Vietnam’s agricultural sector plays a pivotal role in the national economy, with coffee emerging as one of the country’s most significant export commodities. Leveraging favourable climatic conditions, natural resources, and an abundant labour force, Vietnam has developed into the world’s second-largest coffee exporter, following Brazil [1]. Beyond its macroeconomic importance, coffee production contributes substantially to rural livelihoods, providing employment opportunities and improving the living standards of farming households.

In recent years, the global demand for coffee has increased steadily, enhancing Vietnam's position in international markets. According to the Vietnam Coffee-Cocoa Association (VICOFA), the country exported 1.45 million tonnes of coffee during the 2023-2024 crop year, generating USD 5.32 billion in export revenue [2]. This reflects both a growing international preference for Vietnamese coffee and an expansion in market access [3]. In response to these opportunities, Vietnam has adopted a proactive approach to international trade integration through the negotiation and implementation of several Free Trade Agreements (FTAs).

Among these, the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), which came into effect for Vietnam in January 2019, has had a profound impact on the country’s trade landscape. The CPTPP provides Vietnam with preferential access to key markets such as Canada, Mexico, and Peru-countries with which Vietnam had no prior FTAs. Alongside its membership in the World Trade Organization (WTO) and other regional agreements, CPTPP has facilitated greater trade liberalization, reduced tariff barriers, and enhanced the competitiveness of Vietnam’s agricultural exports, including coffee [3,4].

This study aims to examine the impact of the CPTPP on Vietnam’s coffee exports to its 11 partner countries. Specifically, it assesses the influence of key economic, geographic, and policy-related variables on trade flows and evaluates Vietnam’s comparative advantage in the coffee sector under CPTPP commitments. The analysis employs an augmented gravity model combined with the RCA index, using panel data from 11 CPTPP member countries for the period 2013-2022. This study contributes to the literature in three key ways. First, it uniquely examines Vietnam’s coffee exports within the CPTPP framework, an area that remains underexplored compared to other FTAs. Second, by combining the traditional gravity model with the RCA index, the research integrates trade competitiveness into bilateral trade analysis, offering methodological value beyond conventional approaches. Third, the study uses a recent dataset (2013-2022), capturing both the implementation of CPTPP and the COVID-19 shock, thereby providing timely empirical evidence and nuanced policy insights for enhancing Vietnam’s coffee export strategy in high-standard, next-generation trade agreements.

Literature Review and Research Gap

The impacts of free trade agreements (FTAs) on Vietnam’s agricultural trade have been widely examined, yet findings remain mixed. One group of studies highlights the limited or even adverse effects of certain FTAs. For example, [28] showed that agreements such as the ASEAN–Japan FTA and the Vietnam-Japan Economic Partnership Agreement could reduce export values due to heightened competition and stringent quality standards. Similarly, Huynh Ngoc Chuong et al. [5] demonstrated, through HHI and RCA analysis, that Vietnam’s agricultural exports remain concentrated in a few markets, underscoring structural weaknesses and limited diversification despite broader liberalization.

In contrast, a second strand of research emphasizes the positive contributions of newer-generation agreements, particularly the EVFTA and UKVFTA. Studies by Nguyen Tien Hoang and Trinh Thuy Ngan [6], Nguyen Thi Bich Ngoc [7], and Phung Xuan Hoi [3] argue that these agreements promote sustainable agricultural development and technological upgrading. Model-based assessments by Vu Thanh Huong et al. [8] and Le Nguyen Quynh Trang et al. [31] further confirm their export-enhancing potential, although persistent barriers such as SPS, TBTs, and tariff-rate quotas remain important challenges.

A smaller but growing body of literature has shifted focus to coffee, one of Vietnam’s most strategic export commodities. Vo Thuy Dung et al. [6] applied a spatial gravity model to identify macroeconomic and institutional drivers of coffee trade flows, emphasizing the role of GDP, population, distance, and trade barriers. However, most studies analyze Vietnam’s coffee exports in general terms or within older FTAs, while the specific dynamics under the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) remain largely unexplored.

