Credit market development and firm innovation evidence from china

Credit Market Development and Firm Innovation: Evidence from China Abstract From the perspective of credit allocation, this paper analyzes the effect of credit market development on firms’ innovative capacities in China Using a large data set of Chinese industrial firms in 31 provinces, we find out that, from the credit allocation perspective, credit market development enhances both firms’ innovation incentives and outcomes We show that firms’ credit constraints and firms’ performances are two channels through which the credit market development affects firms’ innovative capacities The innovation incentives and outcomes of more credit restrained firms and firms with better performances are affected by credit market development more than those of other firms In addition, we demonstrate that our results are not driven by the increase of the number of firms JEL classifications: G21; O32 1 Introduction Innovation, as the engine of a firm’s development, has been taken as a major driving force of economic growth since Solow (1957) However, what drives innovation is still worth investigating There is a growing literature exploring the factors affecting innovation from various perspectives In this paper, we contribute to this literature by analyzing how the development of the credit market affects firm innovation, from the perspective of credit resource allocation As Levine (2005) pointed out that “…, if finance is to explain economic growth, we need theories that describe how financial development influences resource allocation decisions in ways that foster productivity growth…” It indicates that if credit market development helps allocate more credit to firms or projects that increase productivity growth, credit market development is expected to increase innovation In other words, if the financial intermediaries are active in researching firms, monitoring firms and pooling risks, they are supposed to enhance firms’ innovative capacities In order to measure the credit allocation induced by credit market development, we use an index constructed from the ratio of credits allocated to non-state-owned enterprises (non-SOEs) on total credits of each province in China King and Levine (1993) argue that a financial system which simply allocates credits to state-owned enterprises (SOEs) is different from that allocating to private firms By allocating to SOEs, the financial intermediaries usually just follow the government directives without exerting its function to facilitate credit allocation On the contrary, allocating to non-SOEs could indicate that financial institutions actively examine firms’ performances China provides a good case study for analyzing whether credit market development, from the perspective of credit allocation, affect firms’ innovative capacities The Chinese credit system originates from a mono-bank system, with the People’s Bank of China (PBC) functioning both as a central bank and a commercial bank It is evolving towards a more market-based system gradually Until now, the credit system is composed of various types of banks and non-bank financial institutions to satisfy different kinds of firms The credit allocation also turns from policy lending to more market-based lending As Firth et al (2009) show that the Chinese banks have already been able to use commercial judgments to allocate credits to private firms to some extent We examine how the credit market development affect both firms’ incentives in engaging in innovative activities and innovation outcomes Our data are obtained from the National Bureau of Statistics (NBS) of China from 2000 to 2007 NBS conducted annual surveys on industrial firms including SOEs and non-SOEs above Million RMB in each province One advantage of the data set is that it allows us to analyze the innovative behavior of the non-listed firms More than 99% of the firms in this data set are non-listed Since the non-listed firms are accounted for a much larger part of the Chinese economy, it is very important to explore factors affecting non-listed firms’ innovative capacities Further, compared to listed firms, non-listed firms rely more on credit financing for their operations The second advantage of this data set is that banks apply commercial judgments to a larger degree for the manufacturing industry (Firth et al, 2009) One of the important problems in the finance-innovation literature is the endogeneity problem caused by omitted variables and the reverse causality of finance and innovation We minimize the omitted variable problem by using firm-level analysis Firm-level analysis allows us to control many unobserved variables, such as firm-level, industry-level and province-level unobserved variables which might affect both credit market development and firm innovation We then apply instrumental variable method to solve the reverse causality problem Our results indicate that, from the credit allocation perspective, credit market development enhances both firms’ innovation incentives and outcomes We further demonstrate that there are two possible channels One channel is through relaxing firms’ credit constraints The marginal benefit of the credit market development on the credit restrained firms are larger than that on other firms The innovation incentives and outcomes of more credit restrained firms, such as private firms and small- and middle-sized firms (SMEs), are affected by credit market development more than those of other types of firms Another channel is that by improving credit allocation, financial institutions are more willing to lend to firms with better performances Therefore, the innovation incentives and outcomes of firms with better performances are affected by credit market