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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agriculture Vol.33 No.1 pp.29-39
DOI : https://doi.org/10.12719/KSIA.2021.33.1.29

The Effect of International Technology Cooperation on Bilateral Trade Flows: A Case of The Korea-Africa Food and Agriculture
Cooperation Initiative (KAFACI)

Timothy Mtumbuka*,***, Jeongran Lee*†, Byungmo Lee*, Sinsuk Kang*, Hyeong Sik Eum*, Dayoung Park*, Jeong Jun Kim**
*Korea-Africa Food and Agriculture Cooperation Initiative (KAFACI), Rural Development Administration (RDA), 300 Nongsaengmyeong-ro, Deokjin-gu, Jeonju, Republic of Korea
**Department of Agricultural Environment, National Institute of Agricultural Sciences, RDA, 166 Nongsaengmyeong-ro, Iseo-myeon, Wanju-gun, Republic of Korea
***Ministry of Agriculture, Capital Hill, Box 30134, Lilongwe 3, Malawi
† Corresponding author (Phone) +82-63-238-1126 (E-mail) kongsarang@korea.kr
November 13, 2020 December 8, 2020 December 11, 2020

Abstract


This paper used the gravity model of international trade to analyze the effects of international technology cooperation on bilateral trade flows between member and non-member countries of the Korea-Africa Food and Agriculture Cooperation Initiative (KAFACI). We used panel data of bilateral trade between 45 African countries from the United Nations commodity trade statistics database for the period between 2000 and 2018. To control for endogeneity, selection bias, and correlation within the panels, this study used the Heckman random-effect regression with sample selection technique. Although insignificant, the empirical results indicated a positive effect of international technology cooperation on bilateral trade flows between KAFACI member and non-member countries. Notwithstanding our findings, KAFACI might have significantly increased the bilateral trade flows between its members and main trading partners outside Africa, which were not explored in this paper. Besides, the level and rate of technology adoption by KAFACI member countries were also crucial for influencing the supply side of the economy to unlock trade, but they were not assessed in this study.


Gravity model , Bilateral trade , Heckman random-effect regression , Sample selection , Endogeneity , The Korea-Africa Food and Agriculture Cooperation Initiative (KAFACI) , Correlation

양자무역에서 국제기술협력의 효과: 한-아프리카 농식품기술협력협의체 사례 중심

타모씨 음투부가*,***, 이 정란*†, 이 병모*, 강 신숙*, 엄 형식*, 박 다영*, 김 정준**
*농촌진흥청 한-아프리카농식품기술협력 협의체
**국립농업과학원 환경개선미생물연구단
***말라위 농림부

초록

    Rural Development Administration(RDA)

    INTRODUCTION

    The Rural Development Administration (RDA) of the Republic of Korea is a government organization that facilitates agricultural technology development and dissemination to various stakeholders. The RDA also works in partnership with international bodies, including interna-tional research institutions and government agencies from both developed and developing countries in research projects, expert consultations and capacity building. Since the 1980s, the main focus of RDA with regards to international cooperation has been on transferring agricultural technology to developing countries and creating partnerships for the development of new technologies. To facilitate the development and transfer of technology to developing countries, the RDA introduced multilateral cooperation initiatives covering Asia, Africa and Latin America. The initiatives are named based on the region of collaboration as follows: (i) Asian Food and Agriculture Cooperation Initiative (AFACI); (ii) Korea-Africa Food and Agriculture Cooperation Initiative (KAFACI); and (iii) Korea- Latin America Food and Agriculture Cooperation Initiative (KoLFACI). In addition to multilateral cooperation initiatives, the RDA also introduced the Korea Program for International Cooperation in Agricultural technology (KOPIA) as a bilateral cooperation initiative for the development and sharing of agricultural technologies (RDA, 2015). However, this paper will only concentrate on the technology transfer and development collaboration between the RDA and African countries under KAFACI. As one of the cooperation initiatives under RDA, the KAFACI was established to promote intergovernmental and multilateral cooperation in a quest to improve food production, achieve sustainable agriculture and enhance extension services of African countries. To achieve its objectives, KAFACI promotes knowledge and information sharing on agricultural technologies among member countries. The initiative was officially inaugurated on July 6, 2010, in Seoul, the Republic of Korea with the participation of 16 member countries, namely Angola, Cameroon, Cote d’Ivoire, Democratic Republic of Congo (DRC), Ethiopia, Gabon, Ghana, Kenya, Malawi, Morocco, Nigeria, Senegal, Sudan, Tunisia, Uganda and Zimbabwe. Three more countries joined the initiative as follows: Comoros (2012), Rwanda (2015) and Zambia (2016). Currently, there are 20 member countries of KAFACI including the Republic of Korea. KAFACI envisions to ensure a vibrant and dynamic network among its member countries and enhance their partnership and leadership in the international community. Ultimately, the initiative aims to promote sustainable agricultural development in African member countries to improve people’s livelihoods, reduce food insecurity and contribute to economic development through technological cooperation in the agriculture sector (Cho, 2011;KAFACI, 2020).

