The impact of food safety standard on coffee export the case in vietnam during 2005 2014

UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE IMPACT OF FOOD SAFETY STANDARD ON COFFEE EXPORT THE CASE IN VIETNAM DURING 2005-2014 BY TRUONG TAN TAI MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, OCTOBER -2016 ABSTRACT The objective of this study is to scrutinize the impact of FSS on the quantity of Vietnam’s coffee export Meanwhile, the regulated number of pesticides or average maximum residue levels is usually applied as a measurement of Food Safety Standard of a country The data covers 56 countries from 2005 to 2014 due to the data availability from AgrobaseLogigram’s Homologa database providing coffee FSS The Fixed effect estimator is employed in the panel gravity model Furthermore, Driscoll – Kraay Standard Errors for Fixed effect estimator is used for robustness checks Significantly, the primary findings determine that the regulated number of pesticides has a negative impact while average maximum residue levels have a positive effect on the export of Vietnamese coffee Furthermore, GDP per capita of importing countries, domestic consumption, and TWO member dummy variable demonstrate a contribution to Vietnamese coffee export Meanwhile, the real exchange rate depreciation and price*distance variable indicate a negative influence on the quantity of Vietnam’s coffee export Last but not least important, there are not any significant evidences proving the effect of trade openness and tariff on Vietnamese coffee export in the study Keywords: Food safety standard, Vietnam’s coffee export, panel gravity model i ACKNOWDGEMENT Firstly, I would highly appreciate my advisor Dr Nguyen Huu Dung for his valuable advice, consideration, and agreeable methodology during the time for conducting this thesis If there are not such valuable things, I am unable to complete my thesis in time Secondly, I am grateful to Dr Truong Dang Thuy providing me with precious instructions and encouragement Besides that, I also express my appreciation to dedicated professors and staffs in the Vietnam – Netherlands Programme who always support me during the time at VNP Thirdly, I wish to express my thankfulness to my classmates and my friendly group in Class 20 The kind assistance, useful discussion, and wonderful memories together from them will be imprinted in my heart Finally, I have no word to manifest my deep gratefulness to my loved family They have to sacrifice the best things for me to have this opportunity to study at VNP and complete this thesis ii ABBREVIATIONS CEPII The Centre d’Études Prospectives et d’Informations Internationales EEC European Economic Community EU The European Union FSS Food safety standards FTA Free Trade Agreement MRLs Maximum Residue Levels of Pesticides OLS Ordinary least squares SPS Sanitary and Phytosanitary TBT Technical Barriers to Trade TRAINS The UNCTAD Trade Analysis Information System UN Comtrade The United Nations Commodity Trade Statistics Database WTO World Trade Organization CONTENTS ABSTRACT i ACKNOWDGEMENT ii ABBREVIATIONS iii CONTENTS iv LIST OF TABLES vii LIST OF FIGURES viii CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research questions 1.4 Scope and limitations of the study 1.5 The structure of the study CHAPTER 2: LITERATURE REVIEW 2.1 The definitions 2.1.1 Pesticides .4 2.1.2 A maximum residue level or limit (MRL) 2.2 Some contributions to gravity model theory 2.3 Empirical research 2.3.1 Gravity model estimation using MRLs .8 2.3.2 Gravity model estimation using the regulated numbers of pesticides .10 2.3.3 Gravity model estimation either using MRL or the regulated numbers of pesticides 11 2.3.4 Distance and GDP per capita in gravity model 12 2.3.5 Extended control variables in gravity model .13 2.4 Literature review