Abstract: The European Union Survey on Income and Living
Conditions (EU-SILC) is a popular survey which provides
information on income, poverty, social exclusion and living
conditions of households and individuals in the European Union.
The EU-SILC contains variables which may contain outliers. The
presence of outliers can have an impact on the measures and
indicators used by the EU-SILC. In this paper, we used data sets
from various countries to analyze the presence of outliers. In addition,
we obtain some indicators after removing these outliers, and a
comparison between both situations can be observed. Finally, some
conclusions are obtained.
Abstract: The problem of estimating a proportion has important
applications in the field of economics, and in general, in many areas
such as social sciences. A common application in economics is
the estimation of the headcount index. In this paper, we define the
general headcount index as a proportion. Furthermore, we introduce
a new quantitative method for estimating the headcount index. In
particular, we suggest to use the logistic regression estimator for the
problem of estimating the headcount index. Assuming a real data set,
results derived from Monte Carlo simulation studies indicate that the
logistic regression estimator can be more accurate than the traditional
estimator of the headcount index.
Abstract: This paper explores the extent of the gap in poverty rates between immigrant and native households in Spanish regions and assess to what extent regional differences in individual and contextual characteristics can explain the divergences in such a gap. By using multilevel techniques and European Union Survey on Income and Living Conditions, we estimate immigrant households experiments an increase of 76 per cent in the odds of being poor compared with a native one when we control by individual variables. In relation to regional differences in the risk of poverty, regionallevel variables have higher effect in the reduction of these differences than individual variables.