The effect of banking deregulation on R&D investment: Evidence from the cross-regional operation of city banks

Liu, Peisen; Chen, Shiqi Article The effect of banking deregulation on R&D investment: Evidence from the cross-regional operation of city banks Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Liu, Peisen; Chen, Shiqi (2023) : The effect of banking deregulation on R&D investment: Evidence from the cross-regional operation of city banks, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 8, Iss. 4, pp. 1-13, https://doi.org/10.1016/j.jik.2023.100451 This Version is available at: https://hdl.handle.net/10419/327357 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The effect of banking deregulation on R&D investment: Evidence from the cross-regional operation of city banks Peisen Liu a,b , Shiqi Chen c, * a College of Economics and Management, Southwest University, Chongqing 400715, China b Center for Inclusive Finance and Agricultural & Rural Development, Southwest University, Chongqing 400715, China c School of Environment, Education and Development, University of Manchester, Manchester M139PL, UK ARTICLE INFO Article History: Received 16 September 2022 Accepted 3 November 2023 Available online 13 November 2023 ABSTRACT Improving firms’innovation capability is crucial for promoting growth. In 2006 and 2009, China deregulated the banking industry and allowed city banks to set up branches across regions, which changed the banking structure and competition. Using data from financial licence information and 1,012,321 manufacturing firms in China during the 2004−2010 period, this study investigates the effects of banking deregulation on research and development (R&D) investment and provides a reference for firm innovation. The results show that city bank entry increases R&D investment by 0.084% and R&D investment increases by 0.037 percentage points with a 1% increase in cross-regional branches of city banks. The robustness and endogeneity tests confirm these findings. Mechanism tests reveal that city banks’deregulation encourages firms to participate in R&D activities and improve R&D investment by reducing financing constraints. The impacts of city banks’ cross-regional operations on R&D investment are heterogeneous due to the variety in the geographical location, ownership, size, industry and age of firms. The promotion of banking deregulation on R&D investment comes from firms in the eastern region. The positive effect of banking deregulation is stronger for stateowned enterprises than private and foreign enterprises, stronger for large firms than small and medium firms, stronger for high-tech firms than ordinary firms and stronger for old firms than new firms. These findings provide evidence and implications for optimising financial structure to promote firm innovation and economic growth. © 2023 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Keywords: Banking deregulation City banks R&D investment Financing constraints JEL code: G21 G28 G30 G32 Introduction The mode of economic growth driven by factor input is unsustainable in developing countries, and it is essential to establish an innovation-driven growth pattern with strong research and development (R&D) capabilities (Nielsen, 2020). Although innovation is an endogenous power source for economic growth, R&D intensity is still at a low level in developing countries (Chen et al., 2021). The National Bureau of Statistics of China announced that R&D expenditure intensity was 2.55% in 2022 and innovation quality needed to be improved. Enhancing the innovation ability of firms is crucial for developing countries to skip the middle-income trap (Wei et al., 2017). Finance is a leading factor, and a perfect financial market helps to collect innovative resources for firms and promote innovation. The threshold of direct financing in developing countries is high; thus, small firms have limited opportunities to obtain funds through stock and bond markets to support R&D activities than large firms (Liu & Li, 2020). Therefore, indirect financing is crucial to the smooth progress of R&D activities in a financial market dominated by banks. In the 1990s, the deregulation of interstate banks in the United States enhanced financial inclusiveness, financial security and firm innovation (C elerier & Matray, 2019). China’sfinancial system has made remarkable achievements and met the needs of the economy. In 2006, the Chinese government relaxed the regulation of small banks’ cross-regional operation, and city commercial banks (hereinafter referred to as city banks) can set up branches across prefecture-level cities or even provinces. Competition between city banks and other financial institutions reduces firm financing constraints and volatility, especially for firms that rely on external financing (Jiang et al., 2020). However, the mismatch between the financial structure of capital supply and microstructure of capital demand leads to the misallocation of financial resources and hinders innovation (Lin et al., 2015). There are few studies on the effect of banking deregulation on R&D activities and its mechanisms. Newly industrialised countries are in the stage of transforming the old and new driving forces of growth, and the lack of evidence on the effect of bank branching deregulation * Corresponding author E-mail address: [email protected] (S. Chen). https://doi.org/10.1016/j.jik.2023.100451 2444-569X/© 2023 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Journal of Innovation & Knowledge 8 (2023) 100451 Journal of Innovation &Knowledge https://www.journals.elsevier.com/journal-of-innovation-and-knowledge on R&D activities is regrettable to promote economic growth (Matthess & Kunkel, 2020). This study matches firm data with financial licence information according to firms’addresses and exploits a quasi-natural experiment with the difference in the establishment time of city bank branches in different regions to investigate the effects of banking deregulation on R&D activities. (1) What impact does city banks’cross-regional operation have on R&D investment? (2) What is the impact mechanism of banking deregulation on R&D investment? (3) Does firm heterogeneity affect the effect of banking deregulation on R&D investment? Answers to these questions can evaluate the effect of banking deregulation and