summary .16 CHAPTER 3: SITUATION OF VIETNAMESE COFFEE DURING 2005 - 2014 18 3.1 Advantages 18 3.2 Disadvantages 19 3.3 The top contribution rankings for the importing Vietnamese coffee countries 20 CHAPTER 4: ECONOMETRIC MODEL AND DATA 23 4.1 Specification of the model 23 4.1.1 Gravity model 23 4.1.2 Extended variables in gravity model 24 4.2 Data 25 4.2.1 Data source 25 4.2.2 Data description 25 4.2.3 Descriptive statistical analysis 27 4.3 Econometric models 28 4.3.1 Pooled OLS .28 4.3.2 Fixed Effect Estimation .29 4.3.3 Random Effect estimation 30 4.3.4 Driscoll and Kraay estimation 30 4.4 Choosing between OLS, Fixed Effect, and Random Effect estimation 31 4.4.1 F Test for pooled OLS or Fixed Effect estimation 31 4.4.2 Breusch and Pagan Lagrangian Multiplier Test for Random Effect or OLS .31 4.4.3 The Hausman test 31 4.5 Post-estimation tests 32 4.5.1 Multicollinearity 32 4.5.2 Heteroskedasticity 32 4.5.3 Serial correlation 32 CHARTER 5: EMPIRICAL RESULTS 33 5.1 Correlation matrix of all variables in the model .33 5.2 Estimating the intuitive gravity model .34 5.3 Empirical results .35 5.3.1 The empirical results in the gravity model using the regulated number of pesticides variable 35 5.3.2 The empirical results in the model using average maximum residue levels variable 39 CHAPTER 6: CONCLUSIONS AND POLICY IMPLICATIONS 42 6.1 Conclusions 42 6.2 Main findings 42 6.3 Policy implications 43 6.4 Limitations and future research 44 REFERENCES 45 APPENDICES 49 LIST OF TABLES Table 3.1 Top 10 importing countries (selected data on share export quantity during 2005-2014) 20 Table 4.1: Descriptive statistical analysis 28 Table 5.1: The empirical results using the regulated number of pesticides variable 36 Table 5.2: The empirical results using average maximum residue levels variable 39 vii LIST OF FIGURES Figure 1.1: Vietnam’s coffee export value in the period of 2004-2014 .1 Figure 2.1 : Analytical Framework for Vietnam’s Coffee Export and its influencing factors.17 Figure 3.1: Distribution of Vietnam’s coffee to the major importing countries in the period of 2005-2014 .21 Figure 3.2 : The top ten rankings for the major importing Vietnamese coffee countries annually 22 Figure 5.1: The correlation matrix of variables 33 Figure 5.2: The relationship between Pdistance and Vietnamese coffee export 34 Figure 5.3: The relationship between GDP per capita of importing countries and Vietnamese coffee export 35 viii CHAPTER 1: INTRODUCTION 1.1 Problem statement It is undeniable that Vietnam is an agricultural country although the government is still moving towards industrialization and modernization in the future Meanwhile, agricultural products in general and coffee commodity in particular play a crucial position in export turnover of Vietnam To describe this matter, it may consider the total exports of Vietnam in the period of 2004-2014 which has been increasing significantly recent years Figure 1.1: Vietnam’s coffee export value in the period of 2004-2014 The Vietnamese economy has been entering a new development stage after joining WTO since 2007 wherein coffee export sector also adjusts to a great turning point The value of coffee export reached more than USD 2.1 billion in 2008 Subsequently, it dropped in the two following years with the value export of nearly USD 1.76 and 1.90 billion respectively Nevertheless, there are also some years witnessing the decline in coffee export from Vietnam Specifically, demand from these countries and regions were also dropped slightly in 2010 and 2011 due to the change in food safety regulations However, it resumes good performance period from 2011 to 2014 Furthermore, Vietnamese coffee is noticeably exported to many countries around the world At the same period times, the tariff barriers in the world incline to drop Specifically, as a result of WTO, FTA joining or agreements on bilateral and multilateral treaties among countries, tariff is on the way to be lowered gradually Predictably, it will not be the important barrier in the coming time In practice, Henson and Loader (2001) identify that it appears to Chen, M X., Otsuki, T., & Wilson, J S (2006) Do standards matter for export success? 