1 Introduction
Recent empirical studies have identified the favorable effects of improved competition in banking markets on corporate innovation (e.g. Amore et al.
2013) because of the increased credit supply and lowered costs of finance (Rice and Strahan
2010). Prior studies have also attempted to examine the moderating roles played by firm level characteristics in a ‘bank competition – corporate innovation’ relationship, such as firm age (e.g. Chava et al.
2013), nature of business in terms of public listing (e.g. Cornaggia et al.
2015), dependence on external finance (e.g. Amore et al.
2013) and financial constraints (e.g. Guariglia and Liu
2014). What is little known is about the role played by information specialization when financing corporate innovation activities. This is particularly important because corporate innovation carries a nature of being informationally opaque (Hall et al.
2005; Lobo and Xie
2018) and such a feature may moderate or mediate the favorable effects of bank competition on corporate innovation and the role played by banks in alleviating asymmetric information problems (Hadlock and James
2002). Moreover, bank market deregulation on interstate banking and branching has enabled banks to better diversify risk geographically (Amore et al.
2013). Banks still face a trade-off between funding more diversified innovation activities for risk diversification purpose and financing more specialized innovation for cost efficiency reasons in acquiring information from innovative borrowers.
This paper aims to advance our understanding around the bank market effects on corporate innovation. We commence our baseline analysis by revisiting the favorable effects of bank competition on corporate innovation to test the validity of our key bank competition measure, H-statistic, and our empirical model specification. Our results show robust evidence on the favorable and economically sizable effects of bank competition on corporate innovation where an increase of H-statistic by 0.1 raises patent and citation counts by 16% and 53% respectively. This paper differs from prior literature in three aspects. Firstly, we use unique ‘information’ measures at both industry (productivity growth dispersion) and firm (patent type distribution) level to capture information specialization and asymmetries of innovative firms. We show novel evidence that banking competition has stronger favorable effects on more concentrated patenting activities which are characterized by a higher degree of specialized proprietary information. For example, the marginal effects of improved banking competition are 16% greater for those firms with more concentrated patents than for those with dispersed patents. This result reveals that, in the presence of information asymmetries when financing corporate innovation, banks benefit from the economies of scale in acquiring more specialized information.
This paper also differs from prior literature by offering additional evidence on the favorable effects of bank competition on innovation efficiencies. Recent empirical studies (e.g. Amore et al.
2013) have mainly focused on such effects on the quantity (e.g. number of patents) and quality (e.g. citation, originality) of corporate innovation. This paper, instead, focuses on the efficiencies of corporate innovation and shows that bank competition improves innovation efficiencies, in terms of the numbers of patents per million dollar R&D investment and the profits generated by R&D spending, which have been neglected by recent empirical studies. Our results suggest that bank competition does not only increase credit supply but also improves the efficiencies of resource allocation where bank finance could be channeled to the most productive innovation activities.
Finally, when investigating banking competition effects, existing research has predominantly used
exogenous banking market deregulation (e.g. Chava et al.
2013; Amore et al.
2013) and market development (e.g. Benfratello et al.
2008; Hsu et al.
2014) as a measure of market structure. In this paper, we show evidence on the existence of endogeneity of banking market structure and use a variety of measures, including Panzar-Rosse H-statistic (Panzar and Rosse
1984), RS Index (Rice and Strahan
2010), branch density and Herfindahl–Hirschman Index (HHI) in the analysis. The endogenous issue exists because, first, unobserved state (local) characteristics could jointly determine banking market competition and corporate innovation activities (Butler and Cornaggia
2011). Second, there could be a causal issue where bank finances support corporate innovation while innovation could also lead bank finance to follow (Audretsch et al.
2012). To address this concern, we employ an instrumental variable approach and use ‘state median
1 Tier 1 capital ratio’ as an instrument for banking market competition. The underlying validation of our instrument lies in that the incumbent banks with higher Tier 1 capital ratio would have better ability to accumulate capital to build a buffer against failure and to set up a higher entry barriers for new players on the equilibrium path and thus, market concentration occurs (Corbae and D’Erasmo
2014).
