Technical efficiency

On the base of our analyse we detected that the efficiency of companies increased by subsidies allocation in particular years in average about 0,06% up to 4,36%. All these changes were statistical approved except 2003. The reason of this situation was that except of subsidies the companies took special loans from the state for bypassing higher loss caused by worst climate conditions. In general we can say that the subsidies had positive impact on the technical efficiency of companies which reached the values of efficiency up to 40% compared with companies which produced on the production possibilities frontier. It means that financial support was good divided and helped mainly to companies which really needed that help. On other hand these subsidies were for companies very important but in the consequence of subsidies can endanger degeneration of production environment. Some companies are prosperous just because of subsidies and without them they would be not competitive. Except of based company production envoi the agricultural producers have other function too. We are talking about keeping of employment and regional development in many areas which are not interesting for investors from secondary sector.

Some caveats regarding the technical efficiency estimates. While the random coefficient approach facilitated the identification of a benchmark potential output in a given sample, comparison of performance are made in relation to the dominant observation in the sample; thus, an industry may be inefficient but since most firms are closer to the degree of efficiency of the ‘best practice’ firm, the industry’s technical efficiency may be high. This explains why textile industry in the study despite being documented as an inefficient industry in the Philippines (Pack 1987) appear to be more efficient than the electronic industry which include electronics and is one of the biggest exports of the country. Second, as indicated by Stevenson (1980) and Caves (1990) different assumptions about firm-specific effects make comparisons from different studies on technical efficiency less meaningful. For example in the case of stochastic frontier approach, imposing the assumption of constant returns to scale may cause the mean response coefficients to be intractable. This is because even when the condition of constant returns to scale is imposed on the response coefficients ( ß y ’s ), the possibility that ^ ß *)1 can not be ruled out due to the relationship that ß * = |max ß + v } (Kalirajan

This study examines the relative technical efficiency of 12 hospitals in Eastern Ethiopia. Using six-year-round panel data for the period between 2007/08 and 2012/13, this study examines the technical efficiency, total factor productivity, and determinants of the technical inefficiency of hospitals. Data envelopment analysis (DEA) and DEA- based Malmquist productivity index used to estimate relative technical efficiency, scale efficiency, and total factor productivity index of hospitals. Tobit model used to examine the determinants of the technical inefficiency of hospitals. The DEA Variable Returns to Scale (VRS) estimate indicated that 6 (50%), 5 (42%), 3 (25%), 3 (25%), 4 (33%), and 3 (25%) of the hospitals were technically inefficient while 9 (75%), 9 (75%), 7 (58%), 7 (58%), 7 (58%) and 8 (67%) of hospitals were scale inefficient between 2007/08 and 2012/13, respectively. On average, Malmquist Total Factor Productivity (MTFP) of the hospitals decreased by 3.6% over the panel period. The Tobit model shows that teaching hospital is less efficiency than other hospitals. The Tobit regression model further shows that medical doctor to total staff ratio, the proportion of outpatient visit to inpatient days, and the proportion of inpatients treated per medical doctor were negatively related with technical inefficiency of hospitals. Hence, policy interventions that help utilize excess capacity of hospitals, increase doctor to other staff ratio, and standardize number of inpatients treated per doctor would contribute to the improvement of the technical efficiency of hospitals.

According to Kumbahkar and Tsionas(2003) when technical inefficiency enters multiplicatively into the production function, as an output-output model, the elasticities of product and returns to scale are independent of the technical efficiency. Output-output technical inefficiency can be interpreted as the percent loss of output, ceteris paribus, due to technical inefficiency. In the model used as in Battese and Coelli (1995) the effects of technical inefficiency are random variables, non negative and independently distributed. When variables are represented in log form the efficiency of each farm is given by

The result shows that sex, livestock, household size, extension service and seed type were negatively related with technical inefficiency while only off-farm income participation was positively related with technical inefficiency. Off-farm income: The estimated coefficient of off-farm income shows a positive and significant association between off-farm income technical inefficiency and negative relationship with technical inefficiency production. According to Amare (2005), off-farm incomes can have positive effect on the households’ efficiency by alleviating financial constraints in terms of timely purchase of farm inputs. On the other hand, participating in off-farm activities might be at the expense of own farm activities in terms of less labor and time causing for a negative relationship between technical efficiency and participation in off-farm activities. This implies that an increase in off-farm income participation increases the technical inefficiency of farmers and increase technical inefficiency and decrease technical efficiency. This result related with Singh, et al. (2009) and Geta, et al. (2013) who found negative correlations; and Chang and Wen (2011) who confirmed differential effects of participation in off-farm activity on technical efficiency of farm household.

