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Increase in efficiency of the governmental support to the RF Subjects.

The Program implementation is scheduled for 2002The total volume of the Program funds is Rb. 66323, 3 mln, including:

The federal budget funds Rb. 12413.3 mln;

Budgets of RF Subjects Rb 15168 mln;

Extrabudgetary funds Rb 38742 mln.

The envisaged Program final results are:

Reduction in interregional differences between the RF Subjects in terms of the level of GRP per capita with account of purchasing power and per capita incomes by two times by 2010 and another 25% by 2015;

Contraction of the share of the population below the poverty line by 15% by 2010 and 25% by 2015.

In 2002 alone, it was reported that works were complete on construction and reconstruction of social infrastructure facilities in backward in terms of socio-economic development regions. This would allow to bring the regions closer to the average nationwide development level. The year 2003 saw a continuation of the construction of water supply, heating and gas supply facilities. According to preliminary assessments, in 2004 488 construction projects were completed in 45 Russian regions (243 social facilities, 83 educational, 81 healthcare, 79 other social facilities). As well, 245 engineering facilities (water supply and gas supply) were build and repaired.

In 2002, from all the sources Rb. 3599.7 was earmarked (or 94.5% of the planned amount) to fund the noted works, of which:

Federal budget funds Rb 1958,9 mln., or 100% of the limit of budget obligations;

RF Subjects budget funds Rb.1238 mln., or 91,2 %;

Extrabudgetary funds Rb. 402,8 mln., or 81,7 %.

The following regions have failed to earmark the planned financial resources:

From regional budgets: Kirov oblast (78,2%), Republic of Khakassia (73,8%), Republic of Mordovia (75,3%), Moscow (71,1%), Ivanovo (59,5%), Orel (35,8%), Kurgan (26,8%), Omsk (4,1%), oblasts, Tyva Republic (35,7);

From extrabudgetary sources: Altay Krai (23,9%), Novosibirsk (67,6%), Orel (33%), Tomsk (18,6%), Tula (33%) oblasts.

The Program comprised R&D implemented in 2002 for the purpose of developing a methodology of selection of regions and projects under the Program to be funded from the Fund for Regional Development.

In 2003, the Program allowed to finance 331 facility in 42 RF Subjects, of which 127 were social infrastructure facilities, and 204 engineering infrastructure facilities. Another 96 facilities were put into operation. It should be noted that de-facto only Rb.4295.6 was earmarked from all the sources, or 50% of the Program, with the federal budget alone honoring its obligations in full. In 2003, from the federal budget at the expense of the Fund for Regional Development Rb 2645.6 mln. was earmarked on the Program implementation, which accounts for 99.8 % of annual limits. The RF Subjects budgets earmarked Rb.1257.1 mln.,or 73.4 % of the respective limits(65,9% of the Program Passport). The most alarming situation was noted in respect to the Program financing from extrabudgetary sources, with just as much as Rb 392,mln., or 65.4 % of the envisaged limits (7.8 % of the Program Passport) earmarked.

To test the impact the noted measures have on convergence processes, we assessed the regression model earlier employed for testing the hypothesis of an absolute convergence. The model was complemented by controlling variables that reflect the volume of the federal financial aid and investment in capital assets funded from the budget sources. That de-facto equals the testing of the hypothesis of a conditional convergence where these economic policy measures are recognized as convergence conditions.

Specifically, we used three controlling variables: the average value over the period (19942002), the volume of an aggregate financial aid from a superior-tier budget to GRP ratio, and the averaged over the period (19942002) investment in capital assets funded from the budgets of all levels to GRP ratio. The hypothesis of conditional convergence suggests that in such a regression the sign at the initial level of GRP per capita should be (as before) negative, while the one with the controlling variable positive, i.e.

a greater volume of transfers to a given region, or a greater volume of investment from the budget sources in it result in a more rapid growth of GRP per capita. Results of the assessment of the model with the noted controlling variables are given in Table 2.3.

