Review of Business and Economics Studies, 2014, том 2, № 2
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Review of Business and Economics Studies EDITOR-IN-CHIEF Prof. Alexander Ilyinsky Dean, International Finance Faculty, Financial University, Moscow, Russia ailyinsky@fa.ru EXECUTIVE EDITOR Dr. Alexander Kaffka EDITORIAL BOARD Dr. Mark Aleksanyan Adam Smith Business School, The Business School, University of Glasgow, UK Prof. Edoardo Croci Research Director, IEFE Centre for Research on Energy and Environmental Economics and Policy, Università Bocconi, Italy Prof. Moorad Choudhry Dept.of Mathematical Sciences, Brunel University, UK Prof. David Dickinson Department of Economics, Birmingham Business School, University of Birmingham, UK Prof. Chien-Te Fan Institute of Law for Science and Technology, National Tsing Hua University, Taiwan Prof. Wing M. Fok Director, Asia Business Studies, College of Business, Loyola University New Orleans, USA Prof. Konstantin P. Gluschenko Faculty of Economics, Novosibirsk State University, Russia Prof. George E. Halkos Associate Editor in Environment and Development Economics, Cambridge University Press; Director of Operations Research Laboratory, University of Thessaly, Greece Dr. Christopher A. Hartwell President, CASE - Center for Social and Economic Research, Warsaw, Poland Prof. S. Jaimungal Associate Chair of Graduate Studies, Dept. Statistical Sciences & Mathematical Finance Program, University of Toronto, Canada Prof. Bartlomiej Kaminski University of Maryland, USA; Rzeszow University of Information Technology and Management, Poland Prof. Vladimir Kvint Chair of Financial Strategy, Moscow School of Economics, Moscow State University, Russia Prof. Alexander Melnikov Department of Mathematical and Statistical Sciences, University of Alberta, Canada Prof. George Kleiner Deputy Director, Central Economics and Mathematics Institute, Russian Academy of Sciences, Russia Prof. Kwok Kwong Director, Asian Pacific Business Institute, California State University, Los Angeles, USA Prof. Dimitrios Mavrakis Director, Energy Policy and Development Centre, National and Kapodistrian University of Athens, Greece Prof. Steve McGuire Director, Entrepreneurship Institute, California State University, Los Angeles, USA Prof. Rustem Nureev Head of the Department of Macroeconomics, Financial University, Russia Dr. Oleg V. Pavlov Associate Professor of Economics and System Dynamics, Department of Social Science and Policy Studies, Worcester Polytechnic Institute, USA Prof. Boris Porfiriev Deputy Director, Institute of Economic Forecasting, Russian Academy of Sciences, Russia Prof. Svetlozar T. Rachev Professor of Finance, College of Business, Stony Brook University, USA Prof. Boris Rubtsov Chair of Financial Markets and Financial Engineering, Financial University, Russia Dr. Minghao Shen Dean, Center for Cantonese Merchants Research, Guangdong University of Foreign Studies, China Prof. Dmitry Sorokin Deputy Director, Institute of Economy, Russian Academy of Sciences, Head of the Department of Macroeconomics Regulation, Financial University, Russia Prof. Robert L. Tang Vice Chancellor for Academic, De La Salle College of Saint Benilde, Manila, The Philippines Dr. Dimitrios Tsomocos Saïd Business School, Fellow in Management, University of Oxford; Senior Research Associate, Financial Markets Group, London School of Economics, UK Prof. Sun Xiaoqin Dean, Graduate School of Business, Guangdong University of Foreign Studies, China REVIEW OF BUSINESS AND ECONOMICS STUDIES (ROBES) is the quarterly peerreviewed scholarly journal published by the Financial University under the Government of Russian Federation, Moscow. Journal’s mission is to provide scientific perspective on wide range of topical economic and business subjects. CONTACT INFORMATION Financial University Leningradskiy Av. 51 Building 3, 125993 Moscow Russian Federation Telephone: +7(499) 943-95-23 Website: www.robes.fa.ru AUTHOR INQUIRIES Inquiries relating to the submission of articles can be sent by electronic mail to robes@fa.ru. COPYRIGHT AND PHOTOCOPYING © 2014 Review of Business and Economics Studies. All rights reserved. No part of this publication may be reproduced, stored or transmitted in any form or by any means without the prior permission in writing from the copyright holder. Single photocopies of articles may be made for personal use as allowed by national copyright laws. ISSN 2308-944X
