ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of Social Sciences
MCSER Publishing, Rome-Italy
Vol 5 No 13
June 2014
Alternative Approaches to Measure Poverty in Russian Regions
Rudenko Dmitry Y.
Tyumen State University; Tyumen State Academy of World Economy,
Management and Law; Tyumen, Russia
Email: drudenko@inbox.ru
Doi:10.5901/mjss.2014.v5n13p262
Abstract
The paper proves the need of a complex poverty measurement approach in Russian regions, including the analysis of socioeconomic situation, efficiency of existing institutes, and state of environment. We analyze the official poverty measurement
approach in Russia and consider its main problems. To identify the complex picture of poverty in Russian regions, we compare
indicators characterizing poverty according to the monetary approach.
Keywords: inequality, human development, poverty, regions, Russia.
1. Introduction
The second wave of global economic crisis in 2012 has interrupted the growth of per capita incomes in Russia and has
led to a rise in poverty. In 2013, compared to 2012, the proportion of the poor increased by 0.3 percentage point to
11.1%. Factors and risks of poverty in the period have been: the decline in real incomes; increase of the poverty line as a
result of inflation; job cuts; slowdown in social transfers and wages in the public sector. Structural problems in the
Russian economy are still not resolved, the global financial and commodity markets are reeling, the instability in Ukraine
is growing, debt problem in Europe remains valid – there is a chance of slowing economic recovery. In this regard, the
issue of poverty in Russia is becoming increasingly important.
Three concepts of monetary poverty measurement have been worked out: absolute, relative and subjective. Since
Russia's rank in the global economy is contradictory: on a range of indicators we are lagging behind developed countries
and at the same time we are significantly different from the developing ones (despite their diversity), the problem of
poverty can not be solved without combining the concepts of absolute (common for developing countries), relative and
subjective poverty (typical for the developed ones).
2. Methodology
The Poverty headcount index (the proportion of population with incomes below the subsistence minimum) is a core
element of officially published data on the level of poverty in Russian regions, which reflects the most extreme poverty,
but not a living wage, that is, the amount by which a person can live with dignity. A methodology for determining the
poverty in Russia is based on an absolute monetary approach, which does not meet the minimum required to meet the
needs for human development and to evaluate all factors affecting the structure and dynamics of poverty.
According to Fedorenko (2005) the official approach “makes comparable estimates of poverty and subsistence
either by region or by time period, and does not allow to fix the duration of the state of poverty” (p. 22). Poverty is a
multidimensional category that can not be measured only by monetary indicators. A complex approach to defining and
measuring poverty, including an analysis of the socio-economic situation in the region, access to health care and
education, the effectiveness of existing institutions and the environment is needed.
Rudenko (2013) has systematized approaches to poverty study providing: an approach based on unmet basic
needs; a money metric approach; an approach through the possibilities of human development; an approach through
social exclusion and a participative approach. The study of monetary poverty should include an analysis of population
with incomes below the poverty line with its different variations according to the concepts of absolute, relative and
subjective poverty. The study of unmet basic needs for food and living conditions involves determining the number of
people without housing amenities and modern appliances. It is necessary to consider the proportion of children who are
not receiving general education, the provision of medical services, morbidity (especially active tuberculosis, alcoholism,
substance abuse and drug addiction), disability and mortality in the study of human development opportunities. The study
of social exclusion involves determining the level of unemployment (ILO methodology), the number of the homeless, as
well as street children. The study of subjective poverty evaluates the satisfaction by the financial situation and quality of
̱ʹ͸ʹ̱
Mediterranean Journal of Social Sciences
ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Vol 5 No 13
June 2014
MCSER Publishing, Rome-Italy
life, the prospects for improvement.
The author compares various parameters describing the poverty with a monetary point of view to identify a
complex picture of this phenomenon in regions of Russia. We take the view of Aivazyan (1997) that incomes of Russian
households are likely to be well approximated by a lognormal distribution in the income range typically associated with
observed poverty rates. For a homogeneous population with a lognormal income distribution, the poverty rate, H is given
by
§ ln z − μ ·
H = Φ¨
¸
© σ ¹
where ĭ denotes the standard Normal distribution function, z is a poverty line, ȝ is a mean log income, and ı is
the standard deviation of log incomes.
