Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment

By James J. Heckman, N. Hohmann, Jeffrey A. Smith, M. Khoo. 2000

214 citations recorded by the source corpus.

Subfields: Education, Safety Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c88d91bdbed6ffeecec44c1e22a83d0462104356

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Citations in the corpus

How Important is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration (2006, unknown): “However, as discussed in Heckman et al. (2000), a simple comparison of the treatment and control groups in an experimental context will be biased if control group members substitute for the treatment with a similar program or if treatment group members drop out of the experiment.”
Community Colleges and Upward Mobility (2019, unknown): “This method also enhances program evaluation in settings with crowd-out or substitution bias (Heckman et al., 2000), since the task of evaluating a policy or program with readily-available substitutes—here the encouragement of 2-year enrollment in the presence of the 4-year alternative—is aided by t”
Social Experiments in the Labor Market (2016, unknown): “(Heckman et al., 2000, note the issue, but their analyses focus on non-random 95 selection into training and heterogeneity of training effects, which are related but distinct issues.)”
The Labor Market Impacts of Youth Training in the Dominican Republic (2011, background): “I. Introduction Since 1992 the Inter-American Development Bank (IADB) has financed a series of innovative training programs throughout Latin America.”
Compared to what? Variation in the impacts of early childhood education by alternative care type (2016, methodology): “Of course, the issue of variation in counterfactual treatments is common in program evaluation settings, including for alternative schools (Bloom and Unterman, 2014) and job training programs (Heckman et al., 2000; Schochet et al., 2008).”
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 (2007, background): “Table 15 [reproduced from Heckman et al. (2000)] demonstrates the practical importance of both dropout and substitution bias in experimental evaluations.”
Evaluating Public Programs with Close Substitutes: The Case of Head Start (2016, background): “Similar issues arise in the evaluation of job training programs (Heckman et al. 2000), health insurance (Finkelstein et al.”
The Impacts of International Migration on Remaining Household Members: Omnibus Results from a Migration Lottery Program (2009, background): “But mean comparisons may be biased if control group members substitute for the treatment with a similar program or if treatment group members drop out (Heckman et al., 2000).”
Human Capital Policy (2003, background): “An exception to the rule is classroom training, the returns to which are substantial (Heckman et al., 2000).”
Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation (2007, background): “64 See the evidence on disruption bias and contamination bias arising in randomized trials that is presented in Heckman, LaLonde and Smith (1999), Heckman et al. (2000) and the discussion in Section 9 of Chapter 71.”