    Apart from cooperation among member countries, KAFACI also works in partnership with other organizations to ensure agricultural development in member countries. The main partner organizations include Alliance for a Green Revolution in Africa (AGRA), International Livestock Research Institute (IRLI), AfricaRice, Center on Conflict and Development (ConDev), International Trade Centre (ITC) and African Forum for Agricultural Advisory Services (AFAAS).

    Through the famous green revolution and Saemaul movement program, the Republic of Korea has shared appropriate agricultural knowledge and technology for adoption in KAFACI member countries. The technologies are developed and shared in several forms including group training, on-the-job training, post-graduate programs and visiting fellowship programs (Cho, 2011;KAFACI, 2020). The RDA also dispatches scientists from its institutes to member countries for technical backstopping and provision of support services on technology development and application. The KAFACI technologies enable the member countries to develop new products and processes which are catalysts of international trade and country development. Based on both the technology gap theory (Posner, 1961) and product life cycle theory (Vernon, 1966) of international trade, technology developing countries or first adopters are expected to increase their volume of trade due to comparative advantage in the value chains with the high absorption capacity of technologies. The advantage stems from the technology gap that exists before the other countries copy the technologies and create substitutes for their domestic markets.

    Trade theories underscore the importance of technological advancement in promoting the competitiveness of a country in the international market. Unlike the Heckscher- Ohlin theory and other neoclassical economists who believe that differences in natural endowments are the source of comparative advantage that drives trade, Posner (1961) established that technological advancement is the main source of comparative advantage that drives trade. In his technology gap theory, Posner assumed the case where the trading partners are similarly endowed with factors of production and have the same demand conditions as well as the same price ratios in autarky. He then established that differences in technology level become the main source of the comparative advantage enjoyed by the innovative countries. Building on the technology gap theory, Vernon (1966) came up with the international product life cycle which recognized the degree of technology maturity in determining the international trade flows. However, empirical studies have achieved varied results on the effects of technological advancement on the country’s exports depending on other factors such as the Gross Domestic Product (GDP) and its distance from the trading partners (Loungani et al., 2002). By applying the neoclassical pure trade theory, Ronald (1970) allowed the production function to vary between countries and he observed that the differences in technology level determine the country’s comparative advantage and factor-prices no longer reach an equilibrium state. His study established a direct relationship between the level of technology for an industry and its output. The other significant result of the study was that the on-the-job learning process is more effective in improving production capacities because people learn local techniques. On the other hand, the classical trade theory emphasizes that it is a combination of technological advancement and differences in real wages that drive comparative advantages for a particular country rather than technology alone (Keller, 2004).

