Inter- and Intra-Regional Disparities in Russia: Factors of Uneven Economic Growth
Abstract
:1. Introduction
- (1)
- Determine how a region and municipality contribute to the variation of economic growth;
- (2)
- Assess spatial autocorrelation of economic growth;
- (3)
- Examine how the relationship between economic growth and municipal factors changes depending on included regional features.
2. Literature Review
2.1. Uneven Growth at the Small Scale
2.2. Factors Influencing Economic Growth
3. Methods and Data
3.1. Hierarchical Growth Modeling
3.2. Data
4. Uneven Growth of Russian Regions: Spatial Aspect
5. Model Results
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Breau, S.; Saillant, R. Regional income disparities in Canada: Exploring the geographical dimensions of an old debate. Regional Studies. Reg. Sci. 2016, 3, 463–481. [Google Scholar] [CrossRef] [Green Version]
- Chen, B.; Feng, Y. Determinants of economic growth in China: Private enterprise, education, and openness. China Econ. Rev. 2000, 11, 1–15. [Google Scholar] [CrossRef]
- Goodman, C.B. Political fragmentation and economic growth in U.S. metropolitan areas. J. Urban Aff. 2020, 43, 1355–1376. [Google Scholar] [CrossRef]
- Timiryanova, V.; Grishin, K.; Krasnoselskaya, D. Spatial Patterns of Production-Distribution-Consumption Cycle: The Specifics of Developing Russia. Economies 2020, 8, 87. [Google Scholar] [CrossRef]
- Kilroy, A.; Ganau, R. Economic Growth in European Union NUTS-3 Regions; Policy Research Working Paper No. 9494; World Bank Group: Washington, DC, USA, 2020. [Google Scholar]
- Cutrini, E. Economic integration, structural change, and uneven development in the European Union. Struct. Chang. Econ. Dyn. 2019, 50, 102–113. [Google Scholar] [CrossRef]
- Quatraro, F. Innovation, structural change and productivity growth: Evidence from Italian regions, 1980–2003. Camb. J. Econ. 2009, 33, 1001–1022. [Google Scholar] [CrossRef] [Green Version]
- Zubarevich, N.V. Development of the Russian Space: Barriers and Opportunities for Regional Policy. World New Econ. 2017, 11, 46–57. [Google Scholar]
- Butkus, M.; Cibulskiene, D.; Maciulyte-Sniukiene, A.; Matuzeviciute, K. What Is the Evolution of Convergence in the EU? Decomposing EU Disparities up to NUTS 3 Level. Sustainability 2018, 10, 1552. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Li, C.; Li, W.; Song, J.; Yang, C. Do Administrative Boundaries Matter for Uneven Economic Development? A Case Study of China’s Provincial Border Counties. Growth Chang. 2017, 48, 883–908. [Google Scholar] [CrossRef]
- Liao, F.H.F.; Wei, Y.D. Dynamics, Space, and Regional Inequality in Provincial China: A Case Study of Guangdong Province. Appl. Geogr. 2012, 35, 71–83. [Google Scholar] [CrossRef]
- Doran, J.; Jordan, D. Decomposing US regional income inequality from 1969 to 2009. Appl. Econ. Lett. 2015, 23, 781–784. [Google Scholar] [CrossRef]
