REGIONAL INCOMES STRUCTURE ANALYSIS IN SLOVAK REPUBLIC ON THE BASIS OF EU-SILC DATA
DOI:
https://doi.org/10.1515/saeb-2017-0011Keywords:
regional incomes structure, sampling weights, empirical probability mass function, empirical cumulative distribution functionAbstract
The paper deals with the regional incomes structure analysis in Slovak republic on the basis of European Union statistics on income and living conditions in Slovak republic data. The empirical probability mass function and empirical cumulative distribution function is constructed with aid of given sampling weights. On the basis of these functions the median, medial, standard deviation and population histogram of the whole gross household incomes for the whole Slovak republic and separately for eight Slovak regions are estimated and compared.
JEL Codes - C83, R29References
Atkinson, A. B., and Salverda, W., 2005. Top Incomes in the Netherlands and the United Kingdom over the 20th Century. Journal of the European Economic Association, 3(4), 883-913. doi: http://dx.doi.org/10.1162/1542476054430816
Barnett, V., and Lewis, T., 1994. Outliers in Statistical Data. Hoboken: Wiley and Sons.
Chotikapanich, D., Griffiths, W. E., and Rao, D. S. P., 2007. Estimating and Combining National Income Distributions Using Limited Data. Journal of Business & Economic Statistics, 25(1), 97-109.
Cochran, W. G., 1977. Sampling Techniques. New York: J. Wiley and Sons.
Cowell, F. A., and Flachaire, E., 2007. Income distribution and inequality measurement: The problem of extreme values. Journal of Econometrics, 141(2), 1044-1072. doi: http://dx.doi.org/10.1016/j.jeconom.2007.01.001
Dagnelie, P., 1998. Statistique Theorique et Appliquee. Tom 1 - Statistique Descriptive et Bases de l' Inference Statistique. Paris: DeBoeck and Larcier.
Dowrick, S., and Akmal, M., 2005. Contradictory Trends in Global Income Inequality: A Tale of Two Biases. Review of Income and Wealth, 51(2), 201-229. doi: http://dx.doi.org/10.1111/j.1475-4991.2005.00152.x
EUROSTAT, 2007. European Union Statistics on Income and Living Conditions. from http://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions
Ghosh, M., Nangia, N., and Kim, D. H., 1996. Estimation of Median Income of Four-Person Families: A Bayesian Time Series Approach. Journal of the American Statistical Association, 91(436), 1423-1431. doi: http://dx.doi.org/10.2307/2291568
Halley, R. M., 2004. Measures of Central Tendency, Location, and Dispersion in Salary Survey Research. Compensation & Benefits Review, 36(5), 39-52. doi: http://dx.doi.org/10.1177/0886368704268598
Kloek, T., and van Dijk, H. K., 1978. Efficient estimation of income distribution parameters. Journal of Econometrics, 8(1), 61-74. doi: http://dx.doi.org/10.1016/0304-4076(78)90090-8
Levy, P. S., and Lemeshow, S., 2008. Sampling of Populationas. Methods and Applications (4th ed. ed.). Hoboken: Wiley and Sons. doi:http://dx.doi.org/10.1002/9780470374597
Lohr, S. L., 2010. Sampling: Design and Analysis (2nd ed. ed.). Boston: Brooks/Cole.
Piegorsch, W. W., 2015. Statistical Data Analysis. Foundations for Data Mining, Informatics, and Knowledge Discovery. Chichester: Wiley and Sons.
Sala-i-Martin, X., 2006. The World Distribution of Income: Falling Poverty and … Convergence, Period*. The Quarterly Journal of Economics, 121(2), 351-397. doi: http://dx.doi.org/10.1162/qjec.2006.121.2.351
Terek, M., 2016. Odľahlé dáta a charakteristiky polohy v analýzach miezd a príjmov. Revue sociálno-ekonomického rozvoja : vedecký recenzovaný on-line časopis, 2(1), 114-126.
Tosenovsky, J., and Noskievicova, D., 2000. Statisticke metody pro zlepsovani jakosti. Ostrava: Montanex.
Wang, X., and Woo, W. T., 2011. The Size and Distribution of Hidden Household Income in China. Asian Economic Papers, 10(1), 1-26. doi: http://dx.doi.org/10.1162/ASEP_a_00064
Wonnacott, T. H., and Wonnacott, R., 1984. Statistics for Business and Economics. New York: Wiley and Sons.
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