As the above assessments show, in all the cases the coefficients at the controlling variables bear negative sign and a low statistical significance, while the assessments of the coefficient at the initial GRP per capita have remained statistically significant and close to the case of testing the hypothesis of unconventional convergence.

The respective results can be interpreted as an evidence of the fact that the hypothesis of unconventional convergence, indeed, cannot b rejected for Russias regions for the period 19942002, however, the role of the government regional economic policy in this phenomenon is extremely negligible. Thus. Negative signs at the controlling variables mean that the regions that received greater transfers from the federal budget were demonstrating relatively more modest GRP per capita growth rates (a low statistical significance of the assessments, however, does not allow arguing that this statement is proved).

Table 2.Results of Assessment of the Correlation between the GRP Per Capita Growth Rates and the Initial Level of GRP with the Inclusion of Additional Controlling Variables Logarithm of GRP per capita growth rate in constant prices Explained variable (19942202, annualized) Number of observations 88 88 Coefficient Coefficient Coefficient Constant 0.586** 0.673** 0.672** Logarithm of GRP per capita 0.069** 0.076** 0.077** growth rate in Financial aid to the region to 0.043 GRP ratio Financial aid to the region to the regional budget revenue 0.085* ratio Investment in capital assets from budget sources to GRP 0.692* ratio Adj. R2 0.294 0.376 0.P-value F-statistics 0.000 0.000 0.Note: ** assessment is statistically significant at the 5% level, * assessment is statistically significant at the 10% level.

* * * The results of the present empirical analysis of the concept of convergence between Russias regions appear ambiguous.

First, the average characteristics of distribution of GRP per capita were growing during the whole period concerned (19942002), which evidences general living standards rise. The Inter-regional differentiation is growing too, nonetheless. However, since the rise in the average level has been noted in parallel with that of the median value of GRP per capita, which means that the rise in the level takes place both thanks to a further increase in the richest regions welfare and the rising per capita incomes in poor regions.

The distribution of regions in terms of per capita income remains unimodal.

Second, the hypothesis of convergence was rejected by all the tests we employed.

Thirdly, results of the regression analysis evidence that the concept of unconventional convergence appeared just, as far as Russias regions are concerned. In other words, for the whole period in question, the regions with a lower GRP indices in 1994 demonstrated greater growth rates of the index by 2002.

Fourthly, an additional analysis of the impact of the federal financial aid and the budget investment policy on GRP growth rates (the hypothesis of conditional convergence) showed the absence of a correlation of this kind. More than that, results of our assessments were likely to testify to a negative effect the regional economic policy (or to its use in pursuance of opposite objectives) on growth in regions.

In conclusion it is necessary to compare these results with those of one of the earlier CEPRA projects23, that focused on testing hypotheses on whether various fiscal instruments and, more particularly financial aid to Russian regions, bear a progressive or regressive feature. The 2003 paper argued that this or those fiscal instrument bears either feature in relation to any economic or financial indicator if the employment of the given financial instrument contributes to a lowering of the level of disparity the instrument meas Kadochnikov, Sinelnikov at al (2003).

ures. The authors of the cited paper acquired results that proved that the system of distribution of financial aid bore (only within single time intervals) the progressiveness feature relative to GRPs of Russias regions. However, while assessing a stabilization effect of the federal fiscal system, the authors have failed to identify a stable significant negative correlation between the increment in the financial aid and the increment in GRP.

Hence, our results (in the part of the analysis of the impact of the regional budget policy on convergence processes) to a significant extent correspond to the conclusions drawn at the prior stages of CEPRA project: that is, financial aid to regions has failed to steer a more rapid economic growth in GRP per capita (i.e. it equally failed to accomplish its stabilization mission), or given other conditions being equal, the regions with a greater initial income level received a greater volume of transfers from the federal budget (the progressiveness is rejected during the whole period).

