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19-09-2023

Innovation and governance

Authors: Saurav Roychoudhury, Anuj Bhowmik, Srobonti Chattopadhyay

Published in: Journal of Economics and Finance

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Abstract

Innovation is a key driver of long-term economic growth and has significantly improved living standards. Corporate innovation efforts are at the forefront of this process. We propose a two-period career concern model to better understand the factors that influence a corporation’s decision to undertake risky innovation. In this model, a corporation’s manager is influenced by the company’s corporate governance structure and the level of competition in the product market. Our research shows that strong corporate governance positively impacts a manager’s decision to innovate due to career concerns. However, we also found that firms in industries with low competition benefit more from good governance than those in highly competitive industries. This conclusion is supported by our analysis of a panel data set from the 1990s, which contains information on time-varying patent citations in the US.

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Appendix
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Footnotes
1
for example, see Holmström and Costa 1986; Hirshleifer and Thakor 1992; Holmström 1999; Gao et al. 2018
 
2
Schumpeterian view is that the rents that can be earned from a new product or process depend on the size of the innovation and the extent to which the innovation is protected from imitators.
 
3
As noted by Sir John Hicks (1935, page 8) “best of all monopoly profits is a quiet life."
 
4
For simplicity, we assume only two sectors in our model. Allowing multiple sectors of any number in our model does not change our analysis.
 
5
This situation may occur when the concerned manager gets recruited in the sector under consideration \(S_1\) with no previous experience in the sector and hence has no prior experience from where she (and the market) can assess her ability. We can also assume that the manager and the market hold the same prior beliefs about her ability. The information on which the prior is based could be factors like the education of the manager or the output produced by the manager in previous jobs. The case that the manager has private information can be done analogously.
 
6
Our main result is valid for an arbitrary finite number of abilities, refer to Appendix of this paper. In the main body of the paper, we randomly choose 3 types of abilities.
 
7
Most papers we surveyed have incorporated this asymmetric information not in the manager’s ability but where the manager possess private information regarding the quality of the project. We came across just two papers which assume manager’s having exact information about their abilities - Avery and Chevalier (1999) in a “herding" model and more recently by Chen (2015) in a career-concern game theoretic model.
 
8
Larger number of firms in a market implies that any innovative activity will involve a higher probability of quick replication and thus a very fast diffusion of the innovated technique. This is likely to discourage innovative activities (see Aghion et al. 2005 for a discussion). Thus the more concentrated a market is, the higher is the incentive to undertake innovative activities.
 
9
Of the 3,954 CEO contracts studied between 1992 and 2008, Xu (2013) finds that the average time horizon for 40 percent of these contracts was 2.65 years and the remaining contracts were "at will".
 
10
As Aghion et al. (2013a) point out that the institutional investors cannot fire the manager directly but can pressure the board behind the scene to do so.
 
11
Kaplan and Minton (2012) report that in the US, the annual CEO turnover was 15.8 percent from 1992 to 2007, with an average tenure as CEO of fewer than seven years with internal or board driven turnover rising substantially over this period. Internal turnover was significantly related to three components of firm stock performance - relative to industry, industry performance relative to the overall market, and the performance of the overall stock market.
 
12
The ordering is taken pointwise, that is, \(p_3(c)> p_2(c)> p_1(c)\) for all \(c\in [\eta , 1]\).
 
13
Without patent protection, there may be very little managerial incentive to innovate in highly competitive industries. It could be argued that patent protection will eliminate imitation, but that is not true. A patent protection does not guarantee that the inventor can prevent competition from others, either legally by inventing around the new technology, or illegally by infringing the patent (Gilbert and Katz, 2011) and several studies have shown that patents do not confer substantial protection in many industries (see, e.g., See Levin et al. 1985 and Hall and Ziedonis 2001). What the patent protection may offer is a time interval until the next related innovation occurs and in high competition this time interval is smaller compared to low competition hence the innovation rents may be short lived.
 
