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This paper examines the underexplored impact of the procedures in public contracts on market allocation efficiency that are at risk of corruption. We integrated the GMM-SYS estimator with analyses of a balanced panel database of all the provinces in Italy between the period 2013–2021. The use of a high level of spatial granularity in an under-researched context revealed two distinct scenarios. In competitive markets, a more entrepreneurial and less widespread form of corruption emerged which may enhance market efficiency (bottom-up pattern). Conversely, in a context with limited market competition and low institutional quality, corruption indicators may be less useful due to potential pre-bribery behavior (top-down pattern). Our study underscores the capacity of corrupt suppliers to exploit market opportunities and secure selection. We found that corruption risk practices within public contracts have the potential to improve market efficiency, thus rendering them appealing to corrupt suppliers due to their capacity to deliver economic advantages. This also applies to provinces with high-quality governance. Additionally, the paper highlights the limitations of indicators in alternative contexts. The paper has significant implications for policymakers in defining assignation criteria in high-quality contexts, but also offers insights into the effectiveness of grand corruption indicators for academics. • Examines the impact of public contract procedures on market efficiency. • GMM-SYS estimator and innovative matching DID for panel analysis. • Bottom-up corruption in competitive markets, top-down in less competitive ones. • Corrupt suppliers exploit market opportunities, enhancing efficiency. • Offers insights in assignation criteria, and limitation of indicators. [ABSTRACT FROM AUTHOR]

Food insecurity and food surplus are two pressing issues that have been exacerbated by the COVID-19 pandemic. This paper presents a novel approach to addressing these problems using blockchain technology. More specifically, data from the Italian Regusto platform are analysed to identify factors that determine the volume and economic value of surplus food redistribution. From a methodological statistical perspective, the performance of Stepwise Multiple Linear Regression and Random Forest are compared in a blockchain data analysis. The effective machine-learning-based analysis reveals that the Regusto platform gives people greater access to the surplus food redistributed by non-profit organizations and suppliers through both donations and sales. The study also highlights the potential effects of redistributing surplus food on the beneficiaries' diets and health and we find that redistributing surplus food can reduce household food insecurity among low-income families and communities measured by food poverty and COVID-19 indexes at different spatial locations. Overall, by offering a unique perspective on these issues through the lens of blockchain technology, our study can inspire further research on how this technology can ensure everyone has enough affordable, nutritious food to lead healthy lives. • Blockchain technology can address food insecurity and surplus by unlocking the value of surplus food and ensuring equitable distribution. • The Regusto platform facilitates food surplus management and increases access in areas at risk of food insecurity during COVID-19 crisis. • Surplus food traveled across Italy, not only between big cities and agricultural provinces but also over short distances. • Forward Stepwise MLR and Random Forest models used to identify factors influencing food transactions on Regusto platform. [ABSTRACT FROM AUTHOR]

In recent decades, austerity measures have been widely adopted in public healthcare systems, so as to cope with financial constraints. This paper assesses the impact of a specific austerity policy implemented in Italy since 2007, with the purpose of reducing the budget deficit of regional governments originated by an excessive healthcare expenditure, the so called Recovery Plans (Piani di rientro). We exploit this context for a counterfactual analysis of the effects of Recovery Plans on the technical efficiency of hospitals operating in the regions where the Plans have been implemented, using as a control group similar hospitals in the regions where the policies were not enacted. The empirical analysis is based on a unique sample of administrative data relative to a large panel of hospitals in the period 2003–2010, and it employs, as identification strategy, the exogenous introduction of the austerity policy in some regions. We find that the policy had a detrimental effect on the efficiency of the hospitals operating in the regions subjected to the policy. The results show that the efficiency loss grows over time, suggesting the existence of negative cumulative effects of the austerity policy. • Counterfactual analysis of the impact of austerity on efficiency of Italian hospitals. • Use of a balanced panel of 547 hospitals for the period 2003–2010. • The austerity policy had a detrimental effect on the efficiency of hospitals. • The efficiency loss grows over time, that is austerity had cumulative effects. [ABSTRACT FROM AUTHOR]

This paper builds on the empirical literature that analyses the relationship between government efficiency and economic growth by investigating the link on a sample of about 3800 Italian municipalities. While the correlation has been explored at country or regional level, empirical studies at municipal or city level are virtually absent. This is a severe limitation given the numerous fiscal decentralization reforms that took place in many European countries over the last few decades aimed at increasing the autonomy and responsibilities of sub-national government. Estimation results show that government efficiency, when instrumented, is conducive for economic growth. The effect is larger for low populated municipalities, for those located in the South of Italy, and in more peripheral areas. Results are robust to alternative measures of efficiency and growth. [ABSTRACT FROM AUTHOR]

