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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agriculture Vol.37 No.1 pp.35-50
DOI : https://doi.org/10.12719/KSIA.2025.37.1.35

Determinants of Cambodia’s Agricultural Products Exports to Trade Partners: The Gravity Model Approach

CHEN Sokhan*,**, Su-Hyeon Kim*, Seong-Hyuk Hwang*†
*Department of Agricultural Economics, Jeonbuk National University, Korea
**General Directorate of Rubber, Ministry of Agriculture, Forestry and Fisheries(MAFF), Phnom Penh, Cambodia
† Corresponding author (Phone) +82-63-270-2538 (E-mail) hwangsh@jbnu.ac.kr
January 3, 2025 March 3, 2025 March 5, 2025

Abstract


This study evaluates the determinants of Cambodia’s agricultural product exports from 2004 to 2023 across 35 major export destinations using the gravity model approach. During this period, China accounted for the largest share of export value, followed by Vietnam, France, Malaysia, and Thailand. Cambodia’s GDP and the GDP of its trading partners positively influenced the country’s agricultural exports. Moreover, the historical status of export destinations as former French colonies facilitated Cambodia’s export flows. In contrast, exchange rates and geographical distance negatively affected Cambodia’s agricultural trade. This study confirms the existence of significant untapped export potential between Cambodia and its trading partners. To optimize its agricultural exports, Cambodia can benefit from processing agricultural products before export. Therefore, the Royal Government of Cambodia (RGC) should actively support and promote investment in the agricultural processing industry to enhance the country’s international competitiveness. Additionally, the RGC and policymakers should strategically select export destinations through favorable trade negotiations and focus on the effective implementation of existing export policies.


Cambodia , agricultural products export , gravity model

중력모형을 이용한 캄보디아 농산물 수출 결정 요인 분석

CHEN Sokhan*,**, 김수현*, 황성혁*†
*전북대학교 농업경제학과
**캄보디아 농림수산부 고무총국

초록

    INTRODUCTION

    Cambodia experienced one of the world’s most consistent economic growth patterns from 1998 to 2019, achieving an average growth rate of 7.60%. Cambodia attained lower-middle-income status in 2015 and aims to transition to middle-income status by 2030 and high-income economic status by 2050 (RGC, 2023). The poverty rate in the country declined from 33.8% in 2009 to 17.8% in 2019 (CDRI, 2023). Agriculture plays a crucial role in Cambodia’s economy. As of 2022, the agricultural sector contributed 2 2 % to t he G ross D omestic P roduct (GDP), valued at approximately $29.5 billion. With an average annual growth rate of 4%, agriculture has become a key driver of economic development. In 2020, agricultural labor accounted for 35.5% of total employment (RGC, 2022). Cambodia consists of approximately 2.2 million households, 63% of which are engaged in agricultural production (NIS, 2021). Consequently, agriculture occupies a vital role in job creation in Cambodia.

    In recent years, Cambodia has transitioned from subsistence agriculture to commercial farming, resulting in a significant increase in agricultural exports. The volume of agricultural exports, which was merely 680,000 tons in 2012, surged to 8.45 million tons by 2023.

    Cambodia exported agricultural products to 78 countries, totaling 8,449,414 tons, which included 656,323 tons of milled rice, 2,730,825 tons of paddy rice, and 5,062,266 tons of other commodities in 2023. The total value of agricultural exports amounted to approximately $4,307 million in 2023. Agricultural exports meeting phytosanitary certificate requirements included 2,730,825 tons of paddy rice, 1,289,739 tons of dried cassava slices, 1,961,375 tons of fresh cassava, 656,323 tons of unprocessed cashew nuts, 425,977 tons of fresh bananas, 288,137 tons of maize, and 161,925 tons of fresh mangoes (MAFF, 2024). Cambodia exports agricultural products to its main destinations, including Association of Southeast Asian Nations (ASEAN) countries such as Thailand, Vietnam, Malaysia, and Singapore, as well as European Union (EU) member countries, India, Japan, the Republic of Korea, and the USA (GDA, 2024). Moreover, under free trade agreements, Cambodia also exports products to members of the Regional Comprehensive Economic Partnership Agreement (RCEP), which effectively facilitates trade through global, regional, and bilateral trade agreements (Thangavelu and Hing, 2023).

    Cambodia has limited post-harvest handling and processing of agricultural products. About 10% of all raw materials are processed for export, which represents a small proportion of the total production volume. Most of Cambodia’s unprocessed agricultural products are exported to Thailand and Vietnam (ADB, 2021) due to limited local investment in processing facilities, phytosanitary technical challenges, packaging constraints, and limited market negotiations. As a result, Cambodia’s neighboring countries receive the majority of Cambodia’s raw material exports. Moreover, Cambodia’s domestic logistics incur higher costs compared to those in Thailand and Vietnam (Lim, 2024;Piseth et al., 2021).

    The agriculture sector plays a strategic role in ensuring food availability and achieving food security in Cambodia. However, 58% of Cambodians engage in agriculture primarily for household consumption, while the remaining 42% focus on producing agricultural products to generate household income. Despite efforts to transition toward export- oriented commercial agriculture, Cambodia’s agricultural sector remains subsistence-based, influencing the country’s potential for agricultural production (NIS, 2021).

    Cambodia is a small country located between Thailand and Vietnam, which are major agricultural exporters in ASEAN, and these two countries absorb huge amounts of raw agricultural products from Cambodia. Competition with neighboring countries has impeded Cambodia’s export of agricultural products to countries with differentiated end markets (Ngoy, 2022). Cambodia exports at least 2 million tons of paddy rice to Vietnam, which is sufficient for Vietnam to process and re-export as milled rice (Sokcheng, 2019). Primary materials or semi-processing agricultural products will significantly hinder the competitive advantage of agricultural products, gain low value-added, and minimize other economic activities, which are key challenges for developing countries such as Cambodia (Piseth et al., 2021). Therefore, research on critical factors hindering Cambodia’s agricultural products is needed to assess its position and potential. A study on the factors determining Cambodia’s agricultural exports is required to restore robust agricultural growth, which is a priority for sustaining rapid and inclusive economic growth in the short to long term, with proper information for policymakers.

    The study aims to identify the factors determining Cambodia’s agricultural exports. The study employs the gravity model applicable to the trade context, which contributes to the limited body of literature and enhances research on exports. The analysis utilizes relevant data from 35 countries that are Cambodia’s primary trading partners over a period of 20 years (2004-2023), referring to export value and volume (Trade Map, 2024). This study provides insights into improving export performance and maximizing opportunities in global markets, which will contribute to the development of the nation’s trade strategy.

    There are five sections. The first section introduces the Cambodian economy and agricultural exports. The second section focuses on the literature review and empirical research on theoretical gravity models. Section 3 outlines the research methodology, Section 4 presents the results and discussion, and Section 5 provides conclusions and policy recommendations.

