Identification of Models of the Labor Market

Handbook of Labor Economics

Volumn 4, Issue PART A, 2011, Pages 537-617

  French, Eric ^a   Taber, Christopher ^b  

^a FEDERAL RESERVE BANK OF CHICAGO   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Discrete choice; Identification; Roy model; Selection; Treatment effects

Indexed keywords

EID: 79953120481     PISSN: 15734463     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0169-7218(11)00412-6     Document Type: Book

References (88)

1. Abbring J. Identification of dynamic discrete choice models. Annual Review of Economics 2010, 2:367-394.
2. Abbring, J., Ridder, G., 2009. A note on the non-parametric identification of generalized accelerated failure-time models. Unpublished manuscript, Tilburg University.
3. Abbring J., Heckman J. Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation. Handbook of Econometrics 2007, 5145-5303. North Holland, Amsterdam. Heckman, Leamer (Eds.).
4. Abbring J., van den Berg G. The identifiability of the mixed proportional hazards competing risks model. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 2003, 3:701-710.
5. Aguirregabiria V. Another look at the identification of dynamic discrete decision processes: an application to retirement behavior. Journal of Business and Economic Statistics 2010, 28:201-218.
6. Altonji J., Elder T., Taber C. Selection on observed and unobserved variables: assessing the effectiveness of Catholic schools. Journal of Political Economy 2005, 113.
7. Altonji J., Elder T., Taber C. An evaluation of instrumental variable strategies for estimating the effects of Catholic schooling. Journal of Human Resources 2005.
8. Angrist J., Imbens G. Comment on James J. Heckman, "Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations". The Journal of Human Resources 1999, 34(4):823-827.
9. Angrist J., Imbens G., Rubin D. Identification of causal effects using instrumental variables. Journal of the American Statistical Association 1996, 91(June).
10. Angrist, J., Pischke, S., 2010, The credibility revolution in empirical economics: how better research design is taking the con out of econometrics. NBER working paper 15794.
11. Barlevy G. Identification of search models using record statistics. Review of Economic Studies 2008, 75(1):29-64.
12. Bloom H., Orr L., Bell S., Cave G., Doolittle F., Lin W., Bos J.s The benefits and costs of JTPA title II-A programs: key findings from the national job training partnership act study. Journal of Human Resources 1997, 32(3):549-576.
13. Blundell R., Gosling A., Ichimura H., Meghir C. Changes in the distribution of male and female wages accounting for employment composition using bounds. Econometrica 2007, 75:323-363.
14. Buera, F., 2006. Non-parametric identification and testable implications of the Roy model. Unpublished manuscript, UCLA.
15. Burdett K., Mortensen D.T. Wage differentials, employer size and labor market equilibrium. International Economic Review 1998, 39:257-273.
16. Canals-Cerda, J., 2010. Identification in empirical search models when ages are measured with errors. unpublished manuscript. Federal Reserve Bank of Philadelphia.
17. Carneiro, P., Heckman, J., Vytlacil, E. 2010. Estimating marginal returns to education. unpublished manuscript, University College London.
18. Carneiro P., Lee S. Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality. Journal of Econometrics 2009, 149:191-208.
19. Chamberlain G. Asymptotic efficiency in semiparametric models with censoring. Journal of Econometrics 1986, 32:189-218.
20. Chen X. Large sample sieve estimation of semi-nonparametric models. Handbook of Econometrics 2007, North-Holland, (Chapter 76).
21. Das M., Newey W., Vella F. Nonparametric estimation of sample selection models. The Review of Economic Studies 2003, 70(1):33-58.
22. Davidson J. Stochastic Limit Theory 1994, Oxford University Press, Oxford.
23. Deaton, A.. 2009. Instruments of development: randomization in the tropics, and the search for the elusive keys to economic development. NBER working paper 14690.
24. DiNardo J., Lee D. Program evaluation and research designs. Handbook of Labor Economics 2011, vol. 4a:463-536. Elsevier Science.
25. Doyle J. Child protection and child outcomes: measuring the effects of foster care. The American Economic Review 2007, 97(5):1583-1610.
26. Eckstein Z., van den Berg G Empirical labor search: a survey. Journal of Econometrics 2007, 136:531-564.
