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Credit mismatch and non-financial firms’ shadow banking activities —evidence based on entrusted loan activities

Credit mismatch and non-financial firms’ shadow banking activities —evidence based on entrusted... CHINA JOURNAL OF ACCOUNTING STUDIES 2020, VOL. 8, NO. 2, 249–271 https://doi.org/10.1080/21697213.2020.1822027 ARTICLE Credit mismatch and non-financial firms’ shadow banking activities —evidence based on entrusted loan activities a b c Jun Bai , Xiaoyun Gong and Xiangfang Zhao School of Economics and Management, Research Center of Corporate Governance and Management Innovation, Shihezi University, Shihezi, China; Dongwu Business School, Soochow University, Suzhou, China; Shanghai Lixin University of Accounting and Finance, Shanghai, China ABSTRACT KEYWORDS Credit mismatch; excessive When governments opt for financial repression policies, credit mis- bank loans; shadow banking match becomes more prevalent. This may lead some non-financial activities; entrusted loans firms with excessive loans to financialize their operations and under- take shadow banking activities, that is, entrusted loans. Using all listed Chinese firm's financial data and entrusted loans data from 2008 to 2016, this study investigates the impact of credit mismatch on firm's entrusted loans. Results show that, the more credit mismatch, the higher the tendency and the size of firm's entrusted loans. Above the relationship is more significant when under some certain backgrounds, such as a higher degree of government intervention, tighter monetary policy as well as lack of investment opportunities, and state-owned enterprises. Further analysis reveals that a firm's engagement in entrusted loans can harm its main business activities. This study intends to enhance our understanding of the shadow banking activ- ities as means of funds reallocation within China's financial system. 1. Introduction Since the adoption of the policy of reform and opening-up, China’s economy has been witnessing rapid growth for 40 years. However, the growing prominence of financial repression is extremely mismatched with the economic boom, and the inefficient financial system cannot effectively provide financing services for the real economy (Shao, 2010; Wang & Anders, 2013; Yu et al., 2015). Scholars have addressed this mismatch between financial repression and economic boom from the perspectives of political connections (Claessens et al., 2008; Zhang et al., 2010), trade credit (Wang, 2014; Zhang et al., 2013), and other receivables (Wang et al., 2015). In addition, this leads to the question whether there are other more direct channels for the inter-firm reallocation of funds. As direct lending between firms is deemed illegal, the inter-firm entrusted loans, intermediated by banks, have emerged and developed rapidly. By the end of 2017, the balance of corporate CONTACT Xiaoyun Gong gxyacc@163.com Dongwu Business School, Soochow University, Suzhou, China This article has been republished with minor changes. These changes do not impact the academic content of the article. Paper accepted by Kangtao Ye Entrusted loan refers to a lending arrangement in which a trustor provides funds and commercial banks (trustee) lend out money on behalf of the trustor and assist in supervising the use and collection of the loan. The trustor provides instruction that specifies target borrowers, the use of funds, loan amount, currency, maturity, and interest rate, among others. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 250 J. BAI, ET AL. entrusted loans reached 13.88 trillion RMB. Many non-financial listed companies have started carrying out entrusted loan activities by relying on their capital advantages, which has increasingly attracted the regulator’s attention. It can be seen that these entrusted loans have become one of the important channels for fund reallocation (Qian et al., 2013, 2017). Therefore, it is necessary to clarify the behavioural characteristics of firms that engage in entrusted loan activities and their economic consequences in a credit mismatch scenario. In this given context, this study focuses on the following issues: What kind of firms tend to engage in entrusted loans? What are the motives behind these activities? What are the economic consequences of these activities? Studies have shown that, as an important financing channel to support the development of the real economy, an entrusted loan has met the liquidity needs of both privileged firms, and improve the Pareto of credit resource allocation efficiency as well as increasing corporate value. For example, Qian et al. (2017) find that entrusted loans play the role of shadow banking, which enables the transfer of funds from a party with a financial surplus to a party with a deficit to promote balanced development of regional economies across the country. Qian et al. (2018) also demon- strate that the shadow banking mechanism of entrusted loans is a response to the market lack of formal credit, showing a distinctive reverse credit cycle characteristic. That is, when formal credit contracted, the probability and scale of companies issuing entrusted loans significant increase. Allen et al. (2019) show that the company’s entrusted loan business is a decision based on itself and the market environment, which means that the interest rate of the entrusted loan can fully reflect the borrower’s risk. Further, literature suggests that entrusted loans used by firms will distort market pricing mechanisms, impacting the efficiency of resource allocation. For example, Qian & Li (2013) show that the entrusted loans between affiliated firms demonstrate certain characteristics such as lower interest rate, larger size, and longer maturity, and while the entrusted loans between non- affiliated firms have a higher interest rate and risk, leading to increased financial risks. Li and Han (2019) figure that, when an enterprise uses the entrusted loan or private loan as a credit intermediary to borrow funds from the capital demander, the repayment risk of the borrower will not only reduce the debt repayment ability of the loan enterprise, but also be transmitted to the lender through the ‘accounting mechanism’, which will increase the business risk of the enterprise. In summary, the extant research has not reached a consensus on the economic consequences of entrusted loan activities, and this calls for an in-depth study of the institutional underpinning and the behavioural motives behind these activities. Based on the above, this study examines the impact of credit mismatch on the firms’ propensity to engage in entrusted loan activities and on the size of entrusted loans, using Chinese listed firms’ data and a manually collected dataset on each entrusted loan contract. Spanning a period from 2008 to 2016, the dataset includes the loan amount, maturity, interest rate, and the borrower’s basic characteristics. This study examines the characteristics of these firms and the economic consequences of the entrusted loan activities. Our results show that credit mismatch, that is, a scenario wherein some firms have more excessive bank loans, is significantly and positively associated with the propensity to engage in entrusted loan activities and the size of the entrusted loans. Additionally, the effect of credit mismatch on entrusted loan activities is closely related to macroeconomic factors such as monetary policy and government intervention, as well as firm characteristics, which include CHINA JOURNAL OF ACCOUNTING STUDIES 251 investment opportunities and property rights. Further analysis of the economic conse- quences of the entrusted loans shows that firms’ engagement in entrusted loan activities harms their primary business activities, despite the additional income from these loans. Therefore, they fail to provide effective and sufficient support for the long-term develop- ment of firms. Our study makes the following contributions to the literature. First, our paper supple- ments the related literature on the re-allocation of funds between enterprises under financial repression. Existing research has focused on the issue of firms’ funds reallocation through channels such as commercial credit and other receivables. This article focuses on the widespread entrusted lending behaviour among enterprises. Compared with other alternative paths of funds transfer hidden in the real transactions, entrusted loans’ transac- tion data is considered as less noisy when compared to alternative paths of funds transfer hidden in the real transactions. Additionally, our study benefits from the manually collected dataset