scholarly journals Does Farm Size and Specialization Matter for Productive Efficiency? Results from Kansas

2011 ◽  
Vol 43 (4) ◽  
pp. 515-528 ◽  
Author(s):  
Amin W. Mugera ◽  
Michael R. Langemeier
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Production Technology ◽  
Returns To Scale ◽  
Productive Efficiency ◽  
Farm Size ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Sample Period ◽  
Analysis Techniques

In this article, we used bootstrap data envelopment analysis techniques to examine technical and scale efficiency scores for a balanced panel of 564 farms in Kansas for the period 1993–2007. The production technology is estimated under three different assumptions of returns to scale and the results are compared. Technical and scale efficiency is disaggregated by farm size and specialization. Our results suggest that farms are both scale and technically inefficient. On average, technical efficiency has deteriorated over the sample period. Technical efficiency varies directly by farm size and the differences are significant. Differences across farm specializations are not significant.

Efficiency of glass firms in India: an application of data envelopment analysis

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Mini Kundi ◽  
Seema Sharma
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Design Methodology ◽  
Large Scale ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Efficiency Measures ◽  
Target Values ◽  
Practical Implications

Purpose The purpose of the present study is to evaluate the efficiency of glass firms in India. Design/methodology/approach Data envelopment analysis (DEA) has been employed to study the technical, scale and super efficiency measures of glass firms in India. Findings Major findings of DEA analysis show that 65 percent firms are found to be technically efficient. Returns to scale analysis indicate that five firms are operating at decreasing returns to scale and two firms are exhibiting increasing returns to scale. Further, results show that small– and medium–scale firms are more efficient than large–scale firms. Old firms are more efficient compared to the young firms and foreign-owned firms are technically more efficient compared to the domestic firms. Practical implications The results of this study would help the managers to assess their relative efficiency and take corrective measures to efficiently use their resources. Originality/value This seems to be the first study to apply DEA to analyze the efficiency of glass firms in India. No previous study on glass industry seems to have decomposed the measure of overall technical efficiency into its components, namely pure technical efficiency and scale efficiency and no study seems to have examined whether ownership, age and size of a firm are significant for its efficiency. In addition, no earlier study seems to have ranked the glass firms based on their efficiency values. Further, target values of inputs and outputs are demonstrated in this study. Stability of efficiency scores is also checked.

scholarly journals Evaluation of the technical efficiency in the production of National Cocoa in the main cantons of the province of Guayas

2021 ◽  
Vol 25 (110) ◽  
pp. 14-22
Author(s):  
Martha Bucaram Levarone ◽  
Francisco Quinde Rosales ◽  
Joy Mayorga Ramos ◽  
Martha Bueno Quinonez
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Productive Efficiency ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Frontier Analysis ◽  
Simon Bolivar ◽  
Non Parametric

