1

New Evidence about Regional Income Divergence in Post-Reform Russia

PRE-PRINT VERSION

Abstract

This paper tests for regional income convergence in Russia spanning from 2000 to 2008. Data

for 80 Russian regions is drawn from Russia’s statistical agency Rosstat (formerly Goskomstat).

By doing so, we test the hypothesis in that income divergence across regions of the country

should give place to income convergence as the country moves toward free market economy

with strong market institutions. The study contributes to the existing literature by using the

Exponential Smooth Auto-Regressive Augmented Dickey–Fuller (ESTAR-ADF) unit root test in

a panel setup, a novel econometric technique, which encompasses cross sectional dependence as

advocated by Cerrato et al. (2009). Results show strong evidence of increasing regional income

divergence in post-reform period and are similar to those of Solanko (2003), who finds no

evidence of convergence in pre-reform Russia.

Keywords: Russia, regional income convergence; non-linear panel unit root test, ESTAR

JEL Codes: R, C1, C22, C23, C52

2

1. Introduction

In 1991, Russia’s economy fell along with that of the Soviet Union. The Russian currency, the

Ruble, lost its value. Certain goods were scarce, inflation rose, and living standards fell. Millions

of Russians suffered severe hardships, including job losses and food shortages.

Since then, Russia’s economy has been undergoing a difficult transition from a planned economy

controlled by the state to a market economy based on private ownership. In 1998, Russia

suffered a severe financial crisis. Thereafter, its economy picked up and has since shown strong

and steady growth. The recovery up was in part the result of reforms in banking, labor, and

private property rules, followed by rising world oil prices. The economy made real gains of an

average 7.4 percent per year during 2000-2008, making it the 6th largest economy in the world in

term of Gross Domestic Product (GDP), adjusted by Purchasing Power Parity (PPP).

Rapid economic growth in the 2000s and increased financial capabilities of government have

enabled more even spread of economic benefits between Russian regions. Economic growth

more than halved the income deficit, and lessened its regional differentiation. All Russian

regions reported reduction of infant, maternal and child mortality due to increased financing of

the healthcare system and other modernization. Regional gaps in these indicators also narrowed

significantly. Cellular communications developed rapidly and spread from the center to

peripheral areas: access to mobile telecommunications has increased by more than five times and

indicators of outsider regions have moved closer to those of the national leaders.

Although Russia as a whole achieved impressive economic growth during 2000-2008, individual

regional growth rates varied vastly during the same period. For example, while Russia’s GDP

grew 5.7 percent in 2008, distribution of growth across regions was not even. 15 regions

experienced GRP growth rates of more than 8 percent, while in 7 regions GRP actually

decreased.

Similarly, other figures hint on uneven income distribution within the country. Income of the

poorest quintile (20 percent of the population with the lowest income) as a percentage of total

3

income of the population is one of the indicators that show inequality. An increase in inequality

is typical of countries with transitional economies. According to the 2010 National Human

Development Report for the Russian Federation, in 1990, the poorest 20 percent of Russia’s

people accounted for 9.8 percent of the country’s total personal income, but the figure had

declined to 6 percent by 2000-2003 and 5 percent by 2008. The indicator is at lowest levels in

the richest regions – Moscow and oil and natural gas extracting regions, – where the poorest

quintile accounts for only 3-4 percent of total personal incomes (NHDR for the Russian

Federation, 2010).

As the country moves toward free market economy with strong market institutions, one would

expect that income divergence across regions of the country should give place to income

convergence. This paper is concerned with testing for evidence of regional income convergence

in Russia during 2000-2008. In particular, using gross regional product per capita data drawn

from Russia’s statistical agency “Rosstat (formerly Goskomstat)”, we test for whether there is

evidence of increasing convergence of regional per capita income across 80 Russian regions over

this period. Results show strong evidence of increasing regional income divergence in post-

reform period and are similar to those of Solanko (2003), who finds no evidence of income

convergence in pre-reform Russia.

Our contribution to the literature on income convergence is two fold. First, most empirical

studies on income convergence focus only on developed countries, and the literature on post-

Soviet and transition economies is limited, mainly due to the lack of good quality data. We fill in

this gap in research and empirically estimate the degree of income convergence in post-reform

Russia using panel data, obtained from Rosstat. Second and more importantly, it tests the

convergence hypothesis using a nonlinear panel unit root test as advocated by Cerrato et al.