The remainder of the paper proceeds as follows. Section
2 reviews relevant literature on the effects of bank market competition on credit availability and corporate innovation. Section
3 describes the data and methodology. Section
4 presents the empirical results. Section
5 concludes and summarizes the paper.
Corporate innovation has been an important research topic for decades and recent empirical studies have provided additional empirical evidence on the importance of R&D and innovation. For example, innovation enhances the performance and survival of firms by offering new growth opportunities (Artz et al.
2010). However, due to the inflexibility of R&D investment, R&D may also increase corporate distress risk especially for those financially constrained firms and during the economic downturns (Zhang
2015). In financing corporate innovation activities, venture capital investors have exhibited location bias (Cumming and Dai
2010) and focused more on the commercialization of existing innovations and the growth of the invested innovative firms (Engel and Keilbach
2007). Moreover, their exit strategies rely on the success of innovation activities (e.g. number of patents) of the firms they invested (Wang and Wang
2012).
As one of the most important external finance suppliers to firms, banks play an important role in credit supply, determining cost of finance and bank-firm relationship (e.g. Giannetti
2012) with corporate innovation. There has been ample theoretical and empirical evidence on the roles played by banks and banking market structure in financing innovative firms but empirical evidence is never conclusive. Literature built on the traditional market power hypothesis (e.g. Ongena and Smith
2001; Jayaratne and Strahan
1996) suggests that a decrease in competition restricts the supply of credit and, thereby, decreases innovation. This is because the monopoly power in banking sector would drive interest rates high and credit supply low, resulting in a loss of overall market efficiencies (Stein
2002; Beck et al.
2004). Competition, instead, improves the availability of external finance and lowers the costs of finance for businesses (Lian
2018; Mi and Han
2018).
In contrast, according to the information-based hypothesis, market power enables banks to extract informational rent (e.g. Stiglitz
2002) and banks would have stronger incentives to acquire private information in a concentrated banking market because of their ability to subsidize credit-constrained firms at the beginning of the relationship and to extract the rent later (Sharpe
1990; Petersen and Rajan
1995). Such relationships, however, are not sustainable in a competitive market because of the free-riding problem (Dell’Ariccia and Marquez
2004) and increased capital market competition reduces relationship lending (Fraser et al.
2012). Therefore, credit supply to those informationally opaque and financially constrained firms could be greater in a concentrated banking market. For example, Han et al. (
2009) have shown that small firms are less likely to be financially constrained in terms of being discouraged from borrowing in a more concentrated banking market than in a competitive market. Additional evidence is available from Petersen and Rajan (
1995), Black and Strahan (
2002), and Cetorelli (
2004). Indeed, banks have to face problems of asymmetric information when financing informationally opaque businesses. Instead of the traditional arguments on relationship lending, banking strategic theory proposes that greater competition in local credit markets would improve bank cost efficiency (Chortareas et al.
2016) and drive banks to increase credit supply to small, proximate and opaque borrowers (McKee and Kagan
2018). As a result, banks would create a competitive edge that helps insulate themselves from pure price competition from outside banks (Boot and Thakor
2000; Dell’Ariccia and Marquez
2004).
This paper is motivated by recent research development in bank competition and corporate innovation (e.g. Cornaggia et al.
2015) which has shown consistent evidence on the favorable effects of bank competition and financial development on corporate innovation in Italy (Benfratello et al.
2008), U.S. (Cornaggia et al.
2015), China (Hsu et al.
2013) and cross country (Hsu et al.
2014). The favorable effects come from the increased credit supply (Amore et al.
2013) and reduced cost of finance for businesses (Rice and Strahan
2010) when bank market becomes more competitive and the improved capability of banks to diversify risk after bank deregulation (Amore et al.
