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Estimating the dose-response function through the GLM approach

Author

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  • Barbara Guardabascio

    (Italian National Institute of Statistics, Rome)

  • Marco Ventura

    (Italian National Institute of Statistics, Rome)

Abstract

How effective are policy programs with continuous treatment exposure? Answering this question essentially amounts to estimating a dose-response function as proposed in Hirano and Imbens (2004). Whenever doses are not randomly assigned but are given under experimental conditions, estimation of a dose-response function is possible using the Generalized Propensity Score (GPS). Since its formulation, the GPS has been repeatedly used in observational studies, and ad hoc programs have been provided for Stata users (doseresponse and gpscore, Bia and Mattei 2008). However, many applied works remark that the treatment variable may not be normally distributed. In this case, the Stata programs are not usable because they do not allow for different distribution assumptions other than the normal density. In this paper, we overcome this problem. Building on Bia and Mattei's (2008) programs, we provide doseresponse2 and gpscore, which allow one to accommodate different distribution functions of the treatment variable. This task is accomplished through by the application of the generalized linear models estimator in the first step instead of the application of maximum likelihood. In such a way, the user can have a very versatile tool capable of handling many practical situations. It is worth highlighting that our programs, among the many alternatives, take into account the possibility to consistently use the GPS estimator when the treatment variable is fractional, the flogit case by Papke and Wooldridge (1998), a case of particular interest for economists.

Suggested Citation

  • Barbara Guardabascio & Marco Ventura, 2013. "Estimating the dose-response function through the GLM approach," German Stata Users' Group Meetings 2013 10, Stata Users Group.
  • Handle: RePEc:boc:dsug13:10
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    1. Liu, Jin-Long & Liu, Jin-Tan & Hammitt, James K. & Chou, Shin-Yi, 1999. "The price elasticity of opium in Taiwan, 1914-1942," Journal of Health Economics, Elsevier, vol. 18(6), pages 795-810, December.
    2. Helmut Fryges & Joachim Wagner, 2021. "Exports and Productivity Growth — First Evidence from a Continuous Treatment Approach," World Scientific Book Chapters, in: Joachim Wagner (ed.), MICROECONOMETRIC STUDIES OF FIRMS’ IMPORTS AND EXPORTS Advanced Methods of Analysis and Evidence from German Enterprises, chapter 6, pages 57-86, World Scientific Publishing Co. Pte. Ltd..
    3. Helmut Fryges, 2009. "The export-growth relationship: estimating a dose-response function," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1855-1859.
    4. 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.
    5. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, October.
    6. 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.
    7. 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.
    8. 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.
    9. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    10. 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.
    11. Hausman, Jerry A & Leonard, Gregory K, 1997. "Superstars in the National Basketball Association: Economic Value and Policy," Journal of Labor Economics, University of Chicago Press, vol. 15(4), pages 586-624, October.
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    4. Chepchirchir, R. & Macharia, I. & Murage, A.W. & Midega, C.A.O. & Khan, Z.R., 2016. "Impact assessment of push-pull technology on incomes, productivity and poverty among smallholder households in Eastern Uganda," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246316, African Association of Agricultural Economists (AAAE).
    5. Smale, Melinda & Kusunose, Yoko & Mathenge, Mary K. & Alia, Didier, 2014. "Destination or Distraction? Querying the Linkage between Off-farm Income and Farm Investments in Kenya," Food Security International Development Working Papers 196829, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    6. Chen, Jason V., 2023. "The wisdom of crowds and the market's response to earnings news: Evidence using the geographic dispersion of investors," Journal of Accounting and Economics, Elsevier, vol. 75(2).

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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