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2. The comparison of estimated α values for each year also shows some decrease in the coefficient (though, it’s not monotonous). It proves the assumption that financial aid allocation methodology was improving as long as while calculating financial aid amount the federal center turned from actual expenditure focus to 'normative' values. It is in the same manner that β value dynamics be analyzed. Although no sufficient decrease can be observed within β dynamics, it should be noted that for 1998 β coefficients proved to insignificantly differ from zero. Thus, the results of α and β coefficients' estimation show that regardless of the implemented transfer allocation formula there was a shift within federal financial aid calculations from the focus on actual regional revenues and expenditures to the focus on some expected revenues and necessary expenditures close to the 'normative' values and fiscal capacity estimated. In practice this conclusion can be explained by the fact that a new formula and methodology of financial aid allocation from the federal Fund for financial assistance for the regions was introduced only in 1999 and transfer amount received by the Federation subjects from the FFAR in 1998 was defined in the during the parliamentary debates about the Federal Budget bill not in accordance with any formulas. By 1999 the policy pursued by federal authorities32 as well as constraints on the federal budget funds caused by the 1998 financial crisis had resulted in orientation of federal financial aid to the regions that really prove to be in need.

3. It should be noted that α and β coefficients value decrease not only diagonally within the table (year change for both the transfer and revenues and expenditures) but also downwards (transfer amount calculation by 'normative' values and values for the previous years). The following explanation can be accepted. During the time period under consideration the calculations of transfer amount allocated to the regions within the draft plan of federal budget law conceived for the next year were done on the basis of some revenue and expenditure values based on the budget execution data for 1991 agreed with the regional representatives with a great deal of adjustments as well as a two-year lag. Then, as long as the budget bill was passing through the Parliament, financial aid value was being modified. Further on within budget execution during a year the regions could receive additional grants (that were not envisages by the budget plan) to finance their expenditures and compensate for any financial gaps and the Ministry of Finance could either suspend transfer financing or speed it up within the fiscal year. Thus, the calculations of financial aid based upon 'normative' or potential values are done within drafting the federal budget plan as well as current financial aid amount is determined by actual revenues and expenditures for the current period. It results in the fact that though the model estimating financial aid allocation within some year and based upon equalization needs for the previous years is worse to perform actual procedure of financial support, it tends to be focused upon 'normative' rather than actual values (low coefficients α and β). As long as we turn to the models based upon the information close to the time of actual transfer financing, the model fails to be objective (aggregate federal transfer financing depends upon actual revenues and expenditures rather than upon 'normative' or potential values).

4. In general, it can be stated, that α proves to be less than β for almost all allocation models. It means that, while allocating financial aid, the Federal Center is orientated to actual tax revenues rather than to actual expenditures (or visa versa, to expenditure needs rather that to fiscal capacity), which can be explained by the fact that up to 1999 there were no federal methods of fiscal capacity estimation for transfer allocation formula was based upon actual tax revenues. At the same time, the fact that calculations were based upon 1991 expenditure data obliged the federal authorities to define the adjustment index, which proved to bring 1991 expenditures closer to 'normative' rather than actual values. Another assumption is that the range of regional authorities’ taxing (or in broad sense – revenues) powers is less that the range of expenditure powers, i.e. regional authorities’ decisions within expenditure field can exert a more profound influence upon regional budget financial gap, which federal aid allocation is focused on. Therefore it is lower regional tax revenues that proves to be an objective reason for federal transfer increase rather than budget expenditure overgrowth. It is important that the decrease of possible fiscal incentives gained by regional authorities along with changes in the federal financial aid amount be consequent on the given correlation between α and β. This consequence will be analyzed below.

In order to specify the hypotheses about values of α and β parameters we assumed that while calculating financial aid the latter be decomposed into two parts. The first part consists in the transfers received from the fund of financial support for the regions, which perform a kind of financial aid allocated according to the most formalized rules. Common to all the Federation Subjects formula and approved by Federal budget bill for the next year set the transfer value allocated to the regions. The second part consists in additional financial aid constituted by subsidies, subventions, mutual settlements and budget loan balance. The whole amount of financial aid equals the sum of transfer and additional support, the most part of the latter being dependent upon current regional needs rather than upon objective parameters. Therefore, it can be assumed that bigger α and β values (characteristic of less objective allocation) as well as smaller γ value will be observed within the equations, in which additional financial aid proves to be a dependent variable, as long as smaller α and β values and bigger γ value will be found in the equations, in which a dependent variable substitutes for a transfer in its narrow sense. OLS estimations of equation (56) where dependent variable is the second part of the financial aid are given below:

It should be mentioned that the estimations of the regression (56) for the second part of financial aid are characterized by a smaller adjusted R2. value and a smaller (in general) amount of statistically significant coefficients. It means that additional aid allocation is defined rather arbitrary, though the Federal Center in this case is more orientated to the compensation for the actual deficit, i.e. the gap between revenues and expenditures, in comparison with the previous model. In order to understand the meaning of α and β value exceeding one the modified transfer formula (55) can be used. From the formula it can be concluded that α and β can be interpreted as coefficients as long as actual revenues and expenditure value deviates from 'normative' values. It means that along with γ growth, the part of the transfer compensating for the expenditure deviation from the standards is proportionally increasing. Alongside with that the coefficient might be bigger than one (then it fails to be the weight of actual and 'normative' expenditures). It should be also noted that within the last years of the regarded time-period the most part of the coefficients fails to be significant, which probably can be explained by the reduction in the amount of the second part of financial aid. (see table 2).