2013). The favorable effects are also driven by the improved pricing mechanism of equity markets (Hsu et al.
2014) and the nurturing role of financial systems on innovation (Hsu et al.
2013) in both developed and emerging economies.
The widely accepted favorable effects of bank competition and financial development on corporate innovation have been found to vary over firm level characteristics and the nature of innovation, such as firm age (Acs and Audretsch
1988), dependence on external finance (Cohen and Klepper
1996), financial constraints (Amore et al.
2013), private vs. public firms (Chava et al.
2013), and process vs. product innovation (Boer and During
2001). Overall, it has been shown that firms with a greater dependence on bank credit for innovation, such as those young, small and private firms would benefit more from bank market competition and financial development (e.g. Petersen and Rajan
2002; Rajan and Zingales
1998; Cetorelli and Strahan
2006). However, what is less known about the ‘bank competition—corporate innovation’ relationship is how information moderates the effects of bank competition on innovation and the economic consequences of bank competition to innovation efficiencies. In light of the innovation literature, knowledge-intensive firms have intrinsically higher information and knowledge gap with firm outsiders (Jia
2019). The higher information specialization poses a problem for the innovative firms to terminate or initiate a lending relationship with banks, so that information differentiation captures the degree of specialization in relationship building (Boot and Thakor
2000). Because of the high level of switching costs, firms with intensive proprietary information would not switch banks easily even if the rival banks tend to reduce loan pricing when competition is introduced. In this scene, banks are able to extract information rents in the range of switching costs and the higher the degree of specialization of the information, the steadier the rent of such information will be. Therefore, it expected that the greater competition among banks frequently facilitates more for the financing of those informationally opaque firms through producing the high degree of proprietary information which avoids the adverse effect of ongoing competition on profits.
Overall, the two under-studied areas are important to deepen our understanding on the roles of bank competition and financial development in facilitating corporate innovation because corporate innovation activities are risky and informationally opaque (Hall et al.
2005) and further empirical studies are called to investigate if increased credit supply has been channeled to the most productive innovation activities. To fill in these research gaps, this paper is aimed to examine the moderating effects of information opaqueness and the effects of bank competition on innovation efficiencies.
3 Data and methodology
3.1 Data
Our data are collected from various sources. To measure corporate innovation, we collect data from National Bureau of Economics Research (NBER) patent database and Hall et al. (
2001) and Li et al. (
2014) with detailed information on the patents granted by United States Patent and Trademark Office (USPTO) from 1976 to 2010. We exclude sample patents granted to universities, governments and foreign companies who rely weakly on local banking markets. We collect local (state level) bank information from Federal Deposit Insurance Corporation (FDIC) and firm level data from COMPUSTAT. In addition, we also collect state level venture capital investment information from National Venture Capital Association (NVCA) as a proxy for alternative sources of finance for innovation and state level controls from Federal Reserve Bank of ST. Louis. Our analysis is based on 44,567 firm-year observations between 1992 and 2010
2 which allow us to use both innovation data and bank data and to consider the effects of banking market deregulation in U.S. (Amore et al.
2013).
3.2 Measuring corporate innovation and innovation efficiencies
We measure corporate innovation by a widely used patent-metrics (Nelson et al.
2014) which prevents the problems arising from accounting practices, such as R&D expenditure (Dugan et al.
2016), and represents the output or the commercialization of innovation activities (Ciftci and Zhou
2016). Corporate innovative outputs are captured by the ‘weight-adjusted’ numbers (see detail from Hall et al.
2001) of patents (
\( Patent_{ijt} \)) filed by company
i in state
j in year
t and citations (
\( Citation_{ijt} \)) as a measure of the economic importance of innovation activities. We measure innovation efficiency by (1) the number of patents generated by per million dollar R&D investment (
\( Patent/R\& D \)) and (2) return on R&D (
\( Profit/R\& D \)). We also examine the bank competition effects on R&D expenditure as a measure of innovation inputs.
