Abstract: In recent decades, depopulation has become an irreversible process in many of the most developed regions. This trend is particularly pronounced in inner areas, characterized by limited accessibility to essential services, fewer employment opportunities, and demographic decline, making them less attractive to families, especially those with children. The supply of Early Childhood Education and Care (ECEC) is increasingly recognized as a critical factor in addressing the challenges of depopulation. By investing in ECEC, policymakers can create a more supportive environment for families, thereby promoting higher fertility rates and stabilizing population dynamics in developed regions. To this aim, our study examines the role of ECEC services on residential choices. Using administrative data and a non-parametric Difference-in-Differences approach, we assess how expanding ECEC services influences migration patterns at the municipal level in Italy (2013-2022).

Abstract: This research investigates how local governments overlook funding opportunities within the cohesion policies, utilizing machine learning and analysing data from open calls within the European Next Generation EU funds. The focus is on predicting which local governments may face challenges in utilizing available funding, specifically examining the allocation of funds for Italian childcare services. The results demonstrate that it is possible to make out-of-sample predictions of municipalities likely to abstain from invitations, by identifying key determinants. Population-related factors play an important role in predicting inertia, alongside other demand-related elements, particularly in regions with limited services. The study emphasizes the importance of local institutional quality and individual attributes of policymakers. The factors justifying fund allocation have adverse effects on participation, placing regions with greater investment needs at a competitive disadvantage. Anticipating non-participation in calls can aid in achieving policy targets and optimizing the allocation of funds across various local governments.

Abstract: The public debate on the effectiveness of lockdown measures is far from being settled. We estimate the impact of not having implemented a strict lockdown in the Bergamo province, during the first wave of the COVID-19 pandemic, despite observing an infection rate in this area similar to the one observed in nearby municipalities where a strict lockdown was instead promptly implemented. We estimate the causal effect of this policy decision on daily excess mortality using the synthetic control method (SCM). We find that about two-thirds of the reported deaths could have been avoided had the Italian government declared a Red Zone in the Bergamo province. We also clarify that, in this context, SCM and difference-in-differences implicitly restrict effect heterogeneity. We provide a way to empirically assess the credibility of this assumption in our setting.

Abstract: What is the economic impact of joining a currency union? Is this impact heterogeneous across regions? And how does it change in case of a recession? We answer these questions by investigating the economic impact of joining the euro area for the latecomers, that is, the eastern European countries that adopted the euro after 2002. Differently from previous literature, we use NUTS-2 regions as units of analysis. This novelty allows us to investigate the theoretical predictions of a currency union impact at a more appropriate geographical level. Using a recently developed counterfactual approach, we estimate the overall as well as the disaggregated impact of joining the euro area. We find that the adoption of the euro brought about a small positive effect, which was, however, dampened by the Great Recession. Individual regional estimates suggest heterogeneous returns with benefits accruing mostly to core regions.

Abstract: This chapter provides an overview of the leading research on mortality estimates during the COVID-19 pandemic, with a specific focus on the local level. It first discusses the key data challenges and general methodological issues involving the estimation of the actual number of lives lost to the pandemic at all spatial scales – global, national, and subnational. Next, it reviews the main studies focusing on COVID-19 and local excess mortality for all areas in which reliable figures have been provided, and complements them with an overview of aggregate-level studies for macro-areas and countries in which local estimates currently remain unavailable. The survey highlights the persistence of severe geographic imbalances in data availability and studies coverage, even when focusing on national estimates alone. As it stands, granular excess mortality estimates have been produced only for a tiny and non-representative minority of areas. The study concludes with recommendations about methodological choices and research priorities to reduce existing data and information gaps.

Abstract: The first cluster of coronavirus cases in Europe was officially detected on 21st February 2020 in Northern Italy, even if recent evidence showed sporadic first cases in Europe since the end of 2019. In this study, we have tested the presence of coronavirus in Italy and, even more importantly, we have assessed whether the virus had already spread sooner than 21st February. We use a counterfactual approach and certified daily data on the number of deaths (deaths from any cause, not only related to coronavirus) at the municipality level. Our estimates confirm that coronavirus began spreading in Northern Italy in mid-January.

Abstract: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, associated with the Covid-19 pandemic by looking at the excess mortality trends of an area that registered one of the highest death tolls in the world during the first wave, Northern Italy. We do not find any evidence of a sizable Covid-19 harvesting effect, neither in the summer months after the slowdown of the first wave nor at the beginning of the second wave. According to our estimates, only a minor share of the total excess deaths detected in Northern Italian municipalities over the entire period under scrutiny (February – November 2020) can be attributed to an anticipatory role of Covid-19. A slightly higher share is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect can only account for less than 20% of excess deaths. Furthermore, the lower mortality rates observed in these areas at the beginning of the second wave may be due to several factors other than a harvesting effect, including behavioral change and some degree of temporary herd immunity. The very limited presence of short-run mortality displacement restates the case for containment policies aimed at minimizing the health impacts of the pandemic.

Abstract: Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

Abstract: Smart Specialization Strategy in Italy consists of National Strategy and Regional Strategies. The National Strategy focuses on 5 priority areas, while Regional Strategies on 12 areas of specialization, thus generating potential coordination problems. Analysing the documents of the Regional Strategies, written according to bottom-up processes, on entrepreneurial discovery, and comparing them with some works in the literature, which identify the investment priorities based on the same logic, we highlight some inconsistencies as to choices. To help ensure consistency of the process, a quantitative model based on an empirical analysis was developed, which can be used as an aid tool in identifying the highly or moderately specialized areas of the regions. To identify a measure able to analyse the resources available by region, and at the same time to recognise the prospects of long-term innovation, the research potential, the entrepreneurial fabric and the design capacity were chosen as research dimensions. Four thematic Specialization Indices were then built, then summarized in a single Specialization Index. The results of such Composite Index showed an imperfect correspondence with the choices of regional Administrations. In terms of policy, this composite Index, elaborated in such study, can provide useful indications in the monitoring and evaluation phase of RIS3.