In light of the EU2030 strategy for poverty alleviation, this paper provides an assessment on the quality of estimates of poverty and inequality indicators at sub-national level (NUTS2) in Germany and Italy. Given the elaborated survey design of EU-SILC and the complexity of some poverty and inequality indicators that we consider in this paper, we use the Taylor linearization approach to estimate their variance. As a further assessment on the goodness of these estimates, we use a Fay–Herriot model to see in what extent we can obtain estimates with lower mean squared error using small area estimation. Our results reveal high territorial heterogeneity in living conditions across sub-national domains, in particular in some southern regions of Italy and some eastern government regions of Germany. Moreover, results from Fay–Herriot model reveals higher gains in efficiency for the 36 government regions of Germany especially for income-inequality indicators. • we use the Taylor linearization approach to estimate variance in poverty indicators. • we use a Fay–Herriot model to obtain estimates with lower mean squared error. • we map the spatial distribution of poverty indicators at NUTS-2 level. • Our results reveal high territorial heterogeneity across sub-national domains. [ABSTRACT FROM AUTHOR]

This paper investigates whether the gender of elected politicians affects the performance of Italian local governments in providing equitable and sustainable well-being to citizens. In fact, Italy was the first country in the European Union to include citizens' well-being objectives in economic planning. We use data on more than 6000 Italian municipalities for two years to investigate the relationship between gender representation at three bodies of local government with measures of socio-economic and environmental sustainability and find that larger representation of women in municipal leadership, executive and council bodies is significantly associated with improved municipal well-being. These results are also confirmed by accounting for endogenous issues, by adopting an extended definition of well-being and other robustness checks. Differences among northern and southern regions emerge with regard to the effects of incumbent female politicians. Other heterogeneous impacts are unclear or less marked. • A Wroclaw composite index of well-being is constructed at municipal level. • Gender quotas is used as instrumental variable in 2SLS regressions. • A larger share of women in municipal political bodies improves citizen's well-being. • Heterogeneity effects are detected among North vs. Center-South macro-regions. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between the presence of organised crime into the local socio-economic fabric and educational outcomes in Southern Italy. For this purpose, we rely on different indices of organized crime, the main of which is the well-established Mafia Presence Index (IPM), a composite indicator capturing manifold dimensions (activities, violence, political, and economic infiltration) of the presence of Mafia -type organizations on the national soil. These indices proxy the (scale of) negative Mafia -type values that permeate a local society and that are likely to hinder educational achievements, distorting the incentives of both the youngsters (and/or their parents) to invest in human capital and the local firms to employ those who decide to make this investment. Furthermore, combining contemporary individual-level educational outcomes with historical data on Mafia infiltration, we control for endogeneity concerns through an IV strategy. We provide evidence that the presence of organised crime at local level significantly decreases the scores achieved by primary school students, undertaking the INVALSI test for literacy in Southern Italy. Our results are robust to the use of different measures of organised crime, to the inclusion of different sets of controls, different subsamples and to relaxing the exclusion restriction in the IV strategy. • We explore the link between organized crime (OC) and education outcomes (EO) in Italy • We claim that OC can hinder EOs by distorting incentives to invest in human capital • We rely on different indices of OC at the local level • We find that an increase in OC leads to a decrease in EOs • Results are robust to different controls, subsamples and to IV strategy [ABSTRACT FROM AUTHOR]

Several studies in the mathematical education literature show the effect of students' high school skills in maths on their success at higher levels of education and work. In particular, the importance of maths course taking in US high schools is highlighted to be important for college enrollment and completion. The choice of taking additional maths courses or, as in Italy, of choosing a high-school curriculum with more maths, is not random: it depends on several substantial factors such as gender and socio-economic status. This selection bias implies that the differences in the academic outcomes might be traceable not only to mathematics ability and knowledge. In this paper, the aim is to estimate the treatment effect of attending a relatively new high school curriculum in Italy with more maths, with respect to the traditional track of the scientific "liceo", on two academic outcomes: university enrollment and first-year university performance. After having reduced the selection bias using a caliper multi-level propensity score matching procedure, a multi-state Markov model is used to study the treatment effect on the joint educational outcomes. • The influence of studying more mathematics in high school on academic outcomes. • Multi-level propensity score application on higher education data. • Application of multi-state Markov models. [ABSTRACT FROM AUTHOR]