- Pede, V.O. Diversity and Regional Economic Growth: Evidence From US Counties. J. Econ. Dev. 2013, 38, 111–127. [Google Scholar] [CrossRef]
- Panzera, D.; Postiglione, P. The Impact of Regional Inequality on Economic Growth: A Spatial Econometric Approach. Reg. Stud. 2021, 1–16. [Google Scholar] [CrossRef]
- König, J. European Integration and the Effects of Country Size on Growth. J. Econ. Integr. 2015, 30, 501–531. [Google Scholar] [CrossRef] [Green Version]
- He, S.; Bayrak, M.M.; Lin, H. A comparative analysis of multi-scalar regional inequality in China. Geoforum 2017, 78, 1–11. [Google Scholar] [CrossRef]
- Cheong, T.S.; Wu, Y. Intra-Provincial Inequality in China. In Rebalancing and sustaining growth in China; McKay, H., Song, L., Eds.; ANU Press: Canberra, Australia, 2012; pp. 175–206. [Google Scholar] [CrossRef]
- Liao, F.H.F.; Wei, Y.D.; Huang, L. Regional Inequality in Transitional China, 1st ed.; Routledge: London, UK, 2020. [Google Scholar] [CrossRef]
- Higgins, M.J.; Levy, D.; Young, A.T. Growth and Convergence across the United States: Evidence from County-Level Data. Rev. Econ. Stat. 2006, 88, 671–681. [Google Scholar] [CrossRef]
- Fallah, B.N.; Partridge, M. The elusive inequality-economic growth relationship: Are there differences between cities and the countryside? Ann. Reg. Sci. 2007, 41, 375–400. [Google Scholar] [CrossRef]
- Roth, J. How Does Income Inequality Affect the Growth of U.S. Counties? Econ. Honor. Proj. 2010, 32. Available online: http://digitalcommons.macalester.edu/economics_honors_projects/32 (accessed on 15 April 2021).
- Díaz-Dapena, A.; Rubiera-Morollon, F.; Paredes, D. New Approach to Economic Convergence in the EU. Int. Reg. Sci. Rev. 2018, 42, 335–367. [Google Scholar] [CrossRef]
- Li, G.; Fang, C. Spatial Econometric Analysis of Urban and County-level Economic Growth Convergence in China. Int. Reg. Sci. Rev. 2016, 41, 410–447. [Google Scholar] [CrossRef]
- He, S.; Liao, F.H.; Li, G. A spatiotemporal analysis of county economy and the multi-mechanism process of regional inequality in rural China. Appl. Geogr. 2019, 111, 102073. [Google Scholar] [CrossRef]
- De Jesus, C.S.; Drumond, C.E.; Lopes, T.H.C.R.; Uchôa, F. Personal income distribution and economic growth: The case of Brazilian municipalities. Rev. Econ. 2019, 40, 49–64. [Google Scholar] [CrossRef]
- Díaz-Dapena, A.; Rubiera-Morollón, F.; Pires, M.; Gomes, A. Convergence in Brazil: New evidence using a multilevel approach. Appl. Econ. 2017, 49, 5050–5062. [Google Scholar] [CrossRef]
- Barro, R.J. Inequality and Growth Revisited; ADB Working Paper Series on Regional Economic Integration No. 11; Asian Development Bank (ADB): Manila, Philippines, 2008; Publication Stock No. PPA201608; Available online: http://cpi.stanford.edu/_media/pdf/Reference%20Media/Barro_2008_Income%20and%20Wealth.pdf (accessed on 9 April 2021).
