experts would have a limited knowledge of the range of corrupt dealings ongoing in a country at a given time. So, when ranking countries they might draw on observable information that in their view is indicative of corruption rather than direct experience or knowledge.

With regard to noise, a case from Peru gives reason for doubting the power of general indices to reflect the underlying extent of corruption. In 2000 the government released tape transcripts on the Montesino corruption scandal, which involved the bribery of over 1,600 senior government figures. Following the release of the tapes, the Corruption Perception Index of TI moved fairly dramatically from 4.4 to 3.5 (on a scale of 10 - 1). This seems to suggest that the experts did not really understand corruption in Peru prior to the release of the tapes –before the scandal broke, when the corruption was ongoing, Peru was judged as clean as the Czech Republic.

Comparing firm-level surveys and perceptions of aggregate corruption Knack (2006) shows for Eastern Europe and Central Asia that firm-level data on petty corruption are only weakly correlated with perceptions on aggregate corruption. Both sets of data are not well correlated with firm data on procurement related corruption. Olken (2008) and Donchev and Ujheliyi (2008) also find weak correlation between micro-data on corruption and aggregate perceptions.

Kaufmann, Kraay and Mastruzzi (2006) (hereafter KKM) explore whether biases are reflected in perceptions indicators. First, they find that expert surveys do not seem to exhibit systematic biases compared to surveys based on the observations of samples of individual firms or households. Essentially, they showed that ratings among commercial risk-rating surveys were not more correlated with each other than with firm surveys of corruption. They cast doubt on a study by Razafindrakoto and Roubaud, which shows that for eight African countries, experts significantly misjudged what households would report about corruption. For example, households were asked whether they had been “victims of corruption”. KKM argued that some may have paid bribes, but may not have felt victimized, maybe because bribes helped them get what they wanted. Second, they explored whether social groups with different world views about what constitutes good governance such as business or NGOs might come systematically to different conclusions about the existence of corruption. They find that rankings of corruption produced by such different groups are highly correlated. They consider a few other potential sources of bias such as cultural bias, or bias originating in sampling technique. All in all, they conclude that there might well be biases, but it is not clear whether and in what way they might be systematic.

At the same time, Olken (2008) examined the relationship between people‟s perceptions of corruption in road building in villages in Indonesia and audited actual levels of materials theft from that project. He found that perceived levels of corruption were driven far more by general cynicism about the honesty of politicians, the education level of respondents and the ethnic diversity of the village they lived in than they were by actual levels of corruption. And while perceptions of corruption were considerably higher in diverse villages, objective evidence of 3