Efficiency and Capital Structure in the Italian Cereal Sector

Farm capital structure may have contrasting effects on farm efficiency as a strand of the farm efficiency literature as pointed out ( for a review see for example Davidova and Latruffe 2007). Farmers often use external funding both to cover productions costs and to finance investments (machinery, equipment, buildings) to enhance farm economic performance. The debt is necessary to maintain or improve farm productivity and competitiveness by adopting technological innovation needed to increase farm efficiency. At the same time leverage may affects farm efficiency by influencing farm production decision constrained by lower farm expenditure capacity. In this case, farms response may rely on reducing the necessary expenditures to maintain the production assets with negative consequences on farm productivity, growth and efficiency. Finally, farm leverage may affects the farms capacity to react to market shocks adopting the needed strategic adjustments to maintain productivity, efficiency and competitiveness. A relevant case study for assessing this last effect would be the recent surge in price volatility that affected European and world cereal markets starting from 2008.The objective of this paper is to provide new empirical evidence on the relationship between farm capital structure and farm efficiency. In particular we will try to answer the following research question: does higher leverage lead to better performance? The food price volatility that has affected the cereal market from 2008 onwards is a possible stress for cereal farms that must adapt to the rapid drop in prices such as the one observed in 2010. Our research provide a first insight on the evolution of the cereal farms debt-technical efficiency relationship in times of high price volatility

The impact of financial leverage on farm technical efficiency during periods of price instability

The main purpose of this work is to provide empirical evidence on the relationship between capital structure and technical efficiency for Italian cereal farms during the 2008 – 2014 period. Emphasis is given in the understanding of the relationship between the level of financial leverage for cereal farms and their production performance. The methods employed in this research article are based on non-parametric techniques in order to derive technical efficiency estimates for a sample of Italian cereal farms based on available Farm Accountancy Data Network data to explore in depth the relationship amongst the financial exposure of the sector and the capacity to utilise an efficient and effective production technology. Furthermore, subsidies are considered in the model as a non-discretionary variable and therefore, as an input that farmers cannot directly influence within the production function. Hence, the non-discretionary Data Envelopment Analysis model is a more appropriate framework since it is not penalising farms at a lower level of Pillar I payments when benchmarked with farms that receive higher level of payments. The results show that significant improvements could be achieved for most of the farms in the sample by improving production and management practices. Furthermore, results provide an empirical support of the adjustment theory by showing a negative impact of debt to asset ratio to technical efficiency. This research article provides a first insight on the evolution of the Italian cereal farms debt-technical efficiency relationship in periods where high price instability has been observed

Public Food Procurement: A Systematic Literature Review

Public food procurement (PFP) is a policy instrument that has been used to “link” different objectives at once. We undertake a first systematic review of the scientific literature that deals with PFP in order to 1.) assess the progress of the scientific literature concerning PFP in different areas of the world, 2.) look for differences among them and try to identify the topics on which these studies focuses the most. Accordingly, our research questions deal with the definition of the main conceptual dimensions developed by the academic literature on PFP as well as with the geographical and temporal differences among the dimensions identified. The first evidence is the increase in the number of papers per year during the last decade. Furthermore, the literature on PFP is centred on the concepts of localisation and structured demand and its impacts on food chain actors, on citizen-consumers and on sustainability at large. As a main research result, we provide a conceptual framework of the PFP literature largely based on the concept of linkage that has been first proposed in law and regulation studies

Database of Individual Seismogenic Sources (DISS), Version 3.2.1: A compilation of potential sources for earthquakes larger than M 5.5 in Italy and surrounding areas

Istituto Nazionale di Geofisica e VulcanologiaPublished2T. Deformazione crostale attiva3T. Sorgente sismica4T. Sismicità dell'Italia5T. Sismologia, geofisica e geologia per l'ingegneria sismica6T. Studi di pericolosità sismica e da maremoto4IT. Banche dat

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

The Sex-Specific Detrimental Effect of Diabetes and Gender-Related Factors on Pre-admission Medication Adherence Among Patients Hospitalized for Ischemic Heart Disease: Insights From EVA Study