- Da Silva Duarte, M.A.P.; Simões, M.C.N.; Core-Periphery Patterns Across the European Union. Structural Change, Inequality and Economic Growth: Evidence from a Panel of Central and Eastern European Countries; Pascariu, G.C., Duarte, M.A.P.D.S., Eds.; Emerald Publishing Limited: Bingley, UK, 2017; pp. 143–173. [Google Scholar] [CrossRef]
- Próchniak, M. Determinants of economic growth in Central and Eastern Europe: The global crisis perspective. Post-Communist Econ. 2011, 23, 449–468. [Google Scholar] [CrossRef]
- Lewin, P.A.; Watson, P.; Brown, A. Surviving the Great Recession: The influence of income inequality in US urban counties. Reg. Stud. 2017, 52, 781–792. [Google Scholar] [CrossRef]
- Stansel, D. Local decentralization and local economic growth: A cross-sectional examination of US metropolitan areas. J. Urban Econ. 2005, 57, 55–72. [Google Scholar] [CrossRef]
- Gravier-Rymaszewska, J.; Tyrowicz, J.; Kochanowicz, J. Intra-provincial inequalities and economic growth in China. Econ. Syst. 2010, 34, 237–258. [Google Scholar] [CrossRef] [Green Version]
- Hao, Y.; Wang, L.; Zhu, L.; Ye, M. The dynamic relationship between energy consumption, investment and economic growth in China’s rural area: New evidence based on provincial panel data. Energy 2018, 154, 374–382. [Google Scholar] [CrossRef]
- Qin, X.; Li, Y.; Lu, Z.; Pan, W. What makes better village economic development in traditional agricultural areas of China? Evidence from 338 villages. Habitat Int. 2020, 106, 102286. [Google Scholar] [CrossRef]
- Guo, D.; Jiang, K.; Xu, C.; Yang, X. Clustering, growth and inequality in China. J. Econ. Geogr. 2020, 20, 1207–1239. [Google Scholar] [CrossRef]
- Sterlacchini, A. R&D, higher education and regional growth: Uneven linkages among European regions. Res. Policy 2008, 37, 1096–1107. [Google Scholar] [CrossRef]
- Manole, V.; Spatareanu, M. Trade openness and income—A re-examination. Econ. Lett. 2010, 106, 1–3. [Google Scholar] [CrossRef] [Green Version]
- Yanikkaya, H. Trade openness and economic growth: A cross-country empirical investigation. J. Dev. Econ. 2003, 72, 57–89. [Google Scholar] [CrossRef]
- Hong, L. Does and How does FDI Promote the Economic Growth? Evidence from Dynamic Panel Data of Prefecture City in China. IERI Procedia 2014, 6, 57–62. [Google Scholar] [CrossRef] [Green Version]
- Kaneva, M.; Untura, G. The impact of R&D and knowledge spillovers on the economic growth of Russian regions. Growth Chang. 2019, 50, 301–334. [Google Scholar] [CrossRef] [Green Version]
- Sun, W.; Lin, X.; Liang, Y.; Li, L. Regional Inequality in Underdeveloped Areas: A Case Study of Guizhou Province in China. Sustainability 2016, 8, 1141. [Google Scholar] [CrossRef] [Green Version]
- Tian, L.; Wang, H.H.; Chen, Y. Spatial Externalities in China Regional Economic Growth. China Econ. Rev. 2010, 21, S20–S31. [Google Scholar] [CrossRef]
- Ngarambe, O.; Goetz, S.J.; Debertin, D.L. Regional Economic Growth and Income Distribution: County-Level Evidence from the U.S. South. J. Agric. Appl. Econ. 1998, 30, 325–337. [Google Scholar] [CrossRef] [Green Version]
- Čadil, J.; Petkovová, L.; Blatná, D. Human Capital, Economic Structure and Growth. Procedia Econ. Financ. 2014, 12, 85–92. [Google Scholar] [CrossRef] [Green Version]
- Durmaz, A.; Pabuçcu, H. The effect of government educational expenditure on labor productivity in Turkish manufacturing sector. Zb. Rad. Ekon. Fak. Rijeci 2018, 36, 519–535. [Google Scholar] [CrossRef]
- Tridico, P. Growth, Inequality and Poverty in Emerging and Transition Economies. Transit. Stud. Rev. 2010, 16, 979–1001. [Google Scholar] [CrossRef] [Green Version]
- Awaworyi Churchill, S.; Yew, S.; Ugur, M. Effects of Government Education and Health Expenditures on Economic Growth: A Meta-Analysis; Greenwich Papers in Political Economy 14072; University of Greenwich, Greenwich Political Economy Research Centre: London, UK, 2015. [Google Scholar]
- Kennedy, T.; Smyth, R.; Valadkhani, A.; Chen, G. Long- and Short-Run Effects of Income Inequality on the per Capita Output of Australian States; Discussion Paper 55/15; Monash Business School: Caulfield East, Australia, 2015; Available online: https://www.monash.edu/business/economics/research/publications/publications2/5515incomeinequalitykennedysmythvaladkhanichen.pdf (accessed on 24 March 2021).
