Recent studies have found reticent managers are less likely to report corruption than are non-reticent managers. We confirm this using new data from Bangladesh and Sri Lanka. We find reticence greatly affects estimates of corruption for measures based on both direct and indirect questions. We also find reticence affects response rates. Surprisingly, reticent managers were less likely to refuse to answer questions on corruption than non-reticent managers, possibly because reticent managers believe that refusing to answer seems like a tacit admission of guilt. Throughout the analysis, we control for the potential endogeneity of the reticence measure.
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The data generated from the Sri Lanka survey and the questionnaire are available from the World Bank on the World Bank’s Enterprise Survey website (http://www.enterprisesurveys.org).
Classifications were based on ISIC rev 3.1.
This document is available for registered users of World Bank Enterprise Survey data on the Enterprise Survey webpage (http://www.enterprisesurveys.org)
Transparency International assigns scores on perceptions of public sector corruption ranging from 0 to 100, with 0 representing highly corrupt and 100 representing very clean.
Clarke (2011a) shows managers who answer the question as a percentage of sales report paying far higher bribes than do managers who answer the question in local currency. He also shows this is true even after controlling for other things that might affect bribe payments.
The other four are electricity, access to finance, access to land, and political instability.
Managers who answered ‘do not know’ to whether they had met with or been inspected by tax officials were also asked the indirect question.
In addition, it was asked only to managers who said tax inspectors had not inspected their firm, further suggesting it is not asking about the manager’s own experiences.
Consistent with this, Clarke (2012b) shows managers in Afghanistan who do not bid on government contracts are more likely to say corruption is a serious problem when bidding on government contracts than managers who do bid. Clarke (2012b) argues the most reasonable explanation is firms excluded from bidding due to corruption are more concerned about corruption than firms that bid.
For the ‘any bribes’ question, although they cannot answer ‘yes’ or ‘no’, we will treat a positive amount as a ‘yes’ and zero as a ‘no’ in the discussion.
See Fox and Tracy (1986) for a general discussion or Recanatini et al. (2000) for a discussion directly linked to the Enterprise Surveys. A related technique is list randomization where survey respondents are asked to report how many sensitive statements in a given list are true. Karlan and Zinman (2012) used list randomization in a study looking at loan use by microenterprises.
Lensvelt-Mulders et al. (2005) suggest that random response questions reduce underreporting.
Edgell et al. (1982), for example, found significant underreporting of sensitive behaviors when they used a version of the forced response methodology. They used a random number generator to generate random numbers. On some questions, however, the random number generator was not random and instead always forced the respondents to say yes. Despite this, about one-quarter of respondents answered some questions ‘no’.
The expected distribution assuming 30% have done each behavior is calculated assuming the probability an individual does each act is independent of the probability that they will do the other acts.
We do not have any information on the educational attainment of the respondent (unless the respondent is also the top manager).
We would like to thank an anonymous referee for this observation.
The one difference is that in the model for tax inspections - not inspected, the significance level falls to about 12%.
In particular, the linear probability model can be applied with continuous, binary or categorical variables (Lewbel et al., 2012). The linear probability model is also more robust to heteroskedasticity. Because the error terms in the linear probability are heteroskedastic by construction, we use robust standard errors. Although robust standard errors are less efficient than using weighted least squares, they allow for more general heteroskedasticity.
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The data used in this paper are from the Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Responsibility for all errors, omissions, and opinions rests solely with the authors. We would like to thank Jeff Nugent and Bilin Neyapti and participants at the Western Economic Association annual meetings, Public Choice Society annual meetings, and a seminar at Texas A&M International University for comments on earlier drafts. Finally, we would like to thank Josef Brada and anonymous referees for their helpful advice.
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Clarke, G., Friesenbichler, K. & Wong, M. Do Indirect Questions Reduce Lying about Corruption? Evidence from a Quasi-Field Experiment. Comp Econ Stud 57, 103–135 (2015). https://doi.org/10.1057/ces.2014.43
- Sri Lanka
- indirect questions