Micro-Econometrics for Policy, Program and Treatment Effects

Micro-Econometrics for Policy, Program and Treatment Effects

Volumn , Issue , 2006, Pages 1-262

  Lee, Myoung jae ^a  

^a SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

Author keywords

Controlling covariates; Difference in differences; Instrumental variable estimation; Matching; Regression discontinuity; Selection problem; Study designs; Treatment effects

Indexed keywords

EID: 84920615947     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1093/0199267693.001.0001     Document Type: Book

References (144)

1. Aakvik, A. (2001). Bounding a matching estimator: The case of a Norwegian training program. Oxford Bulletin of Economics and Statistics, 63, 115-43.
2. Abadie, A. (2002). Bootstrap tests for distributional treatment effects in instrumental variable models. Journal of the American Statistical Association, 97, 284-92.
3. Abadie, A. (2003). Semiparametric instrumental variable estimation of treatment response models. Journal of Econometrics, 113, 231-63.
4. Abadie, A. (2005). Semiparametric difference-in-differences estimators. Review of Economic Studies, 72, 1-19.
5. Abadie, A. Angrist, J., and Imbens, G. (2002). Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings. Econometrica, 70, 91-117.
6. Abadie, A. and Imbens, G. (2002). Simple and bias-corrected matching estimators for average treatment effects, unpublished paper.
7. Agodini, R. and Dynarski, M. (2004). Are experiments the only option? A look at dropout prevention programs. Review of Economics and Statistics, 86, 180-94.
8. Allison, P. D. and Long, J. S. (1990). Departmental effects on scientific productivity. American Sociological Review, 55, 469-78.
9. Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery:Evidence from social security administrative records. American Economic Review, 80, 313-35.
10. Angrist, J. D. (2001). Estimation of limited dependent variable models with dummy endogenous regressors: Simple strategies for empirical practice. Journal of Business and Economic Statistics, 19, 2-16.
11. Angrist, J. D. (2004). Treatment effect heterogeneity in theory and practice. Economic Journal, 114, C52-C83.
12. Angrist, J. D. and Evans, W. N. (1998). Children and their parent's labor supply:Evidence from exogenous variation in family size. American Economic Review, 88, 450-77.
13. Angrist, J. D. and Imbens, G. (1995). Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association, 90, 431-42.
14. Angrist, J. D. Imbens, G. W., and Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444-55.
15. Angrist, J. D. and Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106, 979-1014.
16. Angrist, J. D. and Krueger, A. B. (1999). Empirical strategies in labor economics. In Handbook of Labor Economics 3A (ed. O. Ashenfelter and D. Card), North-Holland.
17. Angrist, J. D. and Krueger, A. B. (2001). Instrumental variables and the search for identification:From supply and demand to natural experiments. Journal of Economic Perspectives, 15, 69-85.
18. Angrist, J. D. and Lavy, V. (1999). Using Maimonides' rule to estimate the effect of class size on scholastic achievement. Quarterly Journal of Economics, 114, 533-75.
19. Battistin, E. and Rettore, E. (2002). Testing for programme effects in a regression discontinuity design with imperfect compliance. Journal of the Royal Statistical Society, Series A, 165, 39-57.
20. Behrman, J. R., Cheng, Y., and Todd, P. E. (2004). Evaluating pre-school programs when length of exposure to the program varies: A nonparametric approach. Review of Economics and Statistics, 86, 108-32.
21. Bergstralh, E. J. and Kosanke, J. L. (1995). Computerized matching of cases to controls. Technical Report 56, Mayo Foundation.
22. Berk, R. A. and de Leeuw, J. (1999). An evaluation of California's inmate classification system using a generalized regression discontinuity design. Journal of the American Statistical Association, 94, 1045-52.
23. Bertrand, M., Duflo, E., and Mullainathan, S. (2004). How much should we trust differences-in-differences estimates. Quarterly Journal of Economics, 119, 249-75.
24. Besley, T. and Case, A. (2004). Unnatural experiments? Estimating the incidence of endogenous policies. Economic Journal, 110, F672-F694.