    LITERATURE REVIEW

    Empirical research related to Cambodia’s agricultural products exports

    There is limited previous empirical research on Cambodia’s agricultural product exports. According to Lim (2024), who employed the gravity model with OLS and bootstrapping to study the impact of non-tariff measures on exporting agricultural products in Cambodia, the study’s main findings indicated that sanitary and phytosanitary (SPS) measures and pre-shipment inspection (PSI) non-tariff measures (NTMs) significantly negatively impacted Cambodian agricultural exports, while technical barriers to trade (TBT) measures had a positive impact on exports. Non-technical barriers (NTBs) were not significant, and the study also suggested strengthening SPS standards and encouraging investment in Cambodia’s agricultural sector. Additionally, Yang and Thong (2022) studied the impact of trade facilitation among Regional Comprehensive Economic Partnership (RCEP) members on Cambodia’s exports. The study employed a gravity model with a Random Effect (RE) model to assess the relationship between trade facilitation and export trade. This research also conducted regression analyses at both product-specific and national levels to measure the differential impact of trade facilitation on various types of exports. The results revealed that a 1% increase in trade facilitation boosts Cambodia’s exports to RCEP countries by 2.23%. Factors such as infrastructure and information technology have the most significant positive impact on trade, while tariffs and geographical distance negatively affect trade flows.

    Piseth et al. (2021) studied Cambodia’s agri-food trade during the COVID-19 outbreak using an analysis of trade flows and a review of policies. The study found that Cambodia faces an agri-food trade deficit, primarily exporting low-value primary products while importing higher- value processed goods. During this period, Cambodia’s agricultural sector demonstrated resilience, leading to recommendations to boost domestic agro-processing investments, increase value-added production, and improve technology to diversify exports to global markets.

    Kea et al. (2020) investigated the relative export competitiveness of the Cambodian rice sector using three methods to evaluate Relative Export Competitiveness (REC) indexes: developing the Relative System Export Competitiveness (RSEC) index and utilizing the Short-Run Regression (SRR) model for determinant analysis. The research revealed that the Rectangular Strategy, the Everything But Arms (EBA) agreement, the Belt and Road Initiative (BRI), and an increase in per capita income contributed to the competitiveness of Cambodian rice exports from 1995 to 2018. However, high domestic prices reduced competitiveness, highlighting the need for the Cambodian government to focus on improving policy implementation, particularly for agro-products beyond rice crops.

    Ajmani et al. (2019) assessed Cambodia’s agricultural market integration within and beyond ASEAN using trade potential and competition indicator analysis. The analysis identified Cambodian exports with high potential and low competition. However, rice, cassava, and pepper exhibited both high potential and high competition. The study recommended fostering regional cooperation, promoting investment in processing facilities to enhance export value, and diversifying agricultural exports in the ASEAN market.

    Kea et al. (2019) employed these methodologies to analyze the dynamic panel data of Cambodian rice exports from 1996 to 2018. The findings revealed that historical connections, currency rate policies, and agricultural land reforms facilitated Cambodian rice exports. However, the study also highlighted that economic recessions, as a macroeconomic challenge and obstacle, hinder export flows and necessitate additional policy interventions.

    Chhuor (2017) studied the potential roles of export orientation in Cambodia’s agriculture and agro-industry, employing the Computable General Equilibrium (CGE) Analysis method. The study found that export growth in agriculture, food, beverage, tobacco, and rubber sectors positively impacts Cambodia’s GDP, employment, and household welfare, with more substantial effects observed in the agro-industry sectors.

    Although there has been limited empirical research on Cambodia’s agricultural exports, previous studies have overlooked certain trade variables and relied on limited sample sizes for bilateral trading partners. Furthermore, these studies have not evaluated the factors determining Cambodia’s agricultural export potential with its bilateral trading partners amid global changes in agricultural trade in recent years. Therefore, this study on Cambodia’s agricultural exports employs the gravity model over a period of 20 years, significantly filling the gap in the literature on Cambodia’s agricultural product exports.

    The g ravity m odel t heory

    The selection of the gravity equation estimator has been widely debated among scholars regarding the accuracy of the gravity model. The presence of heteroskedasticity and zero bilateral trade flows in standard empirical approaches has been a key subject of critique (Silva and Tenreyro, 2006;Helpman et al., 2008;Gómez-Herrera, 2013). Silva and Tenreyro (2006) argued that standard empirical techniques for estimating gravity equations are inconsistent and produce biased results. They claim that typical log-linear estimators are adversely affected by heteroskedasticity, potentially leading to biased estimations of true elasticities. Conversely, alternative strategies have been proposed to address the issue of zero trade flows. Some researchers suggest eliminating zero flows from the sample (Linneman, 1966) or adding a constant to all trade flows to estimate the log-linear equation (Rose, 2004). Despite ongoing disagreements and the availability of various estimation methods such as the Heckman model, the FGLS model (Gómez- Herrera, 2013), the Helpman model (Martinez-Zarzoso, 2013), and the Tobit model (Martin and Pham, 2008) determining the optimal method remains a complex task. The choice of method should take into account both economic and econometric considerations, including robust specification checks and diagnostic tests (Linders and De Groot, 2006;Martinez-Zarzoso, 2013).

    The gravity model’s application in agricultural trade

    Numerous empirical studies have employed commodity- specific approaches to examine trade movements by applying the Generalized Least Squares (GLS), Poisson Pseudo-Maximum Likelihood (PPML), and the Heckman model within a single commodity investigation. According to Ravi Kumar et al. (2024), who analyzed trade determinants and opportunities for Indian rice using a dynamic panel gravity model, panel data from 1995 to 2022 was utilized to study bilateral rice trade with ten major importing countries. Various econometric models were applied to estimate the impact of different economic factors on India’s rice exports. The PPML model was employed to address zero trade observations, while the Heckman model was used to evaluate the influence of trade determinants on India’s rice exports and the likelihood of selecting trading partners. The study revealed that trading partners’ economic size and geographical proximity significantly influence India’s rice exports. Additionally, trade agreements and membership in international organizations, such as the World Trade Organization (WTO), were found to substantially boost rice exports.

    Ismaiel Ali et al. (2023) examined the factors and potential of trade by applying the gravity model to the Egyptian rice crop. The data analysis estimated the influence of independent variables on rice crop exports from 2001 to 2020 with 11 main importing countries. The model incorporated factors such as distance, GDP, and population to explain trade volumes and patterns. The study indicated that economic factors, including GDP, population growth, and export prices, are critical determinants of rice exports. It also highlighted that addressing logistical challenges and trade agreements could significantly enhance Egypt’s competitiveness in global rice markets. Shahriar et al. (2019) applied a gravity model to analyze the Chinese meat business from 1996 to 2016 in 31 regularly importing countries. The results demonstrated that GDP, exchange rate, common language, and geographical proximity significantly affect China’s pork export flows. The study suggested that the Chinese government could improve the pork export system with neighboring countries. Additionally, previous empirical studies have investigated agricultural commodity exports using the gravity model. These include sesame and coffee (Eshetu, F., 2024), rice (Nguyen D., 2022; Sazena et al., 2024), leather (Shahriar et al., 2021), and apples (Ke and Zhang, 2024).