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European Review of Agricultural Economics, jbr062 APPENDICES Pooled OLS regression  Pooled OLS regression having ln_regnum Source SS df MS Model Residual 902.199852 593.843761 112.774981 176 3.37411228 Total 1496.04361 184 8.13067181 ln_netweight Coef Std Err ln_pdistance ln_gdpperc ln_regnum ln_dconsum ln_rexrate tradeopn simptax wtomemb _cons -1.324185 0053087 0216337 6708872 1657977 -.0168723 0462819 2.635457 10.58613 2333771 1796989 0797282 1377388 072918 004011 0137057 4699936 2.589798  VIF Variable VIF 1/VIF ln_gdpperc ln_dconsum ln_rexrate simptax tradeopn ln_regnum ln_pdistance wtomemb 2.57 2.36 1.84 1.80 1.59 1.55 1.54 1.12 0.388821 0.424582 0.544775 0.555142 0.628795 0.646813 0.649619 0.895954 Mean VIF 1.79 t -5.67 0.03 0.27 4.87 2.27 -4.21 3.38 5.61 4.09 Number of obs F(8, 176) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.976 0.786 0.000 0.024 0.000 0.001 0.000 0.000 = = = = = = 185 33.42 0.0000 0.6031 0.5850 1.8369 [95% Conf Interval] -1.784763 -.3493333 -.1357127 3990549 0218915 -.0247881 0192331 1.707908 5.475073 -.8636077 3599507 17898 9427194 309704 -.0089566 0733306 3.563005 15.69718  Pooled OLS regression having ln_avermrl Source SS df MS Model Residual 913.472 582.571613 114.184 176 3.31006598 Total 1496.04361 184 8.13067181 ln_netweight Coef Std Err ln_pdistance ln_gdpperc ln_avermrl ln_dconsum ln_rexrate tradeopn simptax wtomemb _cons -1.34452 -.0394638 2040091 6783045 1456513 -.0191771 0402787 2.779218 11.56881 2313359 1682187 109353 1346869 072993 0041369 0135651 4650218 2.542888  VIF Variable VIF 1/VIF ln_gdpperc ln_dconsum ln_rexrate simptax tradeopn ln_pdistance ln_avermrl wtomemb 2.30 2.30 1.87 1.80 1.72 1.54 1.41 1.11 0.435281 0.435612 0.533337 0.555952 0.579883 0.648583 0.710805 0.897842 Mean VIF 1.76 t -5.81 -0.23 1.87 5.04 2.00 -4.64 2.97 5.98 4.55 Number of obs F(8, 176) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.815 0.064 0.000 0.048 0.000 0.003 0.000 0.000 = = = = = = 185 34.50 0.0000 0.6106 0.5929 1.8194 [95% Conf Interval] -1.801069 -.3714492 -.0118029 4124953 0015971 -.0273413 0135074 1.861481 6.550335 -.8879701 2925216 4198211 9441137 2897056 -.0110129 06705 3.696954 16.58729 Random Effect regression  Random Effect regression having ln_regnum Random-effects GLS regression Group variable: id Number of obs Number of groups R-sq: Obs per group: within = 0.3226 between = 0.4284 overall = 0.4753 corr(u_i, X) 185 44 = avg = max = 4.2 = = 98.89 0.0000 Wald chi2(8) Prob > chi2 = (assumed) ln_netweight Coef ln_pdistance ln_gdpperc ln_regnum ln_dconsum ln_rexrate tradeopn simptax wtomemb _cons -1.589887 7470546 -.1303023 6494298 -.0959383 -.0079169 0127173 1.079526 9.969709 2424205 2925865 076967 1662927 120659 006756 0158069 2851447 3.090195 sigma_u sigma_e rho 1.4777486 78617393 77940355 (fraction of variance due to u_i)  = = Std Err z -6.56 2.55 -1.69 3.91 -0.80 -1.17 0.80 3.79 3.23 P>|z| 0.000 0.011 0.090 0.000 0.427 0.241 0.421 0.000 0.001 [95% Conf Interval] -2.065022 1735955 -.2811548 3235022 -.3324256 -.0211585 -.0182637 520653 3.913039 -1.114752 1.320514 0205502 9753574 140549 0053246 0436983 1.6384 16.02638 Breusch and Pagan Lagrangian multiplier test for choosing Pooled OLS or Random Effect regression Breusch and Pagan Lagrangian multiplier test for random effects ln_netweight[id,t] = Xb + u[id] + e[id,t] Estimated results: ln_netw~t e u Test: Var sd = sqrt(Var) 8.130672 6180695 2.183741 2.851433 