provide a useful reference for promoting firm innovation, which is important for improving the efficiency of capital allocation and the transformation of growth mode. The existing research has not obtained a system theory with explanatory power on the relationship between banking deregulation and R&D activities. This study contributes to the literature in the following respects. First, this study is related to the literature that investigates the effect of city banks setting up branches across regions on R&D investment and provides suggestions for formulating policies on banking deregulation and firm innovation. The existing literature focuses on the effect of bank competition on firm financing and wages (Beck et al., 2013;Bens et al., 2022), whereas the effect on R&D activities has not yet led to conclusions (Cornaggia et al., 2015). Second, this study contributes to the literature on the effect of banking deregulation that leads to bank competition on R&D activities and to banking deregulation measurement (Chava et al., 2013;Xin et al., 2022), whereas the previous studies focused on the effect of bank competition on firm financing (Chemmanur et al., 2020;Avramidis et al., 2022). This study uses the changes in the number of crossregional branches of city banks around firms to proxy banking deregulation. Third, this study contributes to the research on the determinants of firm R&D investment (Hall et al., 2015;Adegboye & Iweriebor, 2018). Based on manufacturing firm data, this study investigates the effects and mechanisms of the cross-regional expansion of city banks and examines the heterogeneous effects of firm location, ownership structure, size, industry and age attributes on R&D investment, which provides evidence for firm innovation and a reference for the study of banking deregulation. Finally, this study empirically examines the effect of banking deregulation on R&D activities by using econometric models, including instrumental variables and the Heckman model (Benfratello et al., 2008;Tian et al., 2019). This study reveals that not only do firms hardly affect macro-level banking deregulation but also that banking deregulation comes from exogenous shocks, which provides ideas for future research. Section 2 summarizes the literature review and assumptions. Section 3 describes the methodology and data. Section 4 and Section 5 present the results and discussions. Section 6 is the findings and practical implications. Literature review and research hypothesis China’s banking reform The Chinese financial system makes remarkable reforms that meet the demand for economic growth and financial stability (Brunnermeier et al., 2022). This study focuses on banking deregulation in China for two reasons. First, China is the largest developing country and the second-largest economy in the world. China’sfinancial system is dominated by banks. The proportion of direct financing is still low compared with indirect financing, such as bank loans, which occupy a dominant position. Second, to enhance the ability of financial services to benefit the real economy, the Chinese government relaxes its control over the banking industry. The deregulation of small banks to set up branches across regions intensifies the competition among banks and changes the banking structure (Pu & Yang, 2022). These banking reforms increase the role of market mechanisms in capital allocation and reduce the difficulty and cost of corporate financing (Lin et al., 2009). Before the reform and opening-up in 1978, the People’s Bank of China was the only bank in China that had the functions of commercial and policy financial business. In 1979, the first city credit cooperative was established. However, its cooperative nature was unclear, and the management system was imperfect. Several city credit cooperatives had been in trouble because they relied on high-interest rates to support securities and real estate speculation. After the mid- 1990s, the city credit cooperatives were transformed into city banks to improve operating efficiency and reduce non-performing loans, which were mainly to provide financial services for local firms and residents. The development of city banks promotes the local economy and overcomes the dilemma that the financial market is monopolised by large state-owned banks. After China’s entry into the WTO, China lowered the conditions for foreign banks to enter, which enhanced banking competition and firm supervision. The deregulation of foreign banks entering China reduces the capital costs of domestic firms and extends loan maturity (Lin et al., 2022). Large state-owned and joint-stock banks can set up branches nationwide, whereas small banks, such as city banks, cannot set up branches in other cities. The cross-regional operation of city banks goes through three stages. Before 2006, it was forbidden for city banks to set up branches across cities. A city can only set up one city bank, and the city bank can only set up branches in its own city. A more strictly regulated banking market is less competitive (Claessens & Laeven, 2004). Therefore, this limits the geographical scope of financial services for city banks, which is not conducive to enhancing bank competition and hindering scale economies. Moreover, due to geographical restrictions, it is difficult for city banks to improve their brand influence and popularity across regions. The regulations of city banks increase operational risk. When the operations of city banks are limited to a city, their operating performance is easily affected by local economic fluctuations. Government intervention in the banking industry reduces banks’size and market share, because the regulation forces banks to lower deposit interest rates (Nielsen & Weinrich, 2023). In 2006, the National Administration of Financial Regulation (NAFR) promulgated ‘The Measures for the Administration of Branches of City Commercial Banks in Different Places’. The law lowers the conditions for the cross-regional operation of city banks, and city banks compete for market share with large state-owned and joint-stock banks. The deregulation of city banks changes the banking market structure and enhances banking competition. Majority of the largest shareholders of city banks are local governments and stateowned enterprises. City banks gain more deposits by adding branches