Poverty and Inequality in BRICS Countries

Sociological Research ISSN: 1061-0154 (Print) 2328-5184 (Online) Journal homepage: http://www.tandfonline.com/loi/msor20 Poverty and Inequality in BRICS Countries Vasilii Anikin & Natalia Tikhonova To cite this article: Vasilii Anikin & Natalia Tikhonova (2016) Poverty and Inequality in BRICS Countries, Sociological Research, 55:5, 305-341, DOI: 10.1080/10610154.2016.1294432 To link to this article: http://dx.doi.org/10.1080/10610154.2016.1294432 Published online: 28 Jun 2017. Submit your article to this journal Article views: 7 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=msor20 Download by: [University of Essex] Date: 29 July 2017, At: 11:28 Sociological Research, vol. 55, no. 5, 2016, pp. 305–341. © 2016 Taylor & Francis Group, LLC ISSN: 1061-0154 (print)/ISSN 2328-5184 (online) DOI: https://doi.org/10.1080/10610154.2016.1294432 VASILII ANIKIN AND NATALIA TIKHONOVA Poverty and Inequality in BRICS Countries The Case of Russia This article uses a broad sample of statistical material to show that poverty and inequality have different natures in different BRICS countries (Brazil, Russia, India, China, and South Africa). Using various methods to conceptualize the phenomenon of poverty, the authors are able to classify several types of poverty: preindustrial poverty in modern societies (India, South Africa), early industrial poverty of the lumpen urban poor (Brazil), industrial poverty (China, Russia), and late industrial poverty (Russia). They then draw a conclusion about the overriding heterogeneity of Russian poverty, which includes elements of all these models, but tends toward industrial poverty. They also indicate that the Russian inequality model does not dovetail with any of the inequality models described in this article. Finally, they note the particular relevance of investment, employment, migration, and tax policies to combating poverty “in a way appropriate to the Russian context.” Keywords: BRICS countries, deprivation, industrialization, inequality, models of poverty, poverty, social exclusion English translation © 2016 Taylor & Francis Group, LLC, from the Russian text © 2016 “Obshchestvo i ekonomika.” “Bednost’ i neravenstvo v stranakh BRIKS: rossiiskaia spetsifika,” Obshchestvo i ekonomika, 2016, no. 1, pp. 78–114. Vasilii Anikin is a candidate of economic sciences, associate professor at the National Research University–Higher School of Economics, and senior research fellow at the Institute of Sociology, Russian Academy of Sciences. Natalia Tikhonova is a doctor of sociological sciences, research professor at the National Research University–Higher School of Economics, and chief research fellow at the Institute of Sociology, Russian Academy of Sciences. This article was prepared as part of the “Social Modernization and Sociocultural Dynamics of Russian Society” project conducted by the Institute of Sociology. Translated by Lucy Gunderson. 305 306 SOCIOLOGICAL RESEARCH Recent studies have shown that poverty and inequality in Russia have their own distinct “portraits.” In many ways, these “portraits” depend on what exactly is meant by poverty (and, consequently, how it will be measured) and on what specific inequalities are being referred to. Whatever the case, though, we must examine the situation in Russia against the situations in other countries, especially BRICS countries (Brazil Russia, India, China, and South Africa), some of which are at a relatively similar stage of economic development, in order to answer the question of the specific natures of these portraits, particularly considering how sensitive poverty is to the methods used to measure and conceptualize it on the one hand, and the diverse forms that inequality takes on the other. This comparison will enable us to gain a deeper understanding of the specific nature of “poverty in Russia” as well as the unique features of inequalities in Russian society. The specific nature of socioeconomic development in BRICS countries as the basis for the existence of various types of poverty and inequalities Poverty and inequality exist in BRICS countries in very diverse institutional environments (Table 1). Accordingly, these phenomena differ widely in these countries. Table 1 shows that of the BRICS countries, Russia is the best situated in economic terms. Overall, it has successfully completed the processes of urbanization, industrialization, and the second demographic transition and has low indicators of informal employment and social burden. This combination of indicators that are key to assessing the stage of modernization reform in the country provide evidence that poverty in Russia is relatively superficial and not too widespread.1 Brazil is characterized by average indicators of economic development and high indicators for natural increase and social burden among a large percentage of the urban population. This means not only that it lags far behind Russia in terms of its stage of modernization reform but also that poverty will be linked with high indicators of unemployment and a high birthrate among young people. It can be said that Brazilian poverty is programmed by its excessively high urban population, whose members are either unemployed or informally employed, and by an out-of-control birthrate in a large part of the population. China has shown the greatest gross domestic product (GDP) growth rate per capita of all BRICS countries and is characterized by the most balanced development of modernization processes. The most interesting aspect is the Table 1 Some Statistical Indicators of the Socioeconomic and Demographic Situations in BRICS Countries GDP per capita, PPP dollarsa Brazil Russia India China South Africa 1990 6,475 8,021 1,174 1,007 6,435 2000 8,741 6,825 2,062 2,864 7,554 Employment in agriculture, % 2005 10,560 11,856 2,966 4,963 9,458 2013 15,034 24,120 5,410 11,904 12,503 1990 22.8 13.9 60.5(1994) 60.1 — 2000 81.2 73.4 27.7 35.9 56.9 2005 82.8 72.9 29.2 42.5 59.3 2013 85.1 74.2 32.0 53.0 62.9 1990 23.5 4.9 22.9 14.9 22.6 Level of informal employmentb Brazil Russia India China South Africa 1990–1994 60.0 — 73.7 — — 1995–1999 60.0 — 83.4 18.0 — 2011 15.3 9.7(2009) 51.1(2010) 34.8 4.6 2000–2007 51.1 8.6 83.5 32.0 46.2 2000 18.9 2.2 19.1 12.1 15.1 2005 15.8 –6.5 16.1 6.5 10 2010 13.5 –5.9 14.5 5.4 7.3 Social burden coefficientd 2009–2010 42.2 8.9 83.6 32.6 32.7 1990 66 49.6 70.6 54.0 72.8 2000 54.1 44.1 62.8 48.1 57.3 2005 50.9 40.8 58.6 39.2 54.3 2013 46.2 40.5 52.4 36.8 53.9 SEPTEMBER–OCTOBER 2016 307 1990 73.9 73.4 25.5 26.4 52.0 2005 20.5 10.2 55.8 44.8 7.5 Natural population increase, %c Share of urban population, % Brazil Russia India China South Africa 2000 20.6(2001) 14.5 59.9 50.0 15.6 308 SOCIOLOGICAL RESEARCH Sources: BRICS Joint Statistical Publication, 2015; Brazil, Russia, India, China, South Africa/Rosstat (Moscow: Statistics of Russia, 2015); available at www.gks.ru/free_doc/doc_2015/BRICS_ENG.pdf (accessed September 12, 2015); Eurostat, The European Union and the BRIC countries (Luxembourg: Statistical Office of the European Communities, 2012); World Urbanization Prospects: The 2011 Revision. ST/ESA/SER.A/322 (New York: United Nations, Department of Economic and Social Affairs, Population Division, 2012); World Population Prospects: The 2012 Revision, Highlights and Advance Tables (New York: United Nations, Department of Economic and Social Affairs, Population Division, 2013); available at http://esa.un.org/unpd/wpp/publications/ Files/WPP2012_HIGHLIGHTS.pdf (accessed September 12, 2015); UNU-WIDER: World Income Inequality Database, World Institute for Development Economics Research; available at www.wider.unu.edu/data (accessed 9.12.2015); World Development Indicators: Employment in Agriculture; available at http://data.worldbank.org/indicator/SL.AGR.EMPL.ZS (accessed September 12, 2015), GDP per capita, PPP (current international $); available at http:// data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD (accessed September 12, 2015), Urban population http://data.worldbank.org/indicator/SP.URB.TOTL. IN.ZS (accessed September 12, 2015). World Bank, 2015. The minimum and maximum values for each indicator are given in bold font. a GDP per capita, calculated by the World Bank in terms of purchasing power parity, is given in “current international dollars” for the corresponding period (2013). b The level of informal employment in nonagricultural sectors. The nature of informal employment is these countries varies (J. Charmes, “The Informal Economy Worldwide: Trends and Characteristics,” Margin: The Journal of Applied Economic Research, 2012, no. 2, pp. 103–32). For example, in China informal employment does not provide labor contracts or social insurance and mainly involves the urban population. Data on China for 2005 relate to the urban employed who are residents (this figure reached 84 percent among migrants in cities in 2005). Since the vast majority of these people work in branches other than agriculture, the “cushion” in assessments is not very large (F. Cai, Y. Du, and M. Yang, “Employment and Inequality Outcomes in China,” paper presented at the OECD Seminar on “Employment and Inequality Outcomes: New Evidence, Links, and Policy Responses in Brazil, China, and India,” April 2009, Paris). Data for India (2009, 2010), Brazil (2009), and Russia and China (2010) were obtained using ILO statistics (“Statistical Update on Employment in the Informal Economy” [Geneva: ILO Department of Statistics, 2012]). Data for other years were taken from publications of foreign colleagues (J. Jutting and J.R. Laiglesia, Is Informal Normal? Towards More and Better Jobs in Developing Countries (OECD Publishing, Paris, 2009). It should be noted that official Russian statistics organizations do not collect data on the share of the informally employed. Usually, data are shown on people employed in the informal sector, which in 2010 constituted 12.1 percent in Russia (R.I. Kapeliushnikov, “Neformal’naia zaniatost’ v Rossii: chto govoriat al’ternativnye opredeleniia?” Preprinty. Seriia WP3 Problemy rynka truda, 2012, no. 4). c The natural increase coefficient per 1,000 residents. The effect of migration is excluded (see World Population Prospects: The 2012 Revision, Highlights and Advance Tables, ESA/P/WP.228 [New York: United Nations, Department of Economic and Social Affairs, Population Division, 2013]; available at http:// esa.un.org/unpd/wpp/publications/Files/WPP2012_HIGHLIGHTS.pdf). d The social burden coefficient reflects the relationship of the share of the economically inactive population (under the age of fifteen and over the age of sixty-four) to the share of the workforce (ages fifteen to sixty-four). For example, in 2013, China’s social burden coefficient amounted to 36.8 percent, in other words, for every 100 working-age people, 37 people (both the elderly and children) needed social support. SEPTEMBER–OCTOBER 2016 309 extremely fast-paced growth of its urban population (twice as much as it was a quarter of a century ago), which has occurred due to the large number of first-generation migrants from villages. It is this social group that has traditionally been at increased risk of poverty at the corresponding stage of modernization reforms. South Africa’s indicators for level of economic development are very close to China’s (although its growth rates are not). However, South Africa has the lowest employment rate in agriculture of all the other BRICS countries (4.6 percent). Furthermore, this indicator has been reduced by a factor of three since 2000 against a background of much less significant growth in the share of the urban population and the 37.1 percent of the population that continues to live in rural areas. This means that there is extremely high unemployment and very profound poverty in villages, which is compounded by the highest