on entrusted loan contracts, comprising the loan amount, the maturity, the interest rate, and the two contracting parties. This enables us to investigate the characteristics of the borrowing firms and provides an anchor point for further research. Although there is some literature on entrusted loans (Allen et al., 2019; Li & Han, 2019; Qian et al., 2017; Yu & Li, 2016), the institutional roots of non-financial enterprise entrusted loans have not been discussed in depth. Based on credit mismatch, this article provides a new explanation for the widely existing entrusted loan behaviours, that is, it is not a spontaneous phenomenon in the market, but there are profound institutional causes. In particular, this paper provides direct empirical evidence at the micro-enterprise level. Second, as aforementioned, there have been studies that have disputed the economic consequences of non-financial com- panies engaging in entrusted loans. This research finds that due to credit mismatch, some enterprises have excess borrowings and engage in entrusted loans. This is a short-term profit-seeking behaviour, as a shadow banking activity, and ultimately damages their primary business activities. This conclusion means that the negative effect of entrusted loans is largely due to the dissimilation of these preferentially favoured banks and the loss of attention to the primary business activities, not just the entrusted loan itself. Meanwhile, this finding helps us to re-understand the economic consequences of non-financial com- panies’ entrusted loans from the institutional level. Finally, this paper expands the related research on entrusted loans through conducting a series of heterogeneity analysis on macro factors such as monetary policy, government intervention, and investment oppor- tunities, and the nature of property rights. The remainder of the paper is organised as follows. Section 2 presents the theoretical analysis and develops our research hypotheses. Section 3 describes the research design. Section 4 analyses the empirical results. Section 5 discusses the robustness tests, and Section 6 concludes. 2. Theoretical analysis and hypotheses development Due to the weak legal enforcement and undeveloped intermediary institutions in China, the credit market lacks a complete credit reporting system, resulting in very high transac- tion costs for loan contracts. The banking sector can only implement strict credit ration- ing, such as whether the company has government guarantees or the firm’s size, to enterprises through non-market means (Allen et al., 2005; Ji et al., 2016; Zhang et al., 252 J. BAI, ET AL. 2013). Given a certain amount of funds and the scarcity of credit resources, credit rationing will inevitably lead to inefficient credit rationing, that is, it is easier for some companies to obtain capital from the credit market and occupy excess bank borrowings. The normal financing needs of other companies cannot be met. It can be seen that under the mismatch of credit, corporate financing capabilities and investment opportunities do not match, which in turn leads to the need for capital adjustment between financing- facilitating companies and financing-rare companies. Lu and Yao (2004) point out that there is a significant ‘financial leakage effect’ among non-financial enterprises, that is, large enterprises that are easy to finance will allocate the cheap funds they obtain to small and medium-sized enterprises (SMEs). However, according to Chinese law, non-financial firms, unlike commercial banks, are prohibited from directly engaging in lending activities. Therefore, shadow banking that allows direct lending poses a high legal risk for the contracting parties. Unlike direct lending, entrusted loans allow companies to refinance their own funds to other compa- nies through financial intermediaries such as banks (Qian et al., 2015, 2017). As a channel of funds reallocation, an entrusted loan enables borrowers and lenders to achieve the retransfer of funds among firms based on their supply and demand. On the one hand, companies with dominant positions tend to engage in entrusted loan activities to make a considerable amount of income, since this special lending mechanism is characterised by a high-interest-rate. The interest rate of entrusted loans is substantially higher than that of bank credit (Allen et al., 2019; Qian et al., 2015; Qian & Li, 2013). On the other hand, when presented with promising investment opportunities, private and small and med- ium-sized enterprises that are discriminated by formal financial institutions are willing to pay higher fees to obtain funds from firms with privileged access to cheap capital. For example, Qian et al. (2017) find that entrusted loans have facilitated cross-regional flow of funds, specifically, from firms in east China that have easy access to funds to the firms in midwestern China that face financial constraints. In summary, as a credit transaction with higher degree of interest rate liberalisation, entrusted loan activities can facilitate the flow of funds, because it not only can meet the rigid demand of firms under financial constraints, but also enable fund providers to earn spreads with almost no cost. Based on this, the discussion above leads to the first hypothesis H1: Ceteris paribus, the higher the degree of credit mismatch, the higher would be the motivation of firms to issue entrusted loans and the larger would be the size of these loans. The impact of mismatched credit on corporate entrusted loans is potentially constrained by the macroeconomic environment. To begin with, the monetary policy, as one of the macroeconomic policies of the central bank, directly affects the total amount of credit in the entire market. Many empirical studies have shown that the volatility of monetary policy has a significant impact on firms’ financial decisions (Rao & Jiang, 2013). When the monetary policy is tight, it is more difficult to obtain bank loans and costlier to finance, leading to conflicts between obtaining funds for working capital and capital investment expenditures. Yu et al. (2015) find that when monetary policy restricts the scale of desirable loans, banks tend to ignore the potential investment opportunities of enter- prises, which leads them to prefer large companies with good mortgages. This has allowed large enterprises to have excess borrowings beyond normal demand, while other companies face financing constraints. In this scenario, in order to seize potential CHINA JOURNAL OF ACCOUNTING STUDIES 253 investment opportunities, companies restricted by financing need to seek other shadow banking channels to obtain development funds. Allen et al. (2019) document a significant upward trend in entrusted loans between firms when the interbank rate rises. Therefore, under credit mismatch, the tightening of monetary policy will inevitably increase the financing needs of the capital-scarce parties, thus providing opportunities for fund pre- ferential parties to engage in entrusted loans as a shadow banking activity. In addition, the government has the motivation and ability to exercise appropriate regulation on the market to achieve the corresponding goals. There are various forms of government intervention in the market, such as the control of credit volume and interest rates (Ji et al., 2016), and the implementation of industrial policies. This can promote the flow of low-cost credit funds to government-supported enterprises and industries. Obviously, the higher degree of government intervention in the market, the easier for the supported enterprises to obtain bank loans (Wang et al., 2017) becoming the fund- advantage parties in the credit market. Meanwhile, the funding gap of companies that suffer from financing discrimination will also widen. However, due to the limited informa- tion held by the government, the resources allocated through intervention methods do not always match the financing needs of enterprises. As a result, the incentives for reallocation of credit resources between favoured parties and disadvantaged parties have increased. Based on this, we develop the second hypothesis H2: Ceteris paribus, the tighter the monetary policy or the higher the degree of government intervention, the stronger the positive relationship between the degree of credit mismatch and the firm’s entrusted loans. In fact, firms with credit advantages engaged in entrusted loans may also be significantly related to the micro-level characteristics of companies. First of all, with the slowdown in the growth of investment and consumption, investment opportunities faced by enter- prises are gradually decreasing. In