Archive for the ‘Hypothesis’ Category



DIMEG, Politecnico di Bari, Bari, Viale Japigia, 182, 70126 , Italy. Tel: +390805062720.
(Corresponding author)


DIMEG, Politecnico di Bari, Bari, Italy


The paper conducts an explorative research on the competitive success of the Industrial Districts IDs (GCs), due to their capacity to adapt and evolve to the environment. Our aim is to identify the ID structural features supporting adaptation by using the complexity theory. We consider the IDs as complex adaptive systems (CASs) and identify the ID features on the basis of the main CAS properties that foster adaptation, i.e. interconnectivity, heterogeneity, and control. To formulate the theory linking the values of the ID structural features with the ID competitive success, a multiple case study is carried out. In particular, it is aimed at comparing IDs with different competitive performances in terms of their CAS properties using network theories and measures. Three theoretical propositions are finally derived.

Keywords: Industrial districts, complex adaptive systems, network, complexity science, adaptive capacity

JEL classification:
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World Economy Chair – Belgorod State National Research University, Russia,


World Economy Chair – Belgorod State National Research University, Russia,


The understanding of concentration processes about resources, population, enterprises in some regions and in the cities is very significant for economists and policy-makers. It’s caused by the worldwide urbanization trend and local trend of economic activity agglomeration that increase the regional development differentiation within the country. Issues of economic activity locations and space distribution are solved by scientists over the past two centuries. Recent works show the increasing interest of economists to the Zipf’s law testing in the regional system and the rank-size distribution of the cities. Research aims are to test the Zipf’s law in the Russian cities and to test the hypothesis that the Russian Zipf coefficients depends on the size of the geographical territory of the Federal District.Methodology. In the paper it’s used least square method for tasting the Zipf’s law in Russian cities in general and separately for the federal districts. There is 1,123 Russian cities panel (cities with over 1,000 people population in 2014).Results. The Zipf’s law is confirmed in the territory of Russia in general. According to the Federal Districts the Zipf coefficients range from -0.65 (Far Eastern Federal District) to -0.9 (the Urals and the North Caucasian Federal Districts). Equitability of cities hierarchy in the Ural and the North Caucasus Federal Districts dues to the fact that there are 139 cities located in the 1,789 thous. km2 in the Urals and 56 cities in the 170 thous. km2 in the Caucasus. In the Far East the city location is very disperse – 66 cities in the area of 6000 thous. km2 (Zipf coefficient – 0.65). Conclusions. Testing of the Zipf’s law for the Russian cities in general shows that it’s valid for the small (8,600 – 15,300 peoples) and large cities (66,700 – 331,000 peoples). For cities panel with population exceeds 100 thous. people. The Zipf’s law is not valid for cities of more than 1 million people. (exception – the city of St. Petersburg). The result of the study is the confirmation of the hypothesis that the Zipf coefficient depends on the size of the Federal District.

Keywords: location theories, the Zipf’s law, the city, the rank-size distribution, the cities of Russia

JEL classification: R12
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Estimating Technical Inefficiency: An Empirical Approach to E.U. Industries

Aikaterini Kokkinou

Department of Economics, University of Glasgow

This paper estimates, incorporating a Transcendental Logarithmic Production Function, the technical efficiency level of different industries in selected E.U. countries. The paper considers panel data for inefficiency effects in stochastic production frontier based on Battese and Coelli (1995), providing translog effects, as well as industry effects. The empirical model accommodates not only heteroscedasticity but also allows the possibility that an industry may not always produce the maximum possible output, given the inputs. Unlike most studies, the paper estimates time – varying technical efficiencies (incorporating „learning – by doing‟ behaviour) as industry-specific fixed effects. Furthermore, the model decomposes total factor productivity (TFP) growth into two components: technological growth (essentially, a shift of production possibility frontier, set by best-practice enterprises) and inefficiency changes (i.e., deviations of actual output level from the production possibility frontier). read more

Key Words: Efficiency, Technical Inefficiency, Stochastic Frontier Model