indicator of social burden of all the BRICS countries. In countries that have not yet completed the second demographic transition, this indicator is usually connected with minor children. Thus, profound and large-scale poverty in rural areas is characteristic of South Africa. Finally, India is characterized by the lowest indicators of economic development with comparatively high rates of economic growth, the highest share of rural population of all BRICS countries, very high indicators of employment in agriculture (even though the rural population is shrinking noticeably), a maximum level of informal unemployment in nonagricultural sectors, maximum natural increase, and high indicators of social burden, which in these conditions mainly signifies the burden of children. This shows that of all BRICS countries, India has advanced the least distance on the path to urbanization and industrialization, and that this movement is quite slow (unlike China, the share of the urban population in India has grown by only one-fourth since 1990). Furthermore, although the 14.5 percent rate of natural increase has been reduced by a factor of 1.5 over the past quarter century, it still shows that the second demographic transition has not been completed and that India is lagging very far behind South Africa, which had virtually the same population increase indicators as India in 1990 but was able to reduce this figure by a factor of more than three. This means that, on the one hand, large-scale and profound poverty in rural areas is unavoidable, and, on the other hand, that there will be an enormous number of migrants from rural areas moving to cities who are unemployed or have only informal jobs, which generally do not provide much in the way of income. Thus, poverty in these countries exists in a fundamentally different industrial environment, which is connected with how far each country has 310 SOCIOLOGICAL RESEARCH advanced along the path to modernization reform. This conclusion is also confirmed by data describing the “history of the question.” For example, China is currently experiencing rapid development in its small cities, which have seen an influx of migrants from the country who cannot move to large cities where there are strict limits on residence permits.2 Unlike China, Brazil’s virtually unchecked urbanization process has already reached its “saturation point” (rates of increase in the urban population over the past thirty years have dropped by a factor of almost three), but in recent decades this process has literally flooded the suburbs of large cities with waves of rural residents. As a result, Brazil has more urban dwellers today than Germany or Great Britain. This mass of migrants has had a difficult time adapting to the new environment and has either entered the secondary sector of urban employment or joined the ranks of the unemployed.3 As mentioned above, India is mainly still an agricultural country with semisubsistence agriculture. In regard to South Africa, despite stable economic growth over the past twenty years, the country continues to retain all the features characteristic of the African model of development—a very high unemployment rate and high levels of rural poverty,4 particularly among young people,5 against a background of various forms of racial inequality. Such is the economic and sociodemographic context in which the situation with poverty and inequality in these countries must be viewed. And this context alone suggests that we must use different options for conceptualizing the phenomenon of poverty and methodologies for identifying the poor to analyze poverty in countries at different stages of historical development. Moreover, for some countries, like Russia, that are at the confluence of different stages of historical development (industrial and late industrial, which basically corresponds to the concepts of “developing” and “developed” countries), it would be wise to use different methodologies to identify the poor (different “poverty lines”) reflecting ways to conceptualize poverty in both developed and developing countries at the same time. However, one main approach is sufficient for countries that are either at the stage of industrialization and urbanization (like other BRICS countries) or at the late industrial stage of development. In terms of developed countries, this usually entails relative poverty, since poor people in these countries are viewed as those who are in a relatively adverse situation regarding availability of resources and opportunities and prevalence of hardship in comparison to the majority of members of the given society. Thus, a comparison of their income with the median income distribution or with the prevalence of hardships among them that are not typically experienced by the rest of the given society is generally SEPTEMBER–OCTOBER 2016 311 used to identity the poor in these countries. In developing countries, however, poverty is interpreted through the prism of so-called absolute poverty, and poor people are considered those whose income is below the subsistence minimum (“poverty line”) needed to ensure a person’s basic survival. Moreover, to calculate the national poverty line, the government of each developing country must naturally take into consideration that country’s climate, cultural traditions, and so on. In terms of inequalities, a specific country’s objective (level and model of economic development, historical traditions, etc.) and cultural–civilizational features influence the depth, appearance, and prevalence of inequalities.6 But we will return to the topic of inequalities in BRICS countries in the second part of this article. For now we will look at what determines the specific nature of poverty depending on the current stage of modernization reform in society. In our opinion, the classification of types of poverty seen in the modern world that we propose below will help solve this task.7 Preindustrial poverty in modern societies The type of poverty that humankind first experienced was preindustrial poverty, which accompanied the precapitalism stage of development. It is connected with the low efficiency of agricultural production, most of which is subsistence or semisubsistence farming. The characteristics of this kind of poverty include low labor efficiency, high dependence on geoclimatic conditions, a very low and unstable income level for rural residents, very high rural populations even in spite of the high level of infant mortality, and so forth. The government’s role in combating this type of poverty is usually limited to providing minimal assistance to the poorest people, considering the need to provide for their simple physical survival. Moreover, a different level of support is impractical, not just due to financial limitations imposed by the mass nature of this type of poverty, which dominates over other types of poverty, but also, and mainly, because in poor agrarian societies, this level of life characterizes the daily reality for the majority of their members and is a sort of living standard for the population. For example, in India, where this type of poverty is most widely represented among BRICS countries, since 1978 the poverty line has corresponded to the equivalent of 650 grams of grain per day.8 According to Indian specialists this amount of food provides approximately 2,000 calories, that is, it prevents a person 312 SOCIOLOGICAL RESEARCH from dying of starvation. Given this national poverty line, India’s poverty level was 29.8 percent in 2009–10 and 21.9 percent in 2011–12.9 Naturally, this indicator of the share of poor people in India could be debated from the point of view of the standard of living in more developed countries. It appears to be extremely understated in comparison with the results obtained using methodologies to measure poverty developed by a group of scholars from Oxford University researching poverty and human development. This methodology is specially oriented toward analyzing poverty in lagging developing countries and is based on the deprivation approach, by which poor people are identified based on the deprivations they experience. The Multidimensional Poverty Index is also calculated on the basis of these deprivations.10 This methodology is being used more widely by international organizations in their statistical surveys, although it is extremely doubtful that it is relevant to the more advanced developing countries. Even though this methodology really does provide the poorest countries with the chance for more correct international comparisons than statistics based on national indicators for poverty line (Table 2 shows that the Multidimensional Poverty Index (MPI) for India in 2005 amounted to 53.7 percent, which is almost 1.5 times greater than the official poverty level in India in accordance with the national poverty line), when it is used in comparison with countries at a different level of historical development (like Russia, which is located in a different climate zone and is characterized by different cultural traditions), it loses any point. In fact, it only helps measure the prevalence of profound preindustrial poverty, which does exist in Russia but is not widely prevalent (meanwhile, we should not forget that one percentage point of the Russian population equals 1.5 million people, so, consequently. almost 2 million Russian live in this kind of poverty).11 The strategic elimination of this type of poverty assumes accelerated economic development, the completion of the processes of industrialization and urbanization, the consolidation of a modern, high-performance agricultural industry, including training for workers, and a shift to a controlled birthrate. In tactical terms, the most effective way to combat preindustrial poverty is to expand job training for young people in rural areas in specializations needed not just in the country, but also in the cities, with subsequent organization of migration for part of the young rural population. The fight against large-scale preindustrial poverty is fairly complicated and cannot simply be reduced to seeking financing for social protection measures and other organizational and management measures. And even though this type of poverty is not as widespread in Russia as it is in other BRICS countries, this does not mean that the problem of preindustrial SEPTEMBER–OCTOBER 2016 313 Table 2 Multidimensional Poverty Index (MPI) and Official Poverty Levels in BRICS Countries Percentage of poor people, including Country By People on Poor national the verge of people poverty poverty under Year line under MPI MPI Survey 2005 37.2 16.4 53.7 India DHSa 2003 2.4 6.3 12.5 China WHSb Brazil PNDSc 2006 26.8 7.0 2.7 Russia WHSb 2003 20.3d 0.8 1.3 22.2 13.4 South Africa LCSe 2008/2009 26.3 For reference: According to the most recent international statistics (United Nations, 2014) Country India China Brazil Russia South Africa People living in severe poverty under MPI 28.6 4.5 0.2 0.2 2.4 Population with incomes below the poverty lie ($1.25 per day in terms of PPP) Share of population with incomes below the national poverty line Year 32.7 13.1 6.1 0 10.7 21.9 2.8 9.0 11.0 23.0 2010, 2012 2002 2009, 2012 2013 2006 Sources: Global Multidimensional Poverty Index Databank, University of Oxford, Oxford Poverty and Human Development Initiative, 2014; available at www.dataforall.org/dashboard/ophi/index. php; Multidimensional Poverty Index, Table 5, 2014; available at https://data.undp.org/dataset/ Table-5-Multidimensional-Poverty-Index/7p2z-5b33; Human Development Report, The Rise of the South: Human Progress in a Diverse World (New York: United Nations Development Programme, 2013). a Demographic and Health Surveys; available at www.measurcdhs.com/What-We-Do/SurveyTypes/DHS.cfm. b World Health Survey; available at www.who.int/healthinfo/survey/en/index.html. c Pesquisa Demografia е Saude da Mulher e da Crianca [National Demographic and Health Surveys of Women and Children]; available at http://bvsms.saude.gov.br/bvs/pnds/index.php. d Data for Russia were updated using the state statistics register; available at www.gks.ru/free_ doc/new_site/population/urov/urov_51_g.htm. e Living Conditions Survey; available at www.datafirst.uct.ac.za/dataportal/index.php/catalog/231. This refers to the poverty line determined by expenses adjusted for food and housing expenses. See Poverty Profile of South Africa: Application of the Poverty Lines on the LCS 2008/2009 (Pretoria: Statistics South Africa, 2012). 