Following, the mean of VRS for each farm size is calculated. After the level of education, it is intended to see if farm size has a positive correlation with technical efficiency ( Table 4) . Results show that under the DEA VRS model, farm size has a positive correlation with TE. Small farms with 0.893 score, have overall weaker efficiency compared to the other two groups. Following, the mid-size farms have a higher TE than small farms with 0.996 scores under the VRS. And lastly, the large farms with a score of 1.000, recorded the highest technical efficiency of all groups, and thus are fully efficient relative to other farms in the study. Larger farms tend to have overall more assets, including land and machinery which is used to produce the animal feed. Furthermore, with mixers that prepare the concentrates, the feed at the end tend to be of higher quality and a more suitable form for animals. Accordingly, higher milk production was recorded. Consequently, with present herd/breed and current level of inputs and technology of production, they cannot achieve better results/efficiency. On the other hand, small farms recorded lower percentage of feed cost to total costs relative to other two types of farms, however the feed cost per litre of milk in the small farms is relatively higher than on the other two groups of farms. Hence, high feed cost is a major indicator of low efficiency. Therefore, while the cow’s breed, feeding and milking system are the same, there are possibilities for improvements in being more efficient, and more competitive, and as a result to survive or prosper in this highly competitive market. In the case of the mid-size farms, they are in a much better position than small farms. Their TE score is 99.6%, and just 0.04% needs to be enhanced to reach full efficiency based on best-performing farms in the study. In this respect, mid-size dairy farms in our sample have a relatively low cost of feed per litre of milk, and the overall cost of production. Furthermore, they are well equipped and capable of producing the necessary feed for their animals. However, higher-quality seeds and fertilizer would increase both quality and quantity of animal feed, and thus if not increasing - then maintaining the same level of milk production with fewer inputs. Another comparison between sampled farms is done based on their feeding system. The majority of farms in the study (75%) used

Osun State. Specifically, the study looked at the socio-economic characteristics which influence the technical efficiency of farmers. It estimated and analysed productivity and technical efficiencies of the poultry farms. Data were collected from 86 sampled egg producers with the aid of a structured questionnaire using multistage random sampling technique. The data collected were analysed using descriptive statistics, budgetary analysis and stochastic frontier production function. The study revealed that production of egg was profitable in the study area. Result also indicated that inputs were efficiently allocated and utilized and the farmers operated in the rational zone of production function (Stage II). The inefficiency model showed that only location of the poultry egg farm positively improved TE. It is recommended that farmers should therefore be encouraged to site their poultry farms close to their source of input and environment conducive for poultry production.

where Y is vector of bank outputs , X is s vector of bank inputs, λ is a I × 1 vector of constants. The value of δ ˆ i obtained is the technical efficiency score for the i-th firm. A measure of δ ˆ i =1 indicates that the bank l is technically efficient, and inefficient if δ ˆ i >1. This linear programming problem must be solved n times, once for each bank in the sample. Note that the DEA model can also be estimated using either the constant return to scale (CRS) 3 or variable return to scale (VRS) assumptions and the shape of the frontier will differ depending on the scale assumptions that underline the model. In this paper we rely on the VRS assumption, as the CRS is only correct as long as it is appropriate to assume that banks are operating at an optimal level of scale. Technological advances and regulatory changes might vary across banks in various size groups, so allowing for VRS would permit modelling of the entire range of technology. changes might vary across

This study deals with the nonparametric frontier analysis in the case of the EU 28 countries for a period spanning from 1993 to 2012. It provides statistical inference about the radial output based measure of technical efficiency under the assumption of Constant Returns to Scale (CRS) and it performs scale analysis that allows determining the nature of scale inefficiency of each data point. Furthermore, an order-α approach is developed for determining partial frontiers. Both traditional Malmquist-Luenberger and bootstrapped Malmquist productivity indexes between 1993 and 2012 are constructed. Analysis of productivity change by decomposing the Total Factor Productivity Index into Efficiency Change and Technical Change is performed showing respectively whether productivity gains derive mainly from improvements in efficiency or are mostly the result of technological progress.

The measurement of efficiency in agricultural production determines the efficiency level of households in their farming activities. Farmers in developing countries do not use all potential technological resources, thus making inefficient decisions in their agricultural activities. Therefore, this paper made an effort to measure the technical efficiency (TE) of rice production and identified some determinants of technical efficiency of rice farmers in Vietnam. The Vietnam Household Living Standard Survey 2005-2006 (VHLSS 2006) was analyzed using stochastic frontier analysis method in the Cobb-Douglas production function. The calculated technical efficiency in the study was around 81.6 percent. The study demonstrated that the most important factors having positive impacts on technical efficiency levels are intensive labor in rice cultivation, irrigation and education. These play the important role in terms of TE score change, while agricultural policies did not help farmers cultivate rice more efficiently.

