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Analysis of socio-economic factors affecting technical efficiency of small-holder coffee farming in the Krong Ana Watershed, Dak Lak Province, Vietnam
ANALYSIS OF SOCIO-ECONOMIC FACTORS AFFECTING TECHNICAL EFFICIENCY OF SMALL-HOLDER COFFEE FARMING IN THE KRONG ANA WATERSHED, DAK LAK PROVINCE, VIETNAM UNIVERSITY OF HAWAI῾I AT MANOA MASTER OF SCIENCE IN NATURAL RESOURCES AND ENVIRONMENTAL MANEGEMENT AUGUST 2011 By Thong Quoc Ho Master Committee: John F Yanagida, Chairperson Tung Bui Prabodh Illukpitiya ANALYSIS OF SOCIO-ECONOMIC FACTORS AFFECTING TECHNICAL EFFICIENCY OF SMALL-HOLDER COFFEE FARMING IN THE KRONG ANA WATERSHED, DAK LAK PROVINCE, VIETNAM A THESIS SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI῾I AT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NATURAL RESOURCES AND ENVIRONMENTAL MANEGEMENT AUGUST 2011 By Thong Quoc Ho Master Committee: John F Yanagida, Chairperson Tung Bui Prabodh Illukpitiya ii ANALYSIS OF SOCIO-ECONOMIC FACTORS AFFECTING TECHNICAL EFFICIENCY OF SMALL-HOLDER COFFEE FARMING IN THE KRONG ANA WATERSHED, DAK LAK PROVINCE, VIETNAM A THESIS SUBMITTED TO THE DEPARTMENT OF NATURAL RESOUCES AND ENVIRONMENTAL MANAGEMENT OF THE UNIVERSITY OF HAWAI῾I AT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NATURAL RESOURCES AND ENVIRONMENTAL MANEGEMENT AUGUST 2011 By Thong Quoc Ho Master Committee: John F Yanagida, Chairperson Tung Bui Prabodh Illukpitiya i ACKNOWLEDGEMENTS Since I enrolled in my graduate program at the University of Hawaii at Manoa, I have received tremendous assistance and encouragement from individuals and institutions First, I would like to express sincere thanks to my academic advisor, Prof Dr John F Yanagida, who have continuously encouraged and assisted by providing me invaluable comments and suggestions for my thesis and academic plan as well I am deeply indebted to him for all endless support and kindhearted assistance during my study period I would like to express my endless gratitude to Dr Prabodh Illukpitiya for his helpful comments and encouragement He provided extremely gracious suggestions with productive discussion throughout the thesis research I am grateful to Dr Tung Bui for his invaluable advice on my proposal, keeping track of research implementation conducted in Vietnam I hold my committee members, Dr John F Yanagida, Dr Tung Bui and Dr Prabodh Illukpitiya, in deep reverence I gratefully acknowledge the International Fellowship Program and the East West Center for providing financial support for my graduate studies Much appreciation is extended to students from Tay Nguyen University, namely Tran Ngoc Nam, Ha Van Dung and Do Thanh Tuyen, who helped with data collection I also extend my special thanks to faculty members from Department of economics, Tay Nguyen University for their advice and encouragement during my graduate studies I am grateful to my family and friends, especially my parents, my wife, Hoang Thi Thu and my son, Ho Hoang Quan, who have exhibited the supreme virtue of patience and understanding i I am indebted to more people than I can name Thank you all for being present in this unforgettable stage of my life ii ABSTRACT Title: Analysis of socio-economic factors affecting technical efficiency of small-holder coffee farming in the Krong Ana Watershed, Dak Lak Province, Vietnam Coffee is a major crop in Vietnamese agriculture and plays an important role in the country‟s economy, especially in the Central Highlands of Vietnam Coffee production is a major source of income for farmers in the Dak Lak province Although Vietnam is well-known as one of the largest coffee producers in the world, there is minimal research and information identifying the technical efficiency and socioeconomic factors contributing to production efficiency of coffee The overall objective of this study was to estimate the technical efficiency of coffee production and evaluate factors affecting the level of technical inefficiency of small holder coffee farmers in the Krong Ana Watershed of Dak Lak province The specific objectives were to (i) identify the factors affecting coffee production, (ii) estimate the technical efficiency of coffee farming and (iii) identify factors contributing to technical inefficiency by analyzing the relationship between estimated efficiency levels and farm specific socio-economic factors The study was conducted in four districts of the Dak Lak province Since pooling data was not possible in all districts based on the results of the Chow Test, separate analyses were conducted for Cu Kuin and three combined districts (Krong Ana, Krong Bong and Lak) Maximum likelihood estimates for all the parameters of the stochastic frontier and inefficiency model were simultaneously generated The variance parameters were estimated in terms of parameterization By employing the stochastic frontier iii approach, the results reveal that selected variables significantly affect coffee output (i.e., cost of organic fertilizers, pesticide expenditure, amount of irrigation water and coffee trees for the Cu Kuin district model; labor, inorganic and organic fertilizer expenditure and age of coffee trees for the three combined districts model) The estimated mean technical efficiency scores were 