Background: Sex and gender-related factors have been under-investigated as relevant determinants of health outcomes across non-communicable chronic diseases. Poor medication adherence results in adverse clinical outcomes and sex differences have been reported among patients at high cardiovascular risk, such as diabetics. The effect of diabetes and gender-related factors on medication adherence among women and men at high risk for ischemic heart disease (IHD) has not yet been fully investigated.Aim: To explore the role of sex, gender-related factors, and diabetes in pre-admission medication adherence among patients hospitalized for IHD.Materials and Methods: Data were obtained from the Endocrine Vascular disease Approach (EVA) (ClinicalTrials.gov Identifier: NCT02737982), a prospective cohort of patients admitted for IHD. We selected patients with baseline information regarding the presence of diabetes, cardiovascular risk factors, and gender-related variables (i.e., gender identity, gender role, gender relations, institutionalized gender). Our primary outcome was the proportion of pre-admission medication adherence defined through a self-reported questionnaire. We performed a sex-stratified analysis of clinical and gender-related factors associated with pre-admission medication adherence.Results: Two-hundred eighty patients admitted for IHD (35% women, mean age 70), were included. Around one-fourth of the patients were low-adherent to therapy before hospitalization, regardless of sex. Low-adherent patients were more likely diabetic (40%) and employed (40%). Sex-stratified analysis showed that low-adherent men were more likely to be employed (58 vs. 33%) and not primary earners (73 vs. 54%), with more masculine traits of personality, as compared with medium-high adherent men. Interestingly, women reporting medication low-adherence were similar for clinical and gender-related factors to those with medium-high adherence, except for diabetes (42 vs. 20%, p = 0.004). In a multivariate adjusted model only employed status was associated with poor medication adherence (OR 0.55, 95%CI 0.31–0.97). However, in the sex-stratified analysis, diabetes was independently associated with medication adherence only in women (OR 0.36; 95%CI 0.13–0.96), whereas a higher masculine BSRI was the only factor associated with medication adherence in men (OR 0.59, 95%CI 0.35–0.99).Conclusion: Pre-admission medication adherence is common in patients hospitalized for IHD, regardless of sex. However, patient-related factors such as diabetes, employment, and personality traits are associated with adherence in a sex-specific manner

Multiperiod optimal hedging ratios: Methodological aspects and application to wheat markets

This work deals with methodological and empirical issues related to multiperiod optimal hedging OLS estimators. We propose an analytical formula for the multiperiod minimum variance hedging ratio starting from the triangular representation of a cointegrated system DGP. Since estimating the hedge ratio matching the frequency of data with the hedging horizon leads to a sample size reduction problem, we carry out a Monte Carlo study to investigate the pattern and hedging efficiency of OLS hedging ratio based on overlapping vs non-overlapping observations exploring a range of hedging horizons and sample sizes. Finally, we applied our approach to real data for a cross hedging related to soft wheat

Efficiency and Capital Structure in the Italian Cereal Sector

Farm capital structure may have contrasting effects on farm efficiency as a strand of the farm efficiency literature as pointed out ( for a review see for example Davidova and Latruffe 2007). Farmers often use external funding both to cover productions costs and to finance investments (machinery, equipment, buildings) to enhance farm economic performance. The debt is necessary to maintain or improve farm productivity and competitiveness by adopting technological innovation needed to increase farm efficiency. At the same time leverage may affects farm efficiency by influencing farm production decision constrained by lower farm expenditure capacity. In this case, farms response may rely on reducing the necessary expenditures to maintain the production assets with negative consequences on farm productivity, growth and efficiency. Finally, farm leverage may affects the farms capacity to react to market shocks adopting the needed strategic adjustments to maintain productivity, efficiency and competitiveness. A relevant case study for assessing this last effect would be the recent surge in price volatility that affected European and world cereal markets starting from 2008

The impact of financial leverage on farms capacity to react in market shocks

Evidence in the literature supports that farmers’ ability to choose the best available production technology is restricted when capital structure negatively influences farms’ financial performance. Therefore, empirical evidence is sought to provide an understanding of the relationship between capital structure and technical efficiency of Italian farms in a period of five years (2008 – 2013). It is concluded that significant improvements could be achieved for most of the farms in the sample by improving production and management practices. Furthermore, results provide an empirical support of the adjustment theory by showing a negative impact of debt to asset ratio to technical efficiency