Income distribution embeds a large field of research subjects in economics. It is important to study how incomes are distributed among the members of a population in order for example to determine tax policies for redistribution to decrease inequality, or to implement social policies to reduce poverty. The available data come mostly from surveys (and not censuses as it is often believed) and are often subject to long debates about their reliability because the sources of errors are numerous. Moreover the forms in which the data are available is not always as one would expect, i.e. complete and continuous (micro data) but one also can only have data in a grouped form (in income classes) and/or truncated data where a portion of the original data has been omitted from the sample or simply not recorded.Because of these data features, it is important to complement classical statistical procedures with robust ones, In this paper such methods are presented, especially for model selection, model fitting with several types of data, inequality and poverty analysis and ordering tools. The approach is based on the Influence Function (IF) developed by Hampel (1974) and further developed by Hampel, Ronchetti, Rousseeuw & Stahel (1986), It is also shown through the analysis of real UK and Tunisian data, that robust techniques can give another picture of income distribution, inequality or poverty when compared to classical ones.

Robust Methods for the Analysis of Income Distribution, Inequality and Poverty / Maria-Pia Victoria-Feser. - In: INTERNATIONAL STATISTICAL REVIEW. - ISSN 0306-7734. - STAMPA. - 68:3(2000), pp. 277-293. [10.1111/j.1751-5823.2000.tb00331.x]

Robust Methods for the Analysis of Income Distribution, Inequality and Poverty

Maria-Pia Victoria-Feser
2000

Abstract

Income distribution embeds a large field of research subjects in economics. It is important to study how incomes are distributed among the members of a population in order for example to determine tax policies for redistribution to decrease inequality, or to implement social policies to reduce poverty. The available data come mostly from surveys (and not censuses as it is often believed) and are often subject to long debates about their reliability because the sources of errors are numerous. Moreover the forms in which the data are available is not always as one would expect, i.e. complete and continuous (micro data) but one also can only have data in a grouped form (in income classes) and/or truncated data where a portion of the original data has been omitted from the sample or simply not recorded.Because of these data features, it is important to complement classical statistical procedures with robust ones, In this paper such methods are presented, especially for model selection, model fitting with several types of data, inequality and poverty analysis and ordering tools. The approach is based on the Influence Function (IF) developed by Hampel (1974) and further developed by Hampel, Ronchetti, Rousseeuw & Stahel (1986), It is also shown through the analysis of real UK and Tunisian data, that robust techniques can give another picture of income distribution, inequality or poverty when compared to classical ones.
2000
Robust Methods for the Analysis of Income Distribution, Inequality and Poverty / Maria-Pia Victoria-Feser. - In: INTERNATIONAL STATISTICAL REVIEW. - ISSN 0306-7734. - STAMPA. - 68:3(2000), pp. 277-293. [10.1111/j.1751-5823.2000.tb00331.x]
Maria-Pia Victoria-Feser
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/952916
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