Our group would like to give the sincerest appreciation to Prof. Dinh Thi Thanh Binh for all the lectures you have provided us The knowledge that we have learned from your course was not only interesting but also valuable and practical. Along with the lectures, we received a lot of support from you whenever we had difficulties in studying Econometrics 2. For us, it is an honor and a blessing to have an amazing teacher like you. As a result of the knowledge, we acquired during the course, we devoted all of our effort to completing this report. Although there might be some flaws, we truly hope that you will enjoy it and provide us feedback as well as some suggestions so that we may improve in the future. We are looking forward to the opportunity to accompany you in many subjects during our time at Foreign Trade University. Once again, thank you for your dedication and your time for this course. We will appreciate this knowledge as long as possible. Have a wonderful day our dear teacher Group 10.
Econometrics KTEE318.1 FOREIGN TRADE OF UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS MID-TERM ASSIGNMENT Module: Econometrics FACTORS AFFECTING VIETNAM’S RICE EXPORT QUANTITY TO ASEAN COUNTRIES DURING THE PERIOD OF 2000 - 2020 Group: Class: KTEE318 Instructor: Dinh Thi Thanh Binh Hanoi, June 2023 https://tailieuluatkinhte.com/ Econometrics KTEE318.1 ACKNOWLEDGEMENTS Our group would like to give the sincerest appreciation to Prof Dinh Thi Thanh Binh for all the lectures you have provided us! The knowledge that we have learned from your course was not only interesting but also valuable and practical Along with the lectures, we received a lot of support from you whenever we had difficulties in studying Econometrics For us, it is an honor and a blessing to have an amazing teacher like you As a result of the knowledge, we acquired during the course, we devoted all of our effort to completing this report Although there might be some flaws, we truly hope that you will enjoy it and provide us feedback as well as some suggestions so that we may improve in the future We are looking forward to the opportunity to accompany you in many subjects during our time at Foreign Trade University Once again, thank you for your dedication and your time for this course We will appreciate this knowledge as long as possible Have a wonderful day our dear teacher! Group 10 https://tailieuluatkinhte.com/ Econometrics KTEE318.1 TABLE OF CONTENTS ABSTRACT .6 INTRODUCTION .7 SECTION 1: LITERATURE REVIEW 1.1 Overview of the Rice industry in Vietnam 1.2 Related published researched 1.3 Research hypothesis 10 SECTION 2: MODEL SPECIFICATION AND DATA 11 2.1 Methodology 11 2.1.1 Methodology used to derive model 11 2.1.2 Methodology used to collect and analyze data 12 2.2 Theoretical model specification .12 2.2.1 Specify the model .12 2.2.2 Expected signs of variables and explanation 13 2.3 Data description .15 2.3.1 Sources of data 15 2.3.2 Descriptive statistics 16 2.3.3 Correlation matrix between variables 16 SECTION 3: ESTIMATED MODEL AND STATISTICAL INTERFERENCES .18 3.1 Determine the model type 18 3.2 Dianogsing the model problem 19 3.2.1 Multicollinearity 19 3.2.2 Autocorrelation 19 3.2.3 Heteroskedasticity .20 3.3 Estimation results .20 3.4 Implications 22 CONCLUSION 24 REFERENCES 25 https://tailieuluatkinhte.com/ Econometrics KTEE318.1 https://tailieuluatkinhte.com/ Econometrics KTEE318.1 ABSTRACT The topic of our report is: “Factors affecting Vietnam’s rice export quantity to ASEAN Countries during the period of 2000 - 2020” This essay investigates the effect that domestic consumption of rice in import countries, population of import countries, GDP of Vietnam, and distance between Vietnam and importing countries have on the quantity of exported rice from Vietnam to ASEAN Countries in the period 2000 - 2020 using econometrics method The objective of our report is to find out whether the four factors mentioned above have any significant influence on the quantity or not In this research, secondary data (collecting data that is already existing) in the form of panel data (data that contains observations about different cross sections across time) were used We collect data on rice export quantity from Vietnam to other countries in ASEAN from 2000 to 2020 for the dependent variable and independent variables All data is collected from highly credible sources The data were analyzed using the ordinary least squares method (OLS) and multiple regression model With the help of the STATA program, our group was able to calculate the result in a much more accurate and easy way https://tailieuluatkinhte.com/ Econometrics KTEE318.1 INTRODUCTION Agriculture holds a relatively significant role in the economy of Vietnam Although agriculture is fading as the most important economic sector due to the development of the manufacturing and service sector, it is still the major supplier of raw materials to other industries and hence contributes directly and indirectly to the