According to Kolenikov and Shorrocks (2003) “this explicit formula for the poverty rate helps us to appreciate and
understand the complex (and highly non-linear) way in which the mean income, inequality, and poverty line factors
interact to determine the level of poverty”. Poverty rate estimates from the proposed methodology are necessarily subject
to error as they take no account of variations in incomes and minimum subsistence levels by types of households. They
also rely on highly aggregated quintile share data for the distributional characteristics. Nevertheless, they are able to
reproduce the main features of the Rosstat series of poverty rates. The main advantage of aggregated models is that
they allow the poverty rate to be simulated under various scenarios different from those pertaining in the official series.
These counter-factual experiments are precisely what is required in order to compare indicators characterizing poverty
according to national and international approaches.
3. Results
The prevalence of poverty in the Russian regions in 2012 ranged from 6.5 to 30.8%. The geography of poverty is similar
to the geography of the income purchasing power in many ways, as it is a basic money-metric poverty factor. The lowest
proportion of the poor in 2012 had the Tatarstan Republic (6.5%) and Belgorodskaya oblast (6.5%), and the maximum
level was maintained in Kalmykia (30.8%) and Tyva (28.1%).
The author has used an alternative methodology to assess poverty – absolute (USA – a threefold increase in the
cost of the minimum food basket) and relative (EU – the proportion of people living on less than 50% of per capita
income). Per capita income in 2012 on average in Russia was amounted to RUB 22880 per month, the cost of the
minimum food basket was RUB 2541. So, the poverty line will be considered in the first case as a three times cost of the
minimum food basket – RUB 7623 and a half of per capita income – RUB 11440 in the second case. Thus, the proportion
of the poor in Russia in 2012 was about 15.6% and 31% respectively. The calculation results for the regions are shown in
Table. 1.
Table 1. Regional levels of absolute and relative poverty headcount indexes, %
Russian region
Ⱥ
Belgorod region
Bryansk region
Vladimir region
Voronezh region
Ivanovo region
Kaluga region
Kostroma region
Kursk region
Lipetsk region
Moscow region
Orel region
Ryazan region
Smolensk region
Tambov region
Tver region
Absolute poverty
Official, %
American, %
2002
2012
2002
2012
1
2
3
4
25.4
10.2
58.26
10.4
31.2
15.3
64.1
16.6
35.3
20.4
75.21
22.1
33.8
21.2
63.6
15.5
60.8
23.4
84.18
21.4
35
12.4
70.15
12.7
35.5
19.8
66.24
21.3
33.7
12
58.28
12.3
25.8
10.2
58.48
12.5
27.4
10.1
54.16
8.7
30.6
18.6
62.28
20.8
31.3
16.4
65.91
17.3
25.2
14.8
58.68
19.5
27.2
11.8
55.34
17.2
38.7
14.3
71.17
19.2
̱ʹ͸͵̱
Relative poverty
European, %
2002
2012
5
6
21.21
29.9
21.99
28.4
17.36
25.5
23.68
29.3
18.13
25.2
19.28
28.8
22.49
24.7
22.07
28.2
24.46
28.3
22.53
30.9
24.19
29.2
20.29
26.5
22.07
26.1
24.96
30.4
18.36
24.5
ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of Social Sciences
Tula region
Yaroslavl region
Moscow
Republic of Karelia
Komi Republic
Arkhangelsk region
Vologda region
