IDEAS home Printed from https://ideas.repec.org/p/oec/elsaab/226-en.html
   My bibliography  Save this paper

Faces of joblessness in Australia: An anatomy of employment barriers using household data

Author

Listed:
  • Herwig Immervoll
  • Daniele Pacifico
  • Marieke Vandeweyer

Abstract

Although Australia’s labour market escaped the dramatic negative impact of the global financial economic crisis seen in other OECD countries, a substantial share of working-age Australians either did were not working or worked only to a limited extent as the global recovery gathered pace between 2013 and 2014. The paper extends a method proposed by Fernandez et al. (2016) to measure and visualise employment barriers of individuals with no or weak labour-market attachment, using household micro-data.The most common employment obstacles in Australia are limited work experience, low skills and poor health. A notable finding is that almost one third of jobless or low-intensity workers face three or more simultaneous barriers, highlighting the limits of policy approaches that focus on subsets of these employment obstacles in isolation. A statistical clustering approach points to seven distinct groups, each characterized by unique profiles of employment barriers that call for different configurations of activation and employment-support policies.

Suggested Citation

  • Herwig Immervoll & Daniele Pacifico & Marieke Vandeweyer, 2019. "Faces of joblessness in Australia: An anatomy of employment barriers using household data," OECD Social, Employment and Migration Working Papers 226, OECD Publishing.
  • Handle: RePEc:oec:elsaab:226-en
    DOI: 10.1787/c51b96ef-en
    as

    Download full text from publisher

    File URL: https://doi.org/10.1787/c51b96ef-en
    Download Restriction: no

    File URL: https://libkey.io/10.1787/c51b96ef-en?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 209-224, June.
    2. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
    3. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 235-260, June.
    4. Thøgersen, John, 2017. "Housing-related lifestyle and energy saving: A multi-level approach," Energy Policy, Elsevier, vol. 102(C), pages 73-87.
    5. Gil, J.M. & Diaz-Montenegro, J. & Varela, E., 2018. "A Bias-Adjusted Three-Step approach for analysing the livelihood strategies and the asset mix of cacao producers in Ecuador," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277215, International Association of Agricultural Economists.
    6. Layland, Eric K. & Maggs, Jennifer L. & Kipke, Michele D. & Bray, Bethany C., 2022. "Intersecting racism and homonegativism among sexual minority men of color: Latent class analysis of multidimensional stigma with subgroup differences in health and sociostructural burdens," Social Science & Medicine, Elsevier, vol. 293(C).
    7. Jennifer Oser & Marc Hooghe & Zsuzsa Bakk & Roberto Mari, 2023. "Changing citizenship norms among adolescents, 1999-2009-2016: A two-step latent class approach with measurement equivalence testing," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4915-4933, October.
    8. Yujin Kim & Hyeyoung Woo & Sinn Won Han, 2022. "Work and Family Pathways and Their Associations with Health for Young Women in Korea," IJERPH, MDPI, vol. 19(23), pages 1-17, November.
    9. Lecegui, Antonio & Olaizola, Ana María & López-i-Gelats, Feliu & Varela, Elsa, 2022. "Implementing the livelihood resilience framework: An indicator-based model for assessing mountain pastoral farming systems," Agricultural Systems, Elsevier, vol. 199(C).
    10. Sasso, Alessandro & Hernández-Alava, Mónica & Holmes, John & Field, Matt & Angus, Colin & Meier, Petra, 2022. "Strategies to cut down drinking, alcohol consumption, and usual drinking frequency: Evidence from a British online market research survey," Social Science & Medicine, Elsevier, vol. 310(C).
    11. Sarah R Lowe & Ethan J Raker & Mary C Waters & Jean E Rhodes, 2020. "Predisaster predictors of posttraumatic stress symptom trajectories: An analysis of low-income women in the aftermath of Hurricane Katrina," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-19, October.
    12. Aely Park & Youngmi Kim & Jennifer Murphy, 2023. "Adverse Childhood Experiences and Substance Use Among Korean College Students: Different by Gender?," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(4), pages 1811-1825, August.
    13. Zsuzsa Bakk & Jouni Kuha, 2018. "Two-Step Estimation of Models Between Latent Classes and External Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 871-892, December.
    14. S. C. Noah Uhrig & Nicole Watson, 2020. "The Impact of Measurement Error on Wage Decompositions: Evidence From the British Household Panel Survey and the Household, Income and Labour Dynamics in Australia Survey," Sociological Methods & Research, , vol. 49(1), pages 43-78, February.
    15. Bakk, Zsuzsa & Kuha, Jouni, 2020. "Relating latent class membership to external variables: an overview," LSE Research Online Documents on Economics 107564, London School of Economics and Political Science, LSE Library.
    16. Chen, Runting & Huang, Yueyi & Yu, Meng, 2021. "The latent profile analysis of Chinese adolescents’ depression: Examination and validation," Children and Youth Services Review, Elsevier, vol. 125(C).
    17. Kanchewa, Stella & Christensen, Kirsten M. & Poon, Cyanea Y.S. & Parnes, McKenna & Schwartz, Sarah, 2021. "More than fun and games? Understanding the role of school-based mentor-mentee match activity profiles in relationship processes and outcomes," Children and Youth Services Review, Elsevier, vol. 120(C).
    18. Gugerty, Mary Kay & Mitchell, George E. & Santamarina, Francisco J., 2021. "Discourses of evaluation: Institutional logics and organizational practices among international development agencies," World Development, Elsevier, vol. 146(C).
    19. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.
    20. Paweł A. Atroszko & Bartosz Atroszko & Edyta Charzyńska, 2021. "Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis," IJERPH, MDPI, vol. 18(16), pages 1-29, August.

    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • J6 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers
    • J8 - Labor and Demographic Economics - - Labor Standards

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oec:elsaab:226-en. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/eloecfr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.