Alternative Approaches to Measure Poverty in Russian Regions

Transcription

1 Alternative Approaches to Measure Poverty in Russian Regions Rudenko Dmitry Y. Doi: /mjss.2014.v5n13p262 Tyumen State University; Tyumen State Academy World Economy, Management and Law; Tyumen, Russia Abstract The paper proves the need a complex poverty measurement approach in Russian s, including the analysis socioeconomic situation, efficiency existing institutes, and state environment. We analyze the ficial poverty measurement approach in Russia and consider its main problems. To identify the complex picture poverty in Russian s, we compare indicators characterizing poverty according to the monetary approach. Keywords: inequality, human development, poverty, s, Russia. 1. Introduction The second wave global economic crisis in 2012 has interrupted the growth per capita incomes in Russia and has led to a rise in poverty. In 2013, compared to 2012, the proportion the poor increased by 0.3 percentage point to 11.1%. Factors and risks poverty in the period have been: the decline in real incomes; increase the poverty line as a result 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 slowing economic recovery. In this regard, the issue poverty in Russia is becoming increasingly important. Three concepts monetary poverty measurement have been worked out: absolute, relative and subjective. Since Russia's rank in the global economy is contradictory: on a range 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 poverty can not be solved without combining the concepts absolute (common for developing countries), relative and subjective poverty (typical for the developed ones). 2. Methodology The Poverty headcount index (the proportion population with incomes below the subsistence minimum) is a core element ficially published data on the level poverty in Russian s, 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 poverty. According to Fedorenko (2005) the ficial approach makes comparable estimates poverty and subsistence either by or by time period, and does not allow to fix the duration the state 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 the socio-economic situation in the, access to health care and education, the effectiveness 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 human development; an approach through social exclusion and a participative approach. The study monetary poverty should include an analysis population with incomes below the poverty line with its different variations according to the concepts absolute, relative and subjective poverty. The study unmet basic needs for food and living conditions involves determining the number people without housing amenities and modern appliances. It is necessary to consider the proportion children who are not receiving general education, the provision medical services, morbidity (especially active tuberculosis, alcoholism, substance abuse and drug addiction), disability and mortality in the study human development opportunities. The study social exclusion involves determining the level unemployment (ILO methodology), the number the homeless, as well as street children. The study subjective poverty evaluates the satisfaction by the financial situation and quality

2 life, the prospects for improvement. The author compares various parameters describing the poverty with a monetary point view to identify a complex picture this phenomenon in s Russia. We take the view Aivazyan (1997) that incomes 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 where denotes the standard Normal distribution function, z is a poverty line, is a mean log income, and is the standard deviation 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 poverty. Poverty rate estimates from the proposed methodology are necessarily subject to error as they take no account variations in incomes and minimum subsistence levels by types households. They also rely on highly aggregated quintile share data for the distributional characteristics. Nevertheless, they are able to reproduce the main features the Rosstat series poverty rates. The main advantage aggregated models is that they allow the poverty rate to be simulated under various scenarios different from those pertaining in the ficial 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 poverty in the Russian s in 2012 ranged from 6.5 to 30.8%. The geography poverty is similar to the geography the income purchasing power in many ways, as it is a basic money-metric poverty factor. The lowest proportion the poor in 2012 had the Tatarstan (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 the minimum food basket) and relative (EU the proportion people living on less than 50% per capita income). Per capita income in 2012 on average in Russia was amounted to RUB per month, the cost the minimum food basket was RUB So, the poverty line will be considered in the first case as a three times cost the minimum food basket RUB 7623 and a half per capita income RUB in the second case. Thus, the proportion the poor in Russia in 2012 was about 15.6% and 31% respectively. The calculation results for the s are shown in Table. 1. Table 1. Regional levels absolute and relative poverty headcount indexes, % Absolute poverty Relative poverty Russian Official, % American, % European, % Belgorod Bryansk Vladimir Voronezh Ivanovo Kaluga Kostroma Kursk Lipetsk Orel Ryazan Smolensk Tambov Tver

3 Tula Yaroslavl Karelia Komi Arkhangelsk Vologda Kaliningrad Leningrad Murmansk Novgorod Pskov St. Petersburg Adygea Kalmykia Krasnodar Astrakhan Volgograd Rostov Dagestan Ingushetia Kabardino-Balkar North Ossetia Stavropol Bashkortostan Mari El Mordovia Tatarstan Udmurtia Chuvash Perm Kirov Nizhny Novgorod Orenburg Penza Samara Saratov Ulyanovsk Kurgan Sverdlovsk Tyumen Chelyabinsk Altai Buryatia Tyva Khakassia Altay Trans-Baikal Territory Krasnoyarsk Territory Irkutsk Kemerovo Novosibirsk Omsk Tomsk Sakha