(2009). This novel econometric technique is preferred in modeling income convergence both due

to its sound theoretical base and estimation power, and this argument will be discussed in later

sections.

The remainder of the paper is organized as follows. The next section provides a brief overview

of the existing literature on income convergence, with special reference to Russia. Section 3

4

presents a theoretical and econometrical framework as well as the data used for the analysis.

Empirical results are presented in Section 4. Section 5 concludes.

2. Literature Review

The most common measures of convergence are beta (β) and sigma (σ) convergence. Beta

convergence implies a negative relationship between the growth of per capita income and the

initial level of income across regions over a given time period. In other words, it implies that,

over a long period of time, the per capita income level of a poor region will tend to catch up with

the level of a rich region. Sigma convergence measures the level of income dispersion. It occurs

if the dispersion in income is declining over time. The dispersion of income levels can be

measured by standard deviation, variation, or the coefficient of variation of GDP per capita

among regions or countries.

Based on Solow’s (1956) model, there have been vast amount of studies devoted to economic

growth and convergence (e.g., Barro, 1991; Barro and Sala-i-Martin, 1991; Baumol, 1986; Jones,

1997; Mankiw, Romer and Weil, 1992; Pritchett, 1997). According to these studies, the

conditions of free factor mobility and free trade are essential and contributing to the acceleration

of the convergence process through the equalization of prices of goods and factors of production.

In this context, the tendency for income disparities to decline over time is explained by the

hypothesis that factor costs are lower and profit opportunities are higher in poor regions as

compared to rich regions. Therefore, poor regions tend to grow faster and catch-up the rich ones.

In the long run, factor prices and growth rates tend to equalize across regions.

A number of studies analyzing income convergence in developed countries (e.g., Borts, 1960;

Borts and Stein, 1964; Perloff, 1963) find evidence in support of income convergence. In

contrast, others find no evidence of convergence (e.g., Browne, 1989; Barro and Sala-i-Martin,

1991; Blanchard and Katz, 1992; Carlino, 1992; Mallick, 1993; Crihfield and Panggabean, 1995;

Glaeser, Scheinkman, and Shleifer, 1995; Drennan, Lobo, and Strumsky, 1996; Drennan, Tobier,

and Lewis, 1996; Vohra, 1996; Drennan and Lobo, 1999). Among recent studies, Lau (2010a)

examines the empirical validity of both beta and sigma convergence across the US states using

5

per capita income data for the 1929-2005 period. Using both linear and panel non-linear unit root

tests, the author finds evidence in support of beta and sigma convergence across most states. He

suggests that the convergence process follows a non-linear dynamics because states have very

different structure of their economies.

The empirical analyses on convergence of income for transition countries of Central and Eastern

Europe (CEE) began to appear in the late 1990s. The most recent works are: European

Commission (2001), Wagner and Hlouskova (2002), EEAG (2004), Kaitila (2004), Kutan and

Yigit (2004, 2005), Varblane and Vahter (2005), Prochniak (2008) and Vojinović and Oplotnik

(2008). Although these analyses vary substantially on the period of coverage, the sample of

countries, data, and the method, they all agree that during 1990s the CEE transition countries

grew and 2000s in line with the neoclassical convergence hypothesis.

There are only a few studies devoted to analysis of growth and convergence in Russian regions.

Berkowitz and DeJong (2003) look at the determinants of economic growth for a sample of 48

out of the 89 regions over the period from 1993 to 1997. Their interest is in determining whether

regional policy reform matters for economic growth, and indeed they find a positive

correspondence between price liberalization and growth in per capita incomes. Ahrend (2002)

studies regional growth for a panel of 77 regions for a somewhat longer period. He finds that

economic reform and general reform orientation explain little of the observed differences in

regional growth rates, and concludes that a region’s initial industrial structure and resource

endowment seem to have a large impact on its growth prospects. Dolinskaja (2002) derives a

similar conclusion when she analyzes regional convergence in real incomes using the transition

matrix approach. Her findings confirm that initial industrial structure and natural resources are

significant in explaining regional differences in growth rates. Solanko (2003) investigates

income growth and convergence across Russian regions. Using data for 1992-2001, she finds

strong sigma divergence simultaneously with beta convergence. She suggests that per capita

income in Russian regions may be converging towards two separate steady states with the

poorest regions converging among themselves and other regions being highly heterogeneous.