    Several other studies have been conducted on bilateral trade using the gravity model of international trade and the results support the theoretical premise of technology in unlocking bilateral trade flows between trading partners. A study by Marquez-Ramos et al. (2010) found a positive correlation between technological innovation and export volumes. However, their findings did not exhibit a linear effect but it was observed that for a positive effect to occur, certain levels of technological advancement have to be achieved. More importantly, their conclusion highlighted the need to promote technology adoption for increased exports rather than sorely focusing on the acquisition and dissemination of technologies. Krugman (1982) analyzed the trends of trade as a result of varying technological gaps and intensities using the Ricardian model. The results showed that technological differences are critical for a country to have a comparative advantage in the production of a commodity over others. His findings confirmed the trade and production theories that assume the differences in technology levels among countries as well as their research and development capacities are crucial determinants of trade volumes and income level of a country.

    Another study by Freund and Weilnhold (2002) established that Information Communication Technology (ICT) allows firms to have access to a larger market and wider customer base thereby enjoy economies of scale and increase their profits. Also, these firms can compete with incumbent firms in the world market and develop survival techniques. However, an empirical study on trade gap theory by Karirajan (2007), found that other social, political and institutional constraints hinder the flow of bilateral trade even if the country is technologically advanced. His analysis established that regional economic cooperation is crucial in minimizing the existing trade gaps among other remedies. The most recent study by Xing (2018) in which he examined the role of two technological innovations of internet and e-commerce on promoting bilateral trade flows among 30 developed and 21 developing countries also conform with the international trade theory (technology gap theory). He found that access to advanced ICT and the adoption of e-commerce platforms unlock the flow of bilateral trade between countries. The study stressed on the important role that the level of ICT infrastructure and ecommerce adoption play in promoting export growth by incentivizing firms, producers and exporters. The effect was more pronounced in developing and least developed countries due to an increase in the number of their value chains at the global e-commerce platform. He also found out that not only access to technological innovation is enough to unlock trade flows but also the development of skills, opportunities and capacities as a holistic approach to boosting bilateral trade.

    Since 2010, KAFACI has been facilitating the transfer of agricultural technology to its member countries and creating partnership for new appropriate technology development. In line with the technology gap and product life cycle theories of international trade, adoption of these technologies by member countries will help them leapfrog and quickly improve their comparative advantage over non-member countries in production of specific products that intensively use KAFACI technologies in production process. Consequently, the level of bilateral trade flows between KAFACI members and non-member countries is expected to increase. Therefore, using bilateral trade data for 45 African countries, this paper aims to investigate if the international technology cooperation initiatives of the Republic of Korea has an effect on bilateral trade flows between KAFACI members and non-member countries.

    METHODOLOGY

    The study used bilateral trade data from the United Nations Commodity Trade Statistics Database (UN Comtrade) for a period of 19 years from 2000 to 2018. In order to obtain real bilateral trade flows, data are deflated using the United States of America GDP deflator with 2015 as the base year. The total dataset contains bilateral trade flows between 45 African countries with a total of 18,810 observations. Out of the total observations, 5,502 (29.3%) have missing or zero values of bilateral trade flows. On the other hand, our period of interest from 2010 to 2018 has a total of 8,910 observations of which 2420 (27.2%) have data gaps on the dependent variable. The literature on the gravity model acknowledges the dilemma to ascertain whether zero trade flows in the dataset represent no flows of bilateral trade at all or it’s just a rounded figure of very small trade flows. As a result, alternatives estimation approaches have been proposed or used to estimate the gravity model with zero bilateral trade flows. The approaches include; (a) dropping of the data observations with zero trade flows (truncation); (b) adding a small constant to all observations of bilateral trade before performing log transformation; and (c) estimating the models in levels using Pseudo Poisson Maximum Likelihood (PPML) estimator (Gómez-Herrera, 2013;Helpman et al., 2008;Martínez-Zarzoso et al., 2014; Hag et al., 2012; Santos Silva & Tenreyro, 2006;Westerlund & Wilhelmsson, 2011). Based on the comparison of these different estimation techniques by Gómez-Herrera (2013), the use of PPML and Heckman selection estimators gives more reliable statistics when zero trade flows are systematic. The former also provides unbiased estimates when the data is heteroskedastic. This study will use the Heckman randomeffect regression with sample selection which controls for endogeneity apart from sample selection bias (StataCorp, 2019). The Heckman random-effect regression with sample selection model is used to estimate panel data with endogenous sample selection problem. The model uses maximum likelihood (ML) to estimate the outcome and selection equations with joint normally distributed error terms. The estimator controls for endogeneity, sample selection bias and correlation within panels through the use of panel-level random effects (StataCorp, 2019;Bartus & Roodman, 2014;Roodman, 2009;Heckman, 2012).

