Mangrove and production risk in aquaculture in mekong river delta, vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MANGROVE AND PRODUCTION RISK IN AQUACULTURE IN THE MEKONG RIVER DELTA, VIETNAM BY DO HUU LUAT MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2015 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MANGROVE AND PRODUCTION RISK IN AQUACULTURE IN THE MEKONG RIVER DELTA, VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By DO HUU LUAT Academic Supervisor: TRUONG DANG THUY HO CHI MINH CITY, DECEMBER 2015 DECLARATION This declaration is to certify that this thesis entitled “Mangrove and Production risk in aquaculture in the Mekong river delta, Vietnam”, which is submitted to fulfill the requirements for the degree of Master of Art in Development Economics to the Vietnam – The Netherlands Programme (VNP), constitutes only my original work only All materials used in this thesis have been acknowledged and cited properly following the Programme’s standards DO HUU LUAT ACKNOWLEDGMENTS “Success is no accident It is hard work, perseverance, learning, studying, sacrifice and most of all, love of what you are doing or learning to do.” – Pele This thesis has been finished thanks to supporting and motivation from many people First of all, I am proud of me and would like to give my first thank to myself Two years ago, I did not believe that I could complete this course as well as a scientific thesis I have overcome myself and struggled a lot in order to this Secondly, the authors are grateful to Economy and Environment Program for Southeast Asia (EEPSEA) for funding data collection Thirdly, I would like to give the sincerest thank to my supervisor - Dr Truong Dang Thuy, who has given a precious opportunity to experience a study in reality Besides, he allowed me to utilize his data to serve this thesis and gave invaluable suggestions to me Moreover, I am grateful to all lecturers VNP and staffs VNP who have helped and taught me salutary knowledge over two years Fourthly, I want to spend the best wishes to all my friends at VNP They have been accompanying me in the journey of learning and studying at VNP, this will be my unforgettable experience Finally, I devote my thesis to my parents who always support and give me incentive regardless of what the way I choose DO HUU LUAT ABSTRACT Utilizing survey data in aquaculture activities in 2014 from the Mekong river deltaVietnam, this paper aims to examine the impact of mangrove forests on profit and profit variability in extensive and semi-intensive aquaculture farms (mostly shrimp farms) The Just-Pope framework for a stochastic short-run profit function is applied to examine the impacts of inputs on both the deterministic component and the stochastic component of profit The most crucial characteristics of mangrove forests such as the area of mangrove forests in farm, the density of mangrove trees per 100 square meter, and the area of mangrove forests within 500, 1000, and 2000, are utilized in this paper The main estimation method is the Maximum likelihood (ML) estimator for the loglikelihood function employed to investigate the relationship between mangrove forests and profit as well as its variability Apart from the ML estimator, other estimation methods (including FGLS, Robust S.E, and SUR) are also employed to test robustness of the regression results The results show robust evidences that mangrove forests have negative effect and variance-reducing effect on profit in extensive and semi-intensive aquaculture farms From these results, it implies that when converting more mangrove into water surface area, farmers earn higher profit at higher