Technical efficiency of poultry farms in vietnam non parametric and parametric approaches

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS TECHNICAL EFFICIENCY OF POULTRY FARMS IN VIETNAM NON-PARAMETRIC AND PARAMETRIC APPROACHES BY NGUYỄN THỊ NGỌC LINH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2013 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS TECHNICAL EFFICIENCY OF POULTRY FARMS IN VIETNAM NON-PARAMETRIC AND PARAMETRIC APPROACHES A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS BY NGUYỄN THỊ NGỌC LINH Academic Supervisor: DR TRƢƠNG ĐĂNG THỤY Ho Chi Minh City, December 2013 DECLARATION This is to certify that the thesis entitle “Technical efficiency of Vietnam poultry farms: non-parametric and parametric approaches”, which is submitted by me in partial fulfillment of the requirement for the degree of Master of Art in Development Economic to Vietnam – The Netherlands Programme The thesis comprises only my original work and due supervision and acknowledgement have been made in the text to all materials used Nguyễn Thị Ngọc Linh i ACKNOWLEDGEMENT My appreciation firstly goes to my supervisor, Dr Trƣơng Đăng Thụy, who has made a great effort to support me in this thesis His profound comments have been helpful not only in completing this study but also in improving my knowledge in doing the research I would like to thank my family, especially my mother I would not complete this thesis, as well as study in this program, without their scarification, encouragement and important support For the love and expectation of my family, which motivate my effort to complete this master degree, my mere expression of gratitude here have never been sufficient I am very proud to attend this program I am grateful to all lectures in Vietnam – Netherlands Programme for their dedicated instruction and all the courses during the period I studied at the program Besides, I would like to thank all the academic and technical staffs of the Vietnam – Netherlands Programme for supporting me during that time Moreover, I received the enormous encouragement from my classmates and workmates, especially my special friend who has supported me a lot in the writing process I am very grateful for everything that all of you gave me ii ABBREVIATION AE Allocative efficiency BCC DEA model as study of Banker, Charnes and Cooper CCR DEA model as study of Charnes, Cooper and Rhodes CRS Constant returns to scale DEA Data Envelopment Analysis DMU Decision making unit DPF Deterministic Production Function FAO Food and Agriculture Organization GSO General Statistic Office ML Maximum likelihood MLE Maximum likelihood estimate OLS Ordinary Least Square PDF Probability density function SFM Stochastic Frontier Model TE Technical efficiency VHLSS Vietnam Household Living Standards Survey VND Vietnam Dong VRS Variable returns to scale iii ABSTRACT This study attempts to estimate the technical efficiency as well as determine the factorial effects of technical efficiency level of Vietnam poultry farms under semiindustrial system and traditional system Then, this study employs a two-stage analysis with a household-level dataset in whole country In particular, the first stage estimates technical efficiency level of poultry farms under both systems through non-parametric and parametric approaches, which were represented by Data Envelopment Analysis and Stochastic Frontier Analysis, respectively In the second stage, sources of efficiency will be determined by Tobit regression and least square regression with householdspecific characteristics which represent for human capital in qualitative dimension A sample of 3,356 households in VHLSS 2010 is utilized to analyze the broiler poultry production in Vietnam, wherein 820 poultry farms under semi-industrial system and 2,536 poultry farms under traditional system The results from the first stage show that the average technical efficiency which was obtained from SFM is higher than that in DEA; and the TE scores in SFA exhibit the variability lower than TE scores in DEA Moreover, from the analysis in the second stage, it is can be stated that education level of farmer has significantly effects on the TE differential among poultry farms in the positive way Finally, the results also show that TE scores of poultry farms under both systems located in the Southeast are higher than other agro-ecological regions Keywords: poultry household, data envelopment