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The interest in the study of economic insecurity has grown in recent years. However, the ongoing debate about its measurement remains unresolved. This thesis aims to investigate the measurement of economic insecurity by proposing classes of individual objective retrospective indices. The first proposal introduces relative versions of the Bossert et al. (2022) measures. This modification allows for the evaluation of economic insecurity in terms of the proportion of outcomes, favoring the comparison between individuals and between countries adopting different currencies or having different purchasing power. Moreover, the class of measures is applied to assess the decision to change jobs. Results indicate that individuals' economic insecurity significantly increases the probability of changing jobs. The estimated models demonstrate a better fit to the data when measured by the proposed index compared to indices previously suggested in the literature. The second proposal assumes that economic insecurity depends not only on personal economic conditions but also on the comparison with peers' financial situations. A new definition of economic insecurity and a composite inter-temporal class of indices are proposed. This approach combines a longitudinal component and a cross-sectional component that gauges individuals' relative positions compared to their peers. The components' weights are determined through a novel approach incorporating a subjective economic insecurity indicator. The class of composite inter-temporal indices is applied to compare a set of European countries. Results highlight that the index provides new insights into individual perceptions of well-being, not captured by other poverty and inequality measures. Lastly, an empirical study on the effect of economic insecurity on political instability is proposed, comparing different insecurity indicators. The findings show that economic insecurity has a positive relationship with anti-incumbency, which results

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.

Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macro region 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.

Income inequality estimators are biased in small samples, leading generally to an underestimation. This aspect deserves particular attention when estimating inequality in small domains. After investigating the nature of the bias, we propose a bias correction framework for a large class of inequality measures comprising Gini Index, Generalized Entropy and Atkinson index families by accounting for complex survey designs. The proposed methodology is based on Taylor’s expansions and does not require any parametric assumption on income distribution, being very flexible. Design-based performance evaluation of our proposal has been carried out using data taken from the EU-SILC survey, showing a noticeable bias reduction for all the measures. Lastly, a small area estimation exercise shows the risks of ignoring prior bias correction in a basic area-level model, determining model misspecification.

Income inequality estimators are biased in small samples, leading generally to an underestimation. This aspect deserves particular attention when estimating inequality in small domains. After investigating the nature of the bias, we propose a bias correction framework for a large class of inequality measures comprising Gini Index, Generalized Entropy and Atkinson index families by accounting for complex survey designs. The proposed methodology is based on Taylor’s expansions and does not require any parametric assumption on income distribution, being very flexible. Design-based performance evaluation of our proposal has been carried out using data taken from the EU-SILC survey, showing a noticeable bias reduction for all the measures. Lastly, a small area estimation exercise shows the risks of ignoring prior bias correction in a basic area-level model, determining model misspecification.

Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macro region 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.

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.

The aim of the work is to estimate a model describing households' purchase of durable goods by using data from the italian Consumer Expenditure Survey. Since the sample design turns out to have important consequences for estimation, two approaches are used to incorporate sample weights in the model estimation: the conventional weighting, which suffers some limitations, and a method recently suggested by Skinner (1994) in order to overcome those limitations. The empirical study proves more efficient estimates are obtained by Skinner's approach. nevertheless, it could lead to different estimates of the parameters for different subgroups of the population when qualitative regressor are selected in the model.

We analyze the anti-poverty effect of social cash transfers using a micro-econometric approach. Aggregate analyses, based on comparing average poverty indicators before and after public transfers, fail to address who receives the transfers and how the transfers are distributed among the poor. We consider three dichotomous outcome variables: (i) poverty status before the receipt of transfers; (ii) the receipt of transfers; and (iii) poverty status after the receipt of transfers. We use a trivariate probit model with sample selection, connecting the outcome variables to the characteristics of the household and its head. Our empirical results highlight that the Italian social transfers system overprotects certain household typologies at the expense of others, as social transfers are primarily awarded to employees with permanent positions and the elderly, while the system is not generous enough to large households with dependant children, the self-employed, temporary contract workers, and the unemployed.

We analyze the anti-poverty effect of social cash transfers using a micro-econometric approach. Aggregate analyses, based on comparing average poverty indicators before and after public transfers, fail to address who receives the transfers and how the transfers are distributed among the poor. We consider three dichotomous outcome variables: (i) poverty status before the receipt of transfers; (ii) the receipt of transfers; and (iii) poverty status after the receipt of transfers. We use a trivariate probit model with sample selection, connecting the outcome variables to the characteristics of the household and its head. Our empirical results highlight that the Italian social transfers system overprotects certain household typologies at the expense of others, as social transfers are primarily awarded to employees with permanent positions and the elderly, while the system is not generous enough to large households with dependant children, the self-employed, temporary contract workers, and the unemployed.

This paper analyses the production outsourcing of Emilia-Romagna’s small clothing manufacturers, using a new rich firm-level dataset. This dataset has the considerable advantage of containing interesting information about the outsourcing of certain phases of the production process that no other data source offers. The production process of these firms, which may be of a totally integrated or more fragmented nature, is thus analysed, and the effects of outsourcing are investigated. Since the analysis of the impact of outsourcing at the firm level has always focused more on productivity than on profitability (Olsen 2006), we are going to study the impact of outsourcing decisions both on a firm’s value added and on its gross earnings, by estimating econometric models. These models allow us to incorporate a firm’s outsourcing propensity into the impact evaluation, by adjusting for possible selection bias (Amemija 1985; Schnedler 2005). Our findings show that outsourcing has a positive effect on both productivity and, in particular, on profitability, thus confirming that Italian small clothing businesses tend to place immediate profit before productivity and growth.

A model-based small area method for calculating estimates of poverty rates based on different thresholds for subsets of the Italian population is proposed. The subsets are obtained by cross-classifying by household type and administrative region. The suggested estimators satisfy the following coherence properties: (i) within a given area, rates associated with increasing thresholds are monotonically increasing; (ii) interval estimators have lower and upper bounds within the interval (0, 1); (iii) when a large domain-specific sample is available the small area estimate is close to the one obtained using standard design-based methods; (iv) estimates of poverty rates should also be produced for domains for which there is no sample or when no poor households are included in the sample. A hierarchical Bayesian approach to estimation is adopted. Posterior distributions are approximated by means of MCMC computation methods. Empirical analysis is based on data from the 2005 wave of the EU-SILC survey.

The increasing importance of ICT in a knowledge-based economy highlights the needfor statistical information on its diffusion. In Italy, a few information is collected fromsample surveys on firms, while no information on labor force capabilities is available. Wecarried out a sample survey on individual skills, focusing on the use of core computer applications.The aim is to suggest an instrument that could be integrated into current labourforce surveys for improving statistics on human capital. The experiment was successfuland results highlight practical knowledge of different computer applications is limitedto some subsets of the population.