evidence is large and there is great reliance on peculiar country-specific institutional features that create specific opportunities for measurement. Hence, self-reports from surveys will continue to provide the basis for most research on and assessment of corruption in the future. This is especially the case when the focus is on comparisons across countries.

Existing survey evidence indicates that acts of corruption are amazingly common across the globe. Rose and Peiffer (2015) estimate that over a quarter of the world's population regularly pays bribes for public services. Such acts are by definition illegal. Moreover, for obvious reasons, corruption is much more common in countries where the reach of the rule of law is tenuous. But these are exactly the countries where respondents have the most reason to fear that government officials can force survey firms to violate confidentiality agreements. They are the countries where the evidence necessary to support criminal prosecution—or simply political or administrative persecution—is the weakest. They are the places where legal defense against such prosecution or persecution is least likely to be effective. Hence, in the highest corruption environments, there is very good reason to believe that the traditional approach of survey research—increasing respondent comfort—will not eliminate respondent reticence on corruption questions.

Therefore, in aiming to develop valid, self-report-based, cross-country-comparable measures of corruption, we adopt an approach diametrically opposite to the traditional one. We accept that there will always be large numbers of respondents who have a propensity to give false answers to questions on sensitive issues, and we embrace that fact. Instead, we rely on a methodology that allows us to estimate the rate of false answers and therefore the rate of commission of the corrupt act.

The intuition behind our approach is as follows. We formulate a structural model of the behavior of reticent respondents in answering sensitive survey questions. We then apply the model to data that simultaneously reflects the answers to two different types of questions. First, there is a conventional question (CQ) that asks explicitly whether a particular act of corruption has taken place. Naturally, if respondents are reticent, the distribution of yes/no responses on this question will depend on both the level of corruption and the level of reticence. Using this question alone one cannot disentangle the two. We therefore employ the CQ in tandem with a second type of question, a forcedresponse random-response question (RRQ; Warner 1965, Boruch, 1971). Importantly, we assume that responses to both the RRQ and the CQ are affected by the same degree of respondent reticence.<sup>2</sup> Then, the distribution of responses on the RRQ will depend on the same corruption and reticence parameters as the distribution for the CQ, but in a different way. Hence, by applying our model to the combination of the responses to the two types of questions, we obtain estimates of structural parameters that separately reflect levels of corruption and degree of respondent reticence. Our analytical approach is fully described in Sections 3 and 4, where we set up the model of respondent

> 2 In Appendix B, we argue that this is a reasonable assumption and also summarize some simulation results that show that our estimates of the degree of downward bias in standard measures of corruption are lower than those that would be obtained should the RRQ work as designed.

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