
3.6
3.4
34.0
34.0
Age (years) at 30 September
15–19
8.5
8.1
Not in the labour force
20–24
8.3
9.0
Employment status in main job
25–34
17.1
17.8
(employed persons only)
35–44
18.6
17.3
Employee
90.3
89.2
45–54
15.8
16.6
Employer
2.0
2.9
55–64
14.5
14.1
Own account worker
7.3
7.8
65+
17.3
17.1
Contributing family worker
0.4
0.0
Notes: (a) The HILDA estimates are for people aged 15 years and over in the top-up sample, but include defence force personnel and exclude people living in
very remote parts of Australia and those living in special dwellings. The HILDA estimates have been weighted by the design weight which adjusts for
the probability of selection.
(b) The ABS estimates come from the Labour Force Survey for September 2011 (or May 2011 for education, August 2011 for occupation, industry and
employment status, and 2011 in general for Indigenous status). With the exception of country of birth, the population that these estimates apply to
includes all civilians aged 15 years and over. The country of birth figures exclude people living in institutions.
Combining the main
and top-up samples
One of the main reasons for adding the top-up sample was to address an issue of
bias creeping into the weighted cross-sectional HILDA estimates due to the inability
of the ‘following rules’ to capture a representative sample of recent immigrants.
Figure 1 shows that since the study began in 2001, the cross-sectional HILDA Survey
estimate for the proportion of people aged 15 and older who arrived in Australia in
2001 or later is markedly different from the ABS Labour Force Survey estimate. By
2011, the two estimates differ by 7 percentage points. This gap is greatly reduced by
the inclusion of the top-up sample and the use of the weights developed for the
combined sample.
The amount of bias that this lack of recent immigrants can inject into the crosssectional estimates between 2002 and 2010 will vary depending on how strongly
associated the variables are to immigration. An example of a variable that is
highly affected is country of birth. Figure 2 shows the proportion of the
Australian population aged 15 and over who were born in Australia. Over the
nine-year period between 2001 and 2010, the HILDA estimate suggests that the
proportion of the adult population born in Australia has been increasing over time,
whereas for the same period, the ABS Labour Force estimate has been declining.
Again, combining the main sample with the top-up sample removes this bias from
the HILDA Survey estimates.
Figure 1: Proportion born overseas and arrived in 2001 or later (aged 15+),
2001 to 2011
0.12
HILDA main
HILDA combined
0.10
ABS Labour Force Survey
0.08
0.06
0.04
0.02
0
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
While the impact of the top-up sample on country of birth and year of arrival is
quite large, many variables will only have a small impact or none at all. Table 9
compares the estimates from the ABS Labour Force Survey with those from the main
sample and the combined sample. The combined estimate is generally very similar
to the estimate from the main sample alone. Where there are differences, the
combined estimate is usually closer to the ABS estimate than the main sample.
There are, however, two exceptions: the highest level of education and the number
of hours worked. Differences in the questions asked and the use of proxy
interviews by the ABS may explain some of these results.
Users of the HILDA data who would like more information about the top-up
sample, how the two samples have been combined and the impact this has had on
the cross-sectional estimates are directed to a recently released paper in the HILDA
Technical Paper series entitled Longitudinal and Cross-Sectional Weighting
Methodology for the HILDA Survey.
Figure 2: Proportion born in Australia (aged 15+), 2001 to 2011
0.80
HILDA main
0.78
HILDA combined
ABS Labour Force Survey
0.76
0.74
0.72
0.70
0.68
0.66
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Table 9: Wave 11 respondent characteristics and comparisons with the ABS Labour Force Survey (per cent)
Combined
Main
sample sample
ABS
Relationship in household
Couple with children <15
21.7
21.6
22.0
Combined
Main
sample sample
ABS
Indigenous
2.5
2.2
2.1
Part-time workera
32.0
32.8
30.6
Managers
13.0
13.2
13.0
Professionals
23.3
23.5
21.6
Technicians and trade workers
14.7
13.7
14.2
Community and personal
service workers
9.5
10.0
9.7
Occupationa
Couple with dependent
student (no child <15)
5.5
5.7
4.5
Couple with non-dependent
children
5.7
5.4
5.1
Couple without children
26.3
26.6
27.7
Lone parent with children <15
2.7
2.8
3.0
Lone parent with dependent
student (no child <15)
1.0
1.0
0.8
15.4
14.8
15.1
Lone parent with nondependent children
Clerical and administrative
workers
2.6
2.2
1.7
Sales workers
9.1
9.3
9.4
Dependent student
8.7
8.6
7.1
Machinery operators and drivers
6.0
5.8
6.8
Labourers
9.1
9.7
10.2
Health care and social assistance
12.1
12.5
11.7
Retail trade
10.7
10.6
10.8
Construction
8.4
8.4
9.1
Manufacturing
8.4
8.2
8.3
Other industries
60.6
60.3
60.1
Employee
90.5
90.4
89.2
Employer
2.2
2.1
2.9
Non-dependent child
9.6
8.5
8.5
Other family member
2.7
3.2
2.6
Unrelated to all household
members
1.9
3.0
5.2
Lone person
11.6
11.6
11.8
Highest level of education
(15–64 year olds)
Masters or doctorate
4.1
5.5
4.6
Grad diploma or grad certificate
5.0
5.3
2.1
Bachelor or honours
14.6
15.7
17.0
Advanced diploma or diploma
8.6
8.9
9.1
Certificate IV or III
21.3
20.6
17.4
Year 12
19.0
17.9
20.6
Year 11 or below
(incl. Cert I, II, n.f.d.)b
27.2
26.1
29.1
Undetermined
0.1
0.1
–
Country of birth
Australia
75.5
70.0
70.1
Main English-speaking
8.8
10.8
10.8
Other country
15.7
19.2
19.1
Before 1971
27.9
22.3
24.0
1971–1980
14.2
11.2
11.6
1981–1990
25.7
17.0
16.8
1991–2000
24.6
16.5
16.4
2001–2005
4.0
9.7
9.8
2006–2011
3.6
23.4
21.4
Year of arrival (if born overseas)
Industrya
Employment
statusa
Own account worker
7.1
7.2
7.8
Contributing family member
0.3
0.3
0.0
0
0.1
0.0
0.2
1–15
12.4
12.7
11.6
16–29
13.5
14.0
13.0
30–34
6.0
6.1
5.7
35–39
19.2
19.6
23.4
40
16.9
16.3
19.7
41–44
4.2
4.3
3.2
Usual hours
workeda
45–49
9.2
9.1
7.1
50–59
11.6
10.9
9.3
60+
6.9
6.9
6.7
Notes: (a) Employed sub-sample only.
