regulation.<sup>5</sup> Such results suggest that petty bribery for infrastructure connections is somewhat of an issue in the region, and we will see that there is evidence to suggest construction industries are particularly susceptible to corruption in licensing, taxation and obtaining government contracts, in turn suggesting that infrastructure investment may be an area of concern.

The BEEPS data provides evidence on what construction firms believe is typical payoff as a percentage of the contract value made for securing a government contract in their industry (Table 1). From this we can estimate that the average perceived payoff for a government construction contract in the region is around 7 percent of the contract value (although we will see this number is uncertain).<sup>6</sup> It is worth noting here that reports of grand corruption vary significantly across industries. The BEEPS data suggests that there is no significant correlation between cross-industry estimates of corruption and estimates of corruption given by the subset of construction industries at the national level (see Table 2). We shall see that petty corruption in infrastructure also appears to be weakly correlated with more general measures of corruption.

This suggests the importance of sector-level indicators to accurately report levels of corruption in construction and infrastructure in a particular country, however there is a paucity of cross-country data on estimates of both grand and petty corruption in infrastructure provision in particular. Looking at petty corruption, around 20 World Bank Business Environment Surveys have asked infrastructure-related questions regarding the need to pay ‘gifts’ in order to get a water, electricity or phone connection. With grand corruption, we have no cross-country comparable data of the extent of bribery in infrastructure firms. BEEPS did not survey infrastructure firms, for example. This reflects in part the concentrated nature of infrastructure provision which adds complexity to ensuring the anonymity of survey responses.

Beyond perceptions and survey measures, indirect estimates can use measures of losses as a proxy for the extent of corruption, potentially capturing the impact of both petty and grand corruption. In Andhra Pradesh, transmission and distribution losses were reduced from 38 to 26 percent 1999-2003 in large part through theft control and the regularization of 2.25 million unauthorized connections. This strongly suggests that corruption was significantly linked to losses in this case. In Bangladesh and Orissa, in India, around 55 percent of generated power is paid for, the rest is lost to technical and commercial losses. Of this, perhaps 15-18 percent is accounted for by ‘true technical’ losses, suggesting leakage due to illegal connections or underbilling accounts for as much as 30 percent of generated power (Gulati and Rao, 2006). Davis (2004) suggests that unaccounted for water accounts for 35 percent of total flows in India.

> 5 These are unweighted country average responses.

> 6 This assumes mid-point values for the data ranges (2.5% for the 0-5% range, for example) and 30% for answers of ‘above 20 percent.’  The lowest possible estimate (assuming 0% for the 0-5% range, and so on) is four percent, the highest (assuming 5% for the 0-5 range and so on, and 100% for the ‘above 20%’ answer) is ten percent.

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