analysis, stochastic frontier model, technical efficiency, human capital iv TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problem Statements 1.2 Research Objectives 1.3 Research Organization CHAPTER 2: LITERATURE REVIEW 2.1 Basic concepts of efficiency 2.2 Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) 2.2.1 Data Envelopment Analysis (DEA) 10 2.2.2 Stochastic Frontier Model approach (SFM) 12 2.2.2.1 The Production frontier 12 i Deterministic Production Frontier 12 ii Stochastic Production Frontier 13 2.2.2.2 Estimation method 15 i Modified Ordinary Least Squares (MOLS) 15 ii Maximum likelihood Estimation (MLE) 16 2.2.2.3 Measurement of Efficiency 17 2.2.3 Comparison between DEA and SFM approaches 17 2.3 Empirical studies 19 2.3.1 Measurement of technical efficiency of poultry subsector 19 2.3.2 DEA and SFM approaches on measurement of technical efficiency of agriculture sector 22 2.3.3 Impact of human capital on agriculture productivity 24 CHAPTER 3: OVER VIEW OF POULTRY FARMS IN VIETNAM 27 3.1 General characteristics of poultry production in Vietnam 27 3.2 Poultry production system in Vietnam 29 i Traditional extensive household poultry production (non-intensive system) 31 ii Semi-industrial commercial poultry production (semi-intensive) 32 iii Industrial poultry production (intensive system) 32 v CHAPTER 4: DATA DESCRIPTIONS AND RESEARCH METHODOLOGY 34 4.1 Data Description 34 4.1.1 Data Source 34 4.1.2 Data Descriptions 34 4.2 Method, Model specification and Variables definition 36 4.2.1 The first stage: Measurement of Technical efficiency 36 4.2.1.1 Measurement of Technical efficiency using Data Envelopment Analysis 36 4.2.1.2 Measurement of Technical efficiency using Stochastic Frontier Model 38 i Production function specification 39 ii Estimation method specification 40 4.2.1.3 Variables Description for the first stage 41 4.2.1.4 Hypothesis testing 44 i Functional form specification test 45 ii Estimating method specification test 45 4.2.2 The second stage: Factorial decomposition of Technical efficiency 46 4.2.2.1 The Technical efficiency model 46 4.2.2.2 Variables description for the second stage 49 4.2.3 Research Hypotheses 53 CHAPTER 5: EMPIRICAL RESULTS AND DISSCUSION 54 5.1 The first stage: The estimation of the Technical efficiency scores 54 5.1.1 Testing null hypotheses for SFM approach in the first stage 54 i The Production Functional form specification test 55 ii The model specification test 57 5.1.2 Discussion on results obtained from the first stage 58 5.1.2.1 Comparing the Technical efficiency scores in SFM and DEA approaches 58 5.1.2.2 The distribution of technical efficiency scores in SFM and DEA approaches 60 5.1.2.3 Technical efficiency varies between agro-ecological regions 63 vi 5.2 The second stage: The determinants of Technical efficiency 65 5.2.1 Testing the significant of coefficients simultaneously for regressions 65 5.2.2 Discussion on the results obtained from the second stage 66 5.2.2.1 The effects of human capital on technical efficiency scores 67 5.2.2.2 The difference of technical efficiency scores between various agroecological regions 69 CHAPTER 6: CONCLUSION AND POLICY IMPLICATION 70 6.1 Concluding remarks 71 6.2 Policy implications 72 6.3 Limitations of study and recommendations for future research 73 REFERENCES 74 Appendix 1: Matrix of correlation between variables in Stochastic frontier production function of semi-industrial poultry farms 78 Appendix 2: Matrix of correlation between variables in Stochastic frontier production function of traditional poultry farms 78 Appendix 3: Matrix of correlation between variables in technical efficiency model of semi-industrial poultry farms 79 Appendix 4: Matrix of correlation between variables in technical efficiency model of traditional poultry farms 79 Appendix 5: OLS regression of technical efficiency model of traditional poultry farms 80 Appendix 6: Testing heteroskedasticity for OLS regression of technical efficiency model of traditional poultry farms 80 Appendix 7: Testing heteroskedasticity for OLS-robust regression of technical efficiency model of traditional poultry farms 81 Appendix 8: Summary of Empirical Studies at measuring technical efficiency of poultry sector 82 Appendix 9: Summary empirical studies of comparison DEA and SFM approaches at measuring the technical efficiency 84 vii LIST OF TABLES Table 4-1: Summary statistic of broiler poultry