A comparative analysis of the technical efficiency in the production of national cocoa among the main producing cantons of the province of Guayas was carried out. For this, the study was based on an analysis with inductive reasoning and empirical-analytical paradigm, through the elaboration of surveys to 361 UPA's in the cantons of: Milagro, San Jacinto de Yaguachi, El Empalme, Alfredo Baquerizo Moreno, Naranjal and Simón Bolívar; these data served as the basis for the elaboration of the Data Envelopment Analysis (DEA) model. The results show that on average, the Simón Bolívar canton is the canton with the highest technical efficiency, with 50% of the total UPAs surveyed in the range of 70% and 99% effectiveness. Finally, regarding the observed averages of allocative efficiency, it can be concluded that Jujan has the highest average with 75%. Keywords: Technical and Allocative Efficiency, National Cocoa, Enveloped Data Analysis, Non Parametric Method. References [1]M. Naranjo., «Un Puerto en busca de una Nación, Guayaquil y la idea fundacional del Ecuador como país,» de Seminario Internacional Poder, Política y Repertorios de la Movilización Social en el Ecuador Bicentenario, Quito, 2009. [2]S. C. Mogro, V. Andrade-Díaz y D. P.-. Villacís, «Posicionamiento y eficiencia del banano, cacao y flores del Ecuador en el mercado mundial,» Revista Ciencia UNEMI, vol. 9, nº 19, pp. 48-53, 2016. [3]M. Vassallo, Diferenciación y agregado de valor en la cadena ecuatoriana del cacao, Quito: Editorial IAEN, 2015. [4]M. Pigache y S. Bainville, Cacao tipo ‘Nacional’ vs. Cacao CCN51: ¿Quién ganará el partido?, Quito: Ird Editions, 2007. [5]M. Chiriboga, Jornaleros, grandes propietarios y exportación cacaotera, Quito: Universidad Andina Simón Bolívar, 2013. [6]A. Acosta., Breve Historia Económica del Ecuador, Quito: Editora Nacional, 2006. [7]M. Espinoza y Y. Arteaga., «Diagnóstico de los Procesos de Asociatividad y la Producción de Cacao en Milagro y sus sectores aledaños,» Revista Ciencia UNEMI, vol. 8, nº 14, pp. 105-112, 2015. [8]E. Romero, M. Fernández, J. Macías y K. Zúñiga, «Producción y comercialización del cacao y su incidencia en el desarrollo socioeconómico del cantón Milagro,» Revista Ciencia UNEMI, vol. 9, nº 17, pp. 56-64, 2016. [9]e. I. I. d. C. A. Ministerio de Agricultura y Ganadería, La Agroindustria en el Ecuador. Un diagnóstico integral, Quito: IICA, 2006. [10]R. Rodríguez, M. Brugiafreddo y E. Raña., «Eficiencia técnica en la agricultura familiar: Análisis envolvente de datos (DEA) versus aproximación de fronteras estocásticas (SFA),» Nova Scientia, vol. 9, nº 18, pp. 342-370, 2017. [11]A. Resti., «Evaluating the cost-efficiency of the Italian banking system: what can be learned from the joint application of parametric and non-parametric techniques,» Journal of Banking & Finance, vol. 21, nº 2, pp. 221-250, 1997. [12]T. Coelli y S. Perelman, «A Comparison Of Parametric And Non-Parametric Distance Functions: With Application To European Railways,» European Journal Of Operational Research, vol. 117, nº 2, pp. 326-339, 1999. [13]B. Iráizoz, M. Rapún y I. Zabaleta., «Assessing the technicalb efficiency of horticultural production in Navarra, Spain,» Agricultural Systems, vol. 78, nº 3, pp. 387-403, 2003. [14]K. Sharma, S. Ping y H. Zaleski., «Productive efficiency of the swine industry in Hawaii,» Research Series, vol. 77, pp. 1-24, 1996. [15]D. Tingley, S. Pascoe y L. Coglan, «Factors affecting technical efficiency in fisheries: Stochastic Production Frontier versus Data Envelopment Analysis approaches,» Fisheries Research, vol. 73, nº 3, pp. 363-376, 2005. [16]H. Johansson, «Technical, allocative and economic efficiency in Swedish dairy farms: the Data Envelopment Analysis versus the Stochastic Frontier Approach,» de Poster background paper prepared for presentation at the XIth International Congress of the European Association of Agricultural Economists (EAAE), Copenhagen, 2005. [17]F. Madau, «Technical and scale efficiency in the Italian Citrus Farming: A comparison between Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) Models,» Munich Personal RePEc Archive (MPRA), vol. 41403, nº 18, pp. 1-25, 2012. [18]E. A. S. d. Pedro, Nivel de competitividad y eficiencia de la producción ganadera, Córdoba: Tesis doctoral. Departamento de Producción Animal, 2013. [19]F. Bacon, Novum Organum, Londres, 1620. [20]Seminario Metodología de la Investigación, Bogota: Facultad de Ciencias Económicas, Universidad Nacional de Colombia, 2015.  

scholarly journals Access to Credit and Farm Efficiency in Cameroon: A Data Envelopment Analysis Approach