development more than those of firms with worse performances In addition, we demonstrated that our results are driven by improvement of credit allocation rather than increase of number of non-SOEs in a province Our results are also robust to different estimation methods and different sample Our paper is closely related to the literature on whether and how credit market development affects innovation One part of literature argues that credit market development mobilize and provide appropriate financing to firms and projects which promotes economic growth, and provide research, evaluation and monitor services to firms more effectively and less expensively Since innovation is the driving force of economic development, credit market development enhances firm innovation (Schumpeter, 1911; King and Levine, 1993; Morales, 2003) The other part of the literature argues that credit market development discourages innovation First, banks are conservative, dislike risky innovative projects (Weinstein and Yafeh, 1998; Morck and Nakamura, 1999) Second, banks prefer to use physical assets to secure loans They prefer firms which have large investment in plant and equipment, rather than those which have substantial R&D investment to generate intangible assets The most recent cross-country and within country empirical analysis also reaches contrary conclusions For example, Ayyagari, et al (2011) find that bank financing enhances innovation of SMEs in developing countries Xiao and Zhao (2012) argue that credit market development enhances innovation for countries with lower government ownership of banks Hsu, et al (2014) find that credit market development discourages innovation for more high-tech intensive industries and industries more dependent on external finance Benfratello, et al (2008) argue that for Italian firms, banking development accelerates the probability of process innovation, but less for product innovation Daniele, et al (2013) find that for US listed firms in 1976 to 1995, credit market development enhances the quantity and quality of innovation activities Different from researches which provide country-level and industry-level researches, we provide firm-level evidences for the debate on whether and how credit market development affects innovation We also discuss the underlying mechanisms In addition, our results shed light on the debate on whether financial development affects economic growth The rest of the paper is organized as follows In Section 2, we provide institutional background Section describes data and provides summary statistics Section presents results Section provides robustness check Section concludes Institutional Background After several years of development, the Chinese financial system is gradually going towards a more efficient and effective system The credit resources allocation also becomes more market-based Before 1978, the Chinese financial system was a mono-bank system The People’s bank of China (PBC), functioned both as a central bank and a commercial bank From 1978 to 1984, four state-owned specialized banks were established The credit business was separated from PBC to the four banks However, even there were many new institutions created, the financial system did not change much in its style of operation The four banks just supplied loans to state-owned enterprises based on policy lending From 1986, the Chinese financial system becomes more diversified and competitive Various types of financial institutions are developed in order to satisfy different types of customers From 1986 to 1988, urban credit cooperatives developed rapidly Their assets increased from around billion RMB to 284 billion RMB Since 1995, the government allowed them to combine and transform to city commercial banks in order to help urban economy, small and medium-sized firms and urban households In 1986, the first joint-stock bank, Bank of Communications established Until 2000, there are 10 joint-stock banks They become the major competitors for the four specialized banks Due to their advanced organization culture, their credit allocation style are more market-based than the four specialized commercial banks In 1994, three policy banks were founded, separating the policy businesses from the four specialized banks The four specialized banks gradually became state-owned commercial banks From 1996, the rural credit cooperatives are separated from Agriculture Bank of China The PBC also took some reforms on commercial banks’ lending behavior in 1998 It abolished the loan size restrictions on the four state-owned commercial banks The management style changed from mandatory plans to guiding plans It also asked all commercial banks to rank their loans into five categories according to loan risk from 1998 to 2000 After the entry of WTO in 2001, the Chinese financial system further went through several reforms In 2002, China Union Pay was set up, bank card could be used across banks and regions It greatly improved the information processing speed of the financial system In 2003, the Chinese banking regulatory commission (CBRC) was established The Chinese government tried to reinforce the PBC’s independence in monetary policy In order to improve the corporate governance of the four stateowned banks, and speed up the commercialization of the state-owned banks, the government allowed four state-owned banks to go public from 2005 to 2008 In 2004, the CBRC also expressed its opinion to encourage and support city commercial banks to bring in foreign strategic investor, go public and reconstruct CBRC further allowed these banks to operate across regions gradually In 2006, the government opened the RMB business completely to foreign banks The entry of foreign banks had contributed to the efficiency of the Chinese banking