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International business travel: an engine of innovation?

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Abstract

While it is well known that managers prefer in-person meetings for negotiating deals and selling their products, face-to-face communication may be particularly important for the transfer of technology because technology is best explained and demonstrated in person. This paper studies the role of short-term cross-border labor movements for innovation by estimating the recent impact of U.S. business travel to foreign countries on their patenting rates. Business travel is shown to have a significant effect up and beyond technology transfer through international trade and foreign direct investment. On average, a 10 % increase in business travel leads to an increase in patenting by about 0.2 %, and inward business travel is about one fourth as potent for innovation as domestic R&D spending. We show that the technological knowledge of each business traveler matters by estimating a higher impact for travelers that originate in U.S. states with substantial innovation, such as California. This study provides initial evidence that international air travel may be an important channel through which cross-country income differences can be reduced.

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Notes

  1. On the evidence of such spillover effects in the international context, see Keller (2010). Below we present evidence consistent with both intentional technology transfer and spillovers, the former for joint foreign-domestic patent applications, and the latter for domestic patent applications.

  2. Polanyi (1958) discusses the tacitness of technological knowledge. See Koskinen and Vanharanta (2002) on the role of face-to-face communication in overcoming problems arising from the tacitness of technology.

  3. For example, services exports are close to 40 % of goods exports in the United States. News release of the U.S. Bureau of Economic Analysis, May 11, 2011.

  4. The Open Skies Agreement seeks to liberalize air travel to and from the United States, see http://www.state.gov/e/eeb/tra/ata/. This paper is also relevant for multilateral service liberalizations; see WTO (2006) which discusses key multilateral issues.

  5. Network membership often lowers the costs of interaction (Rauch 2001), and to verify membership face-to-face meetings will often be useful. See also Singh (2005), Agrawal et al. (2006), and Agrawal et al. (2008).

  6. Related work includes Le (2008) and Dowrick and Tani (2011).

  7. The data is described in Sect. 3 below.

  8. Productivity often captures not only technical efficiency but also demand shocks and market power, factor market distortions, and product mix changes (Foster et al. 2008; Hsieh and Klenow 2009; Bernard et al. 2010, respectively). See also Keller (2004) for more discussion.

  9. The incentive of knowledge owners will typically be to prevent leakage of the knowledge to others. This by itself may be a reason for reducing the number of face-to-face meetings, except in cases where the technology transfer is intended as in the case of joint ventures.

  10. The industry dimension is important because industries vary greatly in terms of patenting activity. A list of the industries is given in Table 10 in Appendix.

  11. See Wooldridge (2002, Chap. 19).

  12. We focus on the date of application as opposed to the date of when the patent is granted; this ensures that differences in the processing time of patents do not play a role.

  13. See Hall et al. (2001).

  14. The use of fractions means that our data is not strictly speaking count data; despite this we prefer to employ count data regression models. More information on the patent data construction is given in the Appendix.

  15. OECD statistics provide Gross Domestic Expenditure on R&D for OECD and also some non-member countries. The unavailability of R&D data for many middle- and low-income countries is the main reason why the sample is limited to 34 countries.

  16. The assignment of these patents to countries is based only on the first inventor.

  17. There are alternative approaches to the weights \(\frac{P_{sit}}{GSP_{st}}\) proposed here. To pick up the differential knowledge across travelers, for example, one could introduce variables for different states, such as California versus Nebraska, separately. While this approach can be useful (see Keller 2002; Acharya and Keller 2009), it can also be difficult to estimate multiple additional parameters, which is one reason for why we adopt the weighted sum in Eq. (1). Note that Eq. (1) also scales by the total U.S. patent stock by industry, \(P_{it}\), proxying for the U.S. knowledge in that industry and year (up and beyond general effects picked up by industry and time fixed effects).

