Abstract
This Selected Issues paper for the Russian Federation reviews trends in private capital flows to Russia by decomposing the flows into its subcomponents. Russia became a net lender to the international banking system, as a complement to the prolonged period of large current account surpluses. The nonbank corporate sector in Russia began to have better access to both bank and nonbank sources of external finance, with improving investor perceptions and a favorable external environment. The relatively lackluster performance of equity issuances and foreign direct investment has been an outcome of both global and local factors.
V. Russia’s Regions: Income Volatility, Labor Mobility and Fiscal Policy52
Russia’s regions are heavily exposed to regional income shocks because of an uneven distribution of natural resources and a Soviet legacy of distorted regional specialization. Also, Russia has a limited mobility of labor and lacks fiscal instruments in regions. We assess how these features influence the magnitude and persistence of regional income shocks, through a panel vector auto-regression, drawing on extensive regional data of the last decade. We find that labor mobility associated with regional shocks is far lower than in the U.S. yet higher than in the EU-15, and that regional expenditures tend to expand in booms and contract in recessions. We discuss institutional factors behind these outcomes and conclude with policy implications.
A. Introduction
1. Russia’s regions differ very much from each other in their economic environment. The sheer size of the Russian territory, the largest in the world, spanning 11 time zones, provides a unique and crucial backdrop for regional diversity. Natural resources are distributed highly unevenly across the territory. Moreover, the industrial structures of the regions still carry the Soviet legacy—political and military considerations often overrode economic rationales in building factories, towns and infrastructure across the vast territory (Hill and Gaddy, 2003).
2. This diversity in geography, natural resource endowment, and pattern of industrialization has led to huge income disparities across regions. Figure 1 illustrates the income disparity across regions in Canada, China, the EU-15, Russia, and the U.S., defined as the standard deviation of regional income per capita.53 This figure shows that Russia has one of the largest regional disparities, second only to China. Moreover, Russia and China, unlike advanced economies, show no convergence in regional incomes over time.
Regional Income Dispersion
(standard deviation of regional income)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Regional Income Dispersion
(standard deviation of regional income)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Regional Income Dispersion
(standard deviation of regional income)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
3. More importantly, the heterogeneity across regions has also increased the volatility of regional incomes, exposing regions to very large idiosyncratic economic shocks. Figure 2 shows that regional income shocks in Russia, measured by the standard deviation of detrended regional growth, are about three times bigger than in the U.S., Canada, China and the EU-15. The size of the shocks has declined sharply from the early period of market economy reform but is still persistently high.54
Size of Regional Shocks
(controlling for regional trends)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Size of Regional Shocks
(controlling for regional trends)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Size of Regional Shocks
(controlling for regional trends)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
4. The large magnitude of regional shocks in Russia highlights the importance of shockabsorption mechanisms in Russia’s regions, including labor mobility and fiscal policy. In this chapter, we assess how labor forces react to regional income shocks, we analyze how fiscal policy affects the level and volatility of regional incomes, and we discuss their policy implications. We start by analyzing regional income shocks in Russia and their consequences for labor markets in comparison with those in the U.S. and the EU-15, and discuss their economic implications. We proceed with investigating whether regional fiscal policies and federal transfers to regions have helped mitigate regional shocks, and discuss the institutional factors behind the results. In the concluding section, we discuss the policy implications of these findings.
B. Tale of Three Adjustment Mechanisms
5. An economy can deal with regional shocks in a variety of ways. First, the government can help to absorb the income effects of negative regional shocks on households through budgetary transfers and expenditure programs. Second, depending on the mobility of labor, people can move away from stagnating regions and into booming regions. The mobility of labor depends on many institutional and economic factors, including moving costs, housing markets, and regulations, which are in part determined by historical and geographic factors.
