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Value of Sample Separation Information in a Sequential Probit Model: Another Look at SSA's Disability Determination Process

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  • Chuanming Gao

    (SUNY at Albany)

  • Kajal Lahiri

    (SUNY at Albany and Social Security Administration)

  • Bernard Wixon

    (Social Security Administration)

Abstract

We have estimated a 4-step sequential probit model with and without sample separation information to characterize SSA's disability determination process. Under the program provisions, different criteria dictate the outcomes at different steps o f the process. We used data on health, activity limitations, demographic traits, and work from 1990 SIPP exact matched to SSA administrative records on disability determinations. Using GHK Monte Carlo simulation technique, our estimation results suggest that the correlations in errors across equations that may arise due to unobserved individual heterogeneity are not statistically significant. In addition, we examined the value of administrative data on the basis for allow/deny determinations at each sta ge of the process. Following the marginal likelihood approach adopted by Benitez-Silva, Buchinsky, Chan, Rust, and Sheidvasser (1999), we also estimated the above sequential probit model without the sample separation information for the purpose of direct comparison. We found that without the detailed administrative information on outcomes at each stage of the screening process, we could not properly evaluate the importance of a large number of program-relevant survey-based explanatory v ariables. In terms of both in-sample and jackknife-type out-of-sample predictive analysis, the value of modeling the sequential structure of the determination process in generating correct eligibility probabilities is confirmed.

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Paper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 0340.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:0340

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  1. Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
  2. Benitez-Silva, Hugo & Buchinsky, Moshe & Chan, Hiu Man & Rust, John & Sheidvasser, Sofia, 1999. "An empirical analysis of the social security disability application, appeal, and award process," Labour Economics, Elsevier, vol. 6(2), pages 147-178, June.
  3. Blundell, Richard William & Ham, John & Meghir, Costas, 1987. "Unemployment and Female Labour Supply," CEPR Discussion Papers 149, C.E.P.R. Discussion Papers.
  4. Meng, Chun-Lo & Schmidt, Peter, 1985. "On the Cost of Partial Observability in the Bivariate Probit Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 26(1), pages 71-85, February.
  5. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  6. Jones, Andrew M, 1989. "A Double-Hurdle Model of Cigarette Consumption," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(1), pages 23-39, Jan.-Mar..
  7. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
  8. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
  9. Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-34, March.
  10. Poirier, Dale J., 1980. "Partial observability in bivariate probit models," Journal of Econometrics, Elsevier, vol. 12(2), pages 209-217, February.
  11. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
  12. Cragg, John G, 1971. "Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods," Econometrica, Econometric Society, vol. 39(5), pages 829-44, September.
  13. Schmidt, Peter, 1981. "Further Results on the Value of Sample Separation Information [Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model]," Econometrica, Econometric Society, vol. 49(5), pages 1339-43, September.
  14. Goldfelfd, Stephen M. & Quandt, Richard E., 1975. "Estimation in a disequilibrium model and the value of information," Journal of Econometrics, Elsevier, vol. 3(4), pages 325-348, November.
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