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Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macroregion levels, thus we implement a small area model for a set of inequality measures (Gini, Relative Theil and Atkinson indexes) to obtain microregion estimates. Considering that inequality estimators are unit-interval defined with skewed and heavy-tailed distributions, we propose a Bayesian hierarchical model at area level involving a Beta mixture. An application on EU-SILC data is carried out and a design-based simulation is performed. Our model outperforms in terms of bias, coverage and error the standard Beta regression model. Moreover, we extend the analysis of inequality estimators by deriving their approximate variance functions., Comment: 28 pages, 7 figures, 2 tables, 2 pages of supplementary material

In order to estimate inequality measures at local level, small area estimation methods may be used to improve the reliability of estimates when the sample size is low. Small area models specified at area level, incorporate the design based estimates (direct estimates) as inputs, that are typically unbiased even though unreliable for small samples. Nevertheless, in the case of inequality measures, design based estimates are instead known to be biased for small sample sizes. In this work we focus on the search for a correction that can produce approximately unbiased direct estimators, taking into account the complexity of the survey design. We use data taken from the EU-SILC sample survey for Italy in 2013. Those modified estimators can then be used in small areas models.

This work proposes a class of latent process models which extend the customary hierarchical Bayesian (HB) small areal-level models in order to address typical prob-lems arising in small area estimation (SAE). Our approach envisions, for the entire re-gion under study, an underlying intensity surface which is block-averaged to the areal units relevant to the study. This framework encompasses all the main instances of pa-rameters being considered in SAE studies. E.g., we can think of an incidence surface when the characteristics of interest are area counts or totals, a probability surface when they are percentages, a proper intensity surface when they are continuous vari-ates. Such intensities are commonly used for explaining spatial point patterns and are generally modeled as realizations of (functions) of latent Gaussian processes. The proposed class of models allows for integrating multiple sources of potentially relevant information, even if available on a spatial scale different from that of small areas. These supplementary sources may consist either in auxiliary data or in survey data collected on planned or major domains. Moreover, in the proposed class bench-marking to large area estimates is automatically satisfied. To illustrate our approach we consider the problem of estimating parameters re-lated to firms’ activity and performance (such as the turnover or value added, which typically have a skewed distribution) as well as indicators of innovation efforts under-taken by enterprises (such as the percentage of firms investing in innovation) at Local Labour Market level. The parameters of interest are thus non-continuous or, if con-tinuous, non-symmetrical, as business statistics frequently are. Our proposal will then consider opportune specifications of unmatched models where the sampling error is non-normally distributed as standardly is in SAE problems. We carried out a simulation to compare the performance of our proposed model to that of customary small area models. The simulation is based on the sampling de-sign adopted in business surveys which typically is one-stage stratified. Predictive error of the Bayesian models under comparison is evaluated in terms of standardly used frequentist properties.

The European Community Household Panel (ECHP) is a panel survey covering a wide range of topics regarding economic, social and living conditions. In particular, it makes it possible to calculate disposable equivalized household income, which is a key variable in the study of economic inequity and poverty. To obtain reliable estimates of the average of this variable for regions within countries it is necessary to have recourse to small area estimation methods. In this paper, we focus on empirical best linear predictors of the average equivalized income based on “unit level models” borrowing strength across both areas and times. Using a simulation study based on ECHP data, we compare the suggested estimators with crosssectional modelbased and designbased estimators. In the case of these empirical predictors, we also compare three different MSE estimators. Results show that those estimators connected to models that take units’ autocorrelation into account lead to a significant gain in efficiency, even when there are no covariates available whose population mean is known.

In recent years, the measure of regional disparities on poverty and social exclusion has received increasing attention by policy makers and this has produced a growing demand of sub-national statistical information on income parameters. Taking into account the multidimensionality of this phenomenon, in the Laeken European Council (Eurostat, 2003) a set of financial poverty indicators were suggested to monitor the progress in fighting inequality. In the European Union regional statistical information on income can be obtained from the European Community Household Panel, a survey designed to provide reliable estimates for large regions in the countries. The aim of this work is to estimate at sub-national level some of the income indicators suggested in the Laeken council. We propose Bayesian small area estimators based on multivariate area level models exploiting the correlation between different indicators. The tendency of model based estimates to over-shrink towards the synthetic component can be a draw-back for policy makers interested in capturing regional disparities in financial poverty. To preserve the relationship between different indicators and disparities over areas, we adopt a multivariate constrained Bayes estimator. The comparison between results based on different models allows us to select the estimator realizing the best compromise between gain in efficiency and reduction of the over-shrinkage.

