Chapter 6__________________________________________________________Is Russia’s Economic Growth Pro-Poor?
Yuka Takeda1

Introduction
From 1998 until the global financial crisis of 2008, which stemmed from the financial crisisin the U.S., Russia enjoyed a decade of continuous economic growth. Although the 1998 financial
crisis damaged the Russian economy, the subsequent large depreciation in the rouble and the dramatic rise in oil and gas prices stimulated a recovery. This period of economic growth, the firstseen since the collapse of the Soviet Union, saw a substantial reduction in poverty. The poverty
rate in 1995 and 2000 was 24.8% and 29.1%, respectively, but dropped to 15.3% in 2006
(Goskomstat 1999; Rosstat 2007a). This dramatic decrease was possible because poverty in Russia
is shallow: the consumption (or income) levels of most of the poor are close to the poverty line.
However, while Russia witnessed economic growth and poverty reduction, inequality still remained high. The Gini index in 1998 and 2002 was 0.401 and 0.397, respectively; it reached 0.410
in 2006.
Using data from a large sample of developed and developing countries over the last four
decades, Dollar and Kraay (2002) empirically showed that growth in the average income of thepoor increased or decreased at the same rate as the growth in overall income. They emphasisedthat standard growth-enhancing policies, focusing on low inflation and fiscal discipline, effectively
reduce poverty. This conclusion remains highly contentious: Kakwani et al. (2000) criticised the
Dollar and Kraay study from a theoretical viewpoint and highlighted the importance of government policies aimed at pro-poor growth.
This study examines whether economic growth is pro-poor in Russia, using regional datafrom Rosstat (the Federal State Statistical Service of Russia) for 1995–2006. Although there havebeen only 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. 2008). A
significantly low convergence rate can cause a differential impact of economic growth on regional
poverty. This study is one of the first attempts to address this issue (Takeda 2004a; 2004b; 2006).
If Russian economic growth does not benefit the poorer regions much more, that is, if the growth
is not found to be pro-poor, the government will need to seriously consider formulating economic
policies directly aimed at pro-poor growth. This study aims to shed light on the significance of1This study is funded by a Grant-in-Aid for Young Scientists (B) from the Japanese Ministry of Education, Culture,
Sports, Science and Technology and a Grant for Visiting Fellows from the Global COE program, the Research Unit
for Statistical and Empirical Analysis in Social Sciences (G-COE Hi-Stat Program) at Hitotsubashi University, Japan.

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pro-poor growth policies in Russia.
Section 2 briefly discusses the trends in Russian socio-economic indicators. In addition, we
examine the relationship between the regional poverty rate and gross regional product (GRP). Section 3 discusses the data and methodology employed; Section 4 mainly describes the empiricalresults on the elasticity of the poverty rate with respect to GRP; and Section 5 presents the conclusions.

Economic Growth, Poverty, and Inequality in Russia
Russia, along with the other transitional countries of Eastern and Southern Europe, faced atransformational recession in the 1990s and suffered a recession that lasted longer than that for theothers. However, the country enjoyed continuous economic growth after the 1998 financial crisis(Figure 1). Although the contagion of the Asian financial crisis hit the Russian economy in August
1998, the devaluation of the rouble because of the crisis ultimately led to a recovery in production
by domestic industries and initiated economic growth in Russia. In Figure 1, for the sake of convenience in obtaining estimates in the subsequent sections, the average real per capita GRP is presented as an indicator of economic growth instead of the real per capita gross domestic product(GDP). The real per capita GRP is deflated by an annual consumer price index (CPI) at the regional level. On 1 January 1998, the rouble was officially denominated, with one new rouble
equalling 1,000 old roubles. In this study, we use the year 1995 as the base year to measure the realvalue; the 1995 price is indicated in new roubles. The average real per capita GRP in 2002 was
7,542.5 roubles, while that in 1995 and 1998 was 8,598.7 and 5,273.2, respectively2. According to
the estimate of the European
Figure 1 Socio-economic indicators in Russia 1995-2006
Bank for Reconstruction andDevelopment (EBRD), Russia
had recovered 90% of its 1989
GDP by 2006.
Inequality dramaticallyincreased after the collapse of
the U.S.S.R. and still remains
high (Figure 1). The Gini coefficient is a measure of the concentration of income distribution; it ranges from 0 (perfectly
equal distribution) to 1 (perSources: Average real per capita GRP and C.V. for real per capita GRP
fectly unequal distribution).
are calculated by the authour on the base of Rosstat’s data. Poverty rate
Here, the Gini coefficient isand Gini coefficient are estimated by Rosstat.
2
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.

