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Ivtreatreg: a new STATA routine for estimating binary treatment models with heterogeneous response to treatment under observable and unobservable selection

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This paper presents a new user-written STATA command called ivtreatreg for the estimation of five different (binary) treatment models with and without idiosyncratic (or heterogeneous) average treatment effect. Depending on the model specified by the user, ivtreatreg provides consistent estimation of average treatment effects both under the hypothesis of “selection on observables” and “selection on unobservables” by using Ordinary Least Squares (OLS) regression in the first case, and Intrumental-Variables (IV) and Selection-model (à la Heckman) in the second one. Conditional on a pre-specified subset of exogenous variables x – thought of as driving the heterogeneous response to treatment – ivtreatreg calculates for each model the Average Treatment Effect (ATE), the Average Treatment Effect on Treated (ATET) and the Average Treatment Effect on Non-Treated (ATENT), as well as the estimates of these parameters conditional on the observable factors x, i.e., ATE(x), ATET(x) and ATENT(x). The five models estimated by ivtreatreg are: Cf-ols (Control-function regression estimated by OLS), Direct-2sls (IV regression estimated by direct two-stage least squares), Probit-2sls (IV regression estimated by Probit and two-stage least squares), Probit-ols (IV two-step regression estimated by Probit and ordinary least squares), and Heckit (Heckman two-step selection model). An extensive treatment of the conditions under which previous methods provide consistent estimation of ATE, ATET and ATENT can be found, for instance, in Wooldgrige (2002, Chapter 18). The value added of this new STATA command is that it allows for a generalization of the regression approach typically employed in standard program evaluation, by assuming heterogeneous response to treatment.

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Paper provided by Institute for Economic Research on Firms and Growth - Moncalieri (TO) in its series CERIS Working Paper with number 201203.

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Length: 32 pages Keywords :Micro-econometrics, Treatment models, Instrumental variables, STATA routine
Date of creation: Jun 2012
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Handle: RePEc:csc:cerisp:201203

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  1. Joshua D. Angrist, 1991. "Instrumental Variables Estimation of Average Treatment Effects in Econometrics and Epidemiology," NBER Technical Working Papers 0115, National Bureau of Economic Research, Inc.
  2. Imbens, Guido & Wooldridge, Jeffrey M., 2009. "Recent Developments in the Econometrics of Program Evaluation," Scholarly Articles 3043416, Harvard University Department of Economics.
  3. Giovanni Cerulli, 2010. "Modelling and Measuring the Effect of Public Subsidies on Business R&D: A Critical Review of the Econometric Literature," The Economic Record, The Economic Society of Australia, vol. 86(274), pages 421-449, 09.
  4. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
  5. Guido M. Imbens & Jeffrey M. Wooldridge, 2008. "Recent Developments in the Econometrics of Program Evaluation," NBER Working Papers 14251, National Bureau of Economic Research, Inc.
  6. Lee, Myoung-jae, 2005. "Micro-Econometrics for Policy, Program and Treatment Effects," OUP Catalogue, Oxford University Press, number 9780199267699.
  7. Deborah A. Cobb-Clark & Thomas Crossley, 2003. "Econometrics for Evaluations: An Introduction to Recent Developments," The Economic Record, The Economic Society of Australia, vol. 79(247), pages 491-511, December.
  8. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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