- Banerjee, A.; Duflo, E. Inequality and growth: What can the data say? J. Econ. Growth 2003, 8, 267–299. [Google Scholar] [CrossRef]
- Giambattista, E.; Pennings, S. When is the government transfer multiplier large? Eur. Econ. Rev. 2017, 100, 525–543. [Google Scholar] [CrossRef] [Green Version]
- Awaworyi Churchill, S.; Yew, S.L. Are government transfers harmful to economic growth? A meta-analysis. Econ. Model. 2017, 64, 270–287. [Google Scholar] [CrossRef]
- Kalafsky, R.V.; Graves, W. Exports and Growth: Learning from the Case of Southern, U.S. Metropolitan Areas. Prof. Geogr. 2017, 70, 383–394. [Google Scholar] [CrossRef]
- Webber, D.J.; Jen, M.H.; O’Leary, E. European regional productivity: Does country affiliation matter? Int. Rev. Appl. Econ. 2018, 33, 1–19. [Google Scholar] [CrossRef]
- Li, Y.; Wei, Y.H.D. The spatial-temporal hierarchy of regional inequality of China. Appl. Geogr. 2010, 30, 303–316. [Google Scholar] [CrossRef]
- Wang, Y.; Liang, C.; Li, J. Detecting Village-level Regional Development Differences: A GIS and HLM method. Growth Chang. 2018, 50, 222–246. [Google Scholar] [CrossRef] [Green Version]
- Mura, M.; Longo, M.; Toschi, L.; Zanni, S.; Visani, F.; Bianconcini, S. Multilevel-growth modelling for the study of sustainability transitions. MethodsX 2021, 8, 101359. [Google Scholar] [CrossRef] [PubMed]
- Raudenbush, S.W.; Bryk, A.S.; Cheong, Y.F.; Congdon, R.T.; Toit, M. HLM 7: Hierarchical Linear and Nonlinear Modeling; Scientific Software International, Inc.: Linconwood, IL, USA, 2011; 351p. [Google Scholar]
- Bliese, P.D. Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In Multi-Level Theory, Research and Methods in Organizations: Foundations, Extensions, and New Directions; Klein, K.J., Kozlowski, S.W.J., Eds.; Jossey-Bass: San Francisco, CA, USA, 2000; pp. 249–381. [Google Scholar]
- Moran, P.A.P. The interpretation of statistical maps. J. R. Stat. Soc. 1948, 10, 243–251. [Google Scholar] [CrossRef]
- Rodgers, A. The locational dynamics of soviet industry. Ann. Assoc. Am. Geogr. 1974, 64, 226–240. [Google Scholar] [CrossRef]
- Hill, F.; Gaddy, C.G. The Siberian Curse: How Communist Planners Left Russia Out in the Cold; Brookings Institution Press: Washington, DC, USA, 2003; p. 240. [Google Scholar]
- Dienes, L. Investment Priorities in Soviet Regions. Ann. Assoc. Am. Geogr. 1972, 62, 437–454. [Google Scholar] [CrossRef]
- Cappellen, A.; Fagerberg, J.; Verspagen, B. Lack of regional convergence. In The Economic Challenge for Europe: Adapting to Innovation Based Growth; Fagerberg, J., Guerrieri, P., Verspagen, B., Eds.; Edward Elgar: Cheltenham, UK, 1999. [Google Scholar]
- Sukharev, O.S. Structural analysis of income and risk dynamics in models of economic growth. Quant. Financ. Econ. 2020, 4, 1–18. [Google Scholar] [CrossRef]
Research | Research Sample; Analysis Period | Model | Dependent Variable | Independent Variables |
---|---|---|---|---|
Pede [13] | 3074 counties in the 48 states of the US; 1990 and 2007 | SARAR | per capita income growth | employment diversity, Gini coefficient, amenities, race Black/Hispanic/other, high school grad, some college, associate’s degree, bachelor’s degree, graduate degree, age 5 to 14, age 15 to 17, age 18 to 64, age 65 or over, metropolitan |