system (Xu, 2011) In the meantime, Chinese financial intermediaries were guided to extend more credit to SMEs and private firms According to the speak of the Chinese vice prime minister, Bangguo, Wu, in the 2001 APEC SME supervisor ministering meeting, the number of SMEs is around 99% of total number of enterprises in China; industrial output to total output is around 60%; the employee number is around 75% of total number of urban employees The Minister of All-China Federation of Industry and Commerce (ACFIC) Ministry of Economy Affairs, Xiaoming Ouyang, in the 2007 National development and reform commission (NDRC) press conference said 90% of SMEs were private enterprises Aware of the importance of SMEs and private enterprises in economic growth, in 2003, the law of promoting small and medium-sized enterprises came into effect In 2005, the State Council issued documents to encourage and direct the growth of private firms In 2005, CBRC urged banks to change their opinions on small firms and private firms, optimize the loan-providing process to small firms, and guide banks to establish compatible credit cultures with small and private firms The CBRC president, Mingkang, Liu, in the 2009 Chinese development high rise forum said that one of the reasons that financial institutions were unwilling to provide loans to SMEs was because they were lacking of knowledge and skills to identify promising SMEs CBRC had invited some small financial institutions and city commercial banks which had good experiences in supporting SMEs to introduce their experiences Data and Summary Statistics 3.1 Innovation measure We construct two innovation measures from the value of new products and total products One is firms’ innovation incentives, NP, a dummy variable 𝑖𝑓 𝑓𝑖𝑟𝑚 𝑖 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑛𝑒𝑤 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡 NP𝑖,𝑡 = {0 𝑖𝑓 𝑓𝑖𝑟𝑚 𝑖 𝑑𝑜𝑒𝑠𝑛′ 𝑡 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑛𝑒𝑤 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑡 , 𝑓𝑜𝑟 𝑖 = 1, … 𝑁; 𝑡 = 2000, … 2007 However, this can only measure whether a firm would like to engage in innovative activities It can not differentiate firms with more innovative activities from those having less innovative activities Therefore, the second measure we construct is called innovation outcome, NPr It is measured as the ratio of the new product production of a firm in one year on its total production in that year The higher the NPr, the more innovative the firm is in a particular year Panel A of Table provides the summary statistics for NP and NPr in the full sample and for innovative companies only The sample sizes for the full sample and the innovation companies are 891462 and 127959, respectively The mean and standard deviation of NP is 0.067 and 0.250 The mean and standard deviation of NPr are 0.03 and 0.139 The medians for both NP and NPr are It indicates that there are many zeros in the data For the sample including innovative companies only, the mean and standard deviation of NP are 0.4 and 0.490, respectively The mean and standard deviation of NPr are 0.19 and 0.303, respectively The median becomes 0.021 3.2 financial development measures We use an index constructed from ratio of credits allocated to non-SOEs on total credits for 31 provinces as the measure of credit market development This indicator is designed to assess how the credits are allocated among firms As King and Levine (1993) argues that a financial system, which allocates more financial resources to private firms are more active in researching firms, managing risks, than that only allocating financial resources to SOEs Therefore, a higher value of this index indicates that the credit allocation by the financial institutions are more active in researching firms rather than government policy oriented Panel C and Panel D of Table present the mean of the credit market development index in various years and various provinces Since we lag the credit market development for one period, we provide the results for year 1999 to 2006 From 1999 to 2006, the mean of the credit market increases from 3.27 to 8.73 It indicates that financial institutions are more active in researching firms and improve credit allocation throughout these years The credit market development of different provinces vary a lot The lowest and highest credit market development are only 1.9 and 10.82 on average Jiangsu, Zhejiang and Fujian provinces rank the top three of the credit market development on average Jilin, Heilongjiang and Hubei rank the bottom three Surprisingly, the credit market in Beijing doesn’t rank very high It is probably because the Beijing is the capital city The financial institutions in Beijing are affected by the central government more than many other provinces 3.3 Control variables Following the literature, we include firm, industry and province control variables which might affect firm innovation The firm control variables include firm size, firm age, age square, leverage, investment intensity, whether it is an export firm, roa, ownership types Industry control variable includes industry concentration (hhi) Province control variables include secondary industry production ratio, third industry production ratio and provincial GDP per capita In addition, we include the government subsidy since the Chinese government subsidies the companies in order for them to engage in more innovative activities Results 4.1 Baseline results We investigate how credit market development by improving credit allocation, affects innovation from two perspectives First, we explore whether the improving of the allocation affects firm incentives to produce new product in general Second, we investigate whether the improvement of the allocation encourage firms