and expanding the scope of services, which is an important channel for governments to increase investment and stimulate economic growth (Chen et al., 2020). In 2009, NAFR gave the approval authority for city banks to set up branches in the province to the provincial regulatory authorities. There are no longer restrictions on the number of cross-regional branches for city banks, and city banks enter a period of rapid development that enhances banking competition. Although interstate banking deregulation has no effect on economic growth, intrastate branching deregulation is beneficial to improving the local long-term economy by 0.5 percentage points in the United States (Spierdijk et al., 2021). The cross-regional operation of city banks provides an opportunity to clarify the effect of banking deregulation on R&D activities by separating the role of city banks in stimulating R&D investment from that of large banks. The expansion of city banks intensifies financial competition, forming new ways to ease financing constraints (Gao et al., 2019). The mechanism by which banking deregulation affects loan quality lies in the fact that the diversification of operating areas P. Liu and S. Chen Journal of Innovation & Knowledge 8 (2023) 100451 2 reduces the customer concentration of banks after deregulation, which reduces systemic risk. Therefore, this study investigates the effects of the cross-regional operation of city banks owing to banking deregulation on firm R&D activities. Banking deregulation and R&D investment An imperfect financial market and weak handling ability of soft information by financial institutions lead to financing constraints, whereas a perfect financial structure, low government intervention and financial agglomeration help firms obtain funds (Berger et al., 2017). It is an inherent requirement of firm management to reduce financing constraints. Financial institutions’expansion alleviates financing constraints by collecting information footprints, reducing information asymmetry and financial exclusion, correcting resource mismatches, controlling innovation risk and improving capital allocation efficiency (Fungacova et al., 2017;Caggese et al., 2019). Moreover, financial development reduces finance costs and promotes R&D activities; however, financial friction hinders firm R&D activities (Hall et al., 2015;Bazot, 2018). There are two views on the impact of banking competition on firm financing. The information hypothesis holds that weakening financial competition encourages banks to establish contact with borrowers to obtain information, reduces the adverse effects of information asymmetry and increases relational financing. Information asymmetry and agency problems cause an increase in firm costs, namely, financing constraints (Myers & Majluf, 1984;Gertler, 1992). Compared with big banks, small banks have a more flexible operating mechanism and a higher enthusiasm for obtaining the soft information from firms, thus narrowing the information asymmetry between banks and firms (King & Levine, 1993;Berger & Udell, 2002). In the asymmetric information market, a banking monopoly helps banks overcome adverse selection and moral hazard, encourages banks to obtain borrower information and expands financing supply (Gonz alez, 2020). A medium level of banking deregulation improves financial efficiency, which is the best state of banking competition (Biswas & Koufopoulos, 2020). The market power hypothesis argues that banking deregulation is beneficial to firms as it improves financing availability and alleviates financial constraints (Santiago et al., 2009;Wang et al., 2020). When the monopoly of the banking industry weakens, and banking competition intensifies, banks will provide more attractive terms to compete for high-quality customers. Branching deregulation and increasing small financial institutions promote banking competition, reduce financing costs and stimulate firm innovation (Rice & Strahan, 2010). The deregulation of the British banking industry strengthens the relationship between banks and firms and stimulates firms to increase leverage and R&D investment (Braggion & Ongena, 2019). Branching deregulation reduces bank concentration and results in low market share and loan interest, which are stronger in the competitive industry of strategic alternatives with negative externalities (Saidi & Streitz, 2021). The cross-regional operation of city banks caused by banking deregulation affects firms’financing constraints and R&D investment in two aspects. First, city banks are an important source of funds for firms and are closely connected with local governments, which are the largest shareholders of many city banks (Cheng et al., 2021). The main goal of setting up bank branches across regions is to absorb deposits and issue loans (Thorsten et al., 2010). The deregulation of banks intensifies the competition in the banking market where they are located, forming a new channel to reduce financing constraints and increase R&D investment (Rice & Strahan, 2010). Second, banking deregulation reduces ownership and relationship discrimination; thus, small firms obtain more funds from financial institutions than before. By opening branches across regions, city banks conduct due diligence on firms, alleviate information asymmetry and then reduce credit risk and non-performing loans. Geographic deregulation of banks affects their capital management tools and leads to a higher target capital ratio (Berger et al., 2023). Management compensation is a way through which banking deregulation increases firms’risk incentives and innovation (Bens et al., 2022). If banking deregulation could improve financing availability and reduce debt costs, the crossregional operation of city banks would promote R&D investment by reducing firm financing constraints, as shown in Fig. 1. Based on this, the following hypotheses are formulated. Hypothesis 1: Banking deregulation increases firm R&D investment. Hypothesis 2: Banking deregulation increases firm R&D investment by reducing financing constraints. Banking deregulation, heterogeneity and R&D investment The lending relationship between banks and firms may be sensitive to firm characteristics, market structure, business environment and public policy (Berger & Udell, 2002;Hsieh et al., 2019). Information asymmetry, low financial