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Characterizing the Schooling Cycle

Author

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  • Sadaba, Barbara

    (Bank of Canada)

  • Vujic, Suncica

    (University of Antwerp)

  • Maier, Sofia

    (European Commission Joint Research Centre (JRC))

Abstract

This paper develops a novel and tractable empirical approach to estimate the cycle in schooling participation decisions, which we denominate the schooling cycle. The estimation procedure is based on unobserved components time series models that decompose higher education enrollment rates into a slow-moving stochastic trend and a stationary cyclical factor. By doing so, we obtain a full characterization of the cyclical dynamics of schooling participation and analyze its relationship with the business cycle in a time-varying fashion. Using data for 16–24-year-olds attending full-time post-secondary education in the United Kingdom from 1995Q1 to 2019Q4, we find evidence of a very persistent schooling cycle largely, but not exclusively, explained by the business cycle. Additionally, we find that the direction of the response of schooling participation to the business cycle, say, pro-, counter- or a-cyclical, is largely time-dependent, as is the degree of synchrony between both cycles. We note, however, that results are heterogeneous across gender.

Suggested Citation

  • Sadaba, Barbara & Vujic, Suncica & Maier, Sofia, 2022. "Characterizing the Schooling Cycle," IZA Discussion Papers 15237, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp15237
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    More about this item

    Keywords

    human capital; business cycle; state space; kalman smoother;
    All these keywords.

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • I2 - Health, Education, and Welfare - - Education
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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