    Determinant of agricultural products exports

    Previous studies have identified various factors influencing the exports of agricultural products and commodities. Empirical research has shown that the economic growth, measured by GDP, of exporting countries and their trading partners positively affects export volumes, with higher economic growth leading to increased export flows (Ke and Zhang, 2024;Abdullahi et al., 2021a). Additionally, geographical proximity, shared borders, and access to seaports are critical positive determinants of agricultural exports, as they enhance export efficiency by reducing logistical costs and facilitating smoother trade (Ke and Zhang, 2024). Membership in the EU or trading with EU countries also facilitates and boosts agricultural trade, while countries with historical colonial ties to importing countries demonstrate stronger trade connections (Abdullahi et al., 2021;Kea et al., 2019). Bui and Chen (2017) investigated rice exports in Vietnam from 2004 to 2014 and found that the exchange rate positively influenced Vietnam’s rice exports. Conversely, Abdullahi et al. (2021a) reported that the exchange rate negatively impacted Nigeria’s agri-food trade. Nguyen (2022), in a study on rice and coffee exports, revealed that longer distances between Vietnam and its trading partners negatively affected exports due to increased transport costs and logistical challenges. Similarly, Batra (2006) found that distance had a negative effect on India’s global trade. In addition, when importing countries had larger agricultural land areas, increased local production intensified competition, creating challenges for exporting countries. Atif et al. (2017) discovered that tariffs hindered Pakistan’s agricultural exports. Finally, Tandra and Suroso (2022) examined the potential of Indonesian palm oil downstream exports in the global market and found that trading with landlocked countries lacking direct access to ports reduced exports due to higher transportation costs.

    THE STRUCTURE OF CAMBODIA’S AGRICULTURAL EXPORTS

    Fig. 1 illustrates the average export values of Cambodia’s agricultural products to the top ten countries from 2004 to 2023. The data indicate that China (35.74%) was the largest export market by proportion, followed by Vietnam (21.60%), France (9.76%), Malaysia (7.90%), Thailand (5.97%), the United States (5.19%), the Netherlands (4.01%), Gabon (3.66%), India (3.56%), and Poland (2.61%). Together, these ten countries accounted for approximately 65% of the total volume of agricultural exports between 2004 and 2023 (Trade Map, 2024).

    According to the data shown in Fig. 2, Cambodia produced its primary agricultural products in 2023 (FAOSTAT, 2024). These products included fresh cassava (17.69 million tons), paddy rice (11.62 million tons), maize (1.16 million tons), sugarcane (0.77 million tons), horticultural and other crops (0.64 million tons), oil palm fruit (0.45 million tons), and meat products (0.10 million tons).

    The Net Production Value (NPV) of Cambodia’s agricultural products revealed that cassava accounted for $3,069 million, followed by paddy rice at $2,952 million, sugarcane at $2,870 million, meat products at $676 million, and horticultural and other crops at $425 million (FAOSTAT, 2024). Furthermore, the General Directorate of Rubber (GDR) reported in 2024 that rubber commodities were estimated to produce 0.65 million tons for export, with an NPV of approximately $600 million. Exports are a critical driver of economic development and prosperity. Agricultural product exports are particularly vital for developing countries such as Cambodia, as strategically utilizing scarce resources can enhance their comparative advantage in global trade (Kea et al., 2019).

    According to the results shown in Fig. 3, Cambodia’s agricultural export flows to the global market have led to a diversification of markets for its agricultural products. China is the largest import market by value ($2,557 million), followed by Vietnam ($1,545 million), France ($698 million), Malaysia ($565 million), Thailand ($426 million), the United States ($371 million), the Netherlands ($287 million), Gabon ($261 million), India ($255 million), and Poland ($186 million) (Trade Map, 2024). In late 2020, the Royal Government of Cambodia (RGC) signed a free trade agreement with China, granting access for 340 Cambodian agricultural products to the Chinese market (Piseth et al., 2021).

    Moreover, Ajmani et al. (2019) indicated that a significant portion of Cambodia’s fragrant rice is exported to the EU and ASEAN countries, contributing to the diversification of export markets.

    In addition, the EU’s “Everything But Arms (EBA)” initiative program facilitated Cambodia’s export of agricultural products to countries such as France, Germany, and the United Kingdom. The EBA program potentially benefited Cambodian rice exports by providing a 30 to 40 percent tariff advantage over Vietnam and Thailand in EU markets (WB, 2018). Meanwhile, the Generalized System of Preferences (GSP) removes import duties for products entering the EU market. However, following the EU’s decision on the partial withdrawal of EBA privileges in February 2020, the Royal Government of Cambodia (RGC) should have taken the necessary steps to meet the conditions required for the European Union to fully restore EBA preferential access to the EU market (Tanaka, K., 2022).

    METHODOLOGY OF RESEARCH AND DATA

    Theoretical gravity model

    The gravity equation is one of the most empirically robust models in economics. It correlates bilateral trade flows with GDP, distance, and additional factors that influence t rade barriers. Anderson et a l. (2003) extensively utilized the gravity equation to analyze the impact of institutions on trade flows, including customs unions, exchange rate systems, ethnic affiliations, linguistic identity, and international boundaries. Tinbergen (1963) and Pöyhönen (1963) were the first to apply the theory of universal gravitation to economic analysis. The following structure outlines a comprehensive trade attractiveness model for bilateral commerce:

    X i j = a Y i β 1 Y j β 2 D i j β 3
    (1)

    where

    • Xij is the trade value between exporting country i and importing countries j,

    • Yi represents the country’s economic size country i,

    • Yj represents the size of a country’s national economy country, j and

    • Dij is the economic distance and disparities between exporting country i and importing country j.

    Anderson (1979) thoroughly investigated the gravity model, predicting a positive influence on bilateral trade between two countries. While the distance between two countries negatively impacts bilateral trade, it does not directly affect their GDP. This study mathematically expresses the gravity model for Cambodia’s agricultural exports in a linear form as follows:

    ln ( X i j t ) = α + β 1 ln ( Y i ) + β 2 ln ( Y j ) + β 3 ln ( D i j )
    (2)

    Numerous studies have developed the gravity equation based on the foundation of three major international trade theories: Ricardo’s theory, the Heckscher-Ohlin model, and new trade theory. As a result, researchers have incorporated additional factors such as the GDP of the exporting and importing countries (Abdullahi et al., 2021;Saxena et al., 2024), the real exchange rate (Ravi Kumar et al., 2024), the distance between exporting and importing countries (Nguyen, 2022;Eshetu, 2024), the arable land of the importing country (Zai, 2023), and whether a country is a member of an association (Lim, 2024) or a union group (Jagdambe and Kannan, 2020).