6

Among the studies on Russia discussed above, only the paper by Solanko (2003) offers reliable

analysis of regional income convergence in Russia in pre-reform period. The availability of the

data set for 2000-2008 for Russia and using a nonlinear panel unit root test in this paper allow

testing regional income convergence hypothesis in post-reform Russia.

3. Theoretical and Econometric Framework

In order to evaluate more precisely the convergence of per capita income across Russian regions,

we can apply two concepts of beta and sigma-convergence. Starting with the latter, which

involves measuring the dispersion of income levels by standard deviation over mean and

estimating the trend line of the dispersion in regional income levels, we find no evidence of

sigma convergence across Russian regions during 2000-2008. Figure 1 below shows that the

income differentiation among Russian regions has actually increased over time.

Figure 1: Sigma Convergence of GRP per Capital in 80 Russian Regions, 2000-2008

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

2000 2001 2002 2003 2004 2005 2006 2007 2008

Coe

ffic

ien

t of

var

iati

on o

f G

RP

per

cap

ita

Given that the sigma convergence is not revealed, in our theoretical and econometric framework

presented below we refer to the concept of beta convergence in order to decide on the presence

or absence of income convergence across Russian regions during 2000-2008.

7

As suggested by Evans (1998), suppose yi,t is the log per capita output for region (cross-sectional

unit) i at time t (i=1,…, N, t=1,…,T). Next we consider the difference between yit and the mean

value of yi,t over i=1,…, N, which is defined as titit yyy ，where ty = N-1

N

iity

1.

As proved by Evans (1998), since tit yy = N-1

N

ijtit yy

1

)( , if yit – yjt is stationary for all pairs

of regions i and j, tit yy is also stationary for all i. The converse proof is also valid: since yit –

yjt= )()( tjttit yyyy , if tit yy is stationary for all i, yit – yjt is also stationary for all pairs

(i,j). By using these results of equivalence, we can focus on examining the stochastic properties

of titit yyy for all i instead of yit – yjt for all pairs of i and j. The standard ADF regression

takes the form:

tikti

K

kkitijjti yyy ,,

1,1,,

Tt ,...,1 ; Ni ,...,1 (1)

Rearranging equation (1) becomes:

tikti

K

kkitiiiti yyy ,,

1,1,,

Tt ,...,1 ; Ni ,...,1 (2)

where is the first difference operator, k is the number of augmenting terms and

,{ }i tu ( 1,2.., )i N are white noise series independently distributed across N = 80 regions, i.e.

2, ,~ (0, )i t i tu iid .

As we hypotheses the income difference in Russian regions follows nonlinear path we use the

Exponential Smooth Transition Autoregressive (ESTAR) model to specify the price evolvement

dynamics across regions. Cerrato et al. (2009) developed a new non-linear panel ADF test

under cross-sectional dependence, which is based on the following ESTAR specification, and

8

the model is applied to the de-meaned data series of interest in our study: in its general form,

following the notation of Cerrato et al. (2009) we have:

itdtiitiitiiit yZyyy );( ,1,*

1, Tt ,...,1 Ni ,...,1 , (3)

where

])(exp[1);( 2,, cyyZ dtiidtii (4)

where θi is a positive coefficient and c is the equilibrium value of income difference between

region i and the mean difference across regions due to heterogeneous factors and existence of

transaction cost among regions. The initial value, 0iy , is given, and the error term, μit, has the

one-factor structure:

ittiit f ,

),0.(..~)( 2itit dii (5)

in which ft is the unobserved common factor, and εit is the individual-specific (idiosyncratic)

error. Following the existing literature, the delay parameter d is set to be equal to one so that

equation (4) may be rewritten in first difference form in general as:

ittidtiihti

h

hihtiii

h

hhtijijhtiiiti fyZyyyyy

);(*)( ,,

1

1

*1,

**1

1,1,, (6)

notice that when ,i t dy c , ( ) 0Z and equation (3) is equivalent to a standard linear ADF

model of equation (1). However, when the magnitude of income divergence between ,i t dy and c

becomes too large, ( ) 1Z will generate a new linear ADF model with parameter *i i i . In

contrast, when income divergence is negligible, *i affects the flow of the expenditure

difference in this case. However, when the income divergence becomes more serious, *i plays a

