Sound regional policies are essential for balanced and sustained economic growth. The interaction of federal and regional policies with cross-regional structural differences affects human and physical capital formation, the business climate, private investment, market depth, and competition. This paper summarizes the main elements of Russia’s fiscal federalism, describes the channels through which it operates, and assesses the effectiveness of regional transfers in reducing regional disparities. The results suggest that federal transfers to regions contributed to reducing disparities arising from heterogeneous regional tax bases and fiscal revenues. This allowed regions with initially lower per capita income to increase human and physical capital at higher rates. There is little evidence for transfers contributing to increased cross-regional growth synchronization. The results also suggest that federal transfers did not significantly improve regional fiscal sustainability, a conclusion that is supported by the lack of convergence in per capita real income across Russian regions in the last 15 years.

convergence, federalism, regional policies, Russia, transfers

Sound regional policies are essential for balanced and sustained economic growth. The interaction of federal and regional policies with cross-regional structural differences (e.g., natural resources, market size, distance to markets, historical events) affects human and physical capital formation, the business climate, private investment, market depth, and competition. Policy pitfalls can be costly as they can result in persistent differences in regional per capita income, dependence on federal transfers, and excessive geographic concentration. Balanced regional development is a challenge that is particularly important in geographically large and heterogeneous countries (for a discussion of factors leading to uneven regional development, see Krugman, 1991).

Russia is a federal state in which regions have the legal responsibility — either exclusively or shared with the federal government — for education, health, and infrastructure spending. At the same time, Russia’s fiscal constitution is more centralized than that in other federal countries. Its main building blocks are a relatively centralized tax authority and a complex system of federal transfers. Thus, the federal government plays a significant role in shaping regional outcomes. Federal transfers represented the economic lifeline of lower per capita income regions, which unsurprisingly have the weakest tax bases, for the last 15 years. Consolidated federal transfers, either from the federal budget or from federal extra budgetary funds (EBFs) to the regions (including territorial medical EBFs) amounted to 3.5 percent of GDP in 2016, or about 65 percent of federal oil and gas revenues. These transfers financed a large share of regional fiscal spending (e.g. almost 70 percent in the North Caucasus Federal Region, or about 40 percent in the Far Eastern Federal Region).

From a policy perspective, the large sub-federal share in general government spending — about 40 percent if territorial medical EBFs are considered — suggests that the fiscal stance is determined simultaneously by policies at the regional and federal level.1 Federal transfers may affect the degree of synchronization of regional growth, creating challenges (or improving) the effectiveness of stabilization policies, including monetary policy (Siluanov and Nazarov, 2009). Moreover, a large volume of transfers reduces the downward flexibility of federal spending, which can create challenges (including for a fiscal rule targeting a constant structural balance) as oil revenues gradually decrease. In this regard, federal transfers to regions add to other (earmarked) transfers, including those to the pension system and other EBFs, some of which will likely mount as population ages.

There is a large Russia-focused literature analyzing fiscal federalism and regional development. Some authors discuss the appropriate institutional design of fiscal federalism in Russia: Lavrov et al. (2001), Khristenko (2002), Kadochnikov et al. (2002), Klimanov and Lavrov (2004), Shvetsov (2005), Yakobson (2006), Nazarov (2006), Grigoriev et al. (2008), Bukhval’d (2008), Zubarevich (2014), and Alexeev (2016). Other authors focus on the challenges and outcomes of regional development in Russia, including Mau and Yanovskiy (2001), Granberg (2002), Pelyasov (2003), Alexandrova and Grishina (2005), Zubarevich (2009), Yushkov (2016), and Yushkov et al. (2017b).

This paper attempts to empirically evaluate the relation between fiscal federalism and regional development in Russia. To that end, it compares Russia’s fiscal federalism to that of other federal countries (Section 2), describes the channels through which fiscal federalism operates in Russia, assesses the effectiveness of regional transfers in reducing regional disparities in the provision of public services, analyzes the impact of transfers in synchronizing cross-regional growth, and evaluates the extent to which they have contributed to strengthen the regions’ fiscal sustainability (Section 3). The paper concludes with a discussion of the results, some policy implications and issues for further analysis (Section 4). The results suggest that federal transfers to regions contributed to reducing disparities arising from heterogeneous regional tax bases and fiscal revenues. This allowed regions with initially lower per capita income to increase human and physical capital at higher rates. There is little evidence for transfers contributing to increased cross-regional growth synchronization. The results also suggest that federal transfers did not significantly improve regional fiscal sustainability, a conclusion that is supported by the lack of convergence in per capita real income across Russian regions in the last two decades.2

Fiscal federalism arrangements in Russia are involved. There are three levels of government in Russia — federal, regional, and local — with the local level further subdivided into a hierarchy of municipalities, which in total count more than 22,000. The Budget Code states that each of the three levels is autonomous and should be financially self-sustained. However, a complex system of intra-government transfers (mostly flowing from the federal government) ensures that spending of most regions, territorial EBFs, and federal EBFs remain broadly financed. A large network (counting more than 65,000) of budgetary, extra-budgetary, unitary enterprises, and joint stock companies (most of which operating at the regional level) adds to complexity.

Russia’s legal framework is consistent with an integrated fiscal constitution. The main conclusion in Blöchliger and Kantorowicz (2016) is that through clustering of fiscal constitutions characterized by similar features it is possible to classify countries in either those having integrated fiscal constitutions or those having decentralized ones. Decentralized fiscal constitutions (e.g., Canada and the United States) are consistent with sub-national governments (SNG) having more autonomy and responsibility, lower co-determination of policies, and relatively weaker numerical budget rules and frameworks. Integrated or centralized, fiscal frameworks are characterized by lower autonomy and responsibility of SNGs and, at least de jure, stronger fiscal rules and frameworks.