    Model Specification

    The gravity model has been adopted by vast studies on international trade such as Rose (2004), Helpman et al. (2008), Gómez-Herrera (2013), and Andrei (2017). By the same token, this study will apply the gravity model to determine the effect of international technology cooperation on the bilateral trade flows between KAFACI members and non-member countries in Africa. The model presumes that trade is positively correlated to the Gross Domestic Product (GDP) but negatively correlated to the distance between trading partners. The results of the gravity model on the two parameters have been consistent in many studies and they explain a considerable variation in international trade flows. This paper will build on the following basic gravity model introduced by Rose (2004):

    b t i j t = β 0 ( g d p i t × g d p j t d i s t i j )
    (1)

    Where btijt is the log of real bilateral trade between countries i and j at time t, gdpit is the real GDP of country i at time t, gdpjt is the real GDP of country j at time t and distij is the distance in kilometers between countries i and j.

    The specified augmented gravity model captures the effects of GDP per capita, land size, sharing a land border, use of the same language, regional economic/trade blocs and being colonized by the same country. The model consists of two equations, namely the outcome and sample selection as specified by (2) and (4), respectively.

    The outcome equation is modeled as follows:

    l n ( b t i j t ) = β X i j t + v i j + μ i j t
    (2)

    Where Xijt represents the covariates modeling the flows of bilateral trade, vij represents the random effects at panel level and μijt is the observation-level error. The full outcome model is specified as follows:

    l n ( b t i j ) t = β 0 + β 1 l n ( d i s t i j ) + β 2 l n ( p g d p i j ) t + β 3 l n ( p g d p p c i j ) t + β 4 l n ( p l a i j ) + β 5 b o t h i n i j t + β 6 o n e i n i j t + β 7 l a n d l i j + β 8 l a n d b i j + β 9 c o m l a n g i j + β 10 R E B i j + β 11 c c o l o n i j + v i j + μ i j t
    (3)

    The covariates include: distij – log of distance between countries i and j; pgdpijt – product of real GDPs for countries i and j; gdppcijt – log product of real GDP per capita for countries i and j; plaij - log product of land areas for countries i and j; bothinijt – a dummy variable and is equal to 1 if both countries i and j are KAFACI members at period t and zero otherwise; oneinijt – a dummy variable and is equal to 1 if either country i or j is KAFACI member at period t and zero otherwise; landlij – denotes the number of landlocked countries in each pair of i and j (0/1/2); landbij – a dummy variable and is equal to 1 if both countries i and j share a land border and zero otherwise; comlangij – a dummy variable and is equal to 1 if countries i and j share a common official language and zero otherwise; REBijt – a dummy variable and is equal 1 if both countries i and j belongs to the same regional economic or trade bloc and zero otherwise; ccolonij – a dummy variable and is equal to 1 if both countries i and j were colonized by the same country and zero otherwise; and sreligionij – a dummy variable and equal to 1 if both countries i and j share the same religion and zero otherwise.

