Estimating the economic and environmental efficiency as well as the factors affecting these efficiency indexes for the shrimp farming in coastal transforming areas. From which, policy makers can select the feasible models and appropriate solutions to improve economic efficiency and to minimize environmental pollution for coastal farmers.
MINISTRY OF EDUCATION AND TRAINING CAN THO UNIVERSITY SUMMARY OF DOCTORAL DISSERTATION Major: Agricultural economics Code: 9620115 NGUYEN THUY TRANG ECONOMIC AND ENVIRONMENTAL EFFICIENCY OF INTENSIVE SHRIMP FARMING IN THE COASTAL TRANSFORMING AREAS OF THE MEKONG DELTA Can Tho, 2020 The research has been finished at Can Tho University, Can Tho City, Vietnam Supervisors - The main instructor: Assoc Prof Dr Huynh Viet Khai - Secondary instructor: Dr Tran Minh Hai The dissertation will be defended at the council of the school level at:……………………………………………………… On:…………hour…… date……month…… year……… Discussant 1: Discussant 2: Citing of this dissertation is available at following the libraries: - Learning Resource Center-Can Tho University, Can Tho City - Vietnam National Library, HCM City LIST OF PUBLISHED PAPERS RELATED TO THE DISSERTATION Nguyen Thuy Trang, Huynh Viet Khai, Vo Hong Tu, Tran Minh Hai, 2019 Theoretical and empirical frameworks for measuring environmental efficiency in agricultural production: A case study of shrimp farming in transforming areas of Kien Giang province Journal of Science Ho Chi Minh City Open University, 14 (1/2019), Pp 115-125 Nguyen Thuy Trang, Huynh Viet Khai, Vo Hong Tu, 2018 Economic efficiency of shrimp farming in the coastal areas of Soc Trang province Can Tho University Journal of Science, 07 (54), Pp 146-154 Nguyen Thuy Trang, Huynh Viet Khai, Vo Hong Tu, Mitsuyasu YABE, 2018, The determinants behind changes of farming systems and adaptation to salinity intrusion in the coastal regions of Mekong Delta Journal of Faculty of Agriculture, Kyushu University, 63, Pages 417-422 Nguyen Thuy Trang, Huynh Viet Khai, Vo Hong Tu, Nguyen Bich Hong, 2018 Environmental efficiency of transformed farming systems: a case study of change from sugarcane to shrimp in the Vietnamese Mekong Delta Forestry Research and Engineering: International Journal, 02, Pages 54-60 i LIST OF CONTENTS LIST OF TABLE iv LIST OF FIGURE v CHAPTER 1: INTRODUCTION 1.1 RATIONALE OF THE DISSERTATION 1.2 OBJECTIVES 1.2.1 General objectives 1.2.2 Specific objectives 1.3 RESEARCH QUESTIONS 1.4 RESEARCH SUBJECTS 1.5 SCOPE OF THE STUDY CHAPTER 2: LITERATURE REVIEW 2.1 REVIEW OF NEW TECHNOLOGY ACCEPTANCE MODEL AND FARMING TRANSFORMATION 2.2 REVIEW ABOUT ECONOMIC EFFICIENCY 2.3 REVIEW ABOUT ENVIRONMENTAL EFFICIENCY 2.4 REVIEWS OF FACTORS AFFECTING EFFICIENCY CHAPTER 3: THEORY AND RESEARCH METHODOLOGY 10 3.1 THEORETICAL CONCEPTS 10 3.1.1 Intensive farming 10 3.1.2 Factors affecting changes in farming systems 10 3.1.3 Economic efficiency and measurement methods 11 3.1.4 Environmental efficiency and measurement method 11 ii 3.2 RESEARCH METHODOLOGY 12 3.2.1 Theoretical framework 12 3.2.2 Selection of study sites 13 3.2.3 Analytical methods 13 CHAPTER 4: RESULT AND DISCUSSION 15 4.1 STATUS OF CHANGES IN FARMING SYSTEMS TO SHRIMP 15 4.1.1 Status of changes in farming systems 15 4.1.2 Factors affecting changes in farming systems to shrimp 16 4.2 ECONOMIC AND ENVIRONMENTAL EFFICIENCY OF TRANSFORMED INTENSIVE SHRIMP FARMING 17 4.2.1 Economic efficiency of intensive shrimp farming 17 4.2.1.1 Estimation of economic efficiency 17 4.2.1.2 Factors affecting economic efficiency 21 4.2.2 Environmental efficiency of intensive shrimp farming 22 4.2.2.1 Estimation of environmental efficiency 22 4.2.2.2 Factors affecting environmental efficiency 26 4.3 IMPLICATIONS FOR IMPROVING ECONOMIC AND ENVIRONMENTAL EFFICIENCY 28 4.3.1 Solutions for shrimp farmers 28 4.3.2 Solutions from local authorities 28 CHAPTER 5: CONCLUSION 29 iii LIST OF TABLE Table 4.1: Logit results of factors affecting changes in farming systems 16 Table 4.2: Estimated results of translog variable cost frontier 18 Table 4.3: Economic efficiency of intensive shrimp farming 19 Table 