PI: Cesar A. Hidalgo, Collective Learning Group, MIT Media Lab, MIT.
What is the next product that a country will produce? And where will that country export that product? Recent literature has shown that the probability that a country starts producing a product, develops an industry, or enters a research area, increases with the number of related activities present in it. The study of trade, on the other hand, has shown that countries trade more with those with whom they share culture (same language, religion, or colonial past), suggesting that trade is not only about differences in factor endowments, but also that it is constrained by the ability of people to form the social networks they require to communicate opportunities and establish agreements and trust. Despite the importance of these two findings, little is known about the interaction between them: does knowledge of how to export to a destination also flow among related industries? That is, are countries more likely to start exporting to the destinations to which local related industries are already exporting? In this proposal we aim to predict the probability of success for each product to enter its new market by analyzing bilateral trade networks.
- Project Title: Where will you export next? Knowledge diffusion and the evolution of international trade
- Principal Investigator: Cesar A. Hidalgo, Collective Learning group, MIT Media Lab, MIT, Associate Professor, Asahi Broadcast Career Development Professor
- Grant Period: September 2017 – August 2018
During recent 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 similar to their current exports, suggesting that knowledge diffusion among related industries is a key constraint shaping the diversification of exports. But does knowledge about how to export to a destination also diffuse among related products and geographic neighbors? Bilateral trade data from 2000 to 2015 confirms that countries are more likely to increase their exports of a product to a destination when: (i) they export the same product to the neighbor of a destination, and (ii) they have neighbors who export the same product to that destination. Our new finding is that countries are also more likely to increase their exports of a product to a destination when (iii) they export related products to it. Then, we explore the magnitude of these effects for new, nascent, and experienced exporters and also for groups of products with different levels of technological sophistication. We find that the effects of product and geographic relatedness are stronger for new exporters, and also, that the effect of product relatedness is stronger for more technologically sophisticated products. These findings support the idea that international trade is shaped by information frictions that are reduced not only with experienced geographic neighbors, but also in the presence of related products.
Jun, Bogang, Aamena Alshamsi, Jian Gao, and Cesar A. Hidalgo. "Relatedness, knowledge diffusion, and the evolution of bilateral trade." arXiv preprint arXiv:1709.05392 (2017).
We use bilateral trade data from 2000 to 2015 from MIT’s Observatory of Economic Complexity (Simoes and Hidalgo, 2011). The data is disaggregated into the Harmonized System (HS rev 1992, four-digit level) and consists of imports and exports between countries. Because both exporter and importer report their trade information, we clean the data by comparing the data re- ported by exporters and importers following the work of Feenstra et al. (2005). Also, we exclude countries that have population less than 1.2 million or have a trade volume in 2008 that is below 1 billion in US dollar. Also, we exclude data from Iraq, Chad and Macau.
Macroeconomic data (GDP at market prices in current US dollar and population) comes from the World Bank’s World Development Indicators. Data on geographical and cultural distance (shared language, physical distance between most populated cities, sharing a border, and shared colonial past) comes from GEODIST data from CEPII (Mayer and Zignago, 2011). For language proximity, we use one of the global language networks of Ronen et al. (2014): the one considering the number of books translated from one language to another as a proxy for the number of translators, or bilingual speakers, between two languages.
Does relatedness among products or geographic neighbors help facilitate the knowledge flows needed to increase bilateral trade flows?
To explore this question, we introduce three measures of relatedness. We use these to estimate: (i) the fraction of the geographic neighbors of a country that import a product from the same origin (Importer Relatedness), (ii) the fraction of neighbors of a country that export a product to the same destination (Exporter Relatedness), and (iii) the similarity between a product and the other products that a country already exports to a destination (Product Relatedness). Product Relatedness should help us capture information about knowledge flows between products (which range from knowledge flows among industries to knowledge flows among product lines within a firm). Figure (C) illustrates Product Relatedness in the context of Korea and Chile. In the example, Korea exports Products I and II to Chile (Shirts and Pants), and this may affect the future exports of Product III (Coats) to Chile, when Product III (Coats) is highly related to Products I and II (Shirts and Pants). Our hypothesis is that knowledge flows should be larger among related products, and hence, exports should increase faster when a country exports related products to a destination.
Importer Relatedness helps us capture knowledge flows on how to: (i) import a product from the same origin than a neighbor, or (ii) export to a neighbor of a current destination. In the example of Figure (A), Korea exports Product I (Shirts) to Peru and Argentina and that may affect the future volume of exports of Product I (Shirts) to Chile (who is a geographic neighbor of Peru and Argentina). Here, knowledge on how to import from an origin should be flowing among neighboring importers, or knowledge on how to export to the neighbors of a country’s destinations should be flowing within the exporter.