In what follows, we rely on the data from Blöchliger and Kantorowicz (2016) to look into Russia’s fiscal federalism and compare it with that of other federal countries. Their analysis together with a reading of Russia’s legal framework, allows to understand the relative weight of the federal and regional governments in shaping cross-regional socio-economic outcomes.3 In particular, the framework for intergovernmental fiscal relations is assessed and quantified along five categories: the autonomy of SNGs, the responsibility for their own fiscal policies, their power to shape federal policy, the strength of budget frameworks, and the overall system’s stability. Each of these categories (as well as sub-categories) is evaluated by looking at several sub-indicators whose performance is assessed with an index ranging from zero (low) to one (high). As this analysis is for some of the indicators mainly de jure, the description below notes, when appropriate, any differences with de facto realities in Russia.4

Russia’s SNGs have weaker tax autonomy than spending autonomy relative to other federal countries. The autonomy of SNGs (Fig. 1a) is analyzed looking at both tax and spending autonomy. Tax autonomy is assessed by looking at each tax category and evaluating whether the federal government, SNGs, or both, can affect tax rates, as well as with respect to the clarity with which the law assigns power between different levels of governments. Likewise, spending autonomy is evaluated at each policy area and assessing the respective responsibilities of SNGs and the federal government. In other sub-categories (namely, borrowing and budgetary autonomy), Russia ranks below the average of advanced economies and similar to the average of other emerging markets.

Fig. 1.

Features of Russia’s fiscal federalism.

Source: Authors’ calculations and OECD (2016).

A look at Russia’s SNGs responsibility (Fig. 1b) suggests that the federal government plays a relatively more important role in regional fiscal policy in Russia than in both advanced and other emerging market economies. In terms of the sub-categories, fiscal equalization policy in Russia is more the responsibility of the federal government than that of SNGs, and stabilization policy is fully in the hands of the federal government. The intensity of federal grants (which may be underestimated in Blöchliger and Kantorowicz (2016) as they measure it in terms of aggregate GDP rather than in terms of the gross regional product, GRP, of recipient regions) also suggests an important role for the federal government in shaping regional outcomes. A de jure evaluation of the possibility of regional bailouts or bankruptcies situates Russia in a better position than the average of advanced and other emerging market economies, although de facto the federal government as recently as in 2015–2016 resorted to transfers to ease the burden of public debt in some regions.5

Finally, Russia’s legal framework obtains higher marks than the average of advanced and other emerging market economies in co-determination of federal policies (Fig. 1c) and the stability of its fiscal constitution (Fig. 1d). However, de jure versus de facto considerations play a role in this assessment. For instance, although Russia’s budget code included some form of a fiscal rule since 2008, its parameters changed, and its implementation was suspended a few times. Regarding the stability of the legal framework, Russia suffered numerous modifications of the operational framework establishing the relation between the federal and regional governments, including on tax sharing and transfers.

This section begins with some background information about Russia’s tax sharing arrangements, the types of federal transfers, and the fiscal situation of Russian regions. It then presents an empirical analysis of the effectiveness of federal transfers in equalizing the provision of public services, in increasing the correlation of cross-regional growth rates, and in delivering sustainable regional budgets.

The econometric analysis uses panel data for 79 regions covering a large variety of regional socio-economic variables, including economic activity, labor, fiscal, financial, and structural indicators. The data spans the period 2000–2016, although some variables are available for shorter time periods (i.e., regional fiscal data for 2005–2016, GRP for 2000–2015, and GRP composition for 2004–2015). A cross-sectional dataset is then constructed in which each observation represents some bilateral interaction between two regions (e.g., difference in growth rates, level differences, or correlation) for a given variable.

3.1. Background

Regional revenues include own revenues and federal transfers. The share of federal transfers in regional revenue varies widely across regions, ranging from about 10 percent to 90 percent. Federal taxes (most importantly personal and corporate income tax) are the largest source of regional revenue, representing on average about 70 percent of own revenues. Tax sharing, or primary distribution, allocates tax revenues among different levels of government. Ideally, it should result in vertical fairness, i.e., in a balanced distribution of revenues among federal, regional, and local governments. It is performed directly in the regions where taxes are collected on a tax-by-tax basis at predetermined rates. Sharing arrangements and rates are governed by the Budget Code, and in the case of the corporate income tax by the Tax Code. Regional excises’ shares are determined by the Budget Code with horizontal (i.e., cross-regional) re-distribution. Rates tend to be adjusted frequently (see Appendix A for more details).

The primary distribution of taxes results in a large cross-regional dispersion of fiscal revenues (or horizontal disparity, i.e., differences in the revenues within a level of government), however some relationships hold steady. Specifically, regions with lower per capita real GRP have lower per capita real own revenues (Fig. 2a). In regions in which the public sector’s share in GRP is high, per capita real fiscal revenues tend to be lower (Fig. 2b). Also, and in line with the literature (Leuthold, 1991; Sen Gupta, 2007), per capita fiscal revenues are positively associated with the share of mining in GRP and negatively associated with the share of agriculture (Figs 2c–2d). In other words, regional tax bases are positively associated with per-capita GRP, with the share of mining in GRP, and negatively associated with the share of agriculture, and that of the public sector.

Fig. 2.

Own fiscal revenues, per capita income, and GRP composition.

Note: The black dashed lines represent the linear regression lines. Source: Authors’ calculations.

