but with a coefficient of 0.23, while the intercept, at 0.65, is significantly different from zero and the R-squared is 0.14 (see Figure 2).<sup>16</sup> Reversing the approach, with percentage of government contracts paid in bribes multiplied by percentage of sales to the state as the dependent variable, the coefficient rises to 0.6, however the intercept also rises, to 2.1 (with a 95 percent confidence interval of 1.6 to 2.6). This is worth comparing to the average value for the dependent variable of 2.9.

Given the issue of measurement in bands, it might be better to calculate the lowest and highest potential percentage of revenues going to bribes for government contracts by each method and calculate in what percentage of cases the two bands overlap (see Figure 3 for an illustration). Using this method, 54 percent of estimates overlap. This number drops to 46 percent of estimates amongst firms which report some level of bribe payments as a percentage of government contract values.<sup>17</sup> The results in part reflect different average estimates produced by the two calculations of bribes for contracts as a percentage of revenues. The product of percentage of total revenues in unofficial payments multiplied by percentage of unofficial payments to secure government contracts averages 1.3 percent. The product of percentage of contracts in bribes multiplied by percentage of sales to government averages 2.9 percent.<sup>18</sup> There is some considerable level of information in the survey results regarding the extent of corruption in government contracting, then. Nonetheless, there is also considerable noise and/or bias (as is usual in surveys), suggesting the need for caution in use of the results. Beyond banding, further noise is added by the fact that respondents are sometimes asked to consider their company and sometimes firms ‘like theirs’. Some questions refer to sales, some to contracts and some to revenues. Nonetheless, the results surely also suggest some uncertainty about the usual size of bribe payments to secure government contracts on the part of respondents.

Again, amongst the firms surveyed by BEEPS were 376 construction companies. We can use the data to examine the differences between construction firms and others when it comes to corruption in Eastern Europe and Central Asia (Table 8). It appears that construction firms in the sample think it is more common to pay bribes in their industry than do firms in other industries, that firms like theirs spend a larger percentage of revenues on bribes, and they bribe more frequently to get licenses, deal with taxes and get contracts. Adding a number of controls, it appears (unsurprisingly) that private construction firms where the state is the largest customer are likely to report particularly

> 16 It is worth noting that this is not a test of a model, but what should hopefully be an identity relationship. Not all sales to government involve a formal contract, this may account for some of the variation between the two estimates.

> 17 i.e. assuming that the answer to question 30 is not 1.

> 18 The ranges based on the minimum and maximum answers from banded responses are 0.8 to 2.0 for the first estimate and 1.8 to 4.7 for the second estimate.  It is worth emphasizing this overlap (the standard deviations of the averages also overlap).  This may suggest that the most plausible range for the percentage of revenues going to bribes for this group of firms is 1.8-2.0.

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