9

more important role in governing the adjustment process. We should take note that * 0i i is

the necessary condition for “global stability” to hold. Once the condition of * 0i i is

fulfilled, it is legitimate to have 0i ; if this occurs, the implication is that the income

divergence follows a non-stationary growth path (e.g. a random walk or an explosive innovation

within the “band of inaction” of c) and eventually it converges back to its equilibrium once the

magnitude of income divergence is outside the “band”. If we assume that ,i ty follows a unit root

process in the middle regime, then 0i and equation (6) can be rewritten as:

tititiitiiti fyyy ,2

1,1,*

, )exp(1 (7)

The null hypothesis of non-stationarity is 0 i: 0 ,H i against the alternative of: 1 : 0iH for

i = 1, 2,…, 1N and 0i for i = 1N + 1,…, N.

Because *i is not identified under the null, it is not feasible to test the null hypothesis directly.

Thus, Cerrato et al. (2009) reparameterize equation (7) by using a first-order Taylor series

approximation and obtain the auxiliary regression

tititiiti fyay ,3

1,, (8)

For a more general case where the errors are serially correlated, equation (8) is extended to:

titihti

h

hihtiiti fyyay ,,

1

1

31,,

(9)

Cerrato et al. (2009) further prove that the common factor tf can be approximated by

3

1

1

ttt yb

yf

(10)

where ty

is the mean of ty and 1

1 N

ii

b bN

.

10

Therefore, it follows that equation (9) can be written as the following non-linear cross-

sectionally augmented DF (NCADF) regression:

titititiiiti ydycybay ,

3

13

1,,

(11)

Given the framework above, the authors develop a unit root test in the heterogeneous panel

model based on equation (11). Extending the idea of ity , Kapetanios et al. (2003) derive t-

statistics on ib

:

)ˆ.(.

ˆ),(

i

iiNL

bes

bTNt , (12)

where ib

is the OLS estimate of ib , and . .( )is e b

is its associated standard error. Following

Pesaran (2007), the t-statistic in equation (12) can be used to construct a panel unit root test by

averaging the individual test statistics:

N

iiNLiNL TNt

NTNt

1

),(1

),( (13)

This is a non-linear cross-sectionally augmented version of the IPS test (NCIPS). Consequently,

Pesaran (2007) calculates critical values of both individual and panel NCADF tests for varying

cross section and time dimensions. Difference in income level among Russian regions is possible

because we may anticipate that the economy only experiences a high growth rate when it reaches

the threshold level of human capital accumulation and starts to engage in trade with other regions.

More importantly, the equalization of prices of goods and factors of production follows a non-

linear dynamics as reported by many researchers (e.g. Michael, Nobay and Peel, 1997; Taylor,

Peel and Sarno, 2001; Sarno et al., 2004). All these reasons may be resulted in “bands of

inaction” in the income growth adjustment process among Russian regions.

11

Our analysis uses Goskomstat’s (Russia’s statistical agency) publicly available panel data on

gross regional product per capita to investigate income convergence across 80 Russian regions

for 2000-2008. Goskomstat is our only feasible data source. In theory, the data collected and

published by regional statistical offices (komstats) may more accurately reflect local conditions,

but gathering the data from Russia’s 83 different administrative subjects is clearly out of

question. Moreover, even if Goskomstat data are imperfect, one can at least assume the same

mistakes are made consistently. The possible inaccuracies in Goskomstat data thus do not

preclude comparison of the Russian regions with each other.

5. Empirical Results

Figure 1 shows the regional per capita income differences relative to the mean per capita income

level across regions in Russia. Clearly, no conclusion regarding the degree of per capita income

convergence could be derived from the diagram.

12

Figure 1. Regional Incomes Relative to Mean per Capita Income

-4

-3

-2

-1

0

1

2

00 01 02 03 04 05 06 07 08

RE1RE2RE3RE4RE5RE6RE7RE8RE9RE10

RE11RE12RE13RE14RE15RE16RE17RE18RE19RE20

RE21RE22RE23RE24RE25RE26RE27RE28RE29RE30

Table 1 shows that the growth dynamics across Russian regions does not follow non-linear

dynamics. The proportion of regions that support the convergence hypothesis is only 13 out of 80

regions or 16% of convergence at 5% significance level in Russia. The results clearly show that

the evidence for mean reversion is weak. In line with earlier studies, our findings support the

view that inter-regional equalities are not observed in Russia. The convergence takes place but

only within the group of regions that are located near regions with similar standards of living

(e.g., Mari El Republic, Vladimir oblast, and Moscow oblast; Amur Oblast and Zabaykalsky

Krai).