In broad terms, inter-governmental federal transfers aim at reducing horizontal fiscal inequality.6 These include (i) non-earmarked and non-matching transfers (of which equalization grants are the most important); (ii) subsidies (earmarked matching transfers to finance spending priorities); (iii) subventions (earmarked non-matching transfers to finance devolved spending responsibilities); and (iv) other transfers.7 In addition, there are transfers from the Federal Medical Insurance Fund (a federal EBF), to Territorial Medical Insurance Funds (regional EBFs), which represented 1.7 percent of GDP in 2016.8 Equalization grants constitute about 50 percent of federal government transfers (see Appendix A for more details). In per capita real terms equalization grants flow mostly to regions with both lower per capita income and lower own fiscal revenues. In contrast, subsidies and subventions in per capita real terms are allocated to regions with higher per capita income (Fig. 3).9

Fig. 3.

Federal transfers and per capita income.

Note: The black dashed lines represent the linear regression lines. Source: Authors’ calculations.

Regions and municipalities are largely responsible for social policies as well as for some regional infrastructure. In 2016, regional spending represented 95 percent of general government expenditure for housing and utilities, 80 percent for education and cultural activities, and around 85 percent for health including spending by territorial extra-budgetary medical funds.

3.2. Federal transfers and public goods’ supply disparities

In principle, a large portion of federal transfers to regions aim at reducing disparities arising from heterogeneous regional tax bases and unequal own revenues. A look at the data shows that indeed the distribution of cross-regional per capita real expenditure is situated to the right of the distribution of per capita real own revenues. This implies that lower income regions can afford higher public real per capita spending than warranted by their own regional revenues.

Moreover, higher average federal transfers to regions in 2005–2016 (in per capita real terms) are positively associated with larger increases in per capita real annual spending in health and education, helping lower income regions to partially close the gap with richer regions in per capita social spending (Figs 4a–4b). Larger federal transfers are also positively associated with stronger human capital accumulation. Educational attainment together with employment data allows constructing regional measures of human capital using a methodology similar to that in Hall and Jones (1999), which assumes diminishing returns for additional years of education.10 The resulting human capital measures show that it grows at higher rates in regions that receive higher average transfers (in GRP terms) (Fig. 4c). This result, however, is partially driven by cross-regional differences in labor supply.

Fig. 4.

Federal transfers and accumulation of factors of production.

Note: The black dashed lines represent the linear regression lines. Source: Authors’ calculations.

In addition, investment-to-GRP ratios and physical capital accumulation are generally higher in regions receiving larger federal transfers. The construction of regional capital stocks by means of the perpetual inventory method shows that physical capital accumulation in regions with initially lower per capita income and that receive larger transfers is faster than in other regions (Fig. 4d). The very high investment ratios (in some cases as high as 50 percent of GRP) highlight, however, that initial capital stocks in lower income per capita regions were likely very low when compared with richer regions.11

3.3. Federal transfers and cross-regional growth correlation

Given the central role that the federal government plays in economic stabilization, a federal policy that smooths out aggregate economic cycles and strengthens cross-regional growth correlations should have positive spillovers for the effectiveness of monetary policy.12 To test whether federal transfers contribute to increasing the synchronization of real GRP growth rates, we estimate the following equation:

​ρ_{i, j} (y_i, y_j) = α + β θ_{i, j} (f t_i, f t_j) + γ X_{i, j} + ϵ_{i, j​} (1)

where ρ_{i, j} (y_i, y_j) is the correlation coefficient between the per capita real GRP growth rate, denoted by y, of region i and region j; θ_{i, j} (f t_i , f t_j) is the correlation coefficient between the growth rate of per capita real federal transfers (on aggregate and by type of transfer), denoted by f t, of region i and region j; X_{i, j} is a set of control variables (including proxies for distance, GRP structure, footprint of the state, and international trade) calculated as the pairwise difference between two regions of the variable being considered;13 α is a constant; β and γ are the coefficients of the correlation coefficient and the control variables, respectively; and ϵ_{i, j} is the error term (see Appendix B for the definition of the variables used in the specifications).14

Table 1 presents the results for eleven alternative specifications. We find evidence that aggregate transfers do not have a strong or robust association with bilateral cross-regional growth correlation. Among transfer types, the same applies for per capita real grant growth rates (whose purpose is to reduce cross-regional spending disparities) and per capita real subsidies growth rates. The coefficient for per capita real subventions growth rates are somewhat significant in some specifications, though this should be taken with caution due to potential endogeneity.15 The positive association between per capita real GRP growth correlations and that of per capita real subventions growth rates can be either desirable or not, depending on whether federal fiscal policy amplifies or lessens the severity of overall economic cycles. The evidence for Russia in the last two decades suggests that federal fiscal policy has been somewhat pro-cyclical (Erbil, 2011).

Table 1Download as CSV 

Estimation results for bilateral per capita GRP growth correlations.

Variable		1	2	3	4	5	6	7	8	9	10	11
Constant	α 1	0.514***	0.530***	0.524***	0.561***	0.566***	0.562***	0.559***	0.566***	0.374*	0.149	0.149
Per capita real federal transfers growth corr.	β 2	0.065	0.051	0.047	0.034	0.021	0.023	0.025
Per capita real grant growth corr.	β 3								0.013	0.005	0.002
Per capita real subsidies growth corr.	β 4									0.264	0.209	0.209
Per capita real subventions growth corr.	β 5										0.356*	0.356*
Initial per capita real GRP	γ 6		–0.071	–0.069	–0.142***	–0.124**	–0.104	–0.101	–0.102	–0.096	–0.085	–0.085
Common border	γ 7			0.126	0.086	0.088	0.096	0.097	0.098	0.088	0.070	0.070
Share of public sector in GRP	γ 8				–1.218***	–0.784	–0.814	–0.853	–0.854	–0.807	–0.813	–0.812
Footprint of state	γ 9					–0.090	–0.097	–0.095	–0.096	–0.096	–0.083	–0.083
Urbanization rates	γ 10						–0.155	–0.132	–0.132	–0.148	–0.189	–0.189
Foreign trade	γ 11							–0.043	–0.043	–0.041	–0.054	–0.054
R2		0.003	0.030	0.038	0.136	0.160	0.164	0.166	0.166	0.177	0.209	0.209
Adj. R2		0.003	0.029	0.037	0.135	0.159	0.163	0.164	0.164	0.175	0.207	0.207
Observations		3023	3023	3023	3023	3023	3023	3023	3023	3023	3023	3023

Note: Robust standard errors, * p < 0.1, ** p < 0.05, *** p < 0.01. Source: Authors’ calculations.

