The relationship between the agricultural trade and productivity in vietnam case

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE RELATIONSHIP BETWEEN AGRICULTURAL TRADE AND PRODUCTIVITY IN VIETNAM CASE A thesis submitted in partial fulfillment of requirements for degree of Master of Arts in Development Economics By NGUYEN HOANG DIEP Academic supervisor Dr TRAN TIEN KHAI HO CHI MINH CITY, JANUARY 2015 [Type text] ACKNOWLEDGEMENT So many persons that I want to say thanks to them I write these acknowledgments to express my gratitude to them who helped me directly and indirectly to finish this thesis First of all, Dr Tran Tien Khai who is my supervisor, he helped me to complete my ideas Furthermore, he also gave me advices to writing logical for this thesis, and already gave his hands when I needed The other person that is Dr Tran Khanh Nam, who gave me advice from beginning when I wrote the thesis design In addition, he provided me the VARHS data set to apply in my thesis Important to me is to thank the Vietnam-Netherlands program and all of people and lecturers who supported and gave me their knowledge Finally yet importantly, with all my respect for my parents who gave me a change to study in this program They have encouraged me to continue my studying [Type text] ABBREVIATION FAOSTAT The Food and Agricultural Organization Corporate Statistical Database VARHS The Vietnam Access to Resources Household Survey TFP The Total Factor Productivity OLS The Ordinary Least Square GDP Gross Domestic Product DF Dickey-Fuller Test [Type text] ABSTRACT This thesis tries to find out whether trade has an effect on agricultural productivity in Vietnam case There are two models to estimate relationship between trade and productivity including national-level model and farm-level model The time series data of nine agricultural commodities, which is taken from FAOSTAT, is used in national-level model Farm-level model uses cross section data from VARHS 2010 including 1449 farm household in nine provinces In both models, yield refers as agricultural productivity The both models support for a strong positive relationship between trade and yield The result also indicates that the land, irrigation, human capital, cost of production might be necessary for improve productivity Key words: Agricultural productivity, international trade, cross-section, time series [Type text] TABLE OF CONTENT Page ACKNOWLEDGEMENT ABBREVIATION ABSTRACT TABLE OF CONTENT LIST OF TABLE LIST OF FIGURE Chapter 1: INTRODUCTION 1.1 Problem statement 1.2 Research objective 10 1.3 Thesis structure 11 Chapter 2: LITERATURE REVIEW 12 2.1 Theory .12 2.1.1 Agricultural productivity 12 2.1.2 Agricultural trade 15 2.2 Empirical study 17 2.3 Conceptual framework 19 Chapter 3: RESEARCH METHODOLOGY 21 3.1 Agricultural productivity measurement 21 3.2 The tradability index 22 3.3 Empirical model 23 3.3.1 The national level model 23 3.3.2 The farm level model .24 3.4 Data 25 3.5 Analytic method 28 3.5.1 Test for multicollinearity 29 3.5.2 Test for heteroscedastiscity 30 3.5.3 Test for autocorrelation 30 3.5.4 Test for stationary 31 Chapter 4: RESULT AND DISCUSSION 32 [Type text] 4.1 Overview Vietnam agricultural trade 32 4.1.1 Agricultural production in Vietnam 32 4.1.2 Land 34 4.1.3 Irrigation 35 4.1.4 Fertilizer 35 4.1.5 Farm machinery .36 4.1.6 Human capital 36 4.1.7 Trade .37 4.1.7.1 Export 37 4.1.7.2 Import 38 4.2 Econometric result .39 4.2.1 The product tradability index and national level response 39 4.2.2 The farm tradability index and farm level response 42 Chapter 5: CONCLUSION 47 5.1 Conclusion 47 5.2 Future research 48 REFERENCE 49 APPENDIX 53 [Type text] LIST OF TABLE Page Table 3.1: The description of each variable and expected sign 27 Table 4.1: Production quantity of selected crops 33 Table 4.2: Annual growth rate of quantity of selected crops 33 Table 4.3: Evolution of crop production value per 33 Table 4.4: Water use in Vietnam in 2005 35 Table 4.5: The mechanization rate in agricultural production activities 36 Table 4.6: Labor force in Vietnam by resident region in selected years (thousand people) 36 Table 4.7: Structure of employed population by industrial sector 2000-2013 (percentage) 37 Table 4.8: The ADF test result for yield of nine commodities 40 Table 4.9: The ADF test result of tradable index of nine commodities 40 Table 4.10: Results of national-level analyses (dependent variable = yield per each commodity between 1961-2010) 41 Table 4.11: Summary statistics of variables in farm level model 42 Table 4.12: The correlation among coefficients in farm level model 43 Table 4.13: The correlation result of each variable (VIF, TOL=1/VIF) 43 Table 4.14: Production function result of the farm-level analyses (dependent variable = yield of an important crop in a farm) 44 [Type text] LIST OF FIGURE Page Figure 2.1: The linkage between agricultural productivity