Global COE Hi-Stat Discussion Paper Series 075

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Economic Growth and its Effect on Poverty Reductionin Russia

Hi-Stat Discussion Paper

Yuka Takeda

July 2009

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

Economic Growth and its Effect on Poverty Reduction in Russia*

Yuka Takeda
School of Political Science and Economics, Waseda University

Abstract
This study discusses whether Russia’s economic growth following the 1998 financial crisis is pro-poor. Using Russia’s regional data pertaining to 1995–2002, weestimate the elasticity of poverty to real per capita GRP (gross regional product). The
analysis reveals that the elasticity of poverty to growth after the crisis substantially
falls at both the national and regional levels. Our results also suggest that inequality
increases between and within the richer and poorer regions. We conclude that it isquite necessary to formulate pro-poor policies rather than growth-enhancing ones, inorder to alleviate poverty in Russia.
Keywords: Poverty. Inequality. Pro-poor growth. Russia.

*

This paper is prepared for the first East Asian Conference for Slavic Eurasian Studies on
5–6 February 2009 at Hokkaido University, Japan. I would like to thank Shinichiro Tabata
and Seongjin Kim for their helpful comments at the Conference. However, any mistakes thatremain are my own. This research is financially supported by a Grant-in-Aid for Young Scientists (B) from the Japanese Ministry of Education, Culture, Sports, Science and Technology and a grant for a visiting fellow from the Global COE program, the Research Unit for
Statistical and Empirical Analysis in Social Sciences (G-COE Hi-Stat Program) at
Hitotsubashi University.
11. Introduction
Until the global financial crisis of 2008, which stemmed from the financial crisisin the U.S., Russia has enjoyed economic growth for about 10 years since 1998.
Although the 1998 financial crisis damaged the Russian economy, the considerable
depreciation of the rouble and the drastic rise in the oil and gas prices following thecrisis led to the recovery of the economy. The economic growth, which occurred forthe first time after the collapse of the Soviet Union, substantially reduced poverty. In
Russia, the poverty rate in 1995 and 2000 was 24.8% and 29.1% respectively, but it
reduced to 15.3% in 2006 (Goskomstat 1999; Rosstat 2007). The drastic decrease in
the poverty rate was possible because poverty in Russia is shallow: the consumption(or income) levels of most of the poor are close to the poverty line. While Russia
witnessed economic growth and poverty reduction, inequality remained high. The
Gini index in 1998 and 2002 was 0.401 and 0.397 respectively. Moreover, it reached0.410 in 2006.
Using data from a large sample of developed and developing countries over thelast four decades, Dollar and Kraay (2002) empirically showed that growth in the
average income of the poor increased or decreased at the same rate of growth in theoverall income. They emphasized that the standard growth-enhancing policies focusing on low inflation and fiscal discipline effectively reduce poverty. The results of
their study were highly contentious: Kakwani et al. (2000) theoretically criticized the
Dollar and Kraay study and highlighted the importance of government policies that
aimed at pro-poor growth.
This study examines whether economic growth is pro-poor in Russia, using theregional data of Rosstat (Federal State Statistical Service of Russia) from 1995 to
2002. Although there have been a few studies on income convergence in Russia,
some studies have highlighted the slow convergence rate in the country (Yemtsov
2005; Lugovoi et al. 2007; Kholodilin et al. 2007). If the convergence rate is considerably slow in Russia, it can differentiate the impact of economic growth on regional poverty reduction. This study is one of the first attempts to deal with this issue(Takeda 2004a; 2004b; 2006). If Russia’s economic growth is not found to be
pro-poor, the government will seriously need to consider formulating economic policies aiming at pro-poor growth. The purpose of this study is to shed light on thesignificance of pro-poor growth policies in Russia.
2Section 2 briefly shows the trend of socio-economic indicators in Russia. In addition, we will examine the relationship between the regional poverty rate and GRP(gross regional product). Section 3 discusses the data and methodology employed;
Section 4 shows the empirical results on the elasticity of poverty rate to GRP; andSection 5 presents the conclusions.
2. Economic Growth, Poverty, and Inequality in Russia
Figure 1 shows the trend of the socio-economic indicators in Russia pertaining to
1995–2005. For the sake of convenience in obtaining estimates in the subsequent
sections, the average real per capita GRP, instead of the real per capita GDP, is presented in Figure 1. The real per capita GRP is deflated by an annual CPI (consumer
price index) at the regional level. On 1 January 1998, the rouble was officiallydenominated with 1 new rouble equalling 1,000 old roubles. For measuring the realvalue in this study, we use the year 1995—three years prior to the 1998 financial crisis—as the base year. The 1995 price is set to a denominated value. Although thecontagion of the Asian financial crisis hit the Russian economy in August 1998, thedevaluation of the rouble caused by the crisis recovered the production of its domestic industries and initiated Russia’s economic growth. The average real per capita
GRP in 2002 was 11,839 roubles, while those in 1995 and 1998 were 9566.3 and6654.8 respectively1.
Poverty rate is defined as the proportion of the population with incomes below
the official poverty line. As shown in Figure 1, the poverty rate at the national level
reached 29.9% in 1999, while it decreased to 20.8% in 1997. In the course of the economic development, however, the poverty rate rapidly decreased to 15.3% in 2006.
As expected, the economic growth had a positive effect on poverty reduction in Russia.
The Gini coefficient is a measure of the concentration of income distribution; it
ranges from 0 (perfectly equal distribution) to 1 (perfectly unequal distribution). The
Gini coefficient is indicated in Figure 1 in terms of percentage. In the early stages of
the transition period, the inequality of income distribution significantly deteriorated
1