3.4. Federal transfers and the sustainability of regional budgets

Federal transfers affect regional fiscal sustainability through different channels. To assess the effect of federal transfers on the regions’ fiscal sustainability, we estimate a system of equations allowing for feedback effects among endogenous variables:

​Y_i,t^m = α + βY_i,t^–m + γ Z_i,t + ϵ_i,t​ (2)

where Y_i,t is a matrix of endogenous variables including the long-term change in the revenue-to-expenditure ratio (our proxy for fiscal sustainability), the cumulated per capita real GRP growth, the long-term percentage change in the share of public sector in GRP, and the long-term average of federal transfers in percent of GRP;16 Z_i,t is a matrix of exogenous variables including the level of initial per capita real GRP, the share of mining in GRP, population size, population density, common border (as a proxy for geographic distance), and the footprint of state;17 m is the equation index; α is a vector of constant terms; β is the matrix of coefficients of the endogenous variables; γ is the matrix of coefficients of the exogenous variables; and ϵ_i,t is a vector of the error terms. Table 2 shows the identifying restrictions to estimate the system.

Table 2Download as CSV 

Specifications for federal transfers in a simultaneous equations system.

Endogenous Vector

Exogenous Vector

Rev. to exp. ratio

Per capita real GRP growth

Change in share of public sector in GRP

Federal transfers as a share of GRP

Const.

Initial per capita real GRP

Share of mining

Pop.

Pop. density

Common border

Footprint of state

Stoch. terms

┌

┐

=

┌

┐

+

┌

┐

+

┌

┐

│

1

–β12

β 13 = 0

β 14 = 0

│

│

α 1

│

│

γ 12 = 0

γ 13 = 0

γ 14 = 0

γ 15 = 0

γ 16 = 0

γ 17 = 0

│

│

ε 1

│

│

β 21 = 0

1

–β23

–β24

│

│

α 2

│

│

γ 22

γ 23 = 0

γ 24 = 0

γ 25

γ 26

γ 27 = 0

│

│

ε 2

│

│

β 31 = 0

–β32

1

–β34

│

│

α 3

│

│

γ 32 = 0

γ 33

γ 34 = 0

γ 35 = 0

γ 36 = 0

γ 37 = 0

│

│

ε 3

│

│

β 41 = 0

β 42 = 0

β 43 = 0

1

│

│

α 4

│

│

γ 42

γ 43 = 0

γ 44

γ 45 = 0

γ 46

γ 47

│

│

ε 4

│

└

┘

└

┘

└

┘

└

┘

Note: Coefficients equal to zero represent the exclusion identification restrictions. Source: Authors’ calculations.

The identification of the model assumes that federal transfers affect fiscal sustainability through their impact on regional tax bases, which should expand faster in regions in which cumulated GRP growth is higher. Differences in GRP growth rates are assumed to be endogenously determined by differences in economic structure (i.e., whether the private or the public sector is expanding more rapidly), by differences in federal transfers (regions receiving larger transfers could accumulate factors of production faster), and to depend on a number of predetermined and exogenous variables (the level of initial real per capita GRP, population density, and geographic distance). Regional economic structure is assumed to depend endogenously on federal transfers and per capita real GRP growth, while federal transfers are assumed to be explained by exogenous and predetermined variables (including bilateral differences in the initial level of real per capita GRP).

The results in Table 3 suggest that federal transfers did not significantly contribute to improve regional fiscal sustainability. This is visible by analyzing the channels through which federal transfers led to changes in per capita real GRP growth rates. On the one hand, comparing pairs of regions, those that received larger federal transfers grew faster (direct effect), possibly due to a more rapid accumulation of factors of production, as described above; on the other hand, the share of the public sector expanded comparatively more (10-year cumulative increase) in regions receiving larger federal transfers, which subtracted from per capita real GRP growth dynamism (indirect effect), with the negative (indirect) impact more than offsetting the positive (direct) one. Given the positive association between own revenue-to-expenditure ratio and per capita real GRP growth, which takes place through the positive effect of per capita real GRP growth on tax bases, it can be inferred that federal transfers did not result in an improvement of regional fiscal sustainability. For instance, our estimates suggest that a one-standard deviation difference in the level of federal transfers (about 17 percent of regional GRP) is associated with a negative cumulative bilateral difference in per capita real GRP growth (over 2005–2015) of around 1.2 percentage points, an increase in the bilateral share of public sector in GRP of around 1.5 percentage points, and with no improvement in the (own) revenue-to-expenditure ratio. These results are particularly relevant for around one third of Russia’s regions (28 out of 79 in the sample), which receive federal transfers that are higher than the average by between one and three standard deviations.

Table 3Download as CSV 

Estimations for federal transfers in a simultaneous equations system.

