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Experiment Estimates of Indigenous Employment from Administrative Data

Author

Listed:
  • Cezary A. Kapuscinski

    (Australian Government Department of Employment)

  • Kyle Thomson

    (Australian Government Department of Employment)

Abstract

In this paper we test a ‘proof of concept’ to remedy the lack of timely data on Indigenous labour market outcomes. We utilize Centrelink administrative data and the HILDA survey data to estimate a forecasting equation of the employment to population ratio of Indigenous Australians. In doing so we examine the probabilities of Income Support Recipients and the general population transiting from one labour market state to another. We conclude the paper by discussing the experimental time-series estimates of employment to population ratio of Indigenous Australians derived from our forecasting equation.

Suggested Citation

  • Cezary A. Kapuscinski & Kyle Thomson, 2014. "Experiment Estimates of Indigenous Employment from Administrative Data," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 17(2), pages 139-161.
    • Handle: RePEc:ozl:journl:v:17:y:2014:i:2:p:139-161
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Indigenous employment; Indigenous disadvantage; Closing the Gap; HILDA data; Transition probabilities between labour states;
    All these keywords.

    JEL classification:

    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    Statistics

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