25. Black, S. E. (1999). Do better schools matter? Parental evaluation of elementary education. Quarterly Journal of Economics, 114, 577-99.
26. Bound, J. (1989). The health and earnings of rejected disability insurance applicants. American Economic Review, 79, 482-503.
27. Card, D. (1990). The impact of the Mariel Boatlift on the Miami labor market. Industrial and Labor Relations Review, 43, 245-57.
28. Card, D. and Krueger, A. B. (1994). Minimum wage and employment: A case study of the fast-food industry in New Jersey and Pennsylvania. American Economic Review, 84, 772-93.
29. Card, D. and Krueger, A. B. (2000). Minimum wage and employment: A case study of the fastfood industry in New Jersey and Pennsylvania: Reply. American Economic Review, 90, 1397-420.
30. Corak, M. (2001). Death and divorce: The long-term consequences of parental loss on adolescents. Journal of Labor Economics, 19, 682-715.
31. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, Series B, 34, 187-202.
32. Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B, 41, 1-31.
33. Dehejia, R. H. and Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053-62.
34. Dehejia, R. H. and Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. Review of Economics and Statistics, 84, 151-61.
35. Donohue III, J. J., Heckman, J. J., and Todd, P. E. (2002). The schooling of southern blacks: The roles of legal activism and private philanthropy, 1910- 1960. Quarterly Journal of Economics, 117, 225-68.
36. Eberwein, C., Ham, J. C., and Lalonde, R. J. (1997). The impact of being offered and receiving classroom training on the employment histories of disadvantaged women: Evidence from experimental data. Review of Economic Studies, 64, 655-82.
37. Eissa, N. (1995). Taxation and labor supply of married women: The tax reform act of 1986 as a natural experiment. NBER working paper 5023.
38. Eissa, N. and Liebman, J. B. (1996). Labor supply response to the earned income tax credit. Quarterly Journal of Economics, 111, 605-37.
39. Fan, J. (1996). Local Polynomial Modeling and its Applications. Chapman and Hall.
40. Fraker, T. and Maynard, R. (1987). The adequacy of comparison group designs for evaluations of employment-related programs. Journal of Human Resources, 22, 194-227.
41. Freedman, D. (1999). From association to causation: Some remarks on the history of statistics. Statistical Science, 14, 243-58.
42. Friedberg, R. M. and Hunt, J. (1995). The impact of immigrants on host country wages, employment and growth. Journal of Economic Perspectives, 9, 23-44.
43. Friedlander, D. and Robins, P. K. (1995). Evaluating program evaluations:New evidence on commonly used nonexperimental methods. American Economic Review, 85, 923-37.
44. Frölich, M. (2003). Programme Evaluation and Treatment Choice. Springer-Verlag.
45. Gastwirth, J. L., Krieger, A. M., and Rosenbaum, P. R. (1998). Dual and simultaneous sensitivity analysis for matched pairs. Biometrika, 85, 907-20.
46. Gill, R. and Robins, J. M. (2001). Causal inference for complex longitudinal data: The continuous case. Annals of Statistics, 29, 1785-811.
47. Gordis, L. (2000). Epidemiology. Saunders.
48. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424-38.
49. Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2, 329-52.
50. Gruber, J. (1994). The incidence of mandated maternity benefits. American Economic Review, 84, 622-41.
51. Gu, X. S. and Rosenbaum, P. R. (1993). Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics, 2, 405-20.
52. Hahn, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica, 66, 315-31.
53. Hahn, J. Todd, P., and van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 69, 201-9.
54. Ham, J. C. and Lalonde, R. J. (1996). The effect of sample selection and initial conditions in duration models: Evidences from experimental data on training. Econometrica, 64, 175-205.
55. Härdle, W. (1990). Applied Nonparametric Regression. Cambridge University Press.
56. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153-61.
57. Heckman, J. J. (1996). Comment. Journal of the American Statistical Association, 91, 459-62.
58. Heckman, J. J. (1997). Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources, 32, 441-62.