order to get rid of the constraints of these development difficulties, some companies, in addition to building their own capabilities, will actively seek government help (Yang, 2011). Despite the lack of good investment opportunities, a large number of companies can still rely on external ‘blood transfusions’ and never die (Huang & Chen, 2017). Existing research confirms the economic phenomenon of mis- match between corporate financing and investment opportunities in terms of investment crowding out, innovation crowding out, and tax distortion (Li et al., 2018; Tan et al., 2017). Liu (2011) also points out that with the deterioration of investment opportunities, relatively inefficient companies tend to invest the low-cost credit funds in high-yield companies. Similarly, based on data from corporate consolidated statements, Shin & Zhao (2013) and Wang et al. (2015) find that due to scarce investment opportunities, companies are more inclined to provide funds to other companies. It can be seen that the lack of investment opportunities will cause non-financial companies with financing advantages to engage in more entrusted loans. Secondly, state-owned enterprises (SOEs), as undertaking multiple social goals (such as employment and public facilities), are closely related to state-owned banks that dominate the credit market. Coupled with the imperfect corporate information disclosure and bond protection systems in China, banks generally have ‘ownership discrimination’ against enterprises (Shao, 2010; Song et al., 2011; Zhang et al., 2013). That is, SOEs have great financing advantages that can easily obtain a large amount 254 J. BAI, ET AL. of low-cost credit funds, while private enterprises are faced with greater financing constraints. Yu et al. (2014) show that SOEs enjoyed preferential financing signifi - cantly crowds out the credit financing of non-state-owned enterprises (non-SOEs), resulting in non-SOEs’ investment efficiency lower than that of SOEs’. Besides, this phenomenon is even more obvious under the shock of monetary policy. To obtain more scarce credit resources, the latter often adopts a series of coping strategies, and SOEs are undoubtedly more likely to be providers of funds. Based on this, we put forward the third hypothesis H3: Ceteris paribus, the relationship between the degree of credit mismatch and the company’s external entrusted loans are related to the poor investment opportunities and the nature of state-owned property rights. Then, under credit mismatch, when firms with financing advantages engage in entrusted loan activities, what kind of impact on a firm’s production and operation? Intuitively, if the privileged companies use the excess bank borrowings for external entrusted loans, it seems to make full use of the loans beyond company’s normal operating financing, which will have a positive effect on corporate performance. However, it should be noted that, to begin with, it is a general phenomenon that certain types of enterprises have excessive borrowing, but for a specific enterprise, rent-seeking activities also need to face competi- tion. Therefore, to maintain the short-term benefits of entrusted loans, corporate manage- ment has to spend a lot of time and energy to maintain relations with the government in order to continuously obtain excess borrowings, which is clearly different from the surplus operating funds of their own. Obviously, rent-seeking activities are more difficult to sustain than production activities, and will largely crowd out the resources invested in the main business activities. Besides, Jensen (1986) shows that due to agency problems within the company, management tends to use free cash flow for on-the-job consumption, or to build a corporate empire through investment, thereby reducing the operating efficiency and the value of the enterprise. It can be seen that because the utility functions between management and shareholders are not consistent, abundant resources are not a sufficient condition for management to enhance the long-term value of the enterprise. Under financial repression, the cost of credit financing is significantly lower than the market level (Ji et al., 2016). Therefore, when companies are able to obtain excess borrowings, management facing pressure for performance appraisal is more prone to opportunistic behaviours, which will distort the company’s business decisions. Yu & Li (2016) also document that in order to pursue the high interest brought by entrusted loans in the short term, management will abandon R&D projects with long-term and uncertain returns. Hence, the level of innovation will decline significantly in the future. Similarly, the short-term excess returns obtained through entrusted loans are likely to cause management to spend less effort to run the business, that is, resulting in slack behaviour and less motivation for doing main business activities. A firm’s long-term development still falls back on frontier technology and high-quality products and services, and it is hard to achieve sustain- able development by merely relying on generating profits via entrusted loans. To sum up, it is not difficult to see that although a company engaged in entrusted loans can obtain short-term returns for the company under credit mismatch, it will CHINA JOURNAL OF ACCOUNTING STUDIES 255 ultimately be detrimental to stable development of the company in the long term. For example, crowding out its primary business activities. Based on this, we propose the last hypothesis H4: Ceteris paribus, the higher the degree of credit mismatch, the more the company’s entrusted loans are not conducive to the devel- opment of its main business activities. 3. Research design 3.1. Model design First, to test how credit mismatch affects entrusted loans, that is, H1, we construct the following econometric model based on the research of Qian et al. (2017) and Allen et al. (2019). EL ¼ α þ α CM þ α NLoan þ α Control þ Yearþ Industryþ ε (1) i;t 0 1 i;t 2 i;t 3 i;t 1 i;t where t indexes time, and i indexes firm. The dependent variable EL represents entrusted loans, which is measured by two indicators, EL_dum and EL_size. EL_dum is a dummy variable that equals one if a firm engages in entrusted loan activities, and zero otherwise. EL_size represents the size of the firm’s entrusted loans; it is measured by the ratio of the size of entrusted loans to total assets. The independent variable CM represents credit mismatch, which is measured by two indicators, OLoan1 and OLoan2. Different from prior research, this study attempts to estimate the degree of credit mismatch at the firm level. In China’s institutional setting, the credit mismatch phenomenon is characterised by the scenario wherein some firms can obtain borrowings more than they need for operations (excessive bank loans), while other firms face financial constraints. On this basis, we follow the research of Flannery & Rangan (2006), Lu & Yang (2011), and Deng et al. (2016) to measure the firm-level credit mismatch. First, bank loans obtained by firms are decomposed into two parts – normal bank loans and excessive bank loans. Subsequently, excessive bank loans are used to measure the degree of credit mismatch at the firm level. To avoid the measurement bias caused by using a single method, this study employs two methods. One measure- ment involves dynamic adjustment to capital structure (Deng et al., 2016; Flannery & Rangan, 2006), where we get the first group of indicators for normal bank loans NLoan1 and excess borrowing OLoan1. The other measurement is the industry average method (Deng et al., 2016; Jiang & Liu, 2005); it is employed to calculate the second set of indicators for normal bank loans NLoan2 and excessive bank loans OLoan2. In the robustness test, this study also uses the regression analysis method to obtain the third group of indicators for normal bank loans NLoan3 and excessive bank loans OLoan3. The above calculation methods are shown in the Appendix. Based on prior research (Deng et al., 2016; Qian et al., 2017), we control for the vector Control comprising other control variables that could affect a firm’s entrusted loans in period t-1. Specifically, Control includes the following variables: firm’s size, Size, measured by the natural logarithm of total assets; capital structure, 256 J. BAI, ET AL. Lev, measured by the ratio of total debt to total assets; profitability, Roa, measured by the ratio of net income to total assets; fixed assets, Tangible, measured by the ratio of net-fixed assets to total assets; cash flow from operations activities, Cfo, measured by ratio of net cash flow from operating activities to total assets; cash