Equation	Variable	Coefficient	SUR	2SLS	3SLS	FIML	GMM
			Endogenous
1	Per capita real GRP growth	β 12	0.280***	0.743***	0.717***	1.099***	0.754***
2	Chg. in share of public sector in GRP	β 23	–0.320***	–0.504	–0.431	–0.236	–0.373
2	Federal transfers as a share of GRP	β 24	0.011	0.051	0.034	0.023	0.025
3	Per capita real GRP growth	β 32	–1.360***	–0.628	–0.573	–0.502	–0.683
4	Federal transfers as a share of GRP	β 34	0.083**	0.104**	0.090*	0.087	0.099*
			Exogenous
1	Constant	α 1	0.002	0.004*	0.004*	0.005*	0.004*
2	Constant	α 2	0.001	0.001	0.001	–0.001	0.000
2	Initial per capita real GRP	γ 22	–0.010***	–0.007	–0.011***	–0.009**	–0.011***
2	Population density	γ 25	0.001	0.002	0.001	0.000	0.001
2	Common border	γ 26	0.000	0.003	–0.002	–0.002	–0.002
3	Constant	α 3	0.002	0.004	0.004	0.004	0.003
3	Share of mining	γ 33	–0.053**	–0.046	–0.041	–0.042	–0.042*
4	Constant	α 4	0.022*	0.024*	0.023*	0.024*	0.021
4	Initial per capita real GRP	γ 42	–0.058***	–0.068***	–0.062***	–0.067***	–0.049***
4	Population	γ 44	–0.046***	–0.048***	–0.043***	–0.043***	–0.039***
4	Common border	γ 46	–0.025	–0.032	–0.024	–0.025	–0.024
4	Footprint of state	γ 47	0.033	0.031	0.034	0.034	0.037*

Note: Robust standard errors, * p < 0.1, ** p < 0.05, *** p < 0.01. Source: Authors’ calculations.

Accordingly, regions receiving larger federal transfers did not close (even partially) the gap between their expenditures and own revenues. This is the case as economic growth based on the expansion of government services did not result in an improvement in its own revenue-to-GRP ratios, which (in levels) are positively correlated with the size of the private sector (see Fig. 2). Thus, the financial dependence of many of these regions on federal transfers remained broadly unchanged. This dependence is summarized by the fact that for many of them their own revenues continue to be barely sufficient to finance health and education spending.18

An alternative way to interpret the results is that, at least during the period analyzed, federal transfers were insufficient to jump-start self-sustaining, private-sector led growth in regions receiving relatively more transfers. Federal transfers should, in the short term, increase the size of the public sector as transfers push social and infrastructure spending upwards; however, they should not necessarily result, a priori, in a long-term increase in the share of the public sector in GRP. Indeed, it can be expected that the increased supply of public goods (e.g., in the form of higher human and physical capital) would result in positive spillovers for the private sector, but this is not observed. A possibility is that a by-product of federal transfers is to support a larger state footprint in regions; there is some evidence for this, as federal transfers flowed to regions not only with lower initial per capita real GRP, but also with a relatively larger footprint of the state (equation 4 in Table 3).

We indirectly test the robustness of these results by means of complementary analysis. First, we find that for pairs of regions, total factor productivity (TFP) expanded at lower annual rates in regions receiving relatively higher levels of federal transfers. This means that the distance in productivity levels between low- and high-income regions increased in the last 15 years (Fig. 5). We come to these results by recovering neutral TFP levels for the period 2000–2015 using a production function approach based on an identical Cobb-Douglas production function for all regions. We construct regional capital stocks using the perpetual inventory method and regional investment, and we calculate effective human capital (i.e., corrected for labor utilization) using educational attainment of the employed working-age population.

Fig. 5.

Federal transfers, public sector expansion, and TFP increases.

Note: The black dashed lines represent the linear regression lines. Source: Authors' calculations.

Second, we find no evidence of convergence in real per capita income across Russian federal regions in the period 1998–2015 (Table 4). Following Pedroni and Yao (2006), we test for convergence across federal regions in the sample and across regions in different geographical areas with the panel unit root tests of Im et al. (2003) and Maddala and Wu (1999). Apart from the Far-Eastern Federal District that shows some convergence at the 10 percent significance level, there is no convergence across the identified clubs.

Table 4Download as CSV 

GRP convergence across regions.

	Countries	Im, Pesaran, and Shin (2003)	Maddala and Wu (1999)
Full sample
(yit – ¯yt) ∀i	84	0.105	195.050
Districts
(yit – ¯yt) ∀i ∈ Central Federal District	19	4.075	15.456
(yit – ¯yt) ∀i ∈ Northwestern Federal District	11	–0.049	21.417
(yit – ¯yt) ∀i ∈ Southern Federal District	6	0.145	9.938
(yit – ¯yt) ∀i ∈ North-Caucasus Federal District	6	–1.274	18.264
(yit – ¯yt) ∀i ∈ Volga Federal District	15	0.032	29.083
(yit – ¯yt) ∀i ∈ Ural Federal District	5	–0.536	16.871
(yit – ¯yt) ∀i ∈ Siberian Federal District	12	0.813	23.932
(yit – ¯yt) ∀i ∈ Far-Eastern Federal District	10	–2.863*	43.252*

Note: The null hypothesis of the unit root tests is that all panels contain a unit root, i.e. there is no convergence. Fixed effects are always included. The Schwartz Information Criterion is used to select the optimal lag length. * p < 0.1, ** p < 0.05, *** p < 0.01. Source: Authors’ calculations.