    Equation (4) presents the selection equation where dy is the latent variable. It is only observed if there is some level of bilateral trade flows between countries i and j or not, thus dy =1 if btijt >0 and dy=0 otherwise. The variable Zijt represents the covariates that affect dy which include Xij variables and an additional dummy variable capturing the same religion (sreligionij) between countries i and j. It is equal to 1 if both countries i and j share the same religion and zero otherwise.

    d y i j t = 1 ( α   Z ijt + s ij + ε ijt > 0 )
    (4)

    The random-effects vij and sij have a bivariate normal distribution with a zero mean and variance of σ2. Likewise, the observation-level selection errors μijt and εijt also have a bivariate normal distribution with zero mean and variance of σ2. The full specification of the selection equation is as follows:

      d y i j t = α 0 + α 1 l n ( d i s t i j ) + α 2 l n ( p g d p i j ) t + α 3 l n ( p g d p p c i j ) t + α 4 l n ( p l a i j ) + α 5 b o t h i n i j t + α 6 o n e i n i j t + α 7 l a n d l i j + α 8 l a n d b i j + α 9 c o m l a n g i j + α 10 R E B i j + α 11 c c o l o n i j + α 12 s r e l i g i o n i j + s i j + ε i j t
    (5)

    Using the variance adaptive Gauss-Hermite quadrature approximation with the abscissa and weight pairs for each panel equal to akij and wkij, respectively; where k =1,…….,q., then we can generate the log likelihood for all panels as follows:

    l n L = ∑ i j = 1 N ( l n ∑ k 2 = 1 q ⋯ ∑ k 2 = 1 q [ { ∏ t = 1 N i j f ( b t i j t , d y i j t | ( v i j , s i j ) ′ = L ∝ k ) } { ∑ s = 1 2 W k s } ] )
    (6)

    The conditional mean for ln(btijt) is given by the following equation:

    E ( l n b t i j t | X i j t ) = β X i j t
    (7)

    RESULTS AND DISCUSSIONS

    Prior to the estimation of the models, we conducted some preliminary tests. Summary statistics for the correlation between covariates showed no significant association except for the correlations between GDP and land area (- 0.64), distance and sharing land border (-0.55), and between distance and regional trade/economic blocs (-0.54), which were moderately correlated. An analysis of the residuals and fitted values of the model confirms the presence of heteroskedasticity because the variance of the residuals is not constant as presented in Figure 1 below. Therefore, linear estimation methods are not appropriate for analyzing the data.

    Table 1 presents the results of Heckman random-effect regression with sample selection, where the logarithm of real bilateral trade (lnbt) is the dependent variable for the outcome equation and the latent variable (dy) is the dependent variable for the selection equation. The results show that there is no significant correlation between observation- level errors (e. dy, e. lnbt) for the outcome and the selection models in our uncontrolled models 1 and 4 but there is a significant negative correlation in models (3) and (6) where random effects are constrained to be independent. On the other hand, the correlation coefficients between the random effects of the outcome and the selection models {Corr. (dy[pairid], lnbt[pairid]))} are positive and significantly different from zero in both models (1) and (4) with the coefficients of 0.746 and 0.671, respectively. According to StataCorp (2019), if at least one of the correlation coefficients is significant then it can be concluded that sample selection is endogenous. Therefore, our results confirm the presence of endogeneity in sample selection i.e. unobserved individual-level factors that increase the probability of reporting positive bilateral trade flows tend to increase the level of bilateral trade between trading partners.

    The estimation technique is consistent with other estimation techniques that have used the gravity equation to analyze trade volumes in terms of the direction of the effect for the main covariate (Rose, 2004;Gómez-Herrera, 2013;Westerlund & Wilhelmsson, 2011;Haq et al., 2012;Andrei, 2017). Sharing of the common border, language and colonizer as well as belonging to the same economic or trade bloc significantly increase the level of bilateral trade. Likewise, the level of real GDPs for the trading partners also significantly increases the level of bilateral trade. Other variables such as the real GDP per capita, distance between trading partners and landlocked partners are also significant in explaining the variability of bilateral trade but the effect is negative. Models (1), (2) and (3) use the full sample covering the period from 2000 to 2018. The only difference is that model (2) omits the random effects from the selection model while model (3) constrains the random effects to be independent. The significance of the coefficient estimates is consistent across the three models. However, model (2) with no random effects from the selection model has none of the correlation coefficients significantly different from zero. Models (4), (5) and (6) present the results for the period of interest from 2010 when the KAFACI was established to 2018. Most coefficients of the covariates are consistent with the full sample models (1), (2) and (3) in terms of significance but the magnitude of the effect is different.