holding, Cashhold, measured by the ratio of cash and cash equivalents to total assets; property rights, SOE, an indicator defined based on firm’s ultimate controller that equals 1 if it is SOE, and 0 otherwise; operation risk, Risk, measured by the standard deviation of the return on total assets within three years. Year and Industry represent year and industry fixed effects, respectively; ε is the residual of the model. To further test the impact of the macro-institutional environment and micro-individual characteristics on the firm’s entrusted loans in the credit mismatch scenario, that is, H2 and H3, we construct a model as follows: EL ¼ β þ β CM þ β Dþ β CM � Dþ β Control þ Yearþ Industryþ ε (2) i;t i;t i;t i;t 1 i;t 0 1 2 3 4 Based on the model (1), this model further adds moderator D, which specifically includes four indicators: monetary policy, MP; government intervention, FM; investment opportu- nity, TobinQ; and property rights nature, SOE. Among them, the monetary policy dummy variable MP is set to one for 2009, 2010, 2012, 2013, 2015, and 2016, and zero for other years; the government intervention, FM, which is the Chinese marketisation index devel- oped by Wang et al. (2016), when the government intervention index is less than the annual average, FM takes one, and zero otherwise; the investment opportunity, TobinQ, if the investment opportunity of the enterprise is lower than the annual average of the industry, TobinQ is equal to one, and zero otherwise. We examine which characteristics will affect the relationship between credit mismatch and entrusted loans by focusing on interaction terms between D and credit mismatch, CM. The other variables are the same as the model (1). Finally, this article further explores the economic consequences of entrusted loans, under the backdrop of credit mismatch. We refer to the research design of Zhu et al. (2015) and Yu & Li (2016) and construct the following regression model to conduct empirical tests on the above issues: OUTCOME ¼ γ þþγ EL þ γ CM þ γ EL � CM þ γ Control þ Year þ Industryþ ε i;t i;t i;t i;t i;t i;t 0 1 2 3 4 (3) where t indexes time, and i indexes firm. The dependent variable OUTCOME includes profits from entrusted loans and performance of primary business activities (measured by the change of operating income and the growth rate of sales). Profits from entrusted loan (Shouyi ) equals the amount of the external entrusted loans multiplied by the annualised i,t interest rate and the terms of these loans. The change of operating income (D_Profit ) i,t+1 equals operating income of the next period minus that of current period and then divided by total assets. And the growth rate of sales (Growth ), which is calculated as sales of i,t+1 the next period minus that of the current period, and then divided by that of the current period. The independent variable is interaction term EL*CM, which is the interaction between the amount of entrusted loans and credit mismatch. Control variables include Size, Lev, Risk, Cfo, SOE, Roa, Age and MB. The definition of all the above variables are presented in Table 1. CHINA JOURNAL OF ACCOUNTING STUDIES 257 Table 1. Variable definitions. Variables Definition EL The propensity of issuing entrusted loans, EL_dum, is a dummy variable that equals 1 if the enterprise engages in entrusted loan activities, and 0 otherwise Size of entrusted loans, EL_size, is calculated as the ratio of the size of entrusted loans to total assets CM Credit mismatch, OLoan1, is estimated by using the dynamic adjustment method to calculate excessive bank loans. The specific calculation is in Appendix Credit mismatch, OLoan2, is estimated by using the industry average method to calculate excessive bank loans. The specific calculation is in Appendix NLoan Normal bank loans, NLoan1, is calculated by using the dynamic adjustment method. The specific calculation is available in Appendix Normal bank loans, NLoan2, is calculated by using industry average method. The specific calculation is available in Appendix MP Tight monetary policy is an indicator that equals one if the year is 2009, 2010, 2012, 2013, 2015 and 2016, and 0 zero otherwise FM Government intervention, the government intervention index in the China’s marketisation index developed by Wang et al. (2016), if it is lower than the annual average, the value is 1, and 0 otherwise TobinQ Investment opportunities, a dummy variable that equals 1 if a firm’s investment opportunity is lower than that of the industry average, and 0 otherwise SOE Property rights, an indicator defined on the basis of the firm’s ultimate controller that equals 1 if it is SOE, 0 otherwise Shouyi Profit from entrusted loan income, which is equal to the amount of the external entrusted loan multiplied by the annualised interest rate and the terms of these loans D_Profit Change of operating profits, which is equal to operating income of the next period minus that of the current period and then divided by total assets Growth Sales growth rate, which is equal to the sales of the next period minus that of the current period, and then divided by that of the current period Size Firm size, which is measured by the natural logarithm of total assets Lev Capital structure, which is measured by the ratio of total debt to total assets Roa Profitability, which is measured by the ratio of net income to total assets Cfo Cashflow from operations activities, which is measured by the ratio of net cash flow from operating activities to total assets Cashhold Cash holding, which is measured by the ratio of cash and cash equivalents to total assets Risk Operation risk, which is measured by the standard deviation of the return on total assets within 3 years 3.2. Sample selection and summary statistics We use Chinese A-share listed companies from 2008 to 2016 as our sample. We treat the original data as follows. First, we exclude observations in finance and insurance industries; we also exclude ST and *ST with leverage ratio greater than 1 or with missing values. This yields a sample of 13,242 observations. Second, all continuous variables are winsorised at the 1% and 99% levels. We also manually sort each entrusted loan contract to obtain data on loan amount, maturity, interest rate, and the borrower’s basic characteristics, and finally get 2,429 observations. The above financial data are obtained from the China Stock Market & Accounting Research (CSMAR) database, and the entrusted loans’ contract data are obtained from the Juchao website. The statistical analysis software is Stata15.0. Table 2 reports the summary statistics. Panel A presents the descriptive statistics, and it shows that the average propensity of issuing entrusted loans (EL_dum) is 0.070. It means that about 7% of the listed firms are engaged in entrusted loan activities; the average size of entrusted loans (EL_size) is 0.003, and the minimum and maximum sizes show that the size difference between sample firms is ST means that the company has suffered losses for two consecutive years, and *ST means that the company has suffered losses for three consecutive years. http://www.cninfo.com.cn/new/index. 258 J. BAI, ET AL. significant. The averages of firms’ excessive bank loans (OLoan1, OLoan2) calculated by using different methods are all positive, indicating that the existence of exces- sive bank loans is prevalent. Furthermore, the standard deviation shows that the variance in excessive bank loans between different firms is distinctive, showing signs of credit mismatch. Panel B compares the degree of credit mismatch between SOEs and non-SOEs and between small firms and large firms. The results of this comparison show that SOEs (large firms) have more excessive bank loans than non- SOEs (small firms) and indicates that credit mismatch brings financing advantages for SOEs and large firms. Panel C presents the difference test for entrusted loans in the credit mismatch scenario. It also shows that firms with excessive bank loans are significantly different from those without surplus loans in terms of the propensity to engage in entrusted loan activities and the size of entrusted loans. This finding suggests that credit mismatch makes firms more likely to get involved in entrusted loans activities. Table 2. Summary statistics. Panel A:Descriptive statistics Variables N Mean Std. Min Median Max EL_dum 13,242 0.070 0.255 0.000 0.000 1.000 EL_size 13,242 0.003 0.012 0.000 0.000 0.090 OLoan1 13,242 0.016 0.107 −0.199 0.003 0.306 NLoan1 13,242 0.178 0.071 0.022 0.174 0.358 OLoan2 13,242 0.028 0.130 −0.210 0.014 0.373 NLoan2 13,242 0.165 0.057 0.065 0.159 0.367 Size 13,242 22.035 1.263 19.478 21.855 25.820 Lev 13,242 