to produce more products Table provides the results for both firms’ innovation incentives and innovation outcomes For the innovation incentives, we apply both pooled logit and conditional logit estimation methods The advantage of the pooled logit is that it can utilize the information in all observations It can also provide average partial effect of a variable In comparison with the pooled logit method, the advantage of the conditional logit is that besides unobserved industry and province fixed effects, it also allow us to control unobserved firm fixed effect In other words, it can include many timeinvariant firm characteristics which affect both credit market development and firms’ innovation incentives This reduces the endogeneity problems caused by omitted variables Nevertheless, the conditional logit method sacrifices the average partial effect and the sample size The standard errors are clustered by industry and province The results under pooled logit method are demonstrated in Column (1) of Table It shows that credit market development, through improving of credit allocation, enhances the probability of firms producing new products in a statistically significant way, at 1% level, holding other factors constant When the credit market development increases by point, it induces around 471 (891384/7*0.0037) firms to engage in producing new products in one year, on average Column (2) provides the results under the conditional logit method The credit market development is also significantly positive at 1% significance level All the results confirm the idea that the credit market development, by improving credit allocation, does increase firms’ innovation incentives The results also indicate that firm size, the ratio of firms’ export on production, firms’ performances, GDP per capital in a province and the proportion of third industry production on GDP have statistically positive effects on firms’ incentives in engaging in innovative activities On the contrary, firms’ leverage and the proportion of secondary industry production on GDP in a province have statistically negative effects on firms’ innovation incentives In addition, as firms’ grow older, their innovation incentives first decrease and then increase For firms’ innovation outcomes, the estimation methods we use include both fixed effect regression method and Tobit method The fixed effect regression method is a commonly used method and very easy to apply It can help reduce the endogeneity problem caused by omitted variables However, it cannot account for the fact that the data are censored Since not all firms produce new product each year, there are many zeroes in the data, which might make the fixed effect regression to be less trustworthy In comparison with the fixed effect regression method, the Tobit method is more suitable to the censored data The standard errors are also clustered at province and industry level The results for firms’ innovation outcomes are presented in column (3) and (4) in Table In column (3), we provide the results estimated by the fixed effect regression method The results in column (3) show that the coefficient on credit market development is positive and significant at 5% level When credit market development increases by point, a firm produces 33 RMB (66739.79*0.05%)1 more new products in one year on average The results estimated by the Tobit method are presented in column (4) The coefficient on credit market development is positive and significant at 1% level After accounts for the fact that the data are censored, the coefficient of credit market development on firms’ innovation outcomes increases a lot It shows that when credit market development increases by point, a firm is predicted to produce 1441 RMB (66739.79*2.16%) more new products in one year, on average All the results in Table reinforces the idea that the credit market development, by improving credit allocation in China, does promote industrial firm innovation 66739.79 is the average production in our sample 4.3 Mechanisms In this subsection, we investigate the mechanisms through which the credit market development, through improving credit allocation, affects firm innovation incentives and outcomes Specifically, we examine the credit constraint channel and the firm performance channel 4.3.1 Credit constraint We first examine how the credit market development promotes firms’ innovation incentives and outcomes through alleviating firms’ credit constraints We hypothesis that if financial intermediaries can effectively alleviate firms’ credit constraints through time, more credit restrained firms should be affected by credit market development more It is because the marginal utility provided by credit market development is higher for more credit restrained firms than less restrained firms In order to examine whether the more credit restrained firms are affected by credit market development more, we first test whether private firms are affected by credit market development more than other types of firms The private firms have been found to be discriminated by Chinese banks due to several reasons Firstly, the Chinese financial system originated from a state-owned mono-bank system The financial institutions tend to allocate credits following central government or local governments’ directives (Cull and Xu, 2003) Secondly, due to the short-credit history and non-standard financial reports, the financial institutions discriminate non-listed private firms (Brandt and Li, 2003, Guariglia and Poncet, 2008) However, realizing the importance of the private sector on economic development2 and their problems in shortage of credit support, the Chinese government exert several methods to help the private sector It includes stating that the private firms are