3.3 Measuring banking market competition and controlling for endogeneity
To measure banking market competition, we use Panzar-Rosse H-statistic
3 (
H henceforth) (Panzar and Rosse
1984) with long term equilibrium in the main tests and
RSIndex,
HHI and
Branch Density in the robustness tests.
H has been acknowledged to be robust and superior to other competition measures, being derived from profit-maximizing equilibrium conditions (Shaffer
2004; Claessens and Laeven
2004) and widely used to test banking market competition (e.g. Molyneux et al.
1994; Bikker and Haaf
2002), with a range between 0 (monopolistic markets) and 1 (competitive markets).
A potential endogeneity issue of banking market competition may arise if the level of competition in a local banking market and corporate innovation decisions are jointly determined by unobserved state characteristics. We employ an instrumental variable (IV) approach by using ‘state median Tier 1 risk-based capital ratio’ as an instrument,
4 relying on the fundamental feature of competitive markets with ‘free entry’ for new players and ‘free exit’ for those that fail (Tian and Han
2019). Tier 1 ratio measures how well a bank is capitalized in terms of the amount of core capital that it holds in comparison to the size and risk profile of the bank. In U.S., current capital requirement is based on Basel III accord and a bank is defined as being undercapitalized if its Tier 1 risk-based capital ratio is less than 6%. This rule was enforced jointly by Office of the Comptroller of the Currency (OCC), Board of Governors of the Federal Reserve System and FDIC. Existing literature has shown a strong impact of bank capital on the stability of banks where banks with a high capital ratio have a greater ability to accumulate capital to build a buffer against unexpected losses. For example, a 50% increase in capital requirement in U.S. banking industry would reduce the exit rates of small banks by 45% and lead to a more concentrated industry (Corbae and D’Erasmo
2014). In addition, the capital regulation directly places a constraint on banks’ potential to entry in a local market. Therefore, a state with lower Tier 1 capital ratio would have a more competitive banking market (greater
H). Our statistical evidence shows that the correlation between Tier 1 ratio and
H is—0.1718 (
p < 0.01). Moreover, we have no reason to believe that performed capital ratio of banks directly affects corporate innovation activities.
5
We measure the degree of information asymmetries at both industry and firm levels. At industry level, we follow Duchin et al. (
2010) and measure information asymmetries by productivity growth dispersion which is defined as the standard deviation of productivity (the ratio of sales to number of employees) growth rate based on a 3-digit SIC industry classification. Innovative firms in a specific industry with a greater productivity growth dispersion are deemed to be more informationally opaque because their corporate performance carries a greater degree of idiosyncratic risk.
Because of the information-sensitivity nature of innovation activities (Jia
2019), a greater information specialization would create a problem for the innovative firms to terminate an existing or initiate a new borrowing relationship with banks. The dynamic nature of innovation, in both the evolutionary and resource-based perspectives, implies that firms with unique innovative capabilities would innovate in particular areas of the technological frontier more efficiently than others (Dosi
1982). This would lead to an increase of information rents over time. Therefore, more concentrated patenting activities are characterized by a higher degree of proprietary information specialization. At firm level, we follow Hall et al. (
2001) and categorize patents into six types, including chemical (excluding drugs), computers and communications, drugs and medical, electrical and electronics, mechanical and other. We then measure information specialization by the
kurtosis and variance of distribution of patent types at firm level, which can better reflect the probability of extreme outliers produced by the distribution. We define a sample firm having dispersed patents (
Dispersed patent =1) if its distribution of patent types has a kurtosis (variance) lower than 3 (greater than cross-industry median); concentrated patents (
Dispersed patent =0) otherwise, where kurtosis is greater than 3.
3.5 Additional control variables
We also control for firm, industry and state characteristics that may affect corporate innovation outputs in our analysis, such as firm size and age, profitability (ROA), cash holding, growth opportunity (sales and Tobin’s Q), asset tangibility, leverage, capital to labor ratio, industry concentration, state-level coincident index and venture capital ratio. We winsorize these variables at 1st/99th percentile in the following analysis and variable definitions are provided in Appendix.
