Skip to main navigation Skip to search Skip to main content

A Markov-switching structural vector autoregressive model of boom and bust in the Australian labour market

  • Noel Gaston
  • , Gulasekaran Rajaguru

    Research output: Contribution to journal › Article › peer-review

    4 Citations (Scopus)

    Abstract

    Major economic events, such as the global financial crisis, are episodes of identifiable duration that differ from other time periods. Using monthly data on the unemployment rate, labour force participation rate and employment for Australia for the period from 1978 to 2012, we estimate a Markov-switching SVAR model to examine the relationship between unemployment and labour force participation and the performance of the Australian labour market. Three distinct labour market regimes are identified. We find that the labour market switches between periods of low unemployment and high participation, prolonged periods of relative stability and short, sharp periods of high unemployment and low participation. A key finding is that, due to the behaviour of workers not in the labour force, the long-term effect of an upswing in labour hiring results in a lower unemployment rate and a lower labour force participation rate.

    Original languageEnglish
    Pages (from-to)1271-1299
    Number of pages29
    JournalEmpirical Economics: a quarterly journal of the Institute for Advanced Studies, Vienna
    Volume49
    Issue number4
    DOIs
    Publication statusPublished - 1 Dec 2015

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 8 - Decent Work and Economic Growth
      SDG 8 Decent Work and Economic Growth
    2. SDG 10 - Reduced Inequalities
      SDG 10 Reduced Inequalities

    Keywords

    • Labour force participation
    • Regime switching
    • Unemployment

    Fingerprint

    Dive into the research topics of 'A Markov-switching structural vector autoregressive model of boom and bust in the Australian labour market'. Together they form a unique fingerprint.

    Cite this