6. In this section, we examine the extent to which labor reacts to regional income shocks. For that purpose, we perform panel vector auto-regressions (VAR) for Russia, the U.S. and the EU-15. The panel VAR comprises an equation for income per capita at the regional level and an equation for regional population.55 Each variable contains three lags. In addition, we add a dummy for each region i and a dummy for each year t in order to control for fixed effects and national business cycles. The specification is as follows:
7. Figures 3 and 4 show how regional incomes and regional populations interact with each other when there is a positive regional income shock.56 Figure 3 shows the annual evolution of regional incomes in the EU-15, Russia and the U.S. after a positive shock to regional incomes in year zero. The size of the shock is equivalent to one standard deviation of regional income growth. Similarly, Figure 4 shows the reactions of regional population to a positive income shock, as characterized by the panel VAR.
Annual Evolution of Regional Incomes
(orthogonalized impulse response functions to a positive income shock; grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Annual Evolution of Regional Incomes
(orthogonalized impulse response functions to a positive income shock; grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Annual Evolution of Regional Incomes
(orthogonalized impulse response functions to a positive income shock; grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Annual Evolution of Regional Populations
(orthogonalized impulse response functions to a positive income shock; grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Annual Evolution of Regional Populations
(orthogonalized impulse response functions to a positive income shock; grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Annual Evolution of Regional Populations
(orthogonalized impulse response functions to a positive income shock; grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
8. Figures 3 and 4 reveal several interesting facts:
The size of the standard income shock is much larger in Russia than in Europe and the U.S.;
The shocks in Russia are far less persistent than in regions of other countries, in the sense that they essentially disappear after four years; and
Regional populations react to a regional economic shock more mildly in Russia and Europe than in the U.S. In the U.S., the number of regional residents increases by 0.4 percent in about five years in response to a surprise income increase of about 2 percent. In Russia, regional populations do not grow at all, even with a surge in regional income of 8 percent. In Europe, the response is even slightly negative, but this is not economically meaningful.
9. This analysis illustrates three different types of adjustment to shocks:
The U.S. type. Labor is highly mobile. Even with relatively modest regional income shocks, the population moves rapidly to other regions;57
The European type. Labor mobility is sluggish. Even in the presence of large and persistent shocks, people hardly move. This is explained partly by rigid labor markets and partly by fiscal policy (Mauro, Prasad, and Spilimbergo, 1999). As a primary remedy to regional shocks, several European countries have fiscal transfer programs to poor regions. In addition, the European Union provides structural funds to relatively poor regions within the Union; and
The Russian type. Russian regions face very large, but relatively short-lived, shocks. The population responds in the first year, but there is no lasting movement.
10. The consequences of the different adjustment mechanisms are evident in the labor markets. If labor does not move despite negative local shocks, local unemployment increases above the national average. The coefficients of variation of regional unemployment rates would thus likely be higher in countries with lower labor mobility than in those with higher labor mobility. Indeed, Figure 5 shows that Russia’s and Europe’s variations in regional unemployment rates are significantly higher than in the U.S. Moreover, recent economic growth in Russia seems to have increased regional income disparities, reducing unemployment more in booming regions than in stagnating regions. This finding is consistent with Andrienko and Guriev (2003), who argue that labor mobility in Russia is severely constrained because of underdeveloped housing markets, a host of regional regulations inhibiting movements of labor, and high search and moving costs. They report that, as a result, internal migration in Russia is merely 2 percent of the total population, which is significantly lower than in most OECD countries.
Coefficient of Variation of Regional Unemployment Rates
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Coefficient of Variation of Regional Unemployment Rates
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Coefficient of Variation of Regional Unemployment Rates
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
C. Fiscal Impact of Regional Income Shocks
11. Given the size of regional shocks and the lack of labor mobility, an examination of whether regions have used fiscal policy to cushion the blow of income shocks is of interest. In this section, we analyze the impact of regional income shocks on regional expenditures by using an extensive regional fiscal database covering the period 1992 to 2002. In particular, we are interested in whether regional expenditures tend to expand in booms and contract in recessions (i.e., whether regional fiscal policy is procyclical).
