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The impact of taxes and transfer payments on the distribution of income: A parametric comparison

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  • Samuel Dastrup
  • Rachel Hartshorn
  • James McDonald

Abstract

The Luxembourg Income Study data is used to explore the impact of taxes and transfer payments on the distribution of income across thirteen countries for different years. The five-parameter generalized beta distribution and ten of its special cases are considered as models for the size distribution of income. Maximum likelihood methods are used to estimate the model with corresponding measures of goodness of fit and inequality reported. These results identify the best-fitting two, three, and four- parameter models as well as describe the inter-temporal patterns of inequality corresponding to earnings, total income, and disposable income. A general pattern of increasing inequality is observed for almost all countries considered along with significantly different distributional impacts of taxes and transfer payments across countries.
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  • Samuel Dastrup & Rachel Hartshorn & James McDonald, 2007. "The impact of taxes and transfer payments on the distribution of income: A parametric comparison," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(3), pages 353-369, December.
    • Handle: RePEc:kap:jecinq:v:5:y:2007:i:3:p:353-369
    DOI: 10.1007/s10888-006-9039-3
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    3. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    4. Monique Graf & Desislava Nedyalkova, 2014. "Modeling of Income and Indicators of Poverty and Social Exclusion Using the Generalized Beta Distribution of the Second Kind," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 821-842, December.
    5. Boccanfuso, Dorothée & Richard, Patrick & Savard, Luc, 2013. "Parametric and nonparametric income distribution estimators in CGE micro-simulation modeling," Economic Modelling, Elsevier, vol. 35(C), pages 892-899.
    6. Sung Y. Park & Anil K. Bera, 2018. "Information theoretic approaches to income density estimation with an application to the U.S. income data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(4), pages 461-486, December.
    7. Brzezinski, Michal, 2013. "Asymptotic and bootstrap inference for top income shares," Economics Letters, Elsevier, vol. 120(1), pages 10-13.
    8. Monique Graf & J. Miguel Marín & Isabel Molina, 2019. "A generalized mixed model for skewed distributions applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 565-597, June.
    9. Michał Brzeziński, 2013. "Parametric Modelling of Income Distribution in Central and Eastern Europe," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 207-230, September.
    10. Walter, Paul & Weimer, Katja, 2018. "Estimating poverty and inequality indicators using interval censored income data from the German microcensus," Discussion Papers 2018/10, Free University Berlin, School of Business & Economics.
    11. Enrico Fabrizi & Maria Rosaria Ferrante & Carlo Trivisano, 2020. "A functional approach to small area estimation of the relative median poverty gap," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1273-1291, June.
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