314 SOCIOLOGICAL RESEARCH poverty cannot be actualized in the future taking into account the difference between models of demographic reproduction in various regions of the country due to the differences in their pace of advancement along the path to modernization reform. And it is no accident that even though the bases for widespread preindustrial poverty were eliminated during industrialization in the 1930s to 1950s, its presence in certain regions adds to the difficulty of finding a comprehensive solution to the problem of this kind of poverty in Russia. For example, the rural population in the North Caucasian Federal District accounted for 51.1 percent of the population as of January 1, 2010, and poverty indicators in the district are especially high.12 Moreover, according to data from the Federal State Statistics Service, this picture will change only slightly by 2031, and by this time 22.6 percent of Russia’s population will consist of rural residents,13 which is very high for an industrially developed country. The abundance of rural residents will lead not just to large-scale pendulum labor migration from villages to cities, which is a serious problem given the state of the Russian transportation system, but will also be reflected in the unemployment rate, and, accordingly, wage levels in villages. It should be specially emphasized that even though the classical variant of preindustrial rural poverty can be found today in the North Caucasus and other regions of the country that have a significant share of the rural population and a high birthrate, and where subsistence farming plays an important role (although even in these areas far from all rural poverty can be classified as preindustrial), this is not the type of poverty most typical of Russian rural poverty overall. Instead, the most typical types of poverty are the poverty of dying rural settlements that have stopped feeding themselves “from the earth,” and the poverty of residents of more prosperous rural settlements who have become marginalized and have lost their connection with the earth. This largely characterizes the type of rural poverty seen in Nechernozem’ia or the northwestern region of the country. Judging from data from the national survey “Poverty and Poor People in Contemporary Russia,” which was conducted by the Institute of Sociology in April 2013,14 99 percent of the rural poor (excluding the regions of the North Caucasus) identified on the basis of an absolute approach had no land at all, including small plots, and 87 percent owned no livestock. Moreover, these indicators reached 100 percent and 95 percent, respectively, for rural households where one of the adults lacked a permanent job. Thus, the majority of Russian rural poor are not engaged in subsistence farming, a situation that is fundamentally different from the one in India or China. Nevertheless, the variety of rural poverty is ultimately connected with the technological backwardness of agriculture in certain areas, on the one SEPTEMBER–OCTOBER 2016 315 hand, and the absence of sufficient demand for local labor of the appropriate quality on local labor markets, on the other. In this sense, rural poverty in Russia essentially resembles rural poverty in any country that has incomplete urbanization processes and any village that has an excess population of working-age people with low-quality human capital and a high share of unemployed or partially employed (seasonal, temporary, etc.) people. However, rural poverty in Russia has its own qualitative characteristics in comparison to rural poverty in other BRICS countries, where subsistence farming plays a much greater role in the lives of village residents than it does in Russia, while the share of village residents (and rural poverty) is much higher. Early industrial poverty of the lumpen urban poor In its classical variant, which is known from the history of developed European countries in the eighteenth and nineteenth centuries, this type of poverty arises as the result of uncontrolled, spontaneous, and large-scale migration of the rural population from overpopulated rural localities to cities during the first demographic transition, the emergence of a labor market, and the birth of manufacturing. This process has particularly grave consequences when society lacks the institutions (e.g., residence permits) that could regulate the flow of migrants into cities. Of all the BRICS countries, this type of poverty is most vividly seen today in Brazil, even though it does have its own special features. For example, in Brazil the mass outflow of people from the country into the city over the past thirty years occurred not due to development of the industrial sector in urban locations, which is what happened in Europe, the Soviet Union, and China, but because of the robust technological and institutional modernization of the agricultural industry,15 which occurred in the second half of the twentieth century. Today Brazil’s agrarian sector is represented by large agribusiness and is a high-tech industry that requires a small but highly qualified workforce to service it. The flip side of the modernization of agribusiness was the formation of a special type of urban agglomeration characterized by the chaotic and spontaneous settlement of the suburbs of large megalopolises—the socalled favelas. This type of housing appeared in Brazil as far back as the 1890s, but only became widespread in the second half of the twentieth century. It was Brazilian society’s unique response to the modernization process of agriculture in the absence of a growth in demand for labor in the cities. 316 SOCIOLOGICAL RESEARCH The main reason for the reproduction of poverty among the lumpen urban poor lies in the deficit of jobs for low-qualified workers. The low demand for these workers leads to very low salaries in this market segment and the prevalence of various forms of nonstandard and informal employment and, more important, to very high unemployment indicators. For example, the lowest 20 percent of Brazil’s population is basically completely excluded from creating a national income, and its main part lives in cities. It is no wonder, then, that the unique features of employment and mass unemployment lead to the marginalization of migrants from rural areas and the formation of a massive urban underbelly. In other words, the economic basis for the poverty of “new city dwellers” is the fact that the level of demand does not match supply of workers with the corresponding level of qualification in the cities and the subsequent mass unemployment and extremely low wages in the event that one actually does find work. This type of poverty is not yet characteristic of Russia, although rural areas are becoming overpopulated, especially in the Caucasus, and part of this population is moving to cities in Central Russia, including Moscow. However, even though in Russia, as in China, the main mass of migrants from rural areas is taken in by small cities, where the situation on local labor markets is already complicated,16 the flow of migrants from rural areas into large and medium-size cities should not be underestimated—one in five poor people of working age in Russian cities with populations over 100,000 grew up in the country. In connection with the problem of urban poverty, we should mention in particular the formation of a lumpen “urban underbelly” simultaneously characterizing profound poverty and a drop in economic activity. Thus, judging by data from the above-mentioned 2013 study, almost in ten poor people of working age in cities of over 1 million people that have developed markets and are in need of labor are not working but obvious reasons are not apparent (i.e., they are not pensioners, on maternity leave, students, etc.). All of this compels us to speak about the possibility that the problem of early industrial poverty will actualize in Russian society. This situation is all the more dangerous in that crime and other forms of social deviation (drug addiction, alcoholism, etc.) grow relatively faster in countries where this type of poverty is widespread. The roots of early industrial poverty are outside the labor market. This is the poverty of “superfluous people” who are not even needed as objects of exploitation. Moreover, it is quite difficult to include them in the labor market, at least to the extent that the issue of their poverty would be removed, due to their poor socialization in contemporary culture and their SEPTEMBER–OCTOBER 2016 317 low level of qualification. These people can only be provided with humanitarian aid, while attempts must be made to socialize and teach their children and young people so that over time they will come to occupy a better position in society. At the same time, the amount of assistance for this type of poor people depends, first, on society’s resources and its level of socioeconomic development, and second, on the unique features of its national culture, particularly the prevalence of values of solidarity. Countries dominated by this type of poverty use this as a starting point for establishing a subsistence minimum. And this is exactly why the subsistence minimum in these countries varies so widely in terms of inflation and size and is not just a matter of differing climate conditions. Therefore, countries where early industrial and preindustrial poverty dominate use an absolute approach to identifying the poor—that is, the “poverty line” is defined based on calculations by experts, who create a certain selection of benefits corresponding to notions about the subsistence minimum in the given country. Industrial poverty: The main type of structural poverty in Russia Industrial poverty is caused by excess supply on the labor market for lowskilled and semiskilled workers. This results in low wages for this part of the workforce, regardless of whether a worker is engaged in physical (manual) or nonphysical (trade, etc.) labor. Moreover, considering their salaries, some of these workers are doomed to poverty, while others will fall into poverty with even the slightest deterioration in their family situation or an increase in workload due to the situation of a dependent (birth of a child, especially a second child, a spouse’s loss of work, divorce, sickness of a family member, etc.). Industrial poverty has been eloquently described in the novels of many West European writers of the second half of the nineteenth and early twentieth centuries. This is the “poverty of exploited labor,” when the relationship between supply and demand in certain segments of the labor market works against workers. Since the economic cause of this type of poverty is the specific nature of the relationship between capital and the workforce at each specific historical moment and at a historical point in space, this type of poverty is primarily characteristic of the employed part of the population,17 including those who are formally employed. From the standpoint of the essence of this type of poverty, it is not so important to know who “underpaid” the worker—a specific capitalist or the state. In any 318 SOCIOLOGICAL RESEARCH case the higher the level of industrial poverty, the