an important component in the Chinese economy, protecting private properties by law and reforming banks to meet private firms’ credit requests As time goes on, the situation for private firms is improving slowly The data from Almanac of China’s Finance and Banking indicates that in 2000, the percentage of short-term loans to private firms is 1% It increases to 3% in 2007 We construct a dummy variable, private, where private equals one if the firm is a private firm and zero otherwise We then add the interaction of private and credit market development to the models in Section 4.1 The coefficient of the interaction term captures the effect of the credit market development on private firms’ innovative capacities compared to those of the SOEs In Table 3, we provide the results for both firms’ innovation incentives and outcomes Column (1) and (2) show the results for firm innovation incentives under logit and conditional logit methods The coefficients on the interaction of the credit market development and private are From the Chinese NBS survey for the industrial firms, in 2000, the private sector contributes to 6% of gross industrial output Its contribution increases to 23% in 2007 The number of the private firms is 13.6% of total number of firms in 2000 It increases to 52.6% in 2007 positive and significant at 5% significance level in both regressions It means that the credit market development enhances innovation incentives of the private firms more than those of other types of firms Compared to other types of firms, the effect of credit market development on private firms’ innovation incentives is 0.12% higher Column (3) and (4) present the results for firms’ innovation outcomes The coefficients on the interaction of the credit market development and private are also positive and significant at 5% significance levels Compared to other types of firms, the effect of credit market development on private firms’ innovation outcomes is 0.09% higher under fixed effect regression and 0.59% higher under Tobit method We then test whether the small- and middle-sized firms (SMEs) are affected by credit market development, through improving of credit allocation, more than large firms It has been documented that compared to large firms, SMEs are less able to obtain credit from banks (Beck et al, 2008; Chong et al, 2013) The Chinese government has executed many methods to resolve this problem including modifying banking structures and directing banks to lend to SMEs However, the problem hasn’t been fully resolved yet (Chong et al, 2013) We construct two dummy variables: middle and small Middle equals one if the size of the firm is middle and zero otherwise Small equals one if the size of the firm is small and zero otherwise We add small dummy, middle dummy, interaction of the small dummy and credit market development, and interaction of middle-dummy and credit market development to the models in Section 4.1 The results are presented in Table All coefficients of the interaction terms in Column (1) and (2) are positive and significant at 1% level The results indicate that compared to large firms, the credit market development affects SMEs’ innovation incentives by 0.63% (0.4%+0.23%) more Both coefficients of the interaction terms are also significantly positive at 5% level under Tobit method It shows that compared to large firms, the effect of credit market development on SMEs are 3.14% higher All the results in Table and Table show that credit constraint is an underlying mechanism which explains how the credit market development promotes firm innovation The credit market development by improving credit allocation encourages credit restrained firms to innovate more than it does to less credit restrained firms 4.2.2 Firm performances We then examine whether credit market development, by improving credit allocation, affects firms’ innovation incentives and outcomes with better financial performances more than those with worse financial performances in China We hypothesize that as the financial institutions become more active in investigating firms and projects rather than based on government orders, they are more willing to lend to firms with better performances First, firms with better performances have 10 ability to pay back loans on time Second, firms which have better performances might indicate that the operation, management and the strategy of the firms are better than other types of firms and they might care more about their long-term growth Third, with better performances, firms might have greater ability and more willing to engage in risky innovative projects Therefore, banks might be also more willing to lend to the innovative projects of firms with better performances than those with worse performances We use return on asset (roa) as a proxy for firm performance The higher the roa, the better the firm performance Table also shows that firms’ roa has a positive and significant effect on firms’ innovation incentives and outcomes We generate a dummy variable fp50 In order to construct this variable, we first sort the firms based on their roas among the same industry, province and year Fp50 equals if the firms’ performances are in the higher 50 percentile, and zero otherwise The fp50 and the interaction term of fp50 and credit market development are added to all the models in Section 4.1 A positive and significant interaction term indicates that the firm performance is indeed a channel In Table 5, we present our results Column (1) and (2) show the results for firms’ innovation incentives, estimated by logit and conditional logit methods, respectively Column (3) and (4) show the results for firms’ innovation outcomes, estimated by fixed effect regression method and tobit method The coefficients associated with