Farrell (1957) defined a simple measure of firm efficiency that could account for multiple inputs, stating that technical efficiency is the ability of a firm to obtain maximal output for a given set of inputs. His definition of technical efficiency led to the development of methods for estimating technical efficiencies in the context of a firm. Data Envelopment Analysis (DEA) is a non-parametric linear programming approach and was first introduced by Charnes, Cooper, and Rhodes in 1978 and further formalised by Banker, Charnes and Cooper in 1984. The technique was first used to study hospital production in 1986 (Banker, Conrad and Strauss) using data from a sample of hospitals in the US, followed by Grosskopf and Valdmanis in 1987. A number of more recent studies have also employed DEA to measure hospital efficiency, Magnussen (1996), Hollingsworth and Parkin (1995), Ferrier and Valdmanis (1996), Parkin and Hollingsworth (1997) and Rosenman, Siddharthan and Ahern (1997). In Norway, Biorn, Hagen, Iversen and Magnussen (2002) measure technical efficiency of hospitals to test the hypothesis that hospital efficiency is expected to be greater with activity based funding of hospitals than with fixed budgets. In Northern Ireland, McKillop et al. (1999) estimated the technical efficiency of all hospitals from 1986 to 1992. All acute hospitals were categorised into small, medium and large (based on total number of inpatients and outpatients). In the Republic though, DEA has not yet been applied to data from hospitals.

As shown in Row 5 in Table 5, the REFORM factor shows a positive, albeit insignificant, effect on technical efficiency. The REFORM factor is beneficial to TE improvement (-0.2522+0.0074t < 0), but the effect on TE will finally become negative with the development of privatization (when t>34, -0.2522+0.0074t >0). Although the effect of privatization on efficiency is ambiguous in both theoretic and empirical literatures (Okten and Arin, 2006), it is positive in our estimation. In China, privatization has emancipated the productive forces which have been fettered by the planned economy for a long time. Privatization can induce competition and enhance productivity that eventually can contribute to efficiency improvement. However, even though the effect is large, it is not statistically significant in our estimation since the p-value of the test is 0.2235.

According to Mahjoor (2013), the determinants of technical efficiency in agriculture are the education and age of the farmer, as well as the membership of a producer group. In turn, Rahman and Salim (2013) list the size of agricultural holdings, the investment in research and development, the expenses incurred in the expansion of production and the specialization of plant production and the illiteracy among the factors affecting the management efficiency. The importance of the farm area is also highlighted in the studies by Munroe (2001), O’Neill and Matthews (2001); Hadley (2006), Emvalomatis et al. (2008), Carroll et al. (2009). In addition, Bhalla and Roy (1988) suggest that an important factor influencing the efficiency of agriculture is the quality of the land. This is con- firmed by the studies conducted by Adhikari and Bjørndal (2011). In turn, Onyenweaku et al. (2004), Gul (2006) and Idris et al. (2013) draw attention to the existence of the relationships between the efficiency of agriculture and the age of the farm managers. On the one hand, it is reasonable to assume that there is a positive relationship between the farmer’s age and efficiency, since the age is regarded as a proxy for the farming experience and knowledge-base. On the other hand, younger farmers are supposed to be more willing to use new production methods and management techniques, which may lead to an in- crease in their technical efficiency. For example, Yu et

At the same time, taking into account an unusual structure of the inaccuracies of the efficiency boundary model, which has an asymmetric distribution and consists of two constituents, the remainders of the regression are principally assessed by the method of maximal plausibility. It is also worth mentioning, that at the whole the function model of the efficiency boundaries of the processes, phenomena or objects under research for obtaining the efficiency values, can be defined in a form of translog function or its reductive conception in a form of Kobb-Duglas’s function. For uncovering stochastic efficiency boundary when studying technical efficiency in the analysis of banking activity is made according to the following approaches for the direct description of banking activity:

DOI: 10.4236/jfrm.2018.71007 120 Journal of Financial Risk Management significant positive correlation between technical efficiency and the degree of marketization, R&D expenditure and educational expenditure, which indicated the improvement of the market level, the increase of R&D expenditure and the increase of educational expenditure can improve the level of the national tech- nical efficiency. Specially, the impact of educational expenditure on technical ef- ficiency is most obvious. However, the correlation between FDI and technology efficiency is not consistent with expectations, and the correlation coefficient is only −0.0003655, indicating that foreign investment has not played a positive role in China’s industrial technology efficiency. The empirical results in the East are in agreement with the national empirical results. This is consistent with the conclusions of Wang (1997), the possible reason is: The eastern regions has a high degree of marketization and a large investment in education and research. FDI investment does not help the eastern industrial enterprises to import ad- vanced technology, so FDI plays a little role in improving the efficiency of en- terprises in the eastern regions, while the technical efficiency of the country is more obviously influenced by the East.