more power employers have in the labor market and the weaker the workers’ negotiating positions. This type of poverty is generally measured and analyzed from the standpoint of an absolute approach, even though living wage in countries where this type of structural poverty dominates is defined not based on the task of ensuring a person’s basic physical survival, but considering the need to retain him as a worker and provide for the intergenerational reproduction of a sufficiently qualified workforce. This is exactly why in so-called developed countries the minimum wage has long corresponded to at least 1.5 times the subsistence minimum, which is the International Labor Organization recommendation. However, the period of the absolute predominance of this type of poverty has already passed for these countries. All BRICS countries are examples of the prevalence of this type of poverty, although to varying degrees. For example, in China the risk of poverty characterizes mainly professional groups made up of urban residents that formed during the country’s rapid industrialization and subsequent urbanization in recent decades, that is, for an enormous army of lowskilled and semiskilled workers who came to cities for jobs primarily in the industrial sector(similar to what occurred in Western European countries in the early and mid-nineteenth century).18 In 2012, Chinese cities had a total of 262.61 million labor migrants.19 On the whole, they ended up forming part of the poor population despite their employment, since hourly rates for migrants from rural areas in China are much lower than for city dwellers, and although on average they work more hours, this does not save them from poverty.20 Poverty among migrants in China is also characterized by restrictions on access to better living conditions, free education for children, and social welfare programs, since they have trouble obtaining a local hukou, which is the equivalent of the Russian residence permit.21 Competition for low-skilled and semiskilled positions characterizes all countries that have undergone or are undergoing the industrial stage of development, and it also exists in contemporary Russia. Moreover, this is happening in despite increasing numbers of jobs for low-skilled and semiskilled workers (Table 3). However, unlike other BRICS countries, Russia is seeing a reduction in the share of skilled jobs in the industrial sector at the same time as the number of low-skilled and semiskilled positions is growing. Thus, this growth in low- and semiskilled positions has been ensured in recent years at the expense of jobs involving nonphysical labor, primarily simple jobs in retail and the service sphere. Nevertheless, despite this growth, competition for the low- and semiskilled jobs in Russia is relatively high, since migrants from other countries, SEPTEMBER–OCTOBER 2016 319 Table 3 Changes in the Sizes of Major Professional Groups (ISCO-88) in Terms of Overall Population of Russia in 1994–2012 (% of workers) Major professional groups 1994 2000 2005 2012 Highly skilled “white collar” (classes 0, 1, 2 under ISCO-88) “White-collar” low- and semiskilled (classes 3, 4) Service and sales workers (class 5) Skilled workers (classes 6, 7, 8) Unskilled workers (class 9)* 18.8 18.2 17.1 18.9 20.3 8.2 38.2 14.5 22.1 10.3 35.6 13.8 23.9 10.7 33.7 14.6 26.6 11.7 27.5 15.4 Source: Monitoring Survey of the Health and Economic Welfare of the Russian Population. This survey offers annual versions and a more complete version of the panel of respondents for the entire observation period. The data in the table are from the survey’s representative databases. *Approximately half of this group is composed of representatives of basic forms of employment in the nonindustrial and nonagricultural spheres like cleaners, cloakroom attendants, yard keepers, movers, street vendors, and so on. Notes: Calculations of the size of groups were made by V.A. Anikin using a methodology he developed for coding professional status in accordance with ISCO-88 taking into account the unique features of Russian conditions. For more information, see V.A. Anikin, “Sotsial’no-professional’naia struktura Rossii: metodologiia i tendentsii,” in Professional’nye gruppy: dinamika i transformatsiia, ed. V.A. Mansurov (Moscow: Izdatel’stvo Instituta sotsiologii RAN, 2009); V.A. Anikin, “Professional’naia struktura naseleniia i tip ekonomicheskogo razvitiia strany,” Terra Economicus, 2013, 11(2). including former Soviet countries, are applying for them along with some skilled workers, including those who cannot find jobs fitting their former profiles and are prepared for a sharp drop in their status even to the extent of changing classes (it should be noted, however, that some Russians in this situation prefer to become unemployed, even with the threat of impoverishment). As a result of this competition, salaries for these types of jobs are very low, and the first ones to fall into poverty are specifically the low- and semiskilled workers regardless of whether their jobs are physical or nonphysical (Table 4). Table 4 shows that the composition of working poor in Russia has a very high share of low- and semiskilled workers, including workers making up over half of all working poor as well as the least qualified workers engaged in nonphysical labor. The share of these professional groups is significantly lower in nonpoor strata of the Russian population. Moreover, while lowskilled workers engaged in nonphysical labor are significantly more likely than Russians overall to fall into poverty due to their incomes, this figure is 320 SOCIOLOGICAL RESEARCH Table 4 Professional and Official Positions Held by the “Income” Poor and the Nonpoor, 2012 (% of their working members) Professional and official positions Mangers of various levels, business owners with salaried workers (class 1) Specialists in positions requiring a higher education, professionals (class 2) Assistants to specialists, semiprofessionals (class 3) Public servants (including nonofficers at defense and law enforcement agencies), office workers, technical personnel (class 4) Frontline retail and service workers (salesperson, hairdresser, etc.) (class 5) Skilled workers (classes 6, 7, 8) Unskilled workers (class 9) “Income” poor Nonpoor 3 4 8 16 16 8 20 8 14 11 31 20 27 14 Source: Monitoring Survey of the Health and Economic Welfare of the Russian Population, 2012. still less than half, and in the majority of cases nonskilled workers number among the poor. Thus, the “skeleton” of the working poor in contemporary Russia is made up of people who are traditionally classified as such at the industrial stage of development. However, in addition to deindustrialization and a reduction in the number of professional positions for highly skilled workers, Russia differs from other countries, including BRICS countries, in another significant way in terms of the causes and profile of its type of poverty: its excess supply of low-skilled labor has been actively fueled in recent years by migration from a number of former Soviet republics and other Asian countries, while in other BRICS countries and at one point in Europe, the source of this excess supply came from migration from rural areas to cities and was accompanied by a sharp drop in the share of the rural population. This excess supply of workers in a labor market segment that is already fraught with the heightened risk of poverty automatically adds to a relative drop in the value of this workforce and even of semiskilled workers and helps preserve poverty among workers holding the corresponding positions. In terms of strategy, it is customary to fight industrial poverty by stimulating the creation of highly efficient jobs through investment and SEPTEMBER–OCTOBER 2016 321 tax policy and by developing education systems and ways to obtain qualifications that are accessible to all. It can be seen from the experience of West European countries in recent years that from a tactical standpoint, strict control of external migration, particularly migration by unskilled workers, in-depth development of labor laws, heightened monitoring of compliance with these laws, and the setting of a minimum hourly wage that ensures a socially acceptable lifestyle have worked well.22 Incidentally, this set of measures that was successfully implemented in so-called developed countries at their corresponding stage of development pursued goals associated with the concept of the “welfare state” that were far from humanitarian, regardless of the political rhetoric that accompanied them. This set of measures created workers with a good quality of human capital for the economies of these countries (the experiences of Australia, Canada, the United States, and, until recently, Great Britain are instructive in this respect) and also contributed to an increase in society’s social capital, which ensured additional possibilities for administrative maneuvers for its elite. All of this provided these countries with additional, and very strong, competitive advantages in the global arena and the opportunity for higher economic growth rates. Late industrial poverty This type of poverty, which is usually associated with the concept of the “new poor” was first really spoken about in the West in the 1970s in connection with the start of the transition from industrial to postindustrial society and the globalization of the world economy. In developed countries, the appearance of this type of poverty was caused by the deindustrialization of their economies (or, more precisely, by a reduction in the industrial sector in developed countries and the simultaneous shift of production to developing countries). During these processes, the number of jobs that traditionally guaranteed membership in the middle and working classes fell, while the structural positions of the upper-middle and lower classes simultaneously rose. In other words, society’s class structure underwent a polarization. At the same time, competition for efficient jobs became much more acute among skilled workers. This led to a decline in the value of an easily replaceable generic workforce23 and increasing differentiation in what had been much more homogenized groups, on the one hand, and, on the other hand, to the “expulsion” of a portion of highly skilled workers that was less competitive for various reasons from their own former professional status and 322 SOCIOLOGICAL RESEARCH sometimes even class.24 As a result, many previously successful members of the middle class and particularly their children, who could not find jobs after obtaining an education,25 found themselves among the numbers of the “new” poor. They were called “new” specifically because those with this level of education and family class membership had not previously been found among the poor population in such great numbers. Thus, the economic bases for this type of poverty are structural changes in the economy and a growth in global competition for jobs, i.e. competition within the framework of the international labor market, which makes it difficult to influence its causes. This means that the fundamental difference between the late industrial type of poverty and industrial poverty is that, even though this poverty is also formed with the participation of the labor market, it is not as localized as the global labor market, which makes it difficult to influence its causes. In addition, the path to poverty in societies transitioning from the industrial to the postindustrial stage of development is quite individualized and random, and its arrival in each individual case appears to be accidental in many ways—after all, place of residence no longer predetermines whether or not a specific person will fall into poverty, which is what happens with a peasant family supported by subsistence farming under preindustrial