Higgins et al. [19] | 3058 counties of the US; 1970, 1980 and 1990 | OLS, CROLS, 3SLS | real per capita income growth | land area per capita, water area per capita, the proportion of the population by age group, percentage of Blacks, Hispanics, education, median house value, poverty, percentage of population employed in the various industries, college town |
Fallah & Partridge [20] | 3028 counties of the US; 1990 and 2000 | GLS | per capita income growth | Gini coefficient, welfare expenditures, initial per capita income, spatial lag per capita income, ethnic diversification, education, natural amenity scale, total poverty rate, the population size, the racial/ethnic composition variables, recent immigrants, age composition shares, and industry composition employment shares |
Roth [21] | 3117 counties of the US; 1977 and 2000 | OLS | per-capita income growth rate | the percent of residents holding a college degree, per-capita housing units constructed, local government spending on public welfare, highways, health, and education, the unemployment rate, voter turnout, percentage of a county’s households that earned less than USD 10,000 and above USD 30,000 |
Kilroy & Ganau [5] | 1348 NUTS-3 regions in the EU; 2003 to 2017 | PARDL | GDP per capita growth | industrial structure, innovation, inward FDI, population dynamics and agglomeration forces |
Díaz-Dapena et al. [22] | 1276 NUTS-3 regions in the EU; 2000 to 2014 | OLS, MLM, MLM with spatial dependence | GDP per capita growth | percentage of population with different levels of education, gross capital formation as a percentage of GDP, mean growth of the working population, R&D expenditures as a percentage of GDP |
Li & Fang [23] | 2286 cities and counties of China; 1992, 2000 and 2010 | OLS, SAR, SEM (panel data) | GDP per capita growth | investment return rate, human capital, savings rate, population growth, technology advancement, capital depreciation rate, initial technology level |
He et al. [24] | 2076 cities and counties of China; from 2000 to 2015 | OLS, SLM, SAR, SEM (panel data) | GDP per capita | labor employment, urbanization, urban–rural inequality, fixed investment, industrialization, availability of bank loans, fiscal expenditure decentralization, fiscal autonomy, implementation of “province administering counties” strategy, topography factors (elevation, slope) |
Breau & Saillant [1] | 284 census divisions Canada; for 1996, 2001 and 2006 | SAR (panel data) | real average total income growth | unemployment rate, % employment in finance, insurance and real estate industries, in high-tech, in manufacturing, in government, Gini coefficient, % high education, female participation rate, % visible minority, % young, % senior, urban dummy |
De Jesus et al. [25] | 5565 Brazilian municipalities; 1991, 2000, 2010 | OLS, F.E. panel, R.E. panel | municipal income growth | initial income, income inequality (Gini coefficient, Theil coefficient, top 10 percent income share), education |
Díaz-Dapena et al. [26] | 4067 Brazilian municipalities; 1991, 2010 | OLS, MLM | per capita income growth | population growth, population density, the average years of education, migratory flows, proportion of employment in the industries |
Variable | Definition | Level * | Mean | Standard Deviation | Min | Max |
