Unraveling population trends in Italy (1921-2021) with spatial econometrics.

Testing density-dependence and path-dependence in long-term population dynamics under differentiated local contexts contributes to delineate the changing role of socioeconomic forces at the base of regional disparities. Despite a millenary settlement history, such issue has been rarely investigated in Europe, and especially in highly divided countries such as those in the Mediterranean region. Using econometric modeling to manage spatial heterogeneity, our study verifies the role of selected drivers of population growth at ten times between 1921 and 2021 in more than 8000 Italian municipalities verifying density-dependent and path-dependent dynamics. Results of global and quantile (spatial) regressions highlight a differential impact of density and (lagged) population growth on demographic dynamics along the urban cycle in Italy. Being weakly significant in the inter-war period (1921-1951), econometric models totalized a high goodness-of-fit in correspondence with compact urbanization (1951-1981). Model's fit declined in the following decades (1981-2021) reflecting suburbanization and counter-urbanization. Density-dependence and path-dependence were found significant and, respectively, positive or negative, with compact urbanization, and much less intense with suburbanization and counter-urbanization. A spatial econometric investigation of density-dependent and path-dependent mechanisms of population dynamics provided an original explanation of metropolitan cycles, delineating the evolution of socioeconomic (local) systems along the urban-rural gradient.

Did COVID-19 enlarge spatial disparities in population dynamics? A comparative, multivariate approach for Italy.

A short-term issue that has been occasionally investigated in the current literature is if (and, eventually, how) population dynamics (directly or indirectly) driven by COVID-19 pandemic have contributed to enlarge regional divides in specific demographic processes and dimensions. To verify this assumption, our study run an exploratory multivariate analysis of ten indicators representative of different demographic phenomena (fertility, mortality, nuptiality, internal and international migration) and the related population outcomes (natural balance, migration balance, total growth). We developed a descriptive analysis of the statistical distribution of the ten demographic indicators using eight metrics that assess formation (and consolidation) of spatial divides, controlling for shifts over time in both central tendency, dispersion, and distributional shape regimes. All indicators were made available over 20 years (2002-2021) at a relatively detailed spatial scale (107 NUTS-3 provinces) in Italy. COVID-19 pandemic exerted an impact on Italian population because of intrinsic (e.g. a particularly older population age structure compared with other advanced economies) and extrinsic (e.g. the early start of the pandemic spread compared with the neighboring European countries) factors. For such reasons, Italy may represent a sort of 'worst' demographic scenario for other countries affected by COVID-19 and the results of this empirical study can be informative when delineating policy measures (with both economic and social impact) able to mitigate the effect of pandemics on demographic balance and improve the adaptation capacity of local societies to future pandemic's crises.

A Comparison of Migrant Integration Policies via Mixture of Matrix-Normals.

In recent decades, there has been a growing interest in comparative studies about migrant integration, assimilation and the evaluation of policies implemented for these purposes. Over the years, the Migrant Integration Policy Index (MIPEX) has become a reference on these topics. This index measures and evaluates the policies of migrants' integration in 52 countries over time. However, the comparison of very different countries can be difficult and, if not well conducted, can lead to misleading interpretations and evaluations of the results. The aim of this paper is to improve this comparison and facilitate the reading of the considered phenomenon, by applying a Mixture of Matrix-Normals classification model for longitudinal data. Focusing on data for 7 MIPEX dimensions from 2014 to 2019, our analysis identify 5 clusters of countries, facilitating the evaluation and the comparison of the countries within each cluster and between different clusters.

Measuring consumers' level of satisfaction for online food shopping during COVID-19 in Italy using POSETs.

The pandemic COVID 19 has upset the economic, social, financial, and general behavioral systems. Global crisis has a large impact overall and related fallouts significantly affect existent structural paradigms in every country and region across the world. In particular, the spread of COVID-19 pandemic has led to having to rethink the way we produce and consume food. Within this global change, a rise in the number of consumers who purchase food products online in order to comply with the rules aimed at limiting the circulation of the virus should be emphasized. Consequently, probably causing a long-term positive effect on m-commerce. The purpose is to elaborate on the index of the satisfaction level of consumers of purchasing food online via food shopping channels, by using key factors that characterize the online spending behavior. The analysis was carried out by collection of data deriving from an anonymous online questionnaire administrated via social networks and emails, during the 'hot' months of the pandemic progression in Italy, which is March-May 2020. We analyse both dimensions of customer satisfaction (process and outcome), by means of two systems of indicators. We reduce their complexity using synthesis obtained with the Partially ordered set. Results highlight the differences between the two dimensions of customer satisfaction. Online shopping can surely contribute to reduction of food waste thanks to elimination of frenzied shopping routines at supermarkets and can open space to new fields of study. On the other hand, defining an index of the consumer's satisfaction can alter sales strategies of m-commerce managers and entrepreneurs.