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A Stata package for the application of semiparametric estimators of dose-response functions

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  • BIA Michela
  • FLORES Carlos A.
  • FLORES-LAGUNES Alfonso
  • MATTEI Alessandra

Abstract

In many observational studies the treatment may not be binary or categorical, but rather continuous in nature, so focus is on estimating a continuous dose-response function. In this paper we propose a set of Stata programs to semiparametrically estimate the dose-response function of a continuous treatment, under the key assumption that adjusting for pre-treatment variables removes all biases (uncounfoundedness). We focus on kernel methods and penalized spline models, and use generalized propensity score methods under continuous treatment regimes for covariate adjustment. Several alternative parametric assumptions on the functional form of the generalized propensity score are implemented in our Stata programs, which also allow users to impose a common support condition and evaluate the balancing of the covariates using various approaches. We illustrate our routines by estimating the effect of the prize amount on subsequent labor earnings for Massachusetts lottery winners, using a data set collected by Imbens et al. (2001).

Suggested Citation

  • BIA Michela & FLORES Carlos A. & FLORES-LAGUNES Alfonso & MATTEI Alessandra, 2013. "A Stata package for the application of semiparametric estimators of dose-response functions," LISER Working Paper Series 2013-07, Luxembourg Institute of Socio-Economic Research (LISER).
    • Handle: RePEc:irs:cepswp:2013-07
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    1. Guido W. Imbens & Donald B. Rubin & Bruce I. Sacerdote, 2001. "Estimating the Effect of Unearned Income on Labor Earnings, Savings, and Consumption: Evidence from a Survey of Lottery Players," American Economic Review, American Economic Association, vol. 91(4), pages 778-794, September.
    2. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(2), pages 1-21, June.
    3. Kluve, Jochen & Schneider, Hilmar & Uhlendorff, Arne & Zhao, Zhong, 2007. "Evaluating Continuous Training Programs Using the Generalized Propensity Score," Ruhr Economic Papers 35, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Michela Bia & Alessandra Mattei, 2012. "Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(4), pages 485-516, November.
    5. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    6. Michela Bia & Philippe Van Kerm, 2014. "Space-filling location selection," Stata Journal, StataCorp LP, vol. 14(3), pages 605-622, September.
    7. Carlos A. Flores & Alfonso Flores-Lagunes & Arturo Gonzalez & Todd C. Neumann, 2012. "Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 153-171, February.
    8. Michela Bia & Alessandra Mattei, 2008. "A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score," Stata Journal, StataCorp LP, vol. 8(3), pages 354-373, September.
    9. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    10. Maarten L. Buis & Nicholas J. Cox & Stephen P. Jenkins, 2003. "BETAFIT: Stata module to fit a two-parameter beta distribution," Statistical Software Components S435303, Boston College Department of Economics, revised 03 Feb 2012.
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    2. Chiara Bocci & Marco Mariani, 2015. "L?approccio delle funzioni dose-risposta per la valutazione di trattamenti continui nei sussidi alla r&s," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2015(3 Suppl.), pages 81-102.
    3. Enrico Cristofoletti, 2021. "A Stata package for the estimation of the dose-response function when the treatment is multidimensional," DEM Working Papers 2021/07, Department of Economics and Management.
    4. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    5. Martina Lubyová & Miroslav Štefánik & Pavol Baboš & Daniel Gerbery & Veronika Hvozdíková & Katarína Karasová & Ivan Lichner & Tomáš Miklošovic & Marek Radvanský & Eva Rublíková & Ivana Studená, . "Labour Market in Slovakia 2017+," Books, Institute of Economic Research, Slovak Academy of Sciences, edition 1, number 003.
    6. Tamru, Seneshaw & Minten, Bart, 2018. "Investing in wet mills and washed coffee in Ethiopia: Benefits and constraints," ESSP working papers 121, International Food Policy Research Institute (IFPRI).