the interaction terms of credit market development and fp50 are all positively significant at 5% level It indicates that compared to low-performance firms, the effect of credit market development on high-performance firms’ innovation incentives and outcomes are 0.09% higher, 0.04% higher under fixed effect regression and 0.48% higher under tobit method, respectively Robust Check In this section, we check the robustness of our results We first check the validity of our results using instrumental variable method We then examine whether our results are driven by increase of the number of non-SOE firms rather than improvement of credit allocation In the end, we evaluate our results using firms which produce new products only 5.1 Instrumental variable regression In this subsection, we solve the reverse causality problem using instrumental variable method We construct an instrumental variable as follows We use the average of the credit market development in neighboring provinces as the IV Since the logit methods can not accommodate the instrumental variable method, we only report the results for firms’ innovation outcomes However, we check our results using 11 probit and IV-probit methods.3 The results are quite similar The results are available upon request Table presents the results Column (1) provides the results using the fixed effect regression method Column (2) shows the results using the Tobit method The coefficients obtained by these two methods are all statistically significant at 1% significance level It further reinforces the idea that credit market development, by improving credit allocation, promotes firm innovation 5.2 Improvement of credit allocation or not? Since the credit market development variable is constructed as an index of credits to non-SOEs on total credits, the larger the number of non-SOEs might lead to the higher the value It indicates that the increase of the credit market development might not be due to the improvement of the credit allocation Instead, it is because of the increase of the number of non-SOEs In order to check whether our results are due to improvement of the credit allocation or increase of the number of non-SOEs, we construct a variable nonSOE_ratio, as the ratio of the number of nonSOEs on the total number of firms We then add the nonSOE_ratio into the equations in Section 4.1 In Table 7, our results show that the credit market development variable is still significant for all cases after controlling the nonSOE_ratio 5.3 Firms with new products only Our main results are built on all firms regardless of having new products or not In this part, we restrict our sample to firms producing new products to further check our results We select firms producing new products in at least one year of our sample period The sample size reduces to 129,131 We reports all results using fixed effect regression and tobit method in Table The results are also consistent with our main results The credit market development term under the full sample is positive and statistically significant at 1% level under both methods The interactions of the private and the credit market development term and the interactions of the small and the credit market development term, the middle and credit market development term under both methods are also positive and significant Conclusions In this paper, we examine the effect of credit market development, through The probit method assumes a random effect However, we assume a fixed effect in order to be consistent with the firms’ innovation outcomes 12 improving of credit allocation, on firms’ innovative capacities in China Using a large data set of Chinese industrial firms in 31 provinces from 2000 to 2007, we find out that, from the credit allocation perspective, provincial-level credit market development enhances both firms’ innovation incentives and outcomes We show that firms’ credit constraints and firm performances are two channels through which the credit market development affects firm innovation abilities The innovation incentives and outcomes of more credit restrained firms and firms with better performances are affected by credit market development more than those of other types of firms We further show that our results are not due to increase of the number of non-SOEs in each year Our results are also robust under different estimation methods and different samples In order to solve the endogeneity problem caused by omitted variables, we control many unobserved variables, including firm fixed effect, industry fixed effect and province fixed effect We further use instrumental variable method to solve the reverse causality problem As far as we know, this is the first paper investigating whether the provincial credit market development, through improvement of credit allocation, enhances Chinese firms’ innovation abilities Different from researches which provide country-level and industry-level researches, we provide firm-level evidences for the ongoing debate on whether and how credit market development affects innovation 13 References [1] M.D., Schneider C., Žaldokas A (2013) Credit supply and corporate innovation Journal of Financial Economics 109, 835855 [2] Ayyagari M., Demirgău -Kunt A., Maksimovic V., 2011 Firm innovation in emerging markets: the Role of Finance, Governance, and Competition, JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS 46, 1545–1580 [3] Benfratello L., Schiantarelli F., Sembenelli A., (2008) Banks and innovation: Microeconometric evidence on Italian firms Journal of Financial Economics 90, 197– 217 [4] Brandt L., Li H.B., 2003 Bank discrimination in transition economies: ideology, information, or incentives? 