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A hazard model for welfare durations with unobserved location-specific effects

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  • J. Fitzgerald

Abstract

Many papers have investigated how personal characteristics and environmental variables affect welfare durations of unmarried mothers. This paper estimates proportional hazard models for welfare durations that allow for either fixed state or fixed labor market area effects. Conditioning on residence location by fixed effects can limit the impact of three types of potential bias. (1) Estimates of the effects of personal characteristics can be biased owing to the omission of relevant local area variables. (2) Estimates of the impact of state welfare benefit levels are biased because they proxy for other unmeasured attributes of the state, in particular, the entire state welfare system. Conditioning on state fixed effects limits this bias to the extent that we can use time variation within states to estimate the benefit level effect. (3) With state fixed effects, we can better estimate the impact of local conditions, such as unemployment rates, because they also may have been picking up omitted state-level effects. The models are estimated by the Cox partial likelihood method with time-varying covariates. Data come from the 1984 and 1985 panels of the Survey of Income and Program Participation. I find that some personal characteristics (being black or Hispanic, education) have greater impact after controlling for location-specific effects.

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  • J. Fitzgerald, "undated". "A hazard model for welfare durations with unobserved location-specific effects," Institute for Research on Poverty Discussion Papers 1046-94, University of Wisconsin Institute for Research on Poverty.
    • Handle: RePEc:wop:wispod:1046-94
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    1. O'Connell, Philip J. & Russell, Helen & FitzGerald, John, 2006. "Human Resources," Book Chapters, in: Morgenroth, Edgar (ed.),Ex-Ante Evaluation of the Investment Priorities for the National Development Plan 2007-2013, Economic and Social Research Institute (ESRI).
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    8. Blank, Rebecca M., 1989. "Analyzing the length of welfare spells," Journal of Public Economics, Elsevier, vol. 39(3), pages 245-273, August.
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    Cited by:

    1. Welch, Shawn M., 1998. "Nonparametric estimates of the duration of welfare spells," Economics Letters, Elsevier, vol. 60(2), pages 217-221, August.
    2. Khan, Shakeeb & Tamer, Elie, 2007. "Partial rank estimation of duration models with general forms of censoring," Journal of Econometrics, Elsevier, vol. 136(1), pages 251-280, January.
    3. Hilary Williamson Hoynes, 2000. "Local Labor Markets And Welfare Spells: Do Demand Conditions Matter?," The Review of Economics and Statistics, MIT Press, vol. 82(3), pages 351-368, August.
    4. Sammis B. White & Lori A. Geddes, 2002. "The Impact of Employer Characteristics and Workforce Commitment on Earnings of Former Welfare Recipients," Economic Development Quarterly, , vol. 16(4), pages 326-341, November.
    5. Ridder, Geert & Tunali, Insan, 1999. "Stratified partial likelihood estimation," Journal of Econometrics, Elsevier, vol. 92(2), pages 193-232, October.
    6. Rebecca M. Blank & Patricia Ruggles, 1993. "When Do Women Use AFDC & Food Stamps? The Dynamics of Eligibility vs. Participation," NBER Working Papers 4429, National Bureau of Economic Research, Inc.
    7. Mairead Reidy & Lucy Mackey-Bilaver & Robert M. Goerge & Yizu Yeh & Bong Joo Lee, 1998. "The Dynamics of AFDC, Medicaid, and Food Stamps: A Preliminary Report," JCPR Working Papers 48, Northwestern University/University of Chicago Joint Center for Poverty Research.
    8. Black, Dan A. & McKinnish, Terra G. & Sanders, Seth G., 2003. "Does the availability of high-wage jobs for low-skilled men affect welfare expenditures? Evidence from shocks to the steel and coal industries," Journal of Public Economics, Elsevier, vol. 87(9-10), pages 1921-1942, September.

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