New generations of respondents: assessing the representativity of the HILDA Survey’s child sample
An important aspect of an indefinite life household panel study is to provide a sample of children who become new generations of respondents over time. The representativity of children and young adults in the Household, Income and Labour Dynamics in Australia (HILDA) Survey is assessed after 16 waves. Estimates from the HILDA Survey are compared to official data sources of the Australian Bureau of Statistics (ABS) and include demographic, education, employment, income and residential mobility variables. Both cross-section and longitudinal estimates are assessed. Overall, the HILDA Survey estimates are relatively close to the ABS estimates with the exception of the year of arrival of recent immigrants, having foreign-born parents, having a certificate level qualification, type of relationship in household, having zero income, the main source of income, and residential mobility. Most of these exceptions can be explained by differences in questionnaire design, respondent recall error, linkage error, and differences in the amount of missing data. The estimate of particular concern is the proportion of immigrants arriving in the last five years, which is underestimated in the HILDA Survey due to undercoverage of recent immigrants. This could be addressed by regular refreshment samples of recent immigrants.
-
ABS (Australian Bureau of Statistics) (2015) General Social Survey: summary results, Australia, 2014. Cat. No. 4159.0, https://www.abs.gov.au/statistics/people/people-and-communities/general-social-survey-summary-results-australia/2014.
-
ABS (Australian Bureau of Statistics) (2017) Census of Population and Housing: Understanding the census and census data, Australia, 2016. Cat. No. 2900.0, https://www.abs.gov.au/ausstats/abs@.nsf/mf/2900.0.
-
ABS (Australian Bureau of Statistics) (2018) Childhood education and care, Australia, June 2017. Cat. No. 4402.0, https://www.abs.gov.au/ausstats/abs@.nsf/PrimaryMainFeatures/4402.0?OpenDocument.
-
ABS (Australian Bureau of Statistics) (2019a) Australian demographic statistics, September 2018. Cat. No. 3101.0, https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3101.0Sep%202018?OpenDocument.
-
ABS (Australian Bureau of Statistics) (2019b) Information paper: Australian Census Longitudinal Dataset, methodology and quality assessment, 2006–2016. Cat. No. 2080.5, https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/2080.5~2006-2016~Main%20Features~Data%20Linking%20Methodology~10.
-
ABS (Australian Bureau of Statistics) (2019c) Microdata: Australian Census Longitudinal Dataset, ACLD. Cat. No. 2080.0, https://www.abs.gov.au/ausstats/abs@.nsf/mf/2080.0.
-
Andreski, P., Li, G., Samancioglu, M.Z. and Schoeni, R. (2014) Estimates of annual consumption expenditures and its major components in the PSID in comparison to the CE, American Economic Review, 104(5): 132–5. doi: 10.1257/aer.104.5.132
-
Andreski, P., McGonagle, K. and Schoeni, R. (2009) An Analysis of the Quality of the Health Data in the Panel Study of Income Dynamics, PSID, Ann Arbor: University of Michigan, Technical Series paper 09-02, https://psidonline.isr.umich.edu/publications/Papers/tsp/2009-02_Quality_Health_Data_PSID_.pdf.
-
Austin, P.C. (2009) Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research, Communications in Statistics – Simulation and Computation, 38(6): 1228–34. doi: 10.1080/03610910902859574
-
Benzeval, M., Bollinger, C.R., Burton, J., Crossley, T.F. and Lynn, P. (2020) The Representativeness of Understanding Society, Understanding Society, Colchester: University of Essex, https://www.understandingsociety.ac.uk/research/publications/526039.
-
Borkowska, M. (2019) Improving Population and Sub-group Coverage: Who Is Missing and What Can Be Done About It?, Understanding Society, Colchester: University of Essex, Working Paper no. 2019-15, https://www.understandingsociety.ac.uk/sites/default/files/downloads/working-papers/2019-15.pdf.
-
Buck, N. (2000) Using panel surveys to study migration and residential mobility, in D. Rose (ed) Researching Social and Economic Change: The Uses of Household Panel Studies, London: Routledge, pp 250–72.