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** significance at 5%; *** significance at 1% size 16 (0.0053) 0.5728*** (0.0204) -0.0904 (0.0730) 0.0499** (0.0166) -0.1082* (0.0585) 0.2107** (0.0719) 0.1904*** (0.0518) -2.3018** (1.0191) 0.4606*** (0.1119) 1.8736 (1.2853) 0.2985*** (0.0549) 0.0442 (0.0490) 0.0005 (0.0375) 0.1308 (0.0806) 0.1943** (0.0815) -5.1391*** (1.1289) 15.5376*** (1.3172) 1.1845*** (0.2496) (3) NPr_xtreg 0.0005** Table Credit market development and firm innovation: private vs other CMD CMD*private private (1) NP_Logit 0.0552*** [0.0034] (0.0093) 0.0198** [0.0012] (0.0078) -0.3984*** [-0.0246] (0.0712) -17.6119*** (3.8884) Y Y Y Y (2) NP_xtLogit 0.0559*** (3) NPr_xtreg 0.0003 (4) NPr_Tobit 0.0204*** (0.0055) 0.0427*** (0.0002) 0.0009*** (0.0033) 0.0059** (0.0072) -0.3678*** (0.0002) -0.0066** (0.0028) -0.1352*** (0.0022) (0.0262) -0.2329** -6.2811*** (0.1050) (1.3520) Controls Y Y Y Prov FE Y Y Y Year FE Y Y Y Industry FE Y Y Y Firm FE Y Y N 891384 96497 891462 891462 The first two columns provide results for firms’ innovation incentives Coefficients are estimated by logit and conditional logit methods The third and fourth columns provide results for firms innovation outcomes Coefficients are estimated by fixed effect regression and tobit methods All independent variables are lagged by one period The standard errors are clustered CMD is credit market development index Private is a dummy of private firms CMD*private denotes the interaction of private and CMD The other control variables are the same as those in Table For simplicity, we not report the estimation results for other control variables Standard errors are in parentheses Marginal effects are in square brackets * significance at 10%; ** significance at 5%; *** significance at 1% Constant 17 (0.0724) Table Credit market development and firm innovation: SME vs other CMD CMD*small CMD*mid small mid (1) NP_Logit 0.0159 [0.0010] (0.0106) 0.0644*** [0.0040] (0.0090) 0.0370*** [0.0023] (0.0081) -1.2171*** [-0.0748] (0.0764) -0.5665*** [-0.0348] (0.0687) -16.2577*** (4.1117) Y Y Y Y (2) NP_xtLogit 0.0087 (3) NPr_xtreg 0.0001 (4) NPr_Tobit 0.0073* (0.0070) 0.0727*** (0.0003) 0.0003 (0.0038) 0.0200*** (0.0062) 0.0783*** (0.0002) 0.0018*** (0.0032) 0.0114*** (0.0084) -0.6196*** (0.0005) -0.0017 (0.0030) -0.3874*** (0.0681) -0.3498*** (0.0029) -0.0070 (0.0289) -0.1786*** (0.0045) (0.0243) -0.2646** -5.9158*** (0.1110) (1.4206) Controls Y Y Y Prov FE Y Y Y Year FE Y Y Y Industry FE Y Y Y Firm FE Y Y N 891384 96497 891462 891462 The first two columns provide results for firms’ innovation incentives Coefficients are estimated by logit and conditional logit methods The third and fourth columns provide results for firms innovation outcomes Coefficients are estimated by fixed effect regression and tobit methods All independent variables are lagged by one period The standard errors are clustered CMD is credit market development index Small is a dummy of small-sized firms Middle is a dummy of middlesized firms CMD*small denotes the interaction of small and CMD CMD*mid denotes the interaction of mid and CMD The other control variables are the same as those in Table For simplicity, we not report the estimation results for other control variables Standard errors are in parentheses Marginal effects are in square brackets * significance at 10%; ** significance at 5%; *** significance at 1% constant 18 (0.0830) Table Credit market development and firm innovation: better performance vs other CMD CMD* fp50 fp50 (1) NP_ Logit 0.0540*** [0.0033] (0.0097) 0.0138** [0.0009] (0.0070) 0.1026** [0.0063] (0.0523) -18.9447*** (3.9437) Y Y Y Y (2) NP_ Xtlogit 0.0539*** (3) NPr_ xtreg 0.0003 (4) NPr_ tobit 0.0197*** (0.0060) 0.0241*** (0.0002) 0.0004** (0.0033) 0.0048** (0.0055) -0.0862* (0.0001) -0.0012 (0.0024) 0.0388** (0.0009) (0.0185) -0.2664** -6.6912*** (0.1092) (1.3675) Controls Y Y Y Prov FE Y Y Y Year FE Y Y Y Industry FE Y Y Y Firm FE Y Y N 891384 96497 891462 891462 The first two columns provide results for firms’ innovation incentives Coefficients are estimated by logit and conditional logit methods The third and fourth columns provide results for firms innovation outcomes Coefficients are estimated by fixed effect regression and tobit methods All independent variables are lagged by one period The standard errors are clustered CMD is credit market development index Fp50 is a dummy of profitable firms in the first 50% percentile of all firms in the same province and industry CMD*fp50 denotes the interaction of fp50 and CMD The other control variables are the same as those in Table For simplicity, we not report the estimation results for other control variables Standard errors are in parentheses Marginal effects are in square brackets * significance at 10%; ** significance at 5%; *** significance at 1% contant 19 (0.0508) Table Credit market development and firm innovation: IV method (1) (2) NPr_xtreg NPr_tobit CMD 0.0024*** 0.0338*** (0.0002) (0.0022) size 0.0074*** 0.1889*** (0.0003) (0.0014) lnage -0.0056*** -0.2194*** (0.0012) (0.0089) Lnage2 0.0014*** 0.0584*** (0.0003) (0.0020) leverage -0.0010 -0.0967*** (0.0009) (0.0072) Investment intensity 0.0025** -0.1992*** (0.0011) (0.0085) export 0.0026** 0.1282*** (0.0009) (0.0056) hhi -0.0468** 1.3411*** (0.0171) (0.1240) roa 0.0017 0.2553*** (0.0016) (0.0155) subsidy 0.0458** 0.0638 (0.0212) (0.1941) SOE 0.0027** -0.0325*** (0.0010) (0.0072) COE 0.0017** -0.1853*** (0.0008) (0.0072) private 0.0012* -0.0830*** (0.0007) (0.0056) HMT 0.0016 -0.2149*** (0.0015) (0.0072) foreign 0.0035** -0.1889*** (0.0015) (0.0070) secondary industry -0.1126*** -1.7973*** (0.0183) (0.2076) third industry 0.2020*** 2.6534*** (0.0209) (0.2452) lngdppc 0.0225*** 0.3419*** (0.0038) (0.0464) constant -0.2859*** -6.7775*** (0.0327) (0.3797) Prov FE Y Y Year FE Y Y Industry FE Y Y Firm FE Y N 891462 891462 