(b) n.f.d. denotes not further defined.
Presentations and Publications about
the HILDA Survey and Data
Publications
Watson, N. and Wooden, M., ‘The HILDA Survey:
A Case Study in the Design and Development of a
Successful Household Panel Study’, Longitudinal and
Life Course Studies, vol. 3, no. 3, pp. 369–381.
Wilkins, R. and Warren, D., Families, Incomes and
Jobs, Volume 7: A Statistical Report on Waves 1 to 9
of the Household, Income and Labour Dynamics in
Australia Survey, Melbourne Institute of Applied
Economic and Social Research, University of
Melbourne, July.
Melbourne Institute Working
Paper Series
Watson, N. and Wilkins, R., ‘Experimental Change
from Paper-Based Interviewing to ComputerAssisted Interviewing in the HILDA Survey’, no.
6/12, February.
Presentations/conference papers
Li, N., ‘Testing Survey Data for Rounding Errors’,
Paper presented at the SSAI Australian Statistical
Conference, Adelaide, 9–12 July.
Watson, N., ‘HILDA Data Security’, Presentation at
the Symposium on the Disclosure Risk of Linked and
Longitudinal Data, Canberra, 27 April.
Watson, N., ‘Measuring Employment: Exploring Bias
in Inequality Research’, Paper presented at the RC33
Eighth International Conference on Social Science
Methodology, Sydney, 9–13 July.
Watson, N., ‘A Tale of Two Surveys: Panel Attrition,
Health and Employment’, Paper presented at the
3rd International Panel Survey Methods Workshop,
Melbourne, 5–6 July.
HILDA Discussion Paper Series
Watson, N. and Wilkins, R., ‘The Impact of
Computer-Assisted Interviewing on Interview
Length’, no. 1/12, April.
HILDA Technical Paper Series
Watson, N., ‘Longitudinal and Cross-Sectional
Weighting Methodology for the HILDA Survey’, no.
2/12, December.
Wooden, M., Mackinnon, A., Rodgers, B. and
Windsor, T., ‘The Development of Cognitive Ability
Measures in the HILDA Survey’, no. 1/12, June.
Watson, N., ‘Weighting Issues in Longitudinal
Surveys: HILDA’s Experience’, Presentation at the
Symposium on Analysis of Longitudinal Data,
Canberra, 16 October.
Watson, N., ‘Weighting Strategy to Integrate a New
Top-Up Sample with an Ongoing Longitudinal
Sample’, Paper presented at the SSAI Australian
Statistical Conference, Adelaide, 9–12 July.
Wilkins, R., ‘Recent Developments in the HILDA
Survey’, Presentation to the ABS Labour Statistics
Advisory Group, Canberra, 12 December.
Wooden, M., ‘The Development of a Successful
Household Panel Survey: The HILDA Experience’,
Presentation at the MOTU Research Workshop ‘Issues
and Options for a NZ Household Panel Survey’,
Wellington (NZ), 20 August.
2013 HILDA Survey Research
Conference
The 2013 HILDA Survey Research Conference will be held on 3 and 4 October 2013
at the University of Melbourne. The aim of the conference is to provide a forum for
the discussion of research based on the Household, Income and Labour Dynamics
in Australia (HILDA) Survey. Attendance at the conference is open to all, but should
be of special interest to users of the HILDA Survey data as well as persons with an
interest in the outcomes from longitudinal survey research in the broad field of
economic and social policy.
An official call for papers has been made, and as in past years we will cover all
travel and accommodation costs for Australian-based presenters, as well as offer
travel subsidies for a select number of overseas-based presenters.
There will be two submission streams: a refereed stream and a non-refereed stream.
Selection of papers for the refereed stream will be on the basis of full papers while
selection of papers for the non-refereed stream will be on the basis of abstracts.
Competition for places is expected to be high, with priority being given to papers
that exploit the longitudinal nature of the data. Papers on methods and crossnational comparisons are also welcome. Further details about this upcoming
conference can be found on the HILDA Survey website at
http://www.melbourneinstitute.com/miaesr/events/conferences/
The HILDA Data User Community
Data users:
How many and
who are they?
As shown in Table 10, the total number of persons who have been approved
access to at least one of the 10 public data releases has steadily been growing.
There were just over 200 users of the first version released in late 2002. By the
end of November 2012, this total had grown to 1,726. In addition, there were
90 users of the HILDA-Cross-National Equivalent File (HILDA-CNEF). Further
details about the composition of our user community are provided in Table 11.