transparency and a lack of collateral result in financing constraints (Bollaert et al., 2021). By setting up branches across regions, city banks gain a wider development space, increase the sources of deposits and loans and reduce debt costs and loan risks (Deng & Elyasiani, 2008). Hence, the effects of increased banking deregulation on R&D investment may be heterogeneous due to the diversification of firm characteristics. The ownership competition view holds that it is more difficult for private firms to obtain loans than for state-owned enterprises (Cull & Xu, 2003). There is bank discrimination in the financing market; state-owned enterprises can obtain stable loans, whereas private firms find it difficult to obtain an equal market position. Private firms have obvious characteristics of financial repression, fewer financing channels and a lack of relationships with banks. As a result of interventions by state-owned banks and governments, lending by banks prefers state-owned enterprises to private firms (Cheng et al., 2021). Bank concentration is positively related to long-term debt and maturity structure for state-owned enterprises, whereas lower government intervention is a benefit for private firms to obtain long-term loans (Liu et al., 2018). Therefore, it is easier for state-owned enterprises to obtain financing from the financial market than private firms. Since banking deregulation allows small banks to set up branches across regions, similar to state-owned banks, the impact of banking deregulation on firms is more prominent in areas with a higher proportion of state-owned banks (Chen et al., 2023). Bank competition reduces the unfair treatment faced by private firms and lowers the threshold for them to obtain funds (Liu & Li, 2020), which provides an opportunity for firms to increase R&D investment. The size competition view holds that the monopoly of large banks destroys the balance of loan allocation, which makes the credit funds of financial institutions lean towards large firms. Large banks are more willing to establish long-term cooperative relations with large and transparent firms and provide financing support for these firms, Fig. 1. Banking deregulation, financing constraints and R&D investment. P. Liu and S. Chen Journal of Innovation & Knowledge 8 (2023) 100451 3 which make the financing constraints of large firms lower than those of small firms. Financial openness is conducive to the growth of firms with low information asymmetry, such as large and listed firms (Park et al., 2020). In contrast, small firms have few approaches to access to financial markets than large firms (Mudd, 2013); however, increased banking deregulation improves their position and thus promotes their access to bank loans (Avramidis et al., 2022). Investment by banks in relationship lending is vital for small firms to obtain bank loans (Fungacova et al., 2017). In a homogeneous market dominated by small banks, bank competition helps firms with low transparency obtain funds, whereas in a heterogeneous market controlled by large banks, bank competition prevents firms with less information from obtaining funds (Heddergott & Laitenberger, 2017). There are a number of studies on the relationship between banking deregulation and the financing of small firms. Small- and medium-sized financial institutions’expansion increases bank competition, which promotes the investment and growth of small firms (Hasan et al., 2017). These firms have the characteristics of asymmetric information and less collateral, which makes banks reluctant to take risks to provide loans for them. Banking deregulation is conducive to obtaining funds, reducing the unfair treatment of private and small firms and improving firm performance. Although banking development cannot fully meet the funds’needs of small firms with comparative advantages, city and joint-stock banks reduce the financing constraints of these firms (Chong et al., 2013). The timing of a firm’s entry into the market is related to its financing channel and ability (Cowling et al., 2017;Bai et al., 2018), which may lead to differences in their R&D activities. New firms have less collateral, incomplete information disclosure and high risk, which makes them unfavourable to financial institutions and reduces their possibility of obtaining loans. In contrast, old firms have more advantages, such as asset size and market opportunities, resulting in fewer financing constraints than new firms. Hence, a high level of banking monopoly may promote firm growth, and banking deregulation is conducive to new firms. The changes in banking deregulation may impact firms with financing constraints. The hypothesis to be tested is as follows: Hypothesis 3: The effects of banking deregulation on the R&D investment of different types of firms are heterogeneous. Model specification Empirical methodology Following the previous studies (Chava et al., 2013;Cornaggia et al., 2015), new models are constructed to investigate the effect of banking deregulation on firm R&D investment. R&D_ratioi;t¼aþbBank_dumi;tþ#Controli;tþviþhtþmkþ’jþei;t;k;j ð1Þ R&D_ratioi;t¼aþbBank_numi;tþ#Controli;tþviþhtþmkþ’jþei;t;k;j ð2Þ where i, t, k and jrepresent the firm, year, industry and region, respectively. The dependent variable, R&D_ratio, is the R&D investment at the firm level. The independent variables, Bank_dum and Bank_num, are explanatory variables and represent the cross-regional operation of city banks at the prefecture-level city. The coefficient b captures the change in firm R&D investment. A positive value of b suggests a relatively higher R&D investment level after banking deregulation. Control represents a series of firm-level variables. As a result of reverse causality and the omitted variables, one of the challenges faced by this study is to identify the causal relationship between banking deregulation and R&D activities. First, R&D investment is likely to be endogenous to firm and market characteristics, including banking development. Thus, a correlation between banking development and firms may make it difficult to explain the causal effects of banking deregulation on R&D investment. Moreover, banking deregulation is measured at the prefecture-level city, and R&D investment is measured at the firm level; thus, firm R&D investment cannot affect the decisions of banks