2021 ◽  
pp. 48-62
Author(s):  
Orelien Tresor Feumba Tchamba
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Returns To Scale ◽  
Farm Households ◽  
Farm Size ◽  
Data Envelopment ◽  
Agricultural Field ◽  
Access To Credit ◽  
Farm Efficiency ◽  
Constant Returns To Scale

The aims of this paper is to analyze the effect of access to credit on the technical efficiency of farms in Cameroon’s rural area. Using a sample of 545 farm households, we first estimate a Data Envelopment Analysis (DEA) model with constant returns to scale; then a censored TOBIT model enabling us to identify factors of efficiency, especially the effect of access to credit on efficiency. Two main results emerge from our analysis. First, we find that on average, the level of technical efficiency of farms is 56.78%; showing therefore the possibility of substantial efficiency gains. Second, farm size, association membership, and fertilizer expenditure negatively affect technical efficiency, while access to credit, age and education increase it. Based on these results, we believe that it’s interesting for farm householders to organize themselves in associations to benefit from available credits and financial facilities and to share their experiences in the agricultural field in order to improve their efficiency.

Data envelopment analysis of technical efficiency and productivity growth in the US Pacific Northwest sawmill industry

2008 ◽  
Vol 38 (10) ◽  
pp. 2553-2565 ◽  
Author(s):  
Ted L. Helvoigt ◽  
Darius M. Adams
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Pacific Northwest ◽  
Productivity Growth ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Efficiency Change ◽  
Constant Returns To Scale ◽  
Sawmilling Industry

This paper uses data envelopment analysis (DEA) to characterize the changing production frontier (technical efficiency, productivity growth, technical and efficiency change, and returns to scale) of the sawmilling industry in the Pacific Northwest (PNW) US using geographical panel data for the period 1968–2002. Unlike past DEA studies, we develop confidence intervals for all estimates using an improved bootstrapping method. The results indicate that the gap between the least and most efficient regions in PNW has grown and the least efficient regions are falling further behind the most efficient regions. For the Oregon regions, the null hypothesis of constant returns to scale (CRS) could not be rejected for any year. For the Washington regions, returns to scale varied year by year, although only two of the five regions showed strong tendencies away from CRS. For PNW as a whole, mean productivity growth was 0.5% per year between 1968 and 1992. Between 1992 and 2002, the regional mean was 1.3%, although with wide variation across regions. DEA results indicate that the vast majority of productivity growth in the PNW sawmilling industry between 1968 and 2002 was due to technical change. Improvements in scale efficiency played a very small role, and efficiency change was zero or negative.

Efficiency in the Indian iron and steel industry – an application of data envelopment analysis

2014 ◽  
Vol 11 (1) ◽  
pp. 4-19 ◽  
Author(s):  
Roma Mitra Debnath ◽  
V.J. Sebastian
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Steel Industry ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Annual Income ◽  
Content Type ◽  
Steel Manufacturing ◽  
Constant Returns To Scale

Purpose – The purpose of this paper applies to Indian steel manufacturing industries to evaluate the technical and scale efficiency (SE). Design/methodology/approach – Data envelopment analysis (DEA) has been employed to calculate the relative efficiency of the steel manufacturing units. The selection criteria for the inclusion of a steel manufacturing unit in the analysis has been annual income of more than 50 crores and units manufacturing pig iron, steel and sponge iron. Within the DEA framework, the output-oriented model with constant returns to scale and variable returns to scale were studied. Four input variables, namely, gross fixed assets, total energy cost, total number of employees and currents assets were considered. Among the output variables, the four variables considered are income, sales, PBIT and PAT. Findings – The result of the efficiency scores have been categorized into three parts. The pure technical efficiency represents local efficiency and the reason of inefficiency is due to inefficient operations. Technical efficiency indicates that the respective decision-making units are globally efficient in case the efficiency is 100 per cent. The SE explains that the inefficiency is caused by disadvantageous conditions. As the result shows, that public sector undertaking (PSUs) are operating under disadvantageous conditions as compared to private manufacturing units. One of the possible reasons of location disadvantage condition is manufacturing units for PSUs are scattered throughout India. Some of the units are located in such places where, the raw material, supply chain could be difficult. It has been found that 45 per cent of the private manufacturing units are technically as well as scale inefficient units. Practical implications – The result of the study would benefit the steel industry to develop a performance benchmarking as steel companies must be profitable in the long term to ensure sustainable achievements. Originality/value – This is an original study to apply DEA to get insights on productivity efficiency of the steel manufacturing units in India. Though the manufacturing units were selected on the basis of annual income, the analysis of productivity does not reflect any impact of income on the efficiency of the manufacturing firms.