Table 10: HILDA data user community, Releases 1 to 10
Total data orders
Orders by
new users
Release 1
204
204
204
Release 2
265
169
373
Release 3
279
157
530
Release 4
329
176
706
Release 5
387
196
902
Release 6
401
176
1,078
Release 7
455
199
1,277
Release 8
431
125
1,402
Release 9
500
141
1,543
Release 10
529
180
1,726
Release
Cumulative
number of users
Table 11: HILDA data users by type, Releases 1 to 10
Rel. 1
Rel. 2
Rel. 3
Rel. 4
Rel. 5
Rel. 6
Rel. 7
Rel. 8
Rel. 9
Rel. 10
30 Nov
2012
Academic: Australia
84
112
126
142
169
178
206
199
241
257
Academic: Overseas
5
15
18
19
24
25
37
24
51
65
Student: Honours year
3
13
16
15
13
7
13
17
13
22
Student: Postgraduate
9
16
18
31
42
41
40
44
59
63
Government:
Commonwealth
87
87
82
103
120
134
137
120
112
114
Government:
State/Local
8
14
8
11
8
5
10
6
9
7
Other
8
8
11
8
11
11
12
21
15
1
Total
204
265
279
329
387
401
455
431
500
529
Type of user
Organisational
Licences
At the end of November 2012, the number of individuals on a Deed of
Confidentiality under the Organisational Licence for Release 10 was 366, compared
to 163 individuals who signed an individual Deed of Licence. In addition, there was
a cumulative total of 91 organisations with an Organisational Deed of Licence:
• ANZ Banking Group Limited
• Australian Bureau of Agricultural and Resource
Economics and Sciences
• Australian Bureau of Statistics
• Australian Consortium for Social and Political
Research Incorporated
• Australian Council for Educational Research
• Australian Council of Trade Unions
• Australian Government Department of
Human Services
• Australian Institute of Family Studies
• Australian Institute of Health and Welfare
• Australian Taxation Office
• Australian Treasury
• Bureau of Crime Statistics and Research (NSW)
• Business Council of Australia
• Cape York Institute for Policy and Leadership
• Catholic Archdiocese of Sydney
• Commonwealth Scientific and Industrial Research
Organisation, Human Nutrition Division
• CQ University, Institute for Health and Social
Science Research
• Deakin University
• Deloitte Access Economics Pty Ltd
• Department of Community Services (NSW)
• Department of Education and Communities (NSW)
• Department of Education, Employment and
Workplace Relations
• Department of Families, Housing, Community
Services and Indigenous Affairs
• Department of Finance and Deregulation
• Department of Human Services, Government
of Victoria
• Department of Immigration and Citizenship
• Department of Industry, Innovation, Science,
Research and Tertiary Education
• Department of the Premier and Cabinet,
Government of Western Australia
• Department of the Prime Minister and Cabinet
• Economic and Social Research Institute
• Fair Work Commission
• Flinders University
• Gender and Work Database
• Grattan Institute
• Griffith University
• Industry Super Network
• Ingham Institute for Applied Medical Research
• IZA Institute for the Study of Labor
• James Cook University
• La Trobe University, Rural Health School
• London School of Economics, Research Laboratory
• Macquarie University
• Monash University
• Motu Economic and Public Policy Research
• Murdoch Children’s Research Institute
• Murdoch University
• National Centre for Vocational Education Research
• New Zealand Social Statistics Network
• New Zealand Treasury
• Open University
• Organisation for Economic Co-operation
and Development
• Parenting Research Centre
• Productivity Commission
• Queensland University of Technology
• Reserve Bank of Australia
• Rice Warner Actuaries Pty Ltd
• RMIT University
AHURI Research Centre
School of Economics, Finance and Marketing
• Swinburne University of Technology
• Telethon Institute for Child Health Research
• The Australia Institute
• The Smith Family
• The World Bank Group
• Towers Watson Australia Pty Ltd
• University College Dublin
• University of Adelaide
• University of Canberra, NATSEM
• University of Essex, Institute for Social and
Economic Research
• University of Georgia, Department of Sociology
• University of Melbourne
Centre for Human Resource Management
Department of Economics
Department of Management and Marketing
School of Languages and Linguistics
School of Population Health
• University of New South Wales
• University of Newcastle, Centre of Full
Employment and Equity
• University of Otago
Division of Commerce
Division of Health Sciences
• University of Queensland
• University of Sheffield
• University of South Australia
• University of St Gallen
• University of Sydney
Faculty of Agriculture, Food and
Natural Resources
Faculty of Health Sciences
School of Economics
School of Public Health
• University of Technology, Sydney
• University of Washington, Foster School
of Business
• University of Western Sydney
• University of Wollongong
• University of York
• University of Zurich, Institute of Political Science
• Victoria University of Wellington
HILDA user
training 2012
April 2012 and September 2012
•
Getting Started: Analysing HILDA with STATA
Wednesday 11 April and Thursday 12 April and
Monday 10 September to Wednesday 12 September
Designed for people who are interested in using the HILDA Survey data but
have not yet done so
November 2012
•
•
Panel Data Analysis Techniques with HILDA Examples
Tuesday 27 November to Thursday 29 November
Lecture series plus hands-on training with STATA examples
Dynamic Modelling for Panel Data
Friday 30 November
Lecture series with worked examples (does not include hands-on training)
Mary-Ann Patterson leads interviewer training (Melbourne)
Publications by HILDA Data Users, 2012
and Forthcoming
Journal articles
Ambrey, C.L. and Fleming, C.M.,
‘Valuing Australia’s Protected Areas:
A Life Satisfaction Approach’, New
Zealand Economic Papers, vol. 46,
no. 3, pp. 191–209.
Baker, D., ‘Match Making: Finding
People Missing Out on Government
Assistance’, Journal of Economic and
Social Policy, vol. 15, no. 1, article
no. 3.
Barrett, G.F. and Brzozowski, M.,
‘Food Expenditure and Involuntary
Retirement: Resolving the RetirementConsumption Puzzle’, American
Journal of Agricultural Economics,
vol. 95, no. 4, pp. 945–955.