poverty, just as the nature of employment does not doom low-skilled workers to poverty when the industrial type of poverty dominates. Societies in the late industrial stage of development are generally sufficiently wealthy to provide not just means for basic physical survival but an entirely tolerable living wage for their poor, and it is not so much the lack of funds that comes to the forefront of interpreting the problems of poverty as the exclusion that accompanies poverty—that is, dropping out of the “mainstream” and being unable to support the standard of living of the majority of people. Accordingly, a deprivation approach comes to replace the logic of analyzing poverty through the prism of an absolute approach. Moreover, nonmaterial forms of deprivation mainly connected with access to resources are gradually starting to play a larger role in the signs of deprivation (the concepts of Amartya Sen and Peter Townsend, the Oxford Poverty Index, etc., should be recalled in this connection). It is virtually impossible to reduce late industrial poverty by developing a system of mass education and similar traditional lifts of social mobility. In these conditions, the government’s main strategy to combat poverty consists of measures to regulate the labor market and specific investment and tax policies. In tactical terms, the most effective method to combat this type of poverty for developed countries where the processes of deindustrialization have historically been accompanied by brisk growth in the quaternary SEPTEMBER–OCTOBER 2016 323 sector of the economy has been large-scale programs to retrain workers with the payment of fairly high unemployment benefits to prevent the loss of a group identification during the job search.26 At the same time, the policy of moderate, need-based, targeted benefits has been maintained to ensure that poor people remain integrated in society. This targeting should also prevent the development of a welfare mentality. Finally, over the years migration policy has become stricter in comparison with the lenient policies of the 1950s and 1960s. There is virtually no late industrial poverty in BRICS countries (with the exception of Russia), since the processes of deindustrialization are not being observed in them. In Russia, though, the situation is unique: statistical and sociological data (particularly the data given in Table 3) show that the processes of deindustrialization are occurring in Russia, but, unlike in other developed countries, they are not accompanied by development of the quaternary sector. Instead, Russia is seeing a growth in employment in the tertiary sector, particularly in retail, where the share of low-skilled jobs is high, especially in the shadow part of this branch. In Russia, a characteristic feature of this type of poverty is the fact that the main employer of professionals is the state, which, in spite of a salary increase for civil servants, pays far less then private organizations. For example, according to data from the RF Federal State Statistics Service, the gap in average salaries of specialists amounts to a factor 1.6 to 1.8 for this indicator, and the greatest differentiation is observed among semiskilled specialists.27 In these conditions, it is no surprise that in Russia a significant part of the population with a higher education is poor, including chronically poor. This part even includes professionals, who, more than any other group, are characterized by very significant intragroup differentiation. In the context of the problem of postindustrial poverty in Russia, it is worth mentioning the worsening problem of unemployment among young people, which is a characteristic feature of this type of poverty. Even though data from the Federal State Statistics Service show that unemployment among young people is rising (the share of unemployed young people ages twenty to twenty-four grew from 12.8 percent to 13.4 percent from 2005 to 2012), for most young people unemployment in Russia has a different nature unrelated to the development of deindustrialization processes. There is a reason why unemployment is especially high among young people in villages, where this indicator reached 17.4 percent in 2012. This indicator was especially high due to the abovementioned problem of rural poverty in the North Caucasian Federal District (it reached 15.6 percent among those ages fifteen to nineteen in Chechnya, with an average indicator of 4.8 percent for this category of people for all of Russia), which 324 SOCIOLOGICAL RESEARCH tells us that unemployment among young people in Russia is mainly unemployment of the preindustrial type. Characteristics of the distribution of inequalities in BRICS countries If a country’s level of economic development and its stage of historical development have the greatest impact on poverty (dominance of one type, its prevalence, depth, etc.), then, along with these factors, the special features of its culture, particularly the unique aspects of its normative value systems, have a major impact on the nature of the distribution of inequalities. Moreover, a great deal of “food for thought” about essential features of the model for socioeconomic development a country has selected can be obtained by comparing its types of distributions of inequalities with those of countries with relatively close levels of economic development. From this standpoint, it would be interesting to know what “company” Russia keeps in this regard. Considering the long tradition of analyzing this topic,28 we will not get into detail about it and will try to limit ourselves to the themes that will allow us to illustrate our main thesis on the connection between the distribution of inequalities in society with level of economic development, on the one hand, and type of culture, on the other. However, before we conduct an overall analysis of the picture of the distribution of inequalities in Russia (i.e., try to classify countries by the type of distribution of inequalities in them), we will show what the general situation in Russia looks like in comparison to other BRICS countries (Table 5). At the same time, it is worth clarifying that while the dynamics of the share of national income belonging to the lower 10 percent or 20 percent is evidence of trends to conserve or overcome poverty, the Gini coefficient provides evidence of the degree of inequality in the distribution of incomes throughout society as a whole, and, primarily, in its middle strata. Thus, together these two indicators allow us to obtain a general idea of the key features of inequality dynamics in a certain country. Table 5 shows that Russia’s social structure as compared to other BRICS countries is characterized by a degree of social stratification that is lower than Brazil’s or South Africa’s and is closer to the situation in China. India has the lowest indicators of social stratification. If we take into consideration the method for calculating the rural poverty 10 percent ratio and the deep poverty in India, this means that the majority of members of the upper decile have relatively low incomes. This overall picture is confirmed by Table 5 Changes in Some Statistical Indicators of the Overall Situation With Inequalities in BRICS Countries, 1988–2011 R/P, % Country Brazil Russia India China South Africa 1988 76.8 4.61 6.9 7.2 (1990) 29.5 (1995) 2001 95.3 13.9 — 13.9 (2002) 35.1 (2000) Gini coefficient, incomes 2005 65.9 15.2 7.5 17.9 53.8 (2006) 2009 55.8 16.2 (2013) 7.8 (2010) 17.7 44.2 1988 61.4 23.8 31.9 32.4 (1990) 56.6 (1995) 2001 61.2 42.2 — 44.8 57.8 (2000) 2005 57.4 37.5 33.4 42.5 57.5 (2006) 2011 51.2 41.7 33.9 (2010) 47.4 51.7 (2009) Share of national income belonging to the bottom 10% of the population Share of national income belonging to the bottom 20% of the (lowest decile), % population, (two lowest deciles), % 1988 0.7 4.2 3.9 3.5 (1990) 1.5 (1995) 2001 0.5 2.5 2.3 (2002) 1.3 (2000) 2005 0.7 2.7 3.8 1.8 1.1 (2006) 2009 0.8 2.8 3.7 (2010) 1.7 1.2 1988 2.1 10 8.8 8.0 (1990) 3.6 (1995) 2001 2.1 6.1 — 5.5 (2002) 3.1 2005 2.8 6.5 8.6 5.0 2.3 (2006) 2009 2.9 6.5 8.5 (2010) 4.7 2.7 Source: BRICS Joint Statistical Publication: 2015; Brazil, Russia, India, China, South Africa/Rosstat– (Moscow: Statistics of Russia, 2015); available at www.gks.ru/free_doc/doc_2015/BRICS_ENG.pdf (accessed December 9, 2015); Eurostat, The European Union and the BRIC Countries (Luxembourg: Statistical Office of the European Communities, 2012); World Population Prospects: The 2012 Revision, Highlights and Advance Tables– (New York: United Nations, Department of Economic and Social Affairs, Population Division, 2013); available at http://esa.un.org/unpd/wpp/publications/Files/ WPP2012_HIGHLIGHTS.pdf (accessed December 9, 2015); UNU-WIDER: World Income Inequality Database (World Institute for Development Economics Research, 2015); available at www.wider.unu.edu/data (accessed December 9, 2015); World Development Indicators: Employment in Agriculture; available at http://data.worldbank.org/indicator/SL.AGR.EMPL.ZS (accessed December 9, 2015), World Bank, 2015. SEPTEMBER–OCTOBER 2016 325 Country Brazil Russia India China South Africa 326 SOCIOLOGICAL RESEARCH other indicators (share of national income belonging to the lowest decile and the lowest two deciles, as well as the Gini coefficient). These indicators are all highest in India, while the situation in Russia is similar to that in China and much better than those in Brazil and South Africa. This means that we cannot refer to the development of the situation in Russia in the sphere of the distribution of inequalities as following the Latin American script. This does not mean, however, that the situation with inequalities is satisfactory in Russia—it is no accident that the indicators of share of national income belonging to the lower deciles has still not reached the level of 1990—that is, not only the lowest 20 percent, but even the next three quantiles have become relatively impoverished (Table 6). Moreover, indicators of the Gini coefficient like the share of income attributable to the third quantile show that even the middle strata of Russian society have become impoverished in the past quarter century. Based on the data in Tables 5 and 6, we can formulate the most typical features of the Russian model of the distribution of inequalities: (1) high inequality of income distribution for society as a whole (by European standards), reflected in high Gini indicators (over 0.40), which is fairly typical of other BRICS countries; (2) low incomes for people in the middle strata (which is evidenced by the fact that median incomes in Russia make up only 74 percent of per capita cash income, while the mode comes in at the level of 40.5 percent of per capita income; and (3) consistently low income indicators for people who became impoverished rapidly in comparison with the “underprivileged classes” of the Soviet period (which is evidenced not just by the fact that incomes in the bottom 20 percent are nine times lower than in the top 20 percent, but also by the very high indicator of the R/P 10 percent). And while this differentiation is not as profound as it is in the majority of BRICS countries, when comparing the situation of the underprivileged classes in Russian and other BRICS countries, it is always worth remembering that in Russia lower-class poverty is not the poverty of the uneducated, those engaged in subsistence farming, or the “urban underbelly” of the population, but is mainly the poverty of working city dwellers who have at least secondary diplomas. Thus, this kind of poverty is perceived differently by the poor people themselves and by the population as a whole. In addition, the existing system of inequalities in Russia is the result of the rapid impoverishment of a large part of the population—and for the two lowest deciles, absolute