First, we estimate technical efficiency for all the countries in the sample by using the non-parametric method. Measures of efficiency are based on ratios of observed output levels to the maximum level that could have been obtained for a given inputs level. This maximum level constitutes the efficient frontier which will be the benchmark for measuring the relative efficiency of the observations. There are various techniques to estimate this frontier and these methods have been recently applied to examine the efficiency of public spending (Murillo-Zamorano, 2004). In this study, we will use the data envelopment analysis (DEA) method based on Banker et al. (1984) to estimate technical efficiency. DEA is a non-parametric linear programming-based technique designed to calculate relative efficiency based on the sample countries’ efficient production frontier (Casu and Molyneux, 2003). The most efficient countries operate on the frontier while countries below the frontier are considered to be inefficient. According to Koop et al. (2000), economic growth can occurs if a country is able to avoid inefficient used of resources and move closer to the world production frontier. We employ the output- oriented variable return to scale (VRS) model with the assumption that the government maximize output in each economic sector given a fixed amount of expenditure. Equation 1 shows the Bankers et al. (1984) model for calculating technical efficiency.

Agreement on Trade in Services Gulf Cooperation Council Gross Domestic Product Human Capital Efficiency Human Intellectual Capital Intellectual Capital Performance International Monetary Fund Internationalization of Banks Increasing Returns to Scale Information Technology Middle East and North Africa Non- Performing Loans Pure Technical Efficiency Return on Assets

The rationale of the so-called “composed error” specification is that production is subject to two random disturbances of different origin. The positive disturbance u expresses the fact that each firm’s output lies on or below its frontier. Any deviation is the result of factors controllable by the firm, such as technical efficiency, the capability of the producer and his/her employees, the defective and damaged products, etc. However, the frontier itself may vary randomly over time for the same firm and consequently the frontier is stochastic, with random disturbance v, which expresses external to the firm events, such as statistical noise, observation and measurement error, luck, climate, and exogenous shocks beyond the control of the production unit. Thus, technical efficiency is measured by the ratio:

Food production even under improved technology in developing countries involves substantial inefficiencies due to farmers’ high unfamiliarity with new technology coupled with poor extension, education, credit, and input supply systems. This is even more pronounced in Ethiopia where the gap between the demand for and supply of extension services is growing and consequently the services are of poor quality and have very low coverage.(Alene et.,al(2005). Low agricultural productivity and an increasing population contribute to increased food insecurity and poverty in Sub-Saharan Africa in general and in Ethiopia in particular (Geta et al., 2010). In order to improve maize production and productivity, an efficient use of production inputs should be adopted by smallholder farmers. An understanding of the relationships between productivity, efficiency, policy indicators and farm-specific practices would provide policy makers with information to design programs that can contribute to increasing food production potential among smallholder farmers (Msuya et al. 2008). Therefore if farmers are producing to supply the surplus to the market after feeding themselves with reducing land per capita due to population growth, they need to adopt new farming practices and increase their efficiency (Jema Haji, 2008). It is possible to improve the current productivity by increasing technical efficiency. The current level of production efficiency can be improved by ownership of oxen, access to extension services, access to credit, use of improved seed varieties and by promoting soil and land conservation practices and by promoting small- scale irrigation schemes (sorsie et., al ,2015). Therefore This review aims at assessing the levels of technical efficiency and identify the source that influence levels of technical efficiency in Ethiopia.

The technical efficiency of production of specific commodities was estimated using input-oriented DEA models with variable returns to scale (VRS). The DEA program used in this study has been developed by Aleksandr Usol’tsev on the basis of standard linear programming algorithms published in the literature. The work has been carried out at the Analytical Centre for Agri-Food Economics in Moscow as part of the BASIS Russia project. Input-oriented estimation is more appropriate than the output-oriented alternative because one of the objectives of the study is to determine the efficiency of input use for the production of a given output and find ways to optimize input use. The impact of external factors on technical efficiency was then estimated by second-stage regression analysis.