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

Author

Listed:
  • Cheng Chou

    (University of Leicester)

  • Ruoyao Shi

    (Department of Economics, University of California Riverside)

Abstract

The American Time Use Survey (ATUS) accurately measures hours worked on a single day. Employing the potential outcome framework, we show that weekly labor supply parameters can be consistently estimated using the ATUS daily hours, but recovering weekly hours or their distribution is impossible due to the time specificity problem. We propose and carefully examine the properties of several new estimators. We recommend the impute estimator, a simple modification of the 2SLS estimator by imputing the dependent variable using daily subsamples. We apply it to the ATUS and find substantially different elasticity estimates from the CPS, especially for married women.

Suggested Citation

  • 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.
    • Handle: RePEc:ucr:wpaper:202017
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    References listed on IDEAS

    as
    1. Cheng Chou & Ruoyao Shi, 2021. "What time use surveys can (and cannot) tell us about labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 917-937, November.
    2. Harley Frazis & Jay Stewart, 2012. "How to Think about Time-Use Data: What Inferences Can We Make about Long- and Short-Run Time Use from Time Diaries?," Annals of Economics and Statistics, GENES, issue 105-106, pages 231-245.
    3. Francine D. Blau & Lawrence M. Kahn, 2007. "Changes in the Labor Supply Behavior of Married Women: 1980–2000," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 393-438.
    4. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    5. Mark Aguiar & Erik Hurst, 2007. "Measuring Trends in Leisure: The Allocation of Time Over Five Decades," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 969-1006.
    6. Juhn, Chinhui & Murphy, Kevin M, 1997. "Wage Inequality and Family Labor Supply," Journal of Labor Economics, University of Chicago Press, vol. 15(1), pages 72-97, January.
    7. Blundell, Richard & Macurdy, Thomas, 1999. "Labor supply: A review of alternative approaches," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 27, pages 1559-1695, Elsevier.
    8. John Bound & Charles Brown & Greg J. Duncan & Willard L. Rodgers, 1989. "Measurement Error In Cross-Sectional and Longitudinal Labor Market Surveys: Results From Two Validation Studies," NBER Working Papers 2884, National Bureau of Economic Research, Inc.
    9. Hu, Yingyao & Sasaki, Yuya, 2017. "Identification Of Paired Nonseparable Measurement Error Models," Econometric Theory, Cambridge University Press, vol. 33(4), pages 955-979, August.
    10. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    11. repec:adr:anecst:y:1999:i:55-56:p:09 is not listed on IDEAS
    12. Heckman, James J, 1993. "What Has Been Learned about Labor Supply in the Past Twenty Years?," American Economic Review, American Economic Association, vol. 83(2), pages 116-121, May.
    13. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    14. Mark Aguiar & Erik Hurst & Loukas Karabarbounis, 2012. "Recent Developments in the Economics of Time Use," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 373-397, July.
    15. Jonathan Guryan & Erik Hurst & Melissa Kearney, 2008. "Parental Education and Parental Time with Children," Journal of Economic Perspectives, American Economic Association, vol. 22(3), pages 23-46, Summer.
    16. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    17. Mark Aguiar & Mark Bils & Kerwin Kofi Charles & Erik Hurst, 2021. "Leisure Luxuries and the Labor Supply of Young Men," Journal of Political Economy, University of Chicago Press, vol. 129(2), pages 337-382.
    18. Claudia Goldin, 2014. "A Grand Gender Convergence: Its Last Chapter," American Economic Review, American Economic Association, vol. 104(4), pages 1091-1119, April.
    19. Garry F. Barrett & Daniel S. Hamermesh, 2019. "Labor Supply Elasticities: Overcoming Nonclassical Measurement Error Using More Accurate Hours Data," Journal of Human Resources, University of Wisconsin Press, vol. 54(1), pages 255-265.
    20. Paul J. Devereux, 2004. "Changes in Relative Wages and Family Labor Supply," Journal of Human Resources, University of Wisconsin Press, vol. 39(3).
    21. 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.
    22. repec:adr:anecst:y:2012:i:105-106:p:11 is not listed on IDEAS
    23. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "A comparison of recall and diary food expenditure data," Food Policy, Elsevier, vol. 72(C), pages 53-61.
    24. Jason Abrevaya & Jerry A. Hausman, 1999. "Semiparametric Estimation with Mismeasured Dependent Variables: An Application to Duration Models for Unemployment Spells," Annals of Economics and Statistics, GENES, issue 55-56, pages 243-275.
    25. Daniel S. Hamermesh & Harley Frazis & Jay Stewart, 2005. "Data Watch: The American Time Use Survey," Journal of Economic Perspectives, American Economic Association, vol. 19(1), pages 221-232, Winter.
    26. Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(2), pages 343-366.
    27. Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
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    Cited by:

    1. Cheng Chou & Ruoyao Shi, 2021. "What time use surveys can (and cannot) tell us about labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 917-937, November.
    2. Thomas Le Barbanchon & Johannes Schmieder & Andrea Weber, 2024. "Job Search, Unemployment Insurance, and Active Labor Market Policies," RF Berlin - CReAM Discussion Paper Series 2424, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    3. 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.

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    More about this item

    Keywords

    labor supply; time specificity; impute estimator; relative asymptotic efficiency; survey methods;
    All these keywords.

    JEL classification:

    • 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
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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