    Previous empirical research indicated that the existing trade attractiveness model comprises three primary elements affecting nations’ exports or trade flows: supply-side factors (exporting country i), demand-side factors (importing country j), and facilitating or hindering factors. This study employs a comprehensive trade model inspired by the frameworks proposed by previous authors to examine the factors influencing Cambodia’s agricultural exports using the gravity model. Therefore, the model can be expressed as follows:

    ln ( A g r i   E x i j t ) = α 0 + β 1 ln ( G D P i t ) + β 2 ln ( G D P j t ) + β 3 ln ( L a n d j t ) + β 4 ln ( D i s t i j t ) + β 5 ln ( E x c i j t ) + β 6 ln ( T a r i f f j t ) + γ 1 ( C o l o n y j t ) + γ 2 ( B o r d e r j t ) + γ 3 ( L a n d l o c k j t ) + γ 4 ( E U j t ) + γ 5 ( R C E P j t ) + γ 6 ( A S E A N j t ) + ε i j t
    (3)

    Where:

    • - i is the exporting country (Cambodia)

    • - j is the importing country, which ranges country from 1 to 35

    • - t is the year of export, which ranges from 2004, 2005,… to 2023

    • - lnAgri Expijt = Log of agricultural products export value from Cambodia to the partner countries

    • - lnGDPit = Log of Gross Domestic Product of Cambodia at the period t ($million)

    • - lnGDPjt = Log of Gross Domestic Product of importing country j at the period t ($million)

    • - lnLandjt= Log of agricultural land of importing country at period time - 1 year (ha)

    • - lnDistijt = Log of the distance between Cambodia and importing countries j at period t (km)

    • - lnExcijt = Log of real exchange rate between Cambodia’s currency and its partner countries j at period t (Riel/partners currency)

    • - lnTariffjt = The average tariff rate for country j during the study period time t

    • - lnColonyjt = The dummy variable with value 1 if the country j was France’s colony; otherwise, 0

    • - lnBorderjt = The dummy variable with a value of 1 if the country j shares a common border with Cambodia; otherwise, 0

    • - lnLandlockjt = The dummy variable with a value of 1 if the country j does not have direct access to the seaport; otherwise, 0

    • - EUjt = The dummy variable with a value of 1 if the country j is a member; otherwise, 0

    • - RCEPjt = The dummy variable with a value of 1 if the country j is a member; otherwise, 0

    • - ASEANjt = The dummy variable with a value of 1 if the country j is a member; otherwise, 0

    • - β = The coefficients of independent variables, and is the error.

    Zero Trade Problem

    The logarithmic form of the gravity equation frequently presents challenges for ‘zero’ trade values, as ln(0) is undefined. This issue arises when specific pairs of countries do not engage in trade during certain periods.1) Previous empirical studies have addressed this problem by applying the Poisson Pseudo-Maximum Likelihood (PPML) method and the Heckman Sample Selection Model (Heckman, 1979;Helpman et. al, 2008), which are among the most common methods in gravity model literature for handling zero trade values. However, Silva and Tenreyro (2011) argued that the two-step estimation method does not fully eliminate selection bias and is only valid under the assumption that all random components of the model are homoskedastic. Therefore, this study adopts the PPML estimation method, which is more appropriate for addressing the issues of zero trade values and heteroskedasticity, instead of the Heckman two-step estimation method (Park, 2016). The PPML method enables the estimation of a gravity model that includes zero trade values and allows for a dependent variable that is not in logarithmic form (Silva and Tenreyro, 2006;Kea et al., 2019).

    The PPML model of this study, therefore, can be written as follows:

    A g r i   E x i j t = α 0 + β 1 ln ( G D P i t ) + β 2 ln ( G D P j t ) + β 3 ln ( L a n d j t ) + β 4 ln ( D i s t i j t ) + β 5 ln ( E x c i j t ) + β 6 ln ( T a r i f f j t ) + γ 1 ( C o l o n y j t ) + γ 2 ( B o r d e r j t ) + γ 3 ( L a n d l o c k j t ) + γ 4 ( E U j t ) + γ 5 ( R C E P j t ) + γ 6 ( A S E A N j t ) + ε i j t
    (4)

    Sample sizes and data sources

    The study selected the relevant variables for the gravity model by applying trade theories and adhering to previous empirical research guidelines and criteria in Table 1.

    The panel dataset consists of bilateral agricultural product export trade data from Cambodia to its 35 major agricultural trading partners over a 20-year period from 2004 to 2 02 3. The s tudy u ses the Trade Map database to select the sample frame of 35 countries based on Cambodia’s average annual agricultural export value, as shown in Table 2, which accounts for nearly 80% of Cambodia’s total agricultural export value (Trade Map, 2024). The dataset includes a total of 700 observations (N = 35, T = 20).

    Table 3 summarizes the variables used in this study. The dependent variable is the average annual export volume of Cambodia’s agricultural products, valued at $12,809,000, exported between 2004 and 2023. This value ranges from $0 to $790,880,000. During the study period, Cambodia recorded zero trade with one of its trading partners (Trade Map, 2024).

    RESULTS AND DISCUSSION

    Results of test

    The three models are highly similar regarding variable signs, coefficients, and statistical significance at conventional levels. In this study, OLS was conducted, followed by the application of the Hausman test to assess the appropriateness of the selected model (Fixed Effects (FE) or Random Effects (RE)). The result showed a p-value = 0.0000. Therefore, Fixed Effects (FE) is the appropriate model. To clarify the proper selection between Random Effects (RE) and FE, the Hausman test was employed. The Hausman test indicated that Prob > chi2 = 0.0035, which is less than 0.05; therefore, we reject the null hypothesis (H0), suggesting that the differences in coefficients are systematically different, implying that the FE model is more appropriate than the RE model for this analysis.