risk, and that a risk-averse farmer will plant more mangrove forests in farm than the risk-neutral farmer Keywords: Mangrove forests, Production risk, Aquaculture, Profit function i TABLE OF CONTENT ABSTRACT i ABBREVIATIONS iv LIST OF FIGURES v LIST OF TABLES vii CHAPTER 11 INTRODUCTION 1.1 Problem statement 1.2 Research objective 1.3 Research questions 1.4 Scope of the paper 1.5 Structure of this thesis CHAPTER 25 LITERATURE REVIEW 2.1 The ecological functions of mangrove forests 2.2 The economic value of mangrove systems 2.3 The theory of production risk 12 2.4 Empirical studies 14 2.4.1 The impact of mangrove forests on production process 15 2.4.2 Empirical studies about production risk 16 CHAPTER RESEARCH METHODS 22 3.1 Overview of the Mekong river delta 22 3.2 Analytical framework 24 3.3 Neoclassical economic theory 24 3.3.1 Two approaches in production economics 24 3.3.2 A dual approach - The profit function 26 3.4 Research methods 30 3.4.1 Model specification 30 3.4.2 Data collection method 35 ii 3.4.3 Variable description 36 3.4.4 Estimation methods 39 CHAPTER RESEARCH RESULTS 50 4.1 Descriptive statistic 50 4.2 Bivariate analysis 55 4.2.1 Mangrove forests versus profit 55 4.2.2 Output and input prices versus profit 59 4.2.3 Fixed inputs versus profit 61 4.2.4 Operator’s management ability versus profit 62 4.3 Estimation results 64 4.3.1 Testing for production risk 64 4.3.2 Regression results for the effect of mangroves on profit per square meter 66 4.3.3 Regression results for the impact of mangrove forests on the profit variability in aquaculture production 73 CHAPTER CONCLUSION AND POLICY IMPLICATION 80 5.1 Conclusion remarks 80 5.2 Policy implication 82 5.3 Limitations and further research 82 REFERENCES 84 APPENDIX 91 Appendix A: Questionnaire 91 Appendix B: Test for the presence of heteroskedasticity 94 Appendix C: Correlation matrix and testing for multi-collinearity 101 Appendix D: Technical efficiency 102 iii ABBREVIATIONS FGLS Feasible Generalized Least Squares GDP Gross Domestic Products GLS Generalized Least Squares GSO General Statistics Office ML Maximum Likelihood OLS Ordinary Least Squares SUR Seemingly Unrelated Regressions WRI World Resources Institute iv LIST OF FIGURES Figure 1.1 Land use in the Mekong River delta, 2007 Figure 2.1 The ecological functions of mangrove forests are in seafood production Figure 2.2 The mixed mangrove-aquaculture farming systems Figure 3.1 Profit function with respect to output price 29 Figure 4.1 The distribution of observed aquaculture farms 50 Figure 4.2 Scatter diagram of profit per square meter on the ratio of mangrove area in farms 55 Figure 4.3 Scatter diagram of profit per square meter on the density of mangrove trees in farms 56 Figure 4.4 Scatter diagram of profit per square meter on the area of mangroves in 500 meter 57 Figure 4.5 Scatter diagram of profit per square meter on the area of mangroves in 2000 meter 57 Figure 4.6 Scatter diagram of profit per square meter on output price 58 Figure 4.7 Scatter diagram of profit per square meter on chemical price 59 Figure 4.8 Scatter diagram of profit per square meter on fry price 59 Figure 4.9 Scatter diagram of profit per square meter on total area 60 Figure 4.10 Scatter diagram of profit per square meter on family working hours 61 Figure 4.11 Scatter diagram of profit per square meter on age of household head 62 Figure 4.12 Scatter diagram of profit per square meter on Operator’s schooling years 62 Figure 4.13 Scatter diagram of production risk on the ratio of mangrove area in farm 73 Figure 4.14 Scatter diagram of production risk on the density of mangrove trees in farm 74 v Figure 4.15 Scatter