at the regional level. Second, there may be omitted variables in the models that affect the estimated results, and error terms may include the unobserved regional and industry characteristics that are related to R&D investment and banking deregulation. Ordinary least squares regression makes it difficult to produce correct statistical inferences with omitted variables. The panel dataset allows the elimination of time-invariant, unobservable effects. Therefore, v i ,h t ,m k and ’ j are the firm, time, industry and region fixed effects. e i,t,k,j is a random error term. Variable measures There are three methods to measure firm R&D investment. R&D_ratio is the firm R&D investment intensity. R&D_dum is a dummy variable that represents firm R&D investment. In addition, Ln_R&D is the firm R&D investment size. R&D_dum and Ln_R&D are used for robustness tests. Banking deregulation enhances bank competition; this study adopts four methods to capture the branching deregulation of city banks. The dummy variable, Bank_dum, is assigned to 1 if there is a city bank from other prefecture-level cities that set up a branch locally and 0 otherwise. The continuous variable, Bank_num, is the natural logarithm of the number of branches set up by city banks from other cities. The dummy variable, State_dum, is assigned to 1 if there is a city bank from other provinces that sets up a branch locally and 0 otherwise. The continuous variable, State_num, is the natural logarithm of the number of branches set up by city banks from other provinces. State_dum and State_num are used for robustness tests. Following the previous literature (Giebel & Kraft, 2019;Bouteska et al., 2023), the control variables include firm asset size Asset. The difference in resource endowment among firms may affect their ability to provide funds for R&D projects. The current asset ratio, Current, is an indicator to measure the cash flow of firms. When the Current is higher, it implies that firms have more free cash flow and operate better. The capital intensity, Capital, is measured by the natural logarithm of the ratio of fixed assets to employee numbers. Capital intensity reflects the fixed assets shared by an employee. The debt level, Leverage, affects cash flow and financing constraints and has an impact on R&D investment. Return on assets, Profit, controls the influence of internal financing capacity on firms. The higher the return on assets is, the stronger the profitability is, increasing investment. Subsidy controls governments’support. Firms that conform to policy orientation can receive subsidies, which play a positive role in reducing financing constraints and increasing R&D investment. The concentration of industry, HHI, uses the Herfindahl index of the five firms with the highest sales revenue to measure market power, which controls the impact of industry concentration on R&D investment. Table 1 presents the variable descriptions. Data The data on city bank branches comes from the financial licence information of NAFR, which includes the addresses, establishments, and cancellation times of more than 220,000 bank branches. The information of bank branches would be deleted from the sample after being closed; however, their information before being closed is included in the sample. This study calculates the changes in banking deregulation based on the geographical location of city bank branches. Firm data come from the Annual Survey of Industrial P. Liu and S. Chen Journal of Innovation & Knowledge 8 (2023) 100451 4 Enterprise. This study uses previous literature for reference to screen samples; deletes samples with fewer than eight employees and eliminates samples with at least one of R&D investment, industrial output value, total assets, owner’s equity, liabilities, fixed assets and operating income less than zero or missing. The extreme values other than 1%−99% of continuous variables at the firm level are truncated, and 1,012,321 annual observations from 2004 to 2010 are obtained. The two datasets are matched using prefecture-level location information. Table 2 reports the summary statistics of the variables. Column (6) shows the mean differences in characteristics between firms with and without R&D investment. The Spearman correlation coefficients between variables are less than 0.509, indicating that multicollinearity is not a concern. Empirical results Baseline specification and results Table 3 shows the results. The control variables are not added to columns (1) and (2), and the coefficients of Bank_dum and Bank_num are significantly positive, revealing that the cross-regional operation of city banks is positively related to firm R&D investment. In columns (3)−(6), the coefficients of Bank_dum and Bank_num are significantly positive, indicating that the deregulation of city banks is positively correlated with R&D investment, which is consistent with the findings of columns (1) and (2). The result of column (5) suggests that the R&D investment of firms with cross-regional operation areas of city banks is 0.084% higher than that of firms without cross-regional city banks. Column (6) shows that for every 1% increase in the number of cross-regional branches set up by city banks, firm R&D investment increases by 0.037 percentage points. These results support Hypothesis 1, confirming the applicability of the market power hypothesis in China’sfinancial services to the real economy. Moreover, the goodness of fit of the models is above 0.58. Compared with large banks, city banks have flexible mechanisms and strong innovation ability, which meet the funding needs of R&D activities. The deregulation of city banks setting up branches across regions results in an increase in branches and competition. Therefore, the bargaining position of banks declines, whereas that of firms rises relatively. Financial institutions more easily support R&D activities than before. Banking deregulation improves the innovation ability and production efficiency of firms (Chen et al., 2023). The results reveal that the influence of banking deregulation on R&D investment would be overestimated without considering the effect of firm characteristics. The coefficients of Asset are significantly negative, and there is a negative correlation between firm asset size and R&D investment. The greater the firm size is, the lower the R&D