Baxter, J., Qu, L., Weston, R., Moloney,
L. and Hayes, A., ‘Experiences and
Effects of Life Events: Evidence from
Two Australian Longitudinal Studies’,
Family Matters, vol. 90, pp. 6–18.
Bechtel, L., Lordan, G. and Prasada
Rao, D.S., ‘Income Inequality and
Mental Health: Empirical Evidence
from Australia’, Health Economics,
vol. 21, no. S1, pp. 4–17.
Bentley, R., Baker, E. and Mason, K.,
‘Cumulative Exposure to Poor Housing
Affordability and Its Association with
Mental Health in Men and Women’,
Journal of Epidemiology and
Community Health, vol. 66, no. 9,
pp. 761–766.
Black, D., Polidano, C. and Tseng, Y.,
‘The Re-engagement in Education of
Early School Leavers’, Economic
Papers, vol. 31, no. 2, pp. 202–215.
Booth, A., Leigh, A. and Varganova,
E., ‘Does Ethnic Discrimination Vary
across Minority Groups? Evidence
from a Field Experiment’, Oxford
Bulletin of Economics and Statistics,
vol. 74, no. 4, pp. 547–573.
Brandon, P.D., ‘The Rise of ThreeGeneration Households among
Households Headed by Two Parents
and Mothers Only in Australia’,
Journal of Family and Economic
Issues, vol. 33, no. 3, pp. 376–388.
Breunig, R., Gong, X. and King, A.,
‘Partnered Women’s Labour Supply
and Child Care Costs in Australia:
Measurement Error and the Child Care
Price’, Economic Record, vol. 88,
Special Issue, pp. 51–69.
Brown, M., ‘Responses to Work
Intensification: Does Generation
Matter?’, International Journal of
Human Resource Management,
vol. 23, no. 17, pp. 3,578–3,595.
Brown, H. and Adams, J., ‘The Role
of Time Preference in Smoking
Cessation: A Longitudinal Analysis of
Data from the Household Income and
Labour Dynamics of Australia Survey,
2001–08’, Journal of Epidemiology and
Community Health, vol. 66, no. S1,
pp. A51–A52.
Brown, H. and Adams, J., ‘The Role of
Time Preference in Smoking Cessation:
A Longitudinal Analysis of Data from
the Household Income and Labour
Dynamics of Australia Survey, 2001–
08’, Society for the Study of Addiction,
vol. 108, no. 1, pp. 186–192.
Cai, L. and Waddoups, C.J.,
‘Unobserved Heterogeneity, Job
Training, and the Employer Size–Wage
Effect in Australia’, Australian
Economic Review, vol. 45, no. 2,
pp. 158–175.
Cobb-Clark, D.A., ‘That Pesky Problem
of Persistent Gender Bias’, Australian
Economic Review, vol. 45, no. 2,
pp. 211–215.
Cobb-Clark, D.A. and Ribar, D.,
‘Financial Stress, Family Conflict, and
Australian Youths’ Transitions from
Home and School’, Review of
Economics of the Household, vol. 10,
no. 4, pp. 469–490.
Cobb-Clark, D.A. and Schurer, S.,
‘The Stability of Big-Five Personality
Traits’, Economics Letters, vol. 115,
no. 1, pp. 11–15.
Cvetkovski, S., Reavley, N.J. and Jorm,
A.F., ‘The Prevalence and Correlates of
Psychological Distress in Australian
Tertiary Students Compared to Their
Community Peers’, Australian and
New Zealand Journal of Psychiatry,
vol. 46, no. 5, pp. 457–467.
Dickerson, A. and Green, F., ‘Fears
and Realisations of Employment
Insecurity’, Labour Economics, vol. 19,
no. 2, pp. 198–210.
Dockery, A.M., ‘Deriving the Labor
Supply Curve from Happiness Data’,
Economics Letters, vol. 117, no. 3,
pp. 898–900.
Dorian, J. and Skinner, N., ‘Alcohol
Consumption Patterns of Shiftworkers
Compared with Dayworkers’,
Chronobiology International, vol. 29,
no. 5, pp. 610–618.
Emerson, E., Llewellyn, G., Honey, A.
and Kariuki, M., ‘The Lower WellBeing of Young Australian Adults with
Self-Reported Disability Reflects Their
Poorer Living Conditions Rather than
Health Issues’, Australian and New
Zealand Journal of Public Health,
vol. 36, no. 2, pp. 176–182.
Feeny, S., Ong, R., Spong, H. and
Wood, G., ‘The Impact of Housing
Assistance on the Employment
Outcomes of Labour Market
Programme Participants in Australia’,
Urban Studies, vol. 49, no. 4,
pp. 821–844.
Foster, G., Frijters, P. and Johnston,
D.W., ‘The Triumph of Hope over
Disappointment: A Note on the Utility
Value of Good Health Expectations’,
Journal of Economic Psychology,
vol. 33, no. 1, pp. 206–214.
Frijters, P. and Beatton, T., ‘The
Mystery of the U-Shaped Relationship
between Happiness and Age’,
Journal of Economic Behavior and
Organization, vol. 82, nos 2–3,
pp. 525–542.
Frijters, P., Johnston, D.W. and Shields,
M.A., ‘The Optimality of Tax Transfers:
What Does Life Satisfaction Data Tell
Us?’, Journal of Happiness Studies,
vol. 13, no. 5, pp. 821–832.
Gatina, L., ‘The Impact of Home
Institutions on the Financial Risk of
Immigrants: Evidence from Australia’,
International Journal of Diversity in
Organizations, Communities and
Nations, vol. 11, no. 4, pp. 37–54.
