Trade impacts of the New Silk Road in Africa: Insight from Neural Networks Analysis
DOI:
https://doi.org/10.18096/TMP.2021.03.02Keywords:
China’s Belt and Road Initiative, Bilateral Trade, Gravity Model, Artificial Neural Network methodology, African CountriesAbstract
The Belt and Road Initiative (BRI) is aimed to strengthen the preferential reciprocal trade between China and the Belt-Road nations. Quantitative evaluations of BRI to determine whether it can explicitly provide more insight into China’s bilateral trade among its partners are needed. Hence, improving prediction accuracy while using more superior algorithms for sustainable decision-making remains essential since decision-makers have been interested in predicting the future. Machine learning algorithms, such as supervised artificial neural networks (ANN), outperform several econometric procedures in predictions; therefore, they are potentially powerful techniques to evaluate BRI. This study uses detailed China’s bilateral export data from 1990 to 2017 to analyze and evaluate the impact of BRI on bilateral trade using gravity model estimations and ANN analysis techniques. The finding suggests that China’s bilateral export flow among the BRI countries results in a slight increase in inter-regional trade. The study provides a comparison view on the different estimation procedures of the gravity model – ordinary least squares (OLS) and Poisson pseudo-maximum likelihood (PPML) with the ANN. The ANN associated with fixed country effects reveals a more accurate estimation compared to a baseline model and with country-year fixed effects. Contrarily, the OLS estimator and PPML showed mixed results. Grounded on the study dataset, the ANN estimation of the gravity equation was superior over the other procedures to explain the variability of the dependent variable (export) regarding the prediction accuracy using root mean squared error (RMSE) and R-square.
References
ANDERSON, J. E. (1979). A Theoretical Foundation for the Gravity Equation. American Economic Review, 69(1), 106–116. https://doi.org/10.1126/science.151.3712.867-a
ANDERSON, J. E. & VAN WINCOOP, E. (2003). Gravity with Gravitas: A Solution to the Border Puzzle. American Economic Review, 93(1), 170–192. https://doi.org/10.1257/000282803321455214
ANDERSON, J. E. & VAN WINCOOP, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Economic Review, 93(1), 170–192.
ATHEY, S. (2018). The Impact of Machine Learning on Economics. The Economics of Artificial Intelligence: An Agenda.
AZIZI, N., REZAKAZEMI, M. & ZAREI, M. M. (2019). An intelligent approach to predict gas compressibility factor using neural network model. Neural Computing and Applications, 31(1), 55–64. https://doi.org/10.1007/s00521-017-2979-7
BAIER, S. L. & BERGSTRAND, J. H. (2009). Bonus vetus OLS: A simple method for approximating international trade-cost effects using the gravity equation. Journal of International Economics, 77(1), 77–85. https://doi.org/https://doi.org/10.1016/j.jinteco.2008.10.004
BAJARI, P., NEKIPELOV, D., RYAN, S. P. & YANG, M. (2015). Machine Learning Methods for Demand Estimation. American Economic Review, 105(5), 481–485. https://doi.org/10.1257/aer.p20151021
BALDWIN, R. & TAGLIONI, D. (2006). Gravity for dummies and dummies for gravity equations.
BAXTER, M. (2017). Robust Determinants of Bilateral Trade. Society for Economic Dynamics., 591.
BERGSTRAND, J. H. (1989). The Generalized Gravity Equation, Monopolistic Competition, and the Factor-Proportions Theory in International Trade. Source: The Review of Economics and Statistics, 71(1), 143–153. https://doi.org/10.2307/1928061
BOFFA, M. (2018). Trade Linkages Between the Belt and Road Economies (English). Policy Research Working Paper. https://doi.org/10.1596/1813-9450-8423
BROWNLEE, J. (2019). A Gentle Introduction to the Rectified Linear Unit (ReLU). Machine Learning Mastery.
CARRERE, C. & GRIGORIOU, C. (2011). Landlockedness, Infrastructure and Trade: New Estimates for Central Asian Countries. ffhalshs-00556941.
CHAN, F. T. S. & CHONG, A. Y. L. (2012). A SEM–neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 54(1), 621–630. https://doi.org/10.1016/j.dss.2012.08.009
CHENG, I. & WALL, H. J. (2005). Controlling for Heterogeneity in Gravity Models of Trade and Integration. Federal Reserve Bank of St. Louis Review, 87(1), 49–64. https://doi.org/10.3886/ICPSR01313
CHINA’S TRADE WITH BRI COUNTRIES SURGES TO $1.34 TRILLION IN 2019. (2020). The Economic Times, 15 January 2020.
CHONG, A. Y. L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247. https://doi.org/10.1016/j.eswa.2012.08.067
CHUNG, C. (C. P. . (2018). What are the strategic and economic implications for South Asia of China’s Maritime Silk Road initiative? The Pacific Review, 31(3), 315–332. https://doi.org/10.1080/09512748.2017.1375000
DEARDORFF, A. V. (1998). Determinants of Bilateral Trade: Does Gravity Work in a Neoclassical World? NBER Working Paper No. W5377, January, 7–32. https://doi.org/10.3386/w5377
DENNIS, A. (2006). The Impact of Regional Trade Agreements and Trade Facilitation in the Middle East North Africa Region. In Policy Research Working Paper.