12. The analysis of the effect of income shocks on fiscal variables is complicated by two econometric problems: simultaneity bias and the possibility of frequent structural breaks. The first problem occurs because fiscal policy is not only affected by income but also affects income. This simultaneity bias makes the interpretation of any regression of fiscal variables on income difficult and problematic. The second problem of frequent structural breaks is potentially serious in Russia, which went through sweeping structural changes during the past decade of economic reforms. Such changes would make it problematic to explain the regional dynamics with a single panel regression that constrains the parameters to be constant over time.
13. As regards the first problem of simultaneity bias, various authors have adopted different identification strategies to deal with it. For instance, Blanchard and Perotti (2002) relied on high frequency data to identify the effects of fiscal spending on income. Poterba (1994) constructed an ad-hoc measure of fiscal shocks based on the state forecast of fiscal revenues. Another possible solution is the use of instrumental variables.
14. Using all the available information on the industrial structure of Russia’s regions and the panel structure of our data, we utilize an alternative strategy to deal with simultaneity bias, by identifying an explicit source of the shocks to the regions. We construct two shock variables, an oil shock and an industrial shock, which are meant to reflect the peculiarities of Russia’s regions as discussed in the introduction. The oil shock variable is defined as:
where the oil share variable refers to the share of regional income coming from the hydrocarbon sector in year t-1.58 In this construction, regions specializing in the energy sector will have a positive shock when oil prices are high. Similarly, the industrial shock variable is defined as the share of regional income originating from the manufacturing sector in year t-1 multiplied by the real exchange rate. The real exchange rate is meant to capture competition from foreign companies—a rise in the real exchange rate creates a negative shock to regions, engaged in the production of tradable goods. Both shock variables are exogenous to the fiscal policy of any region, given that they depend on the industrial structure of the previous year, the real exchange rate, and the price of oil.
15. We run two types of panel regressions to test whether these shock variables have any significant effects on regional growth. The results are reported in Table 1. A regression allowing for fixed effects, which capture unobserved regionspecific factors, confirms that they are significantly correlated with local shocks with the expected signs. We get the same results when we introduce dynamic effects by including lagged dependent variables.59
Regional Growth and Regional Shocks
Regional Growth and Regional Shocks
| Fixed effects | Arellano-Bond | |
|---|---|---|
| Oil Shock t | 1.64 *** | |
| Industrial shock t | -0.11 ** | |
| Difference in gdp growth t-1 | -0.04 | |
| Difference in oil shock t | 2.28 *** | |
| Difference in industrial shock t | 0.23 *** | |
| Constant | 2.85 | 1.98 *** |
| Number of observations | 760 | 608 |
| Number of regions | 76 | 76 |
Regional Growth and Regional Shocks
| Fixed effects | Arellano-Bond | |
|---|---|---|
| Oil Shock t | 1.64 *** | |
| Industrial shock t | -0.11 ** | |
| Difference in gdp growth t-1 | -0.04 | |
| Difference in oil shock t | 2.28 *** | |
| Difference in industrial shock t | 0.23 *** | |
| Constant | 2.85 | 1.98 *** |
| Number of observations | 760 | 608 |
| Number of regions | 76 | 76 |
16. As regards the second problem of structural breaks, the economic literature on Russian reforms indicates that the issue warrants special attention. The sources of regional revenues and the patterns of expenditures have varied greatly over time because of frequent changes in the de jure and de facto institutional arrangements over the past decade (Lavrov and others, 2000, Martinez-Vazquez and Boex, 2000). In particular, the fiscal effects of oil and industrial shocks are likely to have changed. For this reason, it is problematic to proceed with a panel regression that constrains the parameters to be constant over time. In addition, the span of time under analysis is too short for standard time series techniques.
17. We address this problem by running several cross-section regressions for every year under analysis. Having identified two exogenous shock variables, we proceed with a reduced form regression as follows:
where i refers to region i. The construction of the shock variables is explained above. This specification is used in ten cross-section regressions—one for each year.