7861739 1.477749 Var(u) = chibar2(01) = Prob > chibar2 = 122.02 0.0000 51  Random Effect regression having ln_avermrl Random-effects GLS regression Number of obs = 185 Group variable: id Number of groups = 44 R-sq: Obs per group: within = 0.3496 = between = 0.4216 avg = 4.2 overall = 0.4838 max = Wald chi2(8) = 106.06 Prob > chi2 = 0.0000 corr(u_i, X) = (assumed) ln_netweight Coef ln_pdistance ln_gdpperc -1.650093 5375461 ln_avermrl ln_dconsum Std Err z P>|z| [95% Conf Interval] 2349301 2906266 -7.02 1.85 0.000 0.064 -2.110548 -.0320715 -1.189639 1.107164 2822333 10275 2.75 0.006 0808471 4836195 6624981 1642132 4.03 0.000 3406462 98435 ln_rexrate -.1205892 1187213 -1.02 0.310 -.3532786 1121002 tradeopn -.0113889 0063205 -1.80 0.072 -.0237769 000999 simptax 0098928 0156543 0.63 0.527 -.020789 0405746 wtomemb 1.033927 2793552 3.70 0.000 4864006 1.581453 _cons 12.52134 2.932639 4.27 0.000 6.773475 18.26921 sigma_u sigma_e 1.4697237 78145674 rho 77960051  (fraction of variance due to u_i) Breusch and Pagan Lagrangian multiplier test for choosing Pooled OLS or Random Effect regression Breusch and Pagan Lagrangian multiplier test for random effects ln_netweight[id,t] = Xb + u[id] + e[id,t] Estimated results: ln_netw~t e u Test: Var sd = sqrt(Var) 8.130672 6106746 2.160088 2.851433 7814567 1.469724 Var(u) = chibar2(01) = Prob > chibar2 = 115.77 0.0000 52 Fixed Effect regression  Fixed Effect regression having ln_regnum Fixed-effects (within) regression Group variable: id Number of obs = Number of groups = R-sq: Obs per group: within = 0.4902 between = 0.0733 overall = 0.1257 corr(u_i, Xb) F(8,133) Prob > F = -0.9097 ln_netweight Coef Std Err ln_pdistance ln_gdpperc ln_regnum ln_dconsum ln_rexrate tradeopn simptax wtomemb _cons -1.84824 6.337632 -.207028 4501706 -.8969454 0169904 -.0031183 6279477 -33.65924 2354093 1.493269 0804611 1967866 1990111 0132153 0157541 2640555 13.12411 sigma_u sigma_e rho 6.5898192 78617393 98596693 t -7.85 4.24 -2.57 2.29 -4.51 1.29 -0.20 2.38 -2.56 P>|t| 0.000 0.000 0.011 0.024 0.000 0.201 0.843 0.019 0.011 (fraction of variance due F test that all u_i=0: F(43, 133) = 19.25 0.0000 185 44 = avg = max = 4.2 = = 15.99 0.0000 [95% Conf Interval] -2.313871 3.384004 -.3661769 0609344 -1.290582 -.009149 -.0342794 1056561 -59.61822 -1.38261 9.29126 -.0478791 8394068 -.5033092 0431298 0280428 1.150239 -7.700265 to u_i) Prob > F = 53  Fixed Effect regression having ln_avermrl Fixed-effects (within) regression Number of obs = 185 Group variable: id Number of groups = 44 R-sq: Obs per group: within = 0.4963 between = 0.0909 = avg = 4.2 overall = 0.1522 max = F(8,133) = 16.38 Prob > F = 0.0000 corr(u_i, Xb) = -0.9116 ln_netweight Coef Std Err t ln_pdistance ln_gdpperc -1.928247 6.042001 2325466 1.498065 -8.29 4.03 0.000 0.000 -2.388215 3.078887 -1.468279 9.005115 ln_avermrl 2798167 0970608 2.88 0.005 0878343 4717991 ln_dconsum ln_rexrate 5331619 -.9158016 1980932 1958537 2.69 -4.68 0.008 0.000 1413412 -1.303193 9249825 -.5284106 tradeopn simptax 0052668 -.0053491 0112188 0157167 0.47 -0.34 0.640 0.734 -.0169236 -.0364361 0274571 025738 wtomemb _cons 5328793 -30.44181 2584609 13.16637 2.06 -2.31 0.041 0.022 0216535 -56.48438 1.044105 -4.399237 sigma_u sigma_e rho 6.4850048 78145674 98568708 (fraction of variance due to u_i) F test that all u_i=0: F(43, 133) = 19.09 P>|t| [95% Conf Interval] Prob > F = 0.0000 Hausman Test  Hausman Test for choosing Fixed Effect or Random Effect for ln_regnum Coefficients (b) (B) fixed_ln_r~m random_ln_~m (b-B) sqrt(diag(V_b-V_B)) Difference S.E ln_pdistance ln_gdpperc -1.84824 6.337632 -1.589887 7470546 -.2583535 5.590577 1.464324 ln_regnum -.207028 -.1303023 -.0767257 0234536 ln_dconsum 4501706 6494298 -.1992592 1052222 ln_rexrate -.8969454 -.0959383 -.8010072 1582619 tradeopn 0169904 -.0079169 0249073 0113579 simptax -.0031183 0127173 -.0158356 wtomemb 6279477 1.079526 -.4515787 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 38.05 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite)  Hausman Test for choosing Fixed Effect or Random Effect for ln_ avermrl Coefficients (b) (B) fixed_ln_a~l random_ln_~l ln_pdistance ln_gdpperc ln_avermrl ln_dconsum ln_rexrate tradeopn simptax wtomemb -1.928247 6.042001 2798167 5331619 -.9158016 0052668 -.0053491 5328793 -1.650093 5375461 2822333 6624981 -.1205892 -.0113889 0098928 1.033927 (b-B) Difference -.2781535 5.504455 -.0024166 -.1293363 -.7952125 0166557 -.0152419 -.5010473 sqrt(diag(V_b-V_B)) S.E 1.469604 1107923 1557688 0092689 0013998 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 14.37 Prob>chi2 = 0.0726 (V_b-V_B is not positive definite) Heteroskedasticity and autocorrelation test  Modified Wald test testing heteroskedasticity for ln_regnum Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (44) = Prob>chi2 =  1.1e+05 0.0000 Wooldridge test testing autocorrelation for ln_regnum Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 29) = 12.918 Prob > F = 0.0012  Modified Wald test testing heteroskedasticity for ln_avermrl Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (44) = Prob>chi2 =  4.9e+29 0.0000 Wooldridge test testing autocorrelation for ln_avermrl Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 29) = Prob > F = 12.958 0.0012 Driscoll-Kraay Standard Errors for Fixed Effects Regression  Driscoll-Kraay Standard Errors for Fixed Effects Regression having ln_regnum Regression with Driscoll-Kraay standard errors Number of obs Method: Fixed-effects regression Number of groups Group variable (i): id F( 8, 43) maximum lag: Prob > F within R-squared ln_netweight ln_pdistance ln_gdpperc ln_regnum ln_dconsum ln_rexrate tradeopn simptax wtomemb _cons Drisc/Kraay Coef Std Err -1.84824 6.337632 -.207028 4501706 -.8969454 0169904 -.0031183 6279477 -33.65924 2869578 1.418901 0495808 1749316 3687689 0078984 0059502 202319 13.72154 t -6.44 4.47 -4.18 2.57 -2.43 2.15 -0.52 3.10 -2.45 P>|t| 0.000 0.000 0.000 0.014 0.019 0.037 0.603 0.003 0.018 = = = = = 185 44 9512.52 0.0000 0.4902 [95% Conf Interval] -2.426946 3.476145 -.3070172 0973874 -1.640639 0010617 -.0151181 2199325 -61.33137 -1.269535 9.199119 -.1070387 8029537 -.1532521 0329191 0088815 1.035963 -5.987113  Driscoll-Kraay Standard Errors for Fixed Effects Regression having ln_avermrl Regression with Driscoll-Kraay standard errors Number of obs Method: Fixed-effects regression Number of groups Group variable (i): id F( 8, 43) maximum lag: Prob > F within R-squared ln_netweight ln_pdistance ln_gdpperc ln_avermrl ln_dconsum ln_rexrate tradeopn simptax wtomemb _cons Drisc/Kraay Coef Std Err -1.928247 6.042001 2798167 5331619 -.9158016 0052668 -.0053491 5328793 -30.44181 2843618 1.55275 0454079 1804924 3355731 0101561 0074505 199476 14.69183 t -6.78 3.89 6.16 2.95 -2.73 0.52 -0.72 2.67 -2.07 P>|t| 0.000 0.000 0.000 0.005 0.009 0.607 0.477 0.011 0.044 = = = = = 185 44 461.56 0.0000 0.4963 [95% Conf Interval] -2.501717 2.910581 1882429 1691642 -1.592549 -.015215 -.0203744 1305977 -60.07072 -1.354777 9.173421 3713906 8971595 -.2390539 0257486 0096763 9351609 -.8129 ... implications for enhancing the coffee export of Vietnam based on the findings of the thesis 1.3 Research questions In order to reach these objectives, there will be two following questions in the thesis... for Vietnam? ??s coffee export and its influencing factors CHAPTER 3: SITUATION OF VIETNAMESE COFFEE DURING 2005 - 2014 The coffee trees have been initially introduced and planted in Vietnam since... to the impact of other elements It creates the psychological effect on the production of coffee growers and businesses purchasing coffee export making coffee production to decline as well Finally,