IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/24899.html
   My bibliography  Save this paper

Impact Evaluation of Multiple Overlapping Programs using Difference-in-differences with Matching

Author

Listed:
  • Nguyen Viet, Cuong

Abstract

Difference-in-differences with matching is a popular method in impact evaluation. Traditional impact evaluation methods including difference-in-differences with matching often deal with impact measurement of a single binary program. Imbens (1999) and Lechner (2001) extend the matching method to the case of multiple mutually exclusive programs. Frölich (2002) discusses different impact evaluation methods in the similar context. In reality, one can participate in several programs simultaneously and the programs may be overlapping. This paper discusses the method of difference-in-differences with matching in a general context of multiple overlapping programs. The method is applied to measure impacts of formal and informal credit in Vietnam using panel data from two Vietnam Household Living Standard Surveys in 2002 and 2004.

Suggested Citation

  • Nguyen Viet, Cuong, 2008. "Impact Evaluation of Multiple Overlapping Programs using Difference-in-differences with Matching," MPRA Paper 24899, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:24899
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/24899/1/MPRA_paper_24899.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Conning, Jonathan & Udry, Christopher, 2007. "Rural Financial Markets in Developing Countries," Handbook of Agricultural Economics, in: Robert Evenson & Prabhu Pingali (ed.), Handbook of Agricultural Economics, edition 1, volume 3, chapter 56, pages 2857-2908, Elsevier.
    2. Markus Frölich, 2004. "Programme Evaluation with Multiple Treatments," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 181-224, April.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    4. Zeller, Manfred & Diagne, Aliou & Mataya, Charles, 1997. "Market access by smallholder farmers in Malawi," FCND discussion papers 35, International Food Policy Research Institute (IFPRI).
    5. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    6. Guido W. Imbens, 1999. "The Role of the Propensity Score in Estimating Dose-Response Functions," NBER Technical Working Papers 0237, National Bureau of Economic Research, Inc.
    7. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    8. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hämäläinen, Kari & Ollikainen, Virve, 2004. "Differential Effects of Active Labour Market Programmes in the Early Stages of Young People's Unemployment," Research Reports 115, VATT Institute for Economic Research.
    2. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    3. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    4. Sascha Becker & Peter Egger, 2013. "Endogenous product versus process innovation and a firm’s propensity to export," Empirical Economics, Springer, vol. 44(1), pages 329-354, February.
    5. Carlos A. Flores & Oscar A. Mitnik, 2013. "Comparing Treatments across Labor Markets: An Assessment of Nonexperimental Multiple-Treatment Strategies," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1691-1707, December.
    6. Raaum, Oddbjørn & Torp, Hege & Zhang, Tao, 2003. "Business cycles and the impact of labour market programmes," Memorandum 14/2002, Oslo University, Department of Economics.
    7. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    8. Lechner, Michael, 1999. "Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption," IZA Discussion Papers 91, Institute of Labor Economics (IZA).
    9. Antoine Marsaudon & Lise Rochaix, 2017. "Impact of acute health shocks on cigarette consumption
      [Impact d'un choc de santé sur la consommation de cigarette]
      ," PSE Working Papers halshs-01626024, HAL.
    10. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    11. Eliasson, Kent, 2006. "How Robust is the Evidence on the Returns to College Choice? Results Using Swedish Administrative Data," Umeå Economic Studies 692, Umeå University, Department of Economics.
    12. Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 761-783, November.
    13. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82, February.
    14. Stephan, Gesine & Pahnke, André, 2008. "The Relative Effectiveness of Selected Active Labour Market Programmes and the Common Support Problem," IZA Discussion Papers 3767, Institute of Labor Economics (IZA).
    15. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2006. "Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components: A Reanalysis of the California GAIN Program," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 521-566, July.
    16. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    17. Kluve, Jochen & Lehmann, Hartmut & Schmidt, Christoph M., 2008. "Disentangling Treatment Effects of Active Labor Market Policies: The Role of Labor Force Status Sequences," Labour Economics, Elsevier, vol. 15(6), pages 1270-1295, December.
    18. Bart, COCKX & Jean, RIES, 2004. "The Exhaustion of Unemployment Benefits in Belgium. Does it Enhance the Probability of Employment ?," LIDAM Discussion Papers IRES 2004016, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    19. Barbara Sianesi, 2002. "An evaluation of the Swedish system of active labour market programmes in the 1990s," IFS Working Papers W02/01, Institute for Fiscal Studies.
    20. Hujer, Reinhard & Wellner, Marc, 2000. "The Effects of Public Sector Sponsored Training on Individual Employment Performance in East Germany," IZA Discussion Papers 141, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    Treatment effect; impact evaluation; multiple programs; difference-in-differences; matching; propensity score.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:24899. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.