18. We test the cyclical behavior of regional fiscal surpluses as follows. Suppose that regional governments run a countercyclical fiscal policy. Then, the coefficient α should be positive because the fiscal surplus of oil-rich regions should increase at higher oil prices as the extra revenues are saved, while the surplus of non-oil regions should be largely unchanged or could even modestly increase because of spillover effects. Similarly, the coefficient β should be negative under a countercyclical policy, because this implies that the fiscal surplus of a highly industrialized region should decline when the ruble strengthens in real terms.
19. The regression outcomes strongly suggest that regions have not been pursuing a countercyclical fiscal policy. Figure 6 reports the coefficients α for the years 1993 to 2002; in the same graph we report the two standard deviation band. Except for the first two years, and for the crisis year of 1998, the coefficient α is never significantly different from zero, indicating that regions have not conducted a countercyclical fiscal policy with respect to oil shocks. A similar pattern is observed in the fiscal reaction to the industrial shocks.
Fiscal Surplus and Oil Shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Fiscal Surplus and Oil Shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Fiscal Surplus and Oil Shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
20. The primary impact of oil prices on regional expenditures comes from the revenue side. We ran the same regression as above, this time using regions’ own revenues as a dependent variable. The results show that regions’ own revenues are highly sensitive to oil prices (Figure 7).60 The value 1 in the regression coefficient indicates that additional revenues from a one-dollar rise in oil prices in a region where the oil sector accounts for a quarter of total regional income, was more than half of a percentage point of regional income in 1998 and more than ¼ of a percentage point in 2000. Regional governments were able to capture a relatively large share of oil revenues up to 1998. Consistent with other studies, however, we find that the federal government has progressively taken away oil revenues since 1998. This effect measures both the direct and indirect effect of an oil boom and so it is not directly comparable with a study at the national level focusing only on the direct effect of oil prices (Kwon, 2003).
Revenues and Oil shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Revenues and Oil shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Revenues and Oil shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
21. We also looked at the response of regions to an oil shock in a dynamic form. In order to capture the dynamic adjustment to an oil shock, we estimate a structural panel VAR comprising oil shock, income, and fiscal surplus. We allow for two annual lags and for regional dummies. It is a structural VAR because we do not allow any feedback from the other variables to the oil shock.61 The ordering of the variables (oil shock, income, fiscal surplus) assumes that fiscal policy has no contemporaneous impact on income, although it could affect it with a lag. The corresponding impulse response functions are shown in Figure 8. A typical oil shock has an immediate effect on both income and fiscal surplus. However, already in the second year after the shock, the fiscal surplus disappears, while the effect on income is more persistent. These results indicate that regional governments that benefited from oil-driven booms, especially before 2000, have used fiscal revenues to finance local expenditure, with a negligible net effect of oil prices on the local surplus. This is consistent with an unreported finding that local expenditure tracks local revenues very closely.
Impulse Responses to a Positive Oil Shock
(grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Impulse Responses to a Positive Oil Shock
(grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Impulse Responses to a Positive Oil Shock
(grey area represents 90-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
22. Our finding that regional governments use procyclical fiscal policy is consistent with the institutional setup. In Russia, most regions have limited discretion in the formulation and conduct of fiscal policy (Lavrov, Litwack, and Sutherland, 2001; and OECD, 2001). Their tax autonomy is lacking and their borrowing authority is severely constrained. Mandates, mostly imposed by the federal government, exceed available resources in most regions by a wide margin, with the gap only partly covered by federal transfers.62 An implication is that regions do not have sufficient incentives to improve their fiscal situation—they risk losing federal assistance or being burdened with extra expenditure responsibilities (Zhuravskaya 1998, Martinez-Vazquez and Boex, 2000, Litwack 2002). As a result, expenditures are driven primarily by the availability of revenues, with regions usually spending windfall revenues in booms rather than saving them. A corollary is that spending is cut in recessions.
23. The ongoing sweeping reform of intergovernmental fiscal relations may help to make regional fiscal policy more countercyclical. The reform aims to allow more fiscal autonomy to sub-national governments while maintaining or even strengthening accountability policy. Substantial progress has been made so far. Two main governing laws were passed into law in late 2003. Also, amendments to the tax and budget codes are expected to be enacted in 2004, in accordance with the new principles of strengthened fiscal autonomy of local self-governments (See Box 1 for details).
