This paper studies the regional geography of perceived inequality in Italy using evidence from a large-scale online survey developed within the (Growing Resilient, Inclusive and Sustainable) GRINS observatory. The survey complements official statistics by capturing dimensions that conventional distributional indicators do not directly observe, including subjective class placement, current economic adequacy, intergenerational comparison, adolescent living conditions, social support, and everyday exposure to richer and poorer groups. The paper has a double aim. Substantively, it documents how inequality is experienced and narrated across Italian regions. Methodologically, it shows why survey-based evidence is essential for interpreting territorial disparities alongside official sources such as the Italian National Institute of Statistics (ISTAT). The results reveal a multidimensional pattern in which perceived inequality is shaped not only by material strain, but also by remembered disadvantage, fragile support networks, and persistent upward comparison. The regional ranking that emerges is not a mechanical reflection of standard narratives of deprivation and cannot be reduced to a simple territorial divide. The paper therefore argues that a fuller understanding of inequality in Italy requires integrating official measures with systematic evidence on how households perceive their social position and relative distance from others.

The Carbon Footprint (CFP), derived from household consumption survey data, is a crucial indicator for assessing the impact of human consumption on greenhouse gas emissions. The definition and measurement of personal CFP rely on data that require the comparability between classifications of firm production and household consumption. In this article, we address three key aspects related to the definition and estimation of personal CFP. First, to compute the CFP, we build upon the methodology developed by Pang et al. (2020, Urban carbon footprints: A consumption-based approach for Swiss households. Environmental Research Communications, 2(1), 011003.) by incorporating a conversion factor matrix into the formulas, using official Eurostat data. This matrix serves as a bridge between macroeconomic data across different statistical classifications of production and consumption. Second, aiming to conduct inferential procedures on CFP, we select the probability distribution that best fits CFP empirical distribution. The Generalized Beta Distribution of the Second Kind (GB2) provides the best fit. Third, in order to map local CFP through reliable estimates we propose a Small Area Estimation (SAE) model based on Generalized Additive Models for Location, Scale, and Shape (SAE-GAMLSS), assuming CFP follows a GB2 distribution. Finally, we emphasize the significance of mapping per-capita CFP based on reliable local estimates to support the implementation of effective place-based policies.

The importance of accurate measurement of economic or social inequality is generally accepted. Estimators of its most popular measure, the Gini coefficient, are commonly criticised for becoming increasingly imprecise for increasingly skewed distributions and small to moderate samples, i.e., two phenomena that we face more frequently today. More robust inequality measures, typically based on quantile ratios, are well studied in theory, but still attract little attention in practice. We compare through simulations bias, mean squared error and sensitivity to outliers of several inequality estimators, namely, the Gini and quantile-based indicators, including the recently proposed quantile ratio index (QRI). Our results, based on Italian SILC and synthetic data, demonstrate that the QRI estimator offers superior precision and robustness, making it a reliable tool for monitoring inequality.

There exist many statistical indicators for measuring economic inequality. Most of them rely on distribution moments or focus on a few selected percentiles at the tails of the distribution. Recently, a so-called quantile ratio index has been introduced. It considers the entire distribution and measures the distance between the (economic) equi-distribution scenario and the average ratio of quantiles below the median to their symmetric counterparts above it. We present a finite population framework for estimating this index and its standard error under some complex sampling designs. Our estimator demonstrates high accuracy and precision, even with relatively small samples. Being solely based on quantiles, this index exhibits remarkable robustness, having limited sensitivity to anomalous values and highly skewed distributions. This is also shown by an analysis of its influence function.

Global crises (2008 and 2020) have determined insecure conditions in economic and financial systems. The recovery has been particularly slow in Europe compared to the USA, though a certain heterogeneity in the recovery speed was observed among European countries. Results from household surveys have highlighted that households are worried about their ability to recover from economic losses. The aim of this work is to propose a definition of economic resilience and a new measure, which can be calculated at individual or household level. The measurement suggested is based on the comparison of the levels of an economic resource observed over a period after a loss. The measure is proven to satisfy some desirable properties. An application of the proposed measures to the SOEP longitudinal dataset highlights the household characteristics that mostly affect economic resilience in Germany.

Several statistical indicators exist for the measurement of economic inequality. They are mostly based on distribution moments or depend on few percentiles at the tails of the distribution, selected a priori. This work analyzes a recently proposed quantile-based income inequality indicator, the Quantile Ratio Index, which solely depends on quantiles and considers the whole distribution. A complete finite population estimation framework for this indicator is here proposed, that works for data collected with complex sampling design. In order to obtain a reliable measure, special attention is dedicated to the task of estimating quantiles, particularly when accounting for sampling weights. Simulations based on Italian EU-SILC 2017 data demonstrates that the proposed direct estimator has large accuracy, precision and robustness to outlying observations, if based on an appropriate choice of quantile estimator.

The goal of this study is to estimate inequality indices for foreigners living in Italy at the regional level, differentiating between urban, peri-urban, and rural areas. This issue requires a Small Area Estimation (SAE) model. We operate in the unit-level context that has brought forth two challenges: the identification of individual covariates and the reduction of computational time. We propose a unit-level Simplified SAE model based on Generalized Additive Models for Location, Scale, and Shape specified without covariates and able to reduce variability in comparison with the direct estimator. A non-parametric bootstrap, suitable without design information, is proposed to estimate the mean square error. The performance of the proposed model used to estimate three different inequality indices (Gini, Theil, and Atkinson) is evaluated based on design-based simulations. The results show that the proposed predictor reduces the variability of the direct estimates even when covariates are not available. This methodology has been adopted to estimated the three indices for foreigners in Italy at the regional level, distinguishing between urban, peri-urban, and rural areas, showing that relevant disparities emerge in inequality between foreigners compared to natives. These results can help to formulate place-based policies aimed at promoting equity and integration.