Taken together, these strands of research reveal two key gaps. First, while agricultural exports under FTAs have been extensively studied, limited work has investigated Vietnam’s coffee exports in the CPTPP context, despite the agreement’s importance for accessing high-standard markets in the Americas and Oceania. Second, few studies integrate the traditional gravity model with measures of trade competitiveness such as the RCA index, which can provide richer insights into how market access and comparative advantage interact under next-generation trade agreements.

Addressing these gaps, the present study combines the gravity model with the RCA index using recent panel data (2013-2022). This approach not only evaluates macroeconomic, geographic, and institutional determinants of Vietnam’s coffee exports but also situates them within the broader dynamics of competitiveness in the CPTPP era, offering both methodological and policy-relevant contributions.

Theory of Gravity Model in Trade

The gravity model, introduced by, explains trade between two countries based on the same principles as Newton’s law of gravity [9,10]. It posits that trade flows are influenced by the economic size and distance between countries, as well as factors like trade barriers, transportation costs, tariffs, political conditions, and exchange rates. The basic theoretical model between two economies, A and B is represented by Krugman and Maurice according to the following formula [11].

In which: Tij: trade turnover between country i and country j; A: attractiveness or hindrance coefficient; Yi: Economic size of country i; Yj: Economic size of country j; Dij: Distance between two countries i and j The model's strength lies in considering factors from both the supply and demand sides, but it may overlook critical variables, leading to potentially misleading results. While it requires less data than other models and can be used for in-depth analysis, it has limitations in dealing with non-linearities and explaining additional variables. Additionally, it only analyzes trade impacts and not the reverse.

Research Model

The gravity model is widely used in trade research to assess export potential, FDI flows, and the effects of FTAs. For instance, [12] analyzed China’s textile exports, and [10] studied Egypt’s agricultural exports - both highlighting GDP, exchange rates, and geographical distance as key factors. Similarly, [13] and [14] applied the gravity model to Vietnam’s exports and global seafood trade, confirming the importance of GDP, population, exchange rates, and distance.

Building on these foundations, this study applies the gravity model, following the approach of [15], to examine Vietnam’s coffee exports to CPTPP countries. Key variables include the GDP of Vietnam and partners, population, geographical distance, exchange rate, and inflation. A notable addition is Vietnam’s inflation rate, which can influence export competitiveness; moderate inflation is generally trade-enhancing. The CPTPP dummy variable is also included to capture the agreement’s policy impact. Together, these variables provide a comprehensive view of trade dynamics, allowing the model to effectively explain how economic and institutional factors shape Vietnam’s coffee export performance in the context of trade liberalization Therefore, the research model of this paper is written in the form of a logarithmic function to reduce the amplitude of fluctuations as follows:

Ln (EXP_ijt) = A + ß_lln (GDP_it) + ß₂ln (GDP_jt) + ß₃ln (POP_it) + ß₄ln (POP_jt) + ß₅ln (DIS_ij) + ß₆ln (RER_ijt) + ß₇ln (INF_it) + ß₈CPTPP + e_ijt

In which:

A: Constant

i = 1 (Vietnam)

j = 2, 3, 4, 5, 6, 7,…, 23 (partner country)

t = 2013, 2012,…, 2022

e_ijt: Error term

ß?, ß?,… ß?: The coefficients ß represent the impact of factors on Vietnam's coffee export turnover with country j.

The dependent variable is Vietnam’s coffee export turnover (EXPijt) to 11 CPTPP and 11 non-CPTPP countries, allowing comparison to assess the impact of CPTPP membership. Key independent variables include Vietnam’s GDP (GDPit), the GDP of importing countries (GDPjt), and geographical distance (DISij)-three core elements of the gravity model. Vietnam’s GDP reflects coffee production capacity, while importing countries’ GDP indicates demand. Geographical distance, measured from Hanoi to each capital city, serves as a proxy for transport costs. Population variables (POPit and POPjt) represent Vietnam’s labor force and the consumption potential of partner countries. The real exchange rate (RERijt), calculated as the nominal exchange rate multiplied by relative price levels, captures the competitiveness of Vietnamese coffee and is influenced by exchange rate policies [16]. Inflation (INFit), measured by the consumer price index, reflects annual changes in the cost of living. The CPTPP variable is a dummy that equals 1 if both Vietnam and country j are CPTPP members in year t; otherwise, it equals 0. These variables comprehensively reflect production, demand, costs, and institutional factors influencing Vietnam’s coffee exports.