27. Elbers C., Ridder G. True and spurious duration dependence: the identifiability of the proportional hazard model. Review of Economic Studies 1982, 64:403-409.
28. Evans W., Schwab R. Finishing high school and starting college: do Catholic schools make a difference?. Quarterly Journal of Economics 1995, 110:947-974.
29. Flinn C., Heckman J. New methods for analyzing structural models of labor force dynamics. Journal of Econometrics 1982, 18:115-168.
30. French, E., Song, J. 2010. The effect of disability insurance receipt on labor supply. unpublished manuscript. Federal Reserve Bank of Chicago.
31. Heckman J. Sample selection bias as a specification error. Econometrica 1979, 47(1):153-162.
32. Heckman J. Varieties of selection bias. American Economic Review 1990, 80.
33. Heckman J. Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations. The Journal of Human Resources 1997, 32(3):441-462.
34. Heckman J. Instrumental variables: Response to Angrist and Imbens. The Journal of Human Resources 1999, 34(4):828-837.
35. Heckman J. Causal parameters and policy analysis in economics: a twentieth century retrospective. Quarterly Journal of Economics 2000, 115:45-97.
36. Heckman J., Honoré B. The empirical content of the Roy model. Econometrica 1990, 58:1121-1149.
37. Heckman J., Honoré B. The identifiability of the competing risks model. Biometrika 1989, 76:325-330.
38. Heckman J., LaLonde R., Smith J. The economics and econometrics of active labor market programs. Handbook of Labor Economics, vol. 3A 1999, 1865-2097. North-Holland, New York. Ashenfelter, Card (Eds.).
39. Heckman J., Lochner L., Taber C. Explaining rising wage inequality: explorations with a dynamic general equilibrium model of labor earnings with heterogeneous agents. Review of Economic Dynamics 1998.
40. Heckman J., Navarro S. Dynamic discrete choice and dynamic treatment effects. Journal of Econometrics 2007, 136:341-396.
41. Heckman J., Robb R. Alternative methods for evaluating the impact of interventions. Longitudinal Analysis of Labor Market Data 1986, 156-245. Cambridge University Press, New York. Heckman, Singer (Eds.).
42. Heckman J., Singer B. The identifiability of the proportional hazard model. Review of Economic Studies 1984, 51:231-241.
43. Heckman J., Singer B. A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 1984, 52:271-320.
44. Heckman J., Taber C. Econometric mixture models and more general models for unobservables in duration analysis. Statistical Methods in Medical Research 1994, 3(3):279-299.
45. Heckman J., Taber C. Roy model. The New Palgrave Dictionary of Economics Second Edition 2008, Palgrave Macmillan. Durlauf, Blume (Eds.).
46. Heckman J., Urzúa S. Comparing IV with structural models: what simple IV can and cannot identify. Journal of Econometrics 2010, 156(1):27-37.
47. Heckman J., Vytlacil E. Local instrumental variables and latent variable models for identifying and bounding treatment effects. Proceedings of the National Academy of Sciences 1999, 96:4730-4734.
48. Heckman J., Vytlacil E. Local instrumental variables. Nonlinear Statistical Inference: Essays in Honor of Takeshi Amemiya 2001, 145. Cambridge University Press, Cambridge. C. Hsiao, K. Morimune, J. Powell (Eds.).
49. Heckman J., Vytlacil E. Structural equations, treatment effects and econometric policy evaluation. Econometrica 2005, 73:669-738.
50. Heckman J., Vytlacil E. Econometric evaluation of social programs, Part I: causal models, structural models and econometric policy evaluation. Handbook of Econometrics 2007, 4779-4874. North Holland, Amsterdam. Heckman, Leamer (Eds.).
51. Heckman J., Vytlacil E. Econometric evaluation of social programs, Part II: using the marginal treatment effect to organize alternative econometric estimators to evaluate social programs, and to forecast their effects in new environments. Handbook of Econometrics 2007, 4875-5143. North Holland, Amsterdam. Heckman, Leamer (Eds.).
52. Honoré B. Identification results for duration models with multiple spells. Review of Economic Studies 1993, 60:241-246.
53. Hu, Y., Shum, M., 2009. Nonparametric identification of dynamic models with unobserved state variables. Working Paper 543, Department of Economics, Johns Hopkins University.
54. Ichimura H., Taber C. Semiparametric reduced form estimation of tuition subsidies. American Economic Review 2002, 92(2):286-292.