14
Note that the income of the manager is sector-specific, that is, the income of the manager in \(S_1\) and \(S_2\) may be different.
 
15
This is quite natural if one considers many different types of abilities. When there are many types, the difference in probability of success for two consecutive types may be very small. In such cases, allowing for same income for two consecutive types seems rational. To retain this aspect and at the same time, keep the analysis simple, we consider just three types of abilities in the main body of our paper. A general model containing \(m(\ge 2)\) many types of abilities is studied in the appendix.
 
16
Here, we use the standard notation of expectation, that is, for the random variable \(f:\Theta \rightarrow \mathbb R_+\) and the probability measure \(\mathbb P_c(\cdot |1):\Theta \rightarrow [0, 1]\), the expression \(\mathbb E^{\mathbb P_c(\cdot |1)}(f)\) denotes the expected value under \(\mathbb P_c(\cdot |1)\).
 
17
In this case, \(|\delta |\) must be very small, as Eq. (4.1) holds for \(c=\eta \) and \(\mathbb E^{\mathbb P_\eta (\cdot |1)}(f)\) becomes very close to \(\mathbb E^{\mathbb P_c}(f)\).
 
18
This does not mean that the manager’s income for successful innovation is higher in \(S_2\) than \(S_1\). For instance, \(f(\theta _3)> g(\theta _3)\), \(f(\theta _2)= g(\theta _2)\), \(f(\theta _1)< g(\theta _1)\) and \(f(\theta _1)+f(\theta _3)= g(\theta _1)+g(\theta _3)\) imply \(\mathbb E^{\mathbb P_c}(g)> \mathbb E^{\mathbb P_c}(f)\) and \(\mathbb E^{\mathbb P_c(\cdot |1)}(g)< \mathbb E^{\mathbb P_c(\cdot |1)}(f)\).
 
19
Using simple raw patent counts as a proxy for innovation success is severely limited by the very large variance in the significance or value of individual patents, rendering patent counts an extremely noisy indicator (Hall et al. 2005).
 
20
Similarly, Anton and Yao (2004) point out that patents serve as property right protection mechanism and represent a tradeoff between proprietary cost and disclosure transparency which benefits investors. In addition, Cohen et al. (2000) suggest that firms often use patents for strategic reasons rather than as a protection for the property rights, such as blocking competition, maintaining the intangible assets, and facilitating the bargaining power, and Png (2017) suggests that the legal system for property rights protection is an important consideration for firm’s patenting choice.
 
21
See Aghion et al. (2005) for a discussion on why the inverse Lerner is appropriate in these kinds of models. We also used the Herfindahl Hirschman index (HHI), but our results were qualitatively unchanged
 
22
Lowess is an acronym for “Locally weighted Scatter plot Smoother”. The smoothing process is considered local because each smoothed value is determined by neighboring data points defined within the span. A regression weight function is defined for the data points contained within the span.
 
23
see Aghion et al. (2013a); Blundell et al. (2002), for example.
 
24
\(g_{d}\)=1 for G-index values of less than 10, and \(g_{d}\)=0 for values of greater than or equal to 10. We also tested the model with \(g_{d}\)=1 for G-index values of less than or equal to the median value of 10, and our results were qualitatively similar.
 
25
We also used additional controls such as ln(Total Assets), Tobin’s q, S &P 500 firm, and CEO tenure in our base regressions. Our main results and significance remained unchanged. We do not report the results for brevity but they can be obtained from the author’s upon request.
 
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Metadata
Title
Innovation and governance
Authors
Saurav Roychoudhury
Anuj Bhowmik
Srobonti Chattopadhyay
Publication date
19-09-2023
Publisher
Springer US
Published in
Journal of Economics and Finance
Print ISSN: 1055-0925
Electronic ISSN: 1938-9744
DOI
https://doi.org/10.1007/s12197-023-09632-z