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Is Russia’s Economic Growth Pro-Poor?

indicated in terms of percentage. Early in the transition period, inequality in income distribution
increased significantly overall. The Gini coefficient rapidly increased from 26.0% in 1991 to
40.9% by 1994, and has stood at around 40% ever since. This is as high as that in Latin Americancountries.
Inequality in the regional economy continuously increased in Russia during the period
1995–2006 (Figure 1). Coefficient of variance (CV) is the ratio of the standard deviation of avariable to its mean. Therefore, the CV for the real per capita GRP is a measure of inequality between regions. Here, it is indicated in terms of percentage. The CV for the GRP increased during
the 1995–2006 period. The CV in 2005 was 88.5, while in 1995, it was 52.9. Moreover, it should
be noted that the CV increases more rapidly when economic growth is high. This could suggestthat the economic growth in Russia was not pro-poor.
Although the poverty rate increased after the collapse of the U.S.S.R., it dramatically decreased during the economic boom following the 1998 financial crisis. The poverty rate is definedas the proportion of the population with incomes below the official poverty line. In 1999, the poverty rate at the national level reached 29.9%, but decreased to 20.8% in 1997 (Figure 1). In thecourse of economic development, however, the poverty rate rapidly decreased to 15.3% in 2006.
As expected, economic growth had a positive effect on poverty in Russia.
Figure 2 Regional poverty and real per capita GRP 1995-2006

Source: Author’s estimations on the base of Rosstat’s data. Rosstat, Regiony Rossii (various years).

Figure 2 shows the relationship between the regional poverty rate (logarithm) and the real
per capita GRP (logarithm). With reference to the estimates shown in the subsequent sections, the1995–2006 period is divided into two shorter ones: the period of turmoil (1995–2000) and the period of continuous economic growth (2001–2006). We observe a negative relationship between
poverty and real per capita GRP in these periods. The equations shown in all the panels of Figure 2
are estimated with no control variables, using pooled data from 888 observations (in 74 oblasts- 131 -

Yuka Takeda

over 12 years). The elasticity of poverty to growth is –0.426 for 1995–2000 and –0.445 for
2001–2006. It seems that an increase of one percentage point in the real per capita GRP could be
more effective in reducing the poverty rate in the period of economic growth. Moreover, it could
suggest that the economic growth in 2001–2006 was pro-poor. In the subsequent sections, from theregional perspective, we conduct a more thorough examination of whether the economic growth inRussia can be considered pro-poor.

Data and Methodology
Data
In this study, we use Rosstat’s official data at the regional level (Rosstat 2007b; Goskomstat
2002b). As of January 2009, the Russian Federation has 83 regions that include oblasts, krai,
autonomous okrugs, republics, and cities of federal subordinations. As mentioned previously, thereal value is measured on the basis of the 1995 denominated value, using an annual CPI at the regional level. Following Lugovoi et al. (2007) and Kholodilin et al. (2008), apart from autonomous
okrugs within oblasts, the Republic of Chechnya, Republic of Ingushetia, Republic of 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 are not available and the data on the remaining
three regions are unreliable. In addition, 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 periods of the turmoil and ofcontinuous economic growth, the 1995–2006 period is used for the analysis.
Methodology
In order to examine how poverty reduction varies in the periods of the turmoil and of continuous economic growth, we estimate the elasticity of regional poverty to real per capita GRP in
the two periods. The basic model is as follows:

ln Pit   0   GRP lnGRPit   Period Period i  i   it(1)

where Pit is the regional poverty rate in region i (i = 1, …, N) at year t (t = 1, …, T); GRPit is thereal per capita GRP in region i at year t; Periodt is a dummy variable for the period of the turmoil(1 if the year ranges from 1995 to 2000); μi is an unobserved individual effect for region i; and εitis an error term. We assume that εit is an AR(1) error term: it   i, t1   it(2)

The autoregression coefficient is significantly different from zero (Table 1). Therefore, in thisstudy we estimate the models with an AR(1) error term.
To analyse the impact of policies on poverty reduction, we add the variables of social policy
and a macroeconomic stabilisation policy, namely, low inflation policy, to the basic model. The
expenditure of socio-cultural measures consists of the sum of expenditures on education, health- 132 -

Is Russia’s Economic Growth Pro-Poor?