intensity is. Current reveals that the current asset ratio is negatively correlated with R&D investment. Capital indicates that capital intensity is positively related to R&D investment. Capital-intensive firms are more willing to invest in R&D projects to promote innovation and update production equipment. When capital intensity is higher, firms adopt more advanced equipment and technology; meanwhile, firms with low capital intensity rely on traditional production modes. Leverage has a positive correlation with R&D investment. Return on Table 1 Variable definitions. Variable Definition R&D_ratio The ratio of R&D investment to operating income. R&D_dum When a firm has an R&D investment, the value is 1; otherwise, it is 0. Ln_R&D The natural logarithm of the R&D investment of a firm (unit: 1,000 Yuan). Bank_dum When there are city banks from other prefecture-level cities that set up branches locally, the value is 1; otherwise, it is 0. Bank_num The natural logarithm of the number of branches set up by city banks from other prefecture-level cities. State_dum When there are city banks from other provinces that set up branches locally, the value is 1; otherwise, it is 0. State_num The natural logarithm of the number of branches set up by city banks from other provinces. Asset The natural logarithm of total assets (unit: 1,000 Yuan). Current Current assets divided by total assets. Capital The natural logarithm of fixed assets divided by the number of employees (unit: 1,000 Yuan). Leverage Total liabilities divided by total assets. ProfitProfit divided by total assets. Subsidy The natural logarithm of the government subsidy plus 1 obtained by afirm (unit: 1,000 Yuan). Age The natural logarithm of time from the year of a firm establishment to the year of observation. HHI The Herfindahl index of operating income of the quartile industry of afirm. Table 2 Summary statistics. (1) (2) (3) (4) (5) (6) Group Full sample With R&D No R&D Mean difference Variable Mean Median SD Mean Mean (4)−(5) R&D_ratio 0.223 0.000 1.497 1.865 0.000 1.865 *** R&D_dum 0.120 0.000 0.325 1.000 0.000 1.000 *** Ln_R&D 588.900 0.000 19651.200 4916.051 0.000 4916.051 *** Bank_dum 0.258 0.000 0.438 0.299 0.252 0.047 *** Bank_num 5.522 0.000 17.502 5.444 5.533 −0.089 *** State_dum 0.117 0.000 0.322 0.153 0.112 0.041 *** State_num 0.589 0.000 2.737 0.787 0.563 0.224 *** Asset 93907.4 16960.0 932633.7 362429.7 57357.6 305072.1 *** Current 0.544 0.569 0.258 0.550 0.543 0.007 *** Capital 105.100 47.650 270.900 137.004 100.707 36.297 *** Leverage 0.553 0.566 0.265 0.557 0.552 0.005 *** Profit0.099 0.047 0.162 0.092 0.101 −0.009 *** Subsidy 273.500 0.000 9928.000 1115.080 158.925 956.155 *** Age 9.551 7.000 8.338 12.409 9.162 3.247 *** HHI 0.038 0.013 0.087 0.044 0.037 0.007 *** Notes: The summary statistics of variables Ln_R&D, Bank_num, State_num, Asset, Capital, Subsidy and Age are the original values. These variables take the natural logarithm in regression tests. Columns (1)−(3) are summary statistics of the full sample. P. Liu and S. Chen Journal of Innovation & Knowledge 8 (2023) 100451 5 assets is negatively correlated with R&D investment. Age suggests a negative correlation between firm operating time and R&D investment. The longer the operating time is, the lower the R&D intensity is. These results show that capital intensity, liability and government subsidy cannot be ignored in increasing firm R&D investment, which confirms the rationality of the models. This study transforms the effect of the cross-regional operation of city banks on R&D investment into the marginal effect in column (6) and then plots it in Fig. 2. The figure shows that increased city bank branches increase R&D investment, revealing that deregulating city banks to set up branches across regions is an effective way to help firms increase their investment in R&D projects. The shaded area is the 90% confidence interval of the marginal effect of banking deregulation. When banking deregulation rose from the lowest level to the highest level, the R&D investment intensity of firms rose from 0.20% to 0.38% (Fig. 2). Robustness tests This subsection presents several methods to examine whether the above findings are robust to alternative measures of variables. Table 4 reports the results. First, to avoid estimation bias caused by measurement errors of variables, this study adopts R&D_dum and Ln_R&D to measure firm R&D investment and assumes State_dum and State_num to measure banking deregulation by the inter-provincial operations of city banks. Columns (1) and (2) of Table 4 are logit model results, and the coefficients of Bank_dum and Bank_num are significantly positive, indicating that the cross-regional operations of city banks encourage firms to participate in R&D activities. In columns (3) and (4), the coefficients of Bank_dum and Bank_num are significantly positive, respectively, showing that banking deregulation increases R&D investment scale. The coefficients of State_dum and State_num are significantly Table 3 Baseline regression results. (1) (2) (3) (4) (5) (6) Variable R&D_ratio R&D_ratio R&D_ratio R&D_ratio R&D_ratio R&D_ratio Bank_dum 0.090 *** (0.005) 0.084 *** (0.005) 0.084 *** (0.005) Bank_num 0.039 *** (0.002) 0.037 *** (0.002) 0.037 *** (0.002) Asset −0.042 *** (0.003) −0.042 *** (0.003) −0.042 *** (0.003) −0.042 *** (0.003) Current −0.081 *** (0.008) −0.079 *** (0.008) −0.081 *** (0.008) −0.078 *** (0.008) Capital 0.008 *** (0.002) 0.008 *** (0.002) 0.008 *** (0.002) 0.008 *** (0.002) Leverage 0.063 *** (0.008) 0.065 *** (0.008) 0.063 *** (0.008) 0.064 *** (0.008) Profit−0.161 *** (0.011) −0.162 *** (0.011) −0.160 *** (0.011) −0.161 *** (0.011) Subsidy 0.006 *** (0.001) 0.006 *** (0.001) 0.006 *** (0.001) 0.006 *** (0.001) Age −0.007* (0.004) −0.008* (0.004) −0.007 (0.004) −0.007* (0.004) HHI 0.018 (0.016) 0.020 (0.016) 0.018 (0.016) 0.020 (0.016) Constant 0.200 *** (0.002) 0.200 *** (0.001) 0.626 *** (0.034) 0.622 *** (0.034) 0.625 *** (0.034) 0.621 *** (0.034) Firm FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Industry FE No No No No Yes Yes Region FE No No No No Yes Yes Observations 1012321 1012321 1012321 1012321 1012321 1012321 Adj. R-squared 0.581 0.581 0.583 0.583 0.584 0.584 Notes: Standard errors are in parentheses. The explained variable is R&D_ratio. *** p<0.01; ** p<0.05; * p<0.1. Fig. 2. The