IDEAS home Printed from https://ideas.repec.org/a/wly/econjl/v127y2017i604p1833-1873.html
   My bibliography  Save this article

Time Aggregation and State Dependence in Welfare Receipt

Author

Listed:
  • Manudeep Bhuller
  • Christian N. Brinch
  • Sebastian Königs

Abstract

Dynamic discrete-choice models have been an important tool in studies of state dependence in benefit receipt. An assumption of such models is that benefit receipt sequences follow a conditional Markov process. This property has implications for how estimated period-to-period benefit transition probabilities should relate when receipt processes are aggregated over time. This paper assesses whether the conditional Markov property holds in welfare benefit receipt dynamics using high-quality monthly data from Norwegian administrative records. We find that the standard conditional Markov model is seriously misspecified. Estimated state dependence is affected substantially by the chosen time unit of analysis, with the average treatment effect of past benefit receipt increasing with the level of aggregation. The model can be improved considerably by permitting richer types of benefit dynamics: Allowing for differences between the processes for entries and persistence we find important disparities especially in terms of the effects of permanent unobserved characteristics. Extending the model further, we obtain strong evidence for duration and occurrence dependence in benefit receipt. Based on our preferred model, the month-to-month persistence probability in benefit receipt for a first-time entrant is 37 percentage points higher than the entry rate of an individual without previous benefit receipt. Over a 12-month period, the average treatment effect is about 5 percentage points.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
  • Handle: RePEc:wly:econjl:v:127:y:2017:i:604:p:1833-1873
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/ecoj.2017.127.issue-604
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rabe-Hesketh, Sophia & Skrondal, Anders, 2013. "Avoiding biased versions of Wooldridge’s simple solution to the initial conditions problem," Economics Letters, Elsevier, vol. 120(2), pages 346-349.
    2. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    3. Wooldridge, Jeffrey M., 2000. "A framework for estimating dynamic, unobserved effects panel data models with possible feedback to future explanatory variables," Economics Letters, Elsevier, vol. 68(3), pages 245-250, September.
    4. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54.
    5. Martin Biewen, 2009. "Measuring state dependence in individual poverty histories when there is feedback to employment status and household composition," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1095-1116.
    6. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    7. Victoria Prowse, 2012. "Modeling Employment Dynamics With State Dependence and Unobserved Heterogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 411-431, April.
    8. Mark B. Stewart, 2007. "The interrelated dynamics of unemployment and low-wage employment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 511-531.
    9. Bergstrom, R & Edin, P-A, 1992. "Time Aggregation and the Distributional Shape of Unemployment Duration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(1), pages 5-30, Jan.-Marc.
    10. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, January.
    11. Stewart, Mark B & Swaffield, Joanna K, 1999. "Low Pay Dynamics and Transition Probabilities," Economica, London School of Economics and Political Science, vol. 66(261), pages 23-42, February.
    12. Knut Røed & Tao Zhang, 2002. "A note on the Weibull distribution and time aggregation bias," Applied Economics Letters, Taylor & Francis Journals, vol. 9(7), pages 469-472.
    13. Dean R. Hyslop, 1999. "State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women," Econometrica, Econometric Society, vol. 67(6), pages 1255-1294, November.
    14. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
    15. Robert F. Engle & Ta-Chung Liu, 1972. "Effects of Aggregation Over Time on Dynamic Characteristics of an Econometric Model," NBER Chapters,in: Econometric Models of Cyclical Behavior, Volumes 1 and 2, pages 673-737 National Bureau of Economic Research, Inc.
    16. Rabe-Hesketh, Sophia & Skrondal, Anders & Pickles, Andrew, 2005. "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects," Journal of Econometrics, Elsevier, vol. 128(2), pages 301-323, October.
    17. David T. Ellwood, 1982. "Teenage Unemployment: Permanent Scars or Temporary Blemishes?," NBER Chapters,in: The Youth Labor Market Problem: Its Nature, Causes, and Consequences, pages 349-390 National Bureau of Economic Research, Inc.