Reform of Intergovernmental Fiscal Relations
One of the top priorities under the Putin presidency is reform of intergovernmental fiscal relations. The reform plan was prepared in late 2002 by the Presidential Commission headed by Mr. Kozak, then Deputy Head of Presidential Administration and currently Minister of Cabinet Administration. The blueprint became law in late 2003 when parliament passed two governing laws including the Law on General Principles in the Organization of Local Self-Government (No. 121-FZ) and the Amendment to the Law on General Principles in the Organization of Judiciary and Executive Bodies of Government Bodies of Subjects of the Russian Federation (No. 95-FZ).
In essence, the reform aims to promote the fiscal autonomy of sub-national governments while preserving policy accountability. The laws, for example, grant more autonomy to municipal governments including full discretion in setting wages of local public employees while allowing regional governments to take over local administration in the case of fiscal insolvency of local governments. The laws also create a fourth level of government within municipalities in order to streamline expenditure assignments.
The government has also prepared amendments to the Budget Code and the Tax Code in accordance with the new principles of local self-government. The bills, expected to be enacted in 2004 and implemented gradually, envisage:
Streamlining of expenditure authority among different levels of government;
Formulation of rules and procedures for spending assignment;
Clarification of tax-sharing arrangements among federal, regional, municipal and sub-municipal or settlement levels;
Clarification of rules and procedures for temporary takeover of local administration by regional governments; and
Formulation of rules and procedures for financial transfers between lower levels of government.
D. The Role of Central Governments in Absorbing Regional Shocks
24. One instrument available to central governments to mitigate the effect of regional shocks is social benefit entitlements. These entitlements, such as unemployment benefits and means-tested minimum benefit programs, automatically rise in recessions and fall in booms. In the U.S., for example, unemployment-sensitive programs, such as unemployment compensation and food subsidies, represent the bulk of cyclical components of public spending. These cyclical expenditures are usually substantial in advanced economies; on labor market programs alone, OECD countries spend over 2 percent of GDP on average, although the sensitivity of such expenditures to business cycles differ by countries and the nature of programs.
25. However, in Russia, these expenditure-based stabilizers have an insignificant impact, if any, on regional economies. First, unemployment benefits are de facto discretionary spending rather than mandatory, largely predetermined by the availability of revenues. Second, the total benefit spending is small, less than a third of one percent of GDP, much lower than in OECD countries and even lower than in advanced transition economies (World Bank, 2002). It is thus not surprising that registered unemployment is estimated at less than 15 percent of total unemployment. Third, other social benefit programs are even less sensitive to regional business cycles since such benefits, often paid in kind, are usually based on age, occupation, and other special criteria (in particular, disability) rather than income levels.
26. Other instruments with which central governments can smooth regional shocks include tax arrangements and federal transfers. For instance, in the U.S., state governments have independent taxing power and do impose their own taxes and set the rates. Personal income tax, one of the most common and important state taxes, provides an automatic stabilizing force as citizens of states experiencing a downturn pay less income taxes. In the European Union, explicit transfers from Brussels provide some stabilizing effects in the long run.
27. However, not much stabilizing power is provided by the tax system in Russia. The corporate income tax and personal income tax, major sources of regional revenues, are federal taxes, of which rates, bases, and sharing rules are governed by the federal authorities. Moreover, tax competition among regional governments, especially over corporate incomes, has constrained the potential stabilizing force of income taxes by discouraging regional governments from collecting more income taxes in booms. Capital gains and property income taxes, which are highly cyclical in nature, are negligible, given underdeveloped real estate markets and inadequate tax administration. Oil taxes, the most cyclical ones, are assigned mainly to the federal budget.