    Though not significant, our variables of interest, onein exhibit a positive effect of international technology cooperation on the level of bilateral trade flows. Therefore, the international technology cooperation positively affected the flow of bilateral trade between KAFACI members and non-member countries [model (4)]. The insignificance of the effect may be attributed to the fact that most of the African countries trade more with countries from other continents than among themselves through non-reciprocal trade agreements such as the General Agreement on Trade and Tariff (GATT), African Growth Opportunity Act (AGOA) and the Generalized System of Preferences (GSP) (Garth & Van Biessebroeck, 2010;Rose, 2004).

    All the coefficients of the models in table 1 are consistent with available literature except for the level of real GDP per capita which shows an inverse relationship with the flow of bilateral trade. Contrary to other studies (Rose, 2004;Kalirajan, 2007;Mnasri & Salem, 2019; and Santos and Tenreyro, 2006), this study found that the level of real GDP per capita reduces the levels of bilateral trade between partner countries. This can be attributed to the non-achievement of demographic dividends by the sampled countries. The sample of study includes the Sub- Saharan countries and other developing countries in Africa whose levels of population growth and unemployment are high. According to Dao (2012), Headey and Hodge (2009) and Sachs (2008), rapid population growth has a significant negative impact on the economic growth of these developing countries. This reduces the growth rate of GDP per capita if the population growth rate is higher than the GDP growth rate hence negatively affects the level of bilateral trade between partner countries in our model.

    Regional blocs and member countries

    Table 2 below presents the list of member countries for each trade/economic bloc in Africa, including KAFACI and the respective dates of establishment.

    Table 3 partials out the effect of individual economic or trade blocs on the flow of bilateral trade between member countries with varying periods. Still, international technology cooperation has a positive effect on bilateral trade flows between KAFACI members and non-member countries but not between member countries themselves. During the first period (2000-2009), EAC, SADC, ECOWAS, COMESA and CEN-SAD significantly increased the volume of bilateral trade among respective member countries. On the contrary, IGAD, AMU and ECCAS did not have any effect on bilateral trade flows among their respective member countries. In the subsequent period between 2010 and 2018, again COMESA, EAC, SADC, ECOWAS and CEN-SAD had a significant effect on bilateral trade flows between member countries. The IGAD, AMU, ECCAS and KAFACI were not significant in stimulating bilateral trade among their member countries.

    The last model considers the whole period of study from 2000 to 2018 and the results are similar to those of the preceding model (2010-2018). The IGAD, AMU, ECCAS and KAFACI had no effect on the level of bilateral trade among their respective member countries. The findings in Table 3 are consistent with the findings of Turkson (2012) who used time-series data from 1960 to 2006 to analyze the effect of regional trade agreements within Sub-Saharan Africa on bilateral trade flows. He found that SADC and ECOWAS had a significant positive effect on bilateral trade among member countries while EAC and IGAD produced inconclusive results.

    Alternative estimation techniques for the gravity model

    In order to analyze the sensitivity of the covariates across different estimation techniques, our augmented gravity equation was also estimated using ordinary least squares (OLS), Heckit model and Pseudo Poisson maximum likelihood (PPML) techniques. Table 4 provides the estimation results which are consistent with our results from the Heckman random-effect regression with sample selection model in terms of significance of the parameters though the magnitude is different. Results from OLS regressions in both periods give higher estimates compared to the Heckman and PPML estimators. In presence of heteroscedasticity, the OLS estimates are inconsistent because truncation of zero values leads to loss of information and the addition of a constant to the values of bilateral trade alters the conditional distribution of the bilateral trade (Gómez-Herrera, 2013). The results from both Heckman sample selection equations (Table 4) are consistent with those of our Heckman random-effect regression with sample selection (Table 1) in terms of significance and direction of effects on bilateral trade flows. Likewise, the significance and direction of most parameters for the PPML are consistent with results from other models in Table 4 and Table 1. However, the magnitude and associated standard errors of the PPML are significantly different because the dependent variable is introduced in levels rather than taking a log transformation (Gómez-Herrera, 2013;Santos & Tenreyro, 2006).