productivity of farms between regions and within systems 35 Table 4-2: Summary statistic of Input and Output variables for Poultry farms under semi-industrial system and traditional system 42 Table 4-3: Statistical description of determinant factors of technical efficiency level 52 Table 5-1: Maximum-likelihood estimates of the Cobb-Douglas and Translog stochastic production frontier models 56 Table 5-2: Tests hypotheses of the production function specification 58 Table 5-3: Summarizing the technical efficiency scores 59 Table 5-4: The technical efficiency distribution in SFM and DEA-BCC 61 Table 5-5: Technical efficiency scores among agro-ecological regions 63 Table 5-6: The factorial effects on technical efficiency scores from SFM and DEABCC 66 LIST OF FIGURES Figure 2-1: Technical efficiency and Allocative Efficiency Figure 2-2: Stochastic Production Frontier 14 Figure 3-1: Growth rate of livestock and poultry with base year of 1990 27 Figure 3-2: Annual growth rate of number of poultry with base year of 1990 28 Figure 3-3: Poultry density of Vietnam in 2006 29 Figure 3-4: Average number of birds per household in 2001 29 Figure 3-5: Regional Characteristics of poultry-holding household in 2002 30 Figure 3-6: Proportion of total chicken in three production systems in 2006 and 2009 in Vietnam 33 Figure 5-1: The distribution of TE scores of semi-industrial and traditional systems from SFM and DEA approaches 62 Figure 5-2: Technical efficiency scores between agro-ecological regions 64 viii CHAPTER CONCLUSION AND POLICY IMPLICATION This study aims to estimate the technical efficiency as well as to determine the effects of human capital on efficiency level of poultry farms under semi-industrial and traditional systems, in different agro-ecological regions A two-stage analysis was employed to quantify these objectives including (i) estimates the technical efficiency of poultry farms; and (ii) decomposes the factorial effects of technical efficiency differential among poultry farms The first stage identifies and analyzes the technical efficiency scores of poultry farms in both systems In this stage, the output variable was determined by the income of farm from broiler poultry in the year 2010, where the proxy of input variables including all of direct cost (initial breeds, feed, fuels and energy, medicine/vaccine, and other small direct costs) and human capital in quantitative dimension (working day of family’s members) The first stage estimates technical efficiency of poultry farms under both systems by non-parametric and parametric approaches Specifically, the Data Envelopment Analysis under the variable returns to scale condition (DEA-BCC) represents for non-parametric approach Whereas in the parametric approach, the Cobb-Douglas and Translog stochastic frontier function under the distributional assumption that inefficiency term follows the non-negative truncation of the normal distribution These two approaches show somewhat different results of technical efficiency scores of poultry farms under both systems The second stage examines the effects of human capital in qualitative dimension on different technical efficiency scores The effect of quality of labor on agriculture productivity is supported by theoretical and empirical studies that the influences of quality of labor on farm production efficiency could be either positive or negative sign In this stage, the human capital is represented by specifications of household head including gender, age, education and participation of agricultural training course In this study, based on the statistical characteristics of TE scores which were obtained from the first stage, the Tobit regression and least square regression were employed to quantify this effect Particularly, the technical efficiency model with dependent variable obtained from DEABCC model was estimated by the Tobit regression because of the proportional censoring of TE scores is significant On the other hand, the technical efficiency with TE scores from 70 SFM model was estimated by least square regression, the OLS-robust was applied to remedy the heteroskedasticity These two estimation methods show mixed results In general, the human capital has positive effect on TE scores differential among poultry farm in both systems except the participation in agricultural training course Moreover, the difference of TE scores between various agro-ecological regions is