4 Kalm ykia Kalm ykia Kalm ykia Tyva Tyva Tyva I ngushet ia Per m Kr ai Bashkor t ost an Alt ai Kr asnoyar sk Ter r it or y Kom i Jewish aut onom ous Kr asnodar O m sk I r kut sk Tam bov Novosibir sk M agadan Bur yat ia Sakhalin Novgor od Belgor od Kar achay- Vor onezh OAst r el akhan Kem er ovo Nizhny Dagest an M Cher ar i El kessia Trans-BaikalTerritory Sakha Novgor od Kaluga Alt ay Kur Primorsky gan rrost egion ov Trans-BaikalTerritory Chelyabinsk Adygea Br yansk Kur sk Lipet sk M urm ansk Tom Kaliningr Ulyanovsk ad Orenburg Yar oslavl r M ar i El M or dovia St avr opol Leningr Primorsky Khakassia ad Penza Khabarovsk Am ur Ar khangelsk Tula Kabar dino- Balkar Bur yat Saratov Ryazan Jewish aut onom ous IChuvash ngushet ia Khakassia Tom sk Pskov egion Kamchat Nor tka Udm h Sm O ur olensk sset t ia ia r- egion Alania Kar achay- Cher kessia M or dovia Alt ay rvladim egion Leningr ir I r vanovo egion Vologda Kirov ad r Alt ai Kar elia Kem er ovo Volgogr Kost Tver r om ad Pskov a r r Chuvash Kur gan Am ur Vladim St avr ir opol Vologda IKost vanovo r om ra egion Adygea Sakha I r kut sk O m sk Kr asnoyar sk Ter r it or y Alt ay Nor Kaliningr t h Oad sset ia - Alania O r el Penza Ast r akhan M ar i El Saratov Jewish aut onom ous Khabarovsk Sm olensk Kar elia Novgor od M agadan Tver Alt ai Bur yat ia Rost ov Ulyanovsk M or dovia Volgogr ad Kamchat ka Kirov Kamchat Trans-BaikalTerritory Orenburg Kr asnodar I ngushet ia I Ryazan r kut sk Tam rudm egion egion bov ur t ria egion Yar oslavl Chelyabinsk Sakhalin Dagest an Sakha Kar achay- Cher kessia Chuvash Khakassia Tom sk Am ur Br yansk Saratov Kur gan Kr asnoyar Ar khangelsk Ter r rit egion or y Kom i Per m Kr ai Bashkor t ost an Vladim ir Kost r om Pskov a Vor onezh Tula Sam ar a Sm olensk Kabar dino- Balkar Primorsky St avr Volgogr Iopol vanovo Khabarovsk Novosibir sk Vologda ad Kar elia Nizhny Novgor od Kom i Rost ov r Penza egion Ulyanovsk Kirov Ar khangelsk Ryazan Orenburg Novosibir Ast r akhan sk Kaluga Kur sk Lipet sk Leningr ad Per m Kr ai Sam ar a Tver Udm ur t ia Adygea Yar oslavl O r el Novgor od Kr rasnoda M urm ansk M urm ansk Sakhalin Kaliningr ad Nor t h O sset ia - Br Alania yansk Kem er ovo O m sk M agadan Vor onezh Chelyabinsk Nizhny Novgor od Bashkor Belgor od t ost an Tam bov Tula Kaluga Kur sk Lipet sk Kalmykia Tyva Ingushetia Dagest an Sam ar a Belgor od St. Pet er sbur g Tyum en Sver dlovsk Tat ar st an St. Pet er sbur g Tyum en Sver dlovsk Tyum en Tat ar st an St. Pet er sbur g Sver dlovsk Tat ar st an St. Peters Tyumen Samara burg Sverdlovsk Perm Bashkortostan Altai Krasnoyarsk Komi Territory Tatarstan Jewish autonomous Kalmykia Krasnodar Chukotka Tam Irkutsk Novosibirsk Magadan bov autonomous Omsk okrug Novgorod Belgorod Astrakhan Voronezh Sakhalin Orel Dagestan Buryatia Nizhny Novgorod Kemerovo Mari El Trans -Baikal Territory Sakha Kaluga Altay Primorsky Chelyabinsk Tyva Murmansk Bryansk Kursk Lipetsk Rostov Kurgan Trans -Baikal Territory Adygea Kaliningrad Ulyanovsk Tomsk Yaroslavl Orenburg Mordovia Leningrad Tula Arkhangelsk Stavropol Mari Khabarovsk Penza Primorsky Amur El Khakassia Kabardino-Balkar Ryazan Kalmykia Smolensk Pskov Vladimir Vologda Ingushetia North Saratov Ud m Ossetia u rti - Buryatia Jewish Chuvash autonomous Kamchatka a Tomsk Alania Khakassia Altay Ki rov Mordovia Tyva Leningrad Ivanovo Karelia Volgograd Kostroma Altai Kemerovo Tver Pskov Chuvash Kurgan Amur Vladimir Vologda Stavropol Kostroma Ivanovo Adygea Sakha Irkutsk Krasnoyarsk Omsk Territory Altay North Ossetia Kaliningrad Penza - Alania Astrakhan Orel Saratov Mari El Jewish Karelia Smolensk autonomous Khabarovsk Novgorod Magadan Tver Ulyanovsk Rostov Alta i Re pub li c Buryatia Volgograd Kamchatka Ki rov Kamchatka reg Mordovia ion Orenburg Trans -Baikal Territory Krasnodar Ryazan Ud m u rti Ingushetia a Yaroslavl Chelyabinsk Sakhalin Tam Dagestan Sakha Irkutsk bov Chuvash Saratov Tomsk Amur Khakassia Bryansk Kurgan Krasnoyarsk Komi St. Peters burg Arkhangelsk Territory Tula Perm Bashkortostan Kostroma Vladimir Smolensk Pskov Voronezh Samara Kabardino-Balkar Primorsky Volgograd Ivanovo Karelia Stavropol Khabarovsk Novosibirsk Nizhny Novgorod Vologda Penza Ulyanovsk Komi Tyumen Arkhangelsk Rostov Sverdlovsk Ki rov reg Ryazan ion Orenburg Kaluga Astrakhan Kursk Lipetsk Novosibirsk Leningrad Perm Samara Murmansk Tver Ud m u rti a Yaroslavl Murmansk Adygea Orel Novgorod Sakhalin Krasnodar Magadan Kaliningrad Bryansk Tyumen North Ossetia - Alania Nizhny Kemerovo Omsk Chelyabinsk Voronezh Novgorod Belgorod Tatarstan Bashkortostan Tula Tam bov St. Peters burg Kaluga Kursk Lipetsk Sverdlovsk Dagestan Belgorod Tatarstan ISSN (online) Kamchatka Primorsky Khabarovsk Amur Magadan Sakhalin Jewish autonomous Based on Rosstat al statistics Dynamics the population with incomes below the subsistence level shows a stable downward trend, but the dynamics poverty, defined by the European methodology, is not so clear and shows that on average, one-third the population are relatively poor. According to the standards developed countries, the poverty in Russia is times higher than the ficial rate. Measuring poverty by a subjective approach suggests that a large number people consider themselves as the poor 48% and 36% respectively in 2009 and According to Ivanov and Suvorov (2006) this suggests that in the face rising living standards the population as a whole as opposed to periods crisis, accompanied by a significant fall-f self-assessment people s financial situation is much more determined not by sufficient funds to meet the most urgent needs, but by comparing the conditions life 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 the median income (p.135). Figures 1 and 2 show that relative poverty in Russian s is as high as the level per capita income and inequality. According to Sheviakov (2005) there is excessive inequality, expressed in excessive concentration and polarization income, including the wealthiest s (p. 62) Money income per capita, corrected according to the price level, RUB per month Absolute poverty according to the ficial Russian approach, % Relative poverty according to the European approach, % Absolute poverty according to the American approach, % Figure 1. The interdependence absolute and relative poverty and money income per capita, corrected according to the price level, ,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 ficial Russian approach, % Relative poverty according to the European approach, % Absolute poverty according to the American approach, % Figure 2. The interdependence absolute and relative poverty and the Gini index, 2012

