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Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

Author

Listed:
  • Bogang Jun

    (Massachusetts Institute of Technology
    Inha University)

  • Aamena Alshamsi

    (Massachusetts Institute of Technology
    Khalifa University)

  • Jian Gao

    (Massachusetts Institute of Technology
    University of Electronic Science and Technology of China)

  • César A. Hidalgo

    (Massachusetts Institute of Technology
    University of Toulouse
    University of Manchester
    Harvard University)

Abstract

During the last two decades, two important contributions have reshaped our understanding of international trade. First, countries trade more with those with whom they share history, language, and culture, suggesting that trade is limited by information frictions. Second, countries are more likely to start exporting products that are related to their current exports, suggesting that shared capabilities and knowledge diffusion constrain export diversification. Here, we join both of these streams of literature by developing three measures of bilateral relatedness and using them to ask whether the destinations to which a country will increase its exports of a product are predicted by these forms of relatedness. The first form is product relatedness, and asks whether a country already exports many similar products to a destination. The second is importer relatedness, and asks whether the country exports the same product to the neighbors of the target destination. The third is exporter relatedness, and asks whether a country’s neighbors are already exporting the same product to the destination. We use bilateral trade data from 2000 to 2015, and a variety of controls in multiple gravity specifications, to show that countries are more likely to increase their exports of a product to a destination when they have more product relatedness, importer relatedness, and exporter relatedness. Then, we use several sample splits to explore whether the effects of these forms of relatedness are stronger for products of higher complexity, technological sophistication, and differentiation. We find that, in the case of product relatedness, the effects are stronger for differentiated, complex, and technologically sophisticated products. Also, we find the effects of common language and shared colonial past to increase with differentiation, complexity, and technological sophistication, while the effects of shared borders decrease with these three variables. These results suggest that product relatedness and common language capture dimensions of knowledge relatedness that are more important for the exchange of more sophisticated and differentiated products. These findings extend the ideas of relatedness to bilateral trade and show that the evolution of bilateral trade networks are shaped by relatedness among products, exporters, and importers.

Suggested Citation

  • Bogang Jun & Aamena Alshamsi & Jian Gao & César A. Hidalgo, 2020. "Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade," Journal of Evolutionary Economics, Springer, vol. 30(2), pages 247-277, April.
  • Handle: RePEc:spr:joevec:v:30:y:2020:i:2:d:10.1007_s00191-019-00638-7
    DOI: 10.1007/s00191-019-00638-7
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    2. Wenshou Yan & Yan Cai & Xuan Guo, 2023. "How can trade partners be chosen when facing food scandals? China's milk scandal as a natural experiment," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(4), pages 603-635, October.
    3. Seung Hwan Kim & Jeong hwan Jeon & Anwar Aridi & Bogang Jun, 2022. "Factors that affect the technological transition of firms toward the industry 4.0 technologies," Papers 2209.02239, arXiv.org.
    4. Marco Due~nas & Federico Nutarelli & V'ictor Ortiz & Massimo Riccaboni & Francesco Serti, 2021. "Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms," Papers 2104.04570, arXiv.org, revised Nov 2024.
    5. Xiyan Mao & Peiyu Wang, 2023. "Import–export nexus and China's emerging trade in environmental goods," Growth and Change, Wiley Blackwell, vol. 54(1), pages 157-181, March.
    6. Jung-In Yeon & Sojung Hwang & Bogang Jun, 2022. "The spillover effect of neighboring port on regional industrial diversification and regional economic resilience," Papers 2204.00189, arXiv.org.
    7. C'esar A. Hidalgo, 2022. "Knowledge is non-fungible," Papers 2205.02167, arXiv.org.
    8. Seung Hwan Kim & Bogang Jun & Jeong-Dong Lee, 2023. "Technological relatedness: how do firms diversify their technology?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4901-4931, September.
    9. Hidalgo, César A., 2023. "The policy implications of economic complexity," Research Policy, Elsevier, vol. 52(9).
    10. Dueñas, Marco & Ortiz, Víctor & Riccaboni, Massimo & Serti, Francesco, 2021. "Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis," Working papers 79, Red Investigadores de Economía.
    11. Matte Hartog & Frank Neffke & J. Ernesto Lopez-Cordova, 2020. "Assessing Ukraine's Role in European Value Chains: A Gravity Equation-cum-Economic Complexity Analysis Approach," CID Working Papers 129a, Center for International Development at Harvard University.
    12. Cesar A. Hidalgo, 2022. "Knowledge is non-fungible," Papers in Evolutionary Economic Geography (PEEG) 2229, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Nov 2022.
    13. Kexuan Zhou & Sanjay Kumar & Futao Lu & Qiaoyun Fang & Linhui Yu, 2022. "Has the Belt and Road Initiative Improved the Quality of Food Imported by Chinese Enterprises?," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 30(2), pages 60-83, March.
    14. Campi, Mercedes & Dueñas, Marco, 2022. "Clusters and Resilience during the COVID–19 Crisis: Evidence from Colombian Exporting Firms," IDB Publications (Working Papers) 12527, Inter-American Development Bank.

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    More about this item

    Keywords

    International trade; Relatedness; Knowledge diffusion; Economic complexity;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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