Research Hypotheses

Through a review of empirical studies, the author proposes research hypotheses and expectations about the signs of the regression coefficients ß in Table 1.

Table 1: Proposed Research Hypotheses And Expected Sign.

Source: Author's synthesis and proposal.

Data Collection Method

Before collecting secondary data, the author researched and identified the necessary information for the research, searched for data sources, and selected reputable sources for in-depth research and collection. Then, based on the information found, the author evaluated and filtered good information to include in this study. The data after synthesis is presented in the form of statistical tables.

The research utilizes secondary panel data gathered by the author from credible sources in Vietnam and internationally to ensure both accuracy and relevance, including World Bank Open Data, United Nations Comtrade database (UN Comtrade), World Integrated Trade Solution (WITS), and General Department of Vietnam Customs (GDVC). Specific data descriptions for each variable of the research model are shown in Table 2. Below.

Table 2: Data Description of Variables in the Research Model.

Source: Author's synthesis and proposal

Due to limitations and delays in data availability from partner countries, this study uses data from 2013 to 2022 to ensure updated and relevant statistical analysis. During this period, Vietnam experienced significant challenges, including the aftermath of the 2008 global financial crisis and the COVID-19 pandemic starting in 2020. Therefore, this ten-year time frame is appropriate to observe economic fluctuations in a realistic context and ensure sufficient data coverage.

The dataset includes Vietnam’s coffee export turnover, GDP, population, geographical distance, and real exchange rate between Vietnam and 22 partner countries. Among them are 11 CPTPP member countries (excluding Vietnam) such as Australia, Canada, Japan, Mexico, and Singapore-economies that are either highly developed or have strong growth potential and account for a significant share of Vietnam’s coffee exports. This makes them suitable for examining the CPTPP’s impact. In addition, 11 non-member countries such as the United States, China, Germany, India, and South Korea-recognized as major global economies with high bilateral trade volumes-are included to strengthen the comparative analysis. The selection of these countries helps to clarify the effect of the CPTPP agreement on Vietnam’s total coffee export turnover.

Data Processing Method

The study used Microsoft Excel for data synthesis and Stata 17 for regression analysis. All variables (except CPTPP) were log-transformed, and missing values were filled with the respective country’s average. Due to data limitations, the CPTPP variable-equal to 1 only for member countries-could not support pre- and post-2019 comparisons, so the analysis spans the full ten-year period.

The estimation began with Pooled OLS, followed by Fixed Effects Model (FEM) and Random Effects Model (REM). Model selection was based on t-statistics, p-values (at 90%, 95%, 99% confidence levels), R-squared, and F-statistics. The Pooled OLS assumes no unobservable heterogeneity and was retained if it outperformed FEM and REM. The F-test verified FEM, while the LM and Breusch-Pagan tests assessed REM. The Hausman test determined whether FEM or REM was more appropriate by checking the correlation between individual effects and explanatory variables.

To correct for heteroskedasticity and autocorrelation, the Feasible Generalized Least Squares (FGLS) method was finally applied. This method enhances result reliability and robustness, enabling more accurate conclusions and policy recommendations.

Descriptive Research Results

Descriptive statistics

Descriptive statistics for variables including coffee export turnover, GDP and population of Vietnam and importing countries, geographical distance, real exchange rate, inflation rate, and CPTPP membership are shown in Table 3 and Table 4. Table 3 provides descriptive statistics to offer an overview of the central tendency and variability in the data set, helping to understand the distribution and characteristics of the variables used in the analysis. To address issues related to data distribution, such as skewness or heterogeneity, and to make the data more suitable for analysis, the author used the logarithms of the variables for consideration [34].