5 St. Peters burg Leningrad Tula Kaluga Penza Belgorod Orenburg Vladimir North Ossetia - Alania Udm u rti a Chuvash Astrakhan Kabardino-Balkar Kirov Sverdlovsk Nizhny Novgorod Kaliningrad Ivanovo Adygea Saratov Bashkortostan Omsk Bryansk Tver Yaroslavl Altai Novosibirsk Novgorod Ryazan Magadan Primorsky Kursk Chelyabinsk Voronezh Tatarstan Tambov Vologda Sakhalin Mordovia Arkhangelsk Orel Mari El Stavropol Kemerovo Trans -Baik Smolensk Pskov al Territory Sakha Rostov Krasnodar Amur Karelia Lipetsk Khakassia Volgograd Jewish Perm autonomous Tyva Ulyanovsk Krasnoyarsk Kamchatka Territory Kostroma M urm an s k Kurgan Tomsk Khabarovsk Sam ara regi on Kalmykia Altay Irkutsk Buryatia Komi Tyumen Ingushetia Dagestan ISSN (online) We can observe that the number the relatively poor was about 38% in with an ficial poverty rate 9.7% in If one had increased a relative poverty line to two-thirds per capita income there would be 51% the poor. The reason for this is the excessive concentration income in the capital. The development strategy, based on the growing inequality in the distribution income between the rich and the poor, actually does not lead to an increase in economic potential. Figure 3 shows that the number billionaires has increased since the world economic crisis, but the number the poor has not varied significantly in Russia. Figure 3. The number billionaires and the poor in Russia, By the number billionaires Russia is one 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 all 110 Russian billionaires was billion U.S. dollars, or nearly 20.2% GDP, estimated by CIA at 2113 billion U.S. dollars at market exchange rates. While the total net wealth all 442 American billionaires was estimated at nearly 11.2% GDP. According to Credit Suisse (2013) Russia has the highest level 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% total household wealth; in Russia today 110 billionaires own 35% all wealth. There is no middle class in Russia, the formation which is prevented by an exceptional income differentiation and the presence a large number residents with incomes below the subsistence minimum. In addition to a positive and statistically significant (coefficient Pearson correlation 0.848) correlation between the level economic development and inequality in the s Russia, there is also a positive and significant (coefficient Pearson correlation 0.64) relationship between the growth rate per capita income and changes in the incomes inequality (Fig. 4). r = 0,6400; p = 0, The growth rate Gini index, % average annual The growth rate per capita income, % average annual Figure 4. The interdependence economic growth and changes in income inequality in Russian s, Thus, different indicators confirm that inequality has intensified during the period economic growth. This is a consequence a very uneven distribution fruits economic growth and revenues from high commodity prices to different social groups and s. As a result, the rich get richer and the poor relatively poorer. While the social