  18. There are a number of missing observations, especially for R&D, which reduces the number of observations. We also focus on countries with positive levels of business travel to the U.S., and on U.S. industries with positive levels of patenting for the computation in Eq. (1). Further, note that variables such as GDP per capita do not vary at the independent level; all country-level variables have at most \(34\times 11=374\) independent observations. Inference is based on country-year clustered standard errors to account for that.

  19. In this analysis we focus on positive numbers of business travelers; our analysis does not necessary apply to patenting in the case when there is no business travel.

  20. We cluster by country-year because some of the variables do not vary by industry; for example, GDP per capita for a given year is employed for all industries. In contrast, patents on the left and the business variable on the right-hand side vary by industry.

  21. From the confidence intervals given in Table 2 we know that the business variable is significant at a 5 % level or less in all specifications of Table 2.

  22. See Blundell and Powell (2003) for an overview and general results of the CF approach, and Wooldridge (2002, Chap. 19) for a textbook treatment.

  23. The definition of this traveler category in the SIAT survey is: “Visit Friends/Relatives”.

  24. This requirement is analogous to a strong first stage correlation in the typical two-state least squares IV estimation.

  25. The other control functions based on Table 4 give similar results.

  26. In order to form high versus low patenting industries, we take into account changes in the composition across countries when creating median and mean patent counts by industry. The high patenting dummy is equal to 1 if patent counts for a given country \(c\) in year \(t\) and industry \(i\) is higher than median/mean.

  27. For example, if the baseline specification of Table 2, column 6, has \(\beta \ln FDI_{ct}\) as a regressor, in column 1 of Table 9 we allow the effect of \(FDI\) to vary by industry: \(\beta _{i}\ln FDI_{ct}\) estimates \(37\) instead of \(1\) coefficient (not shown in Table 9 to conserve space).

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Acknowledgments

We thank four anonymous referees, Nicholas Bloom, Jeff Furman, Ben Jones, William Kerr, Jim Markusen, Keith Maskus, Mushfiq Mobarak, Marc Muendler, Jim Rauch, and David Weil, as well as participants at presentations at the University of Colorado, the CEPR GIST (Ljubljana 2010), and the American Economic Association (Denver 2011) conferences for useful comments. We thank Eric Stuen and especially Jennifer Poole for help with the data. Part of this research was conducted while Keller was Hoover National Fellow at Stanford University, whose hospitality is greatly appreciated.

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Authors and Affiliations

  1. Department of Economics, Loyola University Maryland, 4501 North Charles Street, Baltimore, MD, 21210, USA

    Nune Hovhannisyan

  2. CESifo, University of Munich, Poschingerstrasse 5, 81679, Munich, Germany

    Wolfgang Keller

  3. University of Colorado, Boulder, CO, USA

    Wolfgang Keller

  4. CEPR, London, UK

    Wolfgang Keller

  5. NBER, Cambridge, MA, USA

    Wolfgang Keller

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  1. Nune Hovhannisyan
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  2. Wolfgang Keller
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Correspondence to Wolfgang Keller.

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Appendix

Appendix

This section gives the details on the sources and construction of our variables.

Innovation U.S. patent counts: The data on U.S. patents issued from 1993 to 2003 comes from the United States Patent and Trademark Office (USPTO), Custom Data Extracts. The individual inventor database, which has address information (street, city, state, country of residence, etc.) for each of multiple inventors per patent, is combined with the bibliographical patent database, which has application month and year, as well as original USPTO technological category for each patent. If a patent has multiple inventors, we assign a fraction of \(1/n\) to each inventors country of residence, where \(n\) is the number of inventors. Using the original USPTO technological categories, each patent is assigned to one of 37 subcategories based on NBER patent classification (Hall et al. 2001). Then using application year for each patent, patents are aggregated by foreign country and technological subcategory for each year 1993–2003 to obtain patent counts by foreign countries and industries for each year 1993–2003.

