A third feature of survey results worth noting is that there is a significant variation even within a sector in a given country in the level of corruption occurring in a particular process. Looking at the BEEPS question “when firms in your industry do business with the government, how much of the contract value would they typically offer in additional or unofficial payments to secure the contract?” only around fourteen percent of the variation in answers amongst construction firms can be explained by the country of respondent (Kenny, 2006).

This evidence suggests that general country-level corruption indicators may be poor tools to uncover particularly corrupt construction industries, but also that corruption within the industry may differ markedly by sub-sector or by location within a country. In turn this suggests some danger in assuming a single intervention or model will have a similar or significant impact on corruption in construction across different countries, and the need for a range of targeted and flexible responses at the country and sector level.

# **Differentiating the Extent and Impact of Corruption**

Where we have good unit costs for the price of construction, it appears that they are sometimes correlated (if weakly) with available general corruption measures. Figure 6 shows CPI scores and GDP per capita for a number of countries ranked by their costs per square meter of classroom construction –there is no clear pattern of countries that are perceived to be more corrupt having higher unit costs of construction.<sup>9</sup> A slightly more revealing picture is suggested by Figure 7, which displays average costs per meter squared for reconstruction of a two-lane road between 6-8 meters wide with a bituminous surface, for countries where we have four or more estimates based on individual project data.<sup>10</sup> The average cost across the 28 countries for which we have data was around $33.2 per square meter. For those countries with below average costs, PPP GDP per capita was $6,282, compared to $3,318 in those countries with above average cost. For those countries with below average costs, the CPI averaged 3.6, compared to 2.4 in countries with above average costs. A regression analysis suggests that lower corruption is significantly associated with lower costs in this sample, a result weakly robust to the inclusion of GDP.<sup>11</sup>

> 9 Data on classroom construction costs from Theunynck, 2006.

> 10 Data from the ROCKs database http://www.worldbank.org/transport/roads/rd_tools/rocks_main.htm. 11 The results suggest that an increase in the CPI (reflecting reduced corruption) from Pakistan’s score to Ghana’s would be associated with a drop in Pakistan’s road construction prices of around 20 percent.  The equation is Cost = 51.9 – 5.9*(CPI), with the constant and CPI entering at one percent, R=0.23, N=28. Including GDP per capita reduces the coefficient on CPI to 4.5 and the significance to ten percent (GDP per capita does not enter significantly).  It should be noted that road construction costs vary considerably due to within-country factors including terrain and distance to suppliers.  Within this sample, the average of standard deviations of costs within countries –at $14.6—is only slightly smaller than the standard deviation of average costs across countries –at $15.6—and for countries with more estimates, within-country cost variation rises.  Given we cannot account for such within-country factors with this analysis, this result needs to be treated with some caution.

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