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What Time Use Surveys Can (And Cannot) Tell Us about Labor Supply

Author

Listed:
  • Ruoyao Shi

    (Department of Economics, University of California Riverside)

  • Cheng Chou

    (University of Leicester)

Abstract

It has been widely acknowledged that the measurement of labor supply in the Current Population Survey (CPS) and other conventional microeconomic surveys has nonclassical measurement error, which will bias the estimates of crucial parameters in labor economics, such as labor supply elasticity. Time diary studies, such as the American Time Use Survey (ATUS), only have accurate measurement of hours worked on a single day, hence the weekly hours worked are unobserved. Despite the missing data problem, we provide several consistent estimators of the parameters in weekly labor supply equation using the information in the time use surveys. The consistency of our estimators does not require more conditions beyond those for a usual two stage least square (2SLS) estimator when the true weekly hours worked are observed. We also show that it is impossible to recover the weekly number of hours worked or its distribution function from time use surveys like the ATUS. In our empirical application we find considerable evidence of nonclassical measurement error in the hours worked in the CPS, and illustrate the consequences of using mismeasured weekly hours worked in empirical studies.

Suggested Citation

  • Ruoyao Shi & Cheng Chou, 2019. "What Time Use Surveys Can (And Cannot) Tell Us about Labor Supply," Working Papers 201912, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:201912
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    File URL: https://economics.ucr.edu/repec/ucr/wpaper/201912.pdf
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    References listed on IDEAS

    as
    1. Angrist, Joshua D., 1991. "Grouped-data estimation and testing in simple labor-supply models," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 243-266, February.
    2. Cheng Chou & Ruoyao Shi, 2019. "What Time Use Surveys Can (And Cannot) Tell Us About Labor Supply," Working Papers 202017, University of California at Riverside, Department of Economics, revised Jul 2020.
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    Cited by:

    1. Cheng Chou & Ruoyao Shi, 2020. "Utilizing Two Types of Survey Data to Enhance the Accuracy of Labor Supply Elasticity Estimation," Working Papers 202018, University of California at Riverside, Department of Economics.
    2. Cheng Chou & Ruoyao Shi, 2019. "What Time Use Surveys Can (And Cannot) Tell Us About Labor Supply," Working Papers 202017, University of California at Riverside, Department of Economics, revised Jul 2020.

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    More about this item

    Keywords

    measurement error; missing data; instrumental variable; asymptotic efficiency; labor supply;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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