Kaliningrad region
Leningrad region
Murmansk region
Novgorod region
Pskov region
St. Petersburg
Republic of Adygea
Republic of Kalmykia
Krasnodar region
Astrakhan region
Volgograd region
Rostov region
Republic of Dagestan
Republic of Ingushetia
Kabardino-Balkar Republic
Karachay-Cherkessia
Republic of North Ossetia
Stavropol region
Republic of Bashkortostan
Mari El Republic
Republic of Mordovia
Republic of Tatarstan
Udmurtia
Chuvash Republic
Perm Krai
Kirov region
Nizhny Novgorod region
Orenburg region
Penza region
Samara region
Saratov region
Ulyanovsk region
Kurgan region
Sverdlovsk region
Tyumen region
Chelyabinsk region
Altai Republic
Republic of Buryatia
Republic of Tyva
Republic of Khakassia
Altay region
Trans-Baikal Territory
Krasnoyarsk Territory
Irkutsk region
Kemerovo region
Novosibirsk region
Omsk region
Tomsk region
Republic of Sakha
Vol 5 No 13
June 2014
MCSER Publishing, Rome-Italy
21.8
21.4
20.7
18.8
19.4
26.5
22.8
39.6
42.1
22.6
30.8
27.5
21.2
34.6
56.6
32
26.2
26.8
27.9
59.7
87.4
36.6
40.9
36
39.4
23.2
52.5
43.7
23.6
30.4
40.9
23.4
34.5
22.8
33.3
37.6
27.7
34.4
40.7
45.2
24.3
15.8
29.7
39.3
37.2
48.2
31.8
38.9
44.6
25.6
31.9
23
39.4
24.3
22.5
22.3
12
16.2
10
17.1
16.8
14.7
18.6
13.5
14.8
14.8
17
16.8
9.6
19.3
36.3
18.6
15.4
13
16
9.2
36.1
16.2
16.2
11.1
19.4
11.2
24.4
19.7
8.6
15.3
19.4
15.3
18.3
13.5
16.2
15.1
16.1
19.4
20.4
17.2
11
12.2
11.2
32.6
19.7
29.1
19.1
25.3
20.1
19.2
19.1
12.2
16.6
15.3
17.8
19.5
̱ʹ͸Ͷ̱
63.53
51.92
31.07
45.68
31.48
45.25
50.63
67.53
75.7
35.55
58.34
61.77
41.95
72.86
79.31
61.19
54.51
57.42
53.08
87.26
97.47
75.13
76.72
77.81
68.62
53.94
80.62
74.95
49.48
65.61
74.59
44.22
63.3
52.25
66.46
70.29
50.25
64.65
67.34
67.36
45.97
27.9
58.25
71.65
59.88
75.18
58.31
68.32
68.23
46.11
49.76
43.09
51.75
53.28
51.05
39.45
15.6
17.5
8.9
19.6
16.4
16.2
21.9
20.8
25.9
11.9
19.8
24.8
16.6
21.5
45.5
17.9
20.6
18.2
19.0
17.4
34.5
26.8
29.3
20.8
22.1
16.0
29.1
27.3
10.6
17.4
24.3
16.0
18.1
13.7
18.0
20.4
15.2
20.0
19.1
23.4
13.2
13.7
17.7
31.5
27.0
44.8
26.3
28.5
28.7
21.1
21.3
25.3
12.8
21.4
26.6
21.7
17.71
21.57
48.4
21.57
31.88
22.53
20.98
20.14
16.79
26
23.54
21.99
22.49
21.21
25.5
26.68
21.57
21.57
24.54
23.45
18.13
19.14
22.99
17.86
22.53
25
22.68
20.56
26.14
18.22
18.22
29.12
18.22
21.99
19.28
18.72
32.13
19.64
25
26.23
24.19
34.79
23.04
18.87
29.34
22.77
23.68
24.46
24.69
29.12
29.7
25.1
24.04
24.46
25.5
25.23
27.1
27.7
38.8
25.1
31.5
27.3
25.7
27.7
27.1
28.3
29.7
26.1
34.1
28.4
26.6
31.0
29.3
24.7
28.6
29.6
26.0
26.6
25.6
26.1
27.3
32.1
27.3
25.6
31.6
26.2
26.0
32.1
25.6
29.3
27.7
27.3
33.8
26.5
27.7
28.6
32.4
34.3
28.7
25.0
29.9
25.6
27.1
25.6
29.2
31.6
30.3
29.6
30.5
31.0
27.7
29.3
ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of Social Sciences
Vol 5 No 13
June 2014
MCSER Publishing, Rome-Italy
Kamchatka Krai
Primorsky Krai
Khabarovsk Krai
Amur region
Magadan region
Sakhalin region
Jewish autonomous region
Chukotka autonomous okrug
34.3
46.9
26.5
44.6
20.8
31
38.4
28.6
23.1
19.4
18.8
23.1
16.3
12
22.5
9.3
55.06
66.73
44.29
62.93
42.2
42.58
63.2
46.41
18.5
28.7
19.6
22.9
19.7
17.8
30.9
23.5
23.68
21.48
25.23
23.45
24.46
24.69
22.99
30.37
26.2
27.6
27.3
27.6
30.5
30.1
26.1
30.5
Based on Rosstat regional statistics 2013.