Table 3: Descriptive Statistics Results of Dependent and Independent Variables in the Analysis Model.

Source: Author's calculations using Stata 17 software

The data is calculated based on data from Vietnam and the remaining 11 CPTPP member countries and 11 non-CPTPP member countries in the period from 2013 to 2022, with 220 observations.

Descriptive statistics for 2013-2022 highlight key characteristics of Vietnam’s coffee trade. Average export turnover reached USD 78.3 million, with high variability (ranging from USD 540,000 to USD 502 million), reflecting disparities across partner markets. Vietnam’s GDP averaged USD 300 billion and peaked at USD 409 billion in 2022. Importing countries showed wide GDP differences-from Brunei (USD 16.6 billion) to major economies like the UK, Canada, Japan, the U.S., and China. Vietnam's population averaged 94.4 million, while trading partners averaged 184 million, with India reaching 1.42 billion. Distance to partners varied greatly, averaging 8,050 km, ranging from Thailand (~1,000 km) to Chile (~19,000 km). The exchange rate (RERijt) showed large fluctuations, with a standard deviation of ~11,000 VND; the UK Pound peaked in 2014, while the Korean Won was lowest in 2022. Inflation remained stable, averaging 3.2%, highest in 2013 (6.6%) and lowest in 2015 (0.63%).

Table 4: Descriptive Statistics of Dummy Variable Data of CPTPP Member Countries (2013 - 2022).

Source: Author's calculations from research data

CPTPP dummy variable indicates whether or not they are members of the CPTPP Agreement. This variable takes the value of 1 if Vietnam and country j are common members of the CPTPP, which has been in effect since 2019 (in Vietnam) until now; otherwise, it takes the value of 0. According to Table 4, 27 observations are members of the CPTPP, accounting for 12.27%, and 193 observations that are not members of the Agreement, accounting for 87.73%.

Correlation Matrix

Table 5: Correlation Matrix between Variables in the Research Model.

Source: Author's calculations using Stata 17 software

According to Table 5, it can be seen that the independent variables in the model all have low correlations (below 0.5) with the dependent variable, except for GDPjt (0.7672) and POPjt (0.6752). In addition, most pairs of variables have low correlation coefficients, except for the pair GDPit-POPit (0.9937) and GDPjt-POPjt (0.8379). In addition, the matrix results also show that there are 5 variables with negative correlations with the dependent variable, including GDPit, POPit, DISij, RERijt, and CPTPP, while the remaining variables all have positive correlations.

OLS Regression and Testing Of Model Defects

OLS Regression

The OLS method will select the regression coefficients so that the squared error of the estimated model is the smallest. Typically, there are three main issues to be concerned with: (i) Are the regression coefficients statistically significant, (ii) Is the model significant, and (iii) How explanatory is the model?

Table 6: OLS Regression Results.

Source: Author's calculations using Stata 17 software

Table 6 shows that only two variables, LnGDPjt and LnDISij, are statistically significant at the 5% level in the OLS model. Other variables are not significant. The model is statistically valid overall, as indicated by the F-test (p < 0.05), and explains about 60% of the variation in Vietnam’s coffee export turnover [33].

Model Defect Testing

After Pooled OLS regression, the author tested the defects in the proposed model, including Multicollinearity through the use of the Variance Inflation Factor (VIF) coefficient, Heteroscedasticity through the White test, and Autocorrelation through Wooldridge's test.

Testing For Multicollinearity

In statistics, the VIF coefficient is the ratio of the variance in a multiterm model to the variance in a single-term model. It quantifies the severity of multicollinearity in an OLS regression analysis. It provides a measure of the degree to which the variance (squared standard deviation of the estimate) of an estimated regression coefficient is inflated by collinearity.

Table 7: VIF Coefficient.

