Scale and efficiency measurement using a semiparametric stochastic frontier model: evidence from the U.S. commercial banks

2007 ◽  
Vol 34 (3) ◽  
pp. 585-602 ◽  
Author(s):  
Subal C. Kumbhakar ◽  
Efthymios G. Tsionas
Keyword(s):  
Commercial Banks ◽  
Stochastic Frontier ◽  
Efficiency Measurement ◽  
Stochastic Frontier Model ◽  
Model Evidence ◽  
The U.S

scholarly journals A stochastic frontier model for measuring online bank profit efficiency

2011 ◽  
Vol 42 (3) ◽  
pp. 49-60 ◽  
Author(s):  
M. A. Baten ◽  
A. A. Kamil
Keyword(s):  
Production Function ◽  
Commercial Banks ◽  
Stochastic Frontier ◽  
Foreign Banks ◽  
Stochastic Frontier Model ◽  
Islamic Banks ◽  
Efficiency Function ◽  
Profit Efficiency ◽  
Private Banks ◽  
Sample Data

This study revisited an alternative profit efficiency function specified by Berger & Mester, (1997) and we applied Battese & Coelli, (1995) inefficiency model as a unified and consistent framework in exploring the determinants of important factors causing profit efficiency differential on banking industry in Bangladesh. Using stochastic frontier technique we estimated bank specific profit efficiency for the period 2000 to 2007. This study attempted to examine the changes in the profit efficiency in accordance with NBs (Nationalized Commercial Banks), ISBs (Islamic Banks), FBs (Foreign Banks) and PBs (Private Banks) and significant variations of efficiencies across different kinds of banks in time periods. We found that the profit inefficiency has declined over the reference period and Translog Production Function is more preferable than Cobb-Douglas Production Function. Our results showed that Nationalized Commercial Banks were significantly inefficient and on the contrary ISBs, FBs, and PBs were efficient in producing profit and noteworthy. The estimated year wise average efficiencies of the sample banks from the profit efficiency model was 0.664 while group wise average profit efficiency was 0,639. Dhaka Bank is highly efficient with score 0.89 and AB Bank was found lowest efficient with score 0,35 according to the sample data.

scholarly journals Estimation of technical efficiency of black cumin (Nigella sativa L.) farming in northwest Ethiopia: a stochastic frontier approach

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Abebe Birara Dessie ◽  
Tadie Mirie Abate ◽  
Betelhem Tsedalu Adane ◽  
Tiru Tesfa ◽  
Shegaw Getu
Keyword(s):  
Technical Efficiency ◽  
Nigella Sativa ◽  
Stochastic Frontier ◽  
Market Price ◽  
Northwest Ethiopia ◽  
Flowering Plant ◽  
Yield Potential ◽  
Stochastic Frontier Model ◽  
Black Cumin ◽  
The Mean

Abstract Ethiopia is one of the east African countries which produce and exports various spices to other countries. Black cumin (Nigella sativa L.) is an important stiff annual flowering plant which mainly grows by producers for its seeds. An increasing demand of black cumin seed and oil in local, national and international market for medicinal, consumption and commercial purpose makes the best alternative crop for small holder farmers in Ethiopia. In spite of its importance, not much has been done to improve its production and productivity in Ethiopia. Therefore, this research was designed to examining efficiency variations and factors influencing technical inefficiency levels of producers on black cumin production in northwest Ethiopia. Primary data were collected using a semi-structured questionnaire administered on 188 black cumin producers selected using systematic random sampling technique. Moreover, various data analysis methods such as descriptive statistics and stochastic frontier model were used for analyzing the data. The empirical result obtained by applying maximum likelihood estimate of stochastic frontier model revealed that seed (p < 0.01) labor (p < 0.05), chemical (p < 0.01) and land (p < 0.05) were significant input variables in determining black cumin production. The mean technical efficiency level of black cumin producer was generally low, about 53.1%. The mean value of actual yield, potential yield and yield gap was 3.131, 5.832 and 2.701 quintals, respectively. Moreover, the result of stochastic frontier model together with the inefficiency parameters revealed that market price of black cumin (p < 0.01) and access of extension service (p < 0.1) were significant variables and positively influenced the efficiency levels of black cumin producers. Whereas age of producers (p < 0.05) and distance to farm plot (p < 0.01) negatively influenced the technical efficiency levels of black cumin producers. Therefore, the study recommends that adoption of latest agricultural technologies; development of institutions, agricultural extension services and infrastructure are advisable to improve the efficiency and commercial value of black cumin production.

scholarly journals How Earmarking Government Revenue Affects Efficiency of Road Construction and Maintenance

2021 ◽  
Vol 49 (1) ◽  
pp. 136-163
Author(s):  
Ramon Christen ◽  
Nils C. Soguel
Keyword(s):  
Public Choice ◽  
Public Administration ◽  
Stochastic Frontier ◽  
Road Construction ◽  
Transport Infrastructure ◽  
Government Revenue ◽  
Stochastic Frontier Model ◽  
Public Resources ◽  
Negative Effect ◽  
Per Capita

It is common practice to assign revenue to accomplish specific governmental tasks in general and to provide transport infrastructure in particular. However, neither the literature in public administration nor in public choice has reached a consensus about the effects that earmarking has on efficiency. Building on earlier public choice models, we argue that this mechanism prevents budget debates from occurring and reduces the incentives for ministers to monitor the colleagues whose budgets are financed by earmarked revenues. These latter tend to overuse public resources, thus increasing inefficiency. A stochastic frontier model fed with data from Swiss cantonal ministries of transport from 2000 to 2016 tests this hypothesis. The results reveal a negative effect of earmarking on efficiency. For every 1,000 additional Swiss francs per capita financed out of an earmarked fund, the input requirement increases by 5.4 percent on average.

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