and international trade 20 Figure 4.1: The value of agricultural production 34 Figure 4.2: The land of annual and perennial crops 1990-2012 in Vietnam 34 Figure 4.3: Export value of main agricultural commodities since 1990-2011 38 Figure 4.4: The import value of main agricultural commodities in Vietnam during period 1990-2011 39 Figure 4.5: The relationship between maize yield and tradable index over time 42 [Type text] Chapter INTRODUCTION 1.1 Problem statement Trade helps people, regions, and countries exchange what they have and what they need In food security side, world population might be reach at billion people in 2050; the question is asked how to meet this great demand of food with limited producers, scare land and water resources The answer is productivity, and open trade may encourage farmers to increase quantity of agricultural products to meet a requirement of food in the world The core of role of agriculture can summary in three issues: “raising productivity, providing market, and generating saving for economic diversification” (Johnson, 2009) However, traditional farmers have suspicions about commercialization process that generate new demand, output or more competition An increase agricultural productivity has attracted many economists studying about role of it in development economics in years (Matsuyama, 1992; Machicado et al., 2008) In addition, agricultural productivity has an essential role in industrialization and development economics That means country improves its productivity with using less labors in agriculture, and access labors in agriculture transfer into manufacturing Furthermore, when producers in agriculture sector increase their incomes thanks to efficient production, demand for manufacturing increases to meet more demand On the other hand, some argue that agricultural productivity has a negative relationship with industrialization Field (1978) and Wright (1979) indicate labor force can be a main fight between manufacturing and agriculture sector because of comparative advantage When agriculture has low productivity, manufacturing has an abundant supply of labor with cheap wage Matsuyama (1991) shows explanation about these conflicting debates may be relative to opened economy He debates that in closed economy an increase agricultural productivity might make agricultural labors shift to manufacturing and contribute to economic growth However, in open economy “high productivity and output in the agriculture sector may, without offsetting changes in relative price, [Type text] squeeze out the manufacturing sector and the economy will de-industrialize over time, and in some case, achieve a lower welfare level.” From Doi Moi 1980, Vietnam agriculture has improved from production and especially exporting (Nguyen, 1998) Agriculture sector shifts from self-sufficient to commercialization to supply both domestic and export markets Vietnam became the second biggest rice exporter in the world, and production of coffee, pepper, rubber, cashew nut, fruit and vegetable have increased in quantity and export value During 1980-1990, agricultural export value increased from 339 US$ million to 2,404 US$ million, and total value of trade increased 3.89 times Quantity of export rice went rapidly up from million tons in 1990 to million tons in 1997 Coffee increased from 90,000 tons to 404,000 tons, cashew increased 27,400 tons to 99,000 tons When Vietnam joined World Trade Organization (WTO), the agricultural export coffee went up 1,256,400 tons, and 178,500 tons for cashew nut, respectively These evidences may support for advantage effects of opening trade in agricultural Vietnam Furthermore, the question is asked that how trade affects productivity in individual farm household Correspondingly, this study tends to analyze the linkage between agricultural trade and productivity in Vietnam, contributes to debate above and tries to answer whether international trade (import and export) in agricultural commodities is related to agricultural productivity at national and farm level This paper applies the product tradable index measurement to represent international trade In order to analysis of productivity, country and farm level analyses are implemented with time series and cross-section analysis in Vietnam 1.2 Research objective This thesis concentrates on studying and evaluating effect of trade on agricultural productivity in Vietnam Some questions, which this thesis tries to answer: whether international trade increases agricultural productivity in Vietnam? In order to answer these questions, this thesis uses two different levels of analysis from overview to detail to understand clearly how trade influences agricultural productivity 10 [Type text] Coffee **-3,938 1,573 *487 249 Note: *, **, *** represent significance at 10%, 5%, and 1% level The negative relationship of maize is a consequence of