Dynamics of the population with incomes below the subsistence level shows a stable downward trend, but the dynamics
of poverty, defined by the European methodology, is not so clear and shows that on average, one-third of the population
are relatively poor. According to the standards of developed countries, the poverty in Russia is 1.5-3 times higher than
the official rate. Measuring poverty by a subjective approach suggests that a large number of people consider themselves
as the poor – 48% and 36% respectively in 2009 and 2010. According to Ivanov and Suvorov (2006) this “suggests that in
the face of rising living standards of the population as a whole – as opposed to periods of crisis, accompanied by a
significant fall-off – self-assessment of people’s financial situation is much more determined not by sufficient funds to
meet the most urgent needs, but by comparing the conditions of life of the others. However, as in other countries,
subjectively estimated poverty rate is above the relative poverty rate, and its boundary is probably close to the value of
the median income” (p.135).
Figures 1 and 2 show that relative poverty in Russian regions is as high as the level of per capita income and
inequality. According to Sheviakov (2005) there is “excessive inequality, expressed in excessive concentration and
polarization of income, including the wealthiest regions” (p. 62).
60
50
Republci of Kalm ykia
Republci of Tyva
40
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Republci of I ngushet a
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Republci of Tyva
Republci of Kalm ykia
Republci of Tyva
20
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Chelyabinsk region
Prgan
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0
10000 12000 14000 16000 18000 20000 22000 24000 26000 28000 30000 32000 34000 36000
Money income per capita, corrected according to the price level, RUB per month
Absolute poverty according to the official Russian approach, %
Relative poverty according to the European approach, %
Absolute poverty according to the American approach, %
Figure 1. The interdependence of absolute and relative poverty and money income per capita, corrected according to the
price level, 2012
50
Re p u b l ic o f Ka lm y k ia
Re p u b l ic o f Ty v a
45
40
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Re p u b l ic o f In gu s he ti a
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f Ba
ko
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in syk Nov
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io
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luggi ao nre gi o n
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ts Ka
k io
re
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k re
g
n
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Re p u b l ic o f Ta ta rs ta n
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J e wi s h aRe
utopnuobm
re glm
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o f Ka
30
25
20
15
10
M os c ow
M os c ow
5
0
0,34
0,36
0,38
0,40
0,42
0,44
0,46
0,48
0,50
The Gini index
Absolute poverty according to the official Russian approach, %
Relative poverty according to the European approach, %
Absolute poverty according to the American approach, %
Figure 2. The interdependence of absolute and relative poverty and the Gini index, 2012
̱ʹ͸ͷ̱
ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of Social Sciences
Vol 5 No 13
June 2014
MCSER Publishing, Rome-Italy
We can observe that the number of the relatively poor was about 38% in Moscow with an official poverty rate of 9.7% in
2012. If one had increased a relative poverty line to two-thirds of per capita income there would be 51% of the poor. The
reason for this is the excessive concentration of income in the capital. The development strategy, based on the growing
inequality in the distribution of income between the rich and the poor, actually does not lead to an increase in economic
potential. Figure 3 shows that the number of billionaires has increased since the world economic crisis, but the number of
the poor has not varied significantly in Russia.
Figure 3. The number of billionaires and the poor in Russia, 2004-2013
By the number of billionaires Russia is one of the leaders in the global economy. Thus, according to the annual Forbes
(2013) ranking, Russia was ranked third after the United States and China, but according to the GDP per capita (PPP)
Russia was ranked only 77.
The total net worth of all 110 Russian billionaires was 427.1 billion U.S. dollars, or nearly 20.2% of GDP, estimated
by CIA at 2113 billion U.S. dollars at market exchange rates. While the total net wealth of all 442 American billionaires
was estimated at nearly 11.2% of GDP. According to Credit Suisse (2013) “Russia has the highest level of wealth
inequality in the world, apart from small Caribbean nations with resident billionaires. Worldwide, there is one billionaire for
every USD 170 billion in household wealth; Russia has one for every USD 11 billion. Worldwide, billionaires collectively
account for 1%-2% of total household wealth; in Russia today 110 billionaires own 35% of all wealth”. There is no middle
class in Russia, the formation of which is prevented by an exceptional income differentiation and the presence of a large
number of residents with incomes below the subsistence minimum.
