Technical efficiency of vietnam rice farms a stochastic frontier production approach

UNIVERSITY OF ECONOMICS HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES THE HAGUE VIETNAM THE NETHERLANDS VIETNAM — NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS TECHNICAL EFFICIENCY OF VIETNAM RICE FARMS A STOCHASTIC FRONTIER PRODUCTION APPROACH By NGUYEN THANH DONG TRINH NGUYEN MASTER OF ARTS IN DEVELOPMENT ECONOMICS He Chi Minh, December 2011 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 VIETNAM RICE FARMS A STOCHASTIC FRONTIER PRODUCTION APPROACH A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN THANH DONG TRINH NGUYEN Academic Supervisors DR NGUYEN TRONG HOAI DR PHAM LE THONG He Chi Minh, December 2011 CERTIFICATION “I certificate that the substance of the thesis has not already been submitted for any degree and is not currently submitted for any other degree I certify that to the best of my knowledge and help received in preparing the thesis and all sources used have been acknowledged in the thesis.” Signature Nguyen Thanh Dong Trinh Nguyen Date: ACKNOWLEDGMENTS Firstly, I would like to say thank you to Dr Nguyen Trong Hoai and Dr Pham Le Thong — my academic supervisors, for their devoted recommendation Without their precious advice and instruction could not complete this thesis By the way, I am very proud to attend this program Every teacher sets an example of hard working for me and other students to follow And I will never forget the support from all employees of the program Their enthusiastic and friendly attitude makes we feel comfortable to study and research Moreover, I received the enormous and continue encouragement from my closed friends and my family, especially my mother Their loves have given me more strength and belief to overcome difficulties during the studying I am very grateful for everything that all of you gave me How can I pay your debt of gratitude! Nguyen Thanh Dong Trinh Nguyen ABSTRACT The research investigates the technical efficiency level and determinants of rice production in Vietnam The analysis employs the Vietnam Household Living Standard Survey 2008 data set and stochastic production frontier approach The mean technical efficiency level is 80% Credit approach, land policy, and , experience are not significant elements of technical efficiency models while the irrigation, promotion program, education and gender of household head are significant ones Key words: technical efficiency, rice production, stochastic production frontier TABLE OF CONTENT • CERTIFICATION ACKNOWLEDGMENTS ii ABSTRACT iii TABLE OF CONTENT iv LIST OF FIGURES ×i LIST OF TABLES ×1 LIST OF ABBREVIATION vii CHAPTER I INTRODUCTION 1.1 Problem Statements 1.2 Research Objectives 1.3 Research Questions and Hypotheses .5 I Research Methodology .6 1.6 Thesis Structure CHAPTER II LITERATURE REVIEW ON TECHNICAL EFFICIENCY AND CONCEPTUAL FRAMEWORK 2.1 Key Concepts l Technical Efficiency .8 1.2 Production Frontier l Stochastic Production Frontier 10 2.2 Approaches to Measure Technical Efficiency 1 2.2.1 Data Envelopment Analysis 1 2.2.2 Stochastic Frontier