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Estimating the dose-response function through the GLM approach

Author

Listed:
  • 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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    File URL: http://fmwww.bc.edu/RePEc/dsug2013/ventura_DESUG2013.ppt
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    Cited by:

    1. 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).
    2. Gabriel M Ahlfeldt, 2018. "Weights to Address Non-parallel Trends in Panel Difference-in-differences Models," CESifo Economic Studies, CESifo Group, vol. 64(2), pages 216-240.
    3. 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.
    4. Ruth T. Chepchirchir & Ibrahim Macharia & Alice W. Murage & Charles A. O. Midega & Zeyaur R. Khan, 2017. "Impact assessment of push-pull pest management on incomes, productivity and poverty among smallholder households in Eastern Uganda," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(6), pages 1359-1372, December.
    5. Andrea Filippetti & Giovanni Cerulli, 2018. "Are local public services better delivered in more autonomous regions? Evidence from European regions using a dose‐response approach," Papers in Regional Science, Wiley Blackwell, vol. 97(3), pages 801-826, August.
    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).

    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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