In a context of dwindling resources, an ageing population and increased costs for accessing technologies, improving healthcare systems efficiency has become a matter of great urgency. Public administrators play a crucial role in bringing about the efficient management of resources. Thus, it has become important to assess the efficiency of national health systems and to identify and eliminate inefficiencies in resource management. By exploiting a stochastic frontier analysis, the paper aims at assessing the determinants of health system efficiency in 20 Italian regions, between 2001 and 2018. Political and institutional factors affecting health sector performance have been identified. Italy has been chosen as a case study due to its decentralized structure and its assignment of decision-making roles to regional politicians and administrators. The main finding shows that institutional quality cannot be considered a marginal determinant, since it exerts a direct effect on healthcare systems' activities and an indirect influence on people's life quality. • The study identifies institutional and political determinants of healthcare system performance. • Healthcare system efficiency determinants are assessed through Stochastic frontier analysis. • Health outcomes are proxied through three innovative indexes. • The research studies efficiency of 20 Italian regional healthcare systems. [ABSTRACT FROM AUTHOR]

Improving the performance of the health care system has become a key policy issue to reduce the tensions between increasing demands and limited resources. In this paper, we address an innovative methodology and application framework for measuring health sector performance at a highly disaggregated level, offering a perspective that has been pursued to a limited extent. Using a decentralised country such as Italy as a case study and micro-territorial information, a composite indicator of health demand at the provincial level is first proposed. Then, using a spatially disaggregated method derived from the well-established Chow-Lin techniques, a municipality-level indicator is estimated to identify health needs and territorial imbalances between local supply and demand. It is found that there are specific spatial patterns in both demand and supply that should be taken into account to avoid inequitable supply tied to a regional boundary, and that health risk increases with spatial distance to health facilities. These findings highlight the lower responsiveness of peripheral areas and the ability to maintain an effective network across the territory, due to the simultaneous dismantling and fragmentation of territorial primary health care over the last 20 years in Italy. • A spatially municipal health risk indicator is constructed from provincial level one. • Spatial patterns in health system performance within and between regions are present. • Health systems performance and inequalities are due to supply and demand mismatch. • Health risk increases with spatial distance from health facilities. [ABSTRACT FROM AUTHOR]

Building on the idea that different hospitals may operate with different technologies, the objective of this paper is a spatial characterization of the production function of hospital services through the identification of different spatial regimes. We introduce an original methodology to identify spatially constrained regimes, namely spatially constrained portions of territory in which the production units are maximally homogeneous in functional terms. The empirical algorithm can be described as a k -means cluster-wise regression procedure in which the units are belonging to a proximity graph and where distance is assessed in regressive terms. The analysis is implemented, first, on simulated data and, then, on output and input data of Italian hospitals for the year 2010. Our results, besides their methodological value, allow to shed light on the working of the hospital sector in Italy. The heterogeneity of the identified technological regimes can be associated to spatial heterogeneity of relevant aspects, like demand, internal organization, clinical and managerial governance, etc., and consequential policy implications can be, therefore, gathered. • A methodology to identify spatially constrained regimes, with production units maximally homogeneous in functional terms. • A production function is estimated for a large sample (681) Italian hospitals. • Spatial heterogeneity of relevant aspects associated to the heterogeneity of the spatial regimes, for policy implications. [ABSTRACT FROM AUTHOR]

The current Covid-19 pandemic is severely affecting public health and global economies. In this context, accurately predicting its evolution is essential for planning and providing resources effectively. This paper aims at capturing the dynamics of the positivity rate (PPR) of the novel coronavirus using the Heterogeneous Autoregressive (HAR) model. The use of this model is motivated by two main empirical features arising from the analysis of PPR time series: the changing long-run level and the persistent autocorrelation structure. Compared to the most frequently used Autoregressive Integrated Moving Average (ARIMA) models, the HAR is able to reproduce the strong persistence of the data by using components aggregated at different interval sizes, remaining parsimonious and easy to estimate. The relative merits of the proposed approach are assessed by performing a forecasting study on the Italian dataset. As a robustness check, the analysis of the positivity rate is also conducted by considering the case of the United States. The ability of the HAR-type models to predict the PPR at different horizons is evaluated through several loss functions, comparing the results with those generated by ARIMA models. The Model Confidence Set is used to test the significance of differences in the predictive performances of the models under analysis. Our findings suggest that HAR-type models significantly outperform ARIMA specifications in terms of forecasting accuracy. We also find that the PPR could represent an important metric for monitoring the evolution of hospitalizations, as the peak of patients in intensive care units occurs within 12–16 days after the peak in the positivity rate. This can help governments in planning socio-economic and health policies in advance. • The Covid-19 pandemic is severely affecting public health and global economies. • We propose the HAR approach for fitting and forecasting the Covid-19 positivity rate. • HAR models outperform ARIMA in predicting positivity rates at different horizons. • Accurately predicting positivity rates helps in planning socio-economic strategies. • Our findings are supported by an empirical application to Italian and US data. [ABSTRACT FROM AUTHOR]

