Perhaps partially as a result, general perceptions measures are not robustly correlated with infrastructure outcomes. For example, Estache et. al. (2006) report that a general measure of perceived country-level corruption is associated with lower energy use. At the same time, they found telecoms access _positively_ associated with perceived corruption while measures of access to water were not correlated either way with perceived corruption.<sup>11</sup>

# **Estimates of Petty Corruption in Surveys**

Surveys of service users appear to be a commonsense tool to estimate levels of petty corruption in infrastructure, given that the average respondent is likely to have personal experience of the transactions that can become corrupted. Nonetheless, it is worth noting that even here, survey answers can apparently involve some uncertainty. The Investment Climate Survey results suggest a very high correlation between cross-country estimates of corruption in getting connections to electricity, water and telephone services, despite considerably lower correlations with a number of other corruption variables (See Table 6). If the data reflects reality, it suggests that even despite the very different nature of the sectors (levels of competition, size of firms, involvement of the private sector and so on), corruption is determined almost solely by national-level factors that vary insignificantly between sectors. Perhaps more plausibly, and given that we believe that corruption does significantly vary by sector, the data may reflect a common perception driven by anchored estimates rather than very similar levels of petty corruption.<sup>12</sup> Significant variation within countries in the reported level of petty infrastructure corruption is suggested by the 1999 BEEPS survey. The survey asks respondents how often firms like theirs have to bribe to get connected to public services such as telephone and electricity connections, with answers ranging between ‘always’ (given a value of one) and ‘never’ (given a value of six). Figure 1 displays the average and standard deviation of answers to this question across countries, compared to the average answer across all countries. As can be seen, only in the case of Estonia does the standard deviation not overlap with the cross-country average.

There are notable country differences (the percentage of firms answering ‘never’ varies between 31 percent in Ukraine and 92 percent in Estonia). Nonetheless, the variation in answers within countries is considerably larger than the variation across countries, to the extent that the great majority of average country responses are unlikely to be statistically

> 11 These results, positive and negative alike, are open to all of the usual concerns with econometric exercises regarding questions of causality and the stability of coefficients in the presence of multicolinearity and omitted variables.  Given that corruption is likely to be centered around urban water supply systems, it might be that better results would be uncovered using access to a private water connection in urban areas, but this data is available for fewer countries.

> 12Anchoring is a problem common to survey work, and has a particular impact where questions are vague and respondents are unsure of the answer.  Anchoring involves answers to subsequent questions being considerably affected by an initial question or piece of information, even if it is completely irrelevant (Tversky and Khaneman, 1974).

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