    7. Chung Choe & Alfonso Flores-Lagunes & Sang-Jun Lee, 2015. "Do dropouts with longer training exposure benefit from training programs? Korean evidence employing methods for continuous treatments," Empirical Economics, Springer, vol. 48(2), pages 849-881, March.
    8. Ida D'Attoma & Silvia Pacei, 2018. "Evaluating the Effects of Product Innovation on the Performance of European Firms by Using the Generalised Propensity Score," German Economic Review, Verein für Socialpolitik, vol. 19(1), pages 94-112, February.
    9. Issahaku, Gazali & Abdulai, Awudu, 2020. "Household welfare implications of sustainable land management practices among smallholder farmers in Ghana," Land Use Policy, Elsevier, vol. 94(C).
    10. Carina Steckenleiter & Michael Lechner & Tim Pawlowski & Ute Schüttoff, 2023. "Do local expenditures on sports facilities affect sports participation?," Economic Inquiry, Western Economic Association International, vol. 61(4), pages 1103-1128, October.
    11. Finn McGuire & Noemi Kreif & Peter C. Smith, 2021. "The effect of distance on maternal institutional delivery choice: Evidence from Malawi," Health Economics, John Wiley & Sons, Ltd., vol. 30(9), pages 2144-2167, September.
    12. Vera Chiodi & Gabriel Montes‐Rojas, 2022. "Mentoring as a dose treatment: Frequency matters—Evidence from a French mentoring programme," LABOUR, CEIS, vol. 36(2), pages 145-166, June.
    13. Roberto ESPOSTI, 2014. "To match, not to match, how to match: Estimating the farm-level impact of the CAP-first pillar reform (or: How to Apply Treatment-Effect Econometrics when the Real World is;a Mess)," Working Papers 403, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    14. Christopher Baum & Giovanni Cerulli, CNR-IRCrES, 2016. "Estimating a dose-response function with heterogeneous response to confounders when treatment is continuous and endogenous," EcoMod2016 9388, EcoMod.
    15. Steckenleiter, Carina & Lechner, Michael & Pawlowski, Tim & Schüttoff, Ute, 2019. "Do local public expenditures on sports facilities affect sports participation in Germany?," Economics Working Paper Series 1905, University of St. Gallen, School of Economics and Political Science.
    16. Mehdi Chowdhury & Dragana Radicic, 2019. "Remittances and Asset Accumulation in Bangladesh: A Study Using Generalised Propensity Score," Journal of International Development, John Wiley & Sons, Ltd., vol. 31(6), pages 475-494, August.
    17. Manuela Coromaldi & Alessandra Garbero & Marco Letta, 2019. "Recovering the counterfactual as part of ex-ante impact assessments: an application to the PASIDP – II project in Ethiopia," Economics Bulletin, AccessEcon, vol. 39(3), pages 1844-1854.
    18. A. Giffin & B. J. Reich & S. Yang & A. G. Rappold, 2023. "Generalized propensity score approach to causal inference with spatial interference," Biometrics, The International Biometric Society, vol. 79(3), pages 2220-2231, September.
    19. Kyoji Furukawa & Munechika Misumi & John B. Cologne & Harry M. Cullings, 2016. "A Bayesian Semiparametric Model for Radiation Dose‐Response Estimation," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1211-1223, June.
    20. Sinyolo, Sikhulumile, 2020. "Technology adoption and household food security among rural households in South Africa: The role of improved maize varieties," Technology in Society, Elsevier, vol. 60(C).
    21. Juraj Bodik, 2024. "Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain," Mathematics, MDPI, vol. 12(10), pages 1-36, May.
    22. Ferrara, Antonella Rita & Dijkstra, Lewis & McCann, Philip & Nisticó, Rosanna, 2022. "The response of regional well-being to place-based policy interventions," Regional Science and Urban Economics, Elsevier, vol. 97(C).
    23. Giannetti, Caterina, 2019. "Debt specialization and performance of European firms," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 257-271.
    24. Davide Dottori & Caterina Giannetti, 2017. "The effect of time preferences on altruism," Discussion Papers 2017/226, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

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    More about this item

    Keywords

    dose-response function; generalized propensity score; kernel estimator; penalized spline estimator; weak unconfoundedness;
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J70 - Labor and Demographic Economics - - Labor Discrimination - - - General

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