---|---|---|---|---|---|---|
Dependent variables: | ||||||
The natural log of the volume of social benefits and taxable income of the population per capita, growth rate (2017, 2019) | 1 | 0.07 | 0.14 | −1.54 | 1.47 | |
The natural log of production per capita, growth rate (2017, 2019) ** | 1 | 0.14 | 0.46 | −3.53 | 4.44 | |
Independent variables: | ||||||
Control variables | ||||||
The natural log of volume of social benefits and taxable income of the population per capita, in 2017 | 1 | 5.23 | 0.43 | 4.05 | 8.11 | |
The natural log of production per capita in 2017 ** | 1 | 4.69 | 1.52 | −1.27 | 10.74 | |
The natural log of average production per capita in 2017–2019 ** | 1 | 4.8 | 1.5 | −0.85 | 10.94 | |
The natural log of average volume of social benefits and taxable income of the population per capita, in 2017–2019 | 1 | 5.27 | 0.44 | 4.14 | 8.05 | |
The natural log of average investment volume in fixed assets (excluding budgetary funds) per capita (2017–2019) | 1 | 9.6 | 1.81 | 3.35 | 17.13 | |
The natural log of average population (2017–2019) | 1 | 3.34 | 1.03 | −0.36 | 9.43 | |
The natural log of average ratio of employment in the total population (2017–2019) ** | 1 | −1.76 | 0.44 | −3.39 | 0.78 | |
The natural log of average population density (2017–2019) | 2 | 2.78 | 1.83 | −2.67 | 8.48 | |
Economic structure | ||||||
The natural log of average agriculture, forestry, and fishing fraction in GRP *** (2017–2019) | 2 | 1.67 | 1.1 | −2.3 | 3.15 | |
The natural log of average mining fraction in GRP (2017–2019) | 2 | 2.07 | 1.08 | 0.26 | 4.35 | |
The natural log of ratio small and medium-sized business fraction in GRP in 2019 | 2 | 3.12 | 0.52 | 0.42 | 3.75 | |
Inequality | ||||||
The natural log of average Gini (2017–2019) | 2 | −0.99 | 0.06 | −1.08 | −0.84 | |
The natural log of average ratio of social benefits in monetary income of the population (2017–2019) | 2 | 3.1 | 0.21 | 2.44 | 3.55 | |
Innovation | ||||||
The natural log of average ratio total R&D expenditures in GRP (2017–2019) | 2 | −0.98 | 1.17 | −4.72 | 1.68 | |
The natural log of average ratio research and development staff in total workforce (2017–2019) | 2 | 1.09 | 0.99 | −1 | 3.38 | |
Human capital | ||||||
The natural log of average ratio government educational expenditure in GRP (2017–2019) | 2 | −3.09 | 0.41 | −4.11 | −1.91 | |
The natural log of average ratio government health expenditure in GRP (2017–2019) | 2 | −4.46 | 0.38 | −5.2 | −3.4 | |
Human capital | ||||||
The natural log of average share of employed population with tertiary education: bachelor’s degree or higher (2017, 2019) | 2 | 3.44 | 0.15 | 3.12 | 3.91 | |
The natural log of average share of employed population with college education (2017, 2019) | 2 | 3.79 | 0.18 | 2.96 | 4.02 | |
Openness | ||||||
Dummy variable: 1 if the net export is positive and 0 otherwise | 2 | 0.73 | 0.45 | 0 | 1 |
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
---|---|---|---|---|---|
Intercept, γ00 | 0.067 *** | 0.064 *** | −0.751 ** | −0.267 | 0.064 *** |
Level 1 | |||||
Control variable | |||||
−0.245 *** | −0.243 *** | −0.6 ** | |||
Cross-level interaction: | |||||
−0.039 *** | |||||
0.065 *** | |||||
0.249 *** | |||||
−0.136 ** | |||||
0.002 | |||||
0.012 *** | 0.013 *** | 0.087 *** | |||