Analysis 12 2.3 Stochastic Frontier Analysis Framework 12 2.3.1 Stochastic Frontier Model 12 2.3.2 Estimation method 14 2.4 Empirical Studies 15 2.5 Conceptual Framework 25 CHAPTER III RESEARCH METHODOLOGY FOR TECHNICAL EFFICIENCY AT THE FAMRS LEVEL 31 3.1 Data Source 31 3.2 Models Specification and Variables Definition 31 3.2 Stochastic Frontier Production Function 31 3.2.2 Efficiency Model: 38 CHAPTER IV RESULTS AND DISCUSSION 45 4.1 Results of Data Analysis .45 4.1 I Stochastic Frontier Production Function 45 4.1.2 Efficiency Model 49 • 4.2 Results Discussion 52 4.2.1 Discussion on Determinants of Stochastic Frontier Production Function , 52 4.2.2 Discussion on Determinants of Technical Efficiency 54 CHAPTER V CONCLUSION AND RECOMMENDATION 60 5.1 Conclusion .60 5.2 Policy recommendation 61 5.3 Research limitation and further studies 63 REFERENCE 64 APPENDIX 69 LIST OF FIGURES Figure 2.1: Technical Efficiency Figure 2.2: Production Frontier 10 Figure 2.3: The Stochastic Frontier Production Function 11 Figure 2.4: Conceptual Framework .26 Figure 4.1: Distribution of Technical Efficiency 56 LIST OF TABLES Table 2.1: Summary of Empirical Studies 22 Table I : Definition of Variables in Stochastic Frontier Production Function 32 Table 3.2: Definition of Variables in Technical Efficiency Model 38 Table 4.1: Statistical Summary of Variables in Frontier Model 45 Table 4.2: Maximum Likelihood Estimation of Stochastic Frontier Production Function 46 Table 4.3: Statistical Summary of Variables in Efficiency Model 49 Table 4.4: OLS — Robust Model of Technical Efficiency Determinants .51 Table 4.5: Statistical Summary of Technical Efficiency Level .55 Table 4.6: Distribution of Technical Efficiency 55 LIST OF ABBREVIATION COLS Corrected Ordinary Least Squares DEA Data Envelopment Analysis FAOSTAT Food and Agriculture Organization of the United Nations - Statistics Division GDP Gross Domestic Production GOV government of Vietnam GSO General Statistics Office of Vietnam IPCC International Panel of Climate Change MLE Maximum Likelihood Estimation MOLS Modified Ordinary Least Squares OLS Ordinary Least Squares SBV State Bank of Vietnam TE Technical Efficiency USDA US Department of Agriculture VHLSS Vietnam Household Living Standard Survey Unit Currency of Vietnam — Vietnam Dong CHAPTER I INTRODUCTION • 1.1 Problem Statements , Agriculture is an important sector in Vietnam economy Agriculture accounts for 18.2% of the country's gross domestic product in 2009 In 2008, agricultural export accounts for 12.3% of total export value of the country In 2009, the proportion of labor force in agriculture, forestry and fishery sector is 62.9% Rural population proportion was around 71% (FAOSTAT, 2010) and rural labor force made up 58.5 % of the total labor force (GSO, 2010) Rice is one of the most important crops in agricultural production with the highest cultivating area of food production Rice production is ranked at the fifth in the world Rice yield contributes 90% food production, and is related to 80% labor force of Vietnam In 2010, domestic rice production of whole country was about 40 million