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Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

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.

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Notes

  1. 1.

    Note: We dropped singleton observations, when we apply three-way error clustering (Correia 2015).

  2. 2.

    Available at https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp

  3. 3.

    Available at http://www.freit.org/TradeResources/TradeData.php

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Acknowledgments

We thank Mauricio (Pacha) Vargas and Alex Simoes for help with the data. We also thank Cristian-Jara Figueroa, Flávio Pinheiro, Tarik Roukny, Dogyoon Song, and Robert A Irwin for helpful comments. This project is supported by the MIT Skoltech Program, by the National Research Foundation of Korea (NRF) (No. 2019R1G1A1100322), and by the Cooperative Agreement between the Masdar Institute of Science and Technology (Masdar Institute), Abu Dhabi, UAE and the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA—Reference 02/MI/MIT/CP/11/07633/GEN/G/00. CAH also acknowledges the support of the ANITI Chair from the University of Toulouse. This paper was circulated as a working paper: “Relatedness, knowledge diffusion, and the evolution of bilateral trade.” arXiv preprint arXiv:1709.05392 (Sep 2017).

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Correspondence to Bogang Jun.

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Appendices

Appendix A: Building a product space for 2000–2015

To calculate the ωopd, we need first build a product space. We define the product space by looking at all proximity measures between products (Hidalgo et al. 2007) after aggregating all the data that covers from 2000 to 2015. To capture the significant trade flow, we calculate the revealed comparative advantage (RCA) following Balassa (1965):

$$ RCA_{o,i} = \left.{\frac{x_{o,i}}{{\sum}_{i}x_{o,i}}}\left/ \frac{{\sum}_{o}x_{o,i}}{{\sum}_{o,i}x_{o,i}}\right.\right. $$
(A1)

Based on the result of RCA, we measure the proximity between product by calculating ϕi, j between product i and j (Hidalgo et al. 2007).

$$ \phi_{i,j} = \min \left\{P(RCA_{i}|RCA_{j}, P(RCA_{j}|RCA_{i})) \right\} $$
(A2)

Using this significant trade flow over 2000–2015, we can create 1242 × 1242 matrix, which entities the proximity between products. Figure 4 shows the product space of world market in the period from 2000 to 2015.

Fig. 4
figure4

Product space over 2000 to 2015: a Network representation of product space, b Cumulative distribution of proximity values, c Density distribution of proximity values, and d the product space matrix sorted in increasing order of the is numerical code.

Appendix B: Regression results without three-way error clustering

Table 6 Bilateral trade volume after two years for periods 2000-2006 without three-way error clustering
Table 7 Bilateral trade volume after two years for periods 2000–2006 (pre-financial crisis), 2007–2012 (crisis period) and 2012–2015 (recovery period) without three-way error clustering
Table 8 Bilateral trade volume after two years for five technological categories without three-way error clustering

Appendix C: Fixed effects model

Table 9 Bilateral trade volume after two years with various fixed effects

Appendix D: Rauch classification

Table 10 Bilateral trade volume after two years for homogeneous goods, reference priced, and differentiated products

Appendix E: Summary statistics and correlation table

Table 11 Summary statistics: 2000–2006
Table 12 Summary statistics: 2007–2012
Table 13 Summary statistics: 2012–2015
Table 14 Correlation Matrix: 2000–2006
Table 15 Correlation matrix: 2007–2012
Table 16 Correlation matrix: 2012–2015

Appendix F: Relationship between bilateral trade volume after two years and the three learning channels by exporters’ comparative advantage

We also test the effects of the exporters’ levels of competitiveness on the diffusion of the information needed to trade by dividing exporters of each product into small, medium, and large exporters (Table 17). We do this by calculating the revealed comparative advantage (RCA) of each exporter in each product. RCA is the ratio between the exports of a country in a product, and the exports that are expected based on a country’s total export market and the size of the global market for that product. We classify as small exporters all countries with an RCA below 0.2 in a product (countries that export less than 20% of what they are expected to export by chance). We classify as medium exporters all countries with an RCA between 0.2 and 1. We classify as the large exporters of a product all countries that have revealed comparative advantage in it (RCA > 1). To rule out temporary changes of exporters’ comparative advantage, we restrict the condition for being a small, medium, and large exporter: small, medium, and large exporters need to keep RCA below 0.2, between 0.2 and 1, and above 1 for three years before the beginning of the period.

Table 17 divide country-product pairs into small, medium, and large exporters. The results are consistent with those presented in Table 2, but also reveal two important distinctions. First, the effects of product and geographic relatedness, especially exporter relatedness, are stronger for small exporters, suggesting that knowledge and information frictions impose larger constraints for countries that are not exporting a product on a large scale. Second, the overall explanatory power of the model is considerably larger for large exporters (R2 ≈ 53% vs R2 ≈ 46% for medium exporters and R2 ≈ 28% for small exporters; these are large differences, even considering that the sample sizes are not the same). This suggests that smaller exporters face more uncertainty (less predictable because of lower R-squared), and, hence, other factors are needed to predict their bilateral trade volume.

Table 17 Bilateral trade volume after two years by different levels of exporters’ comparative advantage

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Jun, B., Alshamsi, A., Gao, J. et al. Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade. J Evol Econ (2019) doi:10.1007/s00191-019-00638-7

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Keywords

  • International trade
  • Relatedness
  • Knowledge diffusion
  • Economic complexity

JEL Classification

  • F1
  • O14
  • O33