-
Chipperfield, J., Brown, J.J. and Watson, N. (2017) The Australian Census Longitudinal Dataset: using record linkage to create a longitudinal sample form a series of cross-sections, Australian and New Zealand Journal of Statistics, 59(1): 1–16. doi: 10.1111/anzs.12177
-
Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd edn, New York: Routledge.
-
Davis-Kean, P., Chambers, R.L., Davidson, L.L., Kleinert, C., Ren, Q. and Tang, S. (2017) Longitudinal Studies Strategic Review: 2017 Report to the Economic and Social Research Council, Swindon: Economic and Social Research Council, https://esrc.ukri.org/files/news-events-and-publications/publications/longitudinal-studies-strategic-review-2017/.
-
Department of Social Services, and Melbourne Institute of Applied Economic and Social Research (2019) The Household, Income and Labour Dynamics in Australia (HILDA) Survey, General Release 18 (Waves 1–18), https://dataverse.ada.edu.au/dataset.xhtml?persistentId=doi:10.26193/IYBXHM.
-
Duffy, D. and Sastry, N. (2012) An Assessment of the National Representativeness of Children in the 2007 Panel Study of Income Dynamics, PSID, Ann Arbor: University of Michigan, Technical Series paper 12-01, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.5684&rep=rep1&type=pdf.
-
Duncan, G.J. and Hill, D.H. (1989) Assessing the quality of household panel data: the case of the panel study of income dynamics, Journal of Business & Economic Statistics, 7(4): 441–52.
-
Ferguson, C.J. (2016) An effect size primer: a guide for clinicians and researchers, Professional Psychology: Research and Practice, 40(5): 532–8. doi: 10.1037/a0015808
-
Fitzgerald, J.M. (2011) Attrition in models of intergenerational links using the PSID with extensions to health and to sibling models, The BE Journal of Economic Analysis & Policy, 11(3): art 2, doi: 10.2202/1935-1682.2868.
-
Fitzgerald, J., Gottschalk, P. and Moffitt, R. (1998) An analysis of sample attrition in panel data: the Michigan Panel Study of Income Dynamics, Journal of Human Resources, 33(2): 251–99. doi: 10.2307/146433
-
Fitzpatrick, D. (2017) Self-employment dynamics in Australia and the importance of state dependence, Economic Record, 93(S1): 144–70. doi: 10.1111/1475-4932.12337
-
Harding, S., Jackson Pulver, L., McDonald, P., Morrison, P., Trewin, D. and Voss, A. (2017) Report on the Quality of 2016 Census Data, Census Independent Assurance Panel to the Australian Statistician, Canberra: Australian Bureau of Statistics, https://www.abs.gov.au/websitedbs/d3310114.nsf/Home/Independent+Assurance+Panel.
-
Jenkins, S.P. (2010) Comparisons of BHPS and HBAI distributions of net household income: 1994–2006, https://filestore.iser.essex.ac.uk/d/3a16c2/.
-
Lipps, O. (2007) Attrition in the Swiss household panel, Methoden, Daten, Analysen (mda), 1(1): 45–68.
-
Lynn, P. (ed) (2006) Quality Profile: British Household Panel Survey: Version 2.0 Waves 1–13, Institute for Social and Economic Research, Colchester: University of Essex, https://www.iser.essex.ac.uk/files/bhps/quality-profiles/BHPS-QP-01-03-06-v2.pdf.
-
Nathan, G. (1999) A Review of Sample Attrition and Representativeness in Three Longitudinal Surveys, London: Office for National Statistics, https://openlibrary.org/works/OL6986074W/A_review_of_sample_attrition_and_representativeness_in_three_longitudinal_surveys.
-
Petersen, J. and Rabe, B. (2013) Understanding Society: A Geographical Profile of Respondents, Understanding Society, Colchester: University of Essex, Working Paper no. 2013-01, https://www.understandingsociety.ac.uk/sites/default/files/downloads/working-papers/2013-01.pdf.