Kamchatka Oblast, Evreiskaya Autonomous Oblast, Republic of Chechnya, Republic of
Ingushetia, Chukotsky Autonomous Region, and other autonomous okrugs are excluded
from the analysis. See Section 3.1 for details.
3

in Russia. Although the Gini coefficient in 1991 was 26.0%, it rapidly increased to40.9% by 1994 and has been at around 40% since 1994. The inequality of income
distribution in Russia is as high as that in Latin American countries.
CV (coefficient of variance) is measured by dividing the standard deviation of avariable by its mean. In Figure 1, the CV for the real per capita GRP is indicated interms of percentage. The inequality of the regional economy in Russia has been continuously increasing. Moreover, it should be noted that the CV increases more
rapidly when the economic growth is higher. This could suggest that the economic
growth in Russia was not pro-poor.
Figure 2 shows the relationship between the regional poverty rate (logarithm) andreal per capita GRP (logarithm). With reference to the estimates shown in the subsequent sections, the period ranges from 1995 to 2002. We observe a negative relationship between poverty and real per capita GRP in this period. The equations
shown in all the panels of Figure 2 are estimated with no control variables, using
pooled data from 592 observations (74 oblasts and 8 years). The elasticity of poverty
to growth in the periods before and after the 1998 financial crisis is –0.401 and–0.436 respectively. It seems that an increase of one percentage in the real per capita
GRP could be more effective in reducing the poverty rate in the period after the crisis.
Moreover, it could suggest that the economic growth in 1999–2002 was pro-poor. In
the subsequent sections, we conduct a more thorough examination of whether the
economic growth in Russia can be considered pro-poor.
3. Data and Methodology
3.1 Data
In this study, we used Rosstat’s official data at the regional level (Rosstat 2007;
Goskomstat 2002). As of January 2009, the Russian Federation has 84 regions that
include oblasts, autonomous okrugs, and krais. As mentioned previously, the realvalue is measured on the basis of the 1995 denominated price, using an annual CPI.
Following Lugovoi et al. (2007) and Kholodilin et al. (2008), besides autonomous
okrugs within oblasts, the Republic of Chechnya, Republic of Ingushetia, Republicof Kalmykia, and Chukotsky Autonomous Okrug are excluded from the analysis;
this is because the data on the GRP of the Republic of Chechnya in 1995–2004 are4not available and the data on the remaining three regions are unreliable. In addition,in this study, Kamchatka Oblast and Evreiskaya Autonomous Oblast are excluded
from the analysis because of the lack of data on their regional poverty rates. We thus
have 74 regions for the analysis. To compare the estimates for the period before andafter the 1998 financial crisis, the 1995–2002 period is used for the analysis.
3.2 Methodology
In order to examine how poverty reduction varies in the economic growth before
and after 1998, we estimate the elasticity of regional poverty to real per capita GRP.
The basic model is as follows:

ln Pit = " 0 + " GRP lnGRPit + " AC ACi + µi + #it ! (1)To improve the model’s goodness of fit, we augment the basic model by controlling!