4.4: Estimated results of translog production frontier 23 Table 4.5: Output-oriented technical efficiency 24 Table 4.6: Environmental efficiency of intensive shrimp 25 Table 4.7: Tobit results of factors affecting environmental efficiency 26 iv LIST OF FIGURE Figure 3.1: Theoretical framework of the study 12 Figure 4.1: Trends in changes of farming systems in Soc Trang and Kien Giang 15 Figure 4.2: Observed and possible minimum cost in Soc Trang 20 Figure 4.3: Observed and possible minimum cost in Kien Giang 21 v CHAPTER 1: INTRODUCTION 1.1 RATIONALE OF THE DISSERTATION Climate change has been happening more and more seriously such as weather variability and salinity intrusion (Carew-Reid, 2008; Nhan et al., 2011; Wassmann et al., 2004) The climate change together with market instability, low selling prices while rising input prices have caused changes of farming activities as an inevitable phenomenon to adapt (Binh et al., 2009; Clayton, 2003) The Mekong Delta (MD) is considered as the core region for agricultural production and aquaculture, contributing more than 90% of rice exports and more than 70% of fishery stocks (GSO, 2013) Recently, many farmers in the Mekong Delta’s coastal areas have shifted their production activities to intensive shrimp farming, especially white-legged shrimp According to the report of the Institute of Fisheries Economics and Planning (2015) and the Ministry of Agriculture and Rural Development (2019), the total area of brackish shrimp farming in the Mekong Delta in 2018 reached 679,152 with an average growth rate of 2.1%/year from 2005-2018 Of which white-legged shrimp farming area increased more than 17 times during the period of 2008-2018, namely from 4,747 hectares in 2008 to 78,392 hectares in 2018 The province having the highest growth rate of white-legged shrimp area is Soc Trang with an average growth rate of up to 116.83%/year; while the remaining provinces having the medium growth rates such as Kien Giang 36.24%/year and Bac Lieu 13.47%/year However, the process of agricultural transformation requires high investment and good preparation of production techniques as well as the market outlets, so the risk of the conversion process is quite high (Le Anh Tuan et al , 2014; World Bank, 2016) According to the World Bank (2016), shrimp farming is one of the production activities that have a significant impact on the water environment and generate high greenhouse gas emissions due to the excessive use of inputs Therefore, it is necessary to measure economic and environmental efficiency with a scientific approach for intensive shrimp farming in coastal transforming areas In terms of economic efficiency, so far many studies have been conducted by using the profit or cost frontier function to measure efficiency In particular, some case studies using the profit function include Pham Le Thong et al (2011); Nguyen Van Tien and Pham Le Thong (2014); Pham Le Thong and Nguyen Thi Phuong (2015); Nguyen Minh Hieu (2014) Some other studies using cost frontier include Ferrier and Lovell (1990); Worthington (2000); Rosko (2001); Coelli, et al (2005); Tu & Trang (2015) However, the above-mentioned studies only considered economic efficiency as the profit/cost frontier function by a two-step approach and did not investigate the causes of transformation as well as the inefficient use of the inputs towards cost minimization by one-step model In addition, these studies using Cobb-Doughlas and DEA (Data Envelopment Analysis) methods make it impossibly to separate the noise effects apart from inefficiency effects and to investigate the causes of inefficiency In addition, many studies show that farmers not use input resources effectively, so it is necessary to consider the ability to reduce production costs (Dung & Dung, 1999; Kompas, 2004; Khai & Yabe, 2011; Hoang Linh, 2012; Kompas et al., 2012) Regarding to environmental efficiency, Pittman (1983) is probably considered to be the first to concern about environmental issues when estimating the efficiency for production activities In this study, the author considered the environmental