impoverishment, over a relatively short period of time. Moreover, the grounds for a sharp deepening in social differentiation are totally illegitimate in the eyes of the overwhelming majority of the SEPTEMBER–OCTOBER 2016 327 Table 6 Distribution of Total Cash Income and Characteristics of the Differentiation of Cash Income in Russia’s Population By groups of 20%, in % Year 1970 1980 1990 1995 2000 2005 2012 2013 2014 First (with the lowest incomes) Second Third Fourth 7.8 10.1 9.8 6.1 5.9 5.4 5.2 5.2 5.2 14.8 14.8 14.9 10.8 10.4 10.1 9.8 9.8 9.9 18.0 18.6 18.8 15.2 15.1 15.1 14.9 14.9 14.9 22.6 23.1 23.8 21.6 21.9 22.7 22.5 22.5 22.6 Fifth (with the largest incomes Gini coefficient 36.8 33.4 32.7 46.3 46.7 46.7 47.6 47.6 47.4 0.387 0.395 0.409 0.420 0.419 0.416 Source: Russian Federation Federal State Statistics Service, Distribution of Total Cash Income in the Population in 2014, table 7.10; available at http://www.gks.ru/bgd/regl/b15_11/IssWWW. exe/Stg/dOl/07-lO.htm (accessed December 9, 2015). Note: According to the RF Federal State Statistics Service, this assessment is based on the materials of a survey of household budgets and the macroeconomic indicator of per capita cash income. population and are not connected with the labor efforts of the most successful part of society. This is why the relatively favorable picture with inequalities in Russia as compared to other BRICS countries and the situations with poverty and inequalities in Russia cannot be considered satisfactory with account for the current stage of Russia’s economic development and the previous historical experiences of its population. We can say, moreover, that this situation is frankly dangerous. The danger of the situation with inequalities in Russia can be explained not just by Russians’ dissatisfaction with deepening inequalities in the country, which is fraught with a growth in social tension and risks of sociopolitical instability. Its main danger lies elsewhere. The connection between the depth and nature of the distribution of inequalities and the development of the population’s human potential, which has a direct impact on the pace and prospects of economic development in a given country is well-known. It is no coincidence that the Human Development Index (HD) 328 SOCIOLOGICAL RESEARCH has in recent years been replaced with the Inequality-adjusted Human Development Index (IHDI). Indicators of this index show that Russia is bearing significant losses in terms of the quality of its human potential due to existing inequalities, especially inequalities in income. Moreover, data show that Russia has the lowest IHDI indicator for the past quarter century out of all BRICS countries, excluding South Africa (Table 7). Of course, this could be explained by the “base effect”—that the quality of Russia’s human potential was initially higher than in other BRICS countries. However, the corresponding indicator in Russia (1.08) lags significantly behind indicators in all groups of countries separated out taking into account their IHDI levels, including countries with the highest indicators. If we look at which group of countries Russia falls into taking into account the models of distribution of inequality typical of it in society, look at the IHDI indicators more broadly, and ignore changes in this indicator, then the picture looks moderately optimistic. On the one hand, from this standpoint the structure of inequalities in Russia cannot be classified under either the African or Latin American models. But this can hardly be considered a great achievement given Russia’s level of economic development. On the other hand, this structure also cannot be classified under the European model or the inequality models existing in other former Soviet countries. Instead, we can say that this example of the distribution of inequalities is further confirmation of the thesis that “Russia is not Europe,” and this “not Europe” signifies that in civilizational terms Russia is closer to the more advanced Asian countries than to Western countries, especially considering the fundamental nature of this indicator as a unique feature of the distribution of inequalities in society. However, on the basis of our classification of the distribution of inequalities in society, it would be more correct to say that a unique model of this distribution is specific to Russia. What exactly are Russia’s unique features? In addition to indicators of the unique aspects of the distribution of inequalities (reflected in the R/P, the Gini coefficient, etc.), to answer this question, we must also keep in mind the distribution of key nonmonetary inequalities in society, namely, inequalities in education and access to high-quality medical assistance (inequalities in life expectancy provide indirect proof of this). Considering how difficult it is to obtain data for a large-scale intercountry comparative analysis by type of inequality, as an indirect indicator we used losses of a country’s human potential as a result of the depth of the corresponding inequalities (Table 8).29 The data in Table 8 make it possible to characterize each of the models of the distribution of inequalities cited in it. We start with the typical Table 7 Changes in Inequality-adjusted Human Development Index (IHDI) in BRICS Countries Indicators adjusted taking into account the IHDI Country 1990 2000 2005 2008 2010 2012 Growth since 1990, times* Brazil 0.522 Russia India 0.345 China 0.407 South Africa 0.57 For reference: countries with A very high IHDI 0.773 A high IHDI 0.605 An average IHDI 0.419 A low IHDI 0.315 0.59 0.73 0.41 0.495 0.621 0.669 0.713 0.463 0.59 0.622 0.699 0.753 0.507 0.637 0.604 0.716 0.778 0.533 0.672 0.613 0.726 0.782 0.547 0.689 0.621 0.73 0.788 0.554 0.699 0.629 1.24 1.08 1.35 1.41 1.01 0.817 0.656 0.481 0.35 0.867 0.695 0.549 0.385 0.889 0.725 0.589 0.424 0.898 0.745 0.617 0.448 0.902 0.753 0.631 0.461 0.905 0.758 0.64 0.466 1.11 1.56 1.53 1.33 Source: Authors’ calculations based on Human Development Report 2013, The Rise of the South: Human Progress in a Diverse World (New York: United Nations Development Programme). *A.I. Lipkin, “Rossiia mezhdu nesovremennymi ‘prikaznymi’ institutami i sovremennoi demokraticheskoi kul’turoi,” Mir Rossii, 2012, no. 21(4), pp. 40–62. SEPTEMBER–OCTOBER 2016 329 1980 330 SOCIOLOGICAL RESEARCH American model, which is characterized by very high HDI indicators and comparatively high indicators of losses from inequalities in comparison with other countries. At the same time, this model combines low indicators of losses in human potential due to inequalities in education and life expectancy with fairly high (24.1 percent) indicators of income inequalities. This same tendency toward high income inequalities is demonstrated by the quantile coefficient of differentiation and the Gini coefficient, indicators that are higher in the United States than in all other groups of European countries. In the Scandinavian model, all the indicators of inequality are much lower than in the Western European or, even more, the North American models. As a result, its IHDI is very high and outstrips analogous indicators in the United States, while losses from human potential for each of these indicators are half as much as in the United States. Moreover, Scandinavian countries are close to the United States in terms of GDP per capita, while Norway even exceeds them. Countries with the European model of the distribution of inequalities fall somewhere between West European countries and the United States. Inequality indicators in them are usually within the range of 4.3–4.4 by the quantile coefficient of differentiation and 28–30 by the Gini coefficient, while losses from inequalities in human potential are basically the same as in the Scandinavian model (inequalities in education are slightly less, while inequalities in income are slightly greater). The South African model is characterized by higher inequalities than in countries forming the “nucleus” of Europe, including losses in human potential from them and, accordingly, by a lower IHDI. However, these losses are not so great in comparison to Asian, African, and Latin American countries. In terms of distribution of inequalities, postsocialist countries fall into at least three groups. The first group of postsocialist countries includes European countries like Slovenia, Slovakia, Hungary, Belarus, Ukraine, Serbia, and Bulgaria, and the indicators for the model of the distribution of inequalities in society are overall close to the European model. They have low indicators for the quantile coefficient (3.6–4.8) and the Gini coefficient (26.0–31.2). The structure of losses in HDI from accounting for the distribution of inequalities in life expectancy, education, and income in these countries is also close to the European model. Considering that the majority of these postsocialist countries are characterized by a significantly lower GDP per capita than Austria, Germany, and other countries forming the “nucleus” of the European model (in terms of this indicator, they are closer to South Table 8 Characteristics of Indicators of Economic Development, Inequality-adjusted Human Development Index (IHDI), and Manifestations of Inequalities in Various Groups of Countries Country 2013 GDP per capita 2013 Growth rate of the economy 2012 Share of rural population 2012 Share of population with a secondary education 1.9 17.1 94.5 2012 Share of population with an income of less than $1.25 in terms of PPP per day 2012 losses due to inequalities in life expectancy, %) 2012 Losses in IHDI due to inequalities in education (%) 2012 Losses in IHDI due to income inequalities (%) 2012 HDI 2012 IHDI 2012 Total losses IHDI (%) — 0.937 0.821 12.4 6.6 5.3 24.1 8.4 40.8 Quantile coefficient of income differentiation 2000–2010 2000– 2010 Gini coefficient (income) American model United States 53,142.9 Scandinavian model Finland 38,250.7 –1.4 16.1 100.0 — 0.892 0.839 6.0 3.9 2.4 11.3 3.8 26.9 Sweden 43,454.8 1.5 14.5 85.0 — 0.916 0.859 6.2 3.3 3.8 11.2 4.0 25.0 Norway 65,461.2 0.6 20.1 95.2 — 0.955 0.894 6.4 3.7 2.2 12.8 3.9 25.8 European model 44,167.5 0.4 31.9 100.0 — 0.895 0.837 6.6 4.2 2.5 12.7 4.4 29.2 Germany 43,331.7 0.4 25.8 96.5 — 0.92 0.856 6.9 4.0 1.8 14.5 4.3 28.3 6,683.5 2.8 78.8 95.8 — 0.913 0.849 7.0 4.1 2. 14.3 5.5 33.7 Switzerland Southern European model Spain 32,103.5 -1.2 22.3 66.4 — 0.885 0.796 10.1 4.1 5.5 19.7 6 34.7 Greece 25,651.0 –3.9 38.0 62.0 — 0.86 0.76 11.5 4.8 11.3 18.1 6.2 34.3 Italy 34,302.6 –1.9 31.2 72.8 — 0.881 0.776 11.9 3.9 13.1 18.1 6.5 36 50.1 96.6 0.892 0.84 5.8 4.1 3.3 9.9 4.8 31.2 Postsocialist model no. 1 Slovenia 27,915.4 (2012) –2.5 (2012) 0.1 (Continued ) SEPTEMBER–OCTOBER 2016 331 Austria (Continued) 2012 Share of rural population 2012 Share of population with a secondary education 2012 Share of population with an income of less than $1.25 in terms of PPP per day 2012 HDI 2012 IHDI 2012 Total losses IHDI (%) 2012 losses due to inequalities in life expectancy, %) 2012 Losses in IHDI due to inequalities in education (%) 2012 Losses in IHDI due to income inequalities (%) Quantile coefficient of income differentiation 2000–2010 Country 2013 GDP per capita 2013 Growth rate of the economy Slovakia 25,333.2 (2012) 1.8 (2012) 45.3 98.8 0.1 0.84 0.788 6.3 5.7 1.5 11.3 3.6 26 Hungary 22,189.8 (2012) –1.7 (2012) 29.6 94.8 0.2 0.831 0.769 7.4 5.7 4.1 12.2 4.8 31.2 2000– 2010 Gini coefficient (income) Postsocialist model no. 2 Estonia 25,048.7 0.8 30.4 94.5 0.5 0.846 0.77 9.0 6. 2.6 17.7 6.4 36.0 Poland 23,274.8 1.6 39.2 80.0 - 0.821 0.74 9.9 5.8 6.3 17.1 5.5 34.1 Latvia 21,380.6 (2012) 5.0 (2012) 32.3 98.4 0.1 0.814 0.726 10.9 7.1 3.6 20.9 6.6 36.6 Postsocialist model no. 3 Armenia Kazakhstan 7,774.4 3.5 35.8 94.4 1.3 0.729 0.649 10.9 14.9 3.7 13.9 4.5 30.9 23,205.6 6.0 46.6 99.3 0.1 0.754 0.652 13.6 16.2 6.9 17.3 4.2 29.0 9,432.7 11.7 29.8 82.4 - 0.675 0.568 15.9 18.8 8.9 19.7 6.2 36.5 24,120.3 1.3 25.8 94.7 0 0.788 11.9 7.3 40.1 Mongolia Russian model Russia 10.8 Model in relatively well-off countries in Southeast Asia Thailand 14390.0 1.8 65.1 32.2 0.4 0.69 0.543 21.3% 10.1% 18.