EU‐wide Economic and Environmental Impacts of CAP Greening with High Spatial and Farm‐type Detail

In this paper we analyse the economic and environmental impacts of CAP greening introduced by the 2013 CAP reform using the CAPRI model. CAPRI captures the farm heterogeneity across the EU and it allows to depict the implementation of the greening measures in high detail while integrating the environmental effects and the market feedback of the simulated policy changes. The simulated results reveal that the economic impacts (land use, production, price and income) of CAP greening are rather small, although some farm types, crops (fallow land and pulses) and Member States may be affected more significantly. The CAP greening will lead simultaneously to a small increase in prices and a small decrease in production. Farm income slightly increases because the price effects offset the production decline. Similarly to economic effects, the environmental impacts (GHG emissions, N surplus, ammonia emissions, soil erosion, and biodiversity‐friendly farming practices) of CAP greening are small, although some regions may see greater effects than others. In general, the environmental effects at EU level are positive on a per hectare basis, but the increase in UAA can reverse the sign for total impacts. Overall, simulated GHG and ammonia emissions decrease in the EU, while the total N surplus, soil erosion and biodiversity‐friendly farming practices indicator slightly increase due to the CAP greening.     

Impacts of 2003 CAP reform on land rental prices and capitalization

Based on a graphical analysis, we investigate the impacts of the Fischler Reform of the Common Agricultural Policy on land rental prices and the capitalization of single farm payments (SFP) into land values. The model shows that the degree of capitalization mainly depends on the scarcity/surplus of SFP entitlements relative to eligible hectares, the implementation model (historical versus regional) and the land supply elasticity. If there are more SFP entitlements than eligible hectares, the degree of capitalization into land values may not decline and may even increase due to the inclusion of other supports such as animal premiums in the SFP. We test this hypothesis with an empirical analysis of cross-section data on land rental prices in Bavaria for 2005. Empirical results indicate that decoupled SFP are capitalized into rental prices to a larger degree than the coupled direct payments of the time prior to the reform.     

Optimizing Production Decisions Using a Hybrid Simulation–Genetic Algorithm Approach

Mathematical programming has for a long time been recognized as a powerful tool. Despite its capacity for solving constrained optimization problems under uncertainty, some methodological obstacles have persisted over the years. The main problem is that the eventually complex results of an unbiased statistical analysis (multiple correlated stochastic variables with different distributions and nonadditive links between) cannot be adequately accounted for within minimization of total absolute deviation (MOTAD) or expected value‐variance (EV) models that rely on the algorithmic determination of the variability measure. In this paper, we develop a methodological hybrid consisting of Monte Carlo simulation and genetic algorithms: the Monte Carlo simulation facilitates the easy representation of diverse stochastic processes and correlation, and the genetic algorithm ensures that the optimization procedure remains applicable even in the case of complex stochastic information. This hybrid approach is applied to the production‐planning problem of a German crop farm. Variant calculations are used to account for the unknown risk attitude of the farmer. Model results demonstrate that optimized production programs and expected total gross margins are not only highly sensitive to the risk attitude, but also to the stochastic processes that are estimated (or assumed) for various activities. We furthermore find evidence that the hybrid approach is able to generate considerable improvement in farm‐program decisions and outperforms planning models that assume static distributions. La programmation mathématique est reconnue depuis longtemps comme étant un outil puissant. Malgré sa capacitéà résoudre des problèmes d'optimisation avec contraintes en situation d'incertitude, certains obstacles méthodologiques ont persisté au fil du temps. Le principal problème réside dans le fait que les résultats éventuellement complexes d'une analyse statistique non biaisée (plusieurs variables aléatoires corrélées avec différentes distributions et des liens non additifs entre elles) ne peuvent être adéquatement représentés dans les modèles MOTAD et E‐V qui dépendent de la détermination algorithmique de la mesure de la variabilité. Dans le présent article, nous avons élaboré une méthode hybride à partir d'une simulation de Monte Carlo et d'algorithmes génétiques: la simulation de Monte Carlo facilite la représentation de divers processus stochastiques et de diverses corrélations, et l'algorithme génétique assure que la procédure d'optimisation demeure applicable même dans le cas d'information stochastique complexe. Cette méthode hybride est appliquée au problème de planification de la production auqúel est confrontée une exploitation de cultures en Allemagne. Des calculs de variantes sont utilisés pour tenir compte de l'attitude inconnue du producteur quant au risque. Les résultats du modèle indiquent que les programmes de production optimisés et les marges brutes totales prévues ne sont pas uniquement sensibles à l'attitude face aux risques mais aussi aux processus stochastiques qui sont estimés (ou supposés) pour diverses activités. Nous avons également trouvé que la méthode hybride peut améliorer considérablement les décisions concernant les programmes agricoles et qu'elle est supérieure aux modèles de planification qui supposent des distributions statiques.     