3.6 Summary statistics
Table
1 reports the descriptive statistics for the variables used in the empirical analysis. Our main samples are 32,910 firm-year observations between 1992 and 2010. Averagely, each sample firm obtains 11 patents which receive 137 citations per year. In terms of innovation efficiencies, a typical firm invests US$79 m in R&D and every one dollar R&D investment generates US$0.65 profits. Averagely, every million dollar R&D investment would generate 0.8 patent.
Table 1Descriptive statistics
Innovation variables |
Number of patents \( \left( {Patent} \right)_{it} \) | 44,567 | 17 | 136 | 0 | 6460 |
Number of citations \( \left( {Citation} \right)_{it} \) | 44,567 | 99 | 1123 | 0 | 71,827 |
R&D ($m) \( \left( {R\& D} \right)_{it + 1} \) | 42,706 | 102.849 | 455.128 | 0.000 | 4297.000 |
Profits/R&D \( \left( {\Pr ofit/R\& D} \right)_{it} \) | 44,567 | 10.644 | 28.152 | − 3.432 | 237.920 |
Patents/R&D in $m \( \left( {Patent/R\& D} \right)_{it} \) | 44,567 | 36.413 | 191.699 | − 3.432 | 14,263.881 |
Kurtosis of patent types distribution | 21,002 | 3.935 | 2.930 | − 3.333 | 6.000 |
Variance of patent types distribution | 35,158 | 47.491 | 217.813 | 0.000 | 1363.5 |
Banking market competition variables |
H-statistic (\( {H}_{{{jt}}} \)) | 41,016 | 0.568 | 0.241 | 0.104 | 1.000 |
RSIndex (\( RSIndex_{jt} \)) | 27,982 | 2.603 | 1.381 | 0 | 4 |
HHI (\( HHI_{jt} \)) | 36,992 | 0.012 | 0.026 | 0.001 | 0.465 |
Branch density (\( Density_{jt} \)) | 36,992 | 0.033 | 0.065 | 0.001 | 1.390 |
Other control variables |
Size ($m) | 42,784 | 4223.405 | 15,046.143 | 0.428 | 132,000.000 |
Age | 44,567 | 14.538 | 9.015 | 1.000 | 35.000 |
Return on assets (ROA) | 43,916 | − 0.052 | 0.492 | − 3.155 | 0.391 |
Cash holding | 43,617 | 0.161 | 0.188 | 0.000 | 0.880 |
Asset tangibility | 43,921 | 0.228 | 0.189 | 0.000 | 0.824 |
Capital to labor ratio | 43,688 | 172.673 | 368.204 | 5.800 | 3126.051 |
Bank loan ratio | 44,547 | 0.469 | 0.458 | 0.000 | 1.000 |
Leverage | 43,926 | 0.106 | 0.464 | − 1.685 | 2.766 |
Sales ($m) | 43,925 | 2861.428 | 8869.681 | 0.000 | 72,102.047 |
Tobin’s Q | 40,066 | 2.159 | 3.188 | 0.061 | 21.884 |
Product market HHI | 44,567 | 0.094 | 0.172 | 0.003 | 1.000 |
Industry standard deviation of productivity growth | 42,880 | 171.065 | 133.540 | 0.000 | 1393.517 |
Coincident Index | 41,189 | 82.264 | 12.035 | 57.343 | 108.243 |
Venture capital | 43,993 | 2972.408 | 6398.523 | 0.415 | 42,868.500 |
Overall, the local (state) banking market is monopolistic competitive, measured by either H, RSIndex, HHI or Branch Density. A typical sample firm is 15 years old and has book value assets of US$5.188 billion, ROA of—%, cash-to-assets ratio (cash holding) of 22.7%, Tobin’s Q of 2.519, net property, plants and equipment (PPE)-to-assets ratio (tangibility) of 24.1%, leverage of 1.1%, capital to labor ratio of 4.32, and industry HHI of 0.014. On average, the kurtosis of patent type distribution is 3.8, indicating more concentrated patents generated by the sample firms.
