Sample survey data are broadly used to provide direct estimates of poverty for the whole population and large areas or domains. That is one of the main deficiencies of poverty analysis at a sub-national level (i.e., related either to regions, or provinces). As they are considered very small geographical areas, since the domain-specific sample is not large enough to support direct estimates of adequate precision, they are likely to produce large standard errors, due to the unduly small size of the sample in that area (Ghosh & Rao, 1994). The aim of our paper is to improve the estimation process quality, in terms of efficiency, of some poverty measures for Italian provinces (NUTS3). The adopted approach deals with Area Level RandomEffect Model (Fay & Herriot, 1979) which relates small area direct estimators to domain specific covariates, considering the random areaeffects as independent. Under that model, the Empirical Best Linear Unbiased Predictor (EBLUP) is obtained. We extend the analysis beyond the conventional measures of income poverty that simply dichotomise the population into the “poor” and the “nonpoor” by a threshold value and we also consider a fuzzy monetary measure treating poverty as a matter of degree (Cheli & Lemmi, 1995; Cheli, 1995). Through such an analysis, we determine some of the socio-economic factors contributing to poverty levels and living standards, and we investigate in depth the territorial perspective. In order to evaluate the performance of the estimation process through small area models and, consequently, the contribution of auxiliary information to composite poverty estimates, we have defined some outcome measures and some quality indicators (Rao, 2003) have been computed. They allow us to test the extent to which the modelling modifies the input direct estimates and the degree of improvement in the accuracy level of the estimates provided by modelling and, more generally, to evaluate the performance of small area estimators.

Estimating Poverty in the Italian Provinces Using Small Area Estimation Models

QUINTANO, Claudio;CASTELLANO, Rosalia;PUNZO, Gennaro
2007

Abstract

Sample survey data are broadly used to provide direct estimates of poverty for the whole population and large areas or domains. That is one of the main deficiencies of poverty analysis at a sub-national level (i.e., related either to regions, or provinces). As they are considered very small geographical areas, since the domain-specific sample is not large enough to support direct estimates of adequate precision, they are likely to produce large standard errors, due to the unduly small size of the sample in that area (Ghosh & Rao, 1994). The aim of our paper is to improve the estimation process quality, in terms of efficiency, of some poverty measures for Italian provinces (NUTS3). The adopted approach deals with Area Level RandomEffect Model (Fay & Herriot, 1979) which relates small area direct estimators to domain specific covariates, considering the random areaeffects as independent. Under that model, the Empirical Best Linear Unbiased Predictor (EBLUP) is obtained. We extend the analysis beyond the conventional measures of income poverty that simply dichotomise the population into the “poor” and the “nonpoor” by a threshold value and we also consider a fuzzy monetary measure treating poverty as a matter of degree (Cheli & Lemmi, 1995; Cheli, 1995). Through such an analysis, we determine some of the socio-economic factors contributing to poverty levels and living standards, and we investigate in depth the territorial perspective. In order to evaluate the performance of the estimation process through small area models and, consequently, the contribution of auxiliary information to composite poverty estimates, we have defined some outcome measures and some quality indicators (Rao, 2003) have been computed. They allow us to test the extent to which the modelling modifies the input direct estimates and the degree of improvement in the accuracy level of the estimates provided by modelling and, more generally, to evaluate the performance of small area estimators.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/726
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