Guven, C., Senik, C. and Stichnoth,
H., ‘You Can’t Be Happier than Your
Wife. Happiness Gaps and Divorce’,
Journal of Economic Behavior and
Organization, vol. 82, no. 1,
pp. 110–130.
Hewitt, B. and Baxter, J., ‘Who Gets
Married in Australia? The Characteristics
Associated with a Transition into First
Marriage 2001–6’, Journal of Sociology,
vol. 48, no. 1, pp. 43–61.
Hewitt, B., Turrell, G. and Giskes, K.,
‘Marital Loss, Mental Health and the
Role of Perceived Social Support:
Findings from Six Waves of an
Australian Population Based Panel
Study’, Journal of Epidemiology and
Community Health, vol. 66, no. 4,
pp. 308–314.
Johnston, D.W. and Lee, W., ‘Climbing
the Job Ladder: New Evidence of
Gender Inequity’, Industrial Relations,
vol. 51, no. 1, pp. 129–151.
Jorm, A.F., Bourchier, S.J., Cvetkovski,
S. and Stewart, G., ‘Mental Health of
Indigenous Australians: A Review of
Findings from Community Surveys’,
Medical Journal of Australia, vol. 196,
no. 2, pp. 118–121.
Katsaiti, M.S., ‘Obesity and Happiness’,
Applied Economics, vol. 44, no. 31,
pp. 4,101–4,115.
Kifle, T. and Desta, I.H., ‘The
Relationship between Body Mass Index
and Socioeconomic and Demographic
Indicators: Evidence from Australia’,
International Journal of Public Health,
vol. 57, no. 1, pp. 135–143.
Kim, S., Sargent-Cox, K.A., French,
D.J., Kendig, H. and Anstey, K.J.,
‘Cross-National Insights into the
Relationship between Wealth and
Wellbeing: A Comparison between
Australia, the United States of America
and South Korea’, Ageing and Society,
vol. 32, no. 1, pp. 41–59.
Kortt, M.A. and Dollery, B., ‘Religion
and the Rate of Return to Human
Capital: Evidence from Australia’,
Applied Economics Letters, vol. 19, no.
10, pp. 943–946.
Kortt, M.A., Dollery, B. and Grant, B.,
‘Protestantism and Work Ethic: Evidence
from Australia’, Empirical Economic
Letters, vol. 11, no. 2, pp. 197–202.
Kortt, M.A., Dollery, B. and Pervan, S.,
‘Religion and Education: Recent
Evidence from the US’, Applied
Economics Letters, vol. 19, no. 12,
pp. 1,175–1,178.
Leach, L., Butterworth, P. and
Whiteford, H.A., ‘Private Health
Insurance, Mental Health and Service
Use in Australia’, Australian and New
Zealand Journal of Psychiatry, vol. 46,
no. 5, pp. 468–475.
Leach, L., Pirkis, J. and Kelaher, M.,
‘Poor Mental Health Influences Risk
and Duration of Unemployment: A
Prospective Study’, Social Psychiatry
and Psychiatric Epidemiology, vol. 47,
no. 6, pp. 1,013–1,021.
Lucas, R.E. and Donnellan, M.B.,
‘Estimating the Reliability of SingleItem Life Satisfaction Measures: Results
from Four National Panel Studies’,
Social Indicators Research, vol. 105,
no. 3, pp. 323–331.
Luhmann, M., Lucas, R.E., Eid, M. and
Diener, E., ‘The Prospective Effect of
Life Satisfaction on Life Events’, Social
Psychological and Personality Science,
vol. 4, no. 1, pp. 39–45.
McPhedran, S., ‘The Labour of a
Lifetime? Health and Occupation Type
as Predictors of Workforce Exit among
Older Australians’, Journal of Aging
and Health, vol. 24, no. 3, pp. 345–360.
Magee, C.A., Stefanic, N., Caputi, P. and
Iverson, D.C., ‘The Association between
Job Demands/Control and Health in
Employed Parents: The Mediating Role
of Work-to-Family Interference and
Enhancement’, Journal of Occupational
Health Psychology, vol. 17, no. 2,
pp. 196–205.
Mavromaras, K. and McGuinness, S.,
‘Overskilling Dynamics and Education
Pathways’, Economics of Education
Review, vol. 31, no. 5, pp. 619–628.
Mavromaras, K., Sloane, P. and Wei, Z.,
‘The Role of Education Pathways in
the Relationship between Job
Mismatch, Wages and Job Satisfaction:
A Panel Estimation Approach’,
Education Economics, vol. 20, no. 3,
pp. 303–321.
Nicholas, A. and Ray, R., ‘Duration and
Persistence in Multidimensional
Deprivation: Methodology and
Australian Application’, Economic
Record, vol. 88, no. 280, pp. 106–126.
Oguzoglu, U., ‘Is There a Better
Measure of Self-Assessed Disability?’,
Applied Economics Letters, vol. 19,
no. 14, pp. 1,335–1,338.
Olesen, S.C., Butterworth, P. and
Rodgers, B., ‘Is Poor Mental Health a
Risk Factor for Retirement? Findings
from a Longitudinal Population
Survey’, Social Psychiatry and
Psychiatric Epidemiology, vol. 47,
no. 5, pp. 735–744.
Ong, R. and Shah, S., ‘Job Security
Satisfaction in Australia: Do Migrant
Characteristics and Gender Matter?’,
Australian Journal of Labour
Economics, vol. 15, no. 2, pp. 123–139.
Parr, N., ‘Trends in Differentials in the
Workforce Participation of Mothers
with Young Children in Australia 2002–
2008’, Journal of Population Research,
vol. 29, no. 3, pp. 203–227.
