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Not Much Bounce in the Springboard: On the Mobility of Low Pay Workers

Author

Listed:
  • Pacheco, Gail

    (NZ Work Research Institute)

  • Plum, Alexander T.

    (Auckland University of Technology)

  • Sloane, Peter J.

    (Swansea University)

Abstract

Estimating economic earnings mobility is imperative for understanding the degree to which low pay employment is a temporary or long-term position. The current literature estimates transition probabilities between low and higher pay. This study extends the focus to identify the underlying pecuniary wage change via construction of an intermediate pay zone marginally above low pay. Utilising monthly administrative data we find that individuals with a strong attachment to the low pay sector have a very low probability of shifting into higher pay. Further, these individuals also have a substantially greater risk of experiencing a low pay-no pay cycle relative to those who are intermediate or higher paid. Notably, this finding is only uncovered using within year variation in wages to reveal intensity of labour market attachment.

Suggested Citation

  • Pacheco, Gail & Plum, Alexander T. & Sloane, Peter J., 2020. "Not Much Bounce in the Springboard: On the Mobility of Low Pay Workers," IZA Discussion Papers 12896, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp12896
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    References listed on IDEAS

    as
    1. Hielke Buddelmeyer & Wang‐Sheng Lee & Mark Wooden, 2010. "Low‐Paid Employment and Unemployment Dynamics in Australia," The Economic Record, The Economic Society of Australia, vol. 86(272), pages 28-48, March.
    2. Anders Skrondal & Sophia Rabe-Hesketh, 2014. "Handling initial conditions and endogenous covariates in dynamic/transition models for binary data with unobserved heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 211-237, February.
    3. Lixin Cai & Kostas Mavromaras & Peter Sloane, 2018. "Low Paid Employment in Britain: Estimating State†Dependence and Stepping Stone Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(2), pages 283-326, April.
    4. Mark B. Stewart, 2007. "The interrelated dynamics of unemployment and low-wage employment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 511-531.
    5. Arne Uhlendorff, 2006. "From No Pay to Low Pay and Back Again?: A Multi-State Model of Low Pay Dynamics," Discussion Papers of DIW Berlin 648, DIW Berlin, German Institute for Economic Research.
    6. Clark, Ken & Kanellopoulos, Nikolaos C., 2013. "Low pay persistence in Europe," Labour Economics, Elsevier, vol. 23(C), pages 122-134.
    7. Yin King Fok & Rosanna Scutella & Roger Wilkins, 2015. "The Low-Pay No-Pay Cycle: Are There Systematic Differences across Demographic Groups?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(6), pages 872-896, December.
    8. Lixin Cai, 2014. "State-Dependence and Stepping-Stone Effects of Low-Pay Employment in Australia," The Economic Record, The Economic Society of Australia, vol. 90(291), pages 486-506, December.
    9. Alexander Plum, 2019. "The British low-wage sector and the employment prospects of the unemployed," Applied Economics, Taylor & Francis Journals, vol. 51(13), pages 1411-1432, March.
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    More about this item

    Keywords

    low pay dynamics; transition probability; state dependence; dynamic models; administrative data;
    All these keywords.

    JEL classification:

    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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