tons from 7,500 thousands hectares of cultivated area with average yield of over tons/ha (GSO, 2010) Vietnam annual per capita of rice consumption is very high, 169 kg/person/year (Laillou et aI, 2010), producing about 1,591 calorie intake - 60% in 2007 (Timmer, 2010) Rice has been a Vietnam’s principal agricultural export and a great source of foreign exchange Value of exported rice accounts about 20% of agricultural and forestry products Vietnam has exported rice to 120 countries and its share in global market is about 20%, ranked at the second in the world (USDA, 201 1) In 2010, Vietnam has exported 6.88 million tons of rice worth US$3.23 billion In 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0.35 69 1.00 0.46 0.67 0.06 0.47 -0.01 0.30 1.00 0.54 0.00 0.42 0.07 0.30 1.00 -0.07 0.50 0.02 0.31 1.00 -0.28 -0 14 0.22 1.00 -0.07 12 1.00 -0.02 1.00 APPENDIX B: STOCHASTIC FRONTIER PRODUCTION FUNCTION Stoc Frontier normal/half-nominal model Log likelihood lnY lnFLA lnHLA lnAREA lnFSEED lnBSEED laFERT lnINSECT lnHERB ILL lnHIRED_MACHINE lnHIRED_CATTLE lnCAPITAL cons s u s Likelihood-ratio test ofs 363 Coef 0.003 0.005 0.837 -0.004 0.014 0.091 0.028 0.010 StcLErr 0.004 0.001 0.006 0.001 0.004 0.004 0.002 0.002 0009 0002 0.015 0.007 -0.002 0.001 0.001 0.001 Number ofobs Wald chi2(l 2) Prob > chi2 98,534 4,691 0.9 4.0 132.8 -2.7 3.1 25.0 11.9 5.0 5.6 10.9 4.4 -2.0 2.5 0.39 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.01 0.00 0.113 0.046 0.319 0.006 0.118 0.004 _u=0: clu1›ar2(01) = 4.4e+02Prob>=clul›ar2 = 0.000 70 APPENDIX C: WALD TESTS FOR THE SIGNIFICANCE INDIVIDUAL VARIABLES IN STOCHASTIC FRONTIER MODEL LA c • )- Prob > c [lnY]1nINSECT = chi2(I ) = 141.71 39 Prob > chi2 = 0.0000 [lnY]lnHLA = chi2(1) = 16.25 Prob > chi2 =0.0001 [kiY]lnHERB = chi2(I ) = 25.23 Prob » chi2 =0.0000 [ktY]1nAREA = chi2(I) =I 7626.81 Prob > chi2 =0.0000 [lnY]lnFUEL = chi2(1) = I I Prob > chi2 =0.0000 [lnY)lnFSEED - chi2(I ) =7.34 Prob > chi2 =0.0067 Ilp: Q = [1nY]lnBSEED = chi2(I) - 9.58 [lnY]lnFERT = chi2(1) = 626.28 Prob > chi2 —0.0000 [lnYJlnCAPITAL = chi2(1) = 3.83 Prob > chi2 =0.0504 [lnY]lnHIRED_MAC HINE = chi2(I ) = I 18.76 Prob > chi2 =0.0000 • fi i i ' [lnY]1nHIRED_CATTLE = chi2( I ) = 19.57 Prob » chi2 = 0.0000 OF 71 APPENDIX D: WALD TEST FOR CONSTANT RETURN TO SCALE He: sum of all coefficients = chi2(1) 6.31 Prob > chi2 0.0120 72 APPENDIX E: MATRIX OF CORRELATION BETWEEN VARIABLES IN EFF lC I ENC Y MODEL CER timeLUR RATIO AVE NA 1.00 time LUR CER_RATIO 0.53 1.00 AVE -0.01 0.01 1.00 NA_IR 0.06 0.09 -0 11 MC_IR 0.00 0.04 0.06 MN_IR 0.03 0.02 -0.08 PPRO -0.08 -0.08 -0.02 CRE 0.00 0.01 13 SCH 0.04 0.05 -0 12 SEX -0.01 -0.04 0.05 AGE 14 13 0.02 EXPERIENCE 0.07 0.06 -0.02 RRD -0.07 -0.06 -0.20 0.06 14 0.37 MRD IR MC IR MN IR PPRO 1.00 -0.68 -0 16 -0.01 -0.04 0.05 -0.01 -0.02 0.02 -0.06 -0 15 1.00 -0 13 -0 12 0.00 0.07 -0.04 11 -0.05 0.22 0.28 73 1.00 -0.02 -0.01 0.09 -0.02 -0.01 -0.03 15 -0 10 1.00 0.05 -0 16 -0.02 -0.07 0.07 -0 11 -0.09 EXPE RIEN CRE SCH SEX AGE CE 1.00 -0.06 0.05 -0.07 0.02 -0 16 14 1.00 18 -0 18 -0 15 0.26 -0 19 1.00 -0.21 1.00 -0 12 0.29 -0.04 0.05 -0.02 11 