Third, population concentration in higher-income geographical areas increased in the last 15 years. For instance, the population of the city of Moscow increased by more than 30 percent since the year 2000, and by 10 percent in Saint Petersburg, against the backdrop of a broadly constant total population. This implies that other less densely populated (and generally lower-income) regions experienced population decreases of 15–20 percent. Although concentration has some advantages for recipient regions and cities (e.g., increases economies of scale, supports firm localization, improves job matching, among other), it has symmetrical drawbacks for regions losing population, and results in an increasing per capita cost for federal transfers. More broadly, it results in geographically unbalanced development, a critical issue for a continental-sized country like Russia. Federal transfers — and fiscal federalism in Russia more generally — appear not to have taken into consideration both the advantages or disadvantages related with increased concentration and the associated regional challenges that may arise as a consequence.19

Russia’s fiscal federalism assigns a strong role to the federal government. The system evolved from a somewhat disorderly decentralization in the 1990s into a more centralized system in the last 15 years. Regions play an essential role in human and physical capital formation, but cross-country comparisons of fiscal constitutions suggest that they have less autonomy and exercise less control of their own fiscal policy than in other federal countries. The system is quite complex and the diversity of federal subjects along socio-economic dimensions is wide. Increased coordination between the federal and regional governments to tackle complexity and to address cross-regional infrastructure and human capital bottlenecks could result in a more integrated national market with positive spillovers for inter-regional and international trade and investment.20 Regional convergence can result in a growth dividend and in more balanced geographical development.

Given relatively rigid tax sharing arrangements, federal transfers constitute one of the main levers through which federal policy operates at the regional level. Federal transfers proved effective in supporting factor accumulation in lower per capita income regions. However, there is little evidence that transfers contributed to increased cross-regional growth synchronization, which is not necessarily a negative outcome given that fiscal policy has been somewhat pro-cyclical. Federal transfers were ineffective in supporting self-sustaining per capita real GRP growth and productivity increases. Transfers expanded government services but did not result in a long-term increase in the share of the private sector in GRP. Accordingly, large cross-sectional differences in own fiscal revenues (in per capita and GRP terms) persist, as well as the associated dependence on federal transfers. Importantly, federal transfers flow more heavily to regions where the footprint of the state is larger, which may suggest a self-sustaining pattern.21

Enhanced strategic direction could help increase federal transfers’ growth effectiveness. Open-ended transfers may have had the unintended effect of weakening regional incentives to enlarge their tax bases, supporting a pattern of dependence. Thought should be given to include in the grant allocation formulas a stronger measure of sustainability together with the current objective of equalization. Establishing realistic transition periods to achieve sustainability is essential.

Appropriate federal macroeconomic and tax policies can contribute to the development of regional tax bases, supporting regional sustainability, and the accountability of regional authorities. An option in this regard could be to expand the use of personal property taxes (OECD, 2016). Personal property taxes currently represent only 0.4 percent of the consolidated own revenues of regions. In 2016, 28 regions started a transition to market value-based instead of accounting value-based taxation of property. For instance, the city of Moscow is projecting a five-fold increase in property tax collections by 2020 (with tax collection increasing by 55 percent in 2016). Larger regional tax bases should also balance somewhat the strong de jure role of the federal government.

Given that higher income regions have more space to strengthen their own tax bases (e.g., through taxation of property as indicated above, which is more abundant and of higher value in richer regions), there may be scope to increase the use of horizontal transfers in the margin. The large cross-regional dispersion of per capita own revenues may have contributed to economic and population concentration, which creates negative spillovers for regions with population outflows.22 Consideration should be given to modify incentives with the aim of limiting concentration. The use of horizontal transfers, in the margin, may contribute to that effect and support the use of improved levels of human and physical capital in lower per capita income regions.23

The sustained implementation of a credible fiscal rule should contribute to avoid stop-go cycles caused by terms of trade shocks, promote a more stable and more aligned-with-fundamentals real exchange rate with positive spillovers for lower per capita income regions, where agriculture (a tradable sector) represents a larger share of GRP. This should have beneficial effects for the expansion of regional tax bases. A fiscal rule would also contribute to smooth national and regional economic cycles, simplifying the implementation of stabilization policies (including monetary policy). The role of different types of federal transfers in the synchronization of regional economic cycles deserves further analysis. Rebalancing domestic taxes with a view to taxing labor less heavily should support decreases in informality, which is likely to be more prevalent in low per capita income regions as attested by weaker tax bases.

Finally, there may be room to simplify and increase the transparency of transfers. Streamlining the number of transfers (especially subsidies), in particular for agriculture development, housing and utilities, and education; allocating subsidies one-to-one to government programs (or subprograms), instead of to a multiplicity of them; transforming and further consolidating “other transfers’” into subsidies; and regulating budget loans, which are increasingly used because of their concessional interest rates, should all result in a simpler, more transparent, and easy-to-administer system.

We thank to, without implicating, Suman Basu, Vladimir Kolychev, Alexander Morozov, Aleksei Mozhin, Ernesto Ramirez Rigo, and the participants of the IMF seminars held in May 2017 at the Central Bank of Russia and the Ministry of Finance of the Russian Federation for their comments and suggestions. We also wish to thank Nina Chebotareva and Tatiana Chernisheva for excellent research assistance.

This appendix summarizes revenue sources (including sharing arrangements) and spending responsibilities by different government levels. Concretely, Table A.1 catalogues federal taxes, special tax regimes, regional taxes, local taxes, and federal non-tax revenues, including their tax sharing between different levels of government, as specified in the Russian Legal framework. In turn, Table A.2, describes federal, regional/local, and joint federal-regional spending responsibilities, and specifies devolved federal spending responsibilities to regions (clarifying which are financed by subventions and which not).

A.1. Limits imposed by the federal government on regional budgets

The Budget and Tax Codes establish several fiscal restrictions for sub-federal governments. Monitoring, reporting, and transparency standards and requirements established by the federal government are high. Sanctions for rules violations might be imposed and include, among other, adjustments in the size of transfers (excluding subventions).

Budget balance requirements: the deficit or regions cannot exceed 15 percent of their own revenues (excluding grants). Rules are stricter if federal grants exceed 40 percent of the consolidated region budget revenues (excluding subventions).