0.7466 and 0.6836 respectively for the Cu Kuin district and the three combined districts Formal education of the household head, amount of credit, ethnicity, and coffee farming experience were key factors which can reduce technical inefficiency of coffee production for the combined districts sub-region For the Cu Kuin district, extension services can be used as a conduit to reduce technical inefficiency of coffee production, while ethnicity has the opposite effect as compared to a priori expectations This latter result requires further research and analysis Improvement of technical efficiency by 10% could generate a substantial amount of additional income for coffee farmers The overall findings suggest that water conservation practices, and the proper choice of fertilizers and pesticides could lead to improvements in coffee yields Expanding coverage of formal education and making credit more available can help farmers enhance technical efficiency of coffee production in the combined districts Improvements in both quantity and quality of extension services may increase technical efficiency of coffee production for farmers in the Cu Kuin district More in-depth investigation into population policies is necessary to identify the effects of family labor, number of children and family size on improving technical efficiency of coffee production for both sub-regions iv TABLE OF CONTENTS ACKNOWLEDGEMENTS i ABSTRACT iii TABLE OF CONTENTS v LIST OF TABLES vii LIST OF FIGURES: viii Chapter INTRODUCTION 1.1 Background information 1.2 Problem statement 1.3 Objectives of the study 1.4 Testable hypotheses 1.5 Outline of thesis Chapter LITERATURE REVIEW 2.1 Factors governing coffee production 2.2 Overview of technical efficiency concepts 2.3 Factors affecting technical inefficiency 10 2.4 Conceptual framework 13 2.5 Knowledge gaps 16 Chapter RESEARCH METHODOLOGY 18 3.1 Research design outline 18 3.2 Theoretical Model 18 Chapter DATA SPECIFICATIONS AND COLLECTION PROCEDURES 26 4.1 Approach 26 4.2 Data sources 27 4.3 Variable descriptions 27 4.4 Sampling Procedures 29 4.5 Descriptive Statistics 31 4.5 Common Tests for the robustness of the models 32 4.5.1 Chow test 32 4.5.2 Collinearity Testing 33 4.5.3 Testing for Heteroskedasticity 34 Chapter EMPIRICAL MODELS AND ESTIMATION RESULTS 36 5.1 Empirical Models 36 v 5.2 Results and Discussion: 38 5.2.1 Factors contributing to coffee production 38 5.2.2 Maximum Likelihood Estimates (MLE) and Technical Efficiency 39 5.2.2.1 Analyses of Maximum Likelihood Estimates 41 5.2.2.2 Technical Efficiency 46 5.2.2.3 Analyses for technical inefficiency models 47 5.2.2.4 Efficiency improvements in coffee production 51 Chapter SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 54 6.1 Summary 54 6.2 Conclusions 55 6.3 Limitations of the study 57 6.4 Policy recommendations 58 Appendix A: Descriptive Statistics 71 Appendix B1: Correlation matrix of production factors for Cu Kuin district 72 Appendix B2: Correlation matrix of production factors for the combined districts 73 Appendix C: Pairwise t-test for the mean 74 vi LIST OF TABLES Table 4.1: Description of production variables 28 Table 4.2: Description of variables of the efficiency model 28 Table 4.3: Descriptive statistics for sample size determination 29 Table 4.4 Sample distribution* 30 Table 4.5 Coffee output per hectare statistics of districts 31 Table 4.6 Chow test for different impacts of independent 32 Table 4.7 Multicollinearity Testing 34 Table 5.1 OLS estimates fo coffee production function models 38 Table 5.2 MLE of stochastic production frontier and technical inefficiency models 40 Table 5.3 Frequency distribution of technical efficiency estimates 46 Table 5.4 Scenario for increasing the efficiency 52 Table 6.1 Change in revenue as the inputs increase by 1% 59 vii implement coffee farming experiments and best management practices (in terms of techniques and optimal applications of fertilizers, pesticides, irrigated water); (3) these programs should involve the national, provincial and local governmental structures with roles involving funding and institution support On the other hand, for the technical inefficiency model of coffee production in the combined districts case, there are realistic options for improving technical efficiency The variables representing the coffee farmers‟ level of education, ethnicity, amount of credit, coffee farming experience and child dependency index constitute instruments that can be manipulated within the framework of coffee production development policies in order to reduce technical inefficiency of coffee production in Krong Ana, Krong Bong and Lak districts Higher formal education of the household head is associated with higher technical efficiency in the combined districts region The average number of years of formal education of the household head is 7.6 years Nchare (2007), and Kehinde et al., (2010) also found that formal education helps in minimizing technical inefficiency in agriculture A recent study by Tao Yang (2004) reported that in China, middle and high schooled families systematically make better input decisions than primary schooled families Therefore, education policies for this sub-region should be taken into account Particularly, for short-term solutions, government