Abstract
The current practice in the literature on the impact of exchange rate uncertainty on foreign direct investment is to consider exchange rate volatility. In this paper, we demonstrate the importance of considering also covariances and apply the theoretical arguments to a UK industry panel of FDI in R&D. An increase in the covariance of the euro and sterling, which would be a certain consequence of the UK’s entry into European Monetary Union, will increase foreign R&D into the UK. Increased volatility of the euro-dollar exchange rate tends to relocate R&D investment from the Euro Area into the UK.
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Notes
While we have given an example of home and foreign countries in which a firm might invest, related to our empirical analysis, it is worth noting that the increase in the expected return, due to an increase in the covariance of a country’s return with the average return of other countries, is independent of the country of origin of the investment, other things equal. This is because it is only the covariation with the average of countries that matters for the result. It would also hold true if a US firm invested in the UK, and possibly at home, but not in the Euro Area, for example. In our empirical analysis below, we are interested in the foreign location of a firm’s investment abroad as dependent on the uncertainty of the sterling dollar and euro dollar exchange rates, so we implicitly consider a firm’s choice between increasing its investment abroad in the UK or abroad in the Euro Area, or both.
If we define the VECH operator in the usual way as a stacked vector of the lower triangle of a symmetric matrix then we can represent the standard generalization of the univariate GARCH model as \(\operatorname{VECH} \left( {\Omega _t } \right) = C + A\left( L \right)\operatorname{VECH} \left( {e_t e_t^\prime } \right) + B\left( L \right)\operatorname{VECH} \left( {\Omega _{t - 1} } \right)\), where C is an (N(N + 1)/2) vector and A i and B i are (N(N + 1)/2) × (N(N + 1)/2) matrices. This general formulation rapidly produces huge numbers of parameters as N rises (for just one lag in A and B and a five variable system we generate 465 parameters to be estimated) so for anything beyond the simplest system this system will almost certainly be intractable. A second problem with this system is that without fairly complex restrictions on the system the conditional covariance matrix cannot be guaranteed to be positive semi definite. So much of the literature in this area has focused on trying to find a parameterization which is both flexible enough to be useful and yet is also reasonably tractable.
This empirical specification takes the general form of many models used in studies of total or domestic R&D in several contexts. See, for example, Bloom et al. (2002). The error correction type approach we use is similar to the models estimated in Bond et al. (2003), Guellec and Ioannidis (1997) or Guellec and Van Pottelsberghe de la Potterie (1997).
The quarterly numbers for COV, VARSD and VARED were transformed to annual ones by taking the arithmetic average per year.
This will allow to test for adjustment costs. Theory suggests these are important because of the high cost of temporary hiring and firing of highly qualified labour with firm-specific knowledge, and because a sustained commitment to R&D is often required for projects to be successful. For empirical evidence, see Bernstein and Nadiri (1986), Hall et al. (1986) and Himmelberg and Petersen (1994). Hall (1993) reports that at least 50% of R&D budgets typically consist of the salaries of professional scientists and engineers.
The long-run effects reported in the following refer to regression [3].
Hence the results suggest that on the one hand entry of the UK into EMU would have the potential gain of increasing the UK’s inward foreign R&D, other things equal. On the other hand, the reallocation from the Euro Area to the UK as a result of an increase in the volatility of its exchange rate with the dollar, i.e. the potential decrease in foreign R&D received by the Euro Area, naturally would stop. We thank a referee for pointing this out to us.
In this regression, we replace the macroeconomic factors by time dummies.
Five time dummy variables are excluded from the regression due to the constant term and the four macroeconomic variables.
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Acknowledgements
We wish to thank Nigel Driffield, Ciaran Driver, Sylvia Gottschalk, Matthias Lutz, James Mitchell, Ron Smith, Martin Weale, participants at the EARIE conference in Helsinki, the Money Macro and Finance conference in Cambridge and a seminar at the Leverhulme Centre for Research on Global Economic Policy at the University of Nottingham for helpful comments. Of course, all remaining errors are our own. Financial support from the ESRC grant No. L138250122 is also gratefully acknowledged.
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Data appendix
Data appendix
This Appendix gives a brief description of the data used in the empirical work and their main statistical source. For two-digit SIC(92) industry coverage, see text.
R&D series:
All data have been converted from the Office for National Statistics (ONS) R&D product group (PG) codes to SIC(92) using a concordance provided by the ONS. Data were taken from ONS MA14 or the ONS website where available. According to information from the ONS, MA14 editions with detailed industry data exist only for 1989 and 1993–2000. Data for total R&D (variable R) and for selected other series totals (sum of all PG’s) are published in revised form on the ONS website, including the years 1990–1992.
R—expenditure on R&D performed in UK businesses, ONS website.
G—expenditure on R&D performed in UK businesses and funded by the government (GOVT) as a share of R. Data for GOVT series total were taken from the ONS website for each year. GOVT at the detailed PG level is not available in revised form for 1993–1998 and not available at all for 1991 and 1992. Data for 1993–1998 were collected from ONS MA14, various editions. The data for 1993–1995 were converted from 1993 to 1996 PGs using conversion factors based upon R, the only series for which data in the old and the new form are available for all relevant years. For nine out of twenty PGs, the conversion factor was 1. In absence of any information to the contrary, pre-1993 data at the detailed PG level were then obtained by applying 1993 PG shares in the series total to the revised series totals of the relevant years. In order to match 1999 data revisions, the 1993–1998 data at the detailed PG level were revised in a similar way, using the respective shares in each year.
F—expenditure on R&D performed in UK businesses by foreign-owned firms. Revised data for 1993–1999 were provided by the ONS. Pre-1993 data were not available either for the series total or at the detailed PG level. The series total for those years was thus interpolated using information on foreign funding of business R&D. Data at individual PG level were then obtained as for GOVT.
D—expenditure on R&D performed in UK businesses by indigenous firms (UK) as a share of R. Revised data for 1993–1999 were provided by the ONS. For the earlier years, UK was obtained as R minus FOREIGN.
HE—UK-wide expenditure on R&D performed by higher education, ONS MA14.
Non-R&D series:
Y—gross value added, 1995 prices, ONS Blue Book (website).
I—total net capital expenditure. Data for 1995–1999 from ONS Annual Business Inquiry (website), linked to equivalent series in ONS Census of Production Summary Volume PA1002, various editions, for the earlier years.
IM—import penetration ratio, calculated as value of imports over home demand. Trade data from ONS MQ10 (website). Turnover data obtained as for total net capital expenditure.
Nominal series were deflated using the gross value added deflator, 1995 = 100, as obtained from gross value added at constant and at current prices, ONS Blue Book. Nominal R&D performed by higher education in the UK as a whole was deflated by the GDP deflator. For further information about the data, see Becker and Pain (2008).
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Becker, B., Hall, S.G. Foreign Direct Investment in R&D and Exchange Rate Uncertainty. Open Econ Rev 20, 207–223 (2009). https://doi.org/10.1007/s11079-007-9075-z
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DOI: https://doi.org/10.1007/s11079-007-9075-z