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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]