production and exports from the country Being used as a staple food in everyday Vietnamese cuisine, rice is the most important crop and is one of the most valuable exportable agricultural commodities in the country Thanks to being one of the world’s richest agricultural regions, Vietnam is among the biggest rice producers, specifically the third largest after India and Thailand exporter worldwide of rice at 6.6 million metric tons in the period of 2021/2022 (statistics made by United States Department of Agriculture - USDA) Today, in the context of increasingly promoting world economic activities, trade activities between countries are increasingly strongly promoted, especially in the ASEAN region Many policies to support member countries in the process of exchanging and exporting goods However, because trade activities between countries are increasingly promoted, each country must actively participate in exploiting its advantages in international trade assignment and exchange With the strength of being an agricultural country with a long history of rice production, Vietnam has become one of the world's largest rice exporters Rice from Vietnam is available in more than 150 nations and territories around the world, with the growing importance of ASEAN members as destinations due to the strong commitments to deepening integration into the regional economy From here, the study of factors affecting Vietnam's rice export output to ASEAN countries becomes necessary and receives much attention from domestic and foreign researchers Our research objective is to describe the determinants of rice export from Vietnam to other ASEAN countries from 2000 - 2020 and to analyze the relationship and influence level of each of these factors to Vietnam’s rice export quantity Therefore, we can test and evaluate the research hypotheses to provide solutions and recommendations to advance export quantity https://tailieuluatkinhte.com/ Econometrics KTEE318.1 SECTION 1: LITERATURE REVIEW 1.1 Overview of the Rice Industry in Vietnam Agriculture plays a relatively important role in the economy of Vietnam Agriculture contributes 24% of GDP and generates 20% of export revenues Over 70% of the national labor force is employed in the agriculture sector, and a further 6% is employed in the agricultural post-production sector Vietnam has started to participate in the world’s rice export market with 1.42 million tons since 1989 According to Vietnam Food Association (VFA), rice exports from Vietnam in 2012 hit a record of 7.72 million tons with a value of 3.45 billion USD while India exported 10 million tons of rice worth around billion USD According to USDA, Vietnam ranked second in the list of largest rice exporters in the world in 2012 and even was the leading rice producer and exporter in 2010 The rice export volume has increased from year to year, which is due to the improvement in the productivity of rice production However, from 2013 to 2016, Vietnam rice exports experienced a decrease in volume and value In 2016, the export volume significantly decreased by 25.8% in volume and 21.2% in value compared to 2015, which is estimated at 4.88 million tons and 2.2 billion USD According to statistics of Vietnam Customs, in 2017, Vietnam exported 5.79 million tons of rice with a value of 2.62 billion USD, increased by 20.4% in volume and 21.2% in value compared to 2016 In 2018, rice export output was estimated at 6.1 million tons, up to 3.0 billion USD Rice exports in 2020 reached 6.15 million tons, worth about 3.07 billion USD, decreased about 3.5% compared to 2019, mainly for the purpose of ensuring national food security In 2020, Vietnam accounted for 12.75% of the world rice export market share, behind India (35.61%) and Thailand (15.1%) Vietnam officially became a member of ASEAN in 1995 Since then, trade turnover between Vietnam and ASEAN countries has increased rapidly, significantly contributing to Vietnam's economic development as well as member countries of the Association Because ASEAN is close to Vietnam and has a big market with a population of nearly 700 million people, the room for export growth of many types of Vietnamese goods to the ASEAN region is large Since the ASEAN region does not have many favorable conditions to produce rice, Vietnam has many advantages in exporting this agricultural product In 2019, Vietnam's rice exports to ASEAN reached 1.1 billion USD, increased by 8.5% compared to 2018 Philippines and Malaysia are the two main customers https://tailieuluatkinhte.com/ Econometrics KTEE318.1 1.2 Related Published Research: Many studies around the world have used a variety of models to show the factors affecting agricultural exports as well as rice exports Adhikari et al (2016) used an estimated regression model showing that export price, international price, lagged production, domestic consumption, and exchange rate are the major determinants of rice export from India Kiani et al (2018) examined the rice and cotton trade potential