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.
LITERATURE REVIEW
Overview of the Rice industry in Vietnam
Agriculture is a vital sector in Vietnam's economy, contributing 24% to GDP and generating 20% of export revenues, with over 70% of the labor force employed in agriculture and an additional 6% in post-production Since entering the global rice export market in 1989, Vietnam has seen significant growth, exporting a record 7.72 million tons of rice worth $3.45 billion in 2012, making it the second-largest rice exporter worldwide according to the USDA However, from 2013 to 2016, rice exports declined, with a notable drop in 2016 of 25.8% in volume and 21.2% in value In 2017, exports rebounded to 5.79 million tons valued at $2.62 billion, and in 2018, they reached 6.1 million tons worth $3.0 billion By 2020, rice exports were at 6.15 million tons valued at $3.07 billion, reflecting a 3.5% decrease from 2019, primarily to ensure national food security In that year, Vietnam held a 12.75% share of the global rice export market, trailing behind India and Thailand.
Vietnam joined ASEAN in 1995, leading to a rapid increase in trade turnover with member countries, which has significantly boosted Vietnam's economic growth The proximity of ASEAN, with a population of nearly 700 million, offers substantial export opportunities for various Vietnamese goods Additionally, due to limited rice production capabilities in the region, Vietnam holds a competitive advantage in rice exports, with notable figures in 2019 highlighting the success of this agricultural product in the ASEAN market.
1.1 billion USD, increased by 8.5% compared to 2018 Philippines and Malaysia are the two main customers.
Related published researched
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) identified key factors influencing rice exports from India, including export price, international price, lagged production, domestic consumption, and exchange rate, through an estimated regression model.
Kiani et al (2018) analyzed the trade potential of rice and cotton in Pakistan from 1984 to 2014 using the gravity and random effect models Their findings indicate that Pakistan's exports are positively influenced by production levels, shared borders, and the GDP of partner countries Conversely, distance negatively impacts exports and GDP Additionally, the study highlights that greater trade flows are associated with countries that share a common border with Pakistan.
Ekrem Erdem and Saban Nazlioglu (2014) discovered that Turkish agricultural exports to the EU are positively influenced by factors such as the size of the economy, the population of the importer, the Turkish diaspora in EU countries, a non-Mediterranean climate, and the EU-Turkey Customs Union Agreement Conversely, these exports are negatively affected by the amount of agricultural arable land in EU countries and the geographical distance between Turkey and the EU.
Researchers in Vietnam have identified key factors influencing rice exports, highlighting the significant roles of gross domestic product (GDP), price, population, and exchange rate Bui Thi Hong Hanh and Qiting Chen (2015) utilized the gravity model to analyze data from 2004 to 2013, demonstrating how these elements impact the country's rice export dynamics.
Tran Thi Bach Yen and Truong Thi Thanh Thao (2017) analyzed the factors influencing Vietnam's rice export turnover to the ASEAN market from 2000 to 2015 Their research revealed that Vietnam's gross domestic product (GDP), geographical distance, inflation, and rice-growing area positively impacted rice export value during this period Conversely, economic distance was found to negatively affect rice exports between 2000 and 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
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.
Research hypothesis
Based on the studies mentioned above, our team come to 4 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
MODEL SPECIFICATION AND DATA
Methodology
2.1.1 Methodology used to derive the model
This research employs the gravity model to analyze the factors influencing Vietnam's rice export quantity to the ASEAN market Widely utilized by economists, the gravity model assesses export dynamics between countries over time Originating from the work of Timbergen (1962) and Poyhonen (1963), this trade-attraction force model is based on Newton's law of gravitation, which posits that the attraction between two entities is determined by their masses and the distance separating them.
The gravity model of international trade posits that trade volume between two nations is influenced by their economic sizes, indicated by GDP, and the geographical distance separating them Additionally, factors like cultural and linguistic similarities, trade policies, transportation costs, and trade agreements can also play a significant role in shaping trade dynamics.
After modeling the groups of common factor, the general form equation is as follows:
𝐸𝑋𝑄ij: Export value from country i to country j
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
2.1.2 Methodology used to collect and analyze data
This research aims to identify the significant factors influencing Vietnam's rice export volume to ASEAN countries by analyzing the effects of various independent variables Utilizing secondary panel data from 2000 to 2020, the study examines rice export quantities to nine ASEAN nations, focusing on four key independent variables: domestic rice consumption in importing countries, population of importing countries, distance between capital cities, and Vietnam's GDP All data is sourced from reputable references A multivariable regression model is employed to estimate the relationship between the dependent and independent variables, with data analysis conducted using STATA to finalize the report.
