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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.

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.