The absence of the effect on growth on the part of investment in capital assets financed out of the budget sources proves results of yet another project24: that is, the efficiency of budget investment is extremely low, while they are focus chiefly on the sociopolitical area.

Dneprovskaya, Drobyshevsky at al. (2002).

Annex Table A1GDP Per Capita in 1994 Prices (as Rb. Th.) over 1994 1994 1995 1996 1997 1 2 3 4 5 Aginsky Buryatsky 1 045.96 1 153.68 1 358.46 1 443.38 886.autonomous okrug Altay krai 5 035.78 2 306.01 2 724.11 2 432.88 1 518.Amur oblast 4 103.75 3 646.13 4 521.74 4 997.82 2 832.Arkhangel oblast 3 497.61 3 907.27 4 292.99 4 615.46 2 968.Astarkhan oblast 2 104.30 2 147.94 2 454.95 2 482.10 1 744.Belgorod oblast 2 804.66 3 725.07 3 403.77 3 380.54 2 290.Bryansk oblast 2 242.09 2 027.42 2 472.33 2 135.68 1 300.Vladimir oblast 2 542.11 2 704.99 2 779.13 2 880.12 1 810.Volgograd oblast 3 187.38 3 024.81 3 684.51 3 500.68 2 098.Vologda oblast 4 550.07 5 726.46 4 699.52 4 429.18 3 309.Voronezh oblast 2 353.53 2 751.43 2 589.31 2 677.88 1 593.City of Moscow 6 203.83 6 943.02 9 498.83 11 334.06 6 374.Saint Petesrburg 3 506.72 4 257.41 4 820.14 4 901.02 3 382.Jewish Autonomous 2 855.66 2 358.93 2 369.80 2 368.13 1 320.oblast Ivanovo oblast 2 049.49 2 120.43 2 287.24 1 971.52 1 255.Irkutsk oblast 1 496.07 5 113.48 5 503.27 5 939.78 3 410.Kabardino-Balkar 1 144.68 1 279.02 1 727.49 1 664.17 1 257.Republic Kaliningrad oblast 2 482.40 2 350.77 3 045.47 3 100.51 1 630.Kaluga oblast 2 737.84 2 848.47 2 810.81 2 748.23 1 647.Kamchatka oblast 5 087.93 5 489.86 6 358.88 5 801.24 5 070.KarachayCherkess 1 613.53 1 696.95 2 090.94 2 049.89 1 248.Republic Kemerovo oblast 2 883.11 5 162.84 5 560.97 5 004.32 2 993.Kirov oblast 2 651.19 3 127.45 3 337.49 3 333.71 2 074.Komi-Permyak 2 101.87 2 579.00 2 402.45 2 440.81 1 190.autonomous okrug Koryak autonomous 8 194.26 7 836.62 9 163.61 8 491.25 9 023.okrug Kostroma oblast 2 814.46 2 937.07 2 863.47 3 134.10 1 966.1 2 3 4 5 Krasnodar krai 1 285.55 2 556.12 3 090.93 2 752.25 1 920.Krasnoyarsky krai 3 909.60 5 460.76 5 840.64 5 929.23 4 267.Kurgan oblast 7 019.96 2 281.47 2 425.06 2 243.74 1 428.Kursk oblast 2 781.76 2 857.02 3 182.78 3 146.55 2 157.Leningrad oblast 3 010.10 3 244.55 3 796.21 3 646.83 2 615.Lipetsk oblast 3 810.72 4 244.52 3 781.14 3 343.44 2 291.Magadan oblast 7 934.41 6 538.18 8 973.21 9 431.27 6 602.Moscow oblast 2 924.48 3 436.46 4 247.75 4 432.85 3 206.Murmansk oblast 5 501.87 5 956.56 5 405.80 5 301.73 3 840.Nenetsky autono13 062.65 13 188.45 14 569.49 13 557.25 10 300.mous okrug N.

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