Theoretical model specification
Based on the results of previous studies and on the theoretical basis, we derive the gravity model for rice export quantity of Vietnam as follow:
Population regression model: ln 𝐸𝑋𝑄i = 𝛽( + 𝛽1 ln 𝐷𝑀𝐶i + 𝛽$ ln 𝑃𝑂𝑃i + 𝛽3 ln 𝐺𝐷𝑃𝑉𝑁i + 𝛽& ln 𝐷𝐼𝑆i + 𝑢i
𝐸𝑋𝑄i : Rice export quantity from Vietnam (tons)
𝐷𝑀𝐶i : Domestic consumption of rice in import countries (tons)
𝑃𝑂𝑃i : Population of import countries (person)
𝐺𝐷𝑃𝑉𝑁i : Gross Domestic Product of Vietnam (USD)
𝐷𝐼𝑆i : Distance between Vietnam and importing countries (km)
𝛽(: the intercept term of the model
𝛽1, 𝛽$, 𝛽3, 𝛽&: the regression coefficients of each independent variables it follows
𝑢i: the disturbance term of the model
2.2.2 Expected signs of variables and explanation
- Dependent variable: rice export quantity from Vietnam
- Independent variables: domestic consumption of rice in import countries, population of import countries, GDP of Vietnam, and distance between Vietnam and importing countries
Anup Adhikari (2016) highlighted that domestic consumption significantly influences rice exports from India, a trend also observed in Vietnam, where the rice consumption levels of ASEAN countries impact export quantities In nations with high rice consumption, natural resource conditions such as cultivation area and weather often fall short of meeting demand, necessitating increased imports Consequently, as the consumption rates in importing countries rise, their demand for rice from Vietnam and other exporting nations also escalates, establishing a direct correlation between foreign consumption indices and Vietnam's rice export volumes.
The population of importing countries significantly influences Vietnam's rice export quantity, as highlighted by Bui Thi Hong Hanh (2015) An increase in population leads to higher demand for essential goods like rice, which can boost export turnover However, the impact of population growth also depends on the specific conditions and the quality of the labor force in the importing nation Specifically, a larger population increases demand for exports, while also expanding domestic labor and production capacity This can enable domestic production to meet local demand, potentially resulting in unchanged or decreased export quantities, particularly in the long run.
Tran Thi Bach Yen and Truong Thi Thanh Thao highlight that Vietnam's GDP positively influences the country's rice export turnover GDP, defined as the total monetary value of all finished goods and services produced within a nation's borders during a specific period, serves as a key indicator of economic health It reflects the exporting country's potential supply capacity, where an expanded production scale leads to increased outputs and higher export levels Consequently, GDP also positively impacts the export of electronic components, which is a dependent variable.
A study by Dao Dinh Nguyen (2020) identified distance as a significant barrier to Vietnam's rice and coffee exports, impacting shipping costs, product preservation, and transportation risks Shorter distances lower transportation fees and risks, facilitating trade, which is why countries often prioritize trade with neighboring or regionally close nations Nevertheless, ASEAN's policies promote export activities among member countries by reducing taxes and transportation costs Additionally, resource-scarce nations like Singapore are open to importing essential goods such as rice, despite geographical distances.
Here is a table describing the variables and the expected sign for the dependent variables.
Expected sign of regression coefficient
EXQ Vietnam's rice export quantity
DMC Domestic consumption of rice import countries tons (+)
POP Population of import countries person (+)
GDPVN Gross Domestic Product of VietnamUSD (+)
Independent variables DIS Distance (geographical distance between Vietnam and importing countries, measured by the distance between the capitals of the countries) kilometer (-)
Data description
The data was collected from reliable sources which are shown in the table below:
Rice export quantity from Vietnam Trademap
Domestic rice consumption of ASEAN countries IndexMundi
Population of the import country World Bank
GDP of Vietnam World Bank
Our group have drawn some general conclusion about the data from the table above:
- Variable Rice export quantity from Vietnam (EXQ): the total observation of 189, mean is 218639.4 tons, standard deviation is 418732.2, the minimum quantity is 68 tons whereas the maximum quantity is 2237357 tons.
- Variable Domestic rice consumption of import countries (DMC): the total observation of
189, mean is 222 million tons, standard deviation is 641 million, the minimum value of 2204.62 tons whereas the maximum value of 3.06 billion tons.
- Variable Population of import countries (POP): the total observation of 189, mean is 56.7 million, standard deviation is 73.2 million, the smallest population is 333166 people and the largest population is 272 million people.
- Variable Geographical distance (DIS): the total observation of 189, mean is 1625.064 km, standard deviation is 752.2976, minimum distance: 482.17 km, maximum distance: 3029.78 km.
- Variable Gross Domestic of Vietnam (GDPVN): the total observation of 189, mean is
158 billion USD, standard deviation is 106 billion, the minimum GDP of Vietnam is
31.2 billion USD and the maximum GDP of Vietnam is 347 billion USD.
We conducted correlation analysis to determine connections and interactions among variables. lnEXQ lnDMC lnPOP lnDIS lnGDPVN lnEXQ 1.0000 lnDMC 0.3010 1.0000 lnPOP 0.2645 0.1342 1.0000 lnDIS 0.6755 0.2088 0.1021 1.0000 lnGDPVN 0.0890 -0.1455 0.0454 0.0000 1.0000
The analysis indicates that all independent variables exhibit a positive correlation with the dependent variable, highlighting their interconnectedness.
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.
ESTIMATED MODEL AND STATISTICAL INTERFERENCES
Determine the model type
When assessing the impact of various factors on Vietnamese rice, it is essential to utilize all three models: OLS, FEM, and REM Each model employs different methodologies, leading to varying estimation results Relying solely on the outcomes of individual estimates may result in incorrect conclusions that do not align with the research objectives Consequently, conducting appropriate tests is crucial to select the most suitable research model.
To determine the appropriate model among the three options, we first assess the existence of factor ai using the Breusch-Pagan test in STATA15 software.
The test result show that p_value < 5% Thus, ai 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 > 0 so the model is random effects model Our next step will be adjusted to be suitable for random effects model.
Dianogsing the model problem
We carry out the tests to control for the limitations of the research model.
We check the multicollinearity defects using the variance inflation factor (VIF) If VIF