1.00 -0.08 1.00 -0.06 -0.25 1.00 APPENDIX F: VARIANCE INFLATION FACTOR AND TOLERANCE OF VARIABLES IN EFFICIENCY MODEL Variable MC_IR NA_IR MRD €ER_AA9UO MN_IR sCH AGE AYE EXPERIENCE SEX PPRO CRE Mean VIF VIF 2.80 2.42 1.50 1.47 1.43 1.37 1.28 1.25 1.24 1.21 36 67 68 73 0.78 87 93 1.15 1.09 1.08 1.06 1.45 74 APPENDIX G: OLS MODEL OF TECHNICAL EF FICIENCY DETERMINANTS Linear regression Number ofobs F(14, 4674) Prob > F R-squared Adj R-squared Root MSE TE100 Coef Std Err timeLUR CER_RATIO AVE NA_IR MC_IR MN_IR CRE PPRO SEX AGE SCH EXPERIENCE RRD MRD cons 0.07 0.57 0.00 7.04 8.30 5.49 0.49 -1.83 0.94 0.00 0.25 0.00 3.20 -0.65 69.14 0.03 0.49 0.00 0.57 0.60 1.06 0.42 0.69 0.45 0.01 0.05 0.01 0.42 0.56 0.98 APPENDIX H: TEST FOR t 2.24 1.17 -1.24 12.38 13.73 5.20 1.15 -2.66 2.11 0.19 4.79 -0.12 7.65 -1.16 70.64 P>]t 0.03 0.24 0.21 0.00 0.00 0.00 0.25 0.01 0.04 0.85 0.00 0.91 0.00 0.24 0.00 HETEROSKEDASTIC ITY OF EFFICIENCY MODEL q 4,689.00 41.80 0.00 0.11 11 10.93 Breusch-Pagan / Cook- Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of TEl 00 chi2(I ) = 668 15 Prob > chi2 = 0.0000 75 APPENDIX I: OLS — ROBUST MODEL OF TECHNICAL DETERM INATS Linear regression Number ofobs F( 14, 4674) Prob > F R-squared Root MSE 4689 35.26 0.00 I I 0.93 TE100 Coe f Std Err I P>|t tiineOLD CER_RATIO 0.07 0.57 0.00 7.04 8.30 5.49 0.49 -1.83 0.94 0.00 0.2:5 0.00 3.20 -0.65 69.14 0.03 0.52 0.00 0.69 0.69 1.02 0.46 0.86 0.45 0.01 0.06 0.01 0.35 0.65 1.19 16 1.09 -0.66 10.20 11 96 5.41 1.05 -2.11 2.08 0.18 4.39 -0.12 9.17 -1.00 58.31 0.03 0.28 0.5 I 0.00 0.00 0.00 0.29 0.04 0.04 0.85 0.00 0.91 0.00 0.32 0.00 AYE NA_ER MC_IR MN_IR CRE PPRO SEX AGE sCH EXPERIENCE RRD “cR‘ 76 EFFIC I ENCY APPENDIX J: WALD-TEST FOR THE SIGNIFICANCE OF INDIVIDUAL VARIABLES IN TECHNICAL EFFICIENCY MODEL Ho• i '0 [TE100]tirrieOLD = F(1, 4674) = 4.66 Prob > F — 0.031 o² ' [TEI00]PPRO = F( I , 4674) = 4.47 Prob > F = 0.0346 [TEI00]CER RATIO = F(I , 4674) - 1.19 Prob > F = 0.2753 W: 63 - [TE100]AVE = F(1, 4674) 0.44 Prob > F 0.5083 [TE100]SEX = F(I , 4674) = 4.31 Prob > F 0.0379 H :6 = [TEI 00]AGE = F(I , 4674) =0.03 Prob > F — 0.8538 [TEI 0OJNA_IR - F(1, 4674) = 104.01 Prob > F = 0.000 [TEIOO]SCH = F(1, 4674) = 19.26 Prob > F = 0.000 :6 = [TEI00]MC_IR = F(1, 4674) = 143 Prob > F = 0.000 rci ooJ m- o F(1, 4674) = 29.24 Prob > F = 0.000 lip: ; = [TEI00]EXPERIENCE — F(I, 4674) = 0.01 Prob > F = 0.9058 RRD = F(1, 4674) = 84.05 Prob > F — 0.000 ’ [TEI00]CRE = F(I , 4674) = 1 I Prob > F = 0.2919 F(1, 4674) = 1.01 Prob > k’—— 0.3159 77 ... variable has the value of Lack of capital would cause difficulties in farm management and reduce technical efficiency Accessibility of credit funds may encourage technical efficiency of rice production. .. applies stochastic frontier approach to analysis technical efficiency of rice farming 2.3 Stochastic Frontier Analysis Framework 2.3.1 Stochastic Frontier Model Aigner et al (l 977), Meeusen and... determinants of rice production in Vietnam The analysis employs the Vietnam Household Living Standard Survey 2008 data set and stochastic production frontier approach The mean technical efficiency