Tax limits: Sub-federal governments can set tax rates and reliefs for regional and local taxes. For the CIT, regions can set rates for the regional part of the tax within the limits set by the Tax Code but not reliefs. Excise taxes on gasoline and alcohol are shared annually between regions and federal government. The Tax Code does not allow for regions to legislate on personal income tax (PIT), fees and charges, rates and reliefs, which constitute the remaining 40 percent of their revenues.

Expenditure limits: Regions with a share of federal grants exceeding 10 percent of consolidated region budget revenues (excluding subventions) cannot assume and execute expenditures assigned to regional governments by Constitution and federal laws, and cannot exceed federal norms for budgetary sector wages and regional government activity financing. Similar restrictions exist for municipalities getting equalization grants from regions.

Borrowing constraints: Domestic borrowing is not directly restricted; new foreign borrowing (for deficit financing or refinancing) is allowed only for regions that do not receive federal equalization transfers, do not have debt arrears, and have proper credit ratings from at least two international agencies. Regions receiving federal equalization transfers can borrow externally to refinance existing external debt if no debt arrears and credit rating requirements are satisfied. Total yearly borrowing of regions and municipalities is bound up by deficit financing and debt amortization.

Debt levels and service: Debt is not allowed to exceed own annual revenues (excluding grants). Rules are stricter if federal grants share exceed 40 percent of consolidated region budget revenues (excluding subventions). Debt service (interest payments) should not exceed 15 percent of total expenditures (excluding subventions). Escape clauses introduce flexibility for regional budget implementation (budget credit financing, privatization, use of regional precautionary saving funds). Debt ceilings are currently allowed to be exceeded for an amount equal to federal budget credits.

Table A.1Download as CSV 

Russia: Tax and non-tax revenue sharing agreement.

	Rates (percent)	Share accruing to (in percent of total)
		Federal	Regional	Municipal
Federal taxes
VAT	18 (concessional rate 10 percent)	100
PIT	13	0	85	15
CIT 1)	20	10	90
MET (Oil and Gas)	Formula-based depending on oil price	100
MET (Other subsoil resources, including diamonds)	Ad valorem and specific	40	60
MET (Commonly occurring subsoil resources)	Ad valorem and specific		100
MET (Diamonds)	8		100
Water tax	Specific	100
Excise tax on ethanol from edible raw material 2)	Specific	50	50
Excise tax on ethanol from all material excluding edible 2)	Specific	100
Excise tax on alcohol-containing products 2)	Specific	50	50
Excise tax on spirits 2)	Specific	50	50
Excise tax on wine, beer, other 2) 3)	Specific		100
Excise tax on tobacco 2)	Specific	100
Excise tax on cars and motocycles 2)	Specific	100
Excise on gasoline and motor oil 2) 4) 5)	Specific	12	88
Excise tax on imported excisable goods 2)	Ad valorem and specific	100
Fee (royalty) for exploitation of water biological resources	Specific	20	80
Fee (royalty) for exploitation of animal resources	Specific		100
Stamp duty 6)	Specific	100	100	100
Stamp duty via public multi-service centers		50	50
Special Tax Regimes
Single agricultural tax	6			100
Single imputed income tax	15 (7.5–15)			100
Patent	6			100
Simplified taxation regime	6 or 15	100
Taxes under Product sharing agreements		25	75
Federal Non-Tax Revenues
Property income and earnings from paid services		100	100	100
License fees		100
Customs duties and fees		100
Forests		100	100	100
Water facilities		100	100	100
Environmental fee 7)		5	40	55
Consular fees		100
Disposal fee		100
Subsoil royalty	Formula-based	40	60
Proceeds from sale/lease of federal land ceded to region			50	50
Fees for record extracts		100	100	100
Fees for record extracts via public multi-service center		50	50
Fines and penalties 8)
Regional Taxes
Corporate property tax	Capped at 2.2		100
Gambling tax	Specific		100
Transport tax	Specific		100
Local Taxes
Land tax	Capped at 0.3 and 1.5 for diff. types of land			100
Personal property tax	0.1–2			100
Retail sales fee (so far implemented only in Moscow)	Specific, but no more than patent-based			100

Notes: ¹⁾ The CIT is the only tax whose rate is split between the federal and the regional levels in the Tax Code (sharing of other taxes is established in the Budget Code). Regions are authorized to adjust their portion of the CIT rate down, but no more than to 13.5 percent (12.5 percent in 2017–2020). For 2017–2020, the federal government will receive an additional 1 pp to be redistributed via equalization grants. This may result in a financing gap for some regions. ²⁾ The tax code sets the corresponding rates in rubles for 2017–2019. ³⁾ As established in the Budget Code (article 56, 2.2). For 2017, the distribution of these revenues shall be governed by the Federal Budget Law. ⁴⁾ These shares are suspended for 2017–2020 by law 409-FZ of 30 November 2016. ⁵⁾ Gasoline and diesel oil excise revenues shall be attributed to the federal budget according to the following shares: 38.3 percent in 2017, 42.6 percent in 2018, and 39.8 percent in 2019. The remaining portion will go to the regional budgets. ⁶⁾ Whenever share of federal, regional and local government is reported simultaneously as 100 it means that each of them receives the full share of the tax revenue in application to its own jurisdiction. ⁷⁾ 95 percent in Moscow, Saint Petersburg. The federal 5 percent is planned to be given over to municipalities in 2018. ⁸⁾ Numerous fines and penalties are distributed in various shares (including 100 percent) among different government levels. Sources: Russian Tax Code (articles 13–15; 18; 143–418); Russian Budget Code (articles 46, 56–64).