The results for Russian regional growth dynamics are similar to the findings of Lau (2010b),

which finds that interprovincial inequalities in China have been widening since 1978, implying

strong evidence of income divergence. We therefore suggest further study on conditional

convergence, since heterogeneous factor differences may hinder beta convergence across

regions. Those factors may include inflation rate, infrastructure, human capital, degree of

openness, and use of foreign capital among regions.

Note:

For names of series please refer to Table 1, for example RE1 is per capita income difference relative to the regional per capita

mean income level for Belgorod Oblast.

13

Table 1: Nonlinear Unit Root Test

Region Statistics Sig. Region Statistics Sig.

Belgorod Oblast -0.840773 Republic of Bashkorttostan -2.437.486

Bryansk Oblast -2.463.847 Mari El Republic -3.454.144 **

Vladimir Oblast -3.969.829 ** Republic of Mordovia -1.849.636

Voronezh Oblast -1.285.806 Republic of Tatarstan -2.135.559

Ivanovo Oblast -1.798.140 Udmurt Republic -2.803.041 *

Kaluga Oblast -2.142.467 Chuvash Republic -3.319.109 *

Kostroma Oblast -1.941.485 Perm Krai -3.453.160 **

Kursk Oblast -2.220.032 Kirov Oblast -2.171.014

Lipetsk Oblast -1.558.814 Nizhniy Novgorod Oblast

-2.182.857

Moscow Oblast -4.753.155 ** Orenburg Oblast -2.038.827

Oryol Oblast -2.104.909 Penza Oblast -2.065.945

Ryazan Oblast -3.181.695 * Samara Oblast -2.687.169

Smolensk Oblast -2.889.770 * Saratov Oblast -2.025.408

Tambov Oblast -1.455.142 Ulyanovsk Oblast -2.763.180

Tver Oblast -2.999.245 * Kurgan Oblast -1.576.523

Tula Oblast -2.758.565 Sverdlovsk Oblast -3.094.629 *

Yaroslavl Oblast -1.739.172 Tyumen Oblast -0.88809

City of Moscow -3.162.767 * Chelyabinsk Oblast -3.215.682 *

Republic of Karelia -2.062.676 Altai Republic -3.527.371 **

Komi Republic -3.065.795 * Buryat Republic -2.635.726

Arkhangelsk Oblast -4.240.601 ** Tuva Republic -1.975.694

14

Vologda Oblast -1.933.041 Republic of Khakassia -1.791.084

Kaliningrad Oblast -3.684.554 ** Altai Krai -2.367.702

Leningrad Oblast -3.053.543 * Zabaykalsky Krai -3.895.097 **

Murmansk Oblast -1.644.691 Krasnoyarsk Krai -2.702.709

Novgorod Oblast -2.281.379 Irkutsk Oblast -3.244.292 *

Pskov Oblast -2.063.411 Kemerovo Oblast -2.373.306

City of Saint Petersburg -1.098.889

Novosibirsk Oblast -1.397.341

Republic of Adygea -2.360.040 Omsk Oblast -1.860.037

Republic of Dagestan -2.966.978 * Tomsk Oblast -1.347.693

Republic of Ingushetia -1.876.981

Sakha Republic -2.104.790

Kabardino-Balkar Republic -2.163.807 Kamchatka

Krai -2.566.100

Republic of Kalmykia -3.584.557 **

Primorsky Krai -2.693.850

Karachay-Cherkess Repulic -2.281.045

Khabarovsk Krai -1.242.774

Republic of North Ossetia-Alania -2.755.695 Amur Oblast -3.915.152 **

Krasnodar Krai -3.426.947 ** Magadan Oblast -2.528.678

Stavropol Krai -1.981.865 Sakhalin Oblast -3.499.499 **

Astrakhan Oblast -3.134.139 * Jewish Autonomous Oblast

-2.629.846

Volgograd Oblast -2.567.207 Chukotka Autonomous Oblast

-4.547.318 **

Rostov Oblast -1.629.620

Note: *, **,and *** represent significance levels at 10%, 5%, and 1%, respectively.