In addition to a positive and statistically significant (coefficient of Pearson correlation 0.848) correlation between
the level of economic development and inequality in the regions of Russia, there is also a positive and significant
(coefficient of Pearson correlation 0.64) relationship between the growth rate of per capita income and changes in the
incomes inequality (Fig. 4).
r = 0,6400; p = 0,0000
The growth rate of Gini index, % average annual 2002-2012
103
St. Pe te rs b u rg
L e n i n g ra d re g io n
Tu la reg io n
102
Kal u g a re g i o n
ro d re g i on
Pen z a re gi oBe
n lgbol ic
Oren b u rg re g i o n Vla dRe
i mpi rure
g i oonf No rth Os s e ti a - Al a n ia
Udm u rti a
M os c ow reg io n
Chu v a s h Re p u b l ic
a rd
o-Ba
v lSv
re
k agerio
Re
nlopvusbkli cre gi o n
rd
As tra k h a n Kab
re g io
n i nKiro
indi nre
g ra
d nre gi o n
Iv a n o v o re g i o n
Ni z h n y No v Ka
g o lro
g io
Rep u b li c o f Ad y g e a
Re p u b l ic o f Ba s h k o rto s ta n
Om s k re g io n
Bry
n rs
sTv
k reg
er g
re
iognio n
o zska
iab
Alta
u b li c PriNov
Ry
rekgi ire
on ion
g io
n i Re pNo
oa
rs
yni Kra
M aYa
g arod sa la
n vrel gre
i on
v g o ro dm
re
i on
Kugrs
k re g io n
Voprounbelizchore
g i on
Ch e ly a b i n s k re g i onRe
f Ta
ta rs ta n
Ta
Sakuhbalilicnore
gm
io
noovvre
fM
obrd
i ag io n
Vol o g d a re g i o n ArkRep
h a n g e ls k re
g iore
n g io n
Orel
v roupbolic
l re g i on
M a ri ElSta
Rep
Kem e ro v o re g io n
Tra n s -Ba Ps
ik a l Te rrito
ry
o ng io n
Smkoolevnre
s kg ire
Am
Kra s n
o duarrre
reggio
ionn
Re p u b li c of Sak h a
Ro s to v re g io n
L i p e ts k reg i o n
Re p u b li c of Kare l ia
Re p u b li c o f Kh a k as s ia
Vo lg oJgra
d re
n n o m o u s re g io n
ewis
h gaio
uto
rm
Kra
i
Re p u b l ic o
f Ty vhaa tk aPe
Ka ra c h
es
rkke re
s sgi
iao n
Ulayya-Ch
n ov
Kras n oy aKam
rs k cTe
rrito ryKra i
Ko s tro m a re g i o n
M urm an s k re gi o n
Ku rg a n re g io n
Kh a b a ro v s k Kra i
T om s k re g i o n
Re p u b l ic o f In g u s h e ti a
Sa ra to v re g i o n
101
Re p u b l ic o f Da g e s ta n
Sa m a ra re g i o n re g i o n
Re p u b li c of KalAlmtayyk ia
100
Irk u ts k re gRep
io n u bl ic o f Bu ry a ti a
Chu k o tk a a u to n o m o u s o k ru g
Ko m i Re p u b l ic
Ty u m e n re g io n
99
M os c ow
98
97
112
114
116
118
120
122
124
126
128
130
132
The growth rate of per capita income, % average annual 2002-2012
Figure 4. The interdependence of economic growth and changes in income inequality in Russian regions, 2002-2012.
Thus, different indicators confirm that inequality has intensified during the period of economic growth. This is a
consequence of a very uneven distribution of “fruits” of economic growth and revenues from high commodity prices to
different social groups and regions. As a result, the rich get richer and the poor – relatively poorer. While the social
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ISSN 2039-2117 (online)
ISSN 2039-9340 (print)
Mediterranean Journal of Social Sciences
MCSER Publishing, Rome-Italy
Vol 5 No 13
June 2014
welfare system is not effective enough as it is weakly oriented to support low-income groups.
4. Concluding Remarks
The results of the analysis and discussions indicated that poverty in many Russian regions was a problem of excessively
high inequality and its scope could not be drastically reduced in the short term without a radical reduction of inequality.
However, in the period of 2002-2012, income inequality had increased in almost all regions of Russia. The current
situation in Russia is paradoxical – economic growth (characterized by low quality) under the existing distribution model
only reinforces inequality, does not contribute to the alleviation of poverty. The trend of declining social well-being during
the positive dynamics of socio-economic development indicates the need to review the socio-economic policy through an
introduction of the integrated approach to poverty study. We proposed such a systematic approach to measure poverty
presupposing to explore various dimensions of it from the perspective of different approaches: absolute, relative and
subjective.
5. Acknowledgement
The study was supported by the Ministry of education and science of Russia, project 14.Z56.14.3051-MK
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