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The Mixing Problem in Program Evaluation

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  • Charles F. Manski

Abstract

A common concern of evaluation studies is to learn the distribution of outcomes when a specified treatment policy or assignment rule, determines the treatment received by each member of a specified population. Recent studies have emphasized evaluation of policies providing the same treatment to all members of the population. In particular, experiments with randomized treatments have this objective. Policies mandating homogenous treatment of the population are of interest, but so are ones that permit treatment to vary across the population. This paper examines the use of empirical evidence on programs with homogenous treatments to infer the outcomes that would occur if treatment were to vary across the population. Experimental evidence from the Perry Preschool Project is used to illustrate the inferential problem and the main findings of the analysis.

Suggested Citation

  • Charles F. Manski, 1993. "The Mixing Problem in Program Evaluation," NBER Technical Working Papers 0148, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0148
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    1. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
    2. Horowitz, J.L. & Manski, C.F., 1992. "Identification and Robustness in the Presence of Errors in Data," Working Papers 92-05, University of Iowa, Department of Economics.
    3. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, vol. 58(5), pages 1121-1149, September.
    4. Manski, C.F. & Sandefur, G.D. & Mclanahan, S. & Powers, D., 1990. "Alternative Estimates Of The Effect Of Family Stucture During Adolescence On Hight School Graduation," Working papers 90-31, Wisconsin Madison - Social Systems.
    5. Robinson, Chris, 1989. "The Joint Determination of Union Status and Union Wage Effects: Some Tests of Alternative Models," Journal of Political Economy, University of Chicago Press, vol. 97(3), pages 639-667, June.
    6. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
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    Citations

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    Cited by:

    1. V. Joseph Hotz & Guido W. Imbens & Julie H. Mortimer, 1999. "Predicting the Efficacy of Future Training Programs Using Past Experiences," NBER Technical Working Papers 0238, National Bureau of Economic Research, Inc.
    2. Manski, Charles F., 2000. "Identification problems and decisions under ambiguity: Empirical analysis of treatment response and normative analysis of treatment choice," Journal of Econometrics, Elsevier, vol. 95(2), pages 415-442, April.
    3. Caliendo, Marco & Steiner, Viktor, 2005. "Aktive Arbeitsmarktpolitik in Deutschland : Bestandsaufnahme und Bewertung der mikroökonomischen Evaluationsergebnisse (Active labour market policy in Germany * review ans assessment of the microecono," Zeitschrift für ArbeitsmarktForschung - Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 38(2/3), pages 396-418.
    4. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    5. C. F. Manski, "undated". "Learning about social programs from experiments with random assignment of treatments," Institute for Research on Poverty Discussion Papers 1061-95, University of Wisconsin Institute for Research on Poverty.
    6. Tomas Philipson, 1999. "External Treatment Effects and Program Implementation Bias," Working Papers 9929, Harris School of Public Policy Studies, University of Chicago.
    7. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    8. Vishal Kamat, 2017. "Identification with Latent Choice Sets: The Case of the Head Start Impact Study," Papers 1711.02048, arXiv.org.
    9. Charles F. Manski, 2001. "Designing Programs for Heterogeneous Populations: The Value of Covariate Information," American Economic Review, American Economic Association, vol. 91(2), pages 103-106, May.
    10. Vishal Kamat, 2018. "On the Identifying Content of Instrument Monotonicity," Papers 1807.01661, arXiv.org.
    11. Marco Caliendo & Reinhard Hujer & Stephan Thomsen, 2008. "Identifying effect heterogeneity to improve the efficiency of job creation schemes in Germany," Applied Economics, Taylor & Francis Journals, vol. 40(9), pages 1101-1122.
    12. repec:iab:iabzaf:v:38:i:2/3:p:396-418 is not listed on IDEAS
    13. Fan, Yanqin & Park, Sang Soo, 2009. "Partial identification of the distribution of treatment effects and its confidence sets," MPRA Paper 37148, University Library of Munich, Germany.
    14. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    15. Craig Gundersen & Brent Kreider & John Pepper & Valerie Tarasuk, 2017. "Food assistance programs and food insecurity: implications for Canada in light of the mixing problem," Empirical Economics, Springer, vol. 52(3), pages 1065-1087, May.
    16. John V. Pepper, 1999. "What Do Welfare-to-Work Demonstrations Reveal to Welfare Reformers?," JCPR Working Papers 105, Northwestern University/University of Chicago Joint Center for Poverty Research.
    17. Marco Caliendo & Viktor Steiner, 2005. "Aktive Arbeitsmarktpolitik in Deutschland: Bestandsaufnahme und Bewertung der mikroökonomischen Evaluationsergebnisse," Discussion Papers of DIW Berlin 515, DIW Berlin, German Institute for Economic Research.
    18. Charles F. Manski, 1999. "Statistical Treatment Rules for Heterogeneous Populations: With Application to Randomized Experiments," NBER Technical Working Papers 0242, National Bureau of Economic Research, Inc.

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