decreasing tradable index over period Vietnam has a great demand of maize to supply for feed industry in hog and poultry feed However, the maize production in Vietnam cannot meet demand and maize has a higher price than in the world Therefore, almost maize in Vietnam is imported and the negative sign in econometric result is not surprised The import and export quantity of maize is presented in figure 4.7 On the other hand, the productivity of coffee increase due to increase land, however, farmers lack awareness of effective technique to plant coffee Therefore, demand goes down due to a poor quality There is a same result for pepper Figure 4.7: The import and export quantity of maize commodity in Vietnam during 1961-2011 2000000 1500000 1000000 Import Quantity (tonnes) 500000 1961 1967 1973 1979 1985 1991 1997 2003 2009 Export Quantity (tonnes) Year Source: FAOSTAT 2014 4.2.2 The farm tradability index and farm-level response At the beginning, the overview of statistical analysis of variable in farm level model will provide a comprehensive view with table 4.11 involving mean, standard deviation, and max of each variable Then the relationship and correlation matrix between variables are indicated in table 4.12 It indicates that all independence variables (fti, ir, tland, dm, dsex, l, edu, k) have a positive relationship with dependence variable (yield) Table 4.11: Summary statistics of variables in farm level model Variable Observations Mean 42 Std Dev Min Max [Type text] Ln_Yield 1450 4.336 0.899 0.377 6.959 Farm tradability index 1450 0.312 0.358 0.000 2.048 Irrigation 1450 0.816 0.203 0.007 1.000 Ln_Land 1450 -0.654 1.032 -4.075 2.737 Machinery 1450 0.509 0.500 0.000 1.000 Sex 1450 0.862 0.345 0.000 1.000 Ln_Labor 1450 1.112 0.475 0.000 2.398 Ln_Education 1449 2.077 0.305 0.000 2.552 Ln_Capital 1450 8.281 1.003 4.489 11.657 Source: Own calculation from VAHRS 2010 data set 1.000 0.114 1.000 0.127 0.248 1.000 -0.008 0.042 0.152 1.000 -0.098 0.135 0.276 0.081 0.089 0.038 Ln_Capital Ln_Education 1.000 0.169 0.136 0.302 -0.009 Ln_Labor Machinery 1.000 0.111 0.002 0.064 0.191 0.046 Sex Ln_Land Ln_Yield 1.000 Farm 0.287 1.000 tradable index Irrigation 0.357 0.043 Ln_Land 0.450 0.102 Machinery 0.210 0.139 Sex 0.200 0.038 Ln_Labor 0.407 -0.014 Ln_Educati 0.062 -0.077 on Ln_Capital 0.410 0.452 Source: Own calculation Irrigation Farm tradable index Ln_Yield Table 4.12: The correlation among variables in farm level model 1.000 According to methodology section, the regression result may be misleading because of multicollinearity among regressors Therefore, after running farm regression in stata, VIF is used to detect whether multicollinearity may occur Table 4.13 provides the result of VIF of variables in farm level model The VIF of each regressor and mean of VIF are lower than 10, therefore the multicollinearity may not a problem in this model Table 4.13: The correlation result of each variable (VIF, TOL=1/VIF) 43 [Type text] Variable VIF 1/VIF Ln_Capital 1.39 0.719 Ln_Land 1.15 0.873 Ln_Labor 1.23 0.813 Farm tradable index 1.30 0.771 Machinery 1.11 0.897 Sex 1.08 0.927 Irrigation 1.07 0.932 Ln_Education 1.04 0.961 Mean VIF 1.17 Source: Author’s calculation Another test must be considered in cross section analysis that is heteroscedasticity of error term As mentioned above in methodology, this thesis uses the Breusch-Godfrey test to detect an existence of heteroscedasticity The result shows the value of chi-square obtained 8.78 with statistical significant probability 0.003, hence, the farm level model exists the heteroscedasticity In order to remedy this problem, the “robust” command must be added when running regression The output of Breusch-Pagan test for heteroscedasticity is presented in appendix B The main objective of this thesis is to analysis whether the trade affects agricultural productivity of farming household; hence, the result of econometric statistic of farm level model is presented in table The farm level model runs following the OLS regression with 1450 observation in nine provinces that come from VHRS 2010 data set The OLS regression refers yield as dependent variable and estimates the effect of farm tradable index on crop yield with other independent variables referring as control variables in model The result reports that all variables are positive relationship with crop yield After robust the regression, table 4.14 reported the result of farm-level model Table 4.14: Production function result of the farm-level analyses (dependent variable = yield of an important crop in a farm) Variables Coefficients Std error 44 [Type text] Farm tradable index ***0.237 0.057 Irrigation ***1.362 0.091 Ln_Land ***0.248 0.018 0.043 0.034 Sex ***0.140 0.043 Ln_Labor ***0.409 0.038 0.031 0.052 ***0.295 0.021 Constant 0.211 0.218 R-squared 0.506 0.435 Machinery Ln_Education Ln_Capital Robust standard errors in parentheses *** p