Heckman, James J., Heinrich, Carolyn and Smith, Jeffrey. (2002). "The Performance of Performance Standards," Journal of Human Resources, 37(4): 778-811.
Heckman, James J., Heinrich, Carolyn and Smith, Jeffrey. (1997). "Assessing the Performance of Performance Standards in Public Bureaucracies," American Economic Review, 87(2): 389-395.
Heckman, James J., Ichimura, Hidehiko and Todd, Petra E. (1998). "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, 65(2): 261-294.
Heckman, James J., Ichimura, Hidehiko and Todd, Petra E. (1997). "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, 64(4): 605-654.
Heckman, James J., Ichimura, Hidehiko, Smith, Jeffrey and Todd,
Petra E. (1998). "Characterizing Selection Bias Using Experimental Data," Econometrica, 66(5): 1017-1098.
Heckman, James J. and Smith, Jeffrey A. (1998). "Evaluating the Welfare State," in S. Strom, ed., Econometrics and Economic Theory in the Twentieth Century: The Ragnar Frisch Centennial Symposium. New York: Cambridge University Press. pp. 241-318.
Heckman, James J. and Richard Robb. 1986. "Alternative Identifying Assumptions in Econometric Models of Selection Bias, " in Advances in Econometrics, Volume 5. G. Rhodes, ed. Greenwich, CT: JAI Press, pp. 243-87.
Heckman, James J. and Richard Robb. 1986. "Alternative Methods for Solving the Problem of Selection Bias in Evaluating the Impact of Treatments on Outcomes, " in Drawing Inferences from Self-Selected Samples. H. Wainer, ed. New York: Springer-Verlag, pp. 63-107.
Heckman, James J. and Richard Robb. 1985. "Alternative Methods for Evaluating the Impact of Interventions, " in Longitudinal Analysis of Labor Market Data. James J. Heckman and B. Singer, eds. New York: Cambridge University Press, pp. 156-245.
Heckman, James J. and Richard Robb. 1985. "Using Longitudinal Data to Estimate Age, Period and Cohort Effects in Earnings Equations, " in Cohort Analysis in Social Research: Beyond the Identification Problem. William M. Mason and Stephen E. Fienberg, eds. New York: Springer-Verlag.
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