Browsing by Subject "Stochastic Frontier Analysis"

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Open Access
The role of progress factors explaining inefficiencies in Transition countries
(Springer-Verlag, 2013) Solakoglu, E. G.; Solakoglu, M. N.; Demir, N.
This paper examines whether progress in transition has a significant effect on the economic efficiency for 24 transition countries from 1990 to 2006. It uses nine progress factors to analyze the role of the progress factors to explain inefficiencies. It also questions the effect of the transition countries that recently joined the European Union on efficiency. The results suggest that the average efficiency scores for EU-N10 are much higher than the average efficiency scores for SEE/CIS. The scores increase over time for both groups of transition countries. Reforms also contribute to efficiency in general.
Open Access
Three essays on technical efficiency in Turkish manufacturing industries
(2001) Kale, Pelin
This study includes three essays on technical efficiency in Turkish manufacturing industries during 1983-1994. The first one, presented in Chapter III, investigates the sources of inefficiency in the food, textiles, machinery, chemicals and the aggregate manufacturing industries within a stochastic frontier (SF) framework. Panel data sets with four-digit industries are used. Among possible sources of inefficiency, industry-specific structural and organizational factors are considered. Results suggest that public ownership is detrimental to technical efficiency while higher real wages or engagement in international trade enhances it. Regarding the effects of domestic competition, no common pattern emerges. The second essay, presented in Chapter IV, investigates the time pattern of technical efficiency and technological change. Parametric SF and nonparametric data envelopment analysis (DEA) techniques are applied to five panel data sets used in the first essay. Results suggest that mean efficiency increased in the chemicals industry, declined in the machinery industry and remained time-invariant in the food, textiles and the aggregate manufacturing industries. Malmquist productivity indices show that sources of productivity growth differed across industries. In the food and machinery industries, technological progress accounted for productivity improvements while the chemicals and textiles industries witnessed significant efficiency improvements. The third essay, presented in Chapter V, uses semiparametric methods to construct an efficient frontier for the aggregate manufacturing industry. The benchmark technology is estimated by kernel regressions and efficiency scores calculated by fixed effects models. Comparison of results to those from DEA and SF models suggest that semiparametric and SF models not only yield close mean efficiency estimates but also are highly consistent in ranking industries.