Вестник исследований бизнеса и экономики ГЛАВНЫЙ РЕДАКТОР А.И. Ильинский, профессор, декан Международного финансо вого факультета Финансового университета ВЫПУСКАЮЩИЙ РЕДАКТОР А.В. Каффка РЕДАКЦИОННЫЙ СОВЕТ М. М. Алексанян, профессор Бизнесшколы им. Адама Смита, Университет Глазго (Великобритания) К. Вонг, профессор, директор Института азиатско-тихоокеанского бизнеса Университета штата Калифорния, Лос-Анджелес (США) К. П. Глущенко, профессор Экономического факультета Новосибирского госуниверситета С. Джеимангал, профессор Департамента статистики и математических финансов Университета Торонто (Канада) Д. Дикинсон, профессор Департамента экономики Бирмингемской бизнесшколы, Бирмингемский университет (Великобритания) Б. Каминский, профессор, Мэрилендский университет (США); Университет информационных технологий и менеджмента в Жешове (Польша) В. Л. Квинт, заведующий кафедрой финансовой стратегии Московской школы экономики МГУ, профессор Школы бизнеса Лассальского университета (США) Г. Б. Клейнер, профессор, член-корреспондент РАН, заместитель директора Центрального экономико-математического института РАН Э. Крочи, профессор, директор по научной работе Центра исследований в области энергетики и экономики окружающей среды Университета Боккони (Италия) Д. Мавракис, профессор, директор Центра политики и развития энергетики Национального университета Афин (Греция) С. Макгвайр, профессор, директор Института предпринимательства Университета штата Калифорния, Лос-Анджелес (США) А. Мельников, профессор Департа мента математических и статистических исследований Университета провинции Альберта (Канада) Р. М. Нуреев, профессор, заведующий кафедрой «Макроэкономика» Финансового университета О. В. Павлов, профессор Депар тамента по литологии и полити ческих исследований Ворчестерского политехнического института (США) Б. Н. Порфирьев, профессор, член-корреспондент РАН, заместитель директора Института народнохозяйственного прогнозирования РАН С. Рачев, профессор Бизнес-колледжа Университета Стони Брук (США) Б. Б. Рубцов, профессор, заведующий кафедрой «Финансовые рынки и финансовый инжиниринг» Финансового университета Д. Е. Сорокин, профессор, член-корреспондент РАН, заместитель директора Института экономики РАН, заведующий кафедрой «Макроэкономическое регулирование» Финансового университета Р. Тан, профессор, проректор Колледжа Де Ла Саль Св. Бенильды (Филиппины) Д. Тсомокос, Оксфордский университет, старший научный сотрудник Лондонской школы экономики (Великобритания) Ч. Т. Фан, профессор, Институт права в области науки и технологии, национальный университет Цин Хуа (Тайвань) В. Фок, профессор, директор по исследованиям азиатского бизнеса Бизнес-колледжа Университета Лойола (США) Д. Е. Халкос, профессор, Университет Фессалии (Греция) К. А. Хартвелл, президент Центра социальных и экономических исследований CASE (Польша) М. Чудри, профессор, Университет Брунеля (Великобритания) Сун Цяокин, профессор, декан Высшей школы бизнеса Гуандунского университета зарубежных исследований (КНР) М. Шен, декан Центра кантонских рыночных исследований Гуандунского университета (КНР) Издательство Финансового университета 125993, Москва, ГСП-3, Ленинградский проспект, 51, корп. 3, к. 104. Тел. 8 (499) 943-95-23. Интернет: www.robes.fa.ru. Журнал «Review of Business and Economics Studies» («Вест ник исследований бизнеса и экономики») зарегистрирован в Федеральной службе по надзору в сфере связи, информационных технологий и массовых коммуникаций 9 июля 2013 г. Свидетельство о регистрации ПИ № ФС77-54658. Подписано в печать: 26.06.2014. Формат 60 × 84 1/8. Заказ № 82 от 27.06.2014. Отпечатано в ООП Издательства Финуниверситета (Настасьинский пер., д. 3, стр. 1). 16+
CONTENTS Effect of Income on Political Preferences of Russian voters Rustem Nureev, Sergey Shulgin 5 The Lessons from the Insistence of the U.S.A. in Nuclear Energy Policy Wen-Hsiang Kung 28 Recent Development of New Energy Policy and Legislation in Taiwan, with the Focus on Promotion of Biofuel Yueh-Hsun Tsai 42 Priority Issues for Boosting Smart Grid-Smart Customers in Taiwan Wen-Ling Sun 51 “Social Impact Bonds”: Implications for Government and Non-Profit Organizations Antonio Costa, Paolo Leoci, Alessandra Tafuro 58 Social Media’s Role in Intellectual Capital’s Growth Michał Falkowski 66 The Need to Design a Quality System for Macedonian Textile Companies Elizabeta Mitreva, Nako Taskov 75 Review of Business and Economics Studies Volume 2, Number 2, 2014