care and social policy. Data of the share of social policy only are available for 2000–2006, although those of socio-cultural measures are available for 1995–1998 and 2000–2006. Therefore,
we use the share of socio-cultural measures in a consolidated budget as the proxy of social policy.
In this study, the share of socio-cultural measures in 1999, which is not available in the statisticalsources of Rosstat, is calculated as the means of its share in a consolidated budget in 1998 and2000.
We obtain an augmented model, adapting that proposed by Ravallion and Datt (2002):

ln Pit   0   GRP lnGRPit   Socpol ln Socpolit   Inf ln Inf it   iFO(k )FO(k )i   Period Periodt  i  it(3)kwhere Socpolit is the share of socio-cultural measures in a consolidated budget in region i at year t;
Infit is an inflation rate in region i at year t; FOk indicates the dummy variables for federal regions(federal’nyi okrug) to which region i belongs (k = 1,…, K). The Russian Federation has seven federal regions: Centre, North West, South, Volga, Ural, Siberia, and Far East. We found differencesin the poverty rates—statistically significant at 1%—amongst the federal regions. Thus, we control
the dummies for federal regions in the model.
In order to consider the regimes of the poorer and the richer regions, we also use a regionaldummy as follows:

ln Pit   0   GRP lnGRPit   Socpol ln Socpolit   Inf ln Inf it  HPR HPRit   Period Periodt  i  it(4)

where HPRit is a dummy variable for the regions with higher poverty. Here, we define regions withhigher poverty (HPR) as those in which the poverty rate in 1995 was above the 5th quintile of its
distribution (Appendix 1). The others are defined as regions with lower poverty (LPR).
Following Ravallion and Datt (2002), in order to improve the model’s goodness of fit, we
use the two-year moving average of lnGRPit and the lagged value of lnSocpolit in the models.
Moreover, the selection of these values assumes that it takes time for growth and social policy tohave an effect on poverty.

Empirical Results
Poverty elasticity without consideration of regimes
The estimation results of Equations 3 and 4 are shown in Table 13. According to the Hausman tests, the FE model gives us an efficient and consistent estimator. Thus, we obtain a statistically significant estimate of –0.94 for the elasticity of poverty to growth (p < 0.01). Accordingly,
we can confirm an adverse relationship between poverty and economic growth. However, comparing the estimate of –0.53 obtained by Ravallion and Chen (1997) for Eastern Europe and Central Asia, the estimate of –0.94 by the FE models seems to be too large, while those of –0.71 and3

Summary statistics are shown in Appendix 1.

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Table 1 Regression of Poverty for Russia in 1996-2006

Yuka Takeda- 134 -

Table 2 Regression of Poverty for Russia by Period

Is Russia’s Economic Growth Pro-Poor?

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Yuka Takeda–0.63 by the RE models in our study are close to their estimate4. This could suggest that the models should be applied separately to each of the two periods (the period of the turmoil and that ofcontinuous economic growth), each of the regions (such as the poorer regions and the richer regions), or both of the regimes.
Estimation by regimes of periods
To examine the elasticity of poverty to growth in the periods of turmoil and positive economic growth, we separately re-estimated the augmented equations (Equations 3 and 4) for theperiods (Table 2)5. The specification tests show that the estimates by the FE model are efficientand consistent. Here, we obtain –0.60 for 1995–2000 and –0.70 for 2001–2006. These estimates
are much closer to that obtained by Ravallion and Chen (1997). In Russia, the elasticity of poverty
to growth slightly increases in 2001–2006, that is, in the period of rapid economic growth. Growthis effective in reducing poverty in both the periods. In addition, poverty responds more sensitively
to economic growth in the period of an economic boom.
The estimate for social policy in the period of turmoil by the FE model is statistically significant at the 1% level, while its estimate in the period of continuous economic growth is statistically not significant. Considering the estimates by the RE models as well as the FE models, for the
reduction of poverty, the role of social policy is more important and effective in the period of turmoil than in that of economic growth. The results of the estimates could suggest that during theperiod of turmoil, social policy reduced the poverty that had increased owing to a decrease in
growth. Moreover, the estimates for inflation by the FE model are not statistically significant inboth periods. The estimates for inflation by the RE models, however, are positive and statisticallysignificant. This could indicate that a low inflation policy as a measure for poverty reduction could
be effective in the period of economic growth, while it is not during the period of turmoil.
Estimation by regimes of periods and regions
Does elasticity of poverty to growth vary among the higher poverty regions (HPR) and thelower poverty regions (LPR)? In the analysis, the HPR comprise 15 regions, including Dagestan
Republic, Kurgan Oblast, Buriatiya Republic, Tuba Republic, and Chita Oblast (Appendix 2)6. The
average poverty rate for the HPR in 1995 was 49.8%, while that for the LPR in 1995 was 25.9%.
For the HPR and the LPR, the specification tests show that efficient and consistent estimators areprovided by the FE models (Table 3)7. For both the HPR and LPR, the estimated elasticity of pov4
Ravallion and Chen (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. Ravallion and Chen
(1997) use the models with a white-noise error. In their study, the pooled OLS showed the efficient and consistentestimate of the elasticity of poverty.
5
According to the Chow test, we can reject at the 1% level the hypothesis that a model without regimes of periods
is appropriate.
6
For a definition of the poorer and richer regions, see Section 3.2.
7
According to the Chow test, we can reject at the 1% level the hypothesis that a model without regimes of periods
and regions is appropriate.