scholarly journals Technical and Scale Efficiency Analysis of Cash Crops Production in Pakistan: An Application of Data Envelopment Analysis (DEA)

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Crop Production ◽  
Returns To Scale ◽  
Efficiency Analysis ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Cash Crops ◽  
Parametric Data ◽  
Crops Yield

The study has analyzed the technical efficiency of major cash crops' yield in Pakistanfrom 1948 till 2018. Cotton and sugarcane are the crops selected for analysis with an area ofthousand hectares. For carrying out the analysis, data is taken from the Ministry of Food andAgriculture (MINFA), Pakistan Bureau of Statistics, and economic surveys of Pakistan of differentyears. The technique employed in the study is the non-parametric Data Envelopment Analysis(DEA) technique. The result obtained after technical efficiency analysis reveal the suboptimal cashcrop yield in Pakistan throughout the analysis. The average technical efficiency of cotton andsugarcane crops from 1948 to 2018 are approximately 0.80 and 0.84 respectively. Furthermore,the result of scale efficiency analysis showed the monotonous performance of cash crops'production. The consistent variation in cotton and sugar cane crops' yield over the years reportedby the efficiency analysis, accounts for Constant Return to Scale (CRS) and Variable Return toScale (VRS) models, resulted in varying Return to Scale (RTS). The plausible reasons can beattributed to the Increasing Returns to Scale (IRS) or Decreasing Returns to Scale (DRS) in thecase of cotton and sugarcane production. An interesting finding is unveiled that out of 70 years,cotton and sugarcane crops achieved the optimal scale efficiencies for only one year. The bestpossible level of output of cotton and sugarcane crops in the future can be achieved by allocatinga lesser area under the cultivation of these crops in Pakistan. It is also imperative that farmers adoptand implement modern farm technologies. Increasing the area under crop production is not asolution, especially in the wake of alarming population growth and urbanization in Pakistan. Theformulation of policies encouraging farmers requires modern farm inputs to realize the optimalyield of cash crops in Pakistan. The government should educate the farmers regarding advancedfarming and efficient farm management techniques to help to augment the farm output withoutexpansion of the area for these crops

scholarly journals Measuring Technical Efficiency of Hospitals affiliated with Shahrekord University of Medical Sciences, Using a Combination Method Data Envelopment Analysis (DEA) – Principle Component Analysis (PCA)

2021 ◽  
Author(s):  
Sara Emamgholipour ◽  
Mohammad Arab ◽  
Abbas Rahimi-Foroushani ◽  
Sayede Somaye Forghani Dehnavi ◽  
Shahide Allahverdi ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Principal Components ◽  
Returns To Scale ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Medical Sciences ◽  
Combined Method ◽  
Management Efficiency ◽  
Dea Method