Source: Author's calculations using Stata 17 software

There are two cases to consider the VIF coefficient: (1) if VIF < 10, there is no multicollinearity phenomenon, for topics on engineering and physics that do not use the Likert scale. (2) In topics on economics and society, researchers believe that VIF > 2 will cause a multicollinearity phenomenon. This study falls into case (1). The results in Table 7 show that there are 2 independent variables, LnGDPit and LnPOPit, in the model with VIF coefficients greater than 10. This proves that in these models, there is a multicollinearity phenomenon.

Testing For Heteroscedasticity

The White test is a statistical test to check whether the variances in the sample change or not. This is a test method based on the regression of the residuals on the first and second order of independent variables. The author performs the White test instead of the Breusch-Pagan test because the White test is used for panel data, while the Breusch-Pagan test is used for time series data.

Table 8: Test Results of White Test.

Source: Author's calculations using Stata 17 software

The results obtained (Table 8) show that the p-value (Prob > Chi-squared) in the research model is less than the 5% significance level (0.000 < 0.05), so hypothesis H1 is accepted, this model has the phenomenon of Heteroscedasticity.

Testing For Autocorrelation

Table 9: Test Results Of Wooldridge Test.

Source: Author's calculations using Stata 17 software

For time series data, use the Durbin-Watson or Breusch-Godfrey test, and for panel data, use the Wooldridge test. Therefore, the study needs to use the Wooldridge test for this phenomenon. The results in Table 9 show that the p-value (Prob > F) of the model is

0.000 < 0.05, so hypothesis H1 is accepted. In the model under consideration, the phenomenon of serial autocorrelation occurs.

Because the research model when estimating OLS has both types of defects: Heteroscedasticity and Autocorrelation, the author continued to conduct a regression of FEM and REM models.

FEM and REM models

FEM model: The Fixed Effects Model (FEM) assumes that each unit has distinct characteristics that could influence the explanatory variables. It explores the relationship between the residuals of each unit and these explanatory variables. This method controls for and separates the impact of individual characteristics (which stay constant over time) from the explanatory variables, allowing for a precise estimation of the true effect of the explanatory variables on the dependent variable.

REM model: The key difference between the Random Effects Model (REM) and the Fixed Effects Model (FEM) is in the handling of variation across units. In the FEM, the variation between units is presumed to be correlated with the independent (explanatory) variable, whereas in the REM, this variation is regarded as random and independent of the explanatory variables. Therefore, if the differences between units have an impact on the dependent variable, the REM model is considered more suitable than the FEM. In REM, the residuals of each entity, which are not correlated with the explanatory variables, are treated as an extra explanatory variable.

Hausman test:The Hausman test is used to decide which model, the Fixed Effects Model (FEM) or the Random Effects Model (REM), is more suitable. This test primarily evaluates whether there is autocorrelation between εi and the independent variables.

Table 10: Test Results Of The Hausman Test.

Source: Author's calculations using Stata 17 software

The results in Table 10 show that the p-value = 0.1276 > 0.05, thus at the 5% significance level, there is no basis to reject hypothesis H0 that there is no systematic difference between FEM and REM. Therefore, the REM model is suitable for this study, that is, the error component and the independent variables are not correlated.

Table 11: Estimated Results According To The Rem Model.

Source: Author's calculations using Stata 17 software

The results in Table 11 show that the REM model only produces two statistically significant variables, LnGDPjt at the 5% significance level and LnPOPit at the 10% significance level. After selecting the REM model, the author conducted several tests to determine the model's defects, including the phenomenon of Heteroscedasticity and Serial Autocorrelation. In which, the test of Heteroscedasticity of the REM model will be through the Breusch and Pagan Lagrangian Multiplier test (Table 3.12), and the Serial Autocorrelation test of this model will also be through the Wooldridge test and will have the same result as that of the OLS regression above (Table 3.9).

Table 12: Test Results Of Breusch And Pagan Lagrangian Multiplier Test.

Source: Author's calculations using Stata 17 software

From the results in Table 12, the p-value of 0.000 is less than the 5% significance level, which means that the selected REM model has the phenomenon of Heteroscedasticity. Combined with the results in Table 3.8, this model also has the phenomenon of Autocorrelation. Thus, it can be seen that the REM estimation is still not the most effective because the two above defects still exist. Therefore, the author will use the FGLS estimation method to overcome these phenomena in the research model [28].