0.491 0.196 0.050 0.496 0.895 Roa 13,242 0.036 0.049 −0.156 0.033 0.197 Cfo 13,242 0.040 0.075 −0.194 0.041 0.256 Cashhold 13,242 0.168 0.117 0.012 0.138 0.665 SOE 13,242 0.515 0.500 0.000 1.000 1.000 Risk 13,242 0.235 0.232 0.014 0.172 1.470 Panel B:Difference test of entrusted loans under credit mismatch OLoan1 < 0 OLoan1 > 0 Mean Median Mean Median Mean test Median test EL_dum 0.059 0.000 0.073 0.000 −0.014*** −0.000*** EL_size 0.002 0.000 0.003 0.000 −0.001*** −0.000*** OLoan2 < 0 OLoan2 > 0 Mean Median Mean Median Mean test Median test EL_dum 0.058 0.000 0.074 0.000 −0.016*** −0.000*** EL_size 0.002 0.000 0.003 0.000 −0.001*** −0.000*** We use t-test in mean, and z-test in median test. *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. To further examine the listed companies’ entrusted loans, Table 3 reports detailed information about each entrusted loan contract, including the amount of the entrusted loans, the interest rate, and other basic characteristics of the borrowers. As shown in Table 3, the average size of entrusted loans, Esize, is about 166 (in millions of RMB), ranging from half a million to 6,000 million RMB. On average, the maturity of entrusted loans is around 17 months, which is classified as short-term financing. The average interest rate of entrusted loans, Rate, is about 7%, and the maximum is 24.5%, significantly higher than the banks’ CHINA JOURNAL OF ACCOUNTING STUDIES 259 benchmark lending rates in the same period. It indicates that the financing cost of entrusted loans is higher. Concerning guarantees, Ensure, its average value is approximately 34%, which indicates that about 34% of the sampled companies must provide loan guarantees when obtaining entrusted loans. Concerning the characteristics of the borrowers of the funds, statistics show that about 29% of the entrusted loans are between firms located in the same city, and it is presumed that these firms are familiar with each other. With regard to property rights, 48% of the sampled firms are SOEs, and the rest are non-SOEs. In terms of the establishment age, firm size, debt level and profitability, the overall endowment of these enterprises, whether listed or not listed, is lower than that of the large listed companies. Table 3. Basic characteristics of entrusted loans. Variable N Mean Std. Min Median Max Esize 2,429 166.013 312.092 0.500 70.000 6000.000 Term 2,241 16.718 13.846 1.000 12.000 144.000 Rate 2,169 7.186 3.861 0.000 6.000 24.500 Ensure 2,429 0.339 0.473 0.000 0.000 1.000 R_Citydum 2,429 0.287 0.452 0.000 0.000 1.000 R_State 2,429 0.485 0.500 0.000 0.000 1.000 R_List 2,429 0.011 0.105 0.000 0.000 1.000 R_Age 2,376 8.011 6.374 0.000 6.000 30.000 R_Size 2,329 18.421 1.611 14.509 18.421 22.291 R_Lev 1,359 0.596 0.328 0.000 0.597 1.878 R_Roa 1,181 0.023 0.104 −0.236 0.004 0.500 R_Growth 532 0.081 0.731 −1.000 0.000 6.116 Esize is the amount of each entrusted loan (in millions); Term is the maturity of each entrusted loan (in months); Rate is the annualised rate of each entrusted loan; Ensure is an indicator that equals 1 if the entrusted loan needs guarantee, and 0 otherwise; R_Citydum is a dummy variable that equals 1 if the borrower and the listed company are in the same city, and 0 otherwise; R_State is an indicator that equals 1 if the borrower is a SOE, and 0 otherwise; R_List is an indicator that equals 1 if the borrower is listed company, and 0 otherwise; R_Age is the age of the borrower; R_Size is the natural logarithm of the borrower’s registered capital; R_Lev is the borrower’s debt-to-asset ratio; R_Roa is the borrower’s return on total assets, and R_Growth is the growth rate of the borrower’s sales. 4. Empirical analyses 4.1. Baseline results Table 4 reports the results based on model (1), that is, the baseline regression results between credit mismatch and entrusted loans. In column (1) and column (2), the coeffi - cient estimates of credit mismatch, CM (OLoan1, OLoan2), are significantly positive, regardless of whether the dependent variable is the propensity to engage in entrusted loans activities (EL_dum) or the size of entrusted loans (EL_size). This implies that the higher the degree of credit mismatch, the higher would be the tendency of firms with excessive bank loans to engage in entrusted loan activities and the larger would be the size of these entrusted loans. In contrast, the coefficient estimates of NLoan (NLoan1 and NLoan2, corresponding to OLoan1, OLoan2 in credit mismatch) are not significant. This findings suggest that the normal bank loans that firms borrowed have not been used for entrusted loans, and credit mismatch has raised excessive bank loans beyond the firms’ normal demand, which has led to firms’ engagement in entrusted loan activities. The above results support our H1. 260 J. BAI, ET AL. Table 4. Credit mismatch and entrusted loans: baseline results. Dependent variables EL_dum EL_size i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t (1) (2) (3) (4) CM 1.144** 0.915* 0.005*** 0.004*** (2.361) (1.950) (4.183) (4.299) NLoan −0.778 0.963 0.002 0.005 i,t (−0.512) (0.415) (0.675) (0.728) Size 0.235*** 0.218*** 0.000** 0.000** i,t-1 (4.665) (4.526) (2.069) (1.979) Lev −0.283 −0.468 −0.002** −0.002*** i,t-1 (−0.648) (−1.179) (−2.119) (−2.578) Roa −1.617 −1.729 −0.007** −0.007*** i,t-1 (−1.470) (−1.559) (−2.563) (−2.599) Cfo −0.083 0.428 0.001 0.002 i,t-1 (−0.112) (0.721) (0.672) (1.188) Cashhold −0.331 −0.199 −0.002 −0.001 i,t-1 (−0.755) (−0.455) (−1.575) (−1.470) SOE 0.438*** 0.435*** 0.001*** 0.001*** i,t-1 (3.616) (3.585) (3.661) (3.652) Risk 0.587*** 0.537*** 0.002*** 0.002*** i,t-1 (3.784) (3.647) (3.594) (3.555) Constant −7.778*** −7.662*** −0.003 −0.003 (−7.166) (−6.696) (−1.161) (−1.052) Fixed-Effects Year and Year and Year and Year and Industry Industry Industry Industry N 13,242 13,242 13,242 13,242 Chi 195.770 194.690 194.730 194.210 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respec- i,t i,t tively; (2) z-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. 4.2. Heterogeneity analysis To further investigate the impact of the macro-institutional environment on the relation- ship between credit mismatch and entrusted loans, this study uses two different macro- indicators, that is, government intervention and monetary policy. First, we define a dummy variable, MP, to measure whether the monetary policy is tight, and thereby examine how the monetary policy affects the relationship between credit mismatch and entrusted loans. Column (1) to column (4) of Table 5 show that the coefficient estimates of the interaction term between credit mismatch and monetary policy, CM *MP, are positive and significant at least at the 10% level. This finding suggests the tightening of the monetary policy will result in a decline in the bank credit resources available to firms, which, in turn, will strengthen the demand for funds transfer between firms and thus exacerbate the entrusted loans between firms. Second, column (5) to column (8) of Table 5 report the impact of government intervention on the relationship between credit mismatch and entrusted loans. We can see that the coefficient of the interaction term between credit mismatch and government intervention, CM *FM, are positive and statistically significant at least at the 5% level. This suggests that when financial regulation is tightened by the government, the degree of credit mismatch becomes higher and the funds reallocation of non-financial firms becomes predominant. The above conclusions support H2. In other words, the macro-institutional environment in which the enterprise is located is an important factor influencing a firm’s engagement in entrusted loan activities. CHINA JOURNAL OF ACCOUNTING STUDIES 261 Table 5. Credit mismatch and entrusted loans: the influence of the macro-institutional environment. D = Monetary policy (MP) Government intervention (FM) Dependent variables EL_dum EL_size EL_dum EL_size i,t i,t i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t i,t i,t i,t i,t (1) (2) (3) (4) (5) (6) (7) (8) CM*D 1.652*** 1.076** 0.004* 0.003* 1.387** 1.513*** 0.007*** 0.007*** (2.599) (2.196) (1.695) (1.760) (2.071) (2.806) (3.487) (4.080) CM 0.100 0.100 0.002 0.002 0.458 0.141 0.001 0.001 (0.155) (0.180) (1.298) (1.350) (0.896) (0.318) (0.954) (0.923) D 0.014 −0.012 0.000 0.000 −0.046 −0.083 −0.000** −0.001** (0.206) (−0.178) (0.381) (0.091) (−0.626) (−1.096) (−2.040) (−2.410) Constant −8.585*** −7.315*** −0.004** −0.002 −7.676*** −7.538*** −0.002 −0.002 (−8.651) (−6.426) (−1.998) (−0.753) (−10.713) (−9.593) (−0.845) (−0.805) Control Yes Yes Yes Yes Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry N 13,242 13,242 13,242 13,242 13,242 13,242 13,242 13,242 Chi 185.660 189.650 186.110 186.770 382.560 385.260 209.330 213.380 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respectively; (2) z-statistics are reported in parentheses, and *, ** and *** denote i,t i,t significance at the 10%, 5%, and 1% level, respectively. 