other variables. By adapting the model proposed by Ravallion et al. (2002), we obtain an augmented model:

ln Pit = " 0 + " GRP lnGRPit + " GOV lnGOVit + # " iFO(k )FO(k )i + " AC ACt + µi + $it ! (2)k!

where Pit is the regional poverty rate in region i (i = 1, …, N) at year t (t = 1, …, T);
GRPit is the real per capita GRP in region i at year t; GOVit is the real per capita government expenditure in region i at year t; ACt is a dummy variable for the period after
the crisis (1 if the year ranges from 1999 to 2002); µi is an unobserved individual
effect for region i; and !it is an error term2. In addition, FOk indicates the dummy
variables for federal regions (federal’nyi okrug) to which region i belongs (k = 1,…,
K). The Russian Federation has 7 federal regions: Center, North West, South, Volga,
Ural, Siberia, and Far East. We found differences in the poverty rates—statisticallysignificant at 1%—among the federal regions. Thus, we control the dummies forfederal regions in the model. Following Ravallion et al. (2002), in order to improvethe model’s goodness of fit, we use the 2-year moving average of lnGRPit and the2

Unlike Ravallion et al. (2002), a variable for inflation is not included in the model of thestudy.
5

lagged value of lnGOVit.
4. Empirical Results
The estimation results with the pooled OLS, fixed effect (FE), and random effect(RE) models are shown in Table 1. As the results of the F tests, the Breusch-Pagan
tests and Hausman tests, the FE model gives us the efficient and consistent estimator.
Thus, we obtain an estimate of –0.367 for the elasticity of poverty to growth. In addition, the estimate by the pooled OLS is –0.57, which is very close to the estimate of–0.53 obtained by Ravallion et al. (1997) for Eastern Europe and Central Asia3.
To examine the elasticity of poverty to growth in the periods before and after the1998 financial crisis, we separately re-estimated the augmented equation (equation 2)
by the periods4. The estimation results are shown in Table 2. The specification tests
show that the estimates by the FE model are the efficient and consistent estimators.
Here, we obtain –0.607 for 1996–1998 and –0.195 for 1999–2002. In Russia, theelasticity of poverty to growth drastically falls in 1999–2002, that is, at the beginningof the rapid economic growth. It suggests that although Russia’s economic growth on
the whole rapidly developed after the crisis, the growth cannot be called pro-poor.
Does elasticity of poverty to growth vary among regions with higher or lower
poverty rates? Table 3 shows the estimates of the elasticity of poverty to growth forregions with higher and lower poverty rates5. Here, we define regions with higher
poverty (HPR) as ones in which the poverty rate in 1995 is above the 5th quintile ofits distribution. The others are defined as regions with lower poverty (LPR). In theanalysis, the HPR comprise 15 regions, including Dagestan Republic, Kurgan Oblast,
Buriatiya Republic, Tuba Republic, and Chita Oblast (Appendix 1). The average
3

Ravallion et al. (1997) estimated the elasticity of poverty to income (or expenditure)
growth using national household surveys of developing and transitional countries (67 countries). Their datasets included at least two surveys during the period since the 1980s. As for
Russia, the survey years were 1988 and 1993. In their study, the pooled OLS showed the efficient and consistent estimate of the elasticity of poverty.
4
The Chow test shows that at the 1% significance level, we can reject the hypothesis that thecoefficients of a statistical model do not differ between different regimes of the covariate
space.
5
The Chow test shows that at the 1% significance level, we can reject the hypothesis that thecoefficients of a statistical model do not differ between different regimes of the covariate
space.
6