Combining Risk Assessment and Economics in Managing a Sanitary–Phytosanitary Risk

The Agreement on the Application of Sanitary and Phytosanitary (SPS) Measures provides guidelines for countries to protect domestic production from pests and diseases. The guidelines permit consideration of consumer welfare. We find an optimal set of SPS measures considering total welfare and mitigation strategies. Our model suggests that it is optimal and less restrictive to apply mitigation strategies first and then an additional smaller tariff if necessary. Relative cost of mitigation determines the amount of the mitigation strategies applied in the importing and exporting countries.     

Was treibt staatliche Umverteilung?

This paper investigates the major drivers of governmental redistribution. Extended and harmonised data on effective redistribution recently provided by the newest version of the Standardized World Income Inequality Database allows for the assessment of the origins of governmental redistribution for a broad sample of countries. Our findings confirm the Meltzer-Richard hypothesis, pointing to a robust positive relationship between market inequality and redistribution. We show that perceptions of inequality are often biased and that the redistribution-enhancing effect of gross inequality is even stronger when individuals are aware of national income disparities. The results also suggest that top income shares tend to impede redistributive policies.     

Zinspolitik ade! Wie man Immobilienpreisblasen dennoch überstehen kann

After the financial crisis, some Euro countries are still facing major economic problems. They require favourable interest rate conditions in order to free themselves from their difficulties. But due to the low interest rate policy of the ECB, the risk of a housing price bubble in Germany is increasing. Since a rise in interest rates is not expected in the near future, other national institutional possibilities come to the fore. High capital ratios of both the borrower and the banks, and long rate fixation periods may counteract the development and impact of price bubbles. This might improve the stability of the German real estate market.     

Malleability of taste perception: biasing effects of rating scale format on taste recognition,product evaluation,and willingness to pay

Product-related cues, such as brand or price, can influence consumers’ taste perception. Going beyond this observation, we examine the extent to which a stimulus-extrinsic factor, such as the format of the measurement tool on which consumers describe attributes of a taste sample, influences concurrent taste perception, and in turn, later taste recognition, overall product evaluation, and willingness to pay (WTP). The results of two experiments show that rating scale format (i) influences consumers’ concurrent impression of a taste sample, (ii) systematically biases later identification of the sample in a taste recognition test, and (iii) affects overall product evaluation and WTP. However, scale format (iv) does not influence ratings and downstream judgments when consumers are highly knowledgeable in the product domain. These findings demonstrate that the experience of taste is fleeting and not well represented in memory, and that like other subjective experiences, taste needs to be reconstructed based on accessible cues.     

Algorithms for optimal design with application to multiple polynomial regression

The approximate theory of optimal linear regression design leads to specific convex extremum problems for numerical solution. A conceptual algorithm is stated, whose concrete versions lead us from steepest descent type algorithms to improved gradient methods, and finally to second order methods with excellent convergence behaviour. Applications are given to symmetric multiple polynomial models of degree three or less, where invariance structures are utilized. A final section is devoted to the construction of efficientexact designs of sizeN from the optimal approximate designs. For the multifactor cubic model and some of the most popular optimality criteria (D-, A-, andI-criteria) fairly efficient exact designs are obtained, even for small sample sizeN. AMS Subject Classification: 62K05.Abbreviated Title: Algorithms for Optimal Design.Invited paper presented at the International Conference on Mathematical Statistics,ProbaStat '94, Smolenice, Slovakia.