Cragg-Donald 12370.06 12370.06 Durbin-Wu-Hausman chi-sq(p-value) 175.62 (0.00) 220.20 (0.00) The first column presents estimated results under fixed effect regression method The second column presents results for under tobit method All independent variables are lagged by one period The standard errors are clustered CMD is credit market development index Standard errors are in parentheses * significance at 10%; ** significance at 5%; *** significance at 1% 20 Table Credit market development and firm innovation: control number of non-SOEs (1) (2) (3) (4) NP_logit NP_xtlogit NPr_xtreg NPr_tobit CMD 0.0640*** 0.0822*** 0.0007*** 0.0231*** [0.0040] (0.0090) (0.0054) (0.0002) (0.0011) nonSOE -0.9595** -3.5345*** -0.0492*** -0.3094*** (0.3497) (0.2020) (0.0129) (0.0373) size 0.5580*** 0.5643*** 0.0072*** 0.1891*** (0.0143) (0.0204) (0.0008) (0.0014) lnage -0.6705*** -0.0907 -0.0057** -0.2219*** (0.0532) (0.0732) (0.0018) (0.0089) lnage2 0.1857*** 0.0460** 0.0013*** 0.0588*** (0.0129) (0.0166) (0.0004) (0.0020) leverage -0.2275*** -0.1152** -0.0011 -0.0988*** (0.0608) (0.0586) (0.0009) (0.0072) Investment -0.5087*** 0.2358** 0.0028** -0.1987*** intensity (0.0990) (0.0722) (0.0013) (0.0085) export 0.3470*** 0.1769*** 0.0027* 0.1291*** (0.0853) (0.0521) (0.0015) (0.0056) hhi 2.8510*** -2.3695** -0.0478** 1.3496*** (0.7529) (1.0122) (0.0222) (0.1240) roa 0.7038*** 0.4446*** 0.0026 0.2565*** (0.1457) (0.1120) (0.0022) (0.0156) subsidy -0.0981 2.0609 0.0532** 0.1082 (1.1430) (1.2895) (0.0265) (0.1941) SOE -0.0766** 0.2202*** 0.0015 -0.0404*** (0.0370) (0.0551) (0.0014) (0.0072) COE -0.5205*** 0.0375 0.0016 -0.1877*** (0.0448) (0.0490) (0.0011) (0.0072) private -0.2248*** 0.0070 0.0010 -0.0844*** (0.0305) (0.0375) (0.0009) (0.0056) HMT -0.6187*** 0.1107 0.0012 -0.2175*** (0.0581) (0.0810) (0.0022) (0.0072) foreign -0.5749*** 0.1527* 0.0025 -0.1925*** (0.0483) (0.0817) (0.0021) (0.0070) secondary -2.8971* -0.8903 -0.0871** -1.6284*** industry (1.4997) (1.1511) (0.0399) (0.2097) third industry 8.5186*** 15.5643*** 0.2139*** 2.6344*** (1.6882) (1.3176) (0.0514) (0.2452) lngdppc 0.2932 -0.5306** 0.0065 0.1777*** (0.4197) (0.2689) (0.0094) (0.0499) Constant -14.1391*** -0.1124 -5.2146*** (3.8220) (0.0913) (0.4109) N 891384 96497 891462 891462 The first two columns provide results for firm innovation incentive Coefficients are estimated by logit and conditional logit The third and fourth columns provide results for firm innovation ability Coefficients are estimated by fixed effect regression and tobit method All independent variables are lagged by one period The standard errors are clustered CMD is credit market development index nonSOE denotes the ratio of the number of non-SOEs on total number of firms in a province Standard errors are in parentheses Marginal effect for CMD is in square bracket * significance at 10% ** significance at 5% *** significance at 1% 21 Table 8: credit market development and firm innovation: firms with new products only CMD (1) (2) fullsample xtreg Tobit 0.0036*** 0.0141*** (0.0009) (0.0018) CMD*private private (3) (4) (5) Private xtreg 0.0029** (0.0009) 0.0032** (0.0010) -0.0223** (0.0092) Tobit 0.0123*** (0.0019) 0.0082*** (0.0019) -0.0873*** (0.0183) CMD*small CMD*mid small mid (6) xtreg 0.0012 (0.0010) Tobit 0.0064** (0.0022) 0.0031** (0.0011) 0.0033** (0.0011) -0.0142 (0.0094) -0.0085 (0.0102) 0.0122*** (0.0023) 0.0054** (0.0022) -0.1528*** (0.0178) -0.0861*** (0.0166) fp50 controls Prov FE Year FE Industry FE Firm FE N -1.1011** (0.4637) Y Y Y Y Y 127959 -3.2691*** (0.9755) Y Y Y Y 127959 -0.9443** (0.4583) Y Y Y Y Y 127959 -2.8567** (0.9419) Y Y Y Y 127959 -1.2094** (0.4852) Y Y Y Y Y 127959 (8) Profit CMD*fp50 constant (7) scale -3.1127** (1.0141) Y Y Y Y 127959 xtreg 0.0029*** (0.0009) Tobit 0.0122*** (0.0018) 0.0015** (0.0007) -0.0026 (0.0053) -1.1249** (0.4606) Y Y Y Y Y 127959 0.0043*** (0.0014) 0.0020 (0.0119) -3.3290*** (0.9646) Y Y Y Y 127959 The first two columns provide full sample results for firms’ innovation outcomes Column (3) and (4) are comparisons of private and other types of firms Column (5) and (6) are comparisons of SMEs and large firms Column (7) and (8) are comparison of profiTable and other firms Coefficients are estimated by logit and conditional logit methods Coefficients are estimated by fixed effect regression and tobit methods All independent variables are lagged by one period The standard errors are clustered CMD is credit market development index Fp50 is a dummy of profitable firms in the first 50% percentile of all firms in the same province and industry CMD*fp50 denotes the interaction of fp50 and CMD Small is a dummy of small-sized firms Middle is a dummy of middlesized firms CMD*small denotes the interaction of small and CMD CMD*mid denotes the interaction of mid and CMD Private is a dummy of private firms CMD*private denotes the interaction of private and CMD The other control variables are the same as those in Table For simplicity, we not report the estimation results for other control variables Standard errors are in parentheses Marginal effects are in square brackets * significance at 10%; ** significance at 5%; *** significance at 1% 22 23 ... provincial-level credit market development enhances both firms’ innovation incentives and outcomes We show that firms’ credit constraints and firm performances are two channels through which the credit market. .. market development affects firm innovation abilities The innovation incentives and outcomes of more credit restrained firms and firms with better performances are affected by credit market development. .. credit market development promotes firm innovation The credit market development by improving credit allocation encourages credit restrained firms to innovate more than it does to less credit