for Pakistan for the period of 1984 to 2014 by using the gravity model and random effect model The empirical results for rice and cotton suggest that Pakistan exports are positively affected through production, common border and GDP of partner countries The results show that the distance has a negative impact on exports and gross domestic product as well The findings also suggest that higher trade flows are attributed to the country that shares a common border with Pakistan Ekrem Erdem and Saban Nazlioglu (2014) find that Turkish agricultural exports to the EU are positively correlated with the size of the economy, the importer population, the Turkish population living in the EU countries, the non-Mediterranean climatic environment, and the membership to the EU-Turkey Customs Union Agreement while they are negatively correlated with agricultural arable land of the EU countries and geographical distance between Turkey and the EU countries In Vietnam, some researchers have pointed out the factors affecting rice export from Vietnam to other regions In order to describe the processes involved in factors influencing rice export in Vietnam, Bui Thi Hong Hanh and Qiting Chen (2015) have used the gravity model with research time from 2004 to 2013 to reveal that the biggest impacts on Vietnam rice export are gross domestic product (GDP), price, population, and exchange rate Tran Thi Bach Yen and Truong Thi Thanh Thao (2017) studied the factors affecting Vietnam's rice export turnover to the ASEAN market in the period 2000-2015 They found that the factors of Vietnam’s gross domestic product (GDP), geographical distance, Vietnam's inflation, and Vietnam's rice-growing area have positive effects on the value of Vietnam's rice export turnover during the research period In contrast, the factors of economic distance have a negative impact on the value of rice exports in the period 2000-2015 Do Thi Hoa Nha (2017) used an extended gravity model to analyze the main factors affecting the export of Vietnamese agricultural products to the EU market in the period https://tailieuluatkinhte.com/ Econometrics KTEE318.1 2005-2015 The estimated results show that GDP per capita, population, technology index have a positive impact, while transportation costs (proxied by distance) have a negative impact on exports 1.3 Research hypotheses: Based on the studies mentioned above, our team come to hypotheses: H1: Domestic consumption of rice in importing countries has a positive relationship with Vietnam's rice export quantity to ASEAN countries H2: Population of importing countries has a positive relationship with Vietnam's rice export quantity to ASEAN countries H3: Gross Domestic Product of Vietnam has a positive relationship with Vietnam's rice export quantity to ASEAN countries H4: Distance between Vietnam and importing countries has a negative relationship with Vietnam's rice export quantity to ASEAN countries https://tailieuluatkinhte.com/ Econometrics KTEE318.1 SECTION 2: MODEL SPECIFICATION AND DATA 2.1 Methodology 2.1.1 Methodology used to derive the model In this research, our group decide to use the gravity model to determine what factors affecting the rice export quantity of Vietnam to the ASEAN market The gravity model is a popular theoretical model used by many economists to measure and analyze the factors affecting exports between two or more countries over the years The trade- attraction force model in international trade was first used to measure the value of exports between two countries, constructed by the two scientists Timbergen (1962) and Poyhonen (1963) based on Newton's physical gravity model - a model that simulates Newton's law of gravitation, which states that the attraction force between two objects depends on the distance between them and the mass of each object In the context of international trade, the gravity model suggests that the volume of trade between two countries depends on the sizes of their economies (measured by GDP) and the distance between them Other factors that may affect trade volume, such as cultural or linguistic similarities, trade policies, transportation costs, and trade agreements, can also be incorporated into the model After modeling the groups of common factor, the general form equation is as follows: 𝐸𝑋𝑄ij = 𝐴𝑌𝑎2𝑌𝑎3𝐷𝑎# i j ij Therein: 𝐸𝑋𝑄ij: Export value from country i to country j 𝐴: Constant 𝑎$, 𝑎3, 𝑎&: Parameters of the model 𝑌i: A group of factors influencing the supply of country i 𝑌j: A group of factors influencing the demand of country j 𝐷ij: A group of other hindering and attractive factors Take the natural logarithm on both sides of the equation, we get an empirical equation for basic gravity model: ln 𝐸𝑋𝑄ij = 𝑎1 + 𝑎$ ln 𝑌i + 𝑎3 ln 𝑌j + 𝛽& ln 𝐷ij + 𝑒 https://tailieuluatkinhte.com/ Econometrics KTEE318.1 Based on the above results we can see that all the independent variables have correlation with the dependent variable All of the independent variables have positive correlation towards the