also determined in this stage 6.1 Concluding remarks Based on results from the two stages, there are three primary conclusions First, this study shows the evidence of different results of technical efficiency from two different approaches in the first stage The results show that average TE score in SFM is higher than average TE score in DEA-BCC model Moreover, the TE in SFM exhibits the lower variability than TE in DEA; and the range of TE scores in DEA is wider than the range of TE scores in SFM It can be explained by the effect of random noise factor that farmers cannot control In DEA approach, the inefficiency term includes the random shock factor whereas in SFM, the random noise is separated from the TE level It seems that the result in SFM is more consistent than that in DEA However, it is hard to conclude that semi-industrial and traditional smallholder poultry farms have high level of technical efficiency The technical efficiency scores were estimated from the ratio of output to input of a farm in comparison with other poultry farms Hence, we can only say that from SFM results, TE levels of poultry farms are similar for both systems whereas from DEA, the variance of TE levels in both systems is more widely spread Second, the estimate from second stage indicates that human capital significantly affects the technical efficiency level of poultry farms in both systems The similar findings in both SFM-TE and DEA-TE models show that in semi-industrial system, poultry farms with male household head have higher level of technical efficiency than those with female counterpart In addition, from these two models, the education level of household head has significant effect on TE scores of poultry farms in traditional system in positive way Furthermore, the farmer who participated in agricultural training course does not have any advantage to improve the level of technical efficiency Besides, the evidence of age of household head is indicated in estimations of DEA-TE model for traditional poultry farms, 71 where farms with older household head have significantly higher TE level than those with younger household head Finally, the estimations from second stage show that technical efficiency scores of poultry farms in both systems located in Southeast are highest compare to other agro-ecological regions The estimations from different technical efficiency models in second stage are quite similar to that in the Southeast because poultry farms in both systems have higher technical efficiency levels than other regions This result consistent with the poultry raising situation in Vietnam which was analyzed by FAO (2003) that in the Southeast, even in the small or large scale, farmers tend to raise poultry under intensive or semi-intensive system, using industrial feed and the products are usually marketed commercially 6.2 Policy implications The empirical results for technical efficiency in relation to human capital of poultry sector based on farm level data from 3,356 semi-industrial and traditional smallholder poultry farms were pointed out in this study First, poultry sector plays a significant role in enhancing living standards such as raising income, creating jobs or improving human nutrition in Vietnamese rural areas From the results of the first stage in this study, it is hard to say that TE scores of poultry farms in Vietnam are in the high level It shows an important implication that there is an opportunity for farms to increase their income from broiler poultry by improving the TE level, especially those of the traditional system Specifically, the TE score of traditional poultry farms from Translog stochastic frontier function being 0.8122 indicates that if the farmers improve their TE to the maximum level, they can increase their average income by nearly 300 thousand VND per year at the same level of input Similarly, the results of DEA-BCC model show that the average income of poultry farms in semi-industrial system and traditional system can be increased possibly by 2.5million VND and 700thousand VND, respectively Furthermore, the education level of household heads was proved to be an important factor to increase technical efficiency of poultry farms in traditional system The average education level of farms is still low, 6.5 over 12 according to the formal education system 72 of Vietnam Although education level trivially influences on the TE level of semi-industrial