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Nonparametric Inference on State Dependence in Unemployment

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  • Alexander Torgovitsky

Abstract

This paper is about measuring state dependence in dynamic discrete outcomes. I develop a nonparametric dynamic potential outcomes (DPO) model and propose an array of parameters and identifying assumptions that can be considered in this model. I show how to construct sharp identified sets under combinations of identifying assumptions by using a flexible linear programming procedure. I apply the analysis to study state dependence in unemployment for working age high school educated men using an extract from the 2008 Survey of Income and Program Participation (SIPP). Using only nonparametric assumptions, I estimate that state dependence accounts for at least 30–40% of the four‐month persistence in unemployment among high school educated men.

Suggested Citation

  • Alexander Torgovitsky, 2019. "Nonparametric Inference on State Dependence in Unemployment," Econometrica, Econometric Society, vol. 87(5), pages 1475-1505, September.
    • Handle: RePEc:wly:emetrp:v:87:y:2019:i:5:p:1475-1505
    DOI: 10.3982/ECTA14138
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    Cited by:

    1. Uguccioni, James, 2022. "The long-run effects of parental unemployment in childhood," CLEF Working Paper Series 45, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    2. Iván Fernández-Val & Martin Weidner, 2018. "Fixed Effects Estimation of Large-TPanel Data Models," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 109-138, August.
    3. Juan Carlos Cuestas & Luis Gil-Alana, 2024. "Unemployment Hysteresis by Sex and Education Attainment in the EU," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 801-827, March.
    4. Pietro Tebaldi & Alexander Torgovitsky & Hanbin Yang, 2023. "Nonparametric Estimates of Demand in the California Health Insurance Exchange," Econometrica, Econometric Society, vol. 91(1), pages 107-146, January.
    5. Han, Sukjin & Yang, Shenshen, 2024. "A computational approach to identification of treatment effects for policy evaluation," Journal of Econometrics, Elsevier, vol. 240(1).
    6. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    7. Kamat, Vishal, 2019. "Identification with Latent Choice Sets," TSE Working Papers 19-1031, Toulouse School of Economics (TSE).
    8. Vishal Kamat, 2017. "Identifying the Effects of a Program Offer with an Application to Head Start," Papers 1711.02048, arXiv.org, revised Aug 2023.
    9. Anna Manzoni & Irma Mooi-Reci, 2020. "The cumulative disadvantage of unemployment: Longitudinal evidence across gender and age at first unemployment in Germany," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    10. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2023. "Constrained Conditional Moment Restriction Models," Econometrica, Econometric Society, vol. 91(2), pages 709-736, March.
    11. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2022. "Optimal Decision Rules when Payoffs are Partially Identified," Papers 2204.11748, arXiv.org, revised May 2023.
    12. Kamat, Vishal, 2024. "Identifying the effects of a program offer with an application to Head Start," Journal of Econometrics, Elsevier, vol. 240(1).
    13. Khan, S. & Ponomareva, M. & Tamer, E., 2023. "Identification of dynamic binary response models," Journal of Econometrics, Elsevier, vol. 237(1).
    14. Zheng Fang & Andres Santos & Azeem M. Shaikh & Alexander Torgovitsky, 2023. "Inference for Large‐Scale Linear Systems With Known Coefficients," Econometrica, Econometric Society, vol. 91(1), pages 299-327, January.
    15. James J. Heckman & Bridget Galaty & Haihan Tian, 2023. "The Economic Approach to Personality, Character and Virtue," NBER Working Papers 31258, National Bureau of Economic Research, Inc.
    16. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    17. Shakeeb Khan & Maria Ponomareva & Elie Tamer, 2019. "Identification of Dynamic Panel Binary Response Models," Boston College Working Papers in Economics 979, Boston College Department of Economics.
    18. Florian Gunsilius, 2019. "A path-sampling method to partially identify causal effects in instrumental variable models," Papers 1910.09502, arXiv.org, revised Jun 2020.
    19. Bulat Gafarov, 2019. "Simple subvector inference on sharp identified set in affine models," Papers 1904.00111, arXiv.org, revised Jul 2024.
    20. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Fu Ouyang & Thomas Tao Yang, 2022. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," Papers 2202.12062, arXiv.org, revised Feb 2024.
    22. MaCurdy, Thomas & Glick, David & Sherpa, Sonam & Nagavarapu, Sriniketh, 2024. "Profiling the plight of disconnected youth in America," Journal of Econometrics, Elsevier, vol. 238(2).
    23. Fu Ouyang & Thomas Tao Yang, 2020. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," ANU Working Papers in Economics and Econometrics 2020-671, Australian National University, College of Business and Economics, School of Economics.
    24. Fu Ouyang & Thomas Tao Yang, 2020. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," Discussion Papers Series 626, School of Economics, University of Queensland, Australia.
    25. Yechan Park & Yuya Sasaki, 2024. "The Informativeness of Combined Experimental and Observational Data under Dynamic Selection," Papers 2403.16177, arXiv.org.

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