Background: Measuring the efficiency of hospitals due to the high proportion of budget allocated to them on the one hand, and the need to ensure the best practices regarding the use of scarce resources on the other hand, is of particular importance. The purpose of this study is to evaluate the technical efficiency of the affiliated hospitals of Shahrekord University of Medical Sciences by using a combination of Principal Component Analysis and (PCA) & Data Envelopment Analysis (DEA). Methods: This was an analytical and cross-sectional study measuring the technical efficiency of all 8 hospitals affiliated to Shahrekord University of Medical Sciences. The required information was collected from the medical records unit of each hospital. For better differentiation between efficient and inefficient units, and the increase of research accuracy and further differentiation between hospitals in terms of efficiency, at first, 17 indicators were selected to assess and adjust these parameters to 3 components proportional to the number of the hospitals by using PCA and SPSS 16 software. After doing the PCA, 7 studied input variables became 7 principal components among which the first input component reflecting the 83 % of scattering data was selected as principal input component, and for being more influenced by human resource variables, it was named as a human resource index. Furthermore, among the output variables, the first 2 output components, which represented 76% of the variance of the data, were selected as the 2 principal components of the output for the study, which were mostly affected by these variables, respectively, the number of admissions and length of stay. Then, the modified input and output components were entered into the software Windeap 2.1 and the technical efficiency of hospitals and their rank were calculated by assuming constant and variable efficiency with respect to the scale. In order to evaluate the effect of using the combined method instead of the conventional method of efficiency measurement, the results of the PCA - DEA method were compared with the results of the conventional DEA method. Results: The result of DEA on the selected components showed the capacity to upgrade the Technical Efficiency (TE) of hospitals is 15 % (TE: 0.852). Moreover, out of 8 hospitals, 1 hospital was increasing return to scale, 3 decreasing returns to scale and 4 constant returns to scale. The technical efficiency of 3 hospitals was 1 (TE = 1), 2 hospitals had the technical efficiency between 0.80 to 1 (1 > TE > 0.80) and that in 3 hospitals was less than 0.80 (TE < 0.80).  The scale efficiency for 50 % of hospitals and the management efficiency for 62/5 % of them were equal 1. Conclusion: The average of total technical efficiency, management efficiency and scale efficiency were calculated to be 0.999, 1 and 0.999, respectively based on the usual comprehensive analysis method; while using the combined method, the average total technical efficiency, management efficiency and scale efficiency were 0.852, 0.947 and 0.902 respectively. The results confirm that the use of PCA method, due to its important role in reducing alignments, increases research accuracy and better differentiates between hospitals in terms of efficiency.

scholarly journals The Measurement of Innovation Efficiency of Chinese High-tech Industry Using Data Envelopment Analysis

2015 ◽  
Vol 65 (s2) ◽  
pp. 101-113 ◽  
Author(s):  
Ling Jiang ◽  
Yunyu Jiang ◽  
Zhijun Wu ◽  
Dongsheng Liao ◽  
Runfa Xu
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Environmental Variables ◽  
Scale Efficiency ◽  
High Tech ◽  
Data Envelopment ◽  
High Tech Industry ◽  
Economic Competitiveness ◽  
Development Level ◽  
Using Data

In the era of knowledge economy, a country’s economic competitiveness depends largely on the development level of high-tech industry. This paper evaluates the efficiency of China’s high-tech industry in 31 provinces in 2012 with data envelopment analysis. The empirical results are summarized as following. Firstly, when the effects of exogenous environmental variables are not controlled, the comprehensive technical efficiency of 31 provinces will be overestimated, the pure technical efficiency will be underestimated, and the scale efficiency value will be overestimated. Secondly, after eliminating the environmental impact, the comprehensive technical efficiency of 31 provinces with the average of 0.395 is rather low, due to the low scale efficiency.

Efficiency evaluation of Indian oil and gas sector: data envelopment analysis

2019 ◽  
Vol 14 (2) ◽  
pp. 362-378 ◽  
Author(s):  
Vikas Vikas ◽  
Rohit Bansal
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Capital Investment ◽  
Oil And Gas ◽  
Stock Exchange ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Content Type ◽  
Indian Economy ◽  
Oil And Gas Sector