poverty rate for the HPR is 49.8%, while that for the LPR is 25.9%. For the HPR and
LPR, the efficient and consistent estimator is provided by the RE and FE models respectively. As shown in Table 3, before the 1998 financial crisis, the estimated elasticity of poverty to growth is almost at the same level in the HPR and LPR, that is,–0.557 and –0.573 respectively (statistically significant at the 1% level). After thecrisis, however, the elasticity for the HPR to growth sharply decreased. The estimate
for the HPR is –0.142, while that for the LPR is –0.224. It should be noted that theestimate for the former after the crisis is not statistically significant. It could suggestthat the economic growth after the crisis had little effect on poverty reduction in the
HPR. In addition, it is also noteworthy that its 95% CI (confidence interval) ranges
from –0.408 to 0.124, while that for the LPR after the crisis ranges from –0.421 to–0.027. It could suggest that the elasticity of poverty to growth for the HPR in
1999–2002, if any, is lower than that for the LPR.
To sum up, at both the national and regional levels, Russia’s economic growth
after the 1998 financial crisis is not pro-poor. Although Russia enjoyed a high economic growth, the growth after the crisis benefited the richer regions (or people)
more than it did the poorer regions (or people).
5. Conclusions
To examine the effect of economic growth on poverty reduction in Russia, thestudy estimates the elasticity of poverty to real per capita GRP, using Russia’s
regional data pertaining to 1995–2002. We find that Russia’s economic growth,which occurred after the 1998 financial crisis, is not pro-poor. At the initial stage ofthe high economic growth after the crisis, the elasticity of poverty to growth drastically falls at the national level. Moreover, it substantially decreases at the regional
level in both the poorer and higher poverty regions. It should be noted that although
the elasticity of poverty to growth for the period before the crisis does not vary
among both the regions, it substantially varies among the regions for the period after
the crisis. During the economic development after the crisis, the elasticity of poverty
to growth for the poorer regions falls much more drastically than that for the richer
regions. In Russia, the growth following the crisis benefited the richer regions (people) much more than it did the poorer regions (people). It also suggests that Russia’s
economic growth is more effective in poverty reduction in the richer regions than in7the poorer ones. The findings of the study explain well the Russian peculiarity ofshallow poverty; moreover, inequality is still observed at the high level and is evenslightly growing.
In the period of the high economic growth following the crisis, poverty drastically decreased in Russia. As is the general notion, economic growth is necessary forpoverty reduction. On the other hand, the decrease in the elasticity of poverty to
growth in the period after the crisis, as shown in the study, suggests that economic
growth is not sufficient for poverty reduction in Russia. Taking all these points into
consideration, growth-enhancing economic policies such as liberalization of trade,strongly recommended by Dollar and Kraay (2002), could not be effective in alleviating poverty in Russia. The results of our study suggest that it is quite necessary to
reduce the inequality of distribution within and between regions. The government
could play an important role in this area by formulating pro-poor policies such as thebuilding and development of infrastructure in order to make economic growth
pro-poor.

Reference
Baumol, W. J., 1986. Productivity growth, convergence and welfare: What the longrun data show, American Economic Review, 76, pp.1072-1085.
Dollar, D. and A. Kraay, 2002. Growth is good for the poor, Journal of Economic
Growth, 7(3), pp.195-225.
Ertur, C., J. Le Gallo, and C. Baumont, 2006. The European regional convergenceprocess, 1980-1995: Do spatial regimes and spatial dependence matter?,International Regional Science Review, 29(1), pp.1-34.
Fischer, S., 2003. Globalization and its challenges, American Economic Review,
93(2), pp.1-30.
Gerry, C. J., E. Nivorozhkin and J. A. Rigg, 2008. The great divide: ‘ruralisation’ ofpoverty in Russia, Cambridge Journal of Economics, 32(4), pp.593-607.
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Goskomstat Rossii, 1999. Sotsial’noe polozhenie i uroven’ zhizni naseleniya Rossii,
M.: Goskomstat of Russia.
------------, 2002. Sotsial’noe polozhenie i uroven’ zhizni naseleniya Rossii, M.:
Goskomstat of Russia.
------------, 2002. Regiony Russii: Sotsial’no-ekonomicheskie pokazateli, M.:
Goskomstat Rossii.
Hulme, D., 2003. Conceptualizing chronic poverty, World Development, 31(3),pp.403-423.
Kakwani, N., B. Prakash, and H. Son, 2000. Growth, inequality and poverty: An
introduction, Asian Development Review, 18(2), pp.1-21.
Kholodilin, K.A., A. Oshchepkov and B. Siliverstovs, 2008. The Russian regional
convergence process, 1998-2006: Does the space matter?, the 10th EACES bi-annual
conference. Moscow, Russia 28-30 August 2008. SU-HSE: Moscow.
Lugovoi, O., V. Dashkeyev, I. Mazayev, D. Fomchenko, and A. Polyakov, 2007.
Analysis of economic growth in regions: Geographical and institutional aspect.
Consortium for Economic Policy Research and Advice, Moscow: IET.
Ravallion, M. and G. Datt, 2002. Why has economic growth been more pro-poor insome states of India than others?, Journal of Development Economics, 68,
pp.381-400.
Ravallion, M. and S. Chen, 2003. Measuring pro-poor growth, Economics Letters, 78,
pp.93-99.
------------, 1997, What can new survey data tell us about recent changes in distribution and poverty?, World Bank Economic Review, 11(2), pp.357-382.

