315 research outputs found

    The Crisis of Small Farms in Central Italy: Can Farmer Turnover Slow Down the Downfall?

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    We use original data to assess if the current incentives to farmer turnover may help the competitiveness of small farms in the Lazio Region (central Italy). Our results show that substantial changes in the policy may be needed. The paper analyzes sharp declining trend in small farm number, discusses its causes and evaluates the policies that have been adopted to stop or slow down this downfall. The regional policy makers consider the ageing of the farmers is a key determinant of the decline of small farms. Consequently, they have designed an incentive policy to generational turnover mainly based on installation payments. Given our empirical findings we conclude that this policy may fail to achieve the stated objectives. Firstly, farms that had a generational turnover in the last seven years do not show higher propensity to investment than the control group. These results suggest that farmers’ turnover per se may fail to increase the competitiveness of small farms. Secondly, in almost half of the cases the change in ownership is the result of a long process. Thus the timing of the policy may be wrong. Thirdly the policy is difficult to monitor and opportunistic behavior is possible.Generational turnover in Agriculture, Installation payments, Agricultural and Food Policy, Q10, Q18,

    Incentives to Efficient Investment Decisions in Agricultural Cooperatives

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    Recent studies have questioned the competitiveness of agricultural cooperatives in an industrialized food system, based on empirical results and economic theory. New organizational institutions have been proposed to overcome the cooperative main weaknesses (the so called new generation cooperatives). In this paper, we provide a simple model based on a financial approach to address the issue of cooperative competitiveness and to assess the investment efficiency of both traditional and new generation cooperatives. The main conclusions of the analysis are: i) cooperatives (both traditional and new generation ones) may have incentive to adopt projects that do not maximize the Net Present Value of the firm ii) the institutions of new generation cooperatives are not sufficient to ensure net present value maximization, even though they address some of the main concerns of traditional cooperatives iii) traditional cooperatives may have a competitive advantage in businesses that require the aggregation of a large number of farmers.agricultural cooperatives, investment efficiency, Agribusiness, Agricultural Finance, Q13, Q14,

    Modelling Pricing Behavior with Weak A‐Priori Information: Exploratory Approach

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    In the absence of reliable a priori information, choosing the appropriate theoretical model to describe an industry’s behavior is a critical issue for empirical studies about market power. A wrong choice may result in model misspecification and the conclusions of the empirical analysis may be driven by the wrong assumption about the behavioral model.This paper develops a methodology aimed to reduce the risk of misspecification bias. The approach is based on the sequential application of a sliced inverse regression (SIR) and a nonparametric Nadaraya/ Watson regression (NW). The SIR‐NW algorithm identifies the factors affecting pricing behavior in an industry and provides a nonparametric characterization of the function linking these variables to price. This information may be used to guide the choice of the model specification for a parametric estimation of market power.The SIR NW algorithm is designed to complement the estimation of structural models of market behavior, rather than to replace it. The value of this methodology for empirical industrial organization studies lies in its data driven approach that does not rely on prior knowledge of the industry. The method reverses the usual hypothesis testing approach. Instead of first choosing the model based on a priori information and then testing if it is compatible with the data, the econometrician selects a theoretical model based on the observed data. Thus, the methodology is particularly suited for those cases where the researcher has no a priori information about the behavioral model, or little confidence in the information that is available

    The potential use of biomarkers in predicting contrast-induced acute kidney injury.

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    Contrast-induced acute kidney injury (CI-AKI) is a problem associated with the use of iodinated contrast media, causing kidney dysfunction in patients with preexisting renal failure. It accounts for 12% of all hospital-acquired kidney failure and increases the length of hospitalization, a situation that is worsening with increasing numbers of patients with comorbidities, including those requiring cardiovascular interventional procedures. So far, its diagnosis has relied upon the rise in creatinine levels, which is a late marker of kidney damage and is believed to be inadequate. Therefore, there is an urgent need for biomarkers that can detect CI-AKI sooner and more reliably. In recent years, many new biomarkers have been characterized for AKI, and these are discussed particularly with their use in known CI-AKI models and studies and include neutrophil gelatinase-associated lipocalin, cystatin C (Cys-C), kidney injury molecule-1, interleukin-18, N-acetyl-ÎČ-d-glucosaminidase, and L-type fatty acid-binding protein (L-FABP). The potential of miRNA and metabolomic technology is also mentioned. Early detection of CI-AKI may lead to early intervention and therefore improve patient outcome, and in future any one or a combination of several of these markers together with development in technology for their analysis may prove effective in this respect
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