6. Conclusion

This paper investigates the convergence process in per capita GRP among Russian regions in the

period of 2000-2008. The novelty of our paper is that we use the Exponential Smooth Auto-

15

Regressive Augmented Dickey–Fuller (ESTAR-ADF) unit root test in a panel setup, an

econometric technique, which encompasses cross sectional dependence. We find evidence of

very weak, if any, regional beta convergence in Russia. The proportion of regions that support

the convergence hypothesis is only 13 out of 80 regions or 16 percent of convergence at 5%

significance level in Russia. Our results clearly show that the evidence for mean reversion is

weak. In line with earlier studies, our findings support the view that inter-regional equalities are

not observed in Russia.

Our results may be interpreted as follows. The regional divergence process in Russia, spurred by

the breakdown of the Soviet Union, still is on-going. The convergence takes place but only

within the group of regions that are located near regions with similar standards of living. The rest

of Russia’s regions seem to be having vastly different development characteristics resulting in

large income dispersion. This difference may be a consequence of the variation in economic

policies and their implementation, as well as certain region-specific factors. As a result, the gap

between rich and poor regions will tend to increase over time unless serious efforts aiming at

reducing regional economic disparities will be implemented at the federal level. Further research

might consider studying conditional convergence to explain the gap between rich and poor

regions in Russia.

References

1. Ahrend, Rudiger (2002). Speed of Reform, Initial Conditions, Political Orientation or What?

Explaining Russian Regions’ Economic Performance, DELTA Working Paper no 2002-10,

Paris.

2. Barro, R. J. (1991). Economic Growth in a Cross-Section of Countries, Quarterly Journal of

Economics, 106(2), 407−443.

3. Barro, R. J., & Sala-i-Martin, X. (1991). Convergence across States and Regions, Brookings

Papers on Economic Activity, 1, 107−182.

4. Baumol, W. (1986). Productivity Growth, Convergence, and Welfare: What the Long-Run

Data Show, American Economic Review, 76, 116−131.

5. Berkowitz, Daniel and David N. DeJong (2003). Policy Reform and Growth in Post-Soviet

16

Russia,” European Economic Review, 47, 337-352.

6. Blanchard, O. J., and L. F. Katz (1992). Regional Evolutions. Brookings Papers on Economic

Activity, 1, 1−75.

7. Borts, G. H. (1960). The Equalization of Returns and Regional Economic Growth, American

Economic Review, 50, 319−334.

8. Borts, G. H., and J. L. Stein (1964). Economic Growth in a Free Market. New York:

Columbia University Press.

9. Browne, L. E. (1989). Shifting Regional Fortunes: The Wheel Turns. New England

Economic Review, Federal Reserve Bank of Boston, May/June (pp. 27−40).

10. Carlino, G. A. (1992). Are Regional per Capita Earning Diverging? Business Review,

Federal Reserve Bank of Philadelphia, March/April (pp. 3−12).

11. Cerrato, M., C. De Peretti, and N. Sarantis (2009). A Nonlinear Panel Unit Root Test under

Cross Section Dependence. Working paper, Department of Economics, University of

Glasgow.

12. Crihfield, C. J. and M. Panggabean (1995). Growth and Convergence in US Cities, Journal

of Urban Economics, 38, 138−165.

13. Dolinskaya, Irina (2002). Transition and Regional Inequality in Russia: Reorganization or

Procrastination?, IMF Working Paper 02/169.

14. Drennan, M. P., E. Tobier and J. Lewis (1996). The Interruption of Income Convergence and

Income Growth in Large Cities in the 1980s, Urban Studies, 33, 63−82.

15. Drennan, M. P., J. Lobo (1999). A Simple Test for Convergence of Metropolitan Income in

the United States, Journal of Urban Economics, 46, 350−359.

16. Drennan, M. P., J. Lobo and D. Strumsky (2004). Unit Root Tests of Sigma Income

Convergence across U.S. Metropolitan Areas, Journal of Economic Geography, 4(5).

17. EEAG (2004). Report on the European Economy 2004, CESifo, Munich.

18. European Commission (2001). The Economic Impact of Enlargement,” Enlargement Paper

No. 4.