Rehm, P., Hacker, J.S. and Schlesinger,
M., ‘Insecure Alliances: Risk,
Inequality, and Support for the Welfare
State’, American Political Science
Review, vol. 106, no. 2, pp. 386–406.
Richardson, S., Lester, L.H. and Zhang,
G., ‘Are Casual and Contract Terms of
Employment Hazardous for Mental
Health in Australia?’, Journal of
Industrial Relations, vol. 54, no. 5,
pp. 557–578.
Sargent-Cox, K.A., Anstey, K.J.,
Kendig, H. and Skladzien, E.,
‘Determinants of Retirement Timing
Expectations in the United States and
Australia: A Cross-National Comparison
of the Effects of Health and Retirement
Benefit Policies on Retirement Timing
Decisions’, Journal of Aging and Social
Policy, vol. 24, no. 3, pp. 291–308.
Siminski, P., ‘Are Low Skill Public
Sector Workers Really Overpaid?’,
Applied Economics, vol. 45, no. 14,
pp. 1,915–1,929.
Stavrunova, O. and Yerokhin, O., ‘TwoPart Fractional Regression Model for the
Demand for Risky Assets’, Applied
Economics, vol. 44, no. 1, pp. 21–26.
Stromback, T., ‘The Employment Effect
of Intensive Support’, Australian
Journal of Labour Economics, vol. 15,
no. 1, pp. 57–76.
Watson, N. and Wooden, M., ‘The
HILDA Survey: A Case Study in the
Design and Development of a
Successful Household Panel Study’,
Longitudinal and Life Course Studies,
vol. 3, no. 3, pp. 369–381.
Wood, G., Ong, R. and McMurray, C.I.,
‘Housing Tenure, Energy Consumption
and the Split-Incentive Issue in
Australia’, International Journal of
Housing Policy, vol. 12, no. 4,
pp. 439–469.
Wood, G., Ong, R. and Winter, I.,
‘Stamp Duties, Land Tax and Housing
Affordability: The Case for Reform’,
Australian Tax Forum, vol. 27, no. 2,
pp. 331–349.
Forthcoming journal articles
Ambrey, C.L. and Fleming, C.M., ‘Life
Satisfaction in Australia: Evidence
from Ten Years of the HILDA Survey’,
Social Indicators Research.
Baxter, J. and Hewitt, B., ‘Economic
Independence or Bargaining Power?
The Relationship between Women’s
Earnings and Housework Time in
Australia’, Feminist Economics.
Boyce, C.J., Wood, A.M. and
Powdthavee, N., ‘Is Personality
Fixed? Personality Changes as Much
as “Variable” Economic Factors and
More Strongly Predicts Changes to
Life Satisfaction’, Social Indicators
Research.
French, D.J., Jang, S.N., Tait, R.J. and
Anstey, K.J., ‘Cross-National Gender
Differences in the Socioeconomic
Factors Associated with Smoking in
Australia, the United States of America
and South Korea’, International
Journal of Public Health.
Gray, E., Evans, A. and Reimondos, A.,
‘Childbearing Desires of Childless Men
and Women: When Are Goals Adjusted?’,
Advances in Life Course Research.
Guest, R. and Parr, N., ‘Family Policy
and Couples’ Labour Supply: An
Empirical Assessment’, Journal of
Population Economics.
Headey, B., Muffels, R. and Wagner,
G.G., ‘Choices which Change Life
Satisfaction: Similar Results for
Australia, Britain and Germany’,
Social Indicators Research.
Hulley, H., McKibbin, R., Pedersen, A.
and Thorp, S., ‘Means-Tested Income
Support, Portfolio Choice and
Decumulation in Retirement’,
Economic Record.
Kifle, T., ‘Relative Income and Job
Satisfaction: Evidence from Australia’,
Applied Research in Quality of Life.
Kortt, M.A. and Dollery, B., ‘Religion
and BMI in Australia’, Journal of
Religion and Health.
Ng, S.K. and McLachlan, G.J., ‘Mixture
Models for Clustering Multilevel
Growth Trajectories’, Computational
Statistics and Data Analysis.
Siminski, P. and Yerokhin, O., ‘Is the
Age Gradient in Self-Reported Material
Hardship Explained by Resources,
Needs, Behaviours, or Reporting
Bias?’, Review of Income and Wealth.
Wilkins, R. and Wooden, M., ‘Gender
Differences in Involuntary Job Loss:
Why Are Men More Likely to Lose
Their Jobs?’, Industrial Relations: A
Journal of Economy and Society.
Other publications
Apps, P., Kabatek, J., Rees, R. and
van Soest, A., Labor Supply
Heterogeneity and Demand for Child
Care of Mothers with Young Children,
Research Paper, Sydney University
Law School, February.
Australian Government, Social
Inclusion in Australia: How Australia
Is Faring, 2nd Edition, Australian
Social Inclusion Board.
Australian Institute of Health and
Welfare, The Desire to Age in Place
among Older Australians, Volume 1:
Reasons for Staying or Moving,
Canberra.
Australian Institute of Health and
Welfare, A Picture of Australia’s
Children 2012, Cat. no. PHE 167,
Canberra.
Azpitarte, F., ‘Has Economic Growth in
Australia Been Pro-Poor?: Analysing
Income Changes 2001 to 2008’,
Brotherhood Comment, April, pp. 6–7.
Baker, D., All the Lonely People:
Loneliness in Australia, 2001–2009,
Paper no. 9, Australia Institute,
Canberra, June.
Berger-Thomson, L. and Roberts, N.,
‘Labour Market Dynamics: CrossCountry Insights from Panel Data’,
Reserve Bank of Australia Bulletin,
September, pp. 27–36.