    To estimate Equation (3), a cross-sectional dependence test was first conducted. Cross-sectional dependence is a common phenomenon in panel data, where correlations arise among the error terms across entities (Park & Ryu, 2020). Pesaran (2004) highlighted that the presence of cross-sectional dependence in a panel model may lead to estimation bias. Accordingly, this study examined the presence of cross-sectional dependence in the panel data and model using data from 35 major trading partner countries of Cambodia. For this purpose, Pesaran’s (2004) CD test (cross-sectional dependence test) was employed (De Hoyos & Sarafidis, 2006).2)

    C D = 2 T N ( N − 1 ) ( ∑ i = 1 N − 1 ∑ i + 1 N ρ ^ i j )
    (5)

    where ρ ^ i j = ∑ t = 1 T e i t e j t / ( ∑ t = 1 T e i t 2 ) 1 / 2 ( ∑ t = 1 T e j t 2 ) 1 / 2 , e i t denoting the residuals of OLS

    The results of the Pesaran CD test showed that the test statistic was 12.639, rejecting the null hypothesis (H0 : Coυ(∈it, ∈jt) = 0 for all t and i≠j) of no cross-sectional dependence at the 1% significance level. This indicates that cross-sectional dependence exists in the panel data used in this study.

    Second, the Wooldridge serial autocorrelation test was conducted to examine the presence of serial correlation in the residuals of the panel data (Wooldridge, 2002;Drukker, 2003). The Wooldridge test for autocorrelation in panel data revealed that F (1, 33) = 7.149; Prob > F = 0.0116. Therefore, the model has autocorrelation.

    Third, a slope homogeneity test, proposed by Pesaran and Yamagata (2008), was conducted. The heterogeneous characteristics of countries importing Cambodian agricultural products may influence Cambodia’s agricultural exports. Given that export and import policies, regulations, the level of globalization, and industrial structures vary across countries, the coefficients of the explanatory variables in Equation (3) may not be identical across countries. Considering these characteristics, it is essential to test whether the slopes of the explanatory variables are homogeneous across countries in panel data analysis (Kim & Ryu, 2024). The slope homogeneity test evaluates whether the coefficients obtained from individual country-specific regression models exhibit statistically significant differences across all countries. To conduct this test, the delta test statistic and the adjusted delta test statistic are computed, followed by an assessment of whether the null hypothesis of coefficient homogeneity (H0 : βki = βk for some i) can be rejected. Equations (6)–(8) present the formulas for calculating the delta test statistic and the adjusted delta test statistic.3)

    S = ∑ i = 1 N β i − β WFE ′ X i ′ M τ X i σ i 2 β i − β WFE
    (6)

    Δ = N ( N − 1 S − k 2 k )
    (7)

    Δ a d j = N ( N − 1 S − k 2 k ( T − k − 1 ) T + 1 )
    (8)

    where, βi represents the OLS coefficient for each country, βWFP denotes the coefficient from the weighted fixed effects model, Xi is the matrix of explanatory variables, Mt is a symmetric and idempotent matrix, and k indicates the number of explanatory variables.

    The results of the slope homogeneity test reject the null hypothesis at the 1% significance level, suggesting the presence of heterogeneity in the coefficients of the variables affecting Cambodia’s agricultural exports across countries.4)

    Finally, the variance inflation factor (VIF) was calculated to detect potential multicollinearity in the panel data. Table 5 illustrates the variables. There are lnGDPit, lnGDPjt, lnExcijt, Borderjt, RCEPjt and ASEANjt positively correlates with the dependent variables lnAgri Exijt at 0.4984, 0.1667, 0.445, 0.1176, 0.0871, 0.0801 and 0.1810, respectively. The correlation coefficient suggests that the variables are moderate, with 0.49 being the highest. Additionally, Gujarati (2004) demonstrated the presence of multicollinearity when the correlation coefficient is high and exceeds 0.8. Hausman (2001) indicated that results are more precise when the correlation coefficient is below 0.80. The study used a more thorough multicollinearity estimation method to find the variance inflation factor (VIF) and tolerance levels (1/VIF) for the independent variables. Ke and Zhang (2004) indicated that figures exceeding 0.10 and VIF values below 10 suitable. The analysis shows that multicollinearity has not changed the equation because all of the parameters’ VIF values and tolerance levels are in the correct range. Correlation analysis was conducted to examine the linear relationship between the variables in the model. In addition, lnLandjt, lnDisijt, lnTariffjt, Landlockjt, and EUjt variables are negatively correlated with the dependent variable lnAgri Exijt.

    Nevertheless, there is a significantly high negative correlation (-0.6815) between the EU and RCEP variables. In such cases, including both the EU and RCEP variables in the model simultaneously may lead to mutual influence, potentially resulting in incorrect estimation or under-estimation of the independent effect of each variable. Therefore, the RCEP variable was excluded from the final model, as there are numerous countries that are members of both RCEP and ASEAN.

    The test results presented above indicate the presence of cross-sectional dependence, serial correlation, and heterogeneity across panel units. Accordingly, Equation (3) was estimated using the Panel-Corrected Standard Error (PCSE) model, developed by Beck and Katz (1995). The PCSE method provides robust standard errors in situations where the error terms exhibit cross-sectional correlation and potential serial correlation over time. As an alternative approach to addressing both cross-sectional dependence and serial correlation, the Driscoll-Kraay estimation method (Driscoll & Kraay, 1998) was also employed to estimate Equation (3).

    Results of estimation

    Table 6 shows the results estimated from PCSE, Driscoll- Kraay estimation, and PPML. These three models show the factors that determine Cambodia’s agricultural exports to importing countries.

    The variables include GDP of the exporting and importing countries, agricultural land of importing countries, distance, exchange rate, import tariff of partner country j, former French colony, common border, landlocked status, EU membership, and ASEAN membership of partner country j. The coefficients for GDP of exporting countries, importing countries, former French colony, and common borders positively affect Cambodia’s agricultural exports. At the same time, distance and exchange rate have adverse effects on Cambodia’s agricultural exports in this study.

    In the gravity model literature review on the (GDPit & GDPjt) variables are proxies for market size, which in our case is the market size of Cambodia and its partners. Cambodia has the potential to produce agricultural commodities, while the market size of importing countries reflects their demand for Cambodia’s agricultural exports. The results for this variable are consistent with the empirical findings by numerous researchers, namely Bui and Chen (2017), Atif et al. (2017), Kea et al. (2019), Abdullahi et al. (2021, 2022), Saxena et al. (2024), and Lim (2024).

    If the arable land area of an importing country expands, its domestic agricultural production is expected to increase, leading to a reduction in agricultural imports from Cambodia. Consequently, a negative relationship was initially anticipated. However, the PPML model indicates that the lnLandjt variable has a statistically significant positive effect on Cambodia’s agricultural exports at the 5% significance level. In contrast, the PCSE estimation method and the Driscoll-Kraay estimation method do not reveal any statistically significant causal relationship between the arable land area of the importing country and Cambodia’s agricultural exports. Therefore, it is challenging to assert a definitive causal link between these variables. Nevertheless, the positive relationship observed in the PPML model may be attributed to the characteristics of major importers of Cambodian agricultural products, such as China, Vietnam, and the United States. Despite possessing extensive arable land, these countries exhibit high domestic demand for agricultural products. Furthermore, they tend to rely on imports rather than domestic production due to economic efficiency considerations and the need to accommodate diverse consumer preferences.