Table A.2Download as CSV 

Russia: Spending responsibilities and jurisdiction by level of government.

Area	Federal	Joint Federal Regional	Regional / Local
General	Exclusive Federal Jurisdiction: Authority on federal property, regulation of social and economic development, federal energy systems, national defense and security, international relations, law enforcement; meteorology and statistics.	Areas of joint federal-regional jurisdiction: Public safety and law enforcement; administrative, labor, family, housing, land, subsoil, forest, water relations; environmental protection; emergencies and natural disasters; education, science, culture, sports; public health, social security. Responsibilities are usually divided based on jurisdictional attribution or relevance (e.g. regional roads or federal water facilities), but sometimes are shared between the two levels of government.	Exclusive Regional Jurisdiction: all other government responsibilities beyond those under the federal jurisdiction and joint federal-regional jurisdiction - as stipulated in regional constitutions and legislation. Local Governments’ jurisdiction: Urban, rural settlements; electricity, heating, water, gas, fuel supply; roads; municipal housing; public transport; emergencies, fire safety; public amenities, eateries, retail trade; culture (local cultural heritage, folk art and crafts); physical culture, sports, public entertainment, recreation; archives; cemeteries; local resorts; public safety, rescue operations; waste management; support to agriculture and SMEs; terrorism/extremism prevention; education (less vocational + vacations); public health.
Delegated federal Responsibilities supported by federal subventions			National Census and Agricultural Census; Prevention of homelessness; Housing for disabled, veterans, retired servicemen, etc.; Subsidization of housing and utility payments for veterans, disabled, radiation-exposed, etc.; payouts to radiation-exposed; unemployment benefits; maternity and childcare benefits; monthly compensation payouts to various categories, e.g. exposed to radiation, blood donors, etc.; water and forest relations: management (partial) of federal water facilities and forests; animal world, hunting, fishing (partial); protection and oversight of cultural heritage; education: oversight, licencing, accreditation (all partial); public health: licensing; procurement of drugs, mandatory medical insurance.
Delegated federal Responsibilities unsupported by federal subventions			Audit of construction plans and engineering surveys; environmental audit; land relations: provision of plots of land for construction, demolition of real estate, easement; R&D management.
Education	Universities		Vocational, primary, and secondary schools.
Employment	Unemployment benefits (delegated — see above)		Employment facilitation.
Social security	Social support to war veterans, radiation victims (some responsibilities delegated — see above)		Social support to senior citizens, disabled, orphans, labor veterans, low income households; payment of medical insurance contributions on behalf of non-workers.
Industry support	For instance, Aviation		Support to agriculture (beyond that from federal programs) and to SMEs (since 2015).
Waste management	Radioactive waste		Solid waste.

Note: Responsibilities of regional governments in areas of joint jurisdiction are stipulated in the following legislation/regulations: 114 responsibilities listed in the framework law (No. 184 FZ of 1999); 61 responsibilities prescribed in various specific laws (e.g. No. 52 FZ On Sanitary and Epidemiological Safety); 20 responsibilities arising from Presidential decrees (in particular decrees of May 2012), e.g. social suppor to medical workers, their professional development, employment of disabled, housing, increase in salaries for teachers and cultural workers, etc.; 162 responsibilities according to GoR decrees (minor, many of them recommended, not mandated. Regional governments implement 55 federal government programs and federal special-purpose programs — according to GoR resolutions (financed with own funds and subsidies). Sources: Constitution of Russian Federation (Articles 71–73, 130–133); Federal Laws No. 184 FZ (10/06/1999 amended 12/28/2016; and, No. 131 FZ (10/06/2003 amended 12/28/2016 and updated 02/17/2017); list of regional responsibilities (Ministry of Justice website, http://minjust.ru/ru/razvitie-federativnyh-otnosheniy-i-mestnogo-samoupravleniya/razgranichenie-polnomochiy-mezhdu).

Table B.1 provides the definition for the variables used in the analysis.

Table B.1Download as CSV 

Variable definitions.

Variable	Definition
Change in the share of public sector in GRP	Change in the share of public sector in GRP in 2004–2015 (percent) *
Common border	Dummy identifying regions sharing a common border *
Federal transfers as a share of GRP	Average federal transfers-to-GRP ratio in 2005–2015 (percent) *
Footprint of state	Ln of number of per capita budgetary and non-budgetary state institutions *
Foreign trade	Average Exports plus Imports over GRP for 2009–2015 (percent) *
Initial per capita real GRP	Ln of real per capita GRP in 2003 *
Per capita real federal transfer growth correlation	Bilateral regional corr. of real per capita federal transfer growth for 2005–2015 (excluding tansfers to territorial EBFs)
Per capita real grant growth correlation	Bilateral regional correlation of real per capita federal grants growth for 2005–2015
Per capita real GRP growth	Annual average growth rate (Ln difference) of real per capita GRP in 2004–2015 *
Per capita real GRP growth correlation	Bilateral regional corr. of real per capita GDP growth for 2005–2015
Per capita real subsidy growth correlation	Bilateral regional corr. of real per capita federal subsidies growth for 2005–2015
Per capita real subvention growth correlation	Bilateral regional corr. of real per capita federal subventions growth for 2005–2015
Population	Ln of population (millions) in 2005 *
Population density	Ln of population density (people per square kilometer) in 2005 *
Revenue-to-expenditure ratio	Annual average change of the revenue-to-expenditure ratio in 2005–2015 (percent) *
Share of mining in GRP	Average share of mining in GRP in 2004–2015 (percent) *
Share of public sector in GRP	Average share of public sector in GRP in 2004–2015 (percent) *
Urbanization rates	Average urbanization rates for 2005–2015 (percent) *

Note: Variables marked by * refer to the bilateral difference between any two regions.