The spread of the Covid-19 pandemic in Italy, in the period March–May 2020, quickly triggered a deep crisis, causing an immediate economic slowdown and consequently a strong contraction in domestic demand and trade. The food supply chain faced severe difficulties, although its anti-cyclical nature allowed for greater resilience compared to other economic sectors. In this framework of ongoing uncertainty, it is important to understand the response of farms to the crisis, and their role in the sustainability of the post-pandemic food supply chain, even for future policy interventions in the short and medium term. The purpose of the paper is to investigate how diversification affected the response of farms to the Covid-19 crisis, and explore whether the changes required by the post-crisis scenario can produce opportunities for their activities. The study investigates a sample of fifteen farms in central Italy through semi-structured interviews, performing a lexicon-based text and sentiment analysis. The results highlight the importance of farm diversification in dealing with the Covid-19 crisis, and emphasise the role of diversified farms for the sustainability of the agri-food system. These results have interesting policy implications, particularly regarding support for the competitiveness of farms by improving sustainable logistics, electronic commerce and exchanges of knowledge and innovations among farmers; these measures should be taken into account to target the future agricultural, rural and food policies, at both national and local level. • The first Covid-19 pandemic wave in March–May 2020 strongly hit the Italian food supply chain. • A direct survey on 15 diversified farms was carried out, through semi-structured interviews. • Text and sentiment analysis were applied to understand how farmers tackled the challenges of the crisis period. • Results stress the importance of farm diversification during the pandemic wave. • Policy interventions to reinforce competitiveness are needed to overcome the crisis. [ABSTRACT FROM AUTHOR]

Food waste represents a multi-sectoral issue and influences the economy, society and environment. Considering that over 50% of food waste is generated from household consumption, the issue has been included among the 17 Sustainable Development Goals, with the aim of halving its quantity by 2030. However, the COVID-19 pandemic imposed several variations in the agri-food industry in terms of food manufacturing, storage and distribution, changing at the same time food access, food consumption and food waste behavior. The present paper, through an online-based questionnaire among 831 respondents from Italy and the application of the cumulative logit model, investigates consumer behavior after the lockdown with reference to unpredictable lifestyles, improvements in smart food delivery and never-experienced time management. Results illustrate that always-at-home consumers (forced to stay at home 24 h a day) are more likely to perceive food waste and reduce its amount, whereas discontinuous smart working makes food purchase, preparation and consumption activities even more stressful and complex. Furthermore, smart food delivery tends to increase consumers' awareness of meals, improving buying decisions and indirectly reducing food waste generation. The unjustifiable prevalence of household food waste represents a major barrier to the achievement of food security, health insurance and hunger reduction, but also the most promising entry point to stress in the achievement of private and public benefits. Thus, the active role of education among young generations must be enhanced. • The cumulative logit model represents a useful tool to investigate consumers' behavior. • Always-at-home consumers are more likely to perceive and reduce food waste. • Smart food delivery tents to increase consumers' awareness on meals. • Unemployment and student status have positive effects on food waste perception. • Improvements in time management are essential to reach the goal of sustainability. [ABSTRACT FROM AUTHOR]

The current Covid-19 pandemic is severely affecting public health and global economies. In this context, accurately predicting its evolution is essential for planning and providing resources effectively. This paper aims at capturing the dynamics of the positivity rate (PPR) of the novel coronavirus using the Heterogeneous Autoregressive (HAR) model. The use of this model is motivated by two main empirical features arising from the analysis of PPR time series: the changing long-run level and the persistent autocorrelation structure. Compared to the most frequently used Autoregressive Integrated Moving Average (ARIMA) models, the HAR is able to reproduce the strong persistence of the data by using components aggregated at different interval sizes, remaining parsimonious and easy to estimate. The relative merits of the proposed approach are assessed by performing a forecasting study on the Italian dataset. As a robustness check, the analysis of the positivity rate is also conducted by considering the case of the United States. The ability of the HAR-type models to predict the PPR at different horizons is evaluated through several loss functions, comparing the results with those generated by ARIMA models. The Model Confidence Set is used to test the significance of differences in the predictive performances of the models under analysis. Our findings suggest that HAR-type models significantly outperform ARIMA specifications in terms of forecasting accuracy. We also find that the PPR could represent an important metric for monitoring the evolution of hospitalizations, as the peak of patients in intensive care units occurs within 12–16 days after the peak in the positivity rate. This can help governments in planning socio-economic and health policies in advance. • The Covid-19 pandemic is severely affecting public health and global economies. • We propose the HAR approach for fitting and forecasting the Covid-19 positivity rate. • HAR models outperform ARIMA in predicting positivity rates at different horizons. • Accurately predicting positivity rates helps in planning socio-economic strategies. • Our findings are supported by an empirical application to Italian and US data. [ABSTRACT FROM AUTHOR]