-
Polidano, C., Carter, A., Chan, M., Chigavazira, A., To, H., Holland, J., Nguyen, S., Vu, H. and Wilkins, R. (2020) The ATO Longitudinal Information Files (ALife): A New Resource for Retirement Policy Research, Canberra: Australian National University, https://taxpolicy.crawford.anu.edu.au/sites/default/files/publication/taxstudies_crawford_anu_edu_au/2020-04/complete_ttpi_submission_cpedit_ato_april_2020_0.pdf.
-
Rose, D. (2000) Household panel studies: an overview, in D. Rose (ed) Researching Social and Economic Change: The Uses of Household Panel Studies, London: Routledge, pp 3–35.
-
Rosenthal, J.A. (1996) Qualitative descriptors of strength of association and effect size, Journal of Social Service Research, 21(4): 37–59. doi: 10.1300/J079v21n04_02
-
Summerfield, M., Bright, S., Hahn, M., La, N., Macalalad, N., Watson, N., Wilkins, R. and Wooden, M. (2019) HILDA User Manual: Release 18, Melbourne: Melbourne Institute, https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/0008/3247289/HILDA-User-Manual-Release-18.0.pdf.
-
Thomas, D., Frankenberg, E. and Smith, J.P. (2001) Lost but not forgotten: attrition and follow-up in the Indonesia Family Life Survey, Journal of Human Resources, 36(3): 556–92. doi: 10.2307/3069630
-
Uhrig, S.C.N. (2008) The Nature and Causes of Attrition in the British Household Panel Study, Institute for Social and Economic Research, Colchester: University of Essex, https://www.iser.essex.ac.uk/research/publications/working-papers/iser/2008-05.pdf.
-
Watson, N. (2012) Longitudinal and Cross-sectional Weighting Methodology for the HILDA Survey, Melbourne: Melbourne Institute, HILDA Project Technical Paper, No. 2/12, https://melbourneinstitute.unimelb.edu.au/assets/documents/hilda-bibliography/hilda-technical-papers/htec212.pdf.
-
Watson, N. (2020) Measuring geographic mobility: comparison of estimates from longitudinal and cross-sectional data, Survey Research Methods, 14(1): 1–18.
-
Watson, N. and Lynn, P. (2021) Refreshment sampling for longitudinal surveys, in P. Lynn (ed) Advances in Longitudinal Survey Methodology, Chichester: Wiley, pp 1–25.
-
Watson, N. and Wooden, M. (2009) Identifying factors affecting longitudinal survey response, in P. Lynn (ed) Methodology of Longitudinal Surveys, Chichester: Wiley, pp 157–81.
-
Watson, N. and Wooden, M. (2013) Adding a top-up sample to the household, income and labour dynamics in Australia survey, Australian Economic Review, 46(4): 489–98. doi: 10.1111/1467-8462.12027
-
Wilkins, R. and Sun, C. (2010) Assessing the Quality of the Expenditure Data Collected in the Self-Completion Questionnaire, Melbourne: Melbourne Institute, HILDA Project Discussion Paper, No. 1/10, https://melbourneinstitute.unimelb.edu.au/assets/documents/hilda-bibliography/hilda-discussion-papers/hdps110.pdf.
-
Wolter, K.M. (2007) Introduction to Variance Estimation, 2nd edn, New York: Springer.
-
Wooden, M. (2009) Use of the Kessler Psychological Distress Scale in the HILDA Survey, Melbourne: Melbourne Institute, HILDA Project Discussion Paper, No. 2/09, https://melbourneinstitute.unimelb.edu.au/assets/documents/hilda-bibliography/hilda-discussion-papers/hdps209.pdf.
-
Wooden, M., Freidin, S. and Watson, N. (2002) The Household, Income and Labour Dynamics in Australia (HILDA) Survey: wave 1, The Australian Economic Record, 35(3): 339–48. doi: 10.1111/1467-8462.00252
-
Zimmermann, E., Budowski, M., Gabadinho, A., Scherpenzeel, A., Tillmann, R. and Wernli, B. (2003) Swiss Household Panel 2004–2007, Proposal Submitted to the Swiss National Science Foundation, Working Paper 2_03, Neuchâtel: Swiss Household Panel, https://www.researchgate.net/publication/228554719_The_Swiss_Household_Panel.
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