262 J. BAI, ET AL. Under credit mismatch, the behaviour associated with entrusted loan activities may also be affected by firm-level characteristics. Therefore, this study further examines the impact of investment opportunities and the nature of property rights on the company’s external entrusted loan behaviour from the perspective of micro-enterprises. First, companies that lack investment opportunities are more likely to invest their credit resources via shadow banking activities to maximise their profits, that is, to earn benefits through lending. Column (1) to column (4) of Table 6 present the impact of poor corporate investment opportunities on the relationship between credit mismatch and corporate external entrusted loans. It can be seen that the coefficient estimates of credit mismatch and investment opportunity, CM *TobinQ, are positive and statistically signifi - cant at least at the 10% level. It implies that firms with excessive bank loans are more inclined to issue entrusted loans when lacking attractive investment opportunities. Second, columns (5) to column (8) of Table 6 report the impact of the nature of property rights on the relationship between credit mismatch and corporate entrusted loans. The results show that the coefficient estimates of credit mismatch and property rights, CM *SOE, are positive and significant at least at the 5% level. Overall, compared with private enterprises, SOEs can obtain more excessive credit funds from banks and transfer them to other firms through entrusted loans. The above conclusions verify H3. In other words, the micro-individual characteristics (insufficient investment opportunities, property rights, and the relative position of the firm in the industry chain) are important considerations for firms to engage in entrusted loan activities. 4.3. Economic consequences Table 7 provides the regression results of the model (3). Columns (1) and (2) show that engaging in entrusted loan activities can bring additional benefits to firms. However, column (3) and (4) reveal that, when using the change of operating income (D_Profit ) i,t+1 as the dependent variable, the coefficient on the interaction term, EL_size*CM, is signifi - cantly negative. The above results show that engaging in entrusted loans under credit mismatch is short-term profit-seeking behaviour, which is detrimental to the firm in the long run. Similarly, when using sales growth rate (Growth ) as the dependent variable, i,t+1 the results are as shown in columns (5) and (6), and the coefficient on the interaction term, EL_size*CM, is still negative, which shows that engaging in entrusted loans is harmful to the improvement of overall profitability. This finding is consistent with Zhang & Zhang (2016) that firms’ financialization leads to a decline in real economic investment. 5. Robustness tests To ensure the reliability of our empirical results, this study also conducts several robust- ness tests to address concerns about endogeneity, sample selection, variable definitions, and regression methods. Untabulated results of using EL_dum to measure external entrusted loans are similar. CHINA JOURNAL OF ACCOUNTING STUDIES 263 Table 6. Credit mismatch and entrusted loans: the influence of micro-individual characteristics. D = Investment opportunities (TobinQ) Property rights (SOE) Dependent variables EL_dum EL_size EL_dum EL_size i,t i,t i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t i,t i,t i,t i,t (1) (2) (3) (4) (5) (6) (7) (8) CM*D 0.548** 0.393* 0.005** 0.004** 1.763** 1.271** 0.008*** 0.007*** (2.198) (1.848) (2.375) (2.349) (2.406) (2.181) (4.078) (4.055) CM 0.481 0.361 0.001 0.001 −0.072 0.018 −0.000 0.000 (0.951) (0.829) (0.675) (0.752) (−0.114) (0.035) (−0.081) (0.254) D −0.145*** −0.111*** 0.001** 0.001** 0.395*** 0.384*** 0.001*** 0.001*** (−3.647) (−2.940) (2.570) (2.453) (4.611) (4.401) (3.072) (2.759) Constant −6.239*** −6.454*** −0.000 −0.000 −7.785*** −7.636*** −0.003 −0.003 (−7.096) (−6.770) (−0.147) (−0.104) (−10.942) (−9.783) (−1.155) (−0.996) Control Yes Yes Yes Yes Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Year and Year and Year and Year and Industry Year and Industry Year and Industry Year and Industry Industry Industry Industry Industry N 13,242 13,242 13,242 13,242 13,242 13,242 13,242 13,242 Chi 390.360 384.050 207.290 206.120 382.440 385.430 186.110 186.770 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respectively; (2) z-statistics are reported in parentheses, and *, ** and *** denote i,t i,t significance at the 10%, 5%, and 1% level, respectively. 264 J. BAI, ET AL. Table 7. Credit mismatch and entrusted loans: economic consequence. Dependent variables Shouyi D_Profit Growth i,t i,t+1 i,t +1 CM = OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t i,t i,t (1) (2) (3) (4) (5) (6) EL_size *CM 0.019*** 0.023*** −0.537*** −0.484** −2.471* −0.660 i,t (3.107) (4.504) (−2.580) (−2.040) (−1.780) (−0.417) EL_size −0.000 −0.001 −0.005 0.021 −0.133 −0.077 i,t (−0.541) (−1.292) (−0.160) (0.632) (−0.640) (−0.358) CM 0.000** 0.000** 0.019*** 0.016*** 0.058** 0.037 (1.997) (2.195) (5.287) (3.960) (2.456) (1.393) Constant 0.011*** 0.011*** −0.002 0.004 0.319*** 0.340*** (25.627) (25.737) (−0.273) (0.525) (5.706) (6.195) Control Yes Yes Yes Yes Yes Yes i,t Fixed-Effects Year and Year and Year and Year and Year and Year and Industry Industry Industry Industry Industry Industry N 8,350 8,350 10,598 10,598 10,598 10,598 Adj_R 0.259 0.260 0.027 0.025 0.079 0.078 F 82.056 82.544 9.489 9.077 27.723 27.532 (1) Due to the missing of some variables, the number of observations has decreased;(2) t-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. 5.1. Endogeneity concerns We use a two-stage regression method based on instrumental variables to alleviate the endogenous concerns. Specifically, we use industry average (OLoan1_indmean, OLoan2_indmean) of corporate credit mismatch (OLoan1, OLoan2) in the same year as instrumental variables of enterprise excess bank loans. Many prior studies use the mean value of industry average of the explanatory variables in the same year as the instrumental variables of the explanatory variable, such as Lu (2014), Yang et al. (2015). The main reasons are as follows: first, the industry average of credit mismatch in the same year is closely related to the corporate’s credit mismatch, which satisfies the correlation require- ment of instrumental variables; second, the industry average of credit mismatch in the Table 8. Credit mismatch and entrusted loans: instrumental variable. First stage Second stage Dependent OLoan1 OLoan2 EL_dum EL_size i,t i,t i,t i,t variables (1) (2) (3) (4) (5) (6) CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t FB1 0.837*** (10.573) FB2 0.954*** (12.553) CM 0.211* 0.406*** 1.649* 3.020*** (1.851) (3.398) (1.879) (3.694) Constant 0.255*** 0.147*** −0.456*** −0.390*** −5.115*** −4.802*** (6.000) (4.075) (−9.071) (−8.795) (−16.347) (−14.966) Control Yes Yes Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Year and Year and Year and Year and Year and Industry Industry Industry Industry Industry Industry N 13,242 13,242 13,242 13,242 13,242 13,242 Adj_R 0.428 0.181 F 141.143 70.392 (1) The first stage uses OLS regression, with t values in parentheses; (2) the second stage takes into account the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respectively, with z-statistics i,t i,t in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. CHINA JOURNAL OF ACCOUNTING STUDIES 265 same year only affects the entrusted loan behaviour by influencing firms’ credit funds, satisfying the exogeneity requirement of instrumental variables. Table 8 shows the results of the first-stage regression, and the coefficients of the instrumental variables (OLoan1_indmean, OLoan2_indmean) are significantly positive, and the model’s F value is large, meaning that the credit mismatch at the industry level is an appropriate instru- mental variable. In the second stage of regression, the coefficient of credit mismatch is still significantly positive, indicating that after controlling for endogenous problems, the credit mismatched company’s entrusted loan behaviour still has a significant effect, further supporting the research hypothesis of this article. 