Using copulas for rating weather index insurance contracts

<p>This study develops a methodology for a copula-based weather index insurance design. Because the copula approach is better suited for modeling tail dependence than the standard linear correlation approach, its use may increase the effectiveness of weather insurance contracts designed to provide protection against extreme weather events. In our study, we employ three selected Archimedean copulas to capture the left-tail dependence in the joint distribution of the farm yield and a specific weather index. A hierarchical Bayesian model is applied to obtain consistent estimates of tail dependence using relatively short time series. Our empirical results for 47 large grain-producing farms from Kazakhstan indicate that, given the choice of an appropriate weather index to signal catastrophic events, such as a severe drought, copula-based weather insurance contracts may provide significantly higher risk reductions than regression-based indemnification schemes.</p

Agroholdings membership: does that make a difference in performance?

Comparing different organizational, managerial and economic indicators of the farms in two selected regions the study investigates whether agroholdings perform better and thus can be regarded as a promising organizational model for Russian agriculture. A comparatively poor performance of agroholdings¿ members in both regions implies that this organisational form suffers from principal management problems hampering the move of agricultural production towards efficient trajectories. The insights regarding the selection of agroholdings¿ members, farm restructuring, marketing activities and performance cannot however be simply transferred to Russian agriculture in general. In both regions analyzed the establishing of agroholdings was strongly promoted by the local government which induces a bias in the reason to integrate

Agroholdings membership: does that make a difference in performance?

Comparing different organizational, managerial and economic indicators of the farms in two selected regions the study investigates whether agroholdings perform better and thus can be regarded as a promising organizational model for Russian agriculture. A comparatively poor performance of agroholdings' members in both regions implies that this organisational form suffers from principal management problems hampering the move of agricultural production towards efficient trajectories. The insights regarding the selection of agroholdings' members, farm restructuring, marketing activities and performance cannot however be simply transferred to Russian agriculture in general. In both regions analyzed the establishing of agroholdings was strongly promoted by the local government which induces a bias in the reason to integrat

Modelling farm production decisions under an expenditure constraint

We use the indirect production function approach in the stochastic frontier framework to estimate separately the output losses due to the presence of a budget constraint and technical inefficiency. We develop a methodology for estimating the severity and testing the significance of the expenditure constraint at individual producer level. Our results, based on the farm data from three Russian regions from 1999 to 2003, show that the majority of the farms studied were expenditure-constrained during the study period. Expenditure constraints caused, on average, a potential output loss of 20 per cent. Output loss due to technical inefficiency, on average, is found to be around 13 per cent. Oxford University Press and Foundation for the European Review of Agricultural Economics 2009; all rights reserved. For permissions, please email [email protected], Oxford University Press.

Yield trend estimation in the presence of non-constant technological change and weather effects

The application of yield time series in risk analysis prerequisites the estimation of technological trend which might be present in the data. In this paper, we show that in presence of highly volatile yield time series and non-constant technology, the consideration of the weather effect in the trend equation can seriously improve trend estimation results. We used ordinary least squares (OLS) and MM, a robust estimator. Our empirical analysis is based on weather data as well as farm-level and county-level yield data for a sample of grain-producing farms in Kazakhstan