59. Heckman, J. J. (2001). Accounting for heterogeneity, diversity, and general equilibrium in evaluating social programmes. Economic Journal, 111, F654-F699.
60. Heckman, J. J. Hohmann, N., and Smith, J. (2000). Substitution and dropout bias in social experiments: A study of an influential social experiment. Quarterly Journal of Economics, 115, 651-94.
61. Heckman, J. J. and Hotz, V. (1989). Choosing among alternative nonexperimental methods for estimating the impact of social program: The case of manpower training. Journal of the American Statistical Association, 84, 862-74.
62. Heckman, J. J. Ichimura H., Smith J., and Todd, P. E. (1998). Characterizing selection bias using experimental data. Econometrica, 66, 1017-98.
63. Heckman, J. J. Ichimura H., and Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training program. Review of Economic Studies, 64, 605-54.
64. Heckman, J. J. Lalonde R. J., and Smith, J. A. (1999). The economics and econometrics of active labor market programs. In Handbook of Labor Economics 3B (ed. O. C. Ashenfelter and D. Card), North-Holland.
65. Heckman, J. J. Smith J., and Clements, N. (1997). Making the most out of program evaluations and social experiments: Accounting for heterogeneity in program impacts. Review of Economic Studies, 64, 487-535.
66. Heckman, J. J., Tobias J. L., and Vytlacil, E. (2003). Simple estimators for treatment parameters in a latent-variable framework. Review of Economics and Statistics, 85, 748-55.
67. Heckman, J. J., and Vytlacil, E. J. (1999). Local instrument variables and latent variable models for identifying and bounding treatment effects. Proceedings of National Academy of Science, 96, 4730-4.
68. Hirano, K., Imbens, G. W., and Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71, 1161-89.
69. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-60.
70. Hotz, V. J., Mullin, C. H., and Sanders, S. G. (1997). Bounding causal effects using data from a contaminated natural experiments: Analyzing the effects of teenage childbearing. Review of Economic Studies, 64, 575-603.
71. Hsu, J. C. (1996). Multiple Comparisons: Theory and Methods. Chapman and Hall.
72. Imbens, G. W. (2000). The role of the propensity score in estimating doseresponse functions. Biometrika, 87, 706-10.
73. Imbens, G. W. (2003). Sensitivity to exogeneity assumptions in program evaluation. American Economic Review, Papers and Proceedings, 93, 126-32.
74. Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86, 4-29.
75. Imbens, G. W. and Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467-75.
76. Imbens, G. W. and Rubin, D. B. (1997). Estimating outcome distributions for compliers in instrumental variables models. Review of Economic Studies, 64, 555-74.
77. Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman and Hall.
78. Kang, C. H., Lee, M. J., and Park, C. S. (2005). Effects of ability mixing in high school on adulthood earnings: Quasi-experimental evidence from South Korea, unpublished paper.
79. Krueger, A. B. and Whitmore, D. M. (2001). The effect of attending a small class in the early grades on college-test taking and middle school test results: Evidence from Project Star. Economic Journal, 111, 1-28.
80. Lalonde, R. J. (1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76, 604-20.
81. Lechner, M. (1999). Nonparametric bounds on employment and income effects of continuous vocational training in East Germany. Econometrics Journal, 2, 1-28.
82. Lechner, M. (2000). An evaluation of public-sector sponsored continuous vocational training programs in East Germany. Journal of Human Resources, 35, 347- 75.
83. Lechner, M. (2001). Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In Econometric Evaluation of Labor Market Policies (ed. M. Lechner and F. Pfeiffer), Physica-Verlag.
84. Lechner, M. (2002). Program heterogeneity and propensity score matching: An application to the evaluation of active labor market policies. Review of Economics and Statistics, 84, 205-20.
85. Lee, M. J. (1996). Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. Springer-Verlag.
86. Lee, M. J. (1997). Nonparametric estimation of treatment effects under comparison group bias, unpublished paper.