28. These institutional arrangements leave federal transfers as potentially the most effective instrument for absorbing regional shocks. In fact, the federal government has provided a substantial amount of financial assistance to regions since 1994, based on a formula, although other channels of assistance were often used as well, exceeding the formula-based channel in some years (Trounin, 2001). However, it is doubtful that federal transfers, in their current form, could play an important role in reducing the volatility of regional economies. The formula, despite the merits of transparency and fiscal discipline, is based on notional tax capacity and expenditure needs, which in turn reflect historical data with considerable lags and unrealistic statutory norms. A more fundamental challenge is posed by the gap-filling nature of federal transfers and the annual adjustment process, both of which discourage regions from intensifying tax effort.
29. There are three main economic reasons why a central government may wish to provide transfers to sub-national authorities:
Equalization transfers. If there is a substantial gap in per capita incomes between regions, the central authority may consider financing structural funds to help the development of the less-rich regions. An example of these equalization transfers are the Structural Funds in the European Union;
Insurance transfers. If a region experiences a temporary shock, such as a natural disaster or the closure of an important industry, the central government could decide to compensate this region as a form of “insurance.” Examples of such transfers are the emergency federal funds in the U.S. These transfers are equivalent to an insurance policy for local authorities and administered by the central government; and
Permanent transfers. If there is a discrepancy between local expenditure mandates and local financing, the central government could cover the gap with transfers. These transfers are present even in the absence of regional shocks or regional income disparities. An example of these transfers are the bloc grants given by the federal U.S. government to U.S. states in order to allow them to fulfill their mandated social expenditure after the welfare reform in 1996.
30. In order to investigate the determinants of transfer policy in Russia, we estimate the following specification:
The parameter α is meant to capture the extent of the equalization transfers. Poor regions should receive more transfers in order to finance local investment projects. The parameter β should capture the insurance transfers; a region experiencing a negative oil shock should receive more transfers. Finally, the parameter γ is meant to capture the third reason for transfers; regions with less revenues to cover their expenditure obligations should receive more transfers. This last coefficient is only a very rough approximation of the last reason for transfers, given that it supposes that expenditure mandates per capita are the same across regions. As before, we estimate several cross-sectional regressions.
31. Figure 9 plots the estimated parameter α for each year. It shows that the sensitivity of regional transfers to regional income per capita has increased, that is, poorer regions have been receiving proportionally more transfers over time. It is notable that the equalization effects captured in α are becoming stronger in the later period. This is consistent with the improvement in the operation of the Fund for Financial Support of the Regions (FFSR) after the 1998 crisis (Martinez-Vazquez and Boex, 2000).
Transfers and Level of Income
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Transfers and Level of Income
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Transfers and Level of Income
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
32. Figure 10 shows the results for parameter β. If there is an insurance motivation to transfers, this coefficient should be significantly negative. However, if, for political economy reasons, oil-rich regions with more bargaining power receive additional transfers during oil booms, then the coefficient should be positive. Figure 10 does not provide firm evidence for either explanation. If anything, transfers seem positively correlated to oil shocks in periods in which the central government was weak, such as 1998.
Transfers and Oil Shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Transfers and Oil Shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Transfers and Oil Shocks
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
33. Finally, Figure 11 illustrates the coefficient γ over time. The coefficient γ is almost always significantly negative, indicating that regions with less revenues are receiving more transfers even controlling for the level of income and oil shocks.
Transfers and Revenues
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Transfers and Revenues
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
Transfers and Revenues
(dashed lines indicate a 95-percent confidence interval)
Citation: IMF Staff Country Reports 2004, 316; 10.5089/9781451833119.002.A005
E. Conclusions
34. This chapter has shown empirical evidence that Russia’s regions are much more heavily exposed to regional income shocks than the U.S. and EU-15 countries. This finding reflects the uneven distribution of natural resources across Russian regions, combined with a Soviet legacy of distorted regional specialization. We also find that labor mobility associated with regional shocks is much lower in Russia than in the U.S., stressing the importance of fiscal policy in Russia in dealing with recessionary or overheating pressures in regions.