5.2. Other robustness tests First, since not all listed firms obtain excessive bank loans, and those without excessive bank loans cannot engage in the entrusted loan activities. We rerun our analysis only using the sample with excessive positive loans. The regression results in Table 9 show that the excessive bank loans are significantly and positively correlated with the entrusted loans, indicating that credit mismatch makes these firms have more excessive bank loans Table 9. Credit mismatch and entrusted loans: firms with excessive bank loans. Dependent variables EL_dum EL_size i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t (1) (2) (3) (4) CM 2.910*** 1.755** 0.011*** 0.009*** (3.525) (2.323) (5.002) (5.284) NLoan −1.761 −1.696 −0.002 0.005 i,t (−0.836) (−0.561) (−0.316) (0.532) Control Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry N 6,017 7,142 6,044 7,142 Chi 145.950 168.610 138.100 196.220 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit i,t i,t respectively; (2) z-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. Table 10. Credit mismatch and entrusted loans: alternative measures. Alternative measures of dependent variable Alternative measures of independent variable Dependent variables EL_size (scaled by sales) EL_dumi,t EL_sizei,t i,t CM = OLoan1i,t OLoan2i,t OLoan3 i,t (1) (2) (3) (4) CM 0.016*** 0.016*** 0.822* 0.004*** (5.349) (5.860) (1.748) (4.129) NLoan 0.018* 0.028 3.669** 0.007 i,t (1.844) (1.457) (2.198) (1.445) Control Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry N 13,242 13,242 13,242 13,242 Chi 328.520 328.350 194.460 193.270 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respec- i,t i,t tively; (2) z-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. 266 J. BAI, ET AL. Table 11. Credit mismatch and entrusted loans: alternative regression method. Dependent variables EL_dum EL_size i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t (1) (2) (3) (4) CM 0.072** 0.059* 0.005*** 0.004*** (2.252) (1.897) (2.806) (2.721) NLoan −0.034 0.104 0.002 0.005 i,t (−0.341) (0.654) (0.499) (0.655) Control Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry N 13,242 13,242 13,242 13,242 Adj_R 0.026 0.026 0.012 0.012 F 4.342 4.351 2.391 2.390 All regressions were adjusted for heteroskedasticity and clustered at the company level. t-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. and that the more excessive bank loans they have, the more likely they will engage in entrusted loan activities. Second, alternative proxies are used for the dependent and independent variables. At first, we use sales to scale the dependent variables. The results presented in columns (1) and (2) of Table 10 imply that the baseline results are robust to these alternative measures. Additionally, following Deng et al. (2016), we use the regression method to obtain the third group of proxies for normal bank loans, NLoan3, and excessive bank loans, OLoan3, and the latter proxy of an alternative measure of credit mismatch. The regression results are shown in columns (3) and (4) of Table 10. Our results remain robust to these alternative measures. Third, the results in Table 11 indicate that our results are also robust if the ordinary least squares (OLS) regression is used. 6. Conclusions Based on the phenomenon that non-financial firms are engaged in shadow banking activities. we use Chinese A-share listed firms’ data and entrusted loans’ data, including the loan amount, the maturity, the interest rate, and the borrower’s basic characteristics collected from each entrusted loan contract from 2008 to 2016 as our sample, and investigate the characteristics of a firm’s entrusted loan activities and the economic consequences of such activities, under a credit mismatch scenario. It shows that the higher the number of excessive loans of non-financial firms, the higher would be their tendency to lend entrusted loans and the larger would be the size of these loans. Given the degree of credit mismatch, the size of the firm’s entrusted loans is closely associated with macro-institutional factors, such as monetary policy and government intervention; the entrusted loan’s size is also associated with firm-level characteristics, such as invest- ment opportunities and property rights. We also analyse the economic consequences of a firm’s entrusted loan activities under credit mismatch and find that although engaging in entrusted loan activities can bring additional benefits, it can also significantly damage the firm’s main business activities. The policy implications of this study are as follows. First, the operational mechanism and institutional reform of financial markets are closely related to the behavioural choices of microeconomic entities. The conclusion of this study indicates that the credit mismatch problem caused by financial repression induces enterprises to engage in shadow banking CHINA JOURNAL OF ACCOUNTING STUDIES 267 activities such as entrusted loans activities, which may explain the slow development of the real economy. Second, we find that the interest rate of entrusted loans is high, and the risks are relatively high. Hence, the government should actively promote the construction of a market-oriented financial market and the reform of financial systems, such as banks, accelerate the reform of interest rate liberalisation, and extend multi-level financing channels for small and medium-sized firms, thereby improving firms’ external financing environment. Finally, this study can help regulatory authorities to further alleviate non- financial firms’ financialization. To reduce the shadow banking activities of non-financial firms and promote their returns on physical investment, it will be necessary to address the root cause of the problem wherein some firms have excessive loans, while others face financing constraints. Acknowledgments The authors would like to offer their most sincere thanks to the editors and the reviewers for their insightful comments. In addition, the authors especially thank Dr Liang Han, at the University of Reading, UK, for his valuable advice and proofreading. The authors are responsible for any remaining errors. Disclosure statement No potential conflict of interest was reported by the authors. Funding This work was supported by the National Natural Science Foundation of China [71762026, 71862029]. References Allen, F., Qian, J., & Qian, M. (2005). Law, finance, and economic growth in China. Journal of Financial, 77(5), 57–116. https://doi.org/10.1016/j.jfineco.2004.06.010 Allen, F., Qian, Y., Tu, G., & Yu, F. (2019). 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First, we select main factors that affect firm’s bank loans, and, subsequently, use model (1-a) to estimate the regression coefficient of the period t-1 on the period t, that is: Loan ¼ α þ α Size þ α Tangible þ α Liquidity þ α Growth þ i;t 0 1 i;t 1 2 i;t 1 3 i;t 1 4 i;t 1 α MB þ α Roe þ α CFO þ α Lev þ α TC þ α Risk þ α Age þ 5 i;t 1 6 i;t 1 7 i;t 1 8 i;t 1 9 i;t 1 10 i;t 1 11 i;t 1 α Dividend þ α Bond þ βLoan þ Yearþ Industryþ ε 12 i;t 1 13 i;t 1 i;t 1 i;t where Loan represents firm’s actual bank loans. We control the following variables: firm’s size, Size; the ratio of tangible assets, Tangible; the liquidity of assets, Liquidity; firm’s growth, Growth; market-to- book ratio, MB; return on equity, Roe; cash flow from operating activities, CFO; leverage, Lev; trade credit, TC; operational risk, Risk; establishment age, Age; whether to issue cash dividends, Dividend; whether to issue corporate bonds, and Bond; year fixed effects (Year) and industry fixed effects (Industry). Subsequently, we substitute the coefficient estimate, β, into the following model (1-b): NLoan ¼ Loan β� Loan ε =ð1 βÞ i;t i;t i;t i;t Subsequently, we get the firm’s target bank loans (NLoan1). 2. The calculation of firms’ normal and excessive bank loans (NLoan2, OLoan2) Drawing on the industry average method of Jiang & Liu (2005) and Deng et al. (2016), we estimate the company’s target bank loan. The rationale of this method is to use weights to control the large capital structure differences between different industries, that is, to construct the weighted average bank loan in the industry as the company’s target bank loans. The calculation model is as follows (2-a): n n X X NLoan ¼ Size � Loan = Size i;t i;t i;t i;t i¼1 i¼1 Then, we get the firm’s target bank loans (NLoan2). 