aspect as an unexpected output from production process The author modified the estimated index from the term "translog multilateral productivity index" proposed by Caves et al (1982) Färe et al (1989) proposed the term enhanced hyperbolic productive efficiency measure This term considers simultaneously the difference among the maximum equiproportional increase in desirable outputs, the maximum equiproportional decrease in undesirable outputs and the maximum equiproportional decrease in inputs However, the study measured productive efficiency using nonparametric approach (DEA), which cannot separate noise effect apart from deterministic frontier In addition, measuring undesirable output in agricultural production is a difficult task In order to overcome the drawbacks of the previous studies, Reinhard et al (1999) treated environmental pollution as input surpluses (e.g., fertilizers, pesticides, energy) to estimate environmental efficiency Some case studies in Vietnam used the approach of Reinhard et al (1999) include Vo Hong Tu (2015); Tu et al (2015); Hong, Takahashi and Yabe (2016) However, there has not been any research using this approach to measure environmental efficiency for shrimp farming in the MD so far From the above reasons, the dissertation was conducted to measure the economic efficiency towards the direction of minimizing the cost and to measure environmental efficiency for the intensive white-legged shrimp farming in the coastal transforming areas by employing the one-step approach 1.2 OBJECTIVES 1.2.1 General objectives Estimating the economic and environmental efficiency as well as the factors affecting these efficiency indexes for the shrimp farming in coastal transforming areas From which, policy makers can select the feasible models and appropriate solutions to improve economic efficiency and to minimize environmental pollution for coastal farmers 1.2.2 Specific objectives To achieve the above general objective, the dissertation focuses on the following specific objectives: activities To test this hypothesis, the dissertation uses the variable namely distance from fields/ponds to river as the independent variable to replace for the variable salinity magnitude due to the lack of values of salinity at individual farmers 4.1.2 Factors affecting changes in farming systems to shrimp The estimated results of the effects of the socio-economic variables on the transformation decision are presented in Table 4.1 below: Table 4.1: Logit results of factors affecting changes in farming systems Variables SEX AGE LABOR FEMALE EDUCATION ORGANIZATION CREDIT AGRILAND DISTANCE Soc Trang province (Sugarcane monoshrimp) Coef s.e 0.132 Kien Giang province (Rice-shrimp monoshrimp) dy/dx Coef s.e dy/dx 0.863 0.0329 0.441 0.772 0.1093 -0.027 0.029 -0.0066 0.013 0.019 0.0033 0.016 0.416 0.0039 0.260 0.348 0.0650 * 0.592 -0.2483 0.429 0.499 0.1074 0.153* 0.081 0.0382 0.124* 0.073 0.0309 -1.650* -0.994 0.887 -0.3682 -1.209 0.853 -0.2807 -2.682 *** 0.608 -0.5853 0.158 0.526 0.0395 -0.168 *** 0.053 -0.0422 -0.015 -0.004 *** 0.001 -0.0012 EXPERIENCE ** 0.012 -0.0038 *** 0.001 -0.0009 0.253*** 0.056 0.0633 -0.004 ** Intercept 4.731 2.026 -3.476 1.503 Note: * indicates the significant level; *p < 0.1; **p < 0.05; ***p < 0.01 s.e stands for standard error; dy/dx indicates marginal effects Source: Own estimates; data appendix available from authors For the case of Soc Trang province (conversion from sugarcane to shrimp), Table 4.1 shows the variables namely female labor, credit access, membership in organizations, agricultural land area and distance had negative effects on the adoption decision while 16 educational level is the only variable that has a positive relationship with the dependent variable In the case of Kien Giang province (conversion from rice - shrimp to shrimp), Table 4.1 shows that the distance from the rice field to the river also had a negative effect while the educational level and experience of shrimp farming had positive effects on the adoption decision of new farming activities 4.2 ECONOMIC AND ENVIRONMENTAL EFFICIENCY OF