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In the last year, a central issue in regional economic growth debate has been represented by the empirical analysis of Verdoorn's law related to the long-term dynamic relationship between the rate of growth in output and the productivity growth due to increasing returns. Several papers have tested Verdoorn's law on European countries as well as many other world economies. Recently, attempts have been made to provide foundations for a spatial version of the original law specification. The main contributions were dedicated to the inclusion of spatial dependence in the economic model. Surprisingly, in the literature on Verdoom's law the analysis of the spatial heterogeneity is not often considered. The aim of this paper is the regional analysis of the spatial dependence and heterogeneity in Verdoorn's law, identifying spatial regimes that can be interpreted as clusters of productivity growth in European regions at NUTS 2 level. To pursue this objective, an optimization algorithm for the identification of groups is used. This constitutes a modified version of Simulated Annealing. [ABSTRACT FROM AUTHOR]

In the period of 1990s alone, four waves of financial crises occurred around the world. The repeated occurrence of financial crises stimulated a large number of theoretical and empirical studies on the phenomena, in particular studies on the determinants of or early warning signals of financial crises. Nonetheless, the different studies of early warning systems have achieved mixed results and there remains much room for further investigation. Since, so far, the empirical studies have focused on conventional economic modelling methods such as simplified probabilistic models and regression models, in this study we examine whether new insights can be gained from the application of the fuzzy clustering method. The theories of fuzzy sets and fuzzy logic offer us the means to deal with uncertainties inherent in a wide variety of tasks, especially when the uncertainty is not the result of randomness but the result of unknown factors and relationships that are difficult to explain. They also provide us with the instruments to treat vague and imprecise linguistic values and to model nonlinear relationships. This paper presents empirical results from analysing the Finnish currency crisis in 1992 using the fuzzy C-means clustering method. We first provide the relevant background knowledge and introduce the fuzzy clustering method. We then show how the use of fuzzy C-means method can help us to identify the critical levels of important economic indicators for predicting of financial crises. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY The paper presents a multisectoral growth model which is readily computable with non-linear production functions and is descriptive of economic conditions prevailing in over-populated countries. The purpose is to show the optimal allocation of resources over time in a labor surplus economy if the dominant social consideration is to increase the rate of employment as fast as possible. Given an institutionally stipulated minimum wage rate and certain other assumptions, the path that maximizes the rate of economic growth also approximates the path that minimizes the time needed to attein full employment as long as the rate of increase of the capital stock exceeds the rate of growth of the potential labor force. A rigorous proof of the proposition is presented with the help of the calculus of variations and the numerically computable solution of the growth model is derived from the conditions underlying the optimum at any moment of time. The model is an analogue of competitive markets under appropriate demand conditions. In addition, since it is computable, the model can be used by economic planners in countries where large scale unemploffment or under-employ-ment exists. [ABSTRACT FROM AUTHOR]