87. Lee, M. J. (2000). Median treatment effect in randomized trials. Journal of the Royal Statistical Society, Series B (Series B) 62, 595-604.
88. Lee, M. J. (2002). Panel Data Econometrics: Methods-of-Moments and Limited Dependent Variables. Academic Press.
89. Lee, M. J. (2003a). Complete pairing, propensity score, and endogeneity test for binary selection models, unpublished paper.
90. Lee, M. J. (2003b). Treatment effect and sensitivity analysis for self-selected treatment and selectively observed response, unpublished paper.
91. Lee, M. J. (2003c). Nonparametric test and estimation of treatment effects for randomly censored responses, presented at the Australian Econometric Society Meeting at Sydney.
92. Lee, M. J. (2004). Selection correction and sensitivity analysis for ordered treatment effect on count response. Journal of Applied Econometrics, 19, 323-37.
93. Lee, M. J. and Kobayashi, S. (2001). Proportional treatment effects for count response panel data: Effects of binary exercise on health care demand. Health Economics, 10, 411-28.
94. Lee, M. J. and Lee, S. J., (2004a). Job-training effects with dropouts:Partial likelihood, matching, and causality, unpublished paper.
95. Lee, M. J. and Lee, S. J., (2004b). Sensitivity analysis of job-training effects on reemployment for Korean women, unpublished paper.
96. Lee, M. J. and Lee, S. J., (2005). Analysis of job-training effects on Korean women. Journal of Applied Econometrics, forthcoming.
97. Lehmann, E. L. (1986). Testing Statistical Hypotheses. 2nd ed. Wiley.
98. Lok, J. J. (2001). Statistical modelling of causal effects in time. Ph.D. thesis. Free University, Amsterdam, the Netherlands.
99. Lu, B., Zanutto, E., Hornik, R., and Rosenbaum, P. R. (2001). Matching with doses in an observational study of a media campaign against drug abuse. Journal of the American Statistical Association, 96, 1245-53.
100. Ludwig, J., Duncan, G. J., and Hirschfield, P. (2001). Urban poverty and juvenile crime: Evidence from a randomized housing-mobility experiment. Quarterly Journal of Economics, 116, 655-79.
101. Madrian, B. C. (1994). Employment-based health insurance and job mobility:Is there evidence of job-lock? Quarterly Journal of Economics, 109, 27-54.
102. Manski, C. F. (1995). Identification Problems in the Social Sciences. Harvard University Press.
103. Manski, C. F. (2003). Partial Identification of Probability Distributions. Springer-Verlag.
104. Manski, C. F. Sandefur, G. D., McLanahan, S., and Powers, D. (1992). Alternative estimates of the effect of family structure during adolescence on high school graduation. Journal of the American Statistical Association, 87, 25-37.
105. Marcantonio, R. J. and Cook, T. D. (1994). Convincing quasi-experiments: The interrupted time series and regression-discontinuity designs. In Handbook of Practical Program Evaluation (ed. J. S. Wholey, H. P. Hatry, and K. E. Newcomer), Jossey-Bass Publishers, San Francisco.
106. Meyer, B. D. (1995). Natural and quasi-experiments in Economics. Journal of Business and Economic Statistics, 13, 151-61.
107. Meyer, B. D. Viscusi, W. K., and Durbin, D. L. (1995). Workers' compensation and injury duration: Evidence from a natural experiment. American Economic Review, 85, 322-40.
108. Michalopoulos, C., Bloom, H. S., and Hill, C. J. (2004). Can propensity-score methods match the findings from a random assignment evaluation of mandatory welfare-to-work programs? Review of Economics and Statistics, 86, 156-79.
109. Murphy, S. A. (2003). Optimal dynamic treatment regimes. Journal of the Royal Statistical Society, Series B, 65, 331-55.
110. Pearl, J. (2000). Causality. Cambridge University Press.
111. Pepper, J. V. (2000). The intergenerational transmission of welfare receipt: A nonparametric bound analysis. Review of Economics and Statistics, 82, 472- 88.
112. Pierce, D. A. (1982). The asymptotic effect of substituting estimators for parameters in certain types of statistics. Annals of Statistics, 10, 475-8.
113. Rephann, T. and Isserman, A. (1994). New highways as economic development tools: An evaluation using quasi-experimental matching methods. Regional Science and Urban Economics, 24, 723-51.
114. Robins, J. M. (1998). Structural nested failure time models. In Survival Analysis, vol. 6, Encyclopedia of Biostatistics (ed. P. Armitage and T. Colton), Wiley.
