Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2013 | 11(17) | 179-200

Article title

Rekonstrukcja światowego rozkładu dochodów na podstawie minimalnej informacji statystycznej

Content

Title variants

EN
Reconstruction of world income distribution based on minimal statistical information

Languages of publication

PL

Abstracts

EN
The aim of this paper is to obtain a sample from the world income distribution (WID). We assumed log-logistic form of countries’ income distributions with unit means. This implies that such distributions are fully described by their corresponding Gini indices. Gini estimates came from Deninger-Squire, WID2, and other databases for 119 countries in the years 1990-2005. We generated random sample for every distribution and multiplied its values by GDP/capita. Sample size was controlled by sequential ratio test. The world sample consisted of country samples weighted by population shares. We have found that WID is bimodal with diminishing inequality and poverty during analyzed period.

Year

Issue

Pages

179-200

Physical description

Dates

published
2013

Contributors

  • Politechnika Gdańska
  • Politechnika Gdańska

References

  • Atkinson A.B., Brandolini A., Promise and pitfalls in the use of ‘secondary’ data-sets: income inequality in OECD Countries, “Journal of Economic Literature” 2001, 39, s. 771-99.
  • Deninger K., Squire L., A new data set measuring income inequality, “The World Bank Economic Review” 1996, 10, s. 565-591.
  • DS World Bank database, http://econ.worldbank.org/projects/inequality. Fisk P.R., The graduation of income distributions, “Econometrica” 1961, 29, s. 171-185.
  • Fisz M., Rachunek prawdopodobieństwa i statystyka matematyczna, PWN, Warszawa 1969.
  • Foster J.E., Greer J., Thorbecke E., A class of decomposable poverty indices, "Econometrica” 1984, 52, s. 761-766.
  • Kendall M.G., Stuart A., The advanced theory of statistics, Vol. 2, Griffin & Co. Ltd., London 1961.
  • Kleiber Ch., Kotz S., Statistical Size Distributions in Economics and Actuarial Sciences, Wiley and Sons Publications, New Jersey 2003.
  • McDonald J.B., Some generalized functions for the size distribution of income, ”Econometrica” 1984, 52, s. 647-663.
  • McDonald J.B., Xu Y.J., A generalization of the beta distribution with applications, “Journal of Econometrics” 1995, 66, s. 133-152: erratum: “Journal of Econometrics” 1995, 69, s. 427-428.
  • Pinkovskiy M., Sala-i-Martin X., Parametric estimations of the Word distribution of income, “NBER Working Paper” 2009, No. 15433.
  • Ravallion M., The debate on globalization, poverty and inequality: Why measurement matters, “Policy Research Working Papers”, WPS3031, The World Bank, May 2003.
  • Sala-i-Martin X., The world distribution of income: Falling poverty and… convergence, period, “Quarterly Journal of Economics” 2006, 121(2), s. 351-397.
  • Shorrocks A., Wan G., Ungrouping income distributions: Synthesising samples for inequality and poverty analysis, [w:] K. Basu, R. Kanbur (red.), Arguments for a Better World: Essays in Honor of Amartya Sen, Vol. I: Ethics, Welfare and Measurement, Oxford University Press, Oxford, 2009, s. 414-434.
  • Van Kerm P., Extreme incomes and the estimation of poverty and inequality indicators from EU-SILC, “IRISS Working Paper” 2007-01, CEPS/INSTEAD, Differdange, Luxembourg 2007.
  • WIID2 World income inequality database, UNU-WIDER, Helsinki, May 2005.
  • World Development Indicators, World Bank, Washington 2012.
  • Yitzhaki S., Gini’s mean difference: A superior measure of variability for non-normal distributions, “METRON − International Journal of Statistics” 2003, 41(2), s. 285-316.

Document Type

Publication order reference

Identifiers

ISSN
1644-6739

YADDA identifier

bwmeta1.element.desklight-61e70bed-6f1b-495c-aa3c-bee58927ae5c
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.