Chiara Gigliarano

Composite indicators are reliable tools that have recently gained popularity because of their effectiveness in solving problems of multidimensional measurement. Along with the notable increase in the number of applications, finding optimal weights for input features during aggregation is a topic that creates many controversies but very few radical solutions. This paper presents a novel statistical method designed to assist developers in attaining a plausible weighting scheme for composite indices. The solution obtained is referred to as sensitivity-based weights, where the magnitude of each weight aligns with the proportion of output variance contributed by the corresponding input. Within the context of our theoretical framework, these weights can be identified based on the multivariate distribution of input features. In case the population distribution is unknown, we introduce an optimization procedure for estimating sensitivity-based weights from a finite sample of inputs. Two supporting algorithms are proposed to facilitate the weighting process, including regression-based estimation and coarse estimation. These algorithms are tested in two numerical simulation cases, where the results show that the paramount factor affecting the accuracy and robustness of estimates lies in the sample size, and the coarsening technique exhibits superiority in performance when dealing with inputs from various distributions. Finally, a composite index for identifying fragile municipalities in Italy has been examined and reconstructed to demonstrate the method’s applicability in practice.

Multidimensional indexes are ubiquitous, and popular, but present non negligible normative choices when it comes to attributing weights to their dimensions. This paper provides a more rigorous approach to the choice of weights by defining a set of desirable properties that weighting models should meet. It shows that Bayesian Networks is the only model across statistical, econometric, and machine learning computational models that meets these properties. An example with EU-SILC data illustrates this new approach highlighting its potential for policies.

This paper aims to examine the impact of the Covid-19 pandemic on multidimensional poverty in Italy and its provinces by comparing household poverty levels before and after the outbreak. To capture the multidimensionality of poverty, we analyze various dimensions, including economic well-being, health status, education, neighborhood quality, and subjective well-being. The empirical analysis relies on micro-data from Istat's aspects of daily life (AVQ) survey, covering the years 2018-2021. As the survey's direct estimates are reliable only at the regional level (NUTS 2), we apply small area estimation techniques to produce accurate estimates of provincial (NUTS 3) deprivation incidences. Subsequently, we aggregate the deprivation headcounts across the elementary indicators using penalized power mean composite indicators. The empirical findings indicate that overall multidimensional poverty worsened in most of the Italian provinces, particularly during the second year of the pandemic, with higher levels persisting in southern areas. The various dimensions of poverty exhibited different trends, with education, subjective well-being, and health emerging as the most negatively affected in numerous provinces.

This study investigates the relationship between quality of government and environmental wellbeing in European regions at the NUTS-2 level. First, we find that subnational environmental data are spatially interdependent. Then, we construct a set of composite indicators of environmental wellbeing via Bayesian spatial factor analysis. Finally, by using these composite indicators in spatial regression analysis, we show that institutional quality is a key determinant of environmental wellbeing. We also find that the institutions-environment nexus varies across dimensions of environmental wellbeing – institutions matter especially for the quality of air and soil. Policymakers should be aware that environmental degradation can be tackled by building more effective and well-functioning regional public institutions

The aim of this paper is to measure to which extent income distribution is polarized across European countries by means of polarization measures based on the Bonferroni and De Vergottini indices of inequality. Different from traditional measures of polarization, the indices proposed in this paper are sensitive to progressive transfers, attaching more importance to some part of the income distribution. These indices enriches the analysis and contribute to disentangle the different faces of income polarization. In the empirical application we compare European countries over the period 2010–2019 using EU-SILC data. Results reveal significant changes in polarization over the last decades for most countries.

This paper proposes spatial comprehensive composite indicators to evaluate the well-being levels and ranking of Italian provinces with data from the Equitable and Sustainable Well-Being dashboard. We use a method based on Bayesian latent factor models, which allow us to include spatial dependence across Italian provinces, quantify uncertainty in the resulting estimates, and estimate data-driven weights for elementary indicators. The results reveal that our data-driven approach changes the resulting composite indicator rankings compared to those produced by traditional composite indicators' approaches. Estimated social and economic well-being is unequally distributed among southern and northern Italian provinces. In contrast, the environmental dimension appears less spatially clustered, and its composite indicators also reach above-average levels in the southern provinces. The time series of well-being composite indicators of Italian macro-areas shows clustering and macro-areas discrimination on larger territorial units.

