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Economic Change and Restructuring

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01.05.2013

Do R&D strategies in high-tech sectors differ from those in low-tech sectors? An alternative approach to testing the pooling assumption

verfasst von: Bettina Becker, Stephen G. Hall

Erschienen in: Economic Change and Restructuring | Ausgabe 2/2013

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Abstract

Most studies investigating the determinants of R&D investment consider pooled estimates. However, if the parameters are heterogeneous, pooled coefficients may not provide reliable estimates of individual industry effects. Hence pooled parameters may conceal valuable information that may help target government tools more efficiently across heterogeneous industries. There is little evidence to date on the decomposition of the determinants of R&D investment by industry. Moreover, the existing work does not distinguish between those R&D determinants for which pooling may be valid and those for which it is not. In this paper, we test the pooling assumption for a panel of manufacturing industries and find that pooling is valid only for output fluctuations, adjustment costs and interest rates. Implementing the test results into our model, we find government funding is significant only for low-tech R&D. Foreign R&D and skilled labour matter only in high-tech sectors. These results suggest important implications for R&D policy.

Vorheriger Artikel Banking reforms and the evolution of cost efficiency in Indian public sector banks

Nächster Artikel On the stability of the solutions of an impulsive Solow model with endogenous population

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Maddala et al. (1997) argue that shrinkage estimators, whereby the individual estimates are shrunk towards the pooled estimate, thereby allowing for some but not complete heterogeneity or homogeneity, are superior to either heterogeneous or homogeneous parameter estimates, especially for prediction purposes. However, a number of case studies, based on demand for gasoline (Baltagi and Griffin 1997; Baltagi et al. 2003), cigarettes (Baltagi et al. 2000) and consumption of electricity and natural gas (Baltagi et al. 2002) conclude that pooled estimators outperform their heterogeneous as well as their shrinkage counterparts in terms of their out-of-sample forecasting performance based on mean squared forecast error. In this paper, we do not consider forecasting.

For a recent survey on the returns to R&D, see Hall et al. (2010).

For theoretical arguments, see e.g. Jaffe (1989) and Krugman (1991) for the geography of innovation and Tirole (1988), Kamien et al. (1992) and Dixit (1988) for joint ventures and cooperation on R&D.

For a recent debate on the latter study, see Thompson and Fox-Kean (2005a, b) and Henderson et al. (2005).

However, the results reported in Sect. 3 are robust to treating LRR as exogenous, so any bias resulting from use of the constructed variable is small.

In an analysis of the effect of uncertainty on physical investment, Driver et al. (2004) suggest that interpreting the negative and significant effect they find for the pooled model as holding for all industries may be misleading, as the heterogeneous estimates vary considerably across industries.

Whilst the test can have low power, the Null hypothesis cannot be rejected even at the 50% significance level.

This of course also requires that some of the other coefficients are significantly different from zero, as holds in all results.

Repeating the analysis for the maximum time period for which earlier total industry R&D data were available, 1985–2000, drew a similar picture, with an explanatory power of the first principal component of only 39%. Interestingly, the explanatory power of the first (47%) and the first four (94%) principal components over the first half of the period, 1985–1992, are essentially the same as for the second half.

Using just the data as the criterion for the industry grouping would lead us to the same conclusion, whether using the nominal or real R&D series or the R&D share in sales, as in Zietz and Fayissa (1992) or Gerhaeusser (1991).

It is interesting to note here that when the tests of the pooling assumption suggest that two of the three industry groups can be pooled while the third one cannot, the group of two always involves the medium-tech sector. The high- and the low-tech industry effects are never similar to each other and at the same time significantly different from the effect of the medium group. This supports the notion that the industries in the latter group are indeed of a medium character, displaying characteristics from both of the two extremes.

Similarly to our results for industry R&D investment for the UK, a study on the effect of financial constraints on firm level investment for the US by Hsiao and Tahmiscioglu (1997) finds that the pooled estimate substantially underestimates the impact of liquidity, and that there are considerable differences across firms in terms of their investment behaviour.

This hypothesis finds support from the sub-sample regression results presented in Table 8 below, which suggest that the insignificance of the low-tech coefficient above reflects some correlation with high- or medium-tech terms. We explore to this in more detail below.

Jaffe (1989), using US data on research spending by university departments, finds a more significant and somewhat larger effect on commercial innovative activity, as measured by corporate patents, within technical industries such as drugs and chemicals than in the total across industries.

The results for the medium-tech sample are available upon request.

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Titel: Do R&D strategies in high-tech sectors differ from those in low-tech sectors? An alternative approach to testing the pooling assumption
verfasst von: Bettina Becker
Stephen G. Hall
Publikationsdatum: 01.05.2013
Verlag: Springer US
Erschienen in: Economic Change and Restructuring / Ausgabe 2/2013
Print ISSN: 1573-9414
Elektronische ISSN: 1574-0277
DOI: https://doi.org/10.1007/s10644-012-9122-7

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