Bradbury, B. and Mendolia, S., Living
Standards after Retirement:
Perceptions and Expenditure Patterns,
Final Report from the Project:
‘Expenditure Costs’, Social Policy
Research Centre, University of New
South Wales, May.
Cassells, R. and Miranti, R., Outside
School Hours Care: Social Gradients
and Patterns of Use, Report
prepared for Social Justice Unit,
Uniting Care, March.
Cassells, R., Miranti, R., Abello, A.,
Mohanty, I., D’Souza, G., Watson, I.
and Vidyattama, Y., Women in NSW
2012, Family and Community
Services, Women NSW.
Davis, K., Superannuation over the
Past Decade: Individual Experiences,
Report by the Australian Centre for
Financial Studies for Australian
Institute of Superannuation Trustees
(AIST), March.
Dockery, A.M. and Miller, P.W., OverEducation, Under-Education and
Credentialism in the Australian
Labour Market, Monograph Series
10/2012, National Centre for
Vocational Education Research
(NCVER), Adelaide.
Finlay, R., ‘The Distribution of
Household Wealth in Australia:
Evidence from the 2010 HILDA
Survey’, Reserve Bank of Australia
Bulletin, March, pp. 19–28.
Hulse, K., Burke, T., Ralston, L. and
Stone, W., The Australian Private
Rental Sector: Changes and
Challenges, Positioning Paper no. 149,
Australian Housing and Urban
Research Institute (AHURI),
Melbourne, July.
Leonard, W., Pitts, M., Mitchell, A.,
Lyons, A., Smith, A., Patel, S., Couch,
M. and Barrett, A., Private Lives 2: The
Second National Survey of the Health
and Wellbeing of Gay, Lesbian,
Bisexual and Transgender (GLBT)
Australians, Monograph Series no. 86,
Australian Research Centre in Sex,
Health & Society, La Trobe University,
Melbourne, April.
Palangkaraya, A., Webster, E. and
Cherastidtham, I., Evidence-Based
Policy: Data Needed for Robust
Evaluation of Industry Policies, Report
for the Australian Department of
Industry, Innovation, Science,
Research and Tertiary Education,
Melbourne, October.
Peetz, D., Murray, G. and Muurlink, O.,
Work and Hours amongst Mining and
Energy Workers, Australian Coal and
Energy Survey, First Phase Report,
Centre for Work, Organisation and
Wellbeing, Griffith University, November.
Productivity Commission, Impacts of
COAG Reforms: Business Regulation
and VET, Research Report vol. 3,
Canberra, April.
Reserve Bank of Australia, ‘Household
and Business Balance Sheets’, Financial
Stability Review, March, pp. 41–52.
Education, Analysis supporting
Grattan’s Graduate Winners Report,
Grattan Institute, Melbourne, August.
Reserve Bank of Australia, ‘Household
and Business Balance Sheets’, Financial
Stability Review, September, pp. 37–48.
Stone, W. and Reynolds, M., Social
Inclusion and Housing: Towards a
Household and Local Area Analysis,
Positioning Paper no. 146, Australian
Housing and Urban Research Institute
(AHURI), Melbourne, March.
Richardson, S., Lester, L. and Zhang,
G., Are Casual and Contract Terms of
Employment Hazardous for Mental
Health in Australia?, Changing
Patterns of Work: Impacts on Physical
& Mental Health & the Meditating Role
of Resiliency & Social Capital Research
Project, Adelaide, April.
Rowley, S. and Ong, R., Housing
Affordability, Housing Stress and
Household Wellbeing in Australia,
Final Report no. 192, Australian
Housing and Urban Research Institute
(AHURI), Melbourne, September.
Savage, J. and Norton, A., NonFinancial Benefits of Higher
Wood, G. and Ong, R., Sustaining
Home Ownership in the 21st Century:
Emerging Policy Concerns, Final
Report no. 187, Australian Housing
and Urban Research Institute (AHURI),
Melbourne, April.
Wood, G., Ong, R., Cidgem, M. and
Taylor, E., The Spatial and
Distributional Impacts of the Henry
Review Recommendations on Stamp
Duty and Land Tax, Final Report no.
182, Australian Housing and Urban
Research Institute (AHURI),
Melbourne, April.
Accessing the Data
The Release 11 DVD
Each year, data files are made publicly available. Release 11 became available in
early December 2012. The Release 11 DVD includes unit record data for Wave 11
together with revised data files for the previous waves. All data are provided in SAS,
SPSS and Stata format. In addition, the DVD includes codebooks and other relevant
documentation.
Improvements to
the Release 11 DVD
We have made a number of improvements to the data package for Release 11.
• Wave 11 includes the first sample replenishment for HILDA. Referred to as the
Top-Up sample, two additional forms were used: a top-up household form (TU-HF)
and a top-up new person questionnaire (TU-NPQ). The TU-HF includes additional
questions from the Wave 1 HF (how well speaks English, external characteristics of
the dwelling) which are used in matching the new sample to benchmarks for
weighting. The TU-NPQ does not include the Wave 11 special topic PQ modules on
‘retirement’ and ‘intentions and plans’ as new sample members are not asked any
modular content until their second interview (this was the approach with the initial
HILDA sample at Wave 1). Both new forms are included in ‘marked-up
questionnaires and showcards.pdf’. The top-up data are integrated into the Wave 11
household, person and combined files, with two additional variables to indicate if a
record is a top-up household (khhtuh) or a top-up person (khhtup).
• Biological parent cross-wave identifiers have been added to the datasets. The
biological parent’s xwaveid is carried back or forward to all waves even if the
parent is not a co-resident but the child has been enumerated.