3.7 Baseline specifications
Our basic econometric model
6 (Eq.
1) focuses on the effects of banking market competition measured by Panzar-Rosse H-statistic (
\( H_{jt} \)) on corporate innovation:
$$ ln\left( {Innovation} \right)_{ijt} = \alpha_{2} + \beta_{2} \widehat{{H_{jt} }} + \gamma_{2n} \sum Z_{it} + Industry_{k} + Year_{t} + \varepsilon_{2it} $$
(1)
where
i,
t,
j and
k denote company, year, state and industry respectively.
\( Innovation_{ijt} \) is the measure of the level (
\( Patent \),
\( Citation \)) and efficiency (
\( Patent/R\& D \),
\( Profit/R\& D \)) of innovation activities at firm level.
\( \widehat{{H_{jt} }} \) is the predicted value of
H after controlling for endogeneity by using ‘state median Tier 1 capital ratio’ as an instrument in the first stage and therefore,
\( \beta_{2} \) is expected to capture the unbiased effect of
H on innovative outcomes.
\( Z_{it} \) denotes a vector of firm-level controls. Given that the U.S. patenting activity increased substantially from mid-1980s (Hall
2004), we also control for the aggregate trends by
\( Industry_{k} \) and
\( Year_{t} \) to capture industry and year fixed effects respectively, so that the estimated effect of banking competition on innovation is not driven by an industry- or time-specific trend.
5 Conclusions
Since the implementation of IBBEA in 1990s, U.S. banking market has become more competitive. Differing from literature on the financial aspects of banks and firms (e.g. Rice and Strahan
2010), this paper investigates the impacts of state-level banking market competition on firm level corporate innovation. Our results reinforce the favorable effects of the improved local banking market competition on corporate innovation. We show bank competition increases innovation outputs, such as patents and citations and inputs, such as R&D expenditures. Our results also show that bank competition improves innovation efficiencies in terms of outputs per $m R&D investment and enables informationally more opaque innovative firms to be less financially constrained and to benefit more from the increased credit supply. Our empirical evidence strongly supports market power hypothesis where with improved banking competition, firms would have better access to bank finance with lower costs to support their innovation activities.
What is less understood in literature is how banks deal with asymmetric information issues in financing innovation activities, with improved market competition. We propose that there could be two strategies for banks to allocate additional credit supply in more competitive markets, which are not necessarily mutually exclusive. Banks could diversify risk by investing a wider range of innovation activities and alternatively, they could finance a certain types of innovation to benefit from the economies of scale in more specialized information acquisition. Our results show that the additional credit supply stemming from improved banking competition provides a much stronger support to those innovative firms operating in informationally opaque industries and firms with more specialized information (more concentrated patent types). Therefore, our finding reveals that banks would channel their additional credit supply to a certain types of innovation activities to take advantages of the economies of scale in collecting more specialized information.
Our findings provide important implications. Our results suggest that improving banking market competition is an effective way to enhance firms’ effectiveness in generating innovations because of the increased credit supply and reduced costs of finance. The greater competition in local banking sector would strengthen the exclusive ties between banks and firms and drive banks to reallocate more resources to the borrowers which are mostly affected by information problems. Therefore, policy makers should target those innovative firms which rely more heavily on bank finance when they have difficulties to access external finance, in a scenario of credit supply decrease for example. Due to the unavailability of relevant data, this paper has not considered the impacts of non-negligible government subsidies and government supports to corporate innovation via taxes breaks for instance. We call for future research to further investigate such effects upon the availability of relevant data. In addition, we also call for future research on the possible over-investment with unbridled market competition.
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