DONAUBAUER, J., GLAS, A., MEYER, B. & NUNNENKAMP, P. (2018). Disentangling the impact of infrastructure on trade using a new index of infrastructure. Review of World Economics, 154(4), 745–784.
DUMOR, K. & YAO, L. (2019). Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis. Sustainability, 11(5), 1449. https://doi.org/10.3390/su11051449
EATON, J. & KORTUM, S. (2002). Technology, Geography, and Trade. Econometrica, 70(5), 1741–1779.
ELIF, N. (2014). Estimating And Forecasting Trade Flows By Panel Data Analysis And Neural Networks. Journal of the Faculty of Economics, 64(1), 85–111.
FEENSTRA, R. C. (2015). Advanced international trade: theory and evidence. Princeton university press.
FEENSTRA, R. C., MARKUSEN, J. R. & ROSE, A. K. (2001). Using the gravity equation to differentiate among alternative theories of trade. Canadian Journal of Economics/Revue Canadienne D`Economique, 34(2), 430–447.
FLINT, C. & ZHU, C. (2019). The geopolitics of connectivity, cooperation, and hegemonic competition: The Belt and Road Initiative. Geoforum, 99. https://doi.org/10.1016/j.geoforum.2018.12.008
GARSON, G. . (1991). Interpreting neural network connection weights. Artif. Intell. Expert, 6, 46–51.
GBONGLI, K. (2017). A two-staged SEM-AHP technique for understanding and prioritizing mobile financial services perspectives adoption. European Journal of Business and Management, 9(30), 107–120.
GBONGLI, K., XU, Y. & AMEDJONEKOU, K. M. (2019). Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach. Sustainability, 11(13), 3639. https://doi.org/10.3390/su11133639
GBONGLI, K., XU, Y., AMEDJONEKOU, K. M. & KOVÁCS, L. (2020). Evaluation and Classification of Mobile Financial Services Sustainability Using Structural Equation Modeling and Multiple Criteria Decision-Making Methods. Sustainability, 12(4), 1288. https://doi.org/10.3390/su12041288
GOPINATH, M., BATARSEH, F. A., BECKMAN, J., KULKARNI, A. & JEONG, S. (2021). International agricultural trade forecasting using machine learning. Data & Policy, 3.
GRADOJEVIC, N. & YANG, J. (2000). The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables. Bank of Canada.
HERRERO, A. G. & XU, J. (2017). China’s Belt and Road Initiative: Can Europe Expect Trade Gains? China & World Economy, 25(6), 84–99.
HOLSLAG, J. (2017). How China’s New Silk Road Threatens European Trade. The International Spectator, 52(1), 46–60.https://doi.org/10.1080/03932729.2017.1261517
HUANG, Y. (2016). Understanding China’s Belt & Road Initiative: Motivation, framework and assessment. China Economic Review. https://doi.org/10.1016/j.chieco.2016.07.007
HUSSAIN, Z., HANIF, N., SHAHEEN, W. A. & NADEEM, M. (2019). Empirical analysis of multiple infrastructural covariates: an application of gravity model on Asian economies. Asian Economic and Financial Review, 9(3), 299.
IWANOW, T. & KIRKPATRICK, C. (2009). Trade facilitation and manufactured exports: Is Africa different? World Development, 37(6), 1039–1050.
KHAN, M. K., SANDANO, I. A., PRATT, C. B. & FARID, T. (2018). China’s Belt and Road Initiative: A global model for an evolving approach to sustainable regional development. Sustainability (Switzerland), 10(11). https://doi.org/10.3390/su10114234
KHASHEI, M., RAFIEI, F. M. & BIJARI, M. (2013). Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets. International Journal of Computational Intelligence Systems, 6(5), 954–968. https://doi.org/10.1080/18756891.2013.809937
KIM, K. & MARIANO, P. (2020). Trade Impact of Reducing Time and Costs at Borders in the Central Asia Regional Economic Cooperation Region.
LEE, H. & ITAKURA, K. (2018). The welfare and sectoral adjustment effects of mega-regional trade agreements on ASEAN countries. Journal of Asian Economics, 55, 20–32. https://doi.org/10.1016/j.asieco.2017.09.001
LEITÃO, N. C. (2010). The gravity model and United States’ trade. European Journal of Economics, Finance and Administrative Sciences, 21, 92–100. http://www.scopus.com/inward/record.url?eid=2-s2.0-77955031213&partnerID=40&md5=d5f6b4e2e21db6849172cb4624970d9a
LEUNG, M. T., CHEN, A.-S. & DAOUK, H. (2000). Forecasting exchange rates using general regression neural networks. Computers & Operations Research, 27(11–12), 1093–1110. https://doi.org/10.1016/S0305-0548(99)00144-6
LI, B. & DUAN, Y. (2014). Trade facilitation assessment and its impact on China’s service trade exports: Based on transnational panel data empirical research (in Chinese). Int. Bus, 1, 5–13.