115. Robins, J. M. (1999). Marginal structural models versus structural nested models as tools for causal inference. In Statistical Models in Epidemiology: The Environment and Clinical Trials (ed. M. E. Halloran and D. Berry), Springer, 95-134.
116. Robins, J. M. Mark S. D., and Newey W. K. (1992). Estimating exposure effects by modelling the expectation of exposure conditional on confounder. Biometrics, 48, 479-95.
117. Robinson, P. M. (1988). Root-N consistent semiparametric regression. Econometrica, 56, 931-54.
118. Rosenbaum, P. R. (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. Journal of the Royal Statistical Society, Series B, 147, 656-666.
119. Rosenbaum, P. R. (1987). Sensitivity analysis for certain permutation inferences in matched observational studies. Biometrika, 74, 13-26.
120. Rosenbaum, P. R. (1989). Optimal matching for observational studies. Journal of the American Statistical Association, 84, 1024-32.
121. Rosenbaum, P. R. (1991). A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society, Series B, 53, 597-610.
122. Rosenbaum, P. R. (2002). Observational Studies, 2nd ed. Springer-Verlag.
123. Rosenbaum, P. R. and Rubin, D. B. (1983a). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
124. Rosenbaum, P. R. and Rubin, D. B. (1983b). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society, Series B, 45, 212-18.
125. Rosenbaum, P. R. and Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39, 33-8.
126. Rosenzweig, M. R. and Wolpin, K. I. (1980). Life cycle labor supply and fertility: Causal inferences from household models. Journal of Political Economy, 88, 328-48.
127. Rosenzweig, M. R. and Wolpin, K. I. (2000). Natural 'natural experiments' in economics. Journal of Economic Literature, 38, 827-74.
128. Rosner, B. (1995). Fundamentals of Biostatistics. Duxbury Press.
129. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701.
130. Seifert, B. and Gasser, T. (1996). Finite-sample variance of local polynomials:Analysis and solution. Journal of the American Statistical Association, 91, 267-75.
131. Seifert, B. and Gasser, T. (2000). Data adaptive ridging in local polynomial regression. Journal of Computational and Graphical Statistics, 9, 338-60.
132. Shadish, W. R., Cook, T. D., and Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin Company.
133. Smith, H. (1997). Matching with multiple controls to estimate treatment effects in observational studies. Sociological Methodology, 27, 325-53.
134. Stock, J. H. and Watson, M. W. (2003). Introduction to Econometrics. Addison-Wesley.
135. Thun, M. J., Peto, R., Lopez, A. D., Monaco, J. H., Henley, S. J., Heath, C. W., and Doll, R. (1997). Alcohol consumption and mortality among middle-aged and elderly U.S. adults. New England Journal of Medicine, 337, 1705-14.
136. Triest, R. K. (1998). Econometric issues in estimating the behavioral response to taxation: A nontechnical introduction. National Tax Journal, 51, 761-73.
137. Van der Klaauw, V. (2002). Estimating the effect of financial aid offers on college enrollment: A regression-discontinuity approach. International Economic Review, 43, 1249-87.
138. Van der Laan, M. J. and Robins, J. (2003). Unified Methods for Censored Longitudinal Data and Causality. Springer-Verlag.
139. Vella, F. and Verbeek, M. (1998). Whose wages do unions raise? A dynamic model of unionism and wage determination for young men. Journal of Applied Econometrics, 13, 163-83.
140. Vytlacil, E. (2002). Independence, monotonicity, and latent index models: An equivalence result. Econometrica, 70, 331-41.
141. Wagner, J. (2002). The causal effects of exports on firm size and labor productivity: First evidence from a matching approach. Economics Letters, 77, 287-92.
142. White, J. R. and Froeb, H. F. (1980). Small-airways dysfunction in nonsmokers chronically exposed to tobacco smoke. New England Journal of Medicine, 302, 720-23.
143. Wooldridge, J. M. (2002). Econometric Analysis of Cross-Section and Panel Data. MIT Press.
144. Zhao, Z. (2004). Using matching to estimate treatment effects: Data requirement, matching metrics, and Monte Carlo evidence. Review of Economics and Statistics, 86, 91-107.