    The (lnDistjt) variable exhibits a negative value, consistent with the theoretical framework of the gravity model. This finding suggests an inverse relationship between the distance of the importing country from Cambodia and Cambodia’s agricultural exports. As the distance increases, export-related costs, such as logistics expenses, also rise, thereby negatively impacting Cambodia’s agricultural exports.

    The (Excijt) variable is statistically significant and has a negative coefficient. This finding indicates that the appreciation of the local currency (Riel) against the importing country’s currency dissuades agricultural exports from Cambodia to importing countries. This result aligns with the studies by Abdullahi et al. (2021a) on factors determining agri-food exports and Eshetu, F. (2024) on sesame and coffee exports. Currency appreciation makes agri-food exports more expensive for importing countries. Conversely, the depreciation of the Cambodian currency (Riel) would enhance Cambodia’s trade competitiveness. However, previous studies by Ravi Kumar et al. (2024) and Saxena et al. (2024) found that the exchange rate positively affects exports.

    It has been observed that former French colonies (Colonyjt) import Cambodian agricultural products at a higher rate compared to non-colonized countries. This phenomenon can be attributed to Cambodia’s historical status as a French protectorate, which facilitated economic and cultural exchanges with other former French colonies. As a result, the preference for Cambodian cuisine and agricultural products increased, thereby serving as a contributing factor to the growth of Cambodian agricultural exports.

    The variable of land-sharing between neighboring countries (Borderjt) exhibited statistically significant results at the 5% significance level only in the PCSE estimation method, while no statistically significant results were observed in the Driscoll-Kraay and PPML estimation methods, indicating inconsistent findings across different estimation methods. However, since the PPML estimation method is more effective in handling zero trade values, it is difficult to assert a clear causal relationship between land-sharing among neighboring countries and Cambodia’s agricultural exports. The presence of economic integration frameworks such as RCEP and ASEAN appears to have diminished the importance of borders, reducing their overall impact. Nevertheless, Cambodia shares borders with Laos, Thailand, and Vietnam, exporting unprocessed agricultural products such as cashew nuts, rubber, and rice to Vietnam, while fresh cassava roots, corn, and mangoes are exported to Thailand. These factors appear to have contributed to the positive effect of the border variable (Borderjt) observed in the PCSE estimation method.

    The result of ASEANjt members shows a positive effect on Cambodia’s agricultural products, which is statistically significant at the 10% level in the Driscoll-Kraay estimation method. However, other models do not show statistically significant results, making the evidence inconclusive as to whether Cambodia exports more agricultural products to ASEAN member countries than to non-member countries.

    In this study, the estimation methods revealed significant differences in the variables ln Landjt, Borderjt, and ASEANjt, while the results for other variables remained consistent across models. In panel data analysis, when zero trade flows and heteroskedasticity are present, certain estimation methods may be less robust in handling such variations. In contrast, the PPML model directly addresses these issues by avoiding log transformation and providing consistent estimates even in the presence of heteroskedasticity and zero trade flows. Therefore, the PPML model is considered to produce more appropriate estimation results.

    CONCLUSION AND RECOMMENDATION

    Conclusion

    The paper aims to identify the factors that determine Cambodia’s agricultural exports. The theoretical and empirical studies apply the gravity model, incorporating three analytical approaches: the PCSE, Driscoll-Kraay, and PPML models. These models were employed to overcome the problems of heteroscedasticity, autocorrelation, cross sectional dependence, and zero trade with partners, and for comparative purposes. The study was analyzed using a panel dataset of 20 years from 2004 to 2023 with 35 selected destination countries. The sample framework encompasses Cambodia’s primary trading partners, contributing to the study’s novelty. The study intends to fill a gap in Cambodia’s agricultural trade and reveal its main findings and insights. First, the growth of Cambodia’s GDP has significantly boosted agricultural product exports. China, Vietnam, France, Malaysia, and Thailand are among the most important markets for Cambodia’s agricultural exports. Second, the GDP of importing countries, and the former French colony are the factors that positively influence Cambodia’s agricultural product export flows. Third, agricultural land of importing countries, distance, and exchange rate are the negative factors associated with Cambodia’s agricultural export flows; meanwhile, landlock is a non-significant variable.

    Recommendation

    Cambodia’s economic stability is crucial for sustaining GDP growth. The findings propose policy recommendations to improve Cambodia’s agricultural exports, urging the Royal Government of Cambodia (RGC) and policymakers to develop strategies for maintaining strong bilateral relations with current trading partners by promoting trade and strengthening existing markets through trade agreements. Cambodia has increasing opportunities to expand its agricultural exports not only to China, its largest importing country, but also to neighboring countries such as Vietnam and Thailand, which are experiencing stable economic growth. In particular, these countries have significant market growth potential and benefit from geographical proximity, which reduces transportation costs.

    Cambodia has the potential to produce agricultural commodities; however, most of the primary products are exported to neighboring countries due to limited processing capacity. The policy framework on agricultural processing requires greater support for small and medium enterprises, which could enhance their processing capacity. Therefore, the RGC should promote technological innovation and provide technical and financial support for agricultural product processing manufacturers to maximize pre-export processing. This support could enhance the value-added advantage and reduce the flow of raw materials to neighboring countries. Local investment in agricultural processing should be encouraged by attracting local and foreign investors with favorable policies. Processing agricultural products could maximize the potential of Cambodia’s agricultural product exports and maintain sustainable growth in the agricultural sector.

    Figure

    JKSIA-37-1-35_F1.gif

    Cambodia’s agricultural products export to 10 main countries (%).

    JKSIA-37-1-35_F2.gif

    The illustration of 20 primary Cambodian agricultural products in 2022.

    JKSIA-37-1-35_F3.gif

    The total value of Cambodia’s agricultural products exported to main destination countries from 2004 to 2023 ($1000).

    Table

    Description of the unit, expected sign, and data sources.

    Source: Authors’ elaboration (2024).

    Sample economies and country selection.

    Source: Authors’ elaboration (2024).

    Descriptive statistics of variables.

    Source: Trade Map, 2024 (processed) and author’s calculation, 2024.

    The results of the slope homogeneity test.

    Note:*** Significant at 1% level

    Matrix of correlations.

    Source: Authors’ calculation (2024).

    The estimated coefficients of PCSE, Driscoll-Kraay and PPML.