marginal effect of banking deregulation on R&D investment. P. Liu and S. Chen Journal of Innovation & Knowledge 8 (2023) 100451 6 positive in columns (5) and (6), respectively, suggesting that the inter-provincial expansion of city banks helps firms increase R&D investment. The inter-provincial operations of city banks can better reflect banking deregulation than cross-regional operations in the province. Compared with the results in Table 3, inter-provincial operations of city banks increase R&D investment more than crossregional operations within a province. Second, this study examines the omitted variables. The models may not include factors that change with regions and time and are not directly observed, which results in estimation bias. Therefore, the interaction terms of time and regional effects are added to the models, and the results are shown in columns (7) and (8). The coefficients of Bank_dum and Bank_num are significantly positive, indicating that the factors that change with the time and region do not change the evaluation of city banks’deregulation to increase R&D investment. Third, this study investigates the non-linear relationship between banking deregulation and R&D investment. The quadratic variable of Bank_num, Bank_num 2 , is included in column (9). The coefficient of Bank_num is significantly positive, and the coefficient of Bank_dum 2 is significantly negative. On the left side of the inflection point, increasing the number of cross-regional branches set up by city banks (competition among banks intensifies) increases R&D investment. Meanwhile, on the right side of the inflection point, the increased number of city bank branches reduces R&D investment. Specifically, the inflection point is 2.053, and the branches of cross-regional city banks in most regions are on the left side of the inflection point, suggesting that the positive relationship between banking deregulation and R&D investment is stable. These results confirm the robustness of the findings. Endogeneity tests One of the challenges in this study is identifying the causal relationship between banking deregulation and R&D investment. The cross-regional operation of city banks promotes R&D investment, and areas with more R&D activities tend to have higher economic levels, which attract city banks to set up branches. Thus, banking deregulation may be endogenous, and regional factors may affect the crossregional operation of city banks. Reverse causality may be a factor if regional differences affect firm activities and banking development. First, this study employs instrumental variables as an alternative identification of the effects of banking deregulation on R&D investment. This study divides the regions where the firms are located into three categories to construct instrumental variables. The first is municipalities. The second is sub-provincial cities, provincial capitals and cities with separate plans. The third is county-level regions within a prefecture-level city. This study takes the average level of banking deregulation in the same type of region (excluding the region where the firm itself is located) as an instrumental variable. The asymmetric information of R&D projects is obvious, and the transaction and information collection costs of cross-regional financing by city banks are high. Therefore, the impact of banking deregulation in other regions on local R&D activities is small. Cities at the same level and within the same region have similarities in social, Table 4 Robustness test results. (1) (2) (3) (4) (5) (6) (7) (8) (9) Variable R&D_dum Ln_R&D R&D_ratio Bank_dum 0.363 *** (0.019) 0.083 *** (0.005) 0.052 *** (0.006) Bank_num 0.102 *** (0.007) 0.036 *** (0.002) 0.024 *** (0.003) 0.156 *** (0.007) State_dum 0.178 *** (0.006) State_num 0.099 *** (0.003) Bank_num 2 −0.038 *** (0.002) Asset 1.299 *** (0.010) 1.301 *** (0.010) 0.101 *** (0.003) 0.101 *** (0.003) −0.041 *** (0.003) −0.042 *** (0.003) −0.042 *** (0.003) −0.042 *** (0.003) −0.042 *** (0.003) Current −0.094 *** (0.031) −0.123 *** (0.031) −0.007 (0.008) −0.005 (0.008) −0.078 *** (0.008) −0.063 *** (0.008) −0.055 *** (0.009) −0.056 *** (0.009) −0.081 *** (0.008) Capital −0.218 *** (0.008) −0.223 *** (0.008) −0.013 *** (0.002) −0.013 *** (0.002) 0.008 *** (0.002) 0.010 *** (0.002) 0.012 *** (0.002) 0.012 *** (0.002) 0.008 *** (0.002) Leverage 0.152 *** (0.034) 0.175 *** (0.034) 0.003 (0.008) 0.005 (0.008) 0.059 *** (0.008) 0.056 *** (0.008) 0.043 *** (0.008) 0.043 *** (0.008) 0.062 *** (0.008) Profit0.715 *** (0.055) 0.709 *** (0.055) 0.060 *** (0.011) 0.059 *** (0.011) −0.153 *** (0.011) −0.151 *** (0.011) −0.134 *** (0.011) −0.134 *** (0.011) −0.155 *** (0.011) Subsidy 0.125 *** (0.003) 0.125 *** (0.003) 0.021 *** (0.001) 0.021 *** (0.001) 0.006 *** (0.001) 0.006 *** (0.001) 0.006 *** (0.001) 0.006 *** (0.001) 0.006 *** (0.001) Age 0.304 *** (0.014) 0.312 *** (0.014) −0.005 (0.004) −0.005 (0.004) −0.007 (0.004) −0.009* (0.004) −0.005 (0.004) −0.005 (0.004) −0.007* (0.004) HHI 0.543 *** (0.084) 0.553 *** (0.084) 0.016 (0.016) 0.018 (0.016) 0.011 (0.016) 0.015 (0.016) 0.002 (0.017) 0.002 (0.017) 0.015 (0.016) Constant −19.89 *** (0.098) −19.87 *** (0.099) −0.279 *** (0.034) −0.283 *** (0.034) 0.614 *** (0.034) 0.612 *** (0.034) 0.602 *** (0.034) 0.600 *** (0.034) 0.613 *** (0.034) lnsig2u 3.226 *** (0.006) 3.226 *** (0.006) Firm FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Region FE Yes Yes Yes Yes Yes Yes Yes Year-region FE No No No No Yes Yes No Observations 1012321 1012321 1012321 1012321 1012321 1012321 1012321 1012321 1012321 Adj. R-squared 0.778 0.778 0.584 0.584 0.586 0.586 0.584 Notes: Standard errors are in parentheses. The explained variable of columns (1) and (2) is R&D_dum, the explained variable of columns (3) and (4) is Ln_R&D, and the explained variable of columns (5)−(9) is R&D_ratio. *** p<0.01; ** p<0.05; * p<0.1. P. Liu and S. Chen Journal of Innovation & Knowledge 8 (2023) 100451 7 economic and natural factors. When city banks set up branches, these banks are more willing to select areas with few branches and low competition. Hence, it is relevant for city banks at the same city level or within the same region to set up branches. Columns (1) and (2) of Table 5 are the two-stage least squares results of the second-stage regressions. The F statistic is greater than 10, and the