dependent variable The smallest number is the correlation between GDP of Vietnam variable (lnGDPVN) and Vietnam rice export quantity (lnEXQ): 0.0890 The largest number is the correlation between Distance variable (lnDIS) and Vietnam rice export quantity variable (lnEXQ): 0.6755 In general, all of the correlation coefficient among variables are smaller then 0.8, hence, smaller risk of Multicollinearity https://tailieuluatkinhte.com/ Econometrics KTEE318.1 SECTION 3: ESTIMATED MODEL AND STATISTICAL INTERFERENCES 3.1 Determine the model type Researching the impact measurement of various factors on Vietnamese rice can use all three models: OLS, FEM, and REM However, due to different methods, the estimation results are different; if only looking at the results of each estimate, an incorrect choice will be made that is not suitable for the research objective Therefore, it is necessary to conduct necessary tests to use the right research model First, we check whether factor exist or not to determine which one should be used among the three models In this step, we use Breusch Pagan test in STATA15 software The test result show that p_value < 5% Thus, a i exists so the model is fixed effects model or random effects model Next, we use Hausman test to see whether the model is fixed effects model or random effects model The result from STATA15 software shows that p_value=0.6175 > so the model is random effects model Our next step will be adjusted to be suitable for random effects model https://tailieuluatkinhte.com/ Econometrics KTEE318.1 3.2 Diagnosing the model problem We carry out the tests to control for the limitations of the research model 3.2.1 Multicollinearity We check the multicollinearity defects using the variance inflation factor (VIF) If VIF < 10, we can conclude that the model has no multicollinearity defects Variable VIF 1/VIF lnDMC 1.09 0.920792 lnDIS 1.95 0.950144 lnPOP 1.03 0.972251 lnGDPVN 1.03 0.973864 Mean VIF 1.05 Based on we can see that all the VIF values are smaller than 10, and the mean VIF=1.05 ( 5% Thus, at the 5% significant level, we accept hypothesis H0: the model has no autocorrelation https://tailieuluatkinhte.com/ Econometrics KTEE318.1 We also use Pesaran’s test to detect cross-section correlation in the model After running the test on STATA15, we have the results: Because p_value is greater than 5%, we reject the hypothesis that the model has crosssection correlation Therefore, the model is free from autocorrelation 3.2.3 Heteroskedasticity We use Breusch – Pagan test to detect heteroskedasticity with two hypotheses as follows: H0: The model has no heteroskedasticity H1: The model has heteroskedasticity The test results from STATA15 software show that P_value nearly equals to zero, less than 5% Thus, at the 5% siginificant level, we reject hypothesis H0 so the model has heteroskedasticity We fix the model using GLS regression model 3.3 Estimation results After selecting the model that best fits the dataset and fixing the model’s defects, the estimation results are given in the table below: lnEXQ Coef Std Err z P>z lnDMC 0.261773 0.052963 4.94 0.000 https://tailieuluatkinhte.com/ Econometrics KTEE318.1 lnPOP 0.2285707 0.0666834 3.43 0.001 lnDIS 3.466532 0.2556248 13.56 0.000 lnGDPVN 0.3924491 0.1418693 2.77 0.006 _cons -34.01431 4.188897 -8.12 0.000 Number of obs 189 Hence, our final estimated model is: ln 𝐸𝑋𝑄i = −34.01431 + 0.261773 ln 𝐷𝑀𝐶i + 0.2285707 ln 𝑃𝑂𝑃i + 0.3924491 ln 𝐺𝐷𝑃𝑉𝑁i + 3.466532 ln 𝐷𝐼𝑆i + 𝑢i At the 5% significant level, estimated coefficients are statistically significant if p_value < 5% From the table, we can see that all of the variables that are statistically significant The R-squared statistic is an ordinary least squares (OLS) concept that is useful because of the unique way it breaks down the total sum of squares into the sum of the model sum of squares and the residual sum of squares However, when we estimate the model’s parameters using generalized least squares (GLS), the total sum of squares cannot be broken down in the the same way, making the R-squared statistic less useful as a diagnostic tool for GLS regressions Specifically, an R-squared statistic computed from GLS sums of squares need not be bounded between zero and one and does not represent the percentage of total variation in the dependent variable that is accounted for by the model Also, eliminating or adding variables in a model does not always increase or decrease the computed R-squared value Therefore, our team not inlude the R- squared value but only dicuss about the estimated coefficients Firstly, the estimasted coefficient of lnDMC is 0.262 The coeficient is positive, so there is a positive linear relationship of import countries’ rice domestic consumption on Vietnam’s rice export quantity At the 5% significant level, holding all else constant, when of import countries’ rice domestic consumption increased by 1%, Vietnam’s rice export quantity to those countries increaed by 0.262% https://tailieuluatkinhte.com/