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Quantile estimates of the impact of R&D intensity on firm performance

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Abstract

This paper investigates the relationship between initial research and development (R&D) intensity and firm growth using a unique data set for firms with R&D activities in Austria during the period 1995–2006. Results based on the least absolute deviation (LAD) estimator show that initial R&D intensity has a positive and significant impact on both employment and sales growth in the subsequent 2 years. Quantile regressions for each cross-section reveal that the impact of R&D intensity is significant from 0.3 to the highest quantile of the conditional distribution of employment growth. Furthermore, the elasticity of employment growth with respect to R&D intensity is highest for firms at or slightly below the median of the distribution of firm growth. Finally, we find that the impact of R&D decreases significantly over time.

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Notes

  1. For a survey on the various channels through which innovation affects employment see Spiezia and Vivarelli (2000) and Petit (1995).

  2. R&D intensity in the business sector [measured as the ratio of R&D expenditures in the business sector to gross domestic product (GDP)] doubled since the beginning of the 1990s (from 0.9% in 1993 to 1.7% in 2009).

  3. Ideally sales should be deflated by an industry price index. However, the use of growth rates of sales in nominal prices is probably unlikely to produce a bias, since producer price inflation is quite small during the period and does not vary much across industries during the period. Unreported results show that the R&D coefficients do not change when sales in current prices are replaced by sales deflated by an overall price index.

  4. It is well known that growth of R&D capital stock is a better measure than R&D intensity (Mairesse and Hall 1996). However, the available time series are too short for calculating the R&D capital stock. Therefore, the firm growth equation contains R&D intensity as a proxy for the stock of R&D capital.

  5. We use a 2-year lag of R&D intensity since employment and sales growth rate are calculated over a 2-year period and to avoid a simultaneous bias. Foray et al. (2007) also use the R&D-to-sales ratio lagged by 2 years.

  6. Note that the panel quantile regression technique accounting for unobserved individual effects is only available when T is large. For a short time span, quantile regression techniques for panel data are not available.

  7. Regional dummy variables are never significant and are therefore not included in the final specification.

  8. The funding agency plans to collect industry affiliation data from Statistics Austria, which will allow us to categorize the sample firms into different industries.

  9. OLS estimates are reported in Table 9 in the Appendix for comparison purposes.

  10. Estimation is performed using the SQREG command in STATA 10.0.

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Authors and Affiliations

  1. Austrian Institute of Economic Research (WIFO), Vienna, Austria

    Martin Falk

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  1. Martin Falk
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Correspondence to Martin Falk.

Appendix

Appendix

See Tables 9, 10, 11, and 12

Table 9 OLS results of the impact of R&D intensity on firm performance
Full size table
Table 10 Quantile regression estimates for the impact of the ratio of R&D employment on employment growth (pooled estimates for the period 1996–2006)
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Table 11 Quantile regression estimates of the determinants of employment and sales growth from 2004 to 2006
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Table 12 Quantile regression estimates of the determinants of employment and sales growth from 1998 to 2000
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Falk, M. Quantile estimates of the impact of R&D intensity on firm performance. Small Bus Econ 39, 19–37 (2012). https://doi.org/10.1007/s11187-010-9290-7

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