should offer short courses during the coffee farmer‟s underemployed period, since coffee production involves seasonable agricultural activities Additionally, the results also indicate that farmers with more coffee farming experience are technically more efficient Therefore, providing basic education services (i.e., extending coverage of formal education, basic use of computers 63 and the Internet etc.) may help farmers to update and effectively utilize information related to coffee production and decisions involving investment in input factors of production and marketing of coffee output For long term strategies, education is usually an important stepping stone for a growing economy In the regional and globally integrated economy, education is a key factor for success and coffee producers are not an exception More than four decades ago, Welch (1970) suggested that educated workers are better able to gather and utilize information that is useful for involved decision making He explained that workers not only improve their standard of work, but also contribute to production by effectively allotting the firm‟s resources This also involves allocating their own time efficiently among different responsibilities which can significantly affect worker productivity In addition, Tao Yang (2004) suggested that as a result of allocative effects (for inputs such as capital and labor), education significantly contributed to sustained income growth In Vietnam, the provincial government has attempted to increase benefits to coffee farmers through education For instance, the Buon Ma Thuot Coffee Exchange Centre, founded in 2008, directly interacts with coffee producers and coffee processing and exporting companies The exchange centre introduces modern production and marketing approaches to coffee farmers This example illustrates the role of education in terms of reducing inefficiency In addition, estimated results show that increased credit availability could also reduce technical inefficiency for the combined districts region This means that providing more credit services may help coffee producers overcome financial constraints and thereby improve efficiency This should be considered as sustainable by rural financial 64 institutions and necessary for rural development while improving the efficiency of coffee production More financial sources and increased amounts of loanable funds for coffee production is useful because in this sub-region, the amount of credit available increases efficiency However, it is important that financial support be used effectively Thus, credit lenders and companies providing coffee farmers with financial support should also offer effective strategies for using capital in coffee production For these companies providing financial support to farmers, agreements with coffee producers can stabilize coffee supply Thus, credit and coffee buying businesses should collaborate with local authorities (i.e., at the commune and district levels) to keep tract of loans used for coffee production The importance of providing credit facilities for agricultural development was identified by Binam et al., (2004) In conclusion, coffee production is affected by changes in the socio-economic environment as well as the natural environment This study was successful in identifying factors (e.g., labor, expenditures on fertilizers, and pesticides, irrigation water, and age of coffee trees) affecting coffee production for the two sub-regions In addition, technical efficiency scores were 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districts Std Mean Dev Min Max N Cu Kuin Std Mean Dev Min Max Coffee yield ( in tons per hectare) 95 2.33 0.83 0.50 4.29 103 2.84 0.56 0.73 4.00 Labor (man-days per hectare) Cost of inorganic fertilizer (million VNDs per hectare) Cost of organic fertilizer (million VNDs per hectare) Cost of pesticide (million VNDs per hectare) Amount of water applied (thousand cubic meters s per hectare) Age of coffee trees (in year) 95 194.29 56.82 106.67 425.00 103 198.81 45.64 102.50 340.00 95 12.80 6.38 1.00 32.80 103 11.52 2.72 1.31 18.00 95 2.72 2.60 0.00 12.00 103 1.89 1.13 - 10.88 95 0.75 0.64 0.00 3.33 103 0.74 0.24 0.03 1.50 95 1.60 0.60 0.40 4.00 103 1.74 0.29 0.46 2.48 95 13.45 4.49 3.00 25.00 103 16.67 3.99 3.00 23.00 Age of household head Number of years of formal education of the household head Dummy variable of ethnicity (if Vietnamese Kinh = or otherwise 0) Extension services (yes = or otherwise 0) Amount of credit loaned from banks and credit organizations (in million VND) Number of years of experience in coffee farming by the household head Child dependency index (number of children divide by family size) 95 45.34 9.85 24.00 74.00 103 45.44 4.35 35.00 56.00 95 7.59 3.19 1.00 15.00 103 9.01 3.16 2.00 16.00 95 0.83 0.38 0.00 1.00 103 0.83 0.37 0.00 1.00 95 0.65 0.60 0.00 4.00 103 0.88 0.47 0.00 4.00 95 22.42 26.60 0.00 125.00 103 9.99 11.58 0.00 57.14 95 15.12 4.97 0.00 25.00 103 17.41 4.18 0.00 24.00 95 0.32 0.15 0.00 0.60 103 0.16 0.19 0.00 1.00 Farm-specific δ1 HAge δ2 Edu δ3 Eth δ4 Ext δ5 Creditm δ6 Nofexperience δ7 depindex 71 Appendix B1: Correlation matrix of production factors for Cu Kuin district ln(Cfyield) ln(Cfyield) 1.0000 0.3351 (0.0005) 0.5094 ln(ValueInorF)