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Quasi-randomization by survey date for policy analysis

Author

Listed:
  • Kimin Kim
  • Myoung-Jae Lee

Abstract

In annual surveys, the interview dates are scattered over several months. When a policy takes place during the survey period, quasi-randomized data may be obtained, if individuals interviewed before and after the policy timing are similar. The policy effect can be found with a before–after difference, which is ‘Quasi-Randomization by Survey date (QRS)’. QRS may be viewed as an regression discontinuity (RD) with time as the running variable. When seasonality is present, the RD-style estimator fails, but we develop a difference-in-differences style estimator, which relies on a weaker assumption analogous to parallel trends that controls for sesonality. We provide an empirical example using Korea Labor and Income Panel Study data for a weekly work-hour reduction law in 2004 from 44 to 40 h. We find that the law effect is about 2 h reduction, not 4 h as the law stipulates.

Suggested Citation

  • Kimin Kim & Myoung-Jae Lee, 2023. "Quasi-randomization by survey date for policy analysis," Oxford Economic Papers, Oxford University Press, vol. 75(2), pages 507-525.
    • Handle: RePEc:oup:oxecpp:v:75:y:2023:i:2:p:507-525.
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    File URL: http://hdl.handle.net/10.1093/oep/gpac026
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J88 - Labor and Demographic Economics - - Labor Standards - - - Public Policy

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