Cross-country Spatial Patterns in Terms of Human Development Indices
DOI:
https://doi.org/10.17059/ekon.reg.2021-3-10Keywords:
human development, human development index, cluster analysis, spatial analysis, social geography, political geography, comparative analysis, spatial research, international relations, world countriesAbstract
The present study attempts to answer the question, which is not considered in the United Nations’ annual Human Development Reports, namely, how spatial patterns of the world countries differ in terms of human development indices. The quantitative research includes three phases. First, functional relations between indices were analysed based on Moran’s I and data fitted by linear regression. Second, clustering of the world countries by human development indices revealed seven spatial clusters. Third, the countries were classified by the types of significant human development problems. The classification distinguished various types of countries: prosperous, problematic and problematic in certain areas. Correlation and spatial dependence analysis demonstrated an important relationship between education and child indicators, in particular, years of education and life expectancy. As a result, the territorial concentration of countries with similar human development was noticed. According to all four groups of indices, 51 prosperous countries (the majority of which are members of the Organisation for Economic Co-operation and Development (OECD)) are characterised by the lack of serious problems. The group of problematic countries includes 51 territories mostly located in sub-Saharan Africa, as well as in Asia, Latin America and Oceania. The findings on relative similarity observed in the identified clusters and groups can be used for developing standard solutions to improve human development. Further research in this direction seems promising.References
Aristotle (1984). Politics. Works in 4 volumes. Volume 4 [Politika. Sochineniya v 4 t. T. 4]. Trans. Moscow: Mysl, 381. (In Russ.)
Schultz, T. W. (1961). Investment in Human Capital. American Economic Review, 51, 1–17.
Schultz, T. W. (1981). Investing in People: The Economics of Population Quality. Berkeley: University of California Press, 173. DOI: 10.2307/1240372.
Sen, A. (2004). Development as Freedom [Razvitie kak svoboda]. Trans. from English. Moscow: Novoe izdatelstvo, 432.
Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234–240. DOI: 10.2307/143141
Rogachev, S. V. (2016). Map interpreting lessons (foundations of spatial analysis). Geografiya. Pervoe sentyabrya [Geography. The first of September], 17, 43–46. (In Russ.)
Sokal, R. R., Oden, N. L., Thomson, B. A. & Kim, J. (2010). Testing for Regional Differences in Means: Distinguishing Inherent from Spurious Spatial Autocorrelation by Restricted Randomization. Geographical Analysis, 25(3), 199–210. DOI: 10.1111/j.1538–4632.1993.tb00291.x.
Tiefelsdorf, M. & Boots, B. (1995). The Exact Distribution of Moran’s I. Environment and Planning A, 27(6), 985–999. DOI: 10.1068/a270985
Bartels, C. P. A. & Hordijk, L. (1977). On the Power of the Generalized Moran Contiguity Coefficient in Testing for Spatial Autocorrelation Among Regression Disturbances. Regional Science and Urban Economics, 7, 83–101.
Bjornstad, O. N. & Falck, W. (2001). Nonparametric Spatial Covariance Functions: Estimation and Testing. Environmental and Ecological Statistics, 8, 53–70. DOI: 10.1023/A:1009601932481.
Anselin, L. & Getis, A. (1992). Spatial Statistical Analysis and Geographic Information Systems. Arizona State University. DOI: 10.1007/BF01581478.
Dray, S. & Jombart, T. (2011). Revisiting Guerry’s data: Introducing spatial constraints in multivariate analysis. The Annals of Applied Statistics, 5(4), 2278–2299. DOI: 10.1214/10-AOAS356.
Fotheringham, A. S, Brunsdon, C. & Charlton, M. (2002). Geographically Weighted Regression. Chichester: John Wiley.
Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27(2), 93–115. DOI: 10.1111/j.1538–4632.1995.tb00338.x.
Carr, D. B., Pickle, L. W. (2010). Visualizing Data Patterns with Micromaps. Boca Raton, FL: Chapman & Hall/CRC. DOI: 10.1111/j.1751–5823.2011.00134_14.x.
Andreev, E. M., Shkolnikov, V. M. & Begun, A. Z. (2002). Algorithm for decomposition of differences between aggregate demographic measures and its application to life expectancies, healthy life expectancies, parity-progression ratios and total fertility rates. Demographic Research, 7(14), 499–522. DOI: 10.4054/DemRes.2002.7.14.
Kohler, I. V., Martikainen, P., Smith, K. P. & Elo, T. (2008). Educational differences in all-cause mortality by marital status — Evidence from Bulgaria, Finland and the United States. Demographic Research, 19, 2011–2042. DOI: 10.4054/DemRes.2008.19.60.
Lawson, A. B., Browne, W. J. & Rodeiro, C. L. V. (2003). Disease Mapping with WinBUGS and MLwiN. Chichester: John Wiley.
Khar’kova, T., Nikitina, S. & Andreev, E. (2017). Dependence of life expectancy on the education levels in Russia. Voprosy statistiki, 8, 61–69. (In Russ.)
Mackenbach, J., Menvielle, G., Jasilionis, D. & Gelder, R. D. (2015). Measuring Educational Inequalities in Mortality Statistics. OECD Statistics Working Papers, 2015/08, OECD Publishing, Paris. 2015. DOI: 10.1787/18152031.
Shkolnikov, V. M, Andreev, E. M, Jasilionis, D., Leinsalu, M., Antonova O. I. & McKee M. (2006). The changing relation between education and life expectancy in central and eastern Europe in the 1990s. Journal of Epidemiology & Community Health, 60(10), 875–881. DOI: 10.1136/jech.2005.044719.
Shkolnikov, V. M, Jasilionis, D., Andreev, E. M, Jdanov, D. A, Stankuniene, V. & Ambrozaitiene, D. (2007). Linked versus unlinked estimates of mortality and length of life by education and marital status: evidence from the first record linkage study in Lithuania. Social Science and Medicine, 64(7), 1392–1406. DOI: 10.1016/j.socscimed.2006.11.014.
Shkolnikov, V. M., Leon, D. A., Adamets, S., Andreev, E. & Deev, A. (1998). Educational level and adult mortality in Russia: An analysis of routine data 1979 to 1994. Social Science & Medicine, 47(3), 357–369. DOI: 10.1016/s0277–9536(98)00096–3.
Enos, R. (2017). The Space Between Us: Social Geography and Politics. Cambridge University Press, 314. DOI: 10.1017/9781108354943.
Bryant, J. (2007). Theories of Fertility Decline and the Evidence from Development Indicators. Population and Development Review, 33(1), 101–127. DOI: 10.1111/j.1728–4457.2007.00160.x.
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Copyright (c) 2021 Igor Yu. Okunev, Sergey L. Barinov, Aleksandr A. Belikov, Yana O. Polyakova

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