The aim of this paper is to analyse the effect of COVID-19 on multidimensional well-being in the European population aged 50 and over by measuring changes in individual well being before and after the pandemic outbreak. To capture the multidimensional nature of well-being, we consider diferent dimensions: economic well-being, health status, social connections and work status. We introduce new indices of change in individual well-being that measure non-directional, downward and upward movements. Individual indices are then aggregated by country and subgroup for comparison. The properties satisfed by the indices are also discussed. The empirical application is based on micro-data from waves 8 and 9 of the Survey of Health, Ageing and Retirement in Europe (SHARE), carried out for 24 European countries before the pandemic outbreak (regular survey) and in the first two years of the COVID-19 pandemic (June–August 2020 and June–August 2021). The findings suggest that employed and richer individuals sufered greater losses in well-being, while differences based on gender and education diverge from country to country. It also emerges that while the main driver of well-being changes in the first year of the pandemic was economics, the health dimension also strongly contributed to upward and downward well-being changes in the second year.

Purpose – This study analyses the link between product/service innovation, partnerships and Managerial Control System (MCS). Particularly, it aims to analyse empirically the role of MCS in supporting the innovation partnership successful functioning and management. Design/methodology/approach – The sample of this study consists of 106 Italian manufacturing firms belonging to the sectors of the Italian economy with the largest number of registered patents according to the European trend chart on innovation. Findings – The results show that MCS may play a key role in reducing risks and lowering the likelihood of failure of innovation partnerships. Particularly, the authors found a positive correlation between the use of informal control mechanisms and a partnership’s successful performance. Moreover, among informal control, the findings show that trust is the only true informal mechanism that can guarantee a successful collaboration. The results of this study may offer relevant implications for practitioners. With regard to the control of the partnership’s activities, the initiatives and creativity of those who are actively involved in the innovation process should not be inhibited; therefore, stifling them with strict rules and procedures would be ineffective but if a firm is not willing to give up formal control mechanisms altogether because it does not believe that a trust-based coordination is sufficiently reassuring, it should opt for “weak”, albeit formal, control mechanisms based on a shared production and management of plans and reports, thus ensuring a perfect information symmetry among different partners. Originality/value – Notwithstanding the different opportunities provided by partnerships and strategic alliances to support there is a growing body of evidence of a high failure rate in such organisational forms. One of the causes cited in the literature is the high level of risk associated with alliances as compared to internal development of innovation. The risks mainly arise from the difficulties to obtain cooperation with partners that might have different objectives, and from the potential opportunistic behaviour of some of the partners. This is particularly true in innovation networks where the uncertainty of producing an interesting result is very high and the investments that the partners make are considerable. In this context, MCS could play a relevant role in reducing the risks and decreasing the likelihood of failure.

One prominent change induced by the COVID-19 pandemic concerns the worldwide use of public transportation for commuting purposes. This study focused on university commuting in Italy by examining the propensity to change transport modes under different infection risk scenarios. Data were collected in 2020 through an online survey of college mobility conducted by the Italian University Network for Sustainable Development. Asking the respondents to consider both a pessimistic and an optimistic scenario, with respect to the risk odds of being infected, we followed a two-step approach to study the prospective travel habits of college users. First, we tested a logit model to estimate the propensity to abandon one's pre-COVID-19 commuting mode. Then, we investigated the factors influencing the choice of switching from public transportation to either cars or active modes by estimating a multinomial logit model. By exploiting the novelty of considering two risk scenarios, this study highlighted that, especially in the pessimistic case, the change to active modes was constrained by spatial aspects in favour of motorized vehicles. From a policy perspective, this COVID-19-based natural experiment advocates transportation authorities taking effective actions to ensure that, in case of emergencies, a modal shift would not benefit more-polluting transport means.