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Time Aggregation and State Dependence in Welfare Receipt

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  • 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.
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  • 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
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    File URL: http://hdl.handle.net/10.1111/ecoj.2017.127.issue-604
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    Cited by:

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    2. Pohlan, Laura, 2019. "Unemployment and social exclusion," Journal of Economic Behavior & Organization, Elsevier, vol. 164(C), pages 273-299.
    3. Ivanov, Boris & Pfeiffer, Friedhelm & Pohlan, Laura, 2020. "Do job creation schemes improve the social integration and well-being of the long-term unemployed?," Labour Economics, Elsevier, vol. 64(C).
    4. Tue Gørgens & Dean Robert Hyslop, 2018. "The Specification of Dynamic Discrete-Time Two-State Panel Data Models," Econometrics, MDPI, Open Access Journal, vol. 7(1), pages 1-16, December.
    5. 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.
    6. Gørgens, Tue & Hyslop, Dean, 2018. "Equivalent representations of discrete-time two-state panel data models," Economics Letters, Elsevier, vol. 163(C), pages 65-67.
    7. 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.
    8. Bruckmeier, Kerstin & Dummert, Sandra & Grunau, Philipp & Hohmeyer, Katrin & Lietzmann, Torsten, 2020. "New administrative data on welfare dynamics in Germany: the Sample of Integrated Welfare Benefit Biographies (SIG)," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 54(54), pages .14(1-14).
    9. Bruckmeier, Kerstin & Dummert, Sandra & Grunau, Philipp & Hohmeyer, Katrin & Lietzmann, Torsten, 2020. "New administrative data on welfare dynamics in Germany: the Sample of Integrated Welfare Benefit Biographies (SIG)," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 54, pages 1-14.
    10. Gail Pacheco & Alexander Plum, 2020. "When there is no way up: Reconsidering low-paid jobs as stepping stones," Working Papers 2020-08, Auckland University of Technology, Department of Economics.
    11. Kabir Dasgupta & Alexander Plum, 2020. "Human Capital Formation and Changes in Low Pay Persistence," Working Papers 2020-15, Auckland University of Technology, Department of Economics.
    12. Peter Eibich & Ricky Kanabar & Alexander Plum & Julian Schmied, 2020. "In and out of unemployment - labour market dynamics and the role of testosterone," MPIDR Working Papers WP-2020-033, Max Planck Institute for Demographic Research, Rostock, Germany.
    13. repec:iab:iabjlr:v:54:i::p:art.14 is not listed on IDEAS
    14. Kristian Heggebø & Espen Dahl & Kjetil A van der Wel, 2020. "Disentangling the dynamics of social assistance: A linked survey—Register data cohort study of long-term social assistance recipients in Norway," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    15. Sebastian Königs, 2015. "Micro-level dynamics of social assistance receipt. Evidence from 4 European countries," Discussion Papers 797, Statistics Norway, Research Department.
    16. Kerstin Bruckmeier & Sandra Dummert & Philipp Grunau & Katrin Hohmeyer & Torsten Lietzmann, 2020. "New administrative data on welfare dynamics in Germany: the Sample of Integrated Welfare Benefit Biographies (SIG)," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-12, December.
    17. 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.

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    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

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