CОДЕРЖАНИЕ Влияние уровня доходов на политические предпочтения российских избирателей Рустем Нуреев, Сергей Шульгин 5 Использование опыта США при формировании политики в области атомной энергетики Вень-Сянь Кунь 28 Новая энергетическая политика и законодательство Тайваня, направленные на внедрение биотоплива Юэ-Сунь Цай 42 Приоритетные вопросы развития «умных сетей электроснабжения» в Тайване Вэнь-Линь Сунь 51 «Социальные облигации» и их применение в государственных и некоммерческих организациях Антонио Коста, Паоло Леучи, Алессандра Тафуро 58 Роль социальных медиа в увеличении интеллектуального капитала Михал Фальковский 66 Создание системы обеспечения качества для текстильных компаний Македонии Элизабета Митрева, Нако Тасков 75 Вестник исследований бизнеса и экономики № 2, 2014
Review of Business and Economics Studies Volume 2, Number 2, 2014 Effect of Income on Political Preferences of Russian Voters* Rustem NUREEV, Doctor of economics, Professor Head of Macroeconomics Department, Financial University; Professor at National Research University — Higher School of Economics, Moscow nureev50@gmail.com Sergey ShUlgiN, Ph.D. in economics Russian Academy for National Economy and Public Administration (RANEPA), Moscow sergey@shulgin.ru Abstract. There was direct correlation between the voters’ income and electoral support for incumbent in Russia during the 1990-s and early 2000-s. The results of election to the State Duma (the parliament) in 2011 and Russia’s presidential elections in 2012 show the opposite. Using income data and electoral results in the Russian regions for each candidate (G. Zyuganov, S. Mironov, V. Zhirinovsky, M. Prokhorov, V. Putin) we defined the level of electoral support in different income groups. Results show the substantial changes in last 8 years in voting behavior. There is the effect of Putin’s inversed threshold and the greatest proportion of votes negatively correlated (–1.58), with a group of people with incomes of 14,250 to 21,250 rub/month. Such inverse correlation may be due to a protest voting. Putin’s electoral support has a positive correlation in low-income group. In this paper we analyze the determinants of voting behavior and show how the income distribution affects the voters’ political preferences (based on the results of the presidential elections in 2012). For each candidate we defined the level of electoral support in different income groups. Also we analyzed income distribution of absent voters. Аннотация. В статье анализируются детерминанты электорального поведения и показывается, каким образом распределение населения по доходам влияет на политические предпочтения избирателей (по итогам федеральных выборов 2012 г.) Определен пороговый уровень доходов, при достижении которого люди начинают проявлять социальную активность и заинтересованность в участии в электоральной системе (демократии). Показано влияние распределения доходов в российских регионах на политические предпочтения избирателей. Key words: Political preferences, regional studies, electoral behavior, income distribution. * Влияние уровня доходов на политические предпочтения российских избирателей. 1. REVIEw S. Kuznets was the one of the first who showed the importance of the distribution of income inequality for economic growth and social and economic progress (Kuznets, 1955; 1971; 1979). However, the focus of his research was not the problem of electoral behavior of voters. Almost ten years later, it becomes the subject of a special study (Lewis-Beck, 1988). Analyzing the Western democracies, the author suggested indirect, but reliable way to assess the economic factors on the electoral process. The book summarizes and complements the classical set of economic factors to explain the behavior of voters. M. Lewis-Beck believes that the majority of voters rarely appeal to the main macroeco nomic indicators in assessing the economic situation and prospects of the economy. According to M. Lewis-Beck, changes in voters’ disposable income also have minor effects on electoral behavior. This paradox can be partly explained by the weak faith of the population in the government’s ability to influence the personal financial situation (by arguments like: “The economic policy of the government matters, but does not affect me”). According to sociological researches in 1980-s, influence of government policy on personal well-being was felt by only 45% of population in UK, 44% in France, 40% in Germany, 34% in Italy, 49% in Spain, and only 20% in USA. Lewis-Beck uses his survey to show that in almost all the developed capitalist countries, the economic
