IDEAS home Printed from https://ideas.repec.org/a/tpr/jeurec/v1y2003i4p958-989.html
   My bibliography  Save this article

Electoral Rules and Corruption

Author

Listed:
  • Torsten Persson

    (Stockholm University)

  • Guido Tabellini

    (Bocconi University)

  • Francesco Trebbi

    (Harvard University)

Abstract

Is corruption systematically related to electoral rules? Recent theoretical work suggests a positive answer. But little is known about the data. We try to address this lacuna by relating corruption to different features of the electoral system in a sample of about eighty democ-racies in the 1990s. We exploit the cross-country variation in the data, as well as the time variation arising from recent episodes of electoral reform. The evidence is consistent with the theoretical priors. Larger voting districts-and thus lower barriers to entry-are associated with less corruption, whereas larger shares of candidates elected from party lists-and thus less individual accountability-are associated with more corruption. Individual accountability appears to be most strongly tied to personal ballots in plurality-rule elections, even though open party lists also seem to have some effect. Because different aspects roughly offset each other, a switch from strictly proportional to strictly majoritarian elections only has a small negative effect on corruption. (JEL: E62, H3) Copyright (c) 2003 The European Economic Association.

Suggested Citation

  • Torsten Persson & Guido Tabellini & Francesco Trebbi, 2003. "Electoral Rules and Corruption," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 958-989, June.
  • Handle: RePEc:tpr:jeurec:v:1:y:2003:i:4:p:958-989
    as

    Download full text from publisher

    File URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1542-4774/issues
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Richard Blundell & Monica Costa Dias, 2000. "Evaluation methods for non-experimental data," Fiscal Studies, Institute for Fiscal Studies, vol. 21(4), pages 427-468, January.
    2. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    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. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
    5. John Ferejohn, 1986. "Incumbent performance and electoral control," Public Choice, Springer, vol. 50(1), pages 5-25, January.
    6. Rafael Di Tella & Alberto Ades, 1999. "Rents, Competition, and Corruption," American Economic Review, American Economic Association, vol. 89(4), pages 982-993, September.
    7. 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. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    2. Spitz, Alexandra, 2004. "Using Methods of Treatment Evaluation to Estimate the Wage Effect of IT Usage," ZEW Discussion Papers 04-67, ZEW - Leibniz Centre for European Economic Research.
    3. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    4. Juan Díaz & Miguel Jaramillo, 2006. "An Evaluation of the Peruvian "Youth Labor Training Program"-PROJOVEN," OVE Working Papers 1006, Inter-American Development Bank, Office of Evaluation and Oversight (OVE).
    5. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    6. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    7. Nolan, Anne, 2006. "Evaluating the Impact of Eligibility for Free Care on the Use of GP Services in Ireland: A Difference-in-Difference Matching Approach," Papers HRBWP25, Economic and Social Research Institute (ESRI).
    8. 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.
    9. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2000. "The Long-Term Gains from GAIN: A Re-Analysis of the Impacts of the California GAIN Program," NBER Working Papers 8007, National Bureau of Economic Research, Inc.
    10. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    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. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2003. "Evaluating the impact of education on earnings in the UK: Models, methods and results from the NCDS," IFS Working Papers W03/20, Institute for Fiscal Studies.
    15. Richard Blundell & Monica Costa Dias, 2009. "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44(3).
    16. Czarnitzki, Dirk & Hanel, Petr & Rosa, Julio Miguel, 2011. "Evaluating the impact of R&D tax credits on innovation: A microeconometric study on Canadian firms," Research Policy, Elsevier, vol. 40(2), pages 217-229, March.
    17. John C. Ham & Xianghong Li & Patricia B. Reagan, 2004. "Propensity Score Matching, a Distance-Based Measure of Migration, and the Wage Growth of Young Men," Working Papers 2004_3, York University, Department of Economics.
    18. 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.
    19. Barbara Sianesi, 2001. "Differential effects of Swedish active labour market programmes for unemployed adults during the 1990s," IFS Working Papers W01/25, Institute for Fiscal Studies.
    20. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.

    More about this item

    JEL classification:

    • D7 - Microeconomics - - Analysis of Collective Decision-Making
    • H1 - Public Economics - - Structure and Scope of Government

    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:tpr:jeurec:v:1:y:2003:i:4:p:958-989. 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: Kelly McDougall (email available below). General contact details of provider: https://direct.mit.edu/journals .

    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.