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Rosstat (Federal State Statistical Service), 2007. Sotsial’noe polozhenie i uroven’zhizni naseleniya Rossii, M.: Rosstat.
------------, 2007. Regiony Rossii: Sotsial’no-ekonomicheskie pokazateli, M.: Rosstat.
Shaban, A., ed., 2006. Reducing Poverty through Growth and Social Policy Reformin Russia, Washington, D.C.: World Bank.
Son, H.H. and N. Kakwani, 2008. Global estimates of pro-poor growth, World
Development, 36 (6), pp.1048-1066.
Takeda, Y., 2006. Determinants of poverty alleviation and impoverishment in transitional Russia: Urban and rural areas, Journal of Economics (Keizaigaku Ronsyu),
72(1), pp.30-50. (in Japanese)------------, 2004a, Poverty dynamics in Russia during the 1990s, Slavic Studies, 51,
pp.241-272. (in Japanese)------------, 2004b, Temporary or chronic poverty in Russia?, CIRJE discussion paper
series (CIRJE-F-309), Graduate School of Economics The University of Tokyo, December 2004. (in Russian)Yemtsov, R., 2005. Quo Vadis? Inequality and poverty dynamics across Russian regions. In Kanbur, R. and A. J. Venables, eds. Spatial Inequality and Development.
Oxford University Press, pp.348-397.

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11

Table 1: Regression of Poverty for Russia in 1996-2002

Pooled OLS
Coef.
t-value-0.570 ***-15.670.056
1.53
0.237 ***
10.4

Fixed effect (FE)Coef.
t-value-0.367 ***-7.17-0.124 ***-3.32
0.190 ***
10.53

Log of real per capita GRP (current + lagged)
Log of real per capita GOV (lagged)
AC (vs. 1999-2002)
FO (vs. Center)North West0.089 **
2.53
South
0.092 ***
2.61
Volga
0.146 ***
4.78
Ural
0.241 ***
4.93
Siberia0.309 ***
9.44
Far East
0.425 ***
9.19
Constant
7.856 ***
36.69
7.522 ***
Number of observations
518
518
Number of groups
74
Adj R-sq.
0.620
Within R-sq.
0.52
Between R-sq.
0.43
Overall R-sq.
0.45
F test (prob>F)
0.000 ***
Breusch & Pagan test (prob>chi2)
0.000 ***
Hausman test (prob>chi2)
0.006 ***
Notes: *** is significant at 1% level; ** is significant at 5% level; * is significant at 10% level.
Source: Author’s estimation.
12

18.04

Random effect (RE)Coef.
z-value-0.425 ***-10.01-0.088 **-2.49
0.198 ***
11.33
0.097
0.119
0.151
0.235
0.333
0.471
7.607
518
74
0.51
0.66
0.61

*
**
**
***
***
***

1.36
1.66
2.36
2.34
4.95
5.29
24.06

Table 2: Regression of Poverty by Period before and after the 1998 Financial Crisis

Before the financial crisis: 1996-1998
Fixed effect (FE)Random effect (RE)Coef.
t-valueCoef.
z-value-0.607 ***-10.0-0.563 *** -13.47-0.099 **-2.4-0.053-1.64

Log of real per capita GRP (current+lagged)
Log of real per capita GOV (lagged)
FO (vs. Center)North West0.181 **
South
0.237 ***
Volga
0.267 ***
Ural
0.499 ***
Siberia
0.538 ***Far East
0.647 ***
Constant
9.481 *** 12.57
8.473 ***
Within R-sq.
0.44
0.43
Between R-sq.
0.31
0.65
Overall R-sq.
0.32
0.64
Number of obs
222
222
Number of groups
74
74
F test (prob>F)
0.000 ***
Breusch-Pagan test (prob>chi2)
0.000 ***
Hausman test (prob>chi2)
0.000 ***
Notes: *** is significant at 1% level; ** is significant at 5% level; * is significant at 10% level.
Source: Author’s estimation.