Exporter Relatedness captures (i) knowledge flows among neighboring exporters on how to export to a destination, or (ii) knowledge flows on how to import from a neighbor of a country from where you currently import. In the example of Figure (B), Chile imports Product I (Shirts) from China and Japan, and that may affect the future volume of exports of Product I (Shirts) from Korea (which is a neighbor of the places from where Chile is currently importing Product I). This would be a knowledge flow on how to export to a destination among neighboring exporters, or a knowledge flow within an importer, of how to import from a neighbor of a current origin.
Figure 1: Relatedness among exporters, importers, and product. (A) Importer Relatedness: the fraction of the geographic neighbors of a country that import a product from the same origin, and (B) Exporter Relatedness: the fraction of neighbors of a country that export a product to the same destination. (C) Product Relatedness: the similarity between a product and the other products that a country already exports to a destination.
First, we find that the three relatedness variables correlate positively with future bilateral trade. This means that a country is likely to experience an increase in their exports of productto a destinationwhen: (i) the country is exporting related products to that destination, (ii) it is exporting the same product to the neighbors of a destination (confirming Chaney (2014); Morales et al. (2015)), and (iii) it has neighbors that are already exporting the same product to that destination. This extends Bahar et al. (2014), who showed that having geographic neighbors increases the probability of exporting a new product, since Bahar et al. (2014) did not look at individual export destinations (they aggregate across all destinations). Our findings, therefore, complement Bahar et al. (2014) by showing that having neighbors that export a product does not only increase the total volume of exports, but the volume of exports to the same destinations that the neighbors were exporting to.
When comparing the effects of product and geographical relatedness (variables are standardized), we find that the role of Product Relatedness is on average the largest, while that of Exporter Relatedness is the smallest. In addition to these, we find strong and positive effects for the role of shared borders, colonial past, shared language, and to a lesser extent, number of translations (language proximity). Other standard gravity factors (distance, GDP per capita, and population) behave as expected.
Together, the finding that relatedness among products, the presence of knowledge among geographic neighbors, language, colonial history, shared borders, and language proximity, all have a positive and significant effect in the increase of trade flows for particular products and countries, are evidence in support of the notion that knowledge on how to trade a specific product between a specific pair of countries needs to flow for that trade to be materialized. If this hypothesis is correct, we should also be able to study the varying importance of knowledge flows for new and experienced exporters (exporters with or without comparative advantage), and also, for products with different levels of technological sophistication.
Nevertheless, our results do provide some light in the long quest to understand how social networks, culture, and knowledge flows shape international trade. They tell us that product relatedness plays an important role since the size of its effect is larger than the one observed among geographic neighbors. This suggests that looking at knowledge flows among product lines, and among industries, should be an avenue of inquiry for improving our understanding of the social and economic forces that govern global trade.
Based on the results of the project, we are currently developing an algorithm that predicts the next product and destination in trade using deep learning technique, such as Random Forest Recursive Feature Elimination.
Bahar, D., Hausmann, R., Hidalgo, C. A., 2014. Neighbors and the evolution of the comparative advantage of nations: Evidence of international knowledge diffusion? Journal of International Economics 92 (1), 111–123.
Chaney, T., 2014. The network structure of international trade. American Economic Review 104 (11), 3600–3634.
Feenstra, R. C., Lipsey, R. E., Deng, H., Ma, A. C., Mo, H., 2005. World trade flows: 1962-2000. NBER Working Paper No. 11040.
Mayer, T., Zignago, S., 2011. Notes on CEPII’s distances measures: The geodist database. CEPII Working Paper No. 2011-25.
Morales, E., Sheu, G., Zahler, A., 2015. Extended gravity. NBER Working Paper 21351.
Ronen, S., Gonc ̧alves, B., Hu, K. Z., Vespignani, A., Pinker, S., Hidalgo, C. A., 2014. Links that speak: The global language network and its association with global fame. Proceedings of the National Academy of Sciences of the United States of America 111 (52), E5616–E5622.
Simoes, A. J. G., Hidalgo, C. A., 2011. The economic complexity observatory: An analytical tool for understanding the dynamics of economic development. In: Proceedings of the 17th AAAI Conference on Scalable Integration of Analytics and Visualization. AAAIWS’11-17. AAAI Press, pp. 39–42.