35. Despite the central importance of fiscal policy in absorbing regional shocks, our panel data study suggests that fiscal policy in Russia has been largely procyclical at the regional level. In particular, our regression outcomes, relying on an extensive regional dataset, indicate that regional revenues and expenditures are highly correlated with oil shocks, although the relationship is unstable over time because of changes in oil taxes. Federal transfers do not seem to play much of a role in the shock absorption with the size of the transfers not being explained well by economic factors. An obvious policy implication is that a neutral fiscal stance could be attained at the general government level only if the federal government’s fiscal policy is sufficiently countercyclical so as to offset the procyclical fiscal policy in regions.
36. This evidence of regional procyclical policy reflects, in our view, an underdeveloped tax system, the lack of countercyclical welfare spending, and rigid intergovernmental fiscal arrangements in which sub-national governments have little discretion and little incentive to react to regional shocks. In addition, a weak and fragmented banking system—a large number of small banks, underdeveloped inter-bank markets, and the virtual absence of loan syndication—intensifies rather than attenuates the vulnerability of regions, although the financial sector issue is beyond the scope of our study.
37. The strength of economic recovery since the 1998 crisis, together with evidence of low labor mobility and high volatility in regional incomes, suggests that more attention should be given to the need of an adequate shock-absorption instrument at the regional level. The lack of autonomy in sub-national fiscal policy was probably fully justified for earlier years, when fiscal sustainability and anti-inflation policy were top policy priorities. In those years, “soft-budget constraints” of sub-national governments were also a legitimate concern. However, given the current strong economic situation, shifting the focus from crisis management to macroeconomic stability would be a welcome change. The ongoing sweeping reform of intergovernmental fiscal relations, which intends to promote regional fiscal autonomy while preserving accountability, is a step in the right direction in this regard.
38. Finally, our study shows the importance of removing the obstacles to labor mobility. Presently, labor mobility is very limited due to a system of regulation and housing subsidies. While the provision of some social security net may be desirable, heavy intervention in the housing markets can delay adjustment processes. A reform of the housing subsidies not only would save some public expenditure but also would promote more efficient labor markets.
References
Andrienko, Yuri and Sergei Guriev, 2003, “Determinants of Interregional Mobility in Russia: Evidence from Panel Data,” Moscow, CEFIR, mimeo.
Arellano, M. and S. Bond, 1991, “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations,” The Review of Economic Studies, Vol. 58, pp. 277–97.
Blanchard, Olivier, and Lawrence Katz, 1992, “Regional Evolutions” Brookings Papers on Economic Activity, Vol. 1, pp. 1–61.
Blanchard, Olivier, and Roberto Perotti, 2002, “An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output,” Quarterly Journal of Economics, Vol. 117, No. 4, pp. 1329–68.
Decressin, Jörg, and Antonio Fatás, 1995, “Regional Labor Market Dynamics in Europe,” European Economic Review, Vol. 38, pp. 1627–55.
Desai., M. Raj, Lev Freinkman, Itzhak Goldberg, 2003, “Fiscal Federalism in Rentier Regions: Panel Evidence from Russia,” mimeo.
Hill, Fiona and Clifford G. Gaddy, 2003, The Siberian Curse: How Communist Planners Left Russia Out in the Cold (Washington: Brookings Institution Press).
IMF, 1997, Fiscal Federalism in Theory and Practice, ed. by Teresa Ter-Minassian, (Washington: International Monetary Fund).
Kwon, Goohoon, 2003, “Post-crisis Fiscal Revenue Developments in Russia: From an Oil Perspective,” Public Finance and Management, Vol. 3 (4), pp. 505–530.
Lavrov, Alexei, John Litwack, and Douglas Sutherland, 2001, “Fiscal Federalist Relations in Russia, A Case for Subnational Autonomy,” OECD Centre for Cooperation with Non-Members Working Paper (Paris: OECD).
Litwack, John, 2002, “Central Control of Regional Budgets: Theory with Applications to Russia,” Journal of Comparative Economics, Vol. 30, pp. 51–75.