Review of Business and Economics Studies Volume 2, Number 2, 2014 reasons are the most important in the vote. The motives of party self-identification (right/left in Europe, the Republicans/Democrats in the U.S.), appear much stronger than the motives of social or religious identity. The following factors strengthen the economic value of vote: the openness of the national economy, economic growth or its expectation, the presence of the ruling coalition or single party government. Among the developed countries studied by Lewis-Beck, the economic motives of the electoral behavior are mostly significant in the United States. In any case, the economic motive affects voting through the personal assessment of the economic development of the country’s voters. For assessment Lewis-Beck suggests three components: “Retrospective” (evaluation of the past compared to the present), “Prospective” (assessment of the future) and “Affective” (unexplained irritation, etc.). According to his study, the most important is “Prospective” evaluation of public policies, the second — “affective” component and the last — “retrospective” component of assessment. Respondents were asked to rate the influence of the government on unemployment, inflation, personal well-being, balance of trade, economic growth, public debt and a number of other parameters. Unemployment was the most important parameter in all countries (UK, France, Germany, Italy, Spain) and inflation was on the second place. Other parameters (such as personal well-being, balance of trade, economic growth, public debt, etc.) were significantly less important than unemployment and inflation. We can see from Lewis-Beck that voters in Western democracies assess their economic situation and current trends primarily through their assessment of the future. According to R. Kiewiet and D. Rivers (1984) voters are not inclined to attach great importance to the current macroeconomic situation. Authors believed that voters were rather farsighted than myopic, and votes do not tend to react with enthusiasm for the short-time economic improvement. Voters do not live by one day and are able to assess the dynamics of the economic situation. The authors in their studies used “Eurobarometer” data by George Gallup Institute. The authors suggested that economic motives of voting were particularly strong in the case of deterioration of the situation. Growth of economic indicators, as it turned out, did not lead to a significant increase of electoral support for incumbent. Economic growth matters only in case of a sharp change of direction in economic development (the typical example — Ronald Reagan’s victory in the presidential election in 1984). A. Sobyanin and B. Suhovolskiy (1995) studied the electoral process in Russia and demonstrated numerous examples of electoral frauds using electoral sta tistics. According to A. Lavrov (1997) social structure affects voters’ political preferences. Lavrov argued that the higher share of urban population and share of population employed by the government (in public administration and state industry) and the share of people with tertiary education lead to the stronger electoral support for centrist and democratic candidates. And vice versa, support for the left politicians in 1990-s increased with a higher share of rural and agrarian population and with higher share of pensioners. L. Smirnayagin (1999) studied the stability of political preferences and proposed a “degradation index”, to explain the shifts in voters’ political preferences. He estimated degradation index for Russia in 1990-s as 0.54. This means that 54% of the voters were ready to change their political preferences in the next election. This high percentage of voters who were ready to switch their preference means that formation of civil society in Russia is uncompleted. V. Mau, O. Kochetkova, K. Yanovsky, S. Zhavoronkov, Yu. Lomakina (2000) studied the impact of different economics indications on electoral behavior. They argued that in late 90-s (1995–2000) the most important for electoral behavior were income and wages, tax payments, share of urban population. At that period the higher was the voters’ income (wages etc.) the higher was support for ruling party. The similar findings were in later studies by O. Kochetkova (2004), according to which the support for incumbent politicians