As Table 5 shows, decentralization measured by main decentralization index has a negative effect on corruption in all specifications and for all datasets that we use. When clustered by country standard errors are used the effect is only marginally statistically significant (p-value of 0.05-0.15) when dependent variables are bribe burden, informal gift, or bribe % sales, while for paid bribe it is practically insignificant. However, the average size of a cluster in our data is 646 observations for GCB sample, and 546 observations for WBES sample, while the number of clusters is 57 in GCB, and 60 in WBES. As is argued by Wooldridge (2006), for relatively small number of clusters and big size of clusters inference based on clustered standard errors may be too conservative (meaning that it produces too big standard errors).<sup>9</sup> In Table 6 we report the results from identical (to the ones in Table 5) regressions, but we do not cluster the standard errors (columns (1), (3), (5), (7)). We show only results from OLS, but the results from probit and tobit have the same property: while the point-estimates of the decentralization’s effect on corruption remain the same, the standard errors are now much smaller. In all above-mentioned specifications decentralization has negative and strongly statistically significant effect on corruption.

Ignoring countries’ fixed effects completely - as it is done in columns (1), (3), (5), (7) of Table 6 - would be too optimistic. As an alternative, we use the inference for a clustered data proposed by Donald and Lang (2006). For that we just average our data by country (take means of every series by country) and run the same regressions as before, only with the number of observations, which corresponds to a number of countries in our samples. The standard OLS standard errors would be then correct. The results for each of our four measures of corruption are presented in columns (2), (4), (6), (8) of Table 6. The effect of decentralization on corruption remains negative in all four specifications. It is significantly different from 0 though only if dependent variables are bribe burden and informal gift. These results are supported by the ones in the columns (13)-(14) from Table 5, when we run a regression on a cross-country sample and use TI corruption perception as our measure of corruption.

While the effect of decentralization on corruption is not statistically significant in all of our specifications, Tables 5 and 6 present a strong evidence that it is strictly negative. First, the point-estimates of the effect are negative in all our specifications, and they are insensitive to the inclusion of additional regressors on the right hand side. Second, even if conservative clustered standard errors are used, it is statistically significant on the margin in certain specifications, and this fact is supported by the Donald and Lang (2006) approach. The strongest (statistically) evidence is for the amount of bribes that households pay (bribe burden), and for the frequency of bribery among firms (informal gift). Therefore, both households and firms are affected by decentralization. As our cross-country regressions suggest, it also has a positive effect on corruption perception of a country among experts (which is higher for less corrupted countries).

Decentralization also has significant economic effect on corruption. From column (2) of Table 5 it follows that a 0.1 increase in decentralization of local governments - which effectively means extending their expenditures and own revenues by 10

> 9Difference in cluster sizes (646 vs. 546) may also explain why standard errors in regressions run on GCB sample are in general higher that the ones from WBES sample

24