An Analysis of the Impact of Various Sampling Designs on the Headcount Index: A Simulation Study Based on the EU-SILC

Pablo J. Moya Fernández, Encarnación à Lvarez and à ngela González Medina
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Pablo J. Moya Fernández: Department of Applied Economics, Faculty of Social and Legal Sciences of Melilla, University of Granada, Spain
Encarnación à Lvarez: Department of Quantitative Methods in Economics and Business, Faculty of Economics and Business, University of Granada, Spain
à ngela González Medina: Department of Quantitative Methods in Economics and Business, Faculty of Economics and Business, University of Granada, Spain

The Journal of Social Sciences Research, 2020, vol. 6, issue 10, 914-922

Abstract: The analysis and the comparison of poverty between regions and countries are important topics in social sciences, which have relevant demands of many national (Cáritas, Intermón Oxfam, Cruz Roja, etc) and international (UN, World Bank, OECD, Eurostat, IMF, etc) agencies and organizations. One of the most common poverty indicators in practice is the headcount index, which analyzes the proportion of individuals considered as poor in a population. In this paper, we first analyze the impact on the headcount index when different sampling designs are considered. Note that this study is based on real data sets taken from different countries of the European Union, and the empirical measures for comparisons are based on different Monte Carlo simulation studies. For instance, we observe that stratified sampling has the best performance in comparison to alternative sampling designs. Post-stratification performs similar to simple random sampling without replacement, and the use of auxiliary information provides similar results to ones derived from stratified sampling. Second, we also analyze the empirical performance of different variance estimators under the commented sampling designs. We conclude that they have a similar empirical performance, and they provide, in general, confidence intervals with desirable coverage rates.

Keywords: Poverty line; Proportion; Random sample; Monte Carlo simulation; Confidence interval. (search for similar items in EconPapers)
Date: 2020
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