    18. repec:wly:econjl:v:127:y:2017:i:604:p:1833-1873 is not listed on IDEAS
    19. Uhlendorff, Arne, 2006. "From No Pay to Low Pay and Back Again? A Multi-State Model of Low Pay Dynamics," IZA Discussion Papers 2482, Institute for the Study of Labor (IZA).
    20. Lorenzo Cappellari & Stephen P. Jenkins, 2014. "The Dynamics of Social Assistance Benefit Receipt in Britain," Research in Labor Economics,in: Safety Nets and Benefit Dependence, volume 39, pages 41-79 Emerald Publishing Ltd.
    21. Mercenier, J. & Michel, P., 1991. "A Criterion For Time Aggregation Intertemporal Dynamic Models," Cahiers de recherche 9108, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    22. Alpaslan Akay, 2012. "Finite‐sample comparison of alternative methods for estimating dynamic panel data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1189-1204, November.
    23. Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
    24. Biewen, Martin & Steffes, Susanne, 2010. "Unemployment persistence: Is there evidence for stigma effects?," Economics Letters, Elsevier, vol. 106(3), pages 188-190, March.
    25. Heckman, James J & Borjas, George J, 1980. "Does Unemployment Cause Future Unemployment? Definitions, Questions and Answers from a Continuous Time Model of Heterogeneity and State Dependence," Economica, London School of Economics and Political Science, vol. 47(187), pages 247-283, August.
    26. Bernt Bratsberg & Oddbjørn Raaum & Knut Røed, 2010. "When Minority Labor Migrants Meet the Welfare State," Journal of Labor Economics, University of Chicago Press, vol. 28(3), pages 633-676, July.
    27. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-764, May.
    28. Trond Petersen & Kenneth W. Koput, 1992. "Time-Aggregation Bias in Hazard-Rate Models with Covariates," Sociological Methods & Research, , vol. 21(1), pages 25-51, August.
    29. Arulampalam, Wiji & Booth, Alison L & Taylor, Mark P, 2000. "Unemployment Persistence," Oxford Economic Papers, Oxford University Press, vol. 52(1), pages 24-50, January.
    30. Hansen, Jörgen & Lofstrom, Magnus & Zhang, Xuelin, 2006. "State Dependence in Canadian Welfare Participation," IZA Discussion Papers 2266, Institute for the Study of Labor (IZA).
    31. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    32. Regina T. Riphahn & Christoph Wunder, 2016. "State dependence in welfare receipt: transitions before and after a reform," Empirical Economics, Springer, vol. 50(4), pages 1303-1329, June.
    33. Wiji Arulampalam & Mark B. Stewart, 2009. "Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 659-681, October.
    34. Blank, Rebecca M., 1989. "Analyzing the length of welfare spells," Journal of Public Economics, Elsevier, vol. 39(3), pages 245-273, August.
    35. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    36. Gregg, Paul, 2001. "The Impact of Youth Unemployment on Adult Unemployment in the NCDS," Economic Journal, Royal Economic Society, vol. 111(475), pages 626-653, November.
    37. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters,in: Studies in Labor Markets, pages 91-140 National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tue Gørgens & Dean Hyslop, 2016. "The specification of dynamic discrete-time two-state panel data models," Working Papers 16_01, Motu Economic and Public Policy Research.
    2. Herwig Immervoll & Stephen P. Jenkins & Sebastian Königs, 2015. "Are Recipients of Social Assistance 'Benefit Dependent'?: Concepts, Measurement and Results for Selected Countries," OECD Social, Employment and Migration Working Papers 162, OECD Publishing.
    3. repec:eee:ecolet:v:163:y:2018:i:c:p:65-67 is not listed on IDEAS
    4. Gørgens, Tue & Hyslop, Dean, 2018. "Equivalent representations of discrete-time two-state panel data models," Economics Letters, Elsevier, vol. 163(C), pages 65-67.
    5. repec:wly:econjl:v:127:y:2017:i:604:p:1833-1873 is not listed on IDEAS
    6. Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
    7. Sebastian Königs, 2015. "Micro-level dynamics of social assistance receipt. Evidence from 4 European countries," Discussion Papers 797, Statistics Norway, Research Department.
    8. Tue Gorgens & Sanghyeok Lee, 2017. "Estimation of dynamic models of recurring events with censored data," ANU Working Papers in Economics and Econometrics 2017-655, Australian National University, College of Business and Economics, School of Economics.

    More about this item

    JEL classification:

    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:econjl:v:127:y:2017:i:604:p:1833-1873. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/resssea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.