• Ever co-resident twin cross-wave identifier has been added to the datasets. The
twin’s xwaveid is carried back or forward to all waves even if the twin is not a
co-resident. Adult not-co-resident twins are recorded in the PQ section on
siblings asked in Waves 8 and 12 (section HS), but they are not added to
_hhtwxid (insufficient information to determine if they are part of the sample).
Triplets, quadruplets etc. are not included in the twin identifiers.
• Grandparent cross-wave identifiers have been added to the datasets.
Grandparents where the lineage (paternal or maternal) is not yet known are
excluded (that is, in households where only the grandparents and grandchildren
have been co-resident (across all the waves)). In addition, the variations that step
parents and step grandparents and grand step parents introduce, both in terms of
the number of relationships and the variation over time, have led to step
relationships being excluded. Researchers can create their own extended
grandparent identifiers including these groups.
• The union membership variable _jbmtuea (from Wave 9 onwards) combines
whether the respondent belongs to a trade union, other union or employee
association (_jbou and _jbtu). Respondents who do not know whether they
belong to a trade union, other union or employee association are treated as not
belonging to one. This provides a consistent definition with Waves 1 to 8 where
only one question is asked (_jbmunio).
For Waves 1 to 8 _jbmtuea is the recoding of _jbmunio where respondents who
do not know whether they belong to a trade union or employee association are
treated as not belonging to one.
_jbmtabs (derived from Wave 9 onwards) is a recoding of _jbtu where
respondents who do not know whether they belong to a trade union are treated
as not belonging to one (which is consistent with the ABS definition).
•
•
•
•
•
•
_hgni is a new indicator variable which differentiates enumerated persons into
0. Interviewed adult
1. Not interviewed adult
2. Not interviewed child (aged 0–14)
For the variable _edagels ‘History: Age left school’, there was a problem in the
updating of this variable which did not take into account errors made by
continuing persons at A2 ‘Whether they had spent any time enrolled in school
since last interviewed’. If they said they had spent time in school and then in
the next wave when they were interviewed they said they were not enrolled in
school _edagels would be increased inappropriately. An age restriction (≤ 26)
has been applied to the updates on _edagels, so adults who make a mistake at
A2 do not get adult ages for age left school at a later wave.
To simplify the indexing of loops in their programs, the HF has been padded
or reduced to 16 persons at all waves. (Persons 15 and 16 have been added to
the HF for Waves 1 to 5; the unused variables for Persons 17 to 20 have been
removed from Wave 9 onwards.) In addition, derived variables for householdfamily type, HF18 relationship-pairs and enumerated weights have been
expanded to 16 persons at all waves. The Household Questionnaire items for
who pays board and, in wealth years, who are the property owners have also
been adjusted. In wealth years, the PQ items for household members who are
joint owners of bank accounts and credit cards have been adjusted. Lists of
affected variables have been included in the attachment of changes made to
each wave.
Self-Completion Questionnaire (SCQ) was matched to Person Interview
(_scmatch). An indicator variable for whether an SCQ had been scanned and
matched to a corresponding person interview is now included in both the
General Release and In-Confidence datasets. The old (in-confidence file)
indicators _hhmpid and _hhmatch have been dropped. The office-use ‘SCQ has
been picked up’ or ‘Will be returned by mail’ variables (HF20 _hgscq, _hgsi,
_hgsf and _hgs) have been dropped from the General Release files as they
were being confused with SCQ to PQ matching.
The HILDA-Cross-National Equivalent File (CNEF) data are no longer supplied
as a separate zip on the DVD. The appropriate files are now included in the
SAS, STATA, SPSS and Documentation zips. The dataset filenames have been
changed from the CNEF convention (named ‘hilda-cnef’ rather than ‘hequiv’),
but the contents are otherwise identical to the files you would have previously
received from the CNEF project.
For the geography variable, some inconsistencies in _hhcd01 2001 Census
Collection District had occurred when some, but not all, individuals in a
household had corrections applied to their CD. This led to inconsistencies in
_hhcd01 between the household data and the enumerated person data (the
Household and Combined datasets were correct, so if you only used these two
files, or you used Panelwhiz, you would not be impacted). Between three and
30 households needed correction each wave. _hhcd01 is not supplied in the
General Release datasets.
More details about these changes can be found in the ‘Readme 110.pdf’ file on
the DVD.
Data requests
Individual Licences
Requests for a copy of the Release 11 DVD can be made by sending the appropriate
forms to FaHCSIA. A charge of A$77.00 (GST inclusive) to cover postage and
administrative costs is payable to the Melbourne Institute, University of Melbourne
(A$121.00 for overseas users). The Deed of Licence is available from the HILDA
Survey website.
Organisational Licences
A flat fee of A$330.00 (GST inclusive) is charged for each release of data. Overseas
organisations pay A$330.00 which is GST exclusive. The fee covers all datasets until
the next release is available.
Please contact FaHCSIA at [email protected] if your organisation
would like to apply for an Organisational Licence. Organisational Licences are
ongoing and do not have to be signed each year. However, users at organisations
with an existing Organisational Licence who wish to obtain access to the latest data
release will need to complete a new Deed of Confidentiality, which can be obtained
from your Data Manager.
HILDA website
http://www.melbourneinstitute.com/hilda/
The HILDA Survey website provides further details about the HILDA Survey and its
progress. Copies of all survey instruments, the User Manual and various discussion
and technical papers can be viewed and downloaded from the HILDA Survey
website. Individual Licence Order Forms and Deeds of Licence for the dataset can
also be found on this site.
42710 HILDA Annual Report 2012 COVER.qxd:hilda annual report cover 17/01/13 11:33 AM Page 1
Household, Income and
Labour Dynamics in
Australia (HILDA) Survey
Annual Report 2012
Funded by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs
HILDA Survey
Annual Report 2012

(HILDA) Survey Annual Report 2012

Transcription

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