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Business visits, knowledge diffusion and productivity

Abstract

We investigate whether labor mobility can be a distinct source of growth by studying the productivity impact of business visits (BVs), vis-à-vis that of other well-known drivers of productivity enhancement. Our analysis uses an unbalanced panel—covering on average 16 sectors per year in ten countries during the period 1998–2011—which combines unique and novel data on BVs sourced from the US National Business Travel Association with Organization for Economic Cooperation and Development (OECD) data on R&D and capital formation. We find that mobility through BVs is an effective mechanism to improve productivity, being about half that obtained by investing in R&D. This relevant finding invites viewing short-term mobility as a strategic mechanism and prospective policy tool to overcome productivity slowdowns and foster economic growth.

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Notes

  1. 1.

    In an influential book, Paul Krugman’s noted that “Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker . . . the essential arithmetic says that long-term growth in living standards . . . depends almost entirely on productivity growth.” (Krugman 1990, p. 9). Although subsequent evidence of rising income inequality is in contrast with Krugman’s statement (Dew-Becker and Gordon 2005) recent work has noted that this is due to poor measurements, which, when corrected, reaffirm the fundamental role of productivity in driving real wages and living standards in the long run (Feldstein 2008).

  2. 2.

    Indeed, tacit knowledge embodied in scientists, engineers, managers, and skilled employees plays a key role in many industries, as detailed by Howells (1996) and Cowan et al. (2000).

  3. 3.

    This includes expatriates (Collings et al. 2007), managers of subsidiaries (Riusala and Suutari 2004, Toselli 2017), and employees temporarily working for another employer within a collaborative arrangement (Franco and Filson 2000, Zellner 2003, Amoroso 2017) among others.

  4. 4.

    Institutional programs promoting the international mobility of researchers, like the Erasmus program in the EU, are based on the similar principle. Namely, that a temporary stint in a foreign laboratory favors young researchers’ professional development and opportunities for future international collaborations, regardless of where they will eventually work (Ackers 2005).

  5. 5.

    This is especially the case within organizational settings favoring social or group activities (Nelson and Winter 1982, Dosi et al. 1988, Kogut and Zander 1992, Nonaka and Takeuchi 1995).

  6. 6.

    Skilled here contains occupations in the top three 1-digit categories of the International Standard Classification of Occupations (ISCO). This includes managers, professionals, and associate professionals.

  7. 7.

    In an open-ended question of a survey of business travelers to/from Australia, about a third of the 1982 respondents replied that the counterfactual of their visit not taking place would have been a “knowledge loss” in the form of a wedge between their industry’s best practice and their employer’s competitive position (and their own skills to keep up to it). For example, a health professional responded that she would have not been able to access knowledge about transplant immunology, a manufacturer of woolen products stated that he would not have been able to follow the most recent innovations and designs, while the marketing director of a large multinational company said that he would have been uninformed about his company’s strategy for the next year as well as unable to report to his headquarters the latest developments taking place in Australia. In contrast, only 10% of respondents indicated that marketing products was the main reason for traveling (Tani 2014).

  8. 8.

    These fitted values are in turn aggregated up from fitted values obtained from a first stage where travel shares for each pair of countries are regressed on a set of geographic characteristics unrelated to productivity (country size, distance between countries, and existence of a common border).

  9. 9.

    See Table 5 in the Appendix for the list of the industries included in the analysis and the corresponding number of observations. As can be seen in the last column of the table, the business visit intensity (mean value of business visits per employee, computed as the average of the available information over the investigated time span) is the highest in the knowledge intensive services devoted to scientific research and development.

  10. 10.

    This is what assumed by the reference literature, taking into account that the knowledge capital (in our case, both R&D expenditures and business visits) exhibits a faster degree of obsolescence rather than the physical capital (see Nadiri and Prucha (1996) for singling out 6% as the proper discount rate for physical capital; Hall (2007) and Hall et al. (2009) for proposing 15% as the standard discount rate for R&D).

  11. 11.

    The Breusch-Pagan/Cook-Weisberg test for heteroskedasticity has not rejected the null hypothesis of constant variance of the residuals; therefore, no correction for heteroskedasticity has been introduced.

  12. 12.

    Interestingly enough, shifting to the FE estimates (and so taking into account sectoral time invariant heterogeneity) lowers the coefficient for capital formation, but raises the ones concerning both the R&D and BV stocks; this means that knowledge production and diffusion within a given sector is particularly important in raising productivity.

  13. 13.

    Therefore, the BV stock does not overlap with what measured by the physical and knowledge capital, indeed contributing to better explain the productivity dynamics. Moreover—although POLS is not our preferred estimation—the adjusted R 2 slightly increases when BV is included, supporting the opportunity to add the BV stock to the regressors’ matrix.

  14. 14.

    As far as our key result is concerned, the POLS estimated coefficient is virtually identical while the FE one turns out to be smaller in magnitude (0.030) and less significant (but still over the 95% level of confidence); this might be due to the non-trivial decrease in available observations (from 2262 to 2049) due to the use of lagged values.

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Acknowledgements

We would like to thank the Editor-in-Chief, Prof. Klaus Zimmermann, for his guidance and three anonymous referees for their detailed comments and useful suggestions.

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Correspondence to Marco Vivarelli.

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Appendix

Appendix

Table 4 Expenditures on business visits and R&D as a share of industry output, 1998–2011
Table 5 Sample composition by industries and sectoral BVs intensities
Table 6 Dependent variable, ln (value added per employee), lagged regressors

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Piva, M., Tani, M. & Vivarelli, M. Business visits, knowledge diffusion and productivity. J Popul Econ 31, 1321–1338 (2018). https://doi.org/10.1007/s00148-017-0679-3

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Keywords

  • Business visits
  • Labor mobility
  • Knowledge
  • R&D
  • Productivity

JEL classification

  • J61O33