report results for Afghanistan where our estimate of the rate of guilt is greater than one and thus falls outside the feasible parameter space. The results appear in Table 6, which reports estimates of g , r and q , as well as effective reticence, rq . This latter measure is particularly relevant because it indicates the proportion of sensitive questions that are not answered candidly. Finally, for reference the Table also reports naive estimates of guilt.<sup>13</sup> The estimated rate of effective reticence, rq , ranges from 21% in Indonesia to 48% in India. While these rates are all lower than the estimated rate of effective reticence of 51% in Peru, they still indicate that a substantial fraction of questions are not answered candidly in these countries. Naturally, this prevalence of reticent behavior implies that estimated rates of guilt are higher than those implied by average responses to the CQ. Looking across countries, the fraction of respondents reporting personal experience with corruption ranges from a low of 7% in Indonesia to a high of 23% in India. However, our estimates of guilt range from 9% in Indonesia to 44% in India.

It is also interesting to note that many of the cross-country differences in estimated guilt and reticence are large relative to the corresponding standard errors of the estimates. To illustrate this, we test the null hypothesis that each of the estimated parameters in Table 6 is equal to the corresponding point estimates that we would obtain if we were to pool data from all 10 countries in our estimation. We then indicate with * (**) (***) those cases where we can reject this null hypothesis of parameter equality at the 90 (95) (99) percent significance level. Consider for example the estimated rate of guilt, _g_ . The pooled estimate combining data for all countries is 0.286, and we reject the null hypothesis that the country-specific estimates of _g_ are equal to the pooled estimate in 5 out of 10 countries. For effective reticence, the pooled estimate of _rq_ is equal to 0.381, and we find that in 8 out of 10 countries, country-specific estimates are significantly different from this pooled estimate.

**Figure 5** illustrates the consequences of reticent behavior for estimates of the prevalence of corruption for the full set of 10 countries included in our analysis.  The top panel compares naïve and reticence-adjusted estimates of guilt.  Consider for example the comparison of Indonesia and Malaysia, which have similar naive estimates of the prevalence of corruption of 7% and 9% respectively.  However, our estimates suggest that effective reticence is much more common in Malaysia (44%) than it is in Indonesia (21%).  As a result, our reticence-adjusted estimates of corruption increase much more in Malaysia (to 15%) than they do in Indonesia (to 9%). Note also that the corruption ranking of six of the ten countries differs between the naïve and the estimated rates of guilt.  Overall, our estimates from the GWP show that reticent behavior is likely to lead to substantial downward biases in estimates of corruption based on CQs, and moreover that the size of these biases can differ non-trivially across countries.

> 13 In our estimates, we restrict our sample to those who have answered the RRQs, and we also do not apply the sampling weights that are used to produce the GWP estimates.  As a result, the sample means of the CQ reported in the last row of Table 6 differ from those reported by Gallup.

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