Building on the idea that different hospitals may operate with different technologies, the objective of this paper is a spatial characterization of the production function of hospital services through the identification of different spatial regimes. We introduce an original methodology to identify spatially constrained regimes, namely spatially constrained portions of territory in which the production units are maximally homogeneous in functional terms. The empirical algorithm can be described as a k -means cluster-wise regression procedure in which the units are belonging to a proximity graph and where distance is assessed in regressive terms. The analysis is implemented, first, on simulated data and, then, on output and input data of Italian hospitals for the year 2010. Our results, besides their methodological value, allow to shed light on the working of the hospital sector in Italy. The heterogeneity of the identified technological regimes can be associated to spatial heterogeneity of relevant aspects, like demand, internal organization, clinical and managerial governance, etc., and consequential policy implications can be, therefore, gathered. • A methodology to identify spatially constrained regimes, with production units maximally homogeneous in functional terms. • A production function is estimated for a large sample (681) Italian hospitals. • Spatial heterogeneity of relevant aspects associated to the heterogeneity of the spatial regimes, for policy implications. [ABSTRACT FROM AUTHOR]

The spread of the Covid-19 pandemic in Italy, in the period March–May 2020, quickly triggered a deep crisis, causing an immediate economic slowdown and consequently a strong contraction in domestic demand and trade. The food supply chain faced severe difficulties, although its anti-cyclical nature allowed for greater resilience compared to other economic sectors. In this framework of ongoing uncertainty, it is important to understand the response of farms to the crisis, and their role in the sustainability of the post-pandemic food supply chain, even for future policy interventions in the short and medium term. The purpose of the paper is to investigate how diversification affected the response of farms to the Covid-19 crisis, and explore whether the changes required by the post-crisis scenario can produce opportunities for their activities. The study investigates a sample of fifteen farms in central Italy through semi-structured interviews, performing a lexicon-based text and sentiment analysis. The results highlight the importance of farm diversification in dealing with the Covid-19 crisis, and emphasise the role of diversified farms for the sustainability of the agri-food system. These results have interesting policy implications, particularly regarding support for the competitiveness of farms by improving sustainable logistics, electronic commerce and exchanges of knowledge and innovations among farmers; these measures should be taken into account to target the future agricultural, rural and food policies, at both national and local level. • The first Covid-19 pandemic wave in March–May 2020 strongly hit the Italian food supply chain. • A direct survey on 15 diversified farms was carried out, through semi-structured interviews. • Text and sentiment analysis were applied to understand how farmers tackled the challenges of the crisis period. • Results stress the importance of farm diversification during the pandemic wave. • Policy interventions to reinforce competitiveness are needed to overcome the crisis. [ABSTRACT FROM AUTHOR]

Food waste represents a multi-sectoral issue and influences the economy, society and environment. Considering that over 50% of food waste is generated from household consumption, the issue has been included among the 17 Sustainable Development Goals, with the aim of halving its quantity by 2030. However, the COVID-19 pandemic imposed several variations in the agri-food industry in terms of food manufacturing, storage and distribution, changing at the same time food access, food consumption and food waste behavior. The present paper, through an online-based questionnaire among 831 respondents from Italy and the application of the cumulative logit model, investigates consumer behavior after the lockdown with reference to unpredictable lifestyles, improvements in smart food delivery and never-experienced time management. Results illustrate that always-at-home consumers (forced to stay at home 24 h a day) are more likely to perceive food waste and reduce its amount, whereas discontinuous smart working makes food purchase, preparation and consumption activities even more stressful and complex. Furthermore, smart food delivery tends to increase consumers' awareness of meals, improving buying decisions and indirectly reducing food waste generation. The unjustifiable prevalence of household food waste represents a major barrier to the achievement of food security, health insurance and hunger reduction, but also the most promising entry point to stress in the achievement of private and public benefits. Thus, the active role of education among young generations must be enhanced. • The cumulative logit model represents a useful tool to investigate consumers' behavior. • Always-at-home consumers are more likely to perceive and reduce food waste. • Smart food delivery tents to increase consumers' awareness on meals. • Unemployment and student status have positive effects on food waste perception. • Improvements in time management are essential to reach the goal of sustainability. [ABSTRACT FROM AUTHOR]