poultry farms, investing in rural education is also recommended to facilitate the adaptation of new technology as well as new breeds of poultry because it leads to the enhancement of the TE level of poultry farms Finally, the result shows that the agricultural training program seems inefficient However, the technical support program for farmers is necessary to adapt new technology for poultry and for agriculture sector in general Hence, policy makers should consider improving the quality of these programs 6.3 Limitations of study and recommendations for future research Although this study could provide some implications above, it still has several limitations as follows Firstly, in the analysis of the first stage and the second stage, the production function and technical efficiency function could not take all socio-economic factors into account due to the lack of data Some missing variables should be corrected in further studies to achieve better results Secondly, the breed of poultry should also be considered because breed of poultry is an important factor to determine productivity of farms The local poultry breeds are less productivity than those which were imported from abroad, while traditional poultry farms tend to raise poultry with local breeds However, the data not have information for the breeds of poultry that farmers raised under both systems This problem could be considered in later research, then, the results will be more consistent Finally, this study does not provide a complete comparison in all aspects of production efficiency; it focuses only in pure technical efficiency These problems are expected to be solved in the future studies In conclusion, although this study still faces some limitations, it is expected to make significant contribution to the empirical study on measuring technical efficiency of poultry sector in Vietnam Furthermore, this study also expects to contribute to the interesting research methodology which employs two alternative methods including non-parametric and parametric approaches to measure technical efficiency in agriculture sector 73 REFERENCES Afriat, S N (1972) Efficiency estimation of production functions International Economic Review, 13(3), 568-598 Aigner, D J., & Chu, S F (1968) On estimating the industry production function The American Economic Review, 58(4), 826-839 Aigner, D J., Lovell, C A L., & Schmidt, P (1977) Formulation and estimation of stochastic frontier production function models Journal of econometrics, 6(1), 2137 Alabi, R A., & Aruna, M B (2005) Technical efficiency of family poultry production in Niger-Delta, Nigeria Journal of Central European Agriculture, 6(4), 531-538 Alam, G M., Hoque, K E., Khalifa, M T B., Siraj, S B., & Ghani, M F B A (2009) The role of agriculture education and training on agriculture economics and national development of Bangladesh Afr J Agric Res, 4(12), 1334-1350 Alene, A D., & Zeller, M (2005) Technology adoption and farmer efficiency in multiple crops production in eastern Ethiopia: A comparison of parametric and nonparametric distance functions Agricultural economics review, 6(1), Banker, R D., Charnes, A., & Cooper, W W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis Management science, 30(9), 1078-1092 Battese, G E (1992) Frontier production functions and technical efficiency: a survey of empirical applications in agricultural economics Agricultural economics, 7(3), 185208 Battese, G E., & Coelli, T J (1988) Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data Journal of econometrics, 38(3), 387-399 Battese, G E., & Coelli, T J (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data Empirical economics, 20(2), 325-332 Charnes, A., Cooper, W W., & Rhodes, E (1978) Measuring the efficiency of decision making units European journal of operational research, 2(6), 429-444 74 Cullinane, K., Wang, T F., Song, D W., & Ji, P (2006) The technical efficiency of container ports: comparing data envelopment analysis and stochastic frontier analysis Transportation Research Part A: Policy and Practice, 40(4), 354-374 Davidson, R., & MacKinnon, J G (2004) Econometric theory and methods New York: Oxford University Press Delgado, C L (1999) Livestock to 2020: The next food revolution (Vol 61) Retrieved from: http://www.ifpri.org/sites/default/files/publications/vb61.pdf Desvaux, S., Ton, V D., Thang, P D., & Hoa, P T T (2008) A general review and a description