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Optimal geographic diversification and firm performance: evidence from the U.K.

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Abstract

This paper examines the relationship between multinationality and firm performance. The analysis is based on a sample of over 400 UK multinationals, and encompasses both service sector and manufacturing sector multinationals. This paper confirms the non-linear relationship between performance and multinationality that is reported elsewhere in the literature, but offers further analysis of this relationship. Specifically, by correcting for endogeneity in the investment decision, and for shocks in productivity across countries, the paper demonstrates that the returns to multinationality are greater than those that have been reported elsewhere, and persist to higher degrees of international diversification.

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Notes

  1. While the use of “high tec”, “low tec” etc. is always rather arbitrary, the classification we employ is the definition of technological intensity is based on an OECD–EUROSTAT classification (Laafia, 2002) and distinguishes four types of manufacturing industries: high technology, medium-high technology, medium-low technology and low technology industries (see Appendix A).

  2. This method is an extension of the method developed by Olley and Pakes (1996) which uses a function of investment as proxy of the productivity shock. This approach has become more popular recently, see for example Smarzynska Javorcik (2004); Aghion et al. (2006).

  3. See Appendix B for details on the construction of the estimate of total factor productivity.

  4. We do however present a set of the baseline results, using a standard fixed effects least squares estimate of total factor productivity, for comparison.

  5. Imports penetration is defined as imports divided by domestic output + imports—exports.

  6. Thus in contrast to OLS, the sum of the absolute values of the residuals is minimised, and for this reason this method is also known as the Least Absolute Deviations regression.

  7. Sourafel Girma wishes to express his gratitude to Davide Castellani and Antonello Zanfei for allowing him to use some information from this database.

  8. It should be pointed out here, that for comparison, we compare the results generated using the Levinsohn-Petrin estimates of total factor productivity, with the standard fixed effects ones. The only variable whose coefficient even approaches significant difference in magnitude is the intangible assets variable. The difference is the “correct” sign, that is to say that ignoring the potential endogeneity and using simple least squares will overstate the importance of intangible assets in explaining productivity. For the sake of brevity, we do not replicate all tables with the ones for least squares derived values of total factor productivity.

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Acknowledgement

The authors wish to acknowledge the support of the ESRC under award number RES-000-22-0468. We would also express our thanks to Davide Castellani and other participants at the UK AIB conference, as well as to Jim Love and Bruce Blonogen conference for comments on an earlier draft.

Author information

Authors and Affiliations

  1. Economics and Strategy Group, Aston Business School, Aston University, Birmingham, B4 7ET, UK

    Nigel Driffield & Jun Du

  2. Nottingham University Business School, Jubilee Campus, Nottingham, NG8 1BB, UK

    Sourafel Girma

Authors
  1. Nigel Driffield
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  2. Jun Du
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  3. Sourafel Girma
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Corresponding author

Correspondence to Nigel Driffield.

Appendices

Appendix A

Classification of manufacturing industries by level of technology intensity

Level of technology intensity

NACE two digits code (Divisions)

High-technology sectors

Manufacture of office machinery and computers (30); Manufacture of radio, television and communication equipment and apparatus(32); Manufacture of medical, precision and optical instruments, watches and clocks (33)

Medium-high technology sectors

Manufacture of electrical machinery and apparatus n.e.c. (31); Manufacture of motor vehicles, trailers and semi-trailers (34); Manufacture of chemicals and chemical products (24); Manufacture of machinery and equipment n.e.c. (29); Manufacture of other transport equipment (35)

Medium-low technology sectors

Manufacture of coke, refined petroleum products and nuclear fuel (23); Manufacture of rubber and plastic products(25); Manufacture of other non-metallic mineral products(26); Manufacture of basic metals (27); Manufacture of fabricated metal products, except machinery and equipment (28)

Low technology sectors

Manufacture of food products and beverages (15); Manufacture of tobacco products (16); Manufacture of textiles (17); Manufacture of wearing apparel; dressing and dyeing of fur (18); Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness and footwear (19); Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials (20); Manufacture of pulp, paper and paper products (21); Publishing, printing and reproduction of recorded media (22); Manufacture of furniture; manufacturing n.e.c. (36) Recycling (37)

  1. Source: Eurostat-OECD classification of technology-intensive sectors

Appendix B

Baseline results replicated using OLS based estimate of total factor productivity

Coefficient

OLS with robust standard errors

Median regression

Outlier robust regression

Industry concentration

−0.290 (−1.88)*

−0.261 (−3.87)***

−0.242 (−4.13)***

Imports competition

−0.0958 (−0.95)

−0.0235 (−0.62)

0.00688 (0.21)

Export intensity

0.104 (1.63)

0.122 (5.64)***

0.0881 (4.69)***

Intangible assets

0.0810 (7.27)***

0.0959 (20.3)***

0.125 (30.5)***

Industry diversification

−0.0106 (−1.06)

−0.000849 (−0.22)

−0.00127 (−0.38)

Degree of multinationality

0.0217 (2.77)***

0.0264 (8.64)***

0.0274 (10.3)***

Square of Degree of multinationality

−0.000449 (−1.70)*

−0.000584 (−5.47)***

−0.000594 (−6.41)***

Observations

3252

3252

3252

R-squared

0.06

.

0.28

  1. Robust t statistics in parentheses
  2. *** p < 0.01; ** p < 0.05; * p < 0.1

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Driffield, N., Du, J. & Girma, S. Optimal geographic diversification and firm performance: evidence from the U.K.. J Prod Anal 30, 145–154 (2008). https://doi.org/10.1007/s11123-008-0102-x

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