Assessing the socio-economic dimensions of the rise of organic farming in the European Union

Organic farming is considered one of the most important rural development tools in the European Union, often connected to the socio-economic objectives of small-farm support and employment generation. Using an EU-27 regional panel data-set from 2000 to 2010, I show that the share of a region’s agricultural area under organic methods is positively associated with average farm size. Furthermore, I show that the share of organic farming in a region is not associated with higher application of agricultural labor per hectare. Both results question the connection between organic farming and the aforementioned objectives, and point to the conventionalization of European organic farming.     

Small and Productive: Kenyan Women and Crop Choice

The question of gender differences in agricultural productivity has received particular attention in the development literature. The stylized fact that women produce less than men, while on average occupying smaller farms, presents a quandary as it is also a stylized fact that smaller farms have higher yields per unit of area. Using data from the 2006 Kenya Integrated Household Budget Survey, this study examines whether there is a gap in output per acre between men and women farmers in Kenya. Using ordinary and two-stage least-squares (OLS and 2SLS) analyses, it shows that when crop choice is taken into account, women are as productive as men. Specifically, the study finds that market-oriented crops are the source of differences. This suggests that further research into what determines crop choice is needed, in addition to policy that ensures that women have the same access as men to support for market-oriented crops.     

Examining the role of CSR skepticism using fuzzy-set qualitative comparative analysis

This study shows the value of a set-theoretic comparative technique—namely, fuzzy-set qualitative comparative analysis—as a means to supplement other traditional techniques, such as regression analysis and structural equation modeling. The study illustrates the technique by using the empirical dataset in Skarmeas and Leonidou's (2013) study on consumer skepticism about corporate social responsibility (CSR). The investigation provides more nuanced coverage of the role of CSR skepticism than the conventional “net effect” symmetrical explanation and illustrates that CSR skepticism and its impact are contingent on combinations of complex antecedent conditions and several alternative paths. Specifically, the study expands on Skarmeas and Leonidou's findings by showing that the degree of CSR skepticism depends on the combination of “ingredients” in the CSR-induced consumer attribution causal “recipes.” The study also shows that the deleterious influence of CSR skepticism on consumer-related outcomes, such as retailer equity, resilience to negative information about the retailer, and word of mouth, is conditional and depends on the combination of antecedent conditions that occur in the causal statements.     

The Impact of Competitiveness on the Shadow Economy

International Advances in Economic Research - The shadow economy absorbs critical resources from the formal economy and to a certain extent impedes the implementation of policies that are necessary...     

The important role of pressure in supercritical fluid process development revealed by reaction calorimetry

The technique of reaction calorimetry adapted for use with reactions in supercritical fluids was used to study some safety aspects of the free-radical dispersion polymerization of methyl methacrylate in scCO₂. The reaction heat rate profile was found to change very little once the dispersion was well formed. Furthermore, it provided valuable information for the calculation of the maximum temperature attainable by the synthesis reaction (MTSR) in the case of a hypothetical cooling system failure. Finally, a series of failure scenarios demonstrated the importance of the pressure as far as the safety of the process is concerned, due to the particularity of the supercritical state of the solvent. It was found that the acceleration phase of the reaction is the most critical period, since a cooling system failure during this phase leaves very little time before the pressure overcomes the operational limit of the equipment and results in an accident. Hence, the utility and the importance of defining the reaction heat rate profile become obvious and several safety features have to be taken into consideration when designing a SCF process. © 2009 American Institute of Chemical Engineers Process Saf Prog 2009     

The balance between size and power in testing for linear association for two stationary AR(1) processes

The classical statistical procedure in testing the null hypothesis of zero correlation for two independent stationary AR(1) processes produces spurious correlations, contrast to the alternative testing approach that has been proposed by Agiakloglou and Tsimpanos (2012). This study examines the trade-offs between size distortions and power using both testing techniques, including the case where the true values of the autoregressive parameters are replaced by their estimates.     

Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables

This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets.     

Entrepreneurship,Income Inequality and Public Spending: A Spatial Analysis into Regional Determinants of Growing Firms in Greece

International Advances in Economic Research - One of the key determinants of economic growth, regional prosperity and sustainable development is entrepreneurship. This paper aims to identify...     

Stock market prediction using evolutionary support vector machines: an application to the ASE20 index

The main motivation for this paper is to introduce a novel hybrid method for the prediction of the directional movement of financial assets with an application to the ASE20 Greek stock index. Specifically, we use an alternative computational methodology named evolutionary support vector machine (ESVM) stock predictor for modeling and trading the ASE20 Greek stock index extending the universe of the examined inputs to include autoregressive inputs and moving averages of the ASE20 index and other four financial indices. The proposed hybrid method consists of a combination of genetic algorithms with support vector machines modified to uncover effective short-term trading models and overcome the limitations of existing methods. For comparison purposes, the trading performance of the ESVM stock predictor is benchmarked with four traditional strategies (a naïve strategy, a buy and hold strategy, a moving average convergence/divergence and an autoregressive moving average model), and a multilayer perceptron neural network model. As it turns out, the proposed methodology produces a higher trading performance, even during the financial crisis period, in terms of annualized return and information ratio, while providing information about the relationship between the ASE20 index and DAX30, NIKKEI225, FTSE100 and S&P500 indices.