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Misunderestimating corruption

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  • Kraay, Aart
  • Kraay, Aart
  • Murrell, Peter

Abstract

Estimates of the extent of corruption rely largely on self-reports of individuals, business managers, and government officials. Yet it is well known that survey respondents are reticent to tell the truth about activities to which social and legal stigma are attached, implying a downward bias in survey-based estimates of corruption. This paper develops a method to estimate the prevalence of reticent behavior, in order to isolate rates of corruption that fully reflect respondent reticence in answering sensitive questions. The method is based on a statistical model of how respondents behave when answering a combination of conventional and random-response survey questions. The responses to these different types of questions reflect three probabilities -- that the respondent has done the sensitive act in question, that the respondent exhibits reticence in answering sensitive questions, and that a reticent respondent is not candid in answering any specific sensitive question. These probabilities can be estimated using a method-of-moments estimator. Evidence from the 2010 World Bank Enterprise survey in Peru suggests reticence-adjusted estimates of corruption that are roughly twice as large as indicated by responses to standard questions. Reticence-adjusted estimates of corruption are also substantially higher in a set of ten Asian countries covered in the Gallup World Poll.

Suggested Citation

  • Kraay, Aart & Kraay, Aart & Murrell, Peter, 2013. "Misunderestimating corruption," Policy Research Working Paper Series 6488, The World Bank.
  • Handle: RePEc:wbk:wbrwps:6488
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    References listed on IDEAS

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    Cited by:

    1. Bernard GAUTHIER & Frédéric LESNÉ, 2017. "Measuring corruption in presence of reticent respondents: Theory and Application," Working Papers P207, FERDI.
    2. Andrews, Matt & Pritchett, Lant & Woolcock, Michael, 2013. "Escaping Capability Traps Through Problem Driven Iterative Adaptation (PDIA)," World Development, Elsevier, vol. 51(C), pages 234-244.
    3. Martin Ravallion, 2013. "Knowledgeable bankers? The demand for research in World Bank operations," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 5(1), pages 1-29, March.
    4. Lant Pritchett & Salimah Samji & Jeffrey Hammer, 2012. "It’s All About MeE: Using Structured Experiential Learning (‘e’) to Crawl the Design Space," CID Working Papers 249, Center for International Development at Harvard University.
    5. repec:dau:papers:123456789/14817 is not listed on IDEAS
    6. Bernard GAUTHIER & Frédéric LESNÉ, 2017. "Measuring corruption in presence of reticent respondents: Theory and Application," Working Papers P207, FERDI.
    7. Dobronogov, Anton & Gelb, Alan & Saldanha, Fernando Brant, 2014. "How should donors respond to resource windfalls in poor countries ? from aid to insurance," Policy Research Working Paper Series 6952, The World Bank.

    More about this item

    Keywords

    Information Security&Privacy; Statistical&Mathematical Sciences; Psychology; Scientific Research&Science Parks; Science Education;

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements
    • O43 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Institutions and Growth

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