R&D Characteristics, Innovation Spillover, and Technology-Driven Business Cycles

Abstract

This paper shows that technology shocks have the largest impact when industries adopt innovations of other industries at a high rate, if costs of adopting technologies and adjusting R&D expenditures are low, and if innovators face more competition. It is not the level but the spillover of innovations across industries that is key for these results. Under the conditions mentioned above, R&D becomes less procyclical and smoother yet R&D-driven innovations have a larger impact on output since they spillover at a higher rate. These inferences follow from a general equilibrium framework describing a real economy with endogenous growth.

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Notes

  1. 1.

    We abstain from modeling entry/exit dynamics of firms that we see in Schumpeterian models (e.g., Bilbiie et al. 2012). Aghion et al. (2005a, 2005b), Comin and Gertler (2006), and Queralto (2019) are some of the papers that also do not have firm entry/exit in their models; however, unlike our paper, these papers have expanding varieties model which suggests that innovations lead to new varieties which in turn increase productivity and economic growth.

  2. 2.

    Similar to Anzoategui et al. (2019), we model labor as the main input for R&D. This approach is motivated by the fact that labor costs constitute the largest share of R&D expenditures in most countries (see for example OECD 2015; Goolsbee 1998; Wolf and Reinthaler 2008). We should note, however, that there are other studies that treat R&D as a physical investment (e.g., Bianchi et al. 2019; Queralto 2019; Comin and Gertler 2006).

  3. 3.

    We should also mention that there is micro-evidence that positively relate R&D to economic volatility. Comin and Philippon (2005) show that there has been an increase in firm-level volatility in the USA between 1950 and 2000, which might be related to the increase in R&D activities. Castro et al. (2015) provide supporting evidence for increased volatility that idiosyncratic shocks are larger than aggregate shocks, and this is a more common phenomenon for R&D intensive manufacturing industries. Similarly, Li (2011) shows that R&D intensive firms are subject to higher risk and uncertainty when they face binding financing constraints. Czarnitzki and Toole (2011) state that patent protection can spur innovation and growth by reducing the adverse effect of this risk.

  4. 4.

    Azoulay (2004), for example, finds that it is data-intensive projects that are mostly outsourced to other companies. Veugelers (1997) finds that R&D intense firms are more likely to experience R&D/innovation spillovers. Cassiman and Veugelers (2006) shows that internal and external R&D activities are complements and Berchicci (2013) finds that this complementarity can play a role in achieving higher innovative performance. Part of this literature also compares the relative importance of in-house R&D and external R&D. Based on a dataset populated by Spanish firms’ investment in basic research between 2006 and 2012, Higón (2016) finds that while 22% of firms conduct external R&D, 6% of the firms conduct in-house R&D.

  5. 5.

    Schumpeterian growth models (Schumpeter 1942) suggest that firms with market power are the engines of innovation for reasons such as greater economies of scale, easier access to R&D funding, risk management practices, large fixed costs pertaining to R&D and the ability to internally finance R&D. There is mixed empirical evidence for this theory (Symeonidis 1996 provides an extensive review of studies that test the Schumpeterian hypothesis). Geroski (1990), for example, shows that market power cannot increase innovation using UK data. Acemoglu and Linn (2004) reach the opposite conclusion. Different from these findings, Aghion et al. (2005a, 2005b) and Hashmi (2013) uncover an inverted-U shaped relationship between market power and innovation. Peretto (1999) takes a different angle and shows that R&D investment can increase market power.

  6. 6.

    In Hall and Lerner (2010) large R&D adjustment costs are explained by the fact that firms smooth out their R&D expenditures as a large share of this spending is on skilled labor. A large drop in R&D expenditure during a recession then implies a loss of human capital which in turn can shrink firms’ knowledge base for a long period of time. Therefore, firms protect their R&D spending at the cost of curbing other expenditures during economic downturns (Brown and Petersen 2011, 2015).

  7. 7.

    The coefficient \(\left (N^{M}\right )^{1-\varphi ^{m}}\) is included to execute our symmetric equilibrium. In this equilibrium Yk,t = Yt/Nm.

  8. 8.

    We use the formulation in Eq. 6 to simplify the illustration. The inherent growth mechanism in the model can be described as follows: Assume that firms accumulate stock of knowledge that are a product of R&D activities and that this stock improves labor efficiency. The evolution of the said stock variables for the intermediate good producer k, all other intermediate firms, and innovators, \(RD_{k,t}^{s,M}\), \(RD_{t}^{^{\prime }s,M}\), and \(RD_{t}^{s,I}\), respectively, can be represented as,

  9. 9.

    Simulations that generate these standard deviations are the same as those used to compute model moments in Table 1.

  10. 10.

    Here, one could compare the level of competition in R&D intense industries such as computers, information, professional services, chemical products and transportation that account for the majority of R&D spending in the USA according to the BRDIS surveys.

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Correspondence to Uluc Aysun.

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Appendix: Calibration

Appendix: Calibration

Table 4 Parameter values under the baseline calibration

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Aysun, U., Yom, Z. R&D Characteristics, Innovation Spillover, and Technology-Driven Business Cycles. J Ind Compet Trade (2021). https://doi.org/10.1007/s10842-021-00358-4

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Keywords

  • Research and development
  • Spillover effects
  • Endogenous growth

JEL Classification

  • E30
  • E32
  • O30
  • O33