FGLS Model

The author applied the REM model using FGLS weights to correct its limitations. Unlike OLS, which treats all observations equally, FGLS uses transformed variables that meet classical assumptions to produce more reliable estimates. By applying OLS to these adjusted variables, the model ensures unbiased and efficient results. Therefore, the corrected results in Table 13 are used as the final basis for analysis in this study.

Table 13: Estimated Results Using FGLS Model.

Source: Author's calculations using Stata 17 software

Table 13 shows that Vietnam’s GDP (coefficient 4.90, p < 0.01) has the strongest positive effect on coffee exports, confirming that domestic economic growth expands production and export capacity. The GDP (0.48, p < 0.05) and population (0.48, p < 0.01) of importing countries also have significant positive impacts, highlighting the importance of income and market size in driving demand. By contrast, Vietnam’s population has a large negative effect (–35.46, p < 0.01), suggesting that rising domestic demand reduce

The estimation results from the FGLS model provide a clearer understanding of the main factors driving Vietnam’s coffee exports between 2013 and 2022. Among the examined variables, four factors show statistically significant impacts: Vietnam’s GDP, the GDP of importing countries, Vietnam’s population, and the population of importing countries. These variables are central to explaining the dynamics of Vietnam’s coffee trade, while other variables such as distance, real exchange rate, inflation, and CPTPP membership provide additional but less robust insights. In this section, the discussion emphasizes the significant findings to highlight the most consistent relationships [26].

First, Vietnam’s GDP (GDPit) has a strong and positive effect on coffee export turnover. This result demonstrates that domestic economic growth not only improves production capacity but also strengthens the competitiveness of Vietnam’s agricultural sector. Higher GDP growth reflects better infrastructure, improved access to credit, and greater investment in technology and quality standards in the coffee industry. These factors collectively increase the exportable surplus and the ability to meet international demand. This finding is consistent with earlier studies such as Bui and Chen [17], Doumbe and Belinga [18], [14] et al and Yanushevsky and Yanushevsky [19], which all emphasized the positive role of economic growth in enabling agricultural trade. In the context of Vietnam, rapid GDP expansion during the study period has facilitated stronger supply chains and export readiness, reinforcing the validity of this result [25].

Second, the GDP of importing countries (GDPjt) also has a statistically significant and positive effect on Vietnam’s coffee exports. As partner countries grow economically, higher income levels boost consumer demand for imported goods, including coffee. Wealthier markets not only expand in terms of volume but also show a preference for high-quality and value-added coffee products. This presents important opportunities for Vietnam to diversify into premium segments. The finding is consistent with previous empirical research by Ha et al. (2023), Hatab et al. [10], Nguyen [13], and Nguy?n and Lê [20], all of which highlight the importance of foreign income levels in determining export potential. For Vietnam, this suggests a strategic focus on high-GDP CPTPP partners such as Japan, Canada, and Australia, where demand is rising both in quantity and in quality preferences [24].

Third, Vietnam’s population (POPit) shows a strong and statistically significant negative effect on coffee exports. This result implies that as domestic demand grows, less coffee is available for foreign markets. Vietnam has experienced rapid population growth and urbanization, with coffee becoming increasingly popular in everyday consumption. The growth of café culture and rising incomes have further intensified domestic consumption, reducing the exportable surplus. This factor, in fact, has the strongest impact on Vietnam’s coffee exports, as shown by the coefficient magnitude. The finding contrasts with earlier studies such as Bui and Chen [17] and [14] et al. (2015), which did not find such a strong negative relationship. Vietnam’s case highlights a unique tension: while economic growth enhances production, population growth simultaneously increases local consumption, thus constraining export capacity unless productivity is improved. This outcome underscores the need to balance domestic demand with international competitiveness [23].