Joint U.S. patent counts: To identify patents which have a combination of foreign and U.S. coinventors we also calculated foreign patent counts of only patents for which there is at least one U.S. coinventor. Using the same methodology as above, foreign patents with at least one U.S. coinventor are obtained by aggregating by foreign country and industry for each year 1993–2003.

U.S. patent stock by states and by industries: For the sample period 1993–2003, each patent with multiple inventors is assigned a fraction of \( 1/n \), where \(n\) is the number of inventors. Then keeping only U.S. inventors, patent counts are aggregated to a given state for each year during 1993–2003. Similarly, patent counts are aggregated to a given industry for each year 1993–2003.

Travel  The data on international air travel comes from the Survey of International Air Travelers (SIAT), which is conducted by the United States Office of Travel and Tourism Industries, a branch of the International Trade Administration, U.S. Department of Commerce. SIAT collects data on non-U.S. residents traveling to the U.S. and U.S. residents traveling from the U.S (excluding Canada). This survey has been carried out monthly starting from 1983 on randomly selected flights from the major U.S. international gateway airports for over 70 participating domestic and foreign airlines. Questionnaires in 12 languages are distributed onboard U.S. outbound flight to international destinations.

In this paper we use data on U.S. residents traveling from the United States to foreign countries in the period of 1993–2003. Outbound U.S. resident travel data is an individual level database which has information on travelers’ U.S. county of residence, country of citizenship, main purpose of the trip, secondary purposes of the trip, main destination foreign cities, secondary destination foreign cities, occupation, quarter and year of travel. Trips can be made for the purpose of business, visiting friends and relatives, and religious, among others. Possible occupations include homemaker and retired, among others. Main destination and secondary destination cities are both coded. Individual observations are expanded if a particular individual traveled to distinct destination countries, treating each destination as a separate trip. If a particular traveler mentioned multiple purposes of the trip, each purpose is given equal weight. Further, expanded individual travel observations are aggregated by purpose of the trip and occupations by U.S. state and foreign country for each year 1993–2003.

Our main variable of interest is \(B_{sct},\) the number of business travelers from state \(s\) to foreign country \(c\) in year \(t\). We calculated the number of travelers who are visiting friends and family, are traveling for religious reasons, or are retired or homemakers in the same way. These aggregated travel variables are weighted by the ratio of U.S. state-industry patent stock to real state GDP and a given industry’s strength in the U.S. (source: U.S. Department of Commerce, Bureau of Economic Analysis, BEA), see Eq. (1). The final travel variables are in natural logarithms, with one added to each value. Furthermore, in this analysis we only consider positive numbers of business travelers.

Other variables at the country level Population size, real GDP per capita for each year 1993–2003 and country are obtained from Penn World Tables, version 6.2. U.S. exports and imports by country and year 1993–2003 are collected from U.S. Census Bureau (www.usatradeonline.gov). U.S. FDI by destination countries and years 1993–2003 is proxied by the total sales of U.S. majority-owned multinational affiliates and comes from U.S. Bureau of Economic Analysis (BEA). FDI to U.S. by countries and years is captured by the total sales of nonbank U.S. affiliaties of foreign multinational firms and comes from BEA. Gross domestic expenditures on R&D expenditures (GERD) for each country in year 1993–2003 are obtained from OECD Statistics, which has data on OECD countries as well as some non-OECD member economies. Each country’s domestic patent applications (by first named inventor) by residents of that country in 1993–2003 are from World Intellectual Property Organization (WIPO). All control variables employed in the analysis are in natural logarithms. The final dataset is an unbalanced sample for 34 countries and 37 industries for the years 1993–2003 (See Tables 10, 11, 12, 13, and 14).

Table 10 NBER technological subcategories
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Table 11 US patenting by states, 1993–2003
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Table 12 US patenting by industries, 1993–2003
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Table 13 Countries in sample
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Table 14 Countries in sample
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Hovhannisyan, N., Keller, W. International business travel: an engine of innovation?. J Econ Growth 20, 75–104 (2015). https://doi.org/10.1007/s10887-014-9107-7

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