Observed and Unobserved Heterogeneity in Stochastic Frontier Models: An Application to the Electricity Distribution Industry

2009 ◽  
Author(s):  
Maria Kopsakangas-Savolainen ◽  
Rauli Svento
2020 ◽  
Vol 17 (5) ◽  
pp. 669-696
Author(s):  
Pavan Khetrapal

PurposeThe objective of the present study is to evaluate and analyse the performance of Indian electricity distribution utilities post the implementation of landmark Electricity Act 2003.Design/methodology/approachStochastic frontier analysis (SFA) that incorporates exogenous influences on operational efficiency is adopted in the present study. Specifically, a stochastic frontier production function model with a technical inefficiency effects model (Battese and Coelli, 1995) is chosen as a preferred model. In this model, the function that explains the inefficiency scores is estimated in a single stage with the production technology. This avoids the problem of inconsistency which is possible in the two-stage approach.FindingsThe sample involved 52 Indian electricity distribution utilities for seven-year period from 2006 to 2013. Major findings of SFA show that Indian electricity distribution utilities post the implementation of Electricity Act (2003) had, on average, experienced efficiency improvement during the observed period. The overall mean technical effciency score is estimated as 78.5% which indicates that there exist wide scope for effciency improvement in the sector. Further, the empirical findings also indicate that publicly owned distribution utilities obtain average technical efficiencies of 71.3%, which is lower than privately owned distribution utilities, which achieve average technical efficiencies of 85.7%.Research limitations/implicationsPower supply quality indicators such as SAIFI, SAIDI, CAIFI, etc. and unobserved heterogeneity also influence the efficiency analysis of electricity distribution utilities. Hence, these parameters as explanatory variables can be incorporated in the future work.Practical implicationsThe results obtained from this empirical study would likely be helpful for utility managers and policymakers to know how well they are performing, and how a better corporate strategy a particular utility can formulate to improve its operational efficiency and also its position in the marketplace.Originality/valueThis paper is amongst the first significant attempts that implement SFA approach to the panel dataset over a longer period of time – 2006 to 2013, so, as to evaluate and analyse the operational efficiency of Indian electricity distribution utilities in a single framework after the enactment of Electricity Act (2003). Unlike previous studies, this study investigates the degree to which various exogenous (or environmental) factors influence efficiency levels in these utilities.


2015 ◽  
Vol 44 (1) ◽  
pp. 124-148 ◽  
Author(s):  
Magnus A. Kellermann

This study examines in an empirical comparison how different econometric specifications of stochastic frontier models affect the decomposition of total factor productivity growth. We estimate nine stochastic frontier models, which have been widely used in empirical investigations of sources of productivity growth. Our results show that the relative contribution of components to total factor productivity growth is quite sensitive to the choice of econometric model, which points to the need to select the “right” model. We apply various statistical tests to narrow the range of applicable models and identify additional criteria upon which to base the choice of non-nested models.


2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Jose M. Gavilan ◽  
Francisco J. Ortega

In the setting of the Stochastic Frontier Production Models, the productive efficiency of the 28 countries belonging to the European Union is analysed. To this end, panel data encompassing a broad period of time is selected, which facilitates the study into whether there is greater efficiency in the years of crisis as a consequence of the adjustment measures. A translog specification of a Cobb-Douglas model is considered, in which the output is measured through the GDP of the countries and two productive factors (capital and labour). The model also includes a trend component that addresses the possible presence of technological change, and dummy variables for each country in order to separate unobserved heterogeneity from productive inefficiency. In relation to the perturbation that models the inefficiency, a model of the type Battese and Coelli (1995) is considered with a trend component and a variable related to economic growth. Finally, the growth of productivity is decomposed into the sum of the changes in technology, in economies of scale, and in inefficiency. 


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