Coerenza ed ottimalità delle stime calibrate su informazioni da indagini campionarie

Il lavoro affronta il problema della coerenza esterna e dell'ottimalità nel caso in cui si vogliono introdurre nel processo di stima delle informazioni di cui si conoscono i totali di controllo da altre indagini campionarie e, quindi, questi sono affetti da errori campionari. Le espressioni di stimatori che soddisfano queste proprietà sono presentate e le loro performance sono studiate attraverso uno studio di simulazione e l'applicazione a casi reali

Coerenza ed ottimalità delle stime calibrate su informazioni da indagini campionarie

Il lavoro affronta il problema della coerenza esterna e dell'ottimalità nel caso in cui si vogliono introdurre nel processo di stima delle informazioni di cui si conoscono i totali di controllo da altre indagini campionarie e, quindi, questi sono affetti da errori campionari. Le espressioni di stimatori che soddisfano queste proprietà sono presentate e le loro performance sono studiate attraverso uno studio di simulazione e l'applicazione a casi reali

On the estimation of the Lorenz curve under complex sampling designs

This paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with different inclusion probabilities. A (design-based) Hajek type estimator for the Lorenz curve is proposed, and its asymptotic properties are studied. Then, a resampling scheme able to approximate the asymptotic law of the Lorenz curve estimator is constructed. Applications are given to the construction of (i) a confidence band for the Lorenz curve, (ii) confidence intervals for the Gini concentration ratio, and (iii) a test for Lorenz dominance. The merits of the proposed resampling procedure are evaluated through a simulation study

Assessing and Adjusting Bias Due to Mixed-Mode in Aspect of Daily Life Survey

Abstract The mixed-mode (MM) designs are adopted by NSIs both to contrast declining response and coverage rates and to reduce the cost of the surveys. However, MM introduces several issues that must be addressed both at the design phase, by defining the best collection instruments to contain the measurement error, and at the estimation phase, by assessing and adjusting the mode effect. In the MM surveys, the mode effect refers to the introduction of bias effects on the estimate of the parameters of interest due to the difference in the selection and measurement errors specific to each mode. The switching of a survey from single to mixed-mode is a delicate operation: the accuracy of the estimates must be ensured in order to preserve their consistency and comparability over time. This work focuses on the methods chosen for the evaluation of the mode effect in the Italian National Institute of Statistics (ISTAT) mixed-mode survey "Aspects of Daily Life – 2017", in the experimental context for which an independent control single-mode (SM) PAPI sample was planned to assess the introduction of the sequential web/PAPI survey. The presented methods aim to analyze the causes that can determine significant differences in the estimates obtained with the SM and MM surveys

A sampling estimator of the Bonferroni inequality index

A sampling estimator of the Bonferroni inequality index based on the Bonferroni curve, is presented. It is compared through a simulation study with the percentile estimator and the estimator that uses weighted observations

Bonferroni index decomposition and the Shapley method

The Shapley decomposition enables to overcome the problem related to Bonferroni inequality index of not being additively decomposable. The comparison among the results obtained for Gini and Bonferrini indices allows to highlight interersting similarities and differences among the two indioce

Satellite Imagery for Studying Development? The Italian case study

The Human Development Index (HDI) is a well-known measure of development published since the 90s by the United Nation. Among the criticisms of HDI, there are undeniable problems in data collection that can frustrate comparisons between countries. In fact, for some countries old data only are available and few others have not even that. Elvidge et al. (2012) proposed the Night Light Development Index (NLDI), that can be computed solely from nighttime satellite imagery and population density, therefore without monetary data and with ease in data collection. The NLDI, that is a inequality measure of light distribution among inhabitants, has a strong correlation with the HDI at country level. In this paper we show that NLDI can produce the same values for very different development levels. Therefore, a simple correction (NLDI*) for overcoming this drawback is introduced. The original NLDI and our correction have been computed for the Italian case study, that is, the whole territory, the geographical areas (NUTS-1), regions (NUTS-2) and few provinces (NUTS-3) have been derived. The values obtained have been compared with those of others indexes to better understand the meaning of NLDI* in a particular context like the Italian one

Decomposing the Bonferroni Inequality Index by Subgroups: Shapley Value and Balance of Inequality

Additive decomposability is an interesting feature of inequality indices which, however, is not always fulfilled; solutions to overcome such an issue have been given by Deutsch and Silber (2007) and by Di Maio and Landoni (2017). In this paper, we apply these methods, based on the “Shapley value” and the “balance of inequality” respectively, to the Bonferroni inequality index. We also discuss a comparison with the Gini concentration index and highlight interesting properties of the Bonferroni index

The ANOGI for detecting the impact of education and employment on income inequality

The ANOGI proposed by Frick et al. (2006) decomposes the overall inequality measured by Gini index by three components: within, between and over- lapping. Based on ADSILC data we analyze the inequality of income, by separating two aspects: inequality due to education and to the career. The overlapping compo- nent shows how intertwined the subgroups are, whereas increases in stratification can cause a negative effect on inequality. In this context, its definition has been extended to compare pairs of workers according to their career and education. We demonstrated that workers with higher education are associated to the highest level of stratification