    All the model results have robust standard errors clustered by pairid presented in brackets. The regressand is the logarithm of the product of bilateral trade between countries i and j except for the PPML regression where the dependent variable is categorized into levels. All the models include time fixed effects.

    CONCLUSION

    The study applied the gravity model to analyze the nexus of international technology cooperation and bilateral trade flows between KAFACI members and non-member countries in Africa. Trade data for 45 African countries were used to analyze the effect over the period of KAFACI existence. To control for endogeneity, sample selection bias and variation within panels, this paper adopted the Heckman random-effect regression with sample selection technique to estimate the augmented gravity equation. The results showed positive effect of international technology cooperation on bilateral trade flows between KAFACI members and non-member countries, even though the effect was not significant. This suggests that member countries might have a comparative advantage over nonmember countries in production of the products that intensively use the developed or acquired KAFACI technologies in their production process. However, it is important to note that the average level of Africa’s intra-regional trade only ranges from 10-12% (Tafirenyika, 2014). That means African countries trade more with other continents than among themselves hence KAFACI might have increased the bilateral trade flows between member countries and their major trading partners outside Africa which has not been tackled in this paper. Lastly, the introduction or development of new technology acts as an increase in the factors of production and consequently changes the price of the produced commodity. However, the impact is only realized when there is good adoption of these technologies which influences the supply side of the economy. Therefore, it is also imperative to establish the rate of agricultural technology adoption by farmers in these member countries which can inform the planning for the second decade of KAFACI. Technology acquisition or development is not an end in itself but the rate and intensity of technology adoption are critical in achieving food security and economic development in Africa.

    적 요

    한-아프리카 농식품 기술협력 협의체 (KAFACI)는 아프리카 농업 공통현안을 연구과제를 통하여 해결하고자 2010년에 출 범하여 현재 한국 포함 20개국으로 구성되었다. 본 논문은 지 난 10년간 회원국에서 수행한 결과를 바탕으로 KAFACI 회원 국과 비회원국 간의 양자 무역 흐름에 대한 국제 기술 협력의 영향을 분석하였다. 분석은 UN 상품 무역 통계 데이터베이스 에서 2000년에서 2018년 동안 45 개 아프리카 국가 간의 양 자 무역 패널 데이터를 이용하였으며 국제 무역 중력 모델을 적용하였다. 또한, 내생성, 표본 선택 편의, 패널내의 상관관계 를 통제하는 표본 선택 기법을 적용한 핵크만 랜덤 효과 회귀 모형을 사용하였다. 분석결과 경험적 결과는 국제 기술 협력 이 KAFACI 회원국과 비회원 국 간의 양자 무역 흐름에 긍 정적인 영향을 미친다는 것을 보여준다. 이러한 연구 결과 이 외에도 KAFACI는 회원국과 아프리카 이외의 주요 무역 파트 너 간의 양자 무역 흐름을 크게 늘렸을 가능성이 있지만 여기 서는 다루지 않았다. 또한 KAFACI 회원국의 기술 채택 수준 과 비율은 무역을 잠금 해제하기 위해 경제의 공급 측면에 영 향을 미치는데 중요하지만 여기에서는 다루지 않았다.

    ACKNOWLEDGMENTS

    This study was supported by the grant of KAFACI Outcome Analyst Program, Rural Development Administration of Korea.

    Figure

    KSIA-33-1-29_F1.gif

    Heteroskedasticity in the data.

    Table

    Effects of international technology cooperation on bilateral trade flows

    Membership to economic, trade, and cooperation blocs

    Analysis of disaggregated regional blocs and varying periods

    Comparison of alternative estimation models

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