    Source: Trade Map, 2024 (processed) and author’s calculation, 2024. Note: *p < 0.10; **p < 0.05; ***p < 0.01

    Reference

    1. Abdullahi, B.N., Dingchou, M., Yeboah, F.K., Ibrahim, H.Y. 2022. Econometric Analysis of China-ECOWAS Agricultural Products Trade. Ciência Rural. 53:e20210556.
    2. Abdullahi, N.M., Huo, X., Zhang, Q., Bolanle Azeez, A. 2021a. Determinants and potential of agri-food trade using the stochastic frontier gravity model: Empirical evidence from Nigeria. Sage Open. 11:21582440211065770.
    3. Abdullahi, N.M., Shahriar, S., Kea, S., Abdullahi, A.M., Zhang, Q., Huo, X. 2021. Nigeria’s cocoa exports: a gravity model approach. Ciência Rural. 51:e20201043.
    4. Abdullahi, N.M., Zhang, Q., Shahriar, S., Irshad, M.S., Ado, A.B., Huo, X. 2022a. Examining the determinants and efficiency of China’s agricultural exports using a stochastic frontier gravity model. PLoS One. 17:e0274187.
    5. Ajmani, M., Joshi, P.K., Roy, D., Renjini, V.R. 2019. Assessing agricultural market integration of Cambodia within and beyond ASEAN. Vol. 1845. Intl Food Policy Res Inst.
    6. Amemiya, T. 1980. Selection of regressors. International economic review. 21:331-354.
    7. Anderson, J.E. 1979. A theoretical foundation for the gravity equation. The American Economic Review. 69:106-116.
    8. Anderson, J.E., Van Wincoop, E. 2003. Gravity with gravitas: A solution to the border puzzle. American Economic Review. 93:170-192.
    9. Asian Development Bank. 2021. Cambodia Agriculture, Natural Resources and Rural Development Sector Assessment, Strategy and Road Map. Report, Asian Development bank. 76p.
    10. Atif, R.M., Haiyun, L., Mahmood, H. 2017. Pakistan’s agricultural exports, determinants, and potential: an application of stochastic frontier gravity model. The Journal of International Trade & Economic Development. 26:257-276.
    11. Batra, A. 2006. India’s global trade potential: The gravity model approach. Global Economic Review. 35:327-361.
    12. Bera, A.K., Jarque, C.M. 1982. Model specification tests: A simultaneous approach. Journal of Econometrics. 20:59-82.
    13. Braha, K., Qineti, A., Cupak, A., Lazorčakova, E. 2017. Determinants of Albanian agricultural export: The gravity model approach. AGRIS on-line Papers in Economics and Informatics. 9:3-21.
    14. Bui, T.H.H., Chen, Q. 2017. An analysis of factors influencing rice export in Vietnam based on gravity model. Journal of the Knowledge Economy. 8:830-844.
    15. CDRI. 2023. Poverty and Covid-19 in Cambodia: Lessons and Future Preparedness, Policy Brief No 3. Phnom Penh, Cambodia.
    16. Chakradhar, J., Thao, T.T. 2024. The Impact of Participating in the Regional Comprehensive Economic Partnership (RCEP) Agreement and Potential Export Markets for Vietnamese Rice: An Application of Augmented Gravity Model. Journal of International Commerce, Economics and Policy. 15:2450004.
    17. Chhuor, S. 2017. Potential roles of export orientation of Cambodia’s agriculture and agro-industry: An application of CGE analysis. Journal of Economic Structures. 6:1-33.
    18. Dawani, C., Pareek, D. 2024. Analysing India’s Export Competitiveness in ASEAN Economies: Insights from Viner’s Trade Creation Model. Journal of Asian Economic Integration. 6:7-30.
    19. De Hoyos, R.E., Sarafidis, V. 2006. Testing for Cross-sectional Dependence in Panel-data Models. The Stata journal. 6:482-496.
    20. Didier, L., Koenig, P. 2019. Has China replaced colonial trade?. Review of World Economics. 155:199-226.
    21. Driscoll, J.C., Kraay, A.C. 1998. Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. Review of Economics and Statistics. 80:549-560.
    22. Drukker, P. 2003. Testing for Serial Correlation in Linear Paneldata Models. The Stata journal. 3:168-177.
    23. Eshetu, F. 2024. Determinants of Ethiopian sesame and coffee exports to its major trade partners: application of the gravity model. Cogent Social Sciences. 10:2334114.
    24. General Directorate of Agriculture (GDA). 2024. Annual Consolidated Report for 2023 and Directions for 2024. (in Khmer language). Phnom Penh, Cambodia.
    25. General Directorate of Rubber (GDR). 2024. Annual report for Cambodia’s rubber in 2023 and direction implementation in 2024. (in Khmer language). Phnom Penh, Cambodia.
    26. Gomez-Herrera, E. 2013. Comparing alternative methods to estimate gravity models of bilateral trade. Empirical economics. 44:1087-1111.
    27. Gujarati, D.N. 2004. Basic Economics (4th edition). McGraw-Hill Inc. NewYork. p.359.
    28. Hausman, J. 2001. Mismeasured variables in econometric analysis: problems from the right and problems from the left. Journal of Economic perspectives. 15:57-67.
    29. Heckman, J.J. 1979. Sample Selection Bias as a Specification Error. Econometrica. 47:153-161.
    30. Helpman, E., Melitz, M., Rubinstein, Y. 2008. Estimating trade flows: Trading partners and trading volumes. The quarterly journal of economics. 123:441-487.
    31. https://www.fao.org/faostat/en/ database: Cambodia: retrieved 10 September 2024.
    32. https://www.trademap.org/Cambodia/Agricultural Products export. Retrieved: 10 September, 2024.
    33. Ismaiel Ali Ismaiel, M., Zhou, D., Shawky Eladawy, R., El-Rasoul, A.A.E.Y.A., Tawfik Yousef Alkhateeb, T., Ali Ahmed Abdullah, T., Mahmood, H. 2023. Determinants and potential of trade using the gravity model approach: empirical evidence of Egyptian rice crop. Complexity. 2023:4791707.
    34. Jagdambe, S., Kannan, E. 2020. Effects of ASEAN-India Free Trade Agreement on agricultural trade: The gravity model approach. World Development Perspectives. 19:100212.
    35. Ji, S., Yoo, J. 2016. Analysis on Import Determinants of Agricultural Products from ASEAN Countries. J. Korean Soc. Int. Agric. 28:166-173.
    36. Ke, B., Zhang, Q. 2024. Assessing the Efficiency and Potential of China’s Apple Exports. Complexity. 2024:7816792.
    37. Kea, S., Li, H., Shahriar, S., Abdulahi, N.M., Phoak, S., Touch, T. 2019. Factors influencing Cambodian rice exports: An application of the dynamic panel gravity model. Emerging Markets Finance and Trade. 55:3631-3652.
    38. Kea, S., Li, H., Shahriar, S., Abdullahi, N.M. 2020. Relative export competitiveness of the Cambodian rice sector. British Food Journal. 122:3757-3778.