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Is Russia’s Economic Growth Pro-Poor?

erty to growth increases in the period of continuous economic growth. The elasticity for the HPR
is –0.63 during economic growth (p < 0.05), while it is –0.52 for the HPR during the period of
turmoil (p < 0.01). Moreover, the estimates for the LPR are –0.63 for 1995–2000 (p < 0.01) and–0.74 for 2001–2006 (p < 0.01). These results show that both the HPR and the LPR gain and benefit from rapid economic growth. The growth is good for both the poorer and the richer regions.
Table 3 Regression for Higher and Lower Poverty Regions by Period
Table 3-1 Higher Poverty Regions

Table 3-2 Lower Poverty Regions- 137 -

Yuka Takeda

Figure 3 Is growth rate higher in the HPR than in LPR?
Figure 3-1 GRP and its growth rate in 1995-2000 and 2000-2006

Figure 3-2 GRP and its growth rate in 1995-2006

Note: Weighted by regional poverty ratio

It should be noted, however, that the estimates of the real per capita GRP are always lower
in the HPR than in the LPR (Table 3). For 1995–2000, the estimate for the GRP is –0.52 for the
HPR and –0.63 for the LPR, while for 2001–2006, the estimate is –0.63 and –0.74, respectively.
Moreover, the growth rate of GRP varies among regions and the HPR doesn’t necessarily growfaster than the LPR (Figure 3). These results could suggest that even in the period of continuous
economic growth, the HPR did not necessarily benefit from the economic growth any more thanthe LPR did. In addition, the results of the estimates could also suggest that in the period of turmoil, the HPR suffered less from the recession than the LPR. The conclusion is that growth bene-- 138 -

Is Russia’s Economic Growth Pro-Poor?

fits the richer regions more than it does the poorer regions, and, thus, growth is not pro-poor in
Russia.
In both the poorer and richer regions, during the 1990s, social policy could have helped toalleviate the poverty that increased during the turmoil, although a low inflation policy could not
help (Table 3). On the contrary, in the course of economic growth, a low inflation policy could
have helped to alleviate poverty in both the regions, while the adverse effect of social policy onpoverty disappeared in the period8. The results of the estimates might suggest that any social policy helps to alleviate poverty during a period of turmoil, while such policies have to be targetedvery carefully in the period of economic growth.

Conclusion
This study examined the effect of economic growth on poverty reduction in Russia based on
the elasticity of poverty to real per capita GRP, using Russian regional data for 1995–2006. We
found that the elasticity of poverty was more sensitive in the period of economic growth(2001–2006), allowing poverty to be substantially reduced. The shallowness of Russian poverty is
another reason for the large reductions in poverty during the period.
From a regional viewpoint, we find that Russian economic growth since the 1998 financial
crisis has not been pro-poor. The study shows that the elasticity of poverty was more sensitive
during the economic growth period in both the poor and rich regions. Thus, growth reduced poverty in both types of regions. From this viewpoint, growth seems to be good for the poor in Russia.
However, the study also shows that growth benefited the richer regions (or people) more. As a result, from the regional point of view, we could conclude that the growth has not been pro-poor in
Russia. The findings of the study comprehensively explain the Russian peculiarity of shallow poverty with inequality still at a high level and even growing slightly.
The effect of a low inflation policy on poverty reduction in Russia is vague. This might bebecause hyperinflation in the early 1990s had such a great effect on the well-being of the people
that even a low inflation policy could not be effective in alleviating poverty. A low inflation policy,
however, could reduce poverty during the period of economic growth. On the other hand, social
policy could be effective in reducing poverty only during the period of turmoil and not during thatof economic growth. Therefore, the government needs to make a well-designed social policy forperiods of economic growth and increase the efficiency of such policy.
As is the general notion, economic growth is necessary for poverty reduction in Russia.
However, in this study we find that the growth thus far has not been pro-poor. This suggests that
economic growth is not sufficient for poverty reduction. Taking all of these points into consideration, growth-enhancing economic policies such as liberalisation of trade, strongly recommended
by Dollar and Kraay (2002), could not be effective in alleviating poverty in Russia. The results ofour study suggest that it is necessary to reduce the inequality of distribution within and between
8

The estimate for social policy is statistically significant only in the richer regions (p<0.01).

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Yuka Takeda

regions. The government could play an important role in this area by formulating pro-poor policies,
such as working on the building and development of infrastructure, in order to make economic
growth spill over to the poorer regions and thus make economic growth pro-poor.

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Appendix 1

Summary Statistics- 141 -

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Appendix 2

Regions with Higher Poverty in 1995

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