We use aggregated and anonymized information based on international expenditures through corporate payment cards to map the network of global business travel. We combine this network with information on the industrial composition and export baskets of national economies. The business travel network helps to predict which economic activities will grow in a country, which new activities will develop and which old activities will be abandoned. In statistical terms, business travel has the most substantial impact among a range of bilateral relationships between countries, such as trade, foreign direct investments and migration. Moreover, our analysis suggests that this impact is causal: business travel from countries specializing in a specific industry causes growth in that economic activity in the destination country. Our interpretation of this is that business travel helps to diffuse knowledge, and we use our estimates to assess which countries contribute or benefit the most from the diffusion of knowledge through global business travel.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
The Journal of Technology Transfer Open Access 13 May 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The aggregated and anonymized dataset made accessible by the Mastercard Center for Inclusive Growth is available for the duration of this research, after which this dataset and any existing copies will be permanently destroyed. The aggregated data used in our growth estimations, along with the data on economic establishments (from Dun & Bradstreet), are provided as a Supplementary Data ZIP file, hosted by the Mastercard Center for Inclusive Growth and accessible via https://growthlab.cid.harvard.edu/academic-research/business-travel. This archive contains the processed business travel network as shown in Fig. 1, countries’ trade profiles as well as the number of establishments, employment and estimated productivity by industry and country. We added random noise to the business-travel information to preserve confidential information held by the data providers. The remaining publicly available datasets can be downloaded at http://atlas.cid.harvard.edu/engage#data-download and at http://www.michelecoscia.com/?page_id=1612.
World Bank. World Development Indicators, https://data.worldbank.org/indicator/NY.GDP.MKTP.CD (2016).
Nonaka, I. & Takeuchi, H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation (Oxford Univ. Press, 1995).
Auerswald, P. E. The Code Economy: A Forty-Thousand Year History (Oxford Univ. Press, 2017).
Johnson, B., Lorenz, E. & Lundvall, B.-Å Why all this fuss about codified and tacit knowledge? Ind. Corp. Change 11, 245–262 (2002).
Arthur, W. B. The Nature of Technology: What It Is and How It Evolves (Simon and Schuster, 2009).
Kogut, B. & Zander, U. Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 3, 383–397 (1992).
Cowan, R., David, P. A. & Foray, D. The explicit economics of knowledge codification and tacitness. Ind. Corp. Change 9, 211–253 (2000).
Polanyi, M. The Tacit Dimension (Univ. Chicago Press, 1967).
Collins, H. M. & Pinch, T. The Golem: What You Should Know About Science (Cambridge Univ. Press, 1998).
Collins, H. & Pinch, T. The Golem at Large: What You Should Know About Technology (Cambridge Univ. Press, 1999).
MacKenzie, D. & Spinardi, G. Tacit knowledge, weapons design, and the uninvention of nuclear weapons. Am. J. Sociol. 101, 44–99 (1995).
Lissoni, F. Knowledge codification and the geography of innovation: the case of Brescia mechanical cluster. Res. Policy 30, 1479–1500 (2001).
Cristea, A. D. Buyer-seller relationships in international trade: evidence from US states’ exports and business-class travel. J. Int. Econ. 84, 207–220 (2011).
Poole, J. P. Business travel as an input to international trade. in AEA 2010 Annual Meeting (eds Hall, R., et al.) https://www.aeaweb.org/conference/2010/retrieve.php?pdfid=431 (2010).
Hovhannisyan, N. & Keller, W. International business travel: an engine of innovation? J. Econ. Growth 20, 75–104 (2015).
Piva, M., Tani, M. & Vivarelli, M. Business visits, knowledge diffusion and productivity. J. Popul. Econ. 31, 1321–1338 (2018).
Krugman, P. R. Geography and Trade (MIT Press, 1993).
Jaffe, A. B., Trajtenberg, M. & Henderson, R. Geographic localization of knowledge spillovers as evidenced by patent citations. Q. J. Econ. 108, 577–598 (1993).
Hoekman, J., Frenken, K. & Van Oort, F. The geography of collaborative knowledge production in Europe. Ann. Reg. Sci. 43, 721–738 (2009).
Breschi, S. & Lissoni, F. “Cross-firm” Inventors and Social Networks: Localized Knowledge Spillovers Revisited. Ann. Econ. Stat. 79/80, 189–209 (2005).
Agrawal, A., Cockburn, I. & McHale, J. Gone but not forgotten: knowledge flows, labor mobility, and enduring social relationships. J. Econ. Geogr. 6, 571–591 (2006).
Hausmann, R., et al. The Atlas of Economic Complexity: Mapping Paths to Prosperity (Mit Press, 2014).
Bahar, D., Hausmann, R. & Hidalgo, C. A. Neighbors and the evolution of the comparative advantage of nations: evidence of international knowledge diffusion? J. Int. Econ. 92, 111–123 (2014).
Lenormand, M. et al. Influence of sociodemographic characteristics on human mobility. Sci. Rep. 5, 10075 (2015).
Sobolevsky, S. et al. Cities through the prism of people’s spending behavior. PloS ONE 11, e0146291 (2016).
Di Clemente, R. et al. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nat. Commun. 9, 3330 (2018).
Albert, R. & Barabási, A.-L. Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47 (2002).
Newman, M. E. The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003).
Coscia, M. & Neffke, F. M., Network backboning with noisy data. In Proc. 2017 IEEE 33rd International Conference on Data Engineering (ICDE) 425–436 (IEEE, 2017).
Alfaro, L. & Charlton, A. Intra-industry foreign direct investment. Am. Econ. Rev. 99, 2096–2119 (2009).
The authors acknowledge financial support from the Mastercard Center for Inclusive Growth. The funders had no role in study design, data analysis, decision to publish or preparation of the manuscript. The Mastercard Center for Inclusive Growth provided access to the anonymized and aggregated data on expenditures through foreign corporate cards described in section 1.1 of the Supplementary materials. We thank the following people for comments and help in preparing the manuscript: A. Stansbury, D. Bahar, B. Zuccolo and S. Ravinutala. The authors.
The Mastercard Center for Inclusive Growth provided access, subject to strict privacy and data protection safeguards, to an aggregated and anonymized dataset relating to corporate credit card foreign spend. The Center reviewed the paper to ensure that it complied with these privacy and data protection safeguards. However, the Center did not determine any parts of the design, execution or interpretation of the research: all opinions, findings, and conclusions or recommendations expressed in this paper and its Supplementary Informationare those of the authors and do not necessarily reflect the views or opinions of Mastercard. R.H. is a senior fellow at the advisory council of the Mastercard Center for Inclusive Growth.
Peer review information: Primary Handling Editor: Aisha Bradshaw.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Coscia, M., Neffke, F.M.H. & Hausmann, R. Knowledge diffusion in the network of international business travel. Nat Hum Behav 4, 1011–1020 (2020). https://doi.org/10.1038/s41562-020-0922-x
The Journal of Technology Transfer (2022)