minimum eigenvalue statistic is greater than the critical value, indicating that the instrumental variables are suitable. The coefficients of Bank_dum and Bank_num are significantly positive, suggesting that setting up branches across regions of city banks is positively correlated with R&D investment after eliminating the endogenous problems. Banking deregulation is beneficial to increasing firm R&D investment, and the conclusion is valid. Second, this study solves the problem of sample self-selection. A firm’s participation in R&D activities is a two-stage decision. The first stage is that the firm decides whether to participate in R&D activities. The second stage is that the firm determines the R&D investment scale. Sample statistics show that a large number of firms did not participate in innovation activities, and their R&D investment was zero. If the firms that did not participate in R&D activities were deleted or treated equally with those that participated in R&D activities, the estimation results may be biased. Heckman (1978) constructed a two-stage selection model to deal with truncated data and solve the problem of selection bias. In the model, the first stage is the selection model, which examines the determinants of R&D participation. The second stage is the linear regression model, which checks the determinants of the R&D investment scale. Columns (3)−(6) of Table 5 present the results. The inverse Mills ratio is significant, and there is a problem of sample selection bias. It is reasonable to adopt a two-stage selection model. The coefficients of Bank_dum and Bank_num are significantly positive in columns (3) and (5), respectively, revealing that the cross-regional operation of city banks promotes firms to participate in R&D activities. The coefficients of Bank_dum and Bank_num are significantly positive in columns (4) and (6), respectively, showing that the deregulation of city banks to set up branches across regions increases the intensity of firm R&D investment. Mechanism tests If alleviating financing constraints is a way for banks to increase firm R&D investment, banking deregulation plays a more significant role in improving the innovation ability of firms with financing constraints. From the perspective of financing, this study examines how banking deregulation affects firm R&D investment by impacting financing constraints. The models are set as follows: R&D_ratioi;t¼aþbBank_dumi;tþgFini;tþsBank_dumi;t Fini;tþuControli;tþviþhtþmkþ’jþei;t;k;jð3Þ R&D_ratioi;t¼aþbBank_numi;tþgFini;tþsBank_numi;t Fini;tþuControli;tþviþhtþmkþ’jþei;t;k;jð4Þ where Fin i,t indicates the financing constraints faced by ifirm in t year. This study uses three methods to measure financing constraints. Long is the natural logarithm of the increment in firm long-term loans. Leverage is a firm’s debt ratio. Cost is the ratio of interest expenses to liabilities and represents financing costs. The coefficient gcaptures the impacts of financing constraints on firm R&D investment. The models include interaction terms between banking deregulation and firm financing constraints. scaptures how banking deregulation shapes the effects of financing constraints on R&D investment. External finance-dependent firms may take advantage of banking deregulation to reduce financing constraints and increase R&D investment. Table 6 shows that the coefficients of Bank_dum and Bank_num are significantly positive, indicating that the cross-regional operation of city banks improves firm R&D investment. The coefficients of Long and Leverage suggest that the increase in financing makes firms invest more in R&D projects. In columns (1)−(4), the coefficients of interaction terms are significantly positive, revealing that banking deregulation strengthens the positive effects of reducing financing constraints on R&D investment. The coefficients of Cost suggest that increased financing costs force firms to reduce R&D investment. As shown in columns (5) and (6), the coefficients of interaction terms are statistically and significantly negative, indicating that banking deregulation lowers the negative effect of debt costs on R&D investment. These results support Hypothesis 2. The aforementioned results confirm that banking deregulation helps firms obtain loans, reduces financing constraints and provides a better financing environment for R&D projects. These findings are consistent with the market power hypothesis, which argues that bank competition leads to higher financing accessibility for firms. Table 5 Endogeneity test results. (1) (2) (3) (4) (5) (6) Instrumental variable First stage Second stage First stage Second stage Variable R&D_ratio R&D_ratio R&D_dum R&D_ratio R&D_dum R&D_ratio Bank_dum 0.175 *** (0.005) 0.123 *** (0.004) 0.662 *** (0.039) Bank_num 0.060 *** (0.002) 0.021 *** (0.001) 0.161 *** (0.011) Asset −0.013 *** (0.003) −0.011 *** (0.003) 0.328 *** (0.001) −0.980 *** (0.074) 0.328 *** (0.001) −0.996 *** (0.074) Current −0.105 *** (0.006) −0.114 *** (0.006) 0.038 *** (0.007) −0.184 *** (0.052) 0.027 *** (0.007) −0.224 *** (0.052) Capital 0.008 *** (0.002) 0.006 *** (0.002) −0.076 *** (0.002) 0.027 (0.021) −0.077 *** (0.002) 0.023 (0.021) Leverage 0.087 *** (0.007) 0.096 *** (0.007) −0.092 *** (0.007) −0.707 *** (0.057) −0.082 *** (0.007) −0.664 *** (0.057) Profit−0.129 *** (0.011) −0.129 *** (0.011) 0.204 *** (0.012) −2.009 *** (0.103) 0.200 *** (0.012) −1.998 *** (0.104) Subsidy 0.006 *** (0.001) 0.006 *** (0.001) 0.056 *** (0.001) −0.048 *** (0.013) 0.057 *** (0.001) −0.051 *** (0.013) Age 0.041 *** (0.004) 0.043 *** (0.004) 0.094 *** (0.002) −0.242 *** (0.027) 0.095 *** (0.002) −0.246 *** (0.028) HHI 0.005 (0.016) 0.009 (0.016) 0.287 *** (0.019) 1.275 *** (0.158) 0.289 *** (0.019) 1.323 *** (0.159) Constant 0.217 *** (0.029) 0.207 *** (0.029) −4.531 *** (0.015) 18.868 *** (1.249) −4.507 *** (0.015) 19.255 *** (1.255) Firm FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Region FE Yes Yes Yes Yes Yes Yes Observations 1012321 1012321 1012321 1012321 1012321 1012321 Mills ratio −3.601 *** (0.296) −3.665 *** (0.298) Notes: Standard errors are in parentheses. The explained variable of columns (1), (2), (4), and (6) is R&D_ratio, the explained variable of columns (3) and (5) is R&D_dum. *** p<0.01; ** p<0.05; * p<0.1. P. Liu and S. Chen Journal of Innovation & Knowledge 8 (2023) 100451 8