positively correlated with incomes and negatively correlated with unemployment and wage arrears. U. Seresova (2005) agued that economic indicators were significant for the electoral process but were not the most important ones. She suggested that electoral behavior was better explained by the level of regional modernization and the role of traditional culture. However, most of studies analyzed the situation of the electoral behavior of the 1990-s and early twentyfirst century. In this paper we deal with a new political reality. In this article we further develop the approach suggested by S. Shulgin (2005) who examined how income distribution in different countries affected democratic institutions. Author used income distribution to analyze the levels of freedom of press measured by Freedom House. 2. DATA In this paper we use official Russia’s electoral statistics for presidential election 2012. All our findings consequently contain errors associated with reliability of official electoral statistics. There is an extensive literature that indicated the frauds during Russian elections. We discussed this problem in several articles (Economic Sub
Review of Business and Economics Studies Volume 2, Number 2, 2014 jects, 2010, etc.). The article (Enikolopov et al., 2013) discusses the results of the parliamentary elections in 2011. Authors compared election results in Moscow precincts attended by independent observers, with the election results in precinct where observers were not allowed. The second part of our data describes income levels and income distributions in Russian regions. This statistics come from Russian Statistical Agency (RusStat). RusStat estimates income distribution based on data from the Household Budget Survey (HBS). Household Budget Survey was carried out by state statistics on a regular basis in all regions of the Russian Federation. The unit of observation in this survey is the household and its members. 3. DATA ANAlySIS Using statistics on income level and income distribution, for each region we construct income distribution function. Income distribution function for given level of income evaluates the share of people within region who have such level of income. RusStat’s statistical yearbook “Regions of Russia” (Regiony Rossii, 2011) in Table 5.9 gives the distribution of population by per capita income (as a share of regional population). Table 5.8 (from yearbook) gives the share of total income by 20 per cent population groups (from the poorest 20% of population to the richest 20% of population). We use data on income distribution and per capita income level to construct cumulative function that shows how many people has income below a certain value. For example, in the Belgorod region 4.2% of people have incomes of up to 3,500 rubles, 6.3% from 3 500 to 5 000 rubles, 10.6% from 5 000 to 7 000 rubles, 16.2% from 7 000 to 10000, 21.4% from 10 000 to 15 000, 23.0% from 15 000 to 25 000, 9.5% from 25 000 to 35 000 and 8.8% of disposable income — over 35 000 rubles per month (see Table 1). Then, in the Belgorod region cumulative function of income shows that 4.2% of people have incomes of up to 3 500 rubles, 10.5% to 5 000 rubles, 21.1% up to 7 000 rubles, 37.3% to 10 000, 58.7% to 15 000, 81.7% to 25 000, 91.2% to 35 000, and the remaining 8.8% — of disposable income over 35 000 rubles a month. For each region we build a linear approximation of the distribution function of per capita income (see the example in Figure 1). To determine how many people in the Belgorod region have income less than 6 000 rubles, we find average on intervals of distribution function 5 000 (10.5%) and 7.000 (21.1%), and the resulting 15.8%. Income distribution data exist for 82 Russia’s regions (for all 83 regions in Russia, with exception of Republic of Chechnya). We use income distribution functions for each region to construct the variable “share of population with incomes below X”. Figure 2 shows the distribution of Russia’s regions by this variable (“share of population with incomes below X”) on 4 different X (Fig2a X=5000 rubles per month, Fig2b X=10 000 rubles per month, Fig2c X=20 000 rubles per month, Fig2d X=30 000 rubles per month)We use electoral statistics to construct electoral variable “the share of votes for candidate N”. We estimated “the share of votes for candidate N” as a share of voters participated in president election. We constructed electoral variables for all five candidates (Zhirinovsky, Zyuganov, Mironov, Prokhorov, Putin). Also we constructed electoral variable for non-voters — as a share of voters who were registered but did not participate. Next, we looked for correlations between income variables “share of population with incomes below X” and electoral variables “the share of votes for candidate N”. On Figure 3 presented scatterplots for electoral variable “share of votes for Zhirinovsky” and income variable “share of population with incomes below X” for different X (Fig3a X=5000 rubles per month, Fig3b X=10 000 rubles per month, Fig3c X=20 000 rubles per month, Fig3d X=30 000 rubles per month)On each scatterplot on Figure 3, vertical axis represents the same electoral variable (“share of votes for Zhirinovsky”) and horizontal axis — income variable “share of population with incomes below X” for different income levels (5000, 10000, 20000, 30000 rubles per month) Figures 4, 5, 6, 7, 8 present scatterplots (“income variable” vs “electoral variable”) for other candidates (Fig. 4: for Zyuganov, Fig. 5: for Mironov, Fig. 6: for Prokhorov, Fig. 7 for Putin, Fig. 8: for non-voters.) Table 1. Distribution of population by per capita income (as a percentage of the total for the Belgorod Region, 2010). Per capita income, rub. per month to 3500,0 from 3500,1 to 5000,0 from 5000,1 to 7000,0 from 7000,1 to 10000,0 from 10000,1 to 15000,0 from 15000,1 to 25000,0 from 25000,1 to 35000,0 more 35000,0 Belgorod region 4,2 6,3 10,6 16,2 21,4 23,0 9,5 8,8 Source: Regiony Rossii, 2011.
Review of Business and Economics Studies Volume 2, Number 2, 2014 a) b) c) d) Figure 2. Distribution of Russian regions by the share of people with incomes less then: a) 5000 rubles per month, b) 10 000 rubles per month, c) 20 000 rubles per month, d) 30 000 rubles per month. Figure 1. Example of cumulative distribution function approximation of average monthly income (for the Belgorod region, 2010).
Review of Business and Economics Studies Volume 2, Number 2, 2014 4. MODEl: ElECTORAl bEhAVIOR AND INCOME DISTRIbuTION Previously we defined electoral variables as “the share of votes for candidate N” and income variables as “share of population with incomes below X”. In our analysis we are looking for correlations between electoral variables and income variables. We analyze such correlations on all possible income levels (up to 100 000 rubles per month). To analyze correlation between electoral and income variables we used model of simple pair regression (1): Share of votes a a Share of population with income _ _ _ _ _ _ _ 0 1 less than X e _ _ . (1) The advantage of this approach is simplicity (since we use a large number of such pairs of simple regression to assess the most relevant interval). At the same time, simple regression leaves many possible interpretations in addition to correlation between the independent and the dependent variables. For example, we can expect that income depends on other variables, which also affect the electoral preferences (level of urbanization, education level, gender, age, etc.). Realizing that this approach can be criticized, we nonetheless underscore its advantage. It reveals the link between income and electoral support for the candidate. Many other important variables (education, urbanization, gender, age) are correlated with income, but we are interested in correlation between electoral behaviors of different income groups. Figure 9 shows the distribution parameter estimation of the set of regressions where the dependent variable is the share of the vote for Zhirinovsky, and the explanatory variable is the “share of population with incomes below X”. Figure 9a shows the distribution of F-statistics, and Figure 9b — the distribution of t-statistics of the coefficient of the explanatory variable. In simple regression F-statistics coincides with the absolute value of t-statistics, we use t-statistics when the sign is important. Sign in the t-statistics is the sign of correlation between dependent and independent variables. The negative sign indicates the negative correlation between the share of votes for a candidate and a share of people with certain level of income. In Figure 9b points 1–4 correspond to the results of the regression estimates, based on data that are dis Figure 3. The share of votes for Zhirinovsky (vertical axis) vs. “share of people with incomes less then”: a) 5000 rubles per month, b) 10 000 rubles per month, c) 20 000 rubles per month, d) 30 000 rubles per month. a) b) c) d)