Cross-level interaction: | |||||
−0.006 *** | |||||
−0.021 ** | |||||
0.0014 | |||||
0.141 *** | 0.144 *** | 0.524 *** | |||
Cross-level interaction: | |||||
0.401 *** | |||||
−0.014 * | |||||
Level 2 | |||||
Control variable | |||||
−0.019 *** | −0.009 | ||||
Economic structure | |||||
−0.0003 | |||||
−0.004 | |||||
0.024 | |||||
Inequality | |||||
−0.002 | |||||
0.028 | |||||
Innovation | |||||
−0.001 | |||||
0.021 | |||||
Human capital | |||||
−0.052 | |||||
0.042 | |||||
0.112 * | 0.104 * | ||||
0.089 | |||||
Openness | |||||
−0.01 | |||||
Spatial error, λ | 0.51 | −0.38 | 0.42 | 0.43 | |
Regular HLM vs. HLM with spatial dependence model comparison test | χ2 = 8.15 *** | χ2 = 1.43 | χ2 = 5.4 ** | χ2 = 6.55 ** | |
Variance component: | |||||
τ2 | 0.004 | 0.003 | 0.003 | 0.003 | 0.004 |
σ2 | 0.016 | 0.016 | 0.013 | 0.013 | 0.012 |
χ2 | 635.4 *** | 635.4 *** | 593.8 *** | 726.3 *** | 832.4 *** |
Reliability estimate | 0.819 | 0.819 | 0.808 | 0.836 | 0.856 |
The value of the log-likelihood function | 1374.1 | 1378.2 | 1598.8 | 1592.1 | 1678.2 |
Deviance | −2748.2 | −2756.3 | −3197.6 | −3184.2 | −3356.4 |
Model 6 | Model 7 | Model 8 | Model 9 | Model 10 | |
---|---|---|---|---|---|
Intercept, γ00 | 0.148 *** | 0.147 *** | 2.82 *** | 2.47 *** | 0.917 ** |
Level 1 | |||||
Control variable | |||||
−0.183 *** | −0.178 *** | −0.965 *** | |||
Cross-level interaction: | |||||
0.037 *** | |||||
0.216 *** | |||||
0.078 | |||||
0.093 *** | 0.095 *** | 0.334 *** | |||
Cross-level interaction: | |||||
−0.019 *** | |||||
0.068 *** | |||||
0.010 | |||||
0.217 *** | 0.256 *** | 0.171 *** | |||
Cross-level interaction: | |||||
−0.094 ** | |||||
Level 2 | |||||
Control variable | |||||
0.013 | |||||
Economic structure | |||||
0.033 | |||||
0.021 | |||||
−0.117 | |||||
Inequality | |||||
0.821 * | 0.49 | ||||
0.214 | |||||
Innovation | |||||
−0.024 | |||||
0.041 | |||||
Human capital | |||||
−0.052 | |||||
0.12 * | 0.082 | ||||
−0.274 * | −0.204 | ||||
−0.266 * | −0.203 * | −0.202 ** | |||
Openness of the economy | |||||
−0.05 | |||||
Spatial error, λ | −0.279 | ||||
Regular HLM vs. HLM with spatial dependence model comparison test | χ2 = 0.87 | ||||
Variance component: | |||||
τ2 | 0.013 | 0.012 | 0.0131 | 0.0129 | 0.0136 |
σ2 | 0.200 | 0.200 | 0.178 | 0.178 | 0.173 |
χ2 | 212.7 *** | 212.7 *** | 194.7 *** | 215.0 *** | 229.9 *** |
Reliability estimate | 0.583 | 0.578 | 0.604 | 0.602 | 0.618 |
The value of the log-likelihood function | −1419.99 | −1415.4 | −1324.3 | −1303.2 | −1284.5 |
Deviance | 2839.99 | 2830.9 | 2648.6 | 2606.4 | 2568.97 |
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Timiryanova, V.; Krasnoselskaya, D.; Lakman, I.; Popov, D. Inter- and Intra-Regional Disparities in Russia: Factors of Uneven Economic Growth. Sustainability 2021, 13, 13754. https://doi.org/10.3390/su132413754
Timiryanova V, Krasnoselskaya D, Lakman I, Popov D. Inter- and Intra-Regional Disparities in Russia: Factors of Uneven Economic Growth. Sustainability. 2021; 13(24):13754. https://doi.org/10.3390/su132413754
Chicago/Turabian StyleTimiryanova, Venera, Dina Krasnoselskaya, Irina Lakman, and Denis Popov. 2021. "Inter- and Intra-Regional Disparities in Russia: Factors of Uneven Economic Growth" Sustainability 13, no. 24: 13754. https://doi.org/10.3390/su132413754