On Estimating the Poverty Gap and the Poverty Severity Indices with Auxiliary Information

Autor/inn/en	Muñoz, J. F.; Álvarez-Verdejo, E.; García-Fernández, R. M.
Titel	On Estimating the Poverty Gap and the Poverty Severity Indices with Auxiliary Information
Quelle	In: Sociological Methods & Research, 47 (2018) 3, S.598-625 (28 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124115626178
Schlagwörter	Poverty; Simulation; Income; Socioeconomic Status; Expenditures; Well Being; Social Indicators; Equations (Mathematics); Computation; Surveys; Monte Carlo Methods; Foreign Countries; Differences; Measurement; European Union; Bulgaria; Spain; Slovenia; Italy; United Kingdom; Poland; Lithuania; Belgium + Suchen Sie Ihr Suchwort? Armut; Simulation program; Simulationsprogramm; Einkommen; Socio-economic status; Sozioökonomischer Status; Ausgaben; Well-being; Wellness; Wohlbefinden; Social indicator; Sozialer Indikator; Equations; Mathematics; Gleichungslehre; Survey; Umfrage; Befragung; Monte-Carlo-Methode; Ausland; Unterscheiden; Messverfahren; Bulgarien; Spanien; Slowenien; Italien; Großbritannien; Polen; Litauen; Belgien
Abstract	Many poverty measures are estimated by using sample data collected from social surveys. Two examples are the poverty gap and the poverty severity indices. A novel method for the estimation of these poverty indicators is described. Social surveys usually contain different variables, some of which can be used to improve the estimation of poverty measures. The proposed estimation methodology is based on this idea. The variance estimation and the construction of confidence intervals are also topics addressed in this article. Real survey data, extracted from the European Union Survey on Income and Living Conditions and based on various countries, are used to investigate some empirical properties of our estimators via Monte Carlo simulation studies. Empirical results indicate that the suggested methods can be more accurate than the customary estimator. Desirable results are also obtained for the proposed variances and confidence intervals. Various populations generated from the Gamma distribution also support our findings. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01