of the poultry production in Vietnam Hanoi, Viet Nam: CIRAD (PRISE) Djomo, J M N., & Sikod, F (2012) The Effects of Human Capital on Agricultural Productivity and Farmer’s Income in Cameroon International Business Research, 5(4), 149 Fafchamps, M., & Quisumbing, A R (1999) Human capital, productivity, and labor allocation in rural Pakistan Journal of Human Resources, 369-406 Farrell, M J (1957) The measurement of productive efficiency Journal of the Royal Statistical Society Series A (General), 120(3), 253-290 Foster, A D., & Rosenzweig, M R (1995) Learning by doing and learning from others: Human capital and technical change in agriculture Journal of political Economy, 1176-1209 Gallacher, M (1999) Human capital and production efficiency: Argentine agriculture CEMA Retrieved from: http://cdi.mecon.gov.ar/biblio/doc/cema/doctrab/158.pdf Hanh, H P., Burgos, S & Roland-Holst, D (2007) The Poultry Sector in Viet Nam: Prospects for Smallholder Producers in the Aftermath of the HPAI Crisis.Pro-Poor Livestock Policy Initiative (PPLPI) Research Report Heidari, M D., Omid, M., & Akram, A (2011) Using nonparametric analysis (DEA) for measuring technical efficiency in poultry farms Revista Brasileira de Ciência Avícola, 13(4), 271-277 Hoff, A (2007) Second stage DEA: Comparison of approaches for modelling the DEA score European Journal of Operational Research, 181(1), 425-435 Huffman, W E (1977) Allocative efficiency: the role of human capital The Quarterly Journal of Economics, 59-79 75 Huffman, W E (1980) Farm and off-farm work decisions: the role of human capital The Review of Economics and Statistics, 62(1), 14-23 Huffman, W E (2001) Human capital: Education and agriculture Handbook of agricultural economics, 1, 333-381 Jatto, N A., Maikasuwa, M A., Jabo, M S M., & Gunu, U I (2012) Assessing the technical efficiency level of poultry egg producers in Ilorin, Kwara State: A Data Envelopment Analysis approach European Scientific Journal, 8(27), 110-117 Jondrow, J., Knox Lovell, C A., Materov, I S., & Schmidt, P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model Journal of econometrics, 19(2), 233-238 Ike, P C (2011) Resource Use and Technical Efficiency of Small Scale Poultry Farmers in Enugu State, Nigeria: A Stochastic Frontier Analysis International Journal of Poultry Science, 10(11), 895-898 Koopmans, T C (1951) Analysis of production as an efficient combination of activities Activity analysis of production and allocation, 13, 33-37 Lau, L J., & Yotopoulos, P A (1971) A test for relative efficiency and application to Indian agriculture The American Economic Review, 61(1), 94-109 Mastromarco, C (2008) Stochastic Frontier Models Dipartimento di Economia e Matematica-Statistica, Università di Lecce, CIDE 2008 Retrieved from: http://www.camillamastromarco.it/CIDE/STFR.pdf McDonald, J (2009) Using least squares and tobit in second stage DEA efficiency analyses European Journal of Operational Research, 197(2), 792-798 Ohajianya, D O., Mgbada, J U., Onu, P N., Enyia, C O., Henri-Ukoha, A., Ben-Chendo, N G., & Godson-Ibeji, C C (2013) Technical and economic efficiencies in poultry production in Imo State, Nigeria American Journal of Experimental Agriculture, 3(4), 927-938 Rafiee, S., Sefeedpari, P., & Akram, A (2013) Identifying sustainable and efficient poultry farms in the light of energy use efficiency: a Data Envelopment Analysis approach Journal of Agricultural Engineering and Biotechnology, 1(1), 1-8 Rao, D P., O'Donnell, C J., & Battese, G E (2005) An introduction to efficiency and productivity analysis T J Coelli (Ed.) Springer Schultz, T W (1961) Investment in human capital The American economic review, 51(1), 1-17 76 Shao, B., & Lin, W T (2002) Technical efficiency analysis of information technology investments: a two-stage empirical investigation Information & Management, 39(5), 391-401 Sugiyama, M., Iddamalgoda, A., Oguri, K., & Kamiya, N (2003) Development of livestock sector in Asia: an analysis of present situation of livestock sector and its importance for future development Gifu City Women’s College, Gifu, Japan, 52 Theodoridis, A M., & Psychoudakis, A (2008) Efficiency measurement in Greek dairy farms: Stochastic frontier vs data envelopment analysis International Journal of Economic Sciences and Applied Research, (2), 53-66 Tian, W., & Wan, G H (2000) Technical efficiency and its determinants in China's grain production Journal of productivity