30

# C. Economic variables:

- Real (1991 Rubles) per capita GDP - 1994 and 1995 data only * Annual real per capita income (1991 Rubles)

- Annual rate of inflation (%)

- Real industrial output growth (as % of previous year)

- Growth of investment (as % of previous year) * Percent of arrears in enterprise payables (1995 and 1996 data only)

# D. Fiscal variables:

- Federal transfers to regions as percent of total regional expenditures (%) * Regional transfers to local governments as percent of local governments' total expenditures (%) * Share of expenditures on National Economy in the consolidated regional budget expenditures (%) * Share of expenditures on Education in the consolidated regional budget expenditures (%) * Share of expenditures on Health and Sport in the consolidated regional budget expenditures (%) * Share of expenditures on Social Protection in the consolidated regional budget expenditures (%) * Real (1991 Rubles) per capita consolidated regional expenditures on National Economy

- Real (1991 Rubles) per capita consolidated regional expenditures on Education -

- * Budget arrears as percent of consolidated regional budget expenditures (%) 1995 and 1996 data only * Tax arrears as percent of the consolidated regional tax revenues (%) - 1995 data only * Accumulated debt as percent of consolidated budget revenues net of transfers (%) - 1995 and 1996 data only

- Local governments budget balance (negative sign indicates a deficit) as percent of local governments total expenditures (%)

- Regional budget balance (negative sign indicates a deficit) as percent of total regional expenditures (%)

- Consolidated regional budget balance (negative sign indicates a deficit) as percent of consolidated regional budget expenditures (%)

The statistical analysis presented below is conducted with the data on 85 of the 89 regions of the Russian Federation. The regions removed from the sample are Chechnya, Ingushetia, the cities of Moscow and St. Petersburg. The first two are dropped because of the poor quality or lack of data on most of the variables. The special status of the last two as federal cities puts the issue of fiscal decentralization out of context. We use the values of the above listed variables over the period 1994-1996 for each of the remaining 85 regions to construct the panel used in the subsequent regression analysis. If there is no data on a particular variable for a given region in a given year, missing values are recorded in the data set. In the case of variables such as the rate of infant mortality in 1990 and the percent of rural population in 1994, for which we are only interested in how their values in some base year (1990 and 1994 respectively) affect the future outcomes of the dependent variable, we record the base values of these variables three times in the data set - once for every year in the sample. The summary regression results tables presented in this section are constructed using the following convention. We first identify the preferred regression model of the dependent variable using standard econometric techniques. We then test for the presence of heteroskedasticity and correlation in regression residuals using the White test for general heteroskedasticity/2 and the Durbin-Watson testf3 for first-order serial correlation. If the resulting

- 22 The test statistics are computed using auxiliary regressions, in which the squared residuals from the regressions presented in Tables 19,20 and 21 are regressed on a constant, the explanatory variables from the original regressions and all possible (nonredundant) cross products of the explanatory variables, with the exception of those involving dummy variables.