55. Imbens, G., 2009. Better LATE than nothing: some comments on Deaton (2009) and Heckman and Urzua (2009). unpublished manuscript, Harvard University.
56. Imbens G., Angrist J. Identification and estimation of local average treatment effects. Econometrica 1994, 62.
57. Imbens G., Wooldridge J. Recent developments in the econometrics of program evaluation. Journal of Economic Literature 2009, 47(1):5-86.
58. Kashara H., Shimotsu K. Nonparametric identification of finite mixture models of dynamic discrete choice. Econometrica 2009, 77(1):135-175.
59. Keane M., Wolpin K. The effect of parental transfers and borrowing constraints on educational attainment. International Economic Review 2001, 42:1051-1103.
60. Keane M., Todd P., Wolpin K. The structural estimation of behavioral models: discrete choice dynamic programming methods and applications. Handbook of Labor Economics 2011, vol. 4a:331-461. Elsevier Science.
61. Lalonde R. Evaluating the econometric evaluations of training programs with experimental data. American Economic Review 1986, 76:604-620.
62. Leamer E. Let's take the con out of econometrics. American Economic Review 1983, 73(1):31-43.
63. Lee L-F Unionism and wage rates: a simultaneous equations model with qualitative and limited dependent variables. International Economic Review 1978, 19(2):415-433.
64. Lippman S., McCall J. The economics of job search: a survey, Part I. Economic Inquiry 1976, 14:155-189.
65. Magnac T., Thesmar D. Identifying dynamic discrete decision processes. Econometrica 2002, 70(2):801-816.
66. Manski C. Anatomy of the selection problem. The Journal of Human Resources 1989, 24(3):343-360.
67. Manski C. Nonparametric bounds on treatment effects. American Economic Review 1990, 80(2):319-323.
68. Manski C. Identification problems in the social sciences 1995, Harvard University Press, Cambridge Mass.
69. Manski C. Monotone treatment response. Econometrica 1997, 65(6):1311-1334.
70. Manski C., Pepper J. Monotone instrumental variables with an application to the returns to schooling. Econometrica 2000, 68(4):997-1010.
71. Manski C., Pepper J. More on monotone instrumental variables. The Econometric Journal 2009, 12(s1). s200-s216.
72. Matzkin R. Nonparametric and distribution-free estimation of the threshold crossing and binary choice model. Econometrica 1992, 60:239-270.
73. Matzkin R. Nonparametric identification and estimation of polychotomous choice models. Journal of Econometrics 1993, 58:137-168.
74. Matzkin R. Nonparametric identification. Handbook of Econometrics 2007, 5145-5368. North-Holland, Amsterdam. Heckman, Leamer (Eds.).
75. Mortensen D., Pissarides C. Job creation and job destruction in the theory of unemployment. Review of Economic Studies 1994, 61:397-415.
76. Neal D. The effects of catholic secondary schooling on educational attainment. Journal of Labor Economics 1997, 15:98-123.
77. Neal D., Grogger J. Further evidence on the effects of Catholic secondary schooling. Brookings-Wharton Papers on Urban Affairs 2000, 151-193.
78. Postel-Vinay F., Robin J.-M. Wage dispersion with worker and employer heterogeneity. Econometrica 2002, 70(6). 2295-350.
79. Ridder G. The non-parametric identification of generalized accelerated failure-time models. Review of Economic Studies 1990, 57:167-182.
80. Rosenzweig M., Wolpin K. Natural 'natural experiments' in economics. Journal of Economic Literature 2000, 38(4):827-874.
81. Roy A.D. Some thoughts on the distribution of earnings. Oxford Economic Papers (New Series) 1951, 3:135-146.
82. Rust J. Structural estimation of Markov decision processes. Handbook of Econometrics 1994, vol. 4:3082-3139. North Holland, Amsterdam. R. Engle, D. McFadden (Eds.).
83. Shaikh, A., 2010. Identification in Economics, Lecture Notes for Topics in Econometrics, University of Chicago. http://home.uchicago.edu/~amshaikh/classes/topics_winter09.html.
84. Sims, C., 2010. Comment on Angrist and Pischke. unpublished manuscript, Princeton University.
85. Taber C. Semiparametric identification and heterogeneity in dynamic programming discrete choice models. Journal of Econometrics 2000.
86. Van den Berg G. Duration models: Specification, identification and multiple durations. Handbook of Econometrics vol. 5 2001, Elsevier. Heckman, Leamer (Eds.).
87. Vytlacil E. Independence, monotonicity, and latent index models: an equivalence result. Econometrica 2002, 70:331-341.
88. Willis R., Rosen S. Education and self- selection. Journal of Political Economy 1979, 87.