diagram of production risk on the area of mangroves in radius 500 meter 75 Figure 4.16 Scatter diagram of production risk on the area of mangroves in radius 2000 75 vi Lee, S.Y., 1999 The effect of mangrove leaf litter enrichment on macrobenthic colonization of defaunated sandy substrates Estuar Coast Shelf S 49, 703– 712 Levinsohn, J., & Petrin, A (2003) 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HOUSEHOLD B1 Type of farm entity Subsistence; Commercial B2 Are you the head of the house 1.Yes; No - If the answer is "Yes", hold? go straight to Question B5 Age of the head of the Years B3 household B4 Gender of the head of the 1: Female; 2: Male household B5 Your age Years B6 Gender 1: Female; 2: Male B7 Household size (of Number owner/manager of the farm) B8 Education of the head of the Years household (total years) PART 2: SEAFOOD FARMING S1 Farming area Hectare S2 Farming method Intensive; Extensive; Semiintensive S3 Tenure type Own land use; Rented land from other person; Rented land from 91 government; Unused land without ownership; Other (specify) S4 How many (average) number of Years years have you used these seafood farming area? S5 Total rent paid (per month) if Currency amount per month plot is leased S6 How many months your Months seafood farming processes occur annually? S7 Annual output from seafood Kg farming (shrimp, crab, fish, etc.) S8 Average output from each Kg rotation S9 Average price for one kg of VND/kg outputs S10 Total value of annual output VND S11 Total value of outputs from one VND rotation S12 Number of working days per Days week S13 Average number of labors Number of labors working on the field S14 Daily average number of Hours working hours (per labor) S15 Average length of one seafood Days farming rotation 92 S16 Value of seafood farming ponds, VND tools and machines? S17 Amount of seafood seed used in Kg cropping in one rotation S18 Average price for one kg of VND/kg seafood seed Cost for seafood seed for one VND (Amount of seafood seed x rotation Average price) S20 Cost of antibiotics VND S21 Are your seafood farming Yes; No S19 activities area next to the mangrove site (within the distance of less than 100m)? S22 Are your seafood farming Yes; No activities area covered by the mangrove? S23 Do you waste the sewage from Yes; No the process into mangrove site? 93 Appendix B: Test for the presence of heteroskedasticity Model 1: the mangrove proxy is the ratio of mangrove forest within the pond Source SS df MS Model Residual 1.2642e+09 4.3129e+09 196 158020247 22004486.3 Total 5.5770e+09 204 27338437.8 properm2 Coef prioutput pribreed pripes hourlf sumarea age schyears mangratio _cons 0129487 -.7651807 -.0567023 4.260213 -.0517 -24.12223 220.1647 -32.90818 4528.933 Std Err .0061515 395679 0196892 1.827574 0166265 28.80157 105.0131 12.86101 1980.919 t 2.10 -1.93 -2.88 2.33 -3.11 -0.84 2.10 -2.56 2.29 Number of obs F( 8, 196) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.037 0.055 0.004 0.021 0.002 0.403 0.037 0.011 0.023 White test White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(44) Prob > chi2 = = 93.55 0.0000 Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis 93.55 29.08 3.94 44 0.0000 0.0003 0.0472 Total 126.56 53 0.0000 Breush-Pagan LM test 94 = = = = = = 205 7.18 0.0000 0.2267 0.1951 4690.9 [95% Conf Interval] 0008171 -1.545516 -.0955322 6559783 -.0844897 -80.92299 13.06412 -58.27191 622.2812 0250802 0151541 -.0178725 7.864447 -.0189103 32.67853 427.2653 -7.544457 8435.584 Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of properm2 chi2(1) = Prob > chi2 = 98.85 0.0000 Model 2: the mangrove proxy is the density of mangrove trees per 100m2 