33 The available regional data on infant mortality for 1994-96 demonstrate unusually large and unstable (with almost chaotic fluctuations from year to year) changes in this variable after the beginning of transition.27 This may be explained by a scale and nature of impact of transition on social development, which was massive and quite uneven across regions and across different time periods. Also, the first years of transition were accompanied by major migration flows and deterioration in a quality of demographic statistics.

Even after controlling for regional differences in the standard of living, economic mismanagement and geographic location, the administrative type of regions remains an important predictor of the degrees of fiscal decentralization. On the revenue side, the OLS coefficients of the dummy variables Autonomous Okrugs and Republics are (-10.6) and (-8.7), while on the expenditure side the corresponding OLS estimates are (-6.8) and (-8.6). These results imply that the predicted values from the two regressions of the shares of local governments in consolidated budget revenues and final expenditures are significantly lower in autonomous okrugs and republics than in oblasts and krais.

Finally, the regressions suggest that regional location is another significant variable to explain variation in fiscal decentralization. Regions located in three geographic areas (Urals, Western and Eastern Siberia) on average have much higher degree of decentralization when it is measured as a share of local governments' expenditures. There are two possible explanations of this phenomenon. First, regions located next to each other tend to have similar budget arrangements and fiscal policy. This similarity derives from horizontal inter-government interactions that are supported through activities of local associations of regional governments. Second, on average regions in both Western and Eastern Siberia are the regions with the largest territory. The size of region's territory might be in itself an important determinant of decentralization: larger territory requires more delegation of authority to local governments and thus decentralization.

To test the latter hypothesis, we re-estimate the two decentralization regressions adding the relative size of region's territory, measured as percent of the total territory of the Russian Federation, to the explanatory variables discussed above. On the revenue side of fiscal decentralization, using the full sample of 85 regions we obtain positive and statistically significant at the 99% level of confidence OLS coefficient (1.25) of the relative size of region's territory. However, when we re-estimate this augmented regression using all possible sub-samples of 84 regions as a check of the robustness of the OLS coefficients (see the discussion in the following paragraph), we discover that the explanatory power of the relative size of region's territory critically hinges upon one extreme realization of that variable. In particular, the territory of the Sakha Republic constitutes 18.2% of the total territory of the Russian Federation and it is also the region with one of the highest levels of revenue decentralization (in 1996 the share of local governments in consolidated regional budget revenues was 93.5%). The removal of this apparent outlier from the sample, renders the OLS coefficient of the relative size of region's territory statistically insignificant even at the 95% level of confidence. Thus, after controlling for regional differences in the standard of living, economic mismanagement, geographic location and administrative status, the size of region's territory is not an important determinant of the degree of revenue decentralization. On the expenditure side of fiscal decentralization, using the full sample of 85 regions we obtain a much smaller and statistically insignificant at the 90% level of confidence OLS coefficient (0.13) of the relative size of region's territory. Finally, the population density - when used in place of the relative size of region's territory - does not show up significantly in any of the two regressions. In summary, the

> 27 For instance, a correlation coefficient between mortality in 1994 and in 1995 amounts to 0.39 and between 1994 and 1996 - to 0.46.