the image of increased corruption is based at least as much on this widespread reporting of a crisis as much as it is on a far smaller increase in the percentage of stories discussing corruption itself. Across countries, the fact that the WBI measure for control of corruption has a correlation coefficient of 0.95 with both WBI government effectiveness and rule of law measures suggests that it may be very difficult to tease out concerns regarding corruption with broader concerns regarding governance in general using perceptions indices (Thomas, 2006).

This is not to argue that when respondents are asked to think about corruption, they in fact _only_ think about general levels of crisis or weak governance –there is good evidence that this is not the case (Kaufmann et. al., 2006). It is only to point out that general concerns abut a country are likely to play a significant role in responses to a question which is based on perceptions of an opaque, amorphous subject. Aggregating perceptions scores from different sources does not necessarily improve the problem. Variation in sources for country scores within a given year and across years and lack of independence between sources both reduce accuracy. This increases the magnitude of variation between scores required to declare a ‘statistically significant’ difference in perceived corruption –a variation which is already quite large. It also makes comparing scores for a country over time problematic (Knack, 2006, Arndt and Oman 2006).

Where we have estimates of the signal to noise ratio in corruption perceptions, the results are not reassuring. Olken (2006) finds that villager perceptions of corruption in village road projects are correlated with objective measures of corruption estimated from expenditure tracking and physical audits of the roads (‘estimated corruption’), but the correlation accounts for little of the variation in perceptions. A 10 percent increase above the mean in the objective measure of corruption (reflecting an increase in missing expenditures equal to 2.4 percent of total expenditures) is associated with an increase in the probability that a respondent would perceive corruption of 0.3 percent (from a baseline of 36 percent). These are marginal effects, but if they held, this suggests that moving from no objective evidence of corruption in the project to the road not being built at all because 100 percent of expenditure went missing would increase the proportion of villagers who reported corruption by 12 percentage points.

Personal characteristics were significantly more correlated with corruption perceptions than were levels of estimated corruption. Beliefs about corruption in Indonesia were strongly correlated across different levels of government (those who believe the President was corrupt were far more likely to believe that the village head was corrupt). Bettereducated and male respondents were much more likely to report corruption, and the impact of education, for example, on likelihood of reporting corruption was far greater than the impact of increases in estimated corruption in the project itself.

Particularly significant as a determinant of perceived corruption at the project level was ethnic heterogeneity. Moving from a village that was ethnically homogeneous to the most ethnically diverse village in the sample increased the likelihood of a villager reporting corruption in the project by 50 percentage points. This bias is particularly important because estimated corruption was significantly _negatively_ related to measures 9