    39. Kim, S.Y., Ryu, S. 2024. A Study on Factors Affecting Government Health Expenditure in ASEAN using RCCEMG Estimation Method. International Area Studies Review. 28:51-70.
    40. Lim, S. 2024. The Impact of Non-Tariff Measures on the Exporting of Agricultural Products of Cambodia. International Journal of Economics and Financial Issues. 14:76-82.
    41. Linders, G.J., De Groot, H.L. 2006. Estimation of the gravity equation in the presence of zero flows. Tinbergen Institute Discussion Paper.
    42. Linnemann, H. 1966. An econometric study of international trade flows. North-Holland Publishing Co., Amsterdam.
    43. MAFF. 2024. Consolidated annual report for 2023 and directions for 2024. (in Khmer language). Ministry of Agriculture, Forestry and Fisheries. Phnom Penh, Cambodia.
    44. Martin, W., Pham, C.S. 2008. Estimating the gravity equation when zero trade flows are frequent. MPRA Paper.
    45. Martin, W., Pham, C.S. 2020. Estimating the gravity model when zero trade flows are frequent and economically determined. Applied Economics. 52:2766-2779.
    46. Martinez-Zarzoso, I. 2013. The log of gravity revisited. Applied Economics. 45:311-327.
    47. National Institute of Statistics (NIS). 2021. Cambodia Inter-censal Agriculture Survey Final Report. Phnom Penh, Cambodia.
    48. Ngoy, Y. 2022. Determinants of import and export flows between Cambodia and its major trading partners. A modified gravity approach: A thesis submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy at Lincoln University (Doctoral dissertation, Lincoln University).
    49. Nguyen, D.D. 2022. Determinants of Vietnam’s rice and coffee exports: using stochastic frontier gravity model. Journal of Asian Business and Economic Studies. 29:19-34.
    50. Park, M., Ryu, S. 2020. An Analysis of House Price Dynamics Considering Cross-Sectional Dependence. Journal of Korea Regional Economics. 18:5-26.
    51. Park, S. 2016. The Impacts of FTAs on Exports of SMEs: Analysis on Extensive and Intensive margins of Exports. Journal of International Trade and Industry Studies. 21:1-20.
    52. Pesaran, M.H. 2004. General Diagnostic Tests for Cross Sectional Dependence in Panels. Empirical Economics. 60:13-50.
    53. Pesaran, M.H. 2006. Estimation and Inference in Large Heterogenous Panels with a Multifactor Error Structure. Econometrica. 74:967-1012.
    54. Pesaran, M.H., Yamagata, T. 2008. Testing slope homogeneity in large panels. Journal of Econometrics. 142:50-93.
    55. Piseth, S., Monyoudom, Y., Tynarath. 2021. Cambodia’s Agri-Food Trade: Structure, New Emerging Potentials, Challenges and Impacts of Covid-19.
    56. Poyhonen, P. 1963. A tentative model for the volume of trade between countries. Weltwirtschaftliches archiv. 90:93-100.
    57. Ravi Kumar, K.N., Reddy, K.G., Shafiwu, A.B., Mohan Reddy, M.J. 2024. Trade determinants and opportunities for Indian rice: a dynamic panel gravity model perspective. Cogent Economics & Finance. 12:2312367.
    58. RGC. 2021. Cambodia Agricultural Survey 2021. Phnom Penh, Cambodia.
    59. RGC. 2022. National Agricultural Development Policy 2022-2030. Phnom Penh, Cambodia.
    60. RGC. 2023. Pentagonal Strategy - Phase I for Growth, Employment, Equity, Efficiency, and Sustainability: Building the Foundation towards Realizing the Cambodia Vision 2050. Available at: https://faolex.fao.org/ docs/pdf/cam222534.pdf
    61. Rose, A.K. 2004. Do we really know that the WTO increases trade?. American Economic Review. 94:98-114.
    62. Santos Silva, J.M.C., Tenreyro, S. 2006. The Log of Gravity. The Review of Economics and Statistics. 88:641-658.
    63. Santos Silva, J.M.C., Tenreyro, S. 2011. Further Simulation Evidence on the Performance of the Poisson Pseudo-Maximum Likelihood Estimator. Economic Letters. 112:220-222.
    64. Saxena, R., Pant, D.K., Singh, A., Sharma, P., Pant, S.C. 2024. How far India has succeeded in harnessing its export potential in rice? Evidence Using Stochastic Frontier Gravity Model. The Journal of International Trade & Economic Development.
    65. Shahriar, S., Kea, S., Abdullahi, N.M., Rahman, R., Islam, R.M. 2021. Determinants of Bangladesh’s leather exports to its major trade partners: a panel gravity model approach. Global Business Review.
    66. Shahriar, S., Qian, L., Kea, S. 2019. Determinants of exports in China’s meat industry: A gravity model analysis. Emerging Markets Finance and Trade. 55:2544-2565.
    67. Silva, J.S., Tenreyro, S. 2006. The log of gravity. The Review of Economics and Statistics. 88:641-658.
    68. Sokcheng, S. 2019. The Implications of Vietnamese Rice Policy Changes for Rice Producers in Southeastern Cambodia. Resource governance, agriculture, and sustainable livelihoods in the lower Mekong basin. 241p.
    69. Tanaka, K. 2022. The European Union’s withdrawal of trade preferences for Cambodia. The World Economy. 45:3398-3419.
    70. Tandra, H., Suroso, A.I. 2023. The determinant, efficiency, and potential of Indonesian palm oil downstream export to the global market. Cogent Economics & Finance. 11:2189671.
    71. Thangavelu, S.M., Hing, V. 2023. Structural Transformation and the Global Production Value Chain: Potential Impact of the Cambodia - Republic of Korea FTA on Cambodia (No. PB-2022-12).
    72. Thuong, N.T.T. 2018. The effect of Sanitary and Phytosanitary measures on Vietnam’s rice exports. Economia. 19:251-265.
    73. Tinbergen, J. 1963. Shaping the world economy: Suggestions for an international economic policy. Twentieth Century Fund.
    74. Tore, B., Jan, D. 2021. Testing for Slope Homogeneity in Stata. The Stata Journal. 21:51-80.
    75. Wooldridge, J.M. 2002. Econometric analysis of cross section and panel data MIT press. Cambridge, MA. 108:245-254.
    76. World Bank. 2018. Case Study: Modernizing the Rice Sector in Cambodia. Available at: https://documents1.worldbank.org/curated/zh/252361525415105840/pdf/125940-WP–P156637–PUBLICCambodia.pdf
    77. Yang, Y., Thong, S. 2022. Study on the Impact of Trade Facilitation of RCEP Member Countries on Cambodia’s Exports. World Scientific Research Journal. 8:327-33.
    78. Zhai, H. 2023. Evaluation of China-ASEAN trade status and potential: An empirical study based on a gravity model. Plos one. 18:e0290897.
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