A two‐step system for direct bank telemarketing outcome classification

A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two‐step system presented performs better than all its individual components. In addition, the two‐step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two‐step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

The public R&D and productivity growth in Australia's broadacre agriculture: is there a link?

This paper investigates the dynamic relationships between research and development (R&D) expenditure and productivity growth in Australian broadacre agriculture using aggregate time series data for the period 1953 to 2009. The results show a cointegrating relationship between R&D and productivity growth and a unidirectional causality from R&D to TFP (total factor productivity) growth in Australian broadacre agriculture. Using the dynamic properties of the model, data from beyond the sample period are analysed by employing the variance decomposition and the impulse response function. The findings reveal that R&D can be readily linked to the variation in productivity growth beyond the sample period. Furthermore, the forecasting results indicate that a significant out‐of‐sample relationship exists between public R&D and productivity in broadacre agriculture.     

Christian roots of capitalist civilization

A conceptual argument is offered for the centrality of religion to the structure of capitalist civilization. This centrality is documented by examples of the role of Christianity in European civilization.     

Exploring the impact of R&D and climate change on agricultural productivity growth: the case of Western Australia*

This article empirically examines the impact of R&D and climate change on the Western Australian Agricultural sector using standard time series econometrics. Based on historical data for the period of 1977–2005, the empirical results show that both R&D and climate change matter for long‐run productivity growth. The long‐run elasticity of total factor productivity (TFP) with respect to R&D expenditure is 0.497, while that of climate change is 0.506. There is a unidirectional causality running from R&D expenditure to TFP growth in both the short run and long run. Further, the variance decomposition and impulse response function confirm that a significant portion of output and productivity growth beyond the sample period is explained by R&D expenditure. These results justify the increase in R&D investment in the deteriorating climatic condition in the agricultural sector to improve the long‐run prospects of productivity growth.     

Energy,unemployment and trade

This article investigates the dynamic relationships among sectoral economic activities, macro expenditure patterns, renewable and non-renewable energy consumption and unemployment in 41 countries from 1980 to 2014. The state of the art econometric techniques, both linear and non-linear panel and time series estimation techniques are used. The results show that industrialization, services sector, government expenditure and trade openness play a positive role in reducing unemployment, while agriculture and renewable energy consumption increase unemployment. This might be, in part, due to recent technological advancements and large capital intensive investments in agriculture and renewable energy sectors. Therefore, dedicated social and labour market policies need to be adopted to complement greening economic policies.     

The Worldwide Movement in Private Universities: Revolutionary Growth in Post-Secondary Higher Education

The end of the Cold War, which resulted from the collapse of communism and the spread of free market ideas in the former Soviet Union, China, and nations in Eastern Europe, has brought about many dramatic and momentous changes on the world scene. One remarkable development not widely recognized outside specialized academic circles is the emergence of new institutions of higher learning in the private sector of previously command economies and also in many developing countries that had been wedded to a culture of socialism for decades. The “privatization” of higher education is by all accounts a global phenomenon. Very few systematic studies of these new trends in private post‐secondary education exist to date, although the impact of this development could be far‐reaching not only for higher education but also for the social and economic development of many societies in the world. In this paper we attempt to analyze the phenomenon of new private universities in non‐Western nations, focusing on the developing world in general, and Asia in particular. What missions have these institutions chosen? Which models are these institutions following? What challenges do they face? How are they coping with their relationship to existing public universities? How are they dealing with tuition and budgetary issues? What sources of funding are they tapping into? How are they dealing with issues of equity, excellence, and access? In the composition of their faculty, student body, and curriculum, are these institutions sufficiently international? How are the funding institutions and universities in the West helping these fledgling universities? Are these trends expected to accelerate in the future? We attempt to answer these questions within the context of a few case studies that reflect the breadth and diversity of this revolutionary growth in post‐secondary higher education.     

Features selection,data mining and finacial risk classification: a comparative study

The aim of this paper is to compare several predictive models that combine features selection techniques with data mining classifiers in the context of credit risk assessment in terms of accuracy, sensitivity and specificity statistics. The t‐statistic, Battacharrayia statistic, the area between the receiver operating characteristic, Wilcoxon statistic, relative entropy, and genetic algorithms were used for the features selection task. The selected features are used to train the support vector machine (SVM) classifier, backpropagation neural network, radial basis function neural network, linear discriminant analysis and naive Bayes classifier. Results from three datasets using a 10‐fold cross‐validation technique showed that the SVM provides the best accuracy under all features selections techniques adopted in the study for all three datasets. Therefore, the SVM is an attractive classifier to be used in real applications for bankruptcy prediction in corporate finance and financial risk management in financial institutions. In addition, we found that our best results are superior to earlier studies on the same datasets.     

The puzzle of sharecropping: A survey of theories

The geographically pervasive and historically tenacious institution of sharecropping has always been a fruitful source of great economic controversies. While the earlier writings were almost universal in their disapproval of sharecropping (and seemed to ascribe little or no rationale for the persistence of it), an important characteristic of recent writings has been to uphold a plurality of views regarding the virtues of this institution. Not only have the recent papers been quite successful in highlighting the basic rationale behind the continuance of this somewhat enigmatic institution, they have addressed themselves to a number of important issues hitherto unexplored. Many strands of recent theoretical analyses have been brought to bear on the analysis of these problems, ranging from the simple competitive model to the esoteric theory of core, from the tricky Coase theorem of property rights to the intricate theory of portfolio behaviour, the principal-agent formulation and recent advances from the economics of information. Based on the present survey of theories, it seems fair to conclude that these recent sophisticated models have been quite illuminating and hold promise of further insights in future.