analysis, 13(2), 159-174 Tung, D X., & Rasmussen, S (2005) Production function analysis for smallholder semisubsistence and semi-commercial poultry production systems in three agroecological regions in Northern provinces of Vietnam Livestock Research for Rural Development, 17(6), 19 Upton, M (2004) The role of livestock in economic development and poverty reduction Pro-Poor Livestock Policy Initiative Working Paper, 10 Wadud, A., & White, B (2000) Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods Applied economics,32(13), 1665-1673 Zamanian, G R., Shahabinejad, V., & Yaghoubi, M (2013) Application of DEA and SFA on the Measurement of Agricultural Technical Countries International Journal of Applied, 3(2), 43-51 77 Efficiency in MENA Appendix Matrix of correlation between variables in Stochastic frontier production function of semi-industrial poultry farms lnOutput lnInitialbreed lnFeed lnMedicine lnFE lnOther lnWorker lnOutput lnInitialbreed 0.4854 lnFeed 0.7895 0.3982 lnMedicine 0.4556 0.2222 0.4142 lnFE 0.3766 0.2432 0.3928 0.4111 lnOther 0.2499 0.1815 0.2115 0.2906 0.301 lnWorker 0.4624 0.2532 0.3846 0.1666 0.1867 0.0562 Appendix Matrix of correlation between variables in Stochastic frontier production function of traditional poultry farms lnOutput lnOutput lnInitialbreed lnFeed lnMedicine lnFE lnOther lnWorker lnInitialbreed 0.3971 lnFeed 0.6871 0.2713 lnMedicine 0.3552 0.1312 0.3466 lnFE 0.1779 0.0789 0.2447 0.2403 lnOther 0.2511 0.1484 0.2687 0.22 0.2608 lnWorker 0.4263 0.2023 0.2665 0.1232 0.0488 0.0871 78 Appendix Matrix of correlation between variables in technical efficiency model of semi-industrial poultry farms tesfm Male Age Edu Training R1 R2 R3 R4 R5 VIF tesfm Male 0.059 Age -0.029 -0.132 Edu 0.043 0.138 -0.099 Training -0.004 -0.024 0.104 -0.001 R1 -0.089 -0.007 0.121 0.187 0.014 R2 -0.084 0.059 -0.166 -0.024 0.059 -0.420 R3 0.012 0.071 0.030 0.063 -0.041 -0.241 -0.295 R4 0.105 -0.010 -0.092 0.016 0.038 -0.151 -0.184 -0.106 1.33 R5 0.129 -0.058 0.115 -0.022 -0.015 -0.106 -0.130 -0.075 -0.047 1.17 1.05 1.10 1.12 1.03 2.07 2.13 1.68 Appendix Matrix of correlation between variables in technical efficiency model of traditional poultry farms tesfm Male Age Edu Training R1 R2 R3 R4 R5 VIF tesfm Male 0.013 Age -0.014 -0.221 Edu 0.089 0.071 -0.072 Training -0.028 -0.031 0.067 -0.212 R1 -0.020 -0.031 0.122 0.234 -0.048 R2 -0.038 0.064 -0.215 -0.159 0.121 -0.366 R3 0.017 0.008 0.105 0.087 -0.066 -0.281 -0.422 R4 0.051 0.002 -0.048 -0.050 0.010 -0.145 -0.217 -0.167 1.07 R5 0.078 0.005 -0.007 -0.002 -0.041 -0.085 -0.128 -0.098 -0.051 1.27 1.06 1.13 1.15 79 1.07 2.58 3.12 2.74 Appendix OLS regression of technical efficiency model of traditional poultry farms Appendix Testing heteroskedasticity for OLS regression of technical efficiency model of traditional poultry farms 80 Appendix Testing heteroskedasticity for OLS-robust regression of technical efficiency model of traditional poultry farms 81 Appendix Summary of Empirical Studies at measuring technical efficiency of poultry sector Author Alabi, R A., & Aruna, M B (2005) Tung, D X., & Rasmussen, S (2005) Heidari, M D., Omid, M., & Akram, A (2011) Ike, P C (2011) Title Method - Estimating TE: Output: Income from poultry Input: feed expense, medicine expense, income from other livestock - Explanation variables of TE: Age of farmer, family size, gender, index of adopt innovation Technical efficiency of family - SFM poultry production in Niger-Delta, - MLE Nigeria Production function analysis for smallholder semisubsistence and semi-commercial poultry production systems in three agro-ecological regions in Northern provinces of VN Using nonparametric analysis (DEA) for measuring technical efficiency in poultry farms Proxy - Cobbdouglas producti on function - OLS method CCRDEA BCCDEA Resource Use and - SFM Technical Efficiency of Small - CD function Scale Poultry Farmers in Enugu State, Nigeria: A - MLE Stochastic Frontier Analysis 82 Results - TE score is 22% - Increasing TE and production by use more inputs and adoption more innovation - The production technology is difference - Estimating TE: between three regions - Productions within Output: Production of different poultry poultry production systems are similar for each region Input: number of birds, - Increase poultry feed, labor, garden production by increase size, income level and number of birds, feed cost of veterinary per birds and animal health - Estimating TE: Output: Production of Household raise poultry poultry under industrial system Input: cost of labors, has technical efficiency feed, chick, fuel and score around 90% electricity - Estimating TE: Output: Income from poultry Input: farm size (number of birds), capital, initial stock, labor, feed and cost of drugs - Explanation variables of TE: Age, gender, education, experience of farmers, household size, poultry production system, extension contact and membership of cooperatives - TE score is 72% - Technical efficiency is improved by using improved breeds and increasing education of farmer Ohajianya, D O., Mgbada, J U., Onu, P N., Enyia, C O., Henri-Ukoha, A., BenChendo, N G., & GodsonIbeji, C C (2013) Jatto, N A., Maikasuwa, M A., Jabo, M S M., & Gunu, U I (2012) Rafiee, S., Sefeedpari, P., & Akram, A (2013) - SFM Technical and economic efficiencies in poultry production in Imo State, Nigeria Assessing the technical efficiency level of poultry egg producers in Ilorin, Kwara State: A Data Envelopment Analysis approach Identifying sustainable and efficient poultry farms in the light of energy use efficiency: a Data Envelopment Analysis approach - CD function - MLE - Estimating TE Output: Income from poultry Input: feed, labor, medicine, flock size, capital, management and other inputs - All input factors are significant with positive signs - The technical efficiency score is ranged from 0.36 to 0.97 with mean score is 0.75 - Estimating TE Input orientated DEA Output: Egg yields Input: number of birds, kilogram of feed, working day of labors Mean of Technical inefficiency is 26% - Estimating TE Output: Egg yields CCR DEA Input: labor, equipment, fossil fuel, electricity, feed and pullet BCC DEA 83 Poultry farms can increase their profit by reduce input used 22% Appendix Summary empirical studies of comparison DEA and SFM approaches at measuring the technical efficiency Author Wadud, A., & White, B (2000) Title Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods Method CRS-DEA VRS-DEA SFMMLE Theodoridis, A M., & Psychoudaki s, A (2008) Efficiency measurement in Greek dairy farms: Stochastic frontier vs data envelopment analysis Zamanian, G R., Shahabineja d, V., & Yaghoubi, M (2013) Application of DEA and SFA CRS-DEA on the Measurement of Agricultural VRS-DEA Technical SFMEfficiency in MLE MENA Countries CRS-DEA VRS-DEA SFMMLE Proxy Results - Estimating TE: - The mean of technical TE Output: Income from estimated from VRS-DEA is rice greater than TE estimated from Input: land, labor, CRS-DEA and TE estimated irrigation, fertilizer, from SFM pesticides - Both VRS-DEA and CRS-DEA have variability of TE are greater - Explanation than SFM variables of TE: - The distribution of TE from Age, year of CRS, VRS and SFM models are schooling of similar indicating more farms in farmers, land the most efficiency group fragmentation, - Sources of technical inefficiency irrigation are socio-economic, infrastructure, demographic factor, environmental characteristics of farm, nondegradation physical factor and environment factors - Estimating TE: - The mean of technical TE Output: Gross output estimated from VRS-DEA is Input: lower than TE estimated from  Labor CRS-DEA and TE estimated  Fixed capital: from SFM building, machinery, - Both VRS-DEA and CRS-DEA livestock for have variability of TE are greater breeding and than SFM utilization - The distribution of TE from  Variables capital: CRS, VRS and SFM models are Fertilizers, fuel, similar indicating more farms in hired labor, feed, the most efficiency group rent of land and other inputs - The mean of technical TE estimated from VRS-DEA is - Estimating TE: higher than TE estimated from Output: Gross output CRS-DEA and TE estimated from SFM Input: land, labor, - Both VRS-DEA and CRS-DEA tractor, fertilizer, have variability of TE are greater livestock than SFM - Results in DEA and SFM give the same ranking for countries 84 ... Point Q and Q’ lie on the SS’ curve; hence, these points are technical efficiency points while point P indicates the technical inefficiency point The distance QP indicates for amount of waste input...UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS TECHNICAL EFFICIENCY OF POULTRY FARMS. .. both the non- parametric and parametric approaches in which SFM and DEA were employed to investigate the technical, allocative, cost and scale efficiency However, the efficiency analysis in poultry