TRANSFORMED INTENSIVE SHRIMP FARMING 4.2.1 Economic efficiency of intensive shrimp farming 4.2.1.1 Estimation of economic efficiency Prior to specifying the cost frontier, we conduct tests to determine whether the data is best fit with Cobb-Douglas or translog function by using LR - log-likelihood ratio test (Coelli et al., 2005 ; Greene, 2012; Kumbhakar et al., 2015) The LR test result shows that the value of , which is much greater than the critical value and significant at 1% This result shows that the collected data is best fit with translog function The results also show that the translog cost function by a one-step method (taking into account the correlation between economic inefficiency and socio-economic characteristics) is accepted compared to the two-step cost function (excluding independent variables affecting economic inefficiency) through the value of = 34.49 This value is much larger than the critical value at 1% The correlation matrix results also show that there is no multi-collinearity between independent variables, specifically the correlation coefficients are less than 0.6 Because the data were collected from two different provinces and shrimp farmers had different levels of intensification of whitelegged shrimp, it is necessary to test whether we can estimate the pooled cost function or not The results show that there is no 17 significant difference according to the t-test between the two data sets, except for the variable fuel Thus, we can estimate cost function by pooling the data of two groups of shrimp farmers in Kien Giang and Soc Trang Regression results are presented in detail in Table 4.2: Table 4.2: Estimated results of translog variable cost frontier Variables lnW1 lnW2 lnW3 lnW4 lnW5 lnZ1 lnY (lnW1lnW1)/2 lnW1lnW2 lnW1lnW3 lnW1lnW4 lnW1lnW5 lnW1lnZ1 lnW1lnY (lnW2lnW2)/2 lnW2lnW3 lnW2lnW4 lnW2lnW5 Estimated parameters of translog cost frontier Coef s.e Variables Coef 8,578 2,838 -0,986 -7,975 -18,267 -0,636 -4,923 0,414 -0,172 0,266 0,317 -0,555 -0,399 -0,182 0,015 0,008 -0,225 0,039 54,603 3,716 4,252 42,042 45,160 6,673 8,646 1,451 0,262 0,328 2,522 4,184 0,518 0,608 0,018 0,015 0,227 0,173 lnW2lnZ1 lnW2lnY (lnW3lnW3)/2 lnW3lnW4 lnW3lnW5 lnW3lnZ1 lnW3lnY (lnW4lnW4)/2 lnW4lnW5 lnW4lnZ1 lnW4lnY (lnW5lnW5)/2 lnW5lnZ1 lnW5lnY (lnZ1lnZ1)/2 lnZ1lnY (lnYlnY)/2 Constant -0,053** 0,024 -0,002 0,047 -0,152 0,017 -0,050 1,227 -0,141 0,337 -0,373 1,338 0,239 0,629 0,048 -0,004 0,207** 117,308 s.e 0,023 0,034 0,028 0,310 0,254 0,022 0,048 1,627 2,358 0,510 0,309 2,053 0,300 0,481 0,042 0,058 0,103 551,5 Estimated parameters of factors affecting inefficiency (Mu) Variables Coef s.e Variables Coef s.e Education 0,029 0,129 No of ponds 1,039** 0,436 Experience 0,041 0,118 Distance -0,004 0,005 Organization 0,356 1,894 Labor -0,003 0,632 Pond size -1,137** 0,457 Constant -0,124 2,188 Density -0,027* 0,015 Usigma -0,607 0,437 Vsigma -2,919*** 0,179 L-Likelihood -9,27 Lamda 3,176*** 0,165 Wald χ2 value 228,33 Source: Household survey in 2017, n = 125 18 From the estimated results of Table 4.2, one can estimate the economic efficiency for individual shrimp farmers in the study sites The economic efficiency is summarized in Table 4.3: Table 4.3: Economic efficiency of intensive shrimp farming Economic efficiency ≥90 80-90 70-80 60-70 50-60 40-50 30-40 χ2 0,0000 0,073 Obs 125 0,075 0,042 10,76 Source: Household survey in 2017, n = 125 Note : *, ** and *** indicate the significant levels of 10%, 5% 1%, respectively Production frontier Variables Coef lnZ1 1,492** lnZ2 0,559 lnZ3 1,209 lnX1 -0,296 lnX2 0,185 (lnZ1lnZ1)/2 0,003 lnZ1lnZ2 0,057 lnZ1lnZ3 -0,095 lnZ1lnX1 -0,032 lnZ1lnX2 0,031 (lnZ2lnZ2)/2 -0,104** lnZ2lnZ3 -0,028 lnZ2lnX1 0,023 lnZ2lnX2 -0,005 (lnZ3lnZ3)/2 -0,037 lnZ3lnX1 -0,012 lnZ3lnX2 0,089 (lnX1lnX1)/2 0,061 lnX1lnX2 -0,098 (lnX2lnX2)/2 -0,002 Hệ số chặn -7,847 Technical and environmental efficiency are summarized in Table 4.5 and Table 4.6, respectively: 23 Table 4.5: Output-oriented technical efficiency Soc Trang Technical efficiency Frequency % ≥90 72 80,00 80-90 8,89 70-80 4,45 60-70 2,22 50-60 1,11 90 80-90 70-80 60-70