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Determinants of patent quality in U.S. manufacturing: technological diversity, appropriability, and firm size

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Abstract

We study the determinants of patent quality for a panel of U.S. manufacturing firms, focusing mainly on the effects of firm-level technological diversity and appropriability conditions. Technological diversity increases the quality-adjusted patent count on most of the diversity distribution, but its relationship with average patent quality is an inverted-U. We find that appropriability conditions (proxied by the rate of self-citations at the firm level) have similar, non-linear effects on both the average quality of patents, and quality-adjusted patents per R&D, which is consistent with an inverted-U pattern. Firm size has no effect on the average quality of patented innovation at the firm level. Finally, as R&D intensity increases, the rate of corporate innovation falls, but its average quality increases, indicating a quality–quantity trade-off in R&D.

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Notes

  1. Authors have used a number of diversity measures, which commonly are inverted concentration indexes, and less often simpler ones such as the number of technological fields a firm is active in. These measures will be discussed in Sect. 4.

  2. There is a growing line of literature that is interested in how firm diversify, which emphasizes the role of technological relatedness in firms’ diversification strategies. For an introduction, see MacDonald (1985), Teece et al. (1994) and Breschi et al. (2003). We also avoid a detailed discussion on the extant literature on the diversity of product lines, which is indirectly related to the current topic in that product market and technological diversification occur in conjunction with one another. On this, also see Pavitt et al. (1987), Pavitt (1998) and Scott (1993).

  3. Even though groups with zero medians are very few, medians representing one or two raw citations are common. Normalizing using such low numbers can be misleading, hence higher percentiles are prefererred. Nevertheless, we experiment with scores using medians and the 90th percentile, which do not produce different results.

  4. Lanjuow and Schankerman (2004) obtain family size for a random sample of a little over 100,000 patents, which makes up a mere 20% of their entire sample of patents. Hence, including family size is impractical unless one wishes to omit a large fraction of the patent database from the sample.

  5. A few important policy changes regarding patent law occur during the sample period. For a review, see Jaffe (2000).

  6. We thank Pelin Demirel for reminding us of this possibility.

  7. Note that if we were interested in the “impact” of innovations alone, it wouldn’t be desirable to net out visibility effects from coefficients, since increased visibility would be a natural part of a firm’s external impact. This is not true when one is interested in quality as such.

  8. We thank Bronwyn Hall for pointing this out.

  9. Hence, our time window is 11 years. On average, a patent receives 48.6% (Drugs and medical) to 68.3% (Computers and communications) of its lifetime citations during the first 11 years after application depending on its technological category.

  10. Also note that for many patents of great significance (and with high lifetime citations as a result), one may expect fewer citations after the initial few years after grant, as these innovations could take longer time to be understood, adopted, and then cited.

  11. For an argument for a quality-quantity trade-off in innovation, see Rassenfosse (2010), who shows that a firm’s (estimated) propensity to patent is negatively associated with average innovation value.

  12. It is also unlikely that unobserved permanent effects are responsible for quality differences, since these are differenced away in the fixed effects specification.

  13. Conversely, if the error term largely consisted of measurement error, averaging over time would increase this noise. Higher ability of this set of regressions (higher \(R^{2}\) values) to explain the variation in average quality suggests this is not the case.

  14. Flow variables (patents and quality-weighted patents, R&D, sales, industry size) are summed, and stock variables (capital, employment, spillover pools) are averaged for each five year period. Firm age and visibility are taken as the age and visibility at the beginning of the period, while technological diversity is re-calculated using all patents of the firm in the five year interval.

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  1. Yali Mah., I.Tarik Sari Cad. 25/6, Güzelbahçe-Izmir, Turkey

    Burak Dindaroğlu

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Dindaroğlu, B. Determinants of patent quality in U.S. manufacturing: technological diversity, appropriability, and firm size. J Technol Transf 43, 1083–1106 (2018). https://doi.org/10.1007/s10961-017-9587-7

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