Source SS df MS Model Residual 1.1286e+09 4.4484e+09 196 141077321 22696034.4 Total 5.5770e+09 204 27338437.8 properm2 Coef prioutput pribreed pripes hourlf sumarea age schyears mangdens _cons 0124601 -.7409621 -.0560328 4.887379 -.0609027 -24.38125 179.0201 -1.519634 4166.042 Std Err .0062787 4018434 0200037 1.838426 0164617 29.34593 105.3144 2.479506 2014.05 t 1.98 -1.84 -2.80 2.66 -3.70 -0.83 1.70 -0.61 2.07 Breush-Pagan LM test Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of properm2 chi2(1) = Prob > chi2 = 95.24 0.0000 White’s test 95 Number of obs F( 8, 196) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.049 0.067 0.006 0.008 0.000 0.407 0.091 0.541 0.040 = = = = = = 205 6.22 0.0000 0.2024 0.1698 4764 [95% Conf Interval] 0000776 -1.533454 -.0954829 1.261744 -.0933675 -82.25558 -28.67485 -6.409569 194.0505 0248425 0515298 -.0165827 8.513015 -.0284379 33.49307 386.7151 3.370301 8138.033 White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(44) Prob > chi2 = = 84.87 0.0002 Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis 84.87 26.66 4.16 44 0.0002 0.0008 0.0415 Total 115.69 53 0.0000 Model 3: the mangrove proxies are the ratio of mangrove forest within the pond and the area of mangrove trees per 100m2 Source SS df MS Model Residual 1.2656e+09 4.3115e+09 195 140618165 22110142.6 Total 5.5770e+09 204 27338437.8 properm2 Coef prioutput pribreed pripes hourlf sumarea age schyears mangdens mangratio _cons 0127895 -.7678539 -.0568622 4.247236 -.0516555 -24.71035 220.7213 654222 -34.06665 4576.725 Std Err .0061985 3967699 0197466 1.832681 0166673 28.96498 105.2881 2.598501 13.68839 1994.722 t 2.06 -1.94 -2.88 2.32 -3.10 -0.85 2.10 0.25 -2.49 2.29 Breush-Pagan LM test Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of properm2 chi2(1) = Prob > chi2 = 98.78 0.0000 96 Number of obs F( 9, 195) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.040 0.054 0.004 0.022 0.002 0.395 0.037 0.801 0.014 0.023 = = = = = = 205 6.36 0.0000 0.2269 0.1912 4702.1 [95% Conf Interval] 0005648 -1.550365 -.0958066 6328142 -.0845268 -81.8352 13.07171 -4.470553 -61.06294 642.7267 0250143 0146573 -.0179179 7.861658 -.0187842 32.41451 428.3709 5.778997 -7.070361 8510.723 White’s test White's test for against Ho: Ha: homoskedasticity unrestricted heteroskedasticity chi2(54) Prob > chi2 Cameron & Trivedi's = = 96.63 0.0003 decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis 96.63 29.07 3.91 54 0.0003 0.0006 0.0480 Total 129.61 64 0.0000 Model 4: the mangrove proxies are the area of mangrove forest within 500 and 2000 meter Source SS df MS Model Residual 1.1499e+09 4.2575e+09 184 127766761 23138503.2 Total 5.4074e+09 193 28017541.2 properm2 Coef prioutput pribreed pripes hourlf sumarea age schyears mang500 mang2000 _cons 0112465 -.8371313 -.0587967 4.619081 -.0641105 -24.74713 161.4328 000692 0003941 4499.646 Std Err .0064564 4303305 0206458 2.055786 0168719 30.16616 108.6423 0042877 0005645 2071.353 t 1.74 -1.95 -2.85 2.25 -3.80 -0.82 1.49 0.16 0.70 2.17 Breush-Pagan LM test Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of properm2 chi2(1) = Prob > chi2 = 87.24 0.0000 White’s test 97 Number of obs F( 9, 184) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.083 0.053 0.005 0.026 0.000 0.413 0.139 0.872 0.486 0.031 = = = = = = 194 5.52 0.0000 0.2127 0.1741 4810.2 [95% Conf Interval] -.0014917 -1.686148 -.0995296 563137 -.0973978 -84.26316 -52.91207 -.0077674 -.0007197 412.9901 0239846 0118851 -.0180638 8.675024 -.0308233 34.76891 375.7776 0091513 0015079 8586.301 White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(54) Prob > chi2 = = 95.81 0.0004 Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis 95.81 28.37 4.56 54 0.0004 0.0008 0.0327 Total 128.74 64 0.0000 Model 5: the mangrove proxies are the ratio of mangrove forest within the pond, the area of mangrove trees per 100m2, and the area of mangrove forest within 500 and 2000 meter Source SS df MS Model Residual 1.2770e+09 4.1304e+09 11 182 116090574 22694445.8 Total 5.4074e+09 193 28017541.2 properm2 Coef prioutput pribreed pripes hourlf sumarea age schyears mang500 mang2000 mangratio mangdens _cons 0124378 -.8047101 -.0605872 4.074932 -.0554614 -23.24345 208.4213 0007043 0002155 -32.84077 3306334 4838.177 Std Err .0064525 4265045 0204663 2.049241 0171046 29.95576 109.4341 004264 0005681 14.51697 2.657435 2065.099 t 1.93 -1.89 -2.96 1.99 -3.24 -0.78 1.90 0.17 0.38 -2.26 0.12 2.34 Breush-Pagan LM test Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of properm2 chi2(1) = Prob > chi2 = 95.41 0.0000 White’s test 98 Number of obs F( 11, 182) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.055 0.061 0.003 0.048 0.001 0.439 0.058 0.869 0.705 0.025 0.901 0.020 = = = = = = 194 5.12 0.0000 0.2362 0.1900 4763.9 [95% Conf Interval] -.0002935 -1.646239 -.1009691 0316073 -.0892102 -82.34867 -7.501393 -.0077088 -.0009054 -61.48397 -4.912709 763.5639 025169 0368191 -.0202054 8.118256 -.0217126 35.86178 424.344 0091175 0013364 -4.197579 5.573976 8912.791 White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(77) Prob > chi2 = = 114.08 0.0039 Cameron & Trivedi's decomposition of IM-test Source chi2 df p Heteroskedasticity Skewness Kurtosis 114.08 31.90 4.29 77 11 0.0039 0.0008 0.0384 Total 150.27 89 0.0001 Testing for multicollinearity Source SS df MS Model Residual 1.1707e+09 4.2367e+09 10 183 117069788 23151298.2 Total 5.4074e+09 193 28017541.2 properm2 Coef prioutput pribreed pripes hourlf sumarea age schyears mang500 mang1000 mang2000 _cons 0118951 -.8128448 -.0610336 4.765002 -.0645187 -26.40322 168.4073 0115895 -.0049549 0010324 4438.574 Std Err .0064944 4312115 0207859 2.06211 016882 30.22505 108.9212 0122717 0052279 0008788 2072.927 t 1.83 -1.89 -2.94 2.31 -3.82 -0.87 1.55 0.94 -0.95 1.17 2.14 99 Number of obs F( 10, 183) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.069 0.061 0.004 0.022 0.000 0.384 0.124 0.346 0.344 0.242 0.034 = = = = = = 194 5.06 0.0000 0.2165 0.1737 4811.6 [95% Conf Interval] -.0009184 -1.66363 -.1020445 6964345 -.0978272 -86.03759 -46.49552 -.0126227 -.0152696 -.0007016 348.6643 0247086 0379405 -.0200227 8.833569 -.0312102 33.23116 383.3102 0358016 0053597 0027663 8528.484 Variable VIF 1/VIF mang1000 mang500 mang2000 pripes prioutput hourlf schyears age pribreed sumarea 53.04 30.29 8.70 1.20 1.16 1.12 1.11 1.11 1.09 1.07 0.018853 0.033014 0.114903 0.834468 0.864352 0.891183 0.899115 0.900380 0.918872 0.931004 Mean VIF 9.99 Source SS df MS Model Residual 1.1499e+09 4.2575e+09 184 127766761 23138503.2 Total 5.4074e+09 193 28017541.2 properm2 Coef prioutput pribreed pripes hourlf sumarea age schyears mang500 mang2000 _cons 0112465 -.8371313 -.0587967 4.619081 -.0641105 -24.74713 161.4328 000692 0003941 4499.646 Std Err .0064564 4303305 0206458 2.055786 0168719 30.16616 108.6423 0042877 0005645 2071.353 Variable VIF 1/VIF mang500 mang2000 pripes prioutput hourlf schyears age pribreed sumarea 3.70 3.59 1.18 1.14 1.12 1.11 1.11 1.08 1.07 0.270277 0.278315 0.845367 0.874058 0.896179 0.903238 0.903399 0.922128 0.931611 Mean VIF 1.68 t 1.74 -1.95 -2.85 2.25 -3.80 -0.82 1.49 0.16 0.70 2.17 100 Number of obs F( 9, 184) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.083 0.053 0.005 0.026 0.000 0.413 0.139 0.872 0.486 0.031 = = = = = = 194 5.52 0.0000 0.2127 0.1741 4810.2 [95% Conf Interval] -.0014917 -1.686148 -.0995296 563137 -.0973978 -84.26316 -52.91207 -.0077674 -.0007197 412.9901 0239846 0118851 -.0180638 8.675024 -.0308233 34.76891 375.7776 0091513 0015079 8586.301 Appendix C: Correlation matrix and testing for multi-collinearity Table A.4 Correlation matrix and variance inflation factors Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) properm2 1.00 (2) prioutput 0.05 1.00 (3) pribreed -0.14 0.02 1.00 (4) pripes -0.24 0.26 0.07 1.00 (5) hourlf 0.24 -0.07 -0.11 -0.25 1.00 (6) sumarea -0.27 0.12 -0.06 0.06 -0.11 1.00 (7) age -0.16 0.00 0.04 0.17 -0.04 0.14 1.00 (8) schyears 0.10 0.15 -0.12 0.05 -0.12 0.08 -0.19 1.00 (9) mangratio -0.23 0.11 -0.03 0.04 -0.17 0.26 0.02 0.20 1.00 (10) mangdens -0.08 0.14 0.02 0.08 -0.05 0.10 0.09 0.04 0.35 1.00 (11) mang500 0.12 0.15 0.18 -0.04 0.08 -0.03 -0.05 0.05 -0.18 -0.03 1.00 (12) mang1000 0.13 0.15 0.19 -0.04 0.09 -0.05 -0.05 0.05 -0.20 -0.03 0.98 1.00 (13) mang2000 0.12 0.09 0.17 0.01 0.06 -0.08 0.01 0.02 -0.23 0.01 0.84 0.91 101 (13) 1.00 VIF VIF* - - 1.18 1.17 1.09 1.09 1.2 1.19 1.14 1.13 1.13 1.12 1.12 1.11 1.15 1.15 1.36 1.36 1.2 1.18 30.42 3.73 53.6 - 8.96 3.71 Appendix D: Technical efficiency The distribution for the inefficiency term as half-normal (hnormal): Stoc frontier normal/hnormal model Number of obs = Wald chi2(8) = Prob > chi2 = 205 46.00 0.0000 Log likelihood = -2003.5792 properm2 Coef Std Err z P>|z| [95% Conf Interval] Frontier prioutput pripes pribreed hourlf sumarea age schyears mangratio _cons 0123521 -.0509421 -.6370912 1.620231 -.0417693 -27.40071 101.9801 -31.26458 5220.898 0051773 0171444 3480506 1.842599 0131771 23.66733 87.44318 10.58789 1678.385 2.39 -2.97 -1.83 0.88 -3.17 -1.16 1.17 -2.95 3.11 0.017 0.003 0.067 0.379 0.002 0.247 0.244 0.003 0.002 0022047 -.0845444 -1.319258 -1.991198 -.0675959 -73.78782 -69.40541 -52.01647 1931.323 0224995 -.0173398 0450754 5.231659 -.0159428 18.9864 273.3655 -10.51269 8510.473 _cons -61.7051 Vsigma mangratio _cons -.0212165 17.3384 0036009 1454905 -5.89 119.17 0.000 0.000 -.0282742 17.05324 -.0141589 17.62356 E(sigma_v) sigma_u 4423.178 3.99e-14 4258.413 4587.944 Usigma The distribution for the inefficiency term as truncated normal distribution Stoc frontier normal/tnormal model Number of obs = Wald chi2(8) = Prob > chi2 = 205 45.22 0.0000 Log likelihood = -2003.5466 properm2 Coef Std Err z P>|z| [95% Conf Interval] Frontier prioutput pripes pribreed hourlf sumarea age schyears mangratio _cons 012942 -.0508058 -.6231427 1.721082 -.0417862 -23.23023 109.308 -30.64046 4847.977 0051768 0171427 3479769 1.842306 0131751 23.65829 87.51786 10.58729 2747.604 2.50 -2.96 -1.79 0.93 -3.17 -0.98 1.25 -2.89 1.76 0.012 0.003 0.073 0.350 0.002 0.326 0.212 0.004 0.078 0027957 -.0844049 -1.305165 -1.889772 -.0676089 -69.59964 -62.22384 -51.39116 -537.228 0230884 -.0172067 0588795 5.331935 -.0159634 23.13917 280.8399 -9.889747 10233.18 _cons -771.5608 _cons 10.76421 44.72478 0.24 0.810 -76.89475 98.42317 Vsigma mangratio _cons -.0212107 17.33774 0036143 1455759 -5.87 119.10 0.000 0.000 -.0282946 17.05241 -.0141268 17.62306 E(sigma_v) sigma_u 4421.999 217.4793 4863.356 0.04 0.964 4257.319 2.01e-17 4586.678 2.36e+21 Mu Usigma 102 ... examine the effects of mangrove forests on profit and profit variability (production risk) in extensive and semi-intensive aquaculture farms (mostly shrimp farms) in Mekong river delta, Vietnam. .. questions  Do mangrove forests reduce profit in aquaculture activities in Mekong river delta?  Do mangrove forests decrease production risk in aquaculture activities in Mekong river delta? 1.4... CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MANGROVE AND PRODUCTION RISK IN AQUACULTURE