% 34.0% 7.1 40 Turkey 18,975.5 4.0 26.7 34.5 0 0.722 0.56 22.5 12.8 27.4 26.5 7.9 39 7.7 46.9 62.7 13.1 0.699 0.543 22.4 13.5 23.2 29.5 9.6 42.5 Chinese model China Indian model 11,903.6 332 SOCIOLOGICAL RESEARCH Table 8 India 5,410.3 5.0 68.0 37.8 32.7 0.554 0.392 29.3 27.1 42.4 15.8 4.9 33.4 48.3 Latin American model Mexico 16,463.4 1.1 21.3 53.9 1.2 0.775 0.593 23.4 10.9 21.9 35.6 11.3 Peru 11,775.4 5.8 22.1 52.9 4.9 0.741 0.561 24.3 14.8 24.6% 32.5 13.5 48.1 Brazil 15,033.8 2.5 14.9 49.5 6.1 0.73 0.531 27.2 14.4 25.3 39.7 20.6 54.7 South Africa 12,503.7 1.9 37.1 70.4 13.8 0.629 28.4 20.8 25.3 63.1 Namibia 9685.0 4.4 60.5 33.5 31.9 0.608 0.344 43.5 21.1 27.8 68.3% 21.8 63.9 Angola 7538.2 4.1 39.3 — — 0.508 0.285 43.9 46.1 34.6 50.0 30.9 58.6 Very high IHDI 85.9 — 0.905 0.807 10.8 5.2 6.8 19.8 — — African model For reference: Other groups of countries with 64.2 — 0.758 0.602 20.6 12.4 19.9 28.6 — — 50.5 — 0.64 0.485 24.2 19.3 30.2 22.7 — — low IHDI 25.2 — 0.466 0.31 33.5 35.7 38.7 25.6 — — Sources: Human Development Report, The Rise of the South: Human Progress in a Diverse World (New York: United Nations Development Programme, 2013); World Development Indicators: GDP growth (annual %); available at http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG (accessed December 9, 2015); GDP per capita, PPP (current international $) http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD (accessed December 9, 2015); Rural population(% of total) http://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS (accessed December 9, 2015). World Bank, 2015. SEPTEMBER–OCTOBER 2016 333 High IHDI Average IHDI 334 SOCIOLOGICAL RESEARCH European countries), this distribution of inequalities is evidence of their close normative and value-based proximity to West European countries in this relationship. The second group of postsocialist countries (Poland, Estonia, Latvia, Lithuania, and Romania) are characterized by higher indicators for the Gini coefficient and overall IHDI losses from inequalities in general than those found in the European model and the first group of postsocialist countries. In this sense, the countries in this group more resemble other groups of countries on the geographical periphery of the “nucleus” of old Europe, namely, South European countries. However, because of their socialist past, HDI losses due to inequalities in education are much lower than in South European countries; as in South European countries, losses from inequalities in life expectancy are relatively low (9 12.6 percent). As a result, their IHDI is quite high (0.687–0.77), and they reproduce the situation typical of countries with a very high IHDI in terms of the model for loss in human potential from inequalities. The third group includes socialist countries located at the crossroads of Europe and Asia or Central Asian countries like Armenia, Azerbaijan, Kazakhstan, Uzbekistan, Mongolia, Tajikistan, and others. This group is characterized by indicators for the quantile coefficient for differentiating income and the Gini coefficient that are closer to the second group of postsocialist countries and South European countries. This group also has low inequality indicators in education and much higher losses from inequalities in life expectancy than are seen in the above groups. Thus, total losses in human potential due to inequalities in this group are noticeably higher than in other postsocialist groups but are still relatively low compared to countries in Africa, Asia, and Latin America, and do not exceed 18.4 percent. In this sense, the group sits squarely between the above groups of countries and developing countries in Africa, Asia, and Latin America that have no experience with socialism. This demonstrates how significant sociocultural factors and past historical development are for the inequality model. Significantly, the Russian inequality model matches none of the models described above. Russia has more profound income inequalities than Europe and the vast majority of postsocialist countries (which is reminiscent of the situation in the United States in terms of economic inequalities) and average indicators of losses due to inequalities in life expectancy. In fact, Russia most closely resembles Georgia out of all the post-Soviet countries in terms of the depth of inequality throughout society as a whole and not just along the margins of income distribution. Meanwhile, the country that is closest to Russia in terms of the inequality model is SEPTEMBER–OCTOBER 2016 335 Turkey, which is also at the crossroads of Europe and Asia, and is generally well off in comparison to most Asian countries (even though it has a much lower GDP per capita than Russia). The data in Table 8 clearly show that Russia differs significantly from all the other BRICS countries in this regard. It basically cannot be compared to Brazil, India, or South Africa in terms of distribution of inequalities. When measured using the Gini coefficient, income inequality in China is somewhat more profound than in Russia. At the same time, however, losses due to inequality in the quality of human potential are many times higher there than in Russia due to the large share of the population living in profound poverty. Losses in IHDI due to inequality in life expectancy are also much higher in China. Furthermore, in China inequality has a highly depressive effect on all components of the IHDI index that is many times higher than for models of the so-called Western or even postsocialist paths of development. In regard to this effect, the greatest roles are played by inequalities in expected life expectancy and education, and not in income. The picture is significantly different and in many ways paradoxical in India. There, relatively low (approximately as in Belgium) indicators of the Gini coefficient and the quantile coefficient combine with profound social inequalities in polar opposite groups of the population—there is a reason that of all the BRICS countries, India has the highest share of people whose per capita incomes amount to less than purchasing power parity (PPP) $1.25 per day. As a result, inequalities contribute the most to the drop in IHDI compared to the HDI. The main explanation for this picture is that India is in the early stages of urbanization and industrialization, as shown by the profound level of preindustrial poverty and the low share of individuals with a secondary education. Overall, the Chinese and Indian models for determining inequalities in society are unique in nature and, like the Russian model, have no analogues. At the same time, Brazil and South Africa are typical representatives of the Latin American and South African models. Among the unique features of the Latin American model are extremely high indicators of the Gini coefficient and high losses in human potential due to inequalities in education, and especially in income. The distribution of inequalities, particularly in income, in this model is such that both society as a whole and its highest and lowest groups are characterized by profound inequalities, while a large share of the population lives in profound poverty. These unique features are also quite typical of Brazil. The African model (excluding Middle Eastern countries, which have their own model) is characterized by extremely high indicators of loss in human potential due to inequalities in life expectancy that are not typical of 336 SOCIOLOGICAL RESEARCH other models and inequalities in education and income that are just as serious as they are in Latin America. Moreover, in South Africa, these unique aspects of the African model are seen even more vividly than they are in other large African countries. Thus, we can conclude the following. 1. When we speak of poverty, both the specific nature of poverty itself and methods of combating it in BRICS countries depend primarily on the stage of development (historical epoch) of these countries as they move along the path toward modernization reform, primarily urbanization and industrialization. It is the specific nature of this stage that determines how various types of poverty are combined in a given society. The unique institutional features (regulation of internal and external migration, agricultural reforms, etc.) of these countries also play a large role in determining this specific nature. Thus, each BRICS country, including Russia, has its own specific combination of various types of poverty. 2. The main unique aspect of “poverty in Russia33n” is its exceptional heterogeneity, which is due not only to the continuation of Russia’s multistructured economy but also to the colossal differences in levels of socioeconomic development in its regions. This aspect makes it difficult to fight poverty in Russia because the combination of various types of poverty varies widely by region. However, it is impossible to fight poverty at regional levels because the key tools in this battle (investment policy, employment policy, migration policy, tax policy, social policy) are managed mainly by central government authorities, and it is specifically federal structures that must make fullest use of these tools to fight poverty in Russia. 3. The following key instruments must be used to fight poverty within the framework of federal socioeconomic policy: ● ● investment policy, the most important course of which must be to change the proportion of jobs in favor of reducing the share of lowskilled and low-paid labor; employment policy, within the framework of which the state must undertake the functions that are carried out by unions and other workers’ associations in developed countries, namely, regulating minimum hourly wages for various types of workers by raising their minimum indicators by a factor of 1.5 times the regional subsistence minimum for the corresponding categories of workers; SEPTEMBER–OCTOBER 2016 337 a migration policy that offers “occasional” work with an influx of migrants into local labor markets and that takes the situation in various segments of these markets into account, with the simultaneous sharp curtailment of illegal migration; ● a tax policy that introduces deductions in the amount of the regional subsistence minimum for minor children, stimulates, on a sliding scale, any attempts of the population to create a safety net in the event of unexpected adversity (primarily, the use of insurance forms such as life, health, residential, and property insurance), and provides the opportunity for people earning less than the median income for their type of location to lower their tax rates; ● an education policy aimed at strengthening education quality in rural areas and small towns and at increasing access to and quality of various levels of professional training, as well as its accessibility to certain categories of young people if they enter into contracts binding them to “work off” the education they received; ● a social policy aimed at reducing the abundance of inequalities and creating a level playing field for children, young people, and people who have inadvertently fallen on hard times (losing a breadwinner, becoming disabled, etc.). Without a doubt, this is a novel set of tasks to fight poverty that will be quite complicated to manage. However, the experience of developed countries shows that all these tasks are fully realizable and specifically these types of activities are more effective than improving the social welfare system and have also started to play a growing role in combating poverty in all the countries conducting active policy in this area. 4. The situation with inequalities depends on a given country’s current level of socioeconomic development less than the situation with the combination of various types of poverty, although the level of socioeconomic development is, of course, very important. The most important factors determining the inequality model are the unique features of the normative value system characteristic of various civilizational areas and past historical experience. 