Modelling the Income Distributions in the Czech Republic since 1992
DOI:
https://doi.org/10.17713/ajs.v41i2.181Abstract
The goal of this article is to study incomes in the Czech Republic and their development since 1992. The net annual per capita income of Czech households is analysed for all households and their respective subpopulations. Data from the microcensus 1992, 1996, 2002, and EU-SILC 2005–2008 surveys carried out by the Czech Statistical Office are used. The subpopulations are defined by a household’s location (Bohemia or Moravia), and education and age of the head of the household in order to compare the distributions ofthe income in Bohemia and Moravia and to quantify the impact of education and age on incomes. The three-parameter lognormal distribution is chosen as a probability distribution to model the per capita income distribution for the whole population and for subpopulations. To estimate the unknown parameters, the maximum likelihood method and that of L-moments are employed. The medians of equalised incomes are given for the EU members and the average growth in the 2004–2007 period is compared. For the Czech Republic, a comparison of the medians of per capita and equivalised income is made.
References
Bartošová, J. (2006). Logarithmic-normal model of income distribution in the Czech Republic. Austrian Journal of Statistics, 35, 215-222.
Bartošová, J., and Bína, V. (2009). Modelling of income distribution of Czech households in years 1996–2005. Acta Oeconomica Pragensia, 17, 3-18.
Bílková, D. (2008). Application of lognormal curves in modeling of wage distributions. In 7th International Conference APLIMAT 2008 (p. 341-351). Bratislava: Slovak University of Technology.
Cohen, A. C., and Whitten, J. B. (1980). Estimation in the three-parameter lognormal distribution. Journal of American Statistical Association, 75, 399-404.
Dagum, C. (1990). Generation and properties of income distribution functions. In Income and wealth distribution, inequality and poverty: Proceedings of the Second International Conference on Income Distribution by Size: Generation, Distribution,
Measurement and Applications, University of Pavia, Italy, September 23-30, 1989 (p. 1-17).
Dagum, C. (1997). A systemic approach to the generation of income distribution models. Journal of Income Distribution, 6, 105-126.
Flachaire, E., and Nunez, O. (2007). Estimation of the income distribution and detection of subpopulations: an explanatory model. Computational Statistics and Data Analysis, 51, 3368-3380.
Hosking, J. R. M. (1990). L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society, Series B, 52, 105-124.
Hosking, J. R. M., and Wales, J. R. (1997). Regional Frequency Analysis: An Approach Based on L-Moments. New York: Cambridge University Press.
Johnson, N. L., Kotz, S., and Balakrishan, N. (1994). Continuous Univariate Distributions, Volume 1 (2nd ed.). New York: Wiley.
Kleiber, C., and Kotz, S. (2003). Statistical Size Distributions in Economics and Actuarial Sciences. New York: Wiley-Interscience.
Kyselý, J., and Picek, J. (2007). Regional growth curves and improved design value estimates of extreme precipitation events in the Czech Republic. Climate Research, 33, 243-255.
McDonald, J. B. (1984). Some generalized functions for the size distribution of income. Econometrica, 52, 647-665.
Pacáková, V., and Sipková, L. (2007). Generalized lambda distributions of household’ incomes. E+M Ekonomie a Management, 10, 98-107.
Parker, S. C. (1997). The distribution of self-employment income in the United Kingdom, 1976-1991. The Economic Journal, 107, 455-466.
Pavelka, R. (2009). Application of density mixture in the probability model construction of wage distributions. Applications of Mathematics and Statistics in Economy: AMSE 2009, Uherské Hradiště, 341-350.
Smithers, J. C., and Schulze, R. E. (2001). A methodology for the estimation of short duration design storms in South Africa using a regional approach based on Lmoments. Journal of Hydrology, 241, 42-52.
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