13

2.14
2.78
3.54
4.15
6.77
6.03
17.71

After the financial crisis: 1999-2002
Fixed effect (FE)Random effect (RE)Coef.
t-valueCoef.
z-value-0.195 **-2.18-0.336 ***-5.17-0.322 ***-5.09-0.198 ***
-3.67

7.582
0.37
0.40
0.39
296
74
0.000
0.000
0.000

***

***
***
***

14.72

0.068
0.009
0.078
0.093
0.207 ***
0.424 ***
7.855 ***
0.36
0.59
0.54
296
74

0.88
0.12
1.14
0.87
2.87
4.43
22.19

Table 3: Regression of Poverty by Region before and after the 1998 Financial Crisis
Table 3-1: Regions with Higher Poverty
Before the crisis: 1996-1998
After the crisis: 1999-2002
Fixed effect (FE)Random effect (RE)
Fixed effect (FE)Random effect (RE)Coef.
t-valueCoef.
z-valueCoef.
t-valueCoef.
z-valueLog of real per capita GRP (current+lagged) -0.693 ***-5.18-0.557 ***-5.18-0.061-0.30-0.142-1.05
log of real per capita GOV (lagged)-0.183 **-2.71-0.130 **-2.06-0.367 ***-2.89-0.289 ***-2.89
Constant
11.032 ***7.63
9.488 ***
8.13
6.914 ***
5.93
7.060 ***
8.62
Within R-sq.
0.49
0.49
0.40
0.40
Between R-sq.
0.23
0.23
0.03
0.07
Overall R-sq.
0.24
0.24
0.14
0.16
Number of obs
45
45
60
60
Number of groups
15
15
15
15
F test (prob>F)
0.000 ***
0.000 ***
Breusch-Pagan test (prob>chi2)
0.000 ***
0.000 ***
Hausman test (prob>chi2)
0.652
0.195
Notes: *** is significant at 1% level; ** is significant at 5% level; * is significant at 10% level.Regions with higher poverty in 1995 are listed in Appendix 1.
Source: Author’s estimation.

14

(Continued)
Table 3-2: Regions with Lower Poverty
Before the crisis: 1996-1998
Fixed effect (FE)Random effect (RE)Coef.
t-valueCoef.
z-valueLog of real per capita GRP (current+lagged) -0.573 ***-8.32-0.417 ***-9.15
log of real per capita GOV (lagged)-0.056-1.10
0.073 **
2.00
Constant
8.809 ***
9.81
6.434 ***
12.65
Within R-sq.
0.43
0.40
Between R-sq.
0.16
0.21
Overall R-sq.
0.18
0.23
Number of obs
177
177
Number of groups
59
59
F test (prob>F)
0.000 ***
Breusch-Pagan test (prob>chi2)
0.000 ***
Hausman test (prob>chi2)
0.070 *
Notes: *** is significant at 1% level; ** is significant at 5% level; * is significant at 10% level.
Source: Author’s estimation.

15

After the crisis: 1999-2002
Fixed effect (FE)Random effect (RE)Coef.
t-valueCoef.
z-value-0.224 **-2.24-0.313 ***-3.90-0.314 ***-4.22-0.134 **-2.03
7.741 ***
13.41
7.264 ***
18.38
0.37
0.35
0.39
0.44
0.37
0.41
236
236
59
59
0.000 ***
0.000 ***
0.000 ***

Appendix 1: Regions with Higher Poverty in 1995

RegionF.O.

Poverty rate, %Pskov Oblast

North West

42.7

Adygeia Republic

South

46.4

Dagestan Republic

South

71.2

Kabardino-Balkariya Republic

South

42.5

Krachaevo-Cherkessiya Republic

South

45.7

Severnaya Osetiya Republic

South

42.8

Stavropol Krai

South

39.6

Mariy El Republic

Volga

43.2

Orenburg Oblast

Volga

49.3

Kurgan Oblast

Ural

50.4

Buriatiya Republic

Siberia

55.2

Tuva Republic

Siberia73.2

Novosibirsk Oblast

Siberia

39.8

Chita Oblast

Siberia

66.5

Amur Oblast

Far East

37.9

Note: Region with higher poverty is defined as a region in which poverty rate isabove the 5th percentile of its distribution.

16