Economic insecurity is attracting growing attention in the social well-being literature. However, there is still debate about its definition and measurement which deserve further and in depth study. Assuming that economic insecurity relates to the forward-looking perception of future outcomes based on past experience, we suggest a class of relative indices measuring the individual feeling of economic insecurity by considering relative past resource fluctuations. The innovation we implement in this context consists in considering relative changes, supposing that individuals evaluate each fluctuation based on their previous resource level. We take advantage of the measures suggested to study how economic insecurity may affect job mobility. Obtained results show that economic insecurity has a significant impact on the probability of changing jobs, and that its effect differs by gender and working experience.

We analyze income inequality trends during the COVID-19 pandemic in Italy across regions, metropolitan, peripheral, and rural areas, and various household typologies. We consider both gross and disposable income to assess the redistributive impact of taxes and transfers. Inequality is estimated within and between spatial and socio-demographic groups, with detailed breakdowns using small-area estimation techniques. Our findings show increased disparities between household typologies, with households with children experiencing the greatest income losses. Inequalities have risen significantly in Northwest urban areas, heavily impacted by the pandemic. Lastly, we find out that taxes and transfers played a role in mitigating growing disparities.

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.

We propose a small area estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS) for the estimation of household economic indicators. SAE-GAMLSS relax the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate the MSE is proposed. The SAE-GAMLSS estimator shows a largely better performance than the well-known Empirical Best Linear Unbiased Predictor (EBLUP) under various simulated scenarios. Per-capita consumption of Italian and foreign households in Italian regions, in urban and rural areas, is estimated using SAE-GAMLSS. Results show that the well-known Italian North-South divide does not hold for foreigners.

Interest in the study of economic insecurity has grown in recent years. However, the ongoing debate about how to measure it remains unresolved. On the assumption that economic insecurity is related both to the forward-looking perception of future outcomes based on past experience and to the perception of one’s own situation compared to others in the present, we propose a class of objective individual composite inter-temporal indices of economic insecurity. The indices are obtained by combining two components, one longitudinal and one cross-sectional. In order to combine the two components, we propose a novel method that takes advantage of the availability of subjective self-assessments of one’s own economic conditions. The composite inter-temporal index is applied to the European Union-Statistics on Income and Living Conditions (EU-SILC) Longitudinal Dataset, encompassing a selection of European countries. Our analysis shows that the proposed class provides new insights into individual perceptions of well-being that are not captured by poverty and inequality measures. It also provides individual measures that can be used to study the relationship between economic insecurity and other phenomena.

The primary goal of this study is to estimate the Theil index using a unit-level Small Area Estimation (SAE) model. This has lead two primary challenges in the unit-level SAE field: the identification of individual covariates and the reduction of computational burden. We propose a unit-level Simplified SAE model based on Generalized Additive Models for Location, Scale and Shape (GAMLSS), which is specified without covariates and is able to reduce variability in comparison with the direct estimator. The performance of the proposed model used to estimate the Theil index is evaluated based on design-based simulations. An application to the Italian Regions, distinguish between Urban, Peri-Urban and Rural areas, conclude the paper.

Income inequality estimators are biased in small samples, leading generally to an underestimation. This aspect deserves particular attention when estimating inequality in small domains and performing small area estimation at the area level. We propose a bias correction framework for a large class of inequality measures comprising the Gini Index, the Generalized Entropy, and the Atkinson index families by accounting for complex survey designs. The proposed methodology does not require any parametric assumption on income distribution, being very flexible. Design-based performance evaluation of our proposal has been carried out using EU-SILC data, their results show a noticeable bias reduction for all the measures. Lastly, an illustrative example of application in small area estimation confirms that ignoring ex-ante bias correction determines model misspecification.

We propose a Small Area Estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS), for the estimation of household economic indicators. SAE-GAMLSS release the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate MSE is proposed. The SAE-GAMLSS estimator shows a largely better performance than the well-known EBLUP, under various simulated scenarios. Based on SAE-GAMLSS per-capita consumption of Italian and foreign households in Italian regions, in urban and rural areas, is estimated. Results show that the well-known Italian North-South divide does not hold for foreigners.

This analysis aims to provide a comprehensive representation of the role of regional disparities in the nexus between poverty and subjective well-being, by adding the territorial dimension to the definition of poverty conditions. We investigate the nexus using regional poverty lines, including different poverty measures and considering different life domains. The analysis focuses on Italy because of its strong regional socio-economic disparities. Results show that the relevance of being poor on the well-being of citizens is in general higher and significant; the intensity and severity of poverty also change for different life domains. Findings are fundamental in designing local policies against poverty.

Income inequality estimators are biased in small samples, leading generally to an underestimation. This aspect deserves particular attention when estimating inequality in small domains and performing small area estimation at the area level. We propose a bias correction framework for a large class of inequality measures comprising the Gini Index, the Generalized Entropy and the Atkinson index families by accounting for complex survey designs. The proposed methodology does not require any parametric assumption on income distribution, being very flexible. Design-based performance evaluation of our proposal has been carried out using EU-SILC data, their results show a noticeable bias reduction for all the measures. Lastly, an illustrative example of application in small area estimation confirms that ignoring ex-ante bias correction determines model misspecification.