19. Evans, P. (1998). Income Dynamics in Regions and Countries, Working Paper, Department

of Economics, The Ohio State University.

20. Glaeser, E. L., J. A. Scheinkman and A. Shleifer (1995). Economic Growth in a Cross-

Section of Cities, Journal of Monetary Economics, 36, 117−143.

17

21. Jones, C. (1997). Convergence Revisited, Journal of Economic Growth, 2, 131−153.

22. Kaitila, V. (2004). Convergence of Real GDP per Capita in the EU15. How Do the

Accession Countries Fit In?, European Network of Economic Policy Research Institutes

Working Paper, No. 25.

23. Kapetanios, G., Y. Shin and A. Snell (2003). Testing for a Unit Root in the Nonlinear STAR

Framework, Journal of Econometrics, 112, 359−379.

24. Kutan, A. M. and T. M. Yigit (2004). Nominal and Real Stochastic Convergence of

Transition Economies, Journal of Comparative Economics, 32, pp. 23-36.

25. Lau, C. K. (2010a). Convergence across the United States: Evidence from Panel ESTAR

Unit Root Test, International Advances in Economic Research, 16(1), 52-64.

26. Lau, C. K. (2010b). New Evidence about Regional Income Divergence in China, China

Economic Review, 21(2), 293-309.

27. Mallick, R. (1993). Convergence of State per Capita Incomes: An Examination of its

Sources, Growth and Change, 325−334.

28. Mankiw, N. G., D. Romer and D. N. Weil (1992). A Contribution to the Empirics of

Economic Growth, Quarterly Journal of Economics, 107, 407−437.

29. Michael, P., A. R. Nobay and D. A. Peel (1997). Transaction Costs and Nonlinear

Adjustment in Real Exchange Rates: An Empirical Investigation, Journal of Political

Economy, 105(4), 862−879.

30. Perloff, H. S. (1963). How a Region Grows. New York: Committee for Economic

Development.

31. Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross-Section

Dependence. Mimeo, Department of Applied Economics, Cambridge University.

32. Pritchett, Lant (1997). Divergence, Big Time, Journal of Economic Perspectives, 11(3),

3−17.

33. Prochniak, M. (2008). Real Economic Convergence Between Central and Eastern Europe and

the European Union, Presented at the conference “China’s Three Decades of Economic

Reform (1978-2008)” organized by Chinese Economic Association (UK), Cambridge (UK),

1-2 April 2008.

34. Russia’s State Statistical Agency Rosstat (Goskomstat), http://www.gks.ru, last accessed on

November 12, 2010.

18

35. Sarno, Lucio, Mark P. Taylor and Ibrahim Chowdhury (2004). Nonlinear Dynamics in

Deviations from the Law of One Price: A Broad-Based Empirical Study, Journal of

International Money and Finance, 23(1), 1−25.

36. Solanko, Laura (2003). An Empirical Note on Growth and Convergence across Russian

Regions, Bank of Finland, Institute for Economies in Transition Discussion Papers, 9/2003

37. Solow, R. (1956). A Contribution to the Theory of Economic Growth, Quarterly Journal of

Economics, 70(1), 65−94.

38. Taylor, M. P., D. A. Peel and L. Sarno (2001). Nonlinear Mean-Reversion in Real Exchange

Rates: Toward a Solution to the Purchasing Power Parity Puzzles, International Economics

Review, 42(4), 1015−1042.

39. The 2010 National Human Development Report (NHDR) for the Russian Federation,

Millennium Development Goals in Russia: Looking into the Future, The United Nations

Development Program in the Russian Federation, Moscow, 2010

40. Varblane U. and P. Vahter (2005). An Analysis of the Economic Convergence Process in the

Transition Countries, University of Tartu (unpublished).

41. Vohra, R. (1996). How Fast Do We Grow?. Growth and Change, 47−54.

42. Vojinović, B. (2005). Towards Financial Integration in the Euro-Area Financial Segments. Is

there a Convergence?, Croatian International Relations Review, 38/39, pp. 29-35.

43. Wagner, M. and J. Hlouskova (2002). The CEEC10ʼs Real Convergence Prospects,

Washington, CEPR Discussion Paper, No. 3318, CEPR.