Review of Business and Economics Studies Volume 2, Number 2, 2014 played in Fig. 3a — 3d. Point 1 in Fig. 9b corresponds to the t-statistic (–4.60) for the coefficient of the explanatory variable b (–0.155) regression, based on data in Fig. 3a (for the income share of less than 5 thousand rubles). Point 2 in Fig. 9b corresponds to the t-statistic (–3.75) for the coefficient of the explanatory variable b (–0.0687) regression, constructed from data in Figure 3b (for revenue share is less than 10 thousand rubles.). Point 3 in Fig. 9b corresponds to the t-statistic (–2.818) for the coefficient of the explanatory variable b (–0.045) regression, based on data in Fig. 3c (for revenue share is less than 20 thousand rubles.). Point 4 in Fig. 9b corresponds to the t-statistic (–2.198) for the coefficient of the explanatory variable b (–0.045) regression, based on data in Fig. 3c (for revenue share is less than 30 thousand rubles.) In addition to the four points (1–4), for which we have provided examples of the distribution of votes and the percentage of people with a certain level of income (in Fig. 3a-3d), the graph 9b contains coefficients of tstatistics for the income groups built around a set of distributed income from 0 to 100 thousand rubles. Five percent significance level t-statistics (for 82 observations) a) b) c) d) Figure 4. The share of votes for Zyuganov (vertical axis) vs. “share of people with incomes less then”: a) 5000 rubles per month, b) 10 000 rubles per month, c) 20 000 rubles per month, d) 30 000 rubles per month.
Review of Business and Economics Studies Volume 2, Number 2, 2014 corresponds to the level of 1.99 (5%), which on the Fig. 9b reaches a level of income 35 thousand rub. The coefficient of the variable “proportion of people with incomes below the X” is no longer statistically significant when x is greater than 35 thousand rubles per month, in regressions explaining the share of votes cast for Zhirinovsky. Similarly graphs 9a, 9b present the results of regressions explaining the share of votes for Zhirinovsky’s presidential election in 2012, if the schedule 9a contains the results of regression in which the share of votes for Zhirinovsky explained by the percentage of people with incomes from 0 to X, and a deferred variable on the horizontal axis, then the graph 9c shows the results that explain the voting share for Zhirinovsky in the proportion of people with income from Y to X. The curves shown in the graph 9a, a special case of the reduced dependence in graph 9c (at Y = 0). In the graph 9c we consider all possible income groups, for example, not only income group from 0 to 5000 (point 1 on the chart 9a and Figure 3a), but also of income from 1000 to 5000, from 2000 to 5000, from 3000 to 5000, from 4000 to 5000, not only income group Figure 5. The share of votes for Mironov (vertical axis) vs. “share of people with incomes less then”: a) 5000 rubles per month, b) 10 000 rubles per month, c) 20 000 rubles per month, d) 30 000 rubles per month. a) b) c) d)
Review of Business and Economics Studies Volume 2, Number 2, 2014 0 to 20,000 (as a point on the graph 3 9a and Figure 3c), but also of income from 5000 to 20000, 10000 to 20000, 15000 to 20000. Zhirinovsky remains relevant in high-income areas, which suggests that a certain number of supporters of Zhirinovsky are present among middle-income voters, and among the richest of the voters. Fig. 10 shows examples of the distribution of population groups with income from Y to X for the Belgorod region (Fig. 10a) and in Moscow (Figure 10b). Each point on this graph represents the percentage of people (axis Z) in the region with an income in the range from Y to X. For example, a group of people with incomes between 20 and 60 thousand rubles (X = 60000, Y = 20000), in the Belgorod region corresponds to the value of Z = 24.38 (i. e. the number of 24.38% of the total population), while in Moscow Z = 34.13 (i. e. 34.13% of the total Moscow’s population has income of 20 to 60 million). Thus, the graph 9c coordinate Z (height above the plane XY) has the value of F-score statistics regression, in which the share of votes for Zhirinovsky explained by the proportion of people with income from Y to X. a) b) c) d) Figure 6. The share of votes for Prokhorov (vertical axis) vs. “share of people with incomes less then”: a) 5000 rubles per month, b) 10 000 rubles per month, c) 20 000 rubles per month, d) 30 000 rubles per month.