Fourth, the population of importing countries (POPjt) has a positive and statistically significant relationship with Vietnam’s coffee export turnover. A larger population means greater demand for coffee products, especially in densely populated and urbanized markets where modern lifestyles increase consumption of beverages such as coffee. This result is in line with expectations and is consistent with previous studies, including Hatab et al. [10], [14] et al. And Nguy?n and Lê [20]. These findings suggest that populous CPTPP markets like Mexico and Japan present substantial opportunities for Vietnamese coffee exporters, provided that supply chains can adjust to meet the specific demands of these large consumer bases [22].

Beyond these significant results, some other findings deserve brief discussion though they are not statistically robust. The geographical distance (DISij) shows a positive coefficient, significant at around the 10% level, which runs counter to the traditional gravity model. Improvements in logistics and global transportation technology may explain why Vietnamese coffee can successfully reach distant markets such as the Americas and Oceania. However, this outcome contrasts with earlier gravity model results reported by Abbas and Waheed [11], Doumbe and Belinga [18], Ha et al. [1], Hatab et al. [10], [14] et al. and Nguyen [13], who generally found distance to be a trade-reducing factor. This suggests that Vietnam’s case may reflect recent advances in supply chain efficiency rather than a generalizable pattern [30-32].

The real exchange rate (RERijt) and inflation (INFit) were not statistically significant in this study. This aligns with the notion that coffee prices are determined largely by international market conditions, such as global supply and demand, rather than short-term domestic macroeconomic fluctuations. Bui and Chen [17] and Nguyen [13] similarly noted that exchange rate volatility has limited influence on Vietnam’s agricultural exports, given that contracts are usually denominated in stable currencies such as the U.S. dollar. Inflation also appears to have little direct effect, consistent with Nguy?n and Lê [20], since export competitiveness is driven more by yield, quality, and international demand factors than by internal price levels [29].

Finally, CPTPP membership shows a negative coefficient that is marginally significant. While not the strongest result in the model, this finding is noteworthy as it suggests that Vietnam has not yet fully captured the benefits of CPTPP membership in the coffee sector. Several factors explain this outcome: the timing of the agreement’s implementation coincided with the COVID-19 pandemic, which disrupted global supply chains; Vietnam’s exporters face challenges in complying with strict rules of origin and quality standards; and competition from established exporters such as Brazil and Colombia remains strong. While the coefficient is negative in the short term, longer-term effects may be more favorable as Vietnam continues to adjust and upgrade its coffee industry [21].

This study employs the gravity model to investigate the key determinants influencing Vietnam’s coffee exports to CPTPP member countries. The results affirm that Vietnam’s GDP, along with the GDP and population of importing countries, plays a significant role in driving export performance. Nonetheless, the negative coefficient associated with CPTPP membership during the studied period indicates that Vietnam has yet to fully capitalize on the trade advantages offered by the agreement. Interestingly, the unexpected positive impact of geographical distance on coffee exports challenges conventional trade theory, suggesting the need for a more nuanced understanding of trade dynamics in specific commodities such as coffee.

Based on the empirical results, several policy implications can be drawn. First, since both Vietnam’s GDP and the GDP of importing countries positively influence coffee exports, policies should continue to stimulate domestic economic growth and simultaneously deepen trade promotion in large and high-income markets, thereby leveraging rising demand. Second, the strong negative effect of Vietnam’s population highlights the pressure from domestic consumption; thus, it is necessary to expand coffee cultivation areas in a sustainable way and increase productivity to secure exportable surplus. Third, the positive role of importing countries’ population suggests opportunities in populous markets, calling for targeted marketing and distribution strategies to capture these segments. Fourth, the unexpected positive impact of geographical distance underscores the importance of logistics efficiency. Therefore, investment in transport, storage, and port services should focus not only on nearby markets but also on improving connectivity to distant CPTPP partners in the Americas and Oceania. Finally, the negative coefficient of CPTPP membership indicates challenges in meeting stringent standards and origin requirements. Hence, policy efforts should prioritize capacity-building for enterprises in certification, compliance, and value-added processing, aligning Vietnam’s coffee exports with the high standards of next-generation trade agreements.

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