Analyzing the results, it can be premised that the values of coefficients used in financial aid allocation formula prove to be different for different regions, i.e. the Federal Center treats the regions (or groups of the regions) differently while distributing financial aid. Let’s assume that actual financial aid deviation from the unity performed by equation (54) is caused by different γ parameter value and similar α and β values for each region within the same year. If this assumption proves to be true, then the regression residuals (56) can be interpreted as various γ values for different regions, the deviation of which from the average value performs the variety of regional impact on financial aid amount. Analogously it can be assumed that regression residuals (56) is determined by the difference in either α or β It can be also assumed that some regions differ by all the three coefficients.

In order to verify the hypothesis about the difference in the parameters of federal financial aid formula for different regions it is necessary that highly subsidized regions group (i.e. receiving sufficient financial aid per capita) be distinguished out of all the regions. In this case the group can’t be limited to traditionally highly subsidized regions (the degree of freedom for the calculations isn’t large enough). Besides, financial aid amount received by the regions was sometimes radically modified from year to year, therefore, the selection of the regions constituting the mentioned above conventional group was based upon the following criterion: within each year this group consists in the regions receiving the transfer bigger than monthly minimum of subsistence per person33. OLS estimations of the equation (56) for the regions that satisfy this condition (the fact that some region is included in this group depends on the amount of financial aid received in one year and may change from year to year) are the following results for the aggregate financial aid:

Correspondingly, for additional financial aid received by highly subsidized regions we get the following OLS estimation results:

In general, as it is shown in the tables, the results received for (highly subsidized) regions differ by a higher coefficients α (more orientated to actual expenditure), β (more orientated to actual revenues) and γ (a larger share of deficit covered). Alongside with that it can be noted that it is in the same manner with α that γ tends to fall within some time as long as β fails to be significant for the earlier years (i.e. financial aid allocation based upon 'normative' values was introduced earlier in these regions than in all the regions). The number of significant coefficients for additional financial aid is smaller than for all the regions, i.e. its allocation is less systematized within this regional group than within all the regions. Thus, the hypothesis about common approach (common criteria) taken by the Federal Center to all the regions while allocating financial aid isn’t proved by empirical data.

As it was mentioned above, if the formula, by which financial aid is calculated, depends upon regional actual revenues and expenditures, regional authorities possessing enough power can modify own revenues and expenditures relying upon their own priorities and, thus, influencing upon transfer amount. In order to value regional fiscal incentives, that is the impact of financial aid amount on regional revenues and expenditure, the following equations were estimated:

( 59)

( 60)

The OLS estimations of (59) for 1994-1999 are given in the tables below (statistically significant coefficients are written in the bold type):

The OLS estimations of equation (60) for 1994-1999 are given in the tables below (statistically significant coefficients are in the bold type):

The estimations of equations (59) and (60) in order to define revenues, expenditures and financial aid amounts characterize (in terms of theoretical model presented above) regional preferences and their initial choice of taxation and expenditure determined by the transfer amount rather than regional behavior depending on change in transfer amount. It also should be noted that such simple regressions of of tax revenues and expenditures upon transfer amount prove to be badly specified. Thus, for shaping tax revenue it is necessary that the equations, which include, besides transfer, tax base and other factors determinant for revenue amount, be used34. Analogous assumptions can be made in regard to regional budget expenditures. Here it is advisable that various factors determining expenditure value be considered35. In order not to complicate the interpretations of the equations we won’t do such estimations at present stage but we’ll estimate them as equations of tax revenues and expenditures dependence upon transfer growth year after year. It will give a chance to estimate transfer impact on revenues and expenditures within the assumption that other determinant factors prove to be constant (it is obvious that this assumption is weaker than the one about similar value of corresponding factors in models (59), (60):

( 61)

As it was mentioned above, changes in tax revenues opposite in sign to changes in financial aid amount (a1<0) proves to be negative fiscal incentive to provide public goods for a model of such kind (the statistically significant coefficients are given in the bold type).

For most equations estimated (regarding taken lags) financial aid growth doesn’t change the tax revenues.

Let’s analyze similarequations for impact of financial aid growth upon expenditures increase.

( 62)

The results of the OLS estimations of the regression (62) are given below (significant coefficients are given in the bold type).

From the estimations performed in order to define the impact of transfer growth on tax revenues and expenditures increase it can be concluded that:

- transfer growth for most equations (regarding the lags) does not exert any influence upon tax revenue increase.

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