LIMAO, N. (2001). Infrastructure, Geographical Disadvantage, Transport Costs, and Trade. The World Bank Economic Review. https://doi.org/10.1093/wber/15.3.451
LISI, F. & SCHIAVO, R. A. (1999). A comparison between neural networks and chaotic models for exchange rate prediction. Computational Statistics & Data Analysis, 30(1), 87–102. https://doi.org/10.1016/S0167-9473(98)00067-X
METULINI, R., PATUELLI, R. & GRIFFITH, D. A. (2018). A spatial-filtering zero-inflated approach to the estimation of the gravity model of trade. Econometrics, 6(1), 9.
MOÏSÉ, E. & SORESCU, S. (2013). Trade Facilitation Indicators. In OECD Trade Policy Papers no.144. https://doi.org/10.1787/5k4bw6kg6ws2-en
NUMMELIN, T., HÄNNINEN, R. & OTHERS. (2016). Model for international trade of sawnwood using machine learning models. Natural Resources and Bioeconomy Studies, 74.
PIERMARTINI, R. & YOTOV, Y. (2016). Estimating Trade Policy Effects with Structural Gravity. 2016–10.
POUREBRAHIM, N., SULTANA, S., THILL, J. C. & MOHANTY, S. (2018). Enhancing trip distribution prediction with Twitter data: Comparison of neural network and gravity models. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2018. https://doi.org/10.1145/3281548.3281555
PÖYHÖNEN, P. (1963). A Tentative Model for the Volume of Trade between Countries. Weltwirtschaftliches Archiv, 90, 93–100.
RAVEN, J. (2001). Trade and Transport Facilitation : A Toolkit for Audit, Analysis, and Remedial Action.
RIPLEY, B. D. (1996). Pattern Recognition and Neural Networks. In Pattern Recognition and Neural Networks. Cambridge University Press. https://doi.org/10.1017/CBO9780511812651
SALETTE, G. & TINBERGEN, J. (1965). Shaping the World Economy. Suggestions for an International Economic Policy. Revue Économique. https://doi.org/10.2307/3498790
SCOTT, J. E. & WALCZAK, S. (2009). Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Information and Management, 46(4), 221–232. https://doi.org/10.1016/j.im.2008.10.003
SHEELA, K. G. & DEEPA, S. N. (2013). Review on Methods to Fix Number of Hidden Neurons in Neural Networks. Mathematical Problems in Engineering, 2013, 1–11. https://doi.org/10.1155/2013/425740
SILVA, J. M. C. S. & TENREYRO, S. (2006). The Log of Gravity. Review of Economics and Statistics, 88(4), 641–658. https://doi.org/10.1162/rest.88.4.641
SILVA, J. M. C. S. & TENREYRO, S. (2011). Further simulation evidence on the performance of the Poisson pseudo-maximum likelihood estimator. Economics Letters, 112(2), 220–222.
SONIA CIRCLAEYS, C. K. & KUMAZAWA, D. (2017). Bilateral Trade Flow Prediction. Stanford Final Reports.
TINBERGEN, J. (1962). Shaping the world economy; suggestions for an international economic policy.
TKACZ, G., HU, S. & OTHERS. (1999). Forecasting GDP growth using artificial neural networks. Bank of Canada Ottawa.
VARIAN, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3
WILSON, J. S., MANN, C. L. & OTSUKI, T. (2003). Trade Facilitation and Economic Development: A New Approach to Quantifying the Impact. The World Bank Economic Review, 17(3), 367–389. https://doi.org/10.1093/wber/lhg027
WOHL, I. & KENNEDY, J. (2018). Neural Network Analysis of International Trade. Office of Industries. U.S. International Trade Commission (USITC).
WU, B. (1995). Model-free forecasting for nonlinear time-series (with application to exchange rates). Computational Statistics & Data Analysis, 19(4), 433–459.
YOTOV, Y. V. (2012). A simple solution to the distance puzzle in international trade. Economics Letters, 117(3), 794–798. https://doi.org/https://doi.org/10.1016/j.econlet.2012.08.032
YOTOV, Y. V, Piermartini, R., Monteiro, J.-A. & Larch, M. (2016). An advanced guide to trade policy analysis: The structural gravity model.
ZAKI, C. (2014). An empirical assessment of the trade facilitation initiative: econometric evidence and global economic effects. World Trade Review, 13(1), 103–130. https://doi.org/10.1017/S1474745613000256
ZHANG, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
ZHANG, Y. B. & LIU, J. (2016). Trade facilitation measurement of the “Silk Road Economic Belt” and China’s trade potential (in Chinese). Finance. Sci., 5, 112–122.
ZHU, J. B. & LU, J. (2015). Research and application of trade facilitation evaluation indicator system (in Chinese). J. Hunan University, 6, 70–75.
Downloads
Published
How to Cite
Issue
Section