5. The situation with inequalities in Russia is unique and indicates that, from the standpoint of its own model of inequality distribution, Russian society is following its own path, even though it is closer to the more advanced Asian countries than it is to European countries at a similar stage of economic development. Moreover, Russia’s model has nothing in common with the model typical of African countries and is very far removed from the Latin American model. ● 338 SOCIOLOGICAL RESEARCH 6. The aspects of inequality distribution in Russia that make it stand out against other developed and developing countries are: (1) high income inequality (by European standards) throughout all of society; (2) low incomes for the middle strata of the population; (3) relatively superficial income differences in comparison to other BRICS countries; (4) the lowest indicator for growth in the quality of human potential over the past twenty-five years of all the BRICS countries, specifically due to the situation with inequalities. At the same time, though, Russia differs greatly from other postsocialist countries and is following a unique “Eurasian” path that takes it closer to countries like Georgia and Turkey than to former Soviet countries or BRICS countries. Notes 1. We understand modernization in its neomodernization sense as a process that takes various courses taking into account the unique features of national cultures and historical experiences. Thanks to this process, traditional (preindustrial) societies achieve the state of being modern not just by means of economic, cultural, or political modernization but also through social and sociocultural modernization. By social modernization, we mean primarily the development of processes of urbanization that lead to growing differentiation in society and changes in the mechanisms of social control with a shift in priorities from traditions to “written law” and so forth. By sociocultural modernization, we mean the shaping of new normative and value systems and concepts, behavioral patterns, and rational thinking, which, combined, create a basis for the formation and successful functioning of new social institutions. 2. X. Bai, “Urban Transition in China: Trends, Consequences, and Policy Implications,” in The New Global Frontier: Urbanization, Poverty and Environment in the 21st Century, ed. G. Martin, G. McGranahan, M. Montgomery, and R. Fernandez-Castilla (London: Earthscan, 2008), pp. 339–56. 3. H.S. Klein and F.V. Luna. Brazil Since 1980 (New York: Cambridge University Press, 2006). 4. B. O’Laughlin, H. Bernstein, B. Cousins, and P. Peters, “Introduction: Agrarian Change, Rural Poverty and Land Reform in South Africa since 1994,” Journal of Agrarian Change, 2013, vol. 13, no. 1, pp. 1–15. 5. V. Sender, N. Rankin, and G. Roberts. “Accessing the First Job in a Slack Labour Market: Job Matching in South Africa,” Journal of International Development, 2014, vol. 26, no. 1, pp. 1–22. 6. In this regard, it should be noted that although Russia’s basic indicators of socioeconomic development are closer to those of a developed country than a developing country, inequalities (including those reflected in the Gini coefficient and the R/P 20 percent ratio) are very great, and poverty is interpreted at the government level using the category of absolute poverty just as in developing countries that lag far behind it. SEPTEMBER–OCTOBER 2016 339 7. More information about poverty models, including middle-class poverty in postindustrial economies, that are not reviewed in this article can be found in an earlier publication by the authors: N.E. Tikhonova and V.A. Anikin, “Bednost’ v Rossii na fone drugikh stran,” Mir Rossii: Sotsiologiia, etnologiia, 2014, no. 4, pp. 59–95. 8. Planning Commission, The Fifth Five Year Plan (1978–83) (Islamabad: Government of Pakistan, 1978). 9. The so-called Tendulkar methodology was used in India until 2014. See Planning Commission, Press Note on Poverty Estimates, 2011–12 (Delhi: Government of India, 2013). In 2014, the Planning Commission appointed a new expert group headed by C. Rangarajan to review the previous methodology for calculating the poverty line. Under the new methodology, which relies heavily on the ideas of the Nobel Laureate Angus Deaton and the relative understanding of poverty, measurements of poverty in India were significantly adjusted for the worse. For example, in 2011–12 poverty in India was measured at the level of 29.5 percent and at 38.2 percent in 2009–10. 10. Ten indicators joining three large groups (education, health, standard of living) are used to calculate the MPI. The largest group of indicators—standard of living—includes forms of deprivation such as lack of electricity; lack of access to clean drinking water; dirt floors; use of dung, wood, or coal for cooking; ownership of no more than one of the following: radio, television, telephone, bicycle, motorcycle, refrigerator. According to the Oxford methodology, people “on the border of poverty” experience deprivation in at least 20–33 percent of the ten indicators, while people suffering from “severe poverty” are deprived of 50 percent of more of the indicators. Accordingly, those who are deprived of more than a third, but less than half of the indicators are viewed as poor. 11. Although the total number of poor people in Russia has dropped by a factor of 1.5 since the time the index calculated in Table 2 was cited, data from panel studies (see N.E. Tikhonovaand E.D. Slobodeniuk, “Geterogennost’ rossiiskoi bednosti cherez prizmu deprivatsionnogo i absoliutnogo podkhodov,” Obshchestvennye nauki i sovremennost’, 2014, no. 1, pp. 36–49), show that this reduction did not affect the poorest part of the population; mainly the situational poor, whose poverty was not terribly profound, left poverty. This makes it possible to assume that the overall share of Russians living in profound poverty has remained virtually unchanged up to now. 12. Population size of the Russian Federation by cities, urban-type settlements, and districts as of January 1, 2010, is from RF Federal State Statistics Service; available at www.gks.ru/bgd/rcgl/b10_109/Main.htm (accessed December 9, 2015). 13. Projected population of the Russian Federation until 2030 is from RF Federal State Statistics Service; available at www.gks.ru/wps/wcm/connect/rosstat_main/ rosstat/ru/statistics/publications/catalog/do c_1140095525812 (accessed December 9, 2015). 14. M.K. Gorshkov and N.E. Tikhonova, eds., Bednost’ i bednye v sovremennoi Rossii (Moscow: Ves’ Mir, 2014). 15. This refers to Brazil’s comprehensive state support for agriculture through subsidizing loans, buying and regulating surplus agricultural products, and so forth, which was launched in the 1960s. 340 SOCIOLOGICAL RESEARCH 16. At the same time, part of the lumpen urban poor are returning to villages, which further complicates the situation in these villages. To a certain extent, marginalized strata of the urban population that have lost their housing as the result of housing schemes participate in this migration. Moreover, the scale of this migration should not be underestimated: only 42 percent of the Russian poor living in villages actually grew up in those villages, while 29 percent grew up in cities with populations of more than 100,000, including 11 percent who grew up in cities with more than 1 million residents, where housing is particularly expensive and therefore especially interesting to swindlers. Participants in this migration include some migrants from rural areas who previously moved to cities, but then “failed to find their place” in these cities and returned to “old times,” to the “small Motherland.” For more on this, see Gorshkov and Tikhonova, Bednost’ i bednye. 17. Nevertheless, naturally other members of their households also become poor. 18. For more on the role of changes in ownership structure and the structure of industry in the transformation of China’s class structure, see P. Li, “Changes in China’s Social Stratification since 1978,” in Handbook on Social Stratification in the BRICS Countries: Change and Perspective, ed. P. Li, M.K. Gorshkov, C. Scalon, and K.L. Sharma (London: World Scientific, 2013); M.K. Gorshkov, Z.T. Golenkova, and L. Peilin, L., eds., Rossiia i Kitai: izmeneniia v sotsial’noi strukture obshchestva (Moscow: Novyi khronograf, 2012). 19. Statistics show that China’s urban population grows by approximately 21 million people each year, even though the employed urban population grows by only 12.66 million people annually. Thus, the majority of migrants end up working, although almost 40 percent of new arrivals are not involved in the industrial process (see “Statistical Communiqué of the People's Republic of China on the 2012 National Economic and Social Development,” National Bureau of Statistics of China, February 22, 2013; available at www.stats.gov.cn/english/newsevents/ 201302/t20130222_26962.html (accessed December 9, 2015)). Some of these are the dependent family members of migrant workers, and others are members of the preindustrial urban poor who were referred to above as “superfluous people” and are not even attractive as objects of exploitation. 20. A. Park and D. Wang “Migration and Urban Poverty and Inequality in China,” China Economic Journal, 2010, vol. 3, no. 1, pp. 49–67. 21. C. Goh, X. Luo, and N. Zhu, “Income Growth, Inequality and Poverty Reduction: A Case Study of Eight Provinces in China,” China Economic Review, 2009, vol. 20, no. 3, pp. 485–96. 22. The prevalence of nonstandard forms of employment among the poor requires regulation specifically of hourly and not monthly wages. 23. M. Castells, “Informationalism, Networks, and the Network Society: A Theoretical Blueprint,” in The Network Society. A Cross-Cultural Perspective, ed. M. Castells (Northampton: Edward Elgar, 2004); G. Standing, The Precariat: The New Dangerous Class (London: A&C Black, 2011). 24. E. Crettaz, Fighting Working Poverty in Post-industrial Economies: Causes, Trade-offs and Policy Solutions (Northampton: Edward Elgar, 2011). 25. A high level of unemployment among educated young people is one of the most typical features of late industrial poverty in developed countries. See D. Leslie, S. Drinkwater, and N. O’Leary, “Unemployment and Earnings Among SEPTEMBER–OCTOBER 2016 341 Britain;s Ethnic Minorities: Some Signs for Optimism,” Journal of Ethnic and Migration Studies, 1998, vol. 24, no. 3, pp. 489–506. 26. Crettaz, Fighting Working Poverty. 27. On the differentiation of salary by professional group, see Statiskicheskii biulleten’, no. 06 (187) (Moscow: Federal’naia sluzhba gosudarstvennoi statistiki RF, 2014). 28. V.E. Gimpel’son and G.A. Monusova, “Vospriiatie neravenstva i sotsial’naia mobil’nost,” Preprinty. Seriia WP3 Problemy rynka truda, 2014, no. 3; L.M. Girgore’ev and A.A. Salmina, “‘Struktura’ sotsial’nogo neravenstva sovremenogo mira: problemy izmereniia,” Sotsiologichekii zhurnal, 2013, no. 3; A.Iu. Sheviakov, “Snizhenie izbytochnogo neravenstva i bednosti kak factor ekonomicheskoi dinamiki i rosta innovatsionnogo potentsiala Rossii,” Obshchestvo i ekonomika, 2006, no. 11–12, pp. 5–36. 29. In order to reduce its size, we limited Table 8 only to a few models of the greatest interest in terms of comparison to Russia. These models did not include Middle Eastern and Pacific models of the distribution of inequalities. The most typical countries for each model are given.