6 welfare system is not effective enough as it is weakly oriented to support low-income groups. 4. Concluding Remarks The results the analysis and discussions indicated that poverty in many Russian s was a problem excessively high inequality and its scope could not be drastically reduced in the short term without a radical reduction inequality. However, in the period , income inequality had increased in almost all s 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 poverty. The trend declining social well-being during the positive dynamics socio-economic development indicates the need to review the socio-economic policy through an introduction the integrated approach to poverty study. We proposed such a systematic approach to measure poverty presupposing to explore various dimensions it from the perspective different approaches: absolute, relative and subjective. 5. Acknowledgement The study was supported by the Ministry education and science Russia, project 14.Z MK References Aivazyan, S.A. (1997). The model for the formation population distribution in terms per capita income in Russia. Economics and Mathematical Methods, (4), Credit Suisse (2013). Global Wealth Report < (accessed May 30, 2014). Fedorenko, N.P. (2005). Poverty and wealth in modern Russia. Economic science modern Russia, (3), Forbes (2013). Mapping the wealth the world s richest. < (accessed May 30, 2014). Ivanov, V.N. and Suvorov, A.V. (2006). Inequality and poverty: the experience solving problems in Russia and abroad. Studies on Russian Economic Development, (3), Kolenikov, S.., and Shorrocks, A.F. (2003). decomposition analysis al poverty in Russia. < (accessed May 30, 2014). Rosstat (2014). Social and Economic Indicators for Russia s Regions < (accessed May 30, 2014). Rudenko, D.Y. (2013). A comprehensive approach to the analysis al poverty. Region: Ekon. Sotsiol., 79 (3), Sheviakov, A.Y. (2005). Social policy and distribution relations: problems and ways reforming. Economic science modern Russia, (3), UNDP (2013). National Human Development Report for the Russian Federation 2013 / Edited by Pr. Sergey Bobylev/ Translation into English by Ben Hooson. : LLC RS Ilf.