3. The calculation of firms’ normal and excessive bank loans (NLoan3, OLoan3) Drawing on the regression method by Deng et al. (2016), we construct firms’ target capital structure. First, we select the main factors that will affect a firm’s capital structure, and, subsequently, use model (3-a) to estimate the regression coefficient of the period t-1 on the period t, that is: OCS ¼ α þ α Size þ α Tangible þ α NDTS þ α Liquidity þ i;t 0 1 i;t 1 2 i;t 1 3 i;t 1 4 i;t 1 α Growth þ α MB þ α Roe þ α Cashholding þ α Risk þ α Age þ 5 i;t 1 6 i;t 1 7 i;t 1 8 i;t 1 9 i;t 1 10 i;t 1 α Dividend þ Yearþ Industryþ ε 11 i;t 1 i;t where OCS represents firm’s target capital structure. We control the following variables: firm’s size, Size; the ratio of tangible assets, Tangible; the liquidity of assets, Liquidity; firm’s growth, Growth; market to book ratio, MB; return on equity, Roe; cash holding, Cashhold; operational risk, Risk; establishment age, Age; whether to issue cash dividends, and Dividend; year fixed effects (Year) and industry fixed effects (Industry). Then we use model (3-b) and (3-c) to estimate firm’s normal bank loan (NLoan3). CHINA JOURNAL OF ACCOUNTING STUDIES 271 ODebt ¼ OCS � Asset i;t i;t i;t NLoan ¼ ODebt QDebt =Asset i;t i;t i;t i;t where ODebt represents firm’s target debt, QDebt represents firm’s other debt (the median of the difference between total liabilities and debt that includes short-term and long-term debts). Loan is a firm’s actual bank loans, and Asset is a firm’s total assets. Subsequently, we get a firm’s target bank loan (NLoan3). The calculation results of the above three target bank loans (NLoan1, NLoan2, NLoan3) are substituted into the model (4). In other words, by calculating the difference between the actual and the target bank loans of the firm, the company obtains the excessive bank loan (OLoan1, OLoan2, OLoan3). Finally, we substitute the above three estimates of the target bank loan (NLoan1, NLoan2, NLoan3) into the model (4). In other words, we obtain firm’s excessive bank loan by calculating the difference between the firm’s actual and target bank loan. OLoan ¼ Loan NLoan i;t i;t i;t http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Journal of Accounting Studies Taylor & Francis

Credit mismatch and non-financial firms’ shadow banking activities —evidence based on entrusted loan activities

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CHINA JOURNAL OF ACCOUNTING STUDIES 2020, VOL. 8, NO. 2, 249–271 https://doi.org/10.1080/21697213.2020.1822027 ARTICLE Credit mismatch and non-financial firms’ shadow banking activities —evidence based on entrusted loan activities a b c Jun Bai , Xiaoyun Gong and Xiangfang Zhao School of Economics and Management, Research Center of Corporate Governance and Management Innovation, Shihezi University, Shihezi, China; Dongwu Business School, Soochow University, Suzhou, China; Shanghai Lixin University of Accounting and Finance, Shanghai, China ABSTRACT KEYWORDS Credit mismatch; excessive When governments opt for financial repression policies, credit mis- bank loans; shadow banking match becomes more prevalent. This may lead some non-financial activities; entrusted loans firms with excessive loans to financialize their operations and under- take shadow banking activities, that is, entrusted loans. Using all listed Chinese firm's financial data and entrusted loans data from 2008 to 2016, this study investigates the impact of credit mismatch on firm's entrusted loans. Results show that, the more credit mismatch, the higher the tendency and the size of firm's entrusted loans. Above the relationship is more significant when under some certain backgrounds, such as a higher degree of government intervention, tighter monetary policy as well as lack of investment opportunities, and state-owned enterprises. Further analysis reveals that a firm's engagement in entrusted loans can harm its main business activities. This study intends to enhance our understanding of the shadow banking activ- ities as means of funds reallocation within China's financial system. 1. Introduction Since the adoption of the policy of reform and opening-up, China’s economy has been witnessing rapid growth for 40 years. However, the growing prominence of financial repression is extremely mismatched with the economic boom, and the inefficient financial system cannot effectively provide financing services for the real economy (Shao, 2010; Wang & Anders, 2013; Yu et al., 2015). Scholars have addressed this mismatch between financial repression and economic boom from the perspectives of political connections (Claessens et al., 2008; Zhang et al., 2010), trade credit (Wang, 2014; Zhang et al., 2013), and other receivables (Wang et al., 2015). In addition, this leads to the question whether there are other more direct channels for the inter-firm reallocation of funds. As direct lending between firms is deemed illegal, the inter-firm entrusted loans, intermediated by banks, have emerged and developed rapidly. By the end of 2017, the balance of corporate CONTACT Xiaoyun Gong gxyacc@163.com Dongwu Business School, Soochow University, Suzhou, China This article has been republished with minor changes. These changes do not impact the academic content of the article. Paper accepted by Kangtao Ye Entrusted loan refers to a lending arrangement in which a trustor provides funds and commercial banks (trustee) lend out money on behalf of the trustor and assist in supervising the use and collection of the loan. The trustor provides instruction that specifies target borrowers, the use of funds, loan amount, currency, maturity, and interest rate, among others. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 250 J. BAI, ET AL. entrusted loans reached 13.88 trillion RMB. Many non-financial listed companies have started carrying out entrusted loan activities by relying on their capital advantages, which has increasingly attracted the regulator’s attention. It can be seen that these entrusted loans have become one of the important channels for fund reallocation (Qian et al., 2013, 2017). Therefore, it is necessary to clarify the behavioural characteristics of firms that engage in entrusted loan activities and their economic consequences in a credit mismatch scenario. In this given context, this study focuses on the following issues: What kind of firms tend to engage in entrusted loans? What are the motives behind these activities? What are the economic consequences of these activities? Studies have shown that, as an important financing channel to support the development of the real economy, an entrusted loan has met the liquidity needs of both privileged firms, and improve the Pareto of credit resource allocation efficiency as well as increasing corporate value. For example, Qian et al. (2017) find that entrusted loans play the role of shadow banking, which enables the transfer of funds from a party with a financial surplus to a party with a deficit to promote balanced development of regional economies across the country. Qian et al. (2018) also demon- strate that the shadow banking mechanism of entrusted loans is a response to the market lack of formal credit, showing a distinctive reverse credit cycle characteristic. That is, when formal credit contracted, the probability and scale of companies issuing entrusted loans significant increase. Allen et al. (2019) show that the company’s entrusted loan business is a decision based on itself and the market environment, which means that the interest rate of the entrusted loan can fully reflect the borrower’s risk. Further, literature suggests that entrusted loans used by firms will distort market pricing mechanisms, impacting the efficiency of resource allocation. For example, Qian & Li (2013) show that the entrusted loans between affiliated firms demonstrate certain characteristics such as lower interest rate, larger size, and longer maturity, and while the entrusted loans between non- affiliated firms have a higher interest rate and risk, leading to increased financial risks. Li and Han (2019) figure that, when an enterprise uses the entrusted loan or private loan as a credit intermediary to borrow funds from the capital demander, the repayment risk of the borrower will not only reduce the debt repayment ability of the loan enterprise, but also be transmitted to the lender through the ‘accounting mechanism’, which will increase the business risk of the enterprise. In summary, the extant research has not reached a consensus on the economic consequences of entrusted loan activities, and this calls for an in-depth study of the institutional underpinning and the behavioural motives behind these activities. Based on the above, this study examines the impact of credit mismatch on the firms’ propensity to engage in entrusted loan activities and on the size of entrusted loans, using Chinese listed firms’ data and a manually collected dataset on each entrusted loan contract. Spanning a period from 2008 to 2016, the dataset includes the loan amount, maturity, interest rate, and the borrower’