L’obiettivo del lavoro è la ricerca di una strategia per la valutazione degli effetti delle promozioni sulle vendite nella grande distribuzione alternativa a quella che si fonda sul confronto con la baseline. La procedura che si suggerisce prevede la stima di modelli di regressione, entro gruppi omogenei di punti vendita, che tengono conto dell’autoselezione dovuta alla scelta di effettuare promozioni. I risultati ottenuti appaiano confortanti riguardo alla possibilità che tale strategia offra indicazioni utili per le politiche di marketing.

The availability of reliable estimates of income distribution parameters at a sub-national level is essential for the study of regional disparities. For European Union, countries the estimation of income parameters can be obtained from the European Community Household Panel, that provides reliable estimates for large areas within countries. The aim of this work is to find a suitable small area estimator of the average income at a smaller geographical scale, based on data from this survey. Since it is a panel survey, we suggest some different specifications of unit level Linear Mixed Models leading to small area estimators that borrow strength across both areas and times. In the Small Area Estimation context, full time series and “time series and cross-sectional” aggregate models have been applied in the literature, while small area unit level models for panel data have not been studied extensively. We compare the estimators performance by a Monte Carlo simulation study. Results show a significant gain in efficiency of estimators connected to models that take into account units autocorrelation.

The objective of this thesis is the small area estimation of an economic security indicator. Economic security is a complex concept that carries a variety of meanings. In the literature there is no a formal unambiguous definition for economic security and in this work we refer to the definition recently provided for its opposite, economic insecurity, as the “anxiety produced by the possible exposure to adverse economic events and by the anticipation of the difficulty to recover from them” (Bossert and D’Ambrosio, 2013). In the last decade interest for economic insecurity/security has grown constantly, especially since the financial crisis of 2008, but even more in the last year after the economic consequences due to the Covid-19 pandemic. In this research, economic security is measures through a longitudinal indicator that takes into account the income levels of Italian households, from 2014 to 2016. The target areas are groups of Italian provinces, for which the indicator is estimated using longitudinal data taken from EU-SILC survey. We notice that the sample size is too low to obtain reliable estimates for our target areas. Therefore we resort to some Small Area Estimation strategies to improve the reliability of the results. In particular we consider small area models specified at area level. Besides the basic Fay-Herriot area-level model, we propose to consider some longitudinal extensions, including time-specific random effects following an autoregressive processes of order 1 (AR1) and a moving average of order 1 (MA1). We found that all the small area models used show a significant efficiency gain, especially MA1 model.

This study presents an empirical analysis of the production outsourcing effect on firm productivity and profitability in the Italian manufacturing industry. This study uses firm-level panel data that were developed by the Italian National Statistical Institute. Using different estimation strategies, we develop panel data models and correct for possible endogeneity bias of the outsourcing with respect to the target variables. We find a non-significant effect of outsourcing on profitability and a significant negative effect of outsourcing on productivity.

This study attempts to answer the question of whether European manufacturing firms that undertake offshoring, innovation or both benefit from higher productivity and profitability. From a methodological point of view, the driving forces that push firms to innovate and/or to offshore can be seen as self-selection mechanisms that make the estimation of their economic impact more difficult if the confounding factors affecting these mechanisms also affect the economic performance of the firms. To disentangle the effect of both offshoring and innovation on firms’ performances from the effect of firm characteristics, the propensity score matching methodology in a multi-overlapping treatment setting is used. The study targets European countries using the EU-EFIGE/Bruegel-Unicredit dataset. Decisions to offshore and innovate do not seem to have a significant effect on productivity, whereas the decision to innovate only has a significant effect on firm profitability.