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



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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  • Giovanni Cerulli, 2012. "Ivtreatreg: a new STATA routine for estimating binary treatment models with heterogeneous response to treatment under observable and unobservable selection," CERIS Working Paper 201203, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
  • Handle: RePEc:csc:cerisp:201203

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    References listed on IDEAS

    1. 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, September.
    2. repec:spr:portec:v:1:y:2002:i:2:d:10.1007_s10258-002-0010-3 is not listed on IDEAS
    3. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    4. Lee, Myoung-jae, 2005. "Micro-Econometrics for Policy, Program and Treatment Effects," OUP Catalogue, Oxford University Press, number 9780199267699, June.
    5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    6. 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.
    7. Cameron,A. Colin & Trivedi,Pravin K., 2008. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9787111235767, March.
    8. 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.
    9. 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.
    10. Edwin Leuven & Barbara Sianesi, 2003. "PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing," Statistical Software Components S432001, Boston College Department of Economics, revised 01 Feb 2018.
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    Cited by:

    1. Jeremiah Harris & Ralph Siebert, 2015. "Driven by the Discount Factor: Impact of Mergers on Market Performance in the Semiconductor Industry," CESifo Working Paper Series 5199, CESifo Group Munich.
    2. Tannistra Banerjee & Stephen Martin, 2015. "Pharmaceutical Regulation and Innovative Performance: A Decision‐theoretic Model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 36(3), pages 177-190, April.
    3. Ralph Siebert, 2017. "Heterogeneous Merger Impacts on Competitive Outcomes," CESifo Working Paper Series 6607, CESifo Group Munich.
    4. Kwok Chan & Ka Fung & Ender Demir, 2015. "The health and behavioral outcomes of out-of-wedlock children from families of social fathers," Review of Economics of the Household, Springer, vol. 13(2), pages 385-411, June.
    5. Pascale Lengagne, 2016. "Experience Rating and Work-Related Health and Safety," Journal of Labor Research, Springer, vol. 37(1), pages 69-97, March.
    6. Tannista Banerjee & Ralph Siebert, 2013. "The Impact of R&D Cooperation on Drug Variety Offered on the Market: Evidence from the Pharmaceutical Industry," Auburn Economics Working Paper Series auwp2013-20, Department of Economics, Auburn University.
    7. Mekonnen, Tigist, 2017. "Productivity and household welfare impact of technology adoption: Micro-level evidence from rural Ethiopia," MERIT Working Papers 007, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    8. Giovanni Cerulli, 2013. "treatrew: A user-written Stata routine for estimating average treatment effects by reweighting on propensity score," United Kingdom Stata Users' Group Meetings 2013 02, Stata Users Group.
    9. Ana Isabel Moreno-Monroy & Frederico Ramos, 2015. "The impact of public transport expansions on informality: the case of the São Paulo Metropolitan Region," ERSA conference papers ersa15p1551, European Regional Science Association.
    10. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.
    11. Pascale Lengagne, 2015. "Workers Compensation Insurance: Incentive Effects of Experience Rating on Work-related Health and Safety," Working Papers DT64, IRDES institut for research and information in health economics, revised Jan 2015.
    12. repec:eee:indorg:v:53:y:2017:i:c:p:32-62 is not listed on IDEAS
    13. Jean Canil & Bruce Rosser, 2015. "Evidence on exercise pricing in CEO option grants in two countries," Annals of Finance, Springer, vol. 11(3), pages 383-410, November.
    14. Giovanni Cerulli, 2014. "CTREATREG: Stata module for estimating dose-response models under exogenous and endogenous treatment," CERIS Working Paper 201405, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation

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