%0 Journal Article
	%A Encarnación Álvarez and  Rosa M. García-Fernández and  Juan F. Muñoz
	%D 2014
	%J International Journal of Economics and Management Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 94, 2014
	%T A New Method to Estimate the Low Income Proportion: Monte Carlo Simulations
	%U https://publications.waset.org/pdf/9999583
	%V 94
	%X Estimation of a proportion has many applications in
economics and social studies. A common application is the estimation
of the low income proportion, which gives the proportion of people
classified as poor into a population. In this paper, we present this
poverty indicator and propose to use the logistic regression estimator
for the problem of estimating the low income proportion. Various
sampling designs are presented. Assuming a real data set obtained
from the European Survey on Income and Living Conditions, Monte
Carlo simulation studies are carried out to analyze the empirical
performance of the logistic regression estimator under the various
sampling designs considered in this paper. Results derived from
Monte Carlo simulation studies indicate that the logistic regression
estimator can be more accurate than the customary estimator under
the various sampling designs considered in this paper. The stratified
sampling design can also provide more accurate results.

	%P 3281 - 3284