Purpose Data envelopment analysis (DEA), a non-parametric technique is used to assess the efficiency of decision-making units which are producing identical set of outputs using identical set of inputs. The purpose of this paper is to find the technical efficiency (TE), pure technical efficiency and scale efficiency (SE) levels of Indian oil and gas sector companies and to provide benchmark targets to the inefficient companies in order to achieve efficiency level. Design/methodology/approach In the present study, a group of 22 oil and gas companies which are listed on the National Stock Exchange for which the data were available for the period 2013–2017 has been considered. DEA has been performed to compare the efficiency levels of all companies. To measure efficiency, three input variables, namely, combined materials consumed and manufacturing expenses, employee benefit expenses and capital investment and two output variables – operating revenues and profit after tax (PAT) have been considered. On the basis of performance for the financial year ending 2017, benchmark targets based on DEA–CCR (Charnes, Cooper and Rhodes) model have been provided to the inefficient companies that should be focused upon by them to attain the efficiency level. The performance of the companies for the past five years has been examined to check the fluctuations in the various efficiency scores of the companies considered in the study over the years. Findings From the results obtained, it is observed that 59 percent, i.e. 13 out of 22 companies are technically efficient. By considering DEA BCC (Banker, Charnes and Cooper) model, 16 companies are observed to be pure technically efficient. In terms of SE, there are 14 such companies. The inefficient units need to improve in terms of input and output variables and for this motive, specified targets are assigned to them. Some of these companies need to upgrade significantly and the managers must take the concern earnestly. The study has also thrown light on the performance of the companies over last five years which shows Oil India Ltd, Gujarat State Petronet Ltd, Petronet LNG Ltd, IGL Ltd, Mahanagar Gas, Chennai Petroleum Corporation Ltd and BPCL Ltd as consistently efficient companies. Research limitations/implications The present study has made an attempt to evaluate the efficiency of Indian oil and gas sector. The results of the study have significant inferences for the policy makers and managers of the companies operating in the sector. The results of the study provide benchmark target level to the companies of Oil and Gas sector which can help the managers of the relatively less efficient companies to focus on the ways to improve efficiency. The improvement in efficiency of a company would not only benefit the shareholders, but also the investors and other stakeholders of the company. Originality/value In the context of Indian economy, very limited number of studies have focused to measure the efficiency of oil and gas sector in the context of Indian economy. The present study aims to provide the latest insight to the efficiency of the companies especially operating in the Indian oil and gas sector. Further, as per our knowledge, this study is distinctive in terms of analyzing the efficiency of Indian oil and gas sector for a period of five years. The longitudinal study of the sector efficiency provides a bird eye view of the average efficiency level and changes in the efficiency levels of the companies over the years.

scholarly journals Performance of Village Unit Co-Operatives in Yogyakarta Special Region: A Data Envelopment Analysis Approach

2017 ◽  
Vol 1 (2) ◽  
pp. 067
Author(s):  
Abi Pratiwa Siregar ◽  
Jamhari Jamhari ◽  
Lestari Rahayu Waluyati
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Best Practice ◽  
Negative Impact ◽  
Positive Impact ◽  
Three Dimensions ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Rice Milling ◽  
Special Region

This study assessed the performance of 32 village unit co-operatives (KUD) in Yogyakarta Special Region during 2011 to 2012. The efficiency level of the KUD were evaluated by employing the data envelopment analysis and multiple regression analysis using panel data to determine the factors affecting efficiency level. Efficiency analysis was decomposed into three dimensions to explore possible sources of inefficiency. According to Marwa and Aziakpono (2016), the first dimension was technical efficiency, which explored the overall effectiveness of transforming the productive inputs into desired outputs compared to the data-driven frontier of best practice. The second dimension was pure technical efficiency, which captured managerial efficiency in the intermediation process. The third dimension was scale efficiency, which explored whether KUD were operating in an optimal scale of operation or not. The results found that the average scores are 64%, 92%, and 68% for technical, pure technical, and scale efficiency respectively in 2011, while in 2012 the average scores are 57%, 94%, and 60% for technical, pure technical, and scale efficiency. Factors having significantly positive impact on several measures of efficiency are incentive and dummy variables (agriculture inputs and hand tractor). Accounts receivable only has positive relationship to pure technical efficiency. On the other hand, rice milling unit and electricity services have negative impact with several measures of efficiency.

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