Martinez-Vazquez, Jorge, 2002, “Asymmetric Federalism in Russia: Cure or Poison?” Georgia State University, Working Paper No. 03–04.
Martinez-Vazquez, Jorge, and Jameson Boex, 2000, Russia’s Transition to a New Federalism (Washington: World Bank).
Mauro, Paolo, Eswar Prasad, and Antonio Spilimbergo, 1999, “Perspectives on Regional Unemployment in Europe” IMF Occasional Paper No. 177 (Washington: International Monetary Fund).
OECD, 2001, “Fiscal Federalist Relations: Recent Trends and Prospects,” 2001 Economic Review—Russian Federation (Paris: OECD).
Poterba, M. James, 1994, “State Responses to Fiscal Crises: the Effects of Budgetary Institutions and Politics,” Journal of Political Economy. Vol. 102, No. 4, pp. 799–821.
Trounin, Ilya, 2001, “Tendencies in the Federal Budget Financial Aid Allocation to the Subjects of the Russian Federation: 1992–2001,” in: Fiscal Federalism in Russia: Problems, Theory, and Experiences (Moscow: Consortium for Economic Policy Research and Advice).
World Bank, 1994, Russia and the Challenge of Fiscal Federalism, ed. by Christine I. Wallich (Washington: World Bank).
World Bank, 2001, “Decentralization in the Transition Economies: Challenges and the Road Ahead,” (Washington; World Bank).
World Bank, 2002, The Russian Labor Market: From Crisis to Recovery, January, mimeo (Washington: World Bank).
Zhuravkaya, Ekatherina, 1998, “Incentives to Provide Local Public Goods: Fiscal Federalism, Russian Style,” Working Paper No. 99/15, Economics Education and Research Consortium Working Paper Series.
Prepared by Goohoon Kwon and Antonio Spilimbergo.
Regions within each country or economic area are defined as the largest sub-national administrative unit. They correspond to 89 regions for Russia, 51 States and the District of Columbia in the US, 11 provinces for Canada, 30 provinces for China, and 178 regions for the 15 countries of the European Union (EU-15). Data for Russia, the U.S., Canada, and China comes from the respective domestic statistical agencies; data for the European Union comes from Eurostat. Economic and social indictors for Russia’s regions are mostly from the State Statistical Services. Regional real output data for the early 1990s are staff’s estimates based on sectoral data of the State Statistical Services and the ministry of economy and trade. The primary source of regional fiscal data is the ministry of finance. We use the NUTS2 classification of Eurostat to define European regions; note that using the NUTS1 classification we would have much less regions and less regional disparity. While some of these regions have a very limited number of inhabitants, the findings on disparity and shocks are confirmed if regions with less of a million inhabitants are excluded from the sample.
Russian regional shocks remain large even using alternative definitions of shocks, such as the coefficient of variation or regional income growth in excess of region-specific income growth trends. The terms “regional shocks” and “local shocks” are used interchangeably in this chapter, unless noted otherwise.
In this chapter, variables for income and population are expressed in logarithms, unless otherwise noted.
The identification is based on a Cholesky decomposition in which the order is income followed by population. The 90 percent confidence intervals are shown.
The U.S. type of adjustment has been documented by Blanchard and Katz (1992), while Decressin and Fatás (1995) have documented the European type of adjustment.
We use the lagged value of the income composition to avoid the problem that nominal income could grow mechanically when the oil sector expands.
In the second panel regression, we use the Arellano-Bond methodology, which avoids problem of inconsistency in dynamic panels with fixed effects (Arellano and Bond, 1991).
Region’s own revenues are defined as total revenues minus federal cash transfers. Non-cash settlements between the federal government and regional governments, which were often substantial in the pre-crisis period, have not been deducted in the calculation of regions’ own revenues since they are in essence an accounting reflection of earmarked federal expenditures of highly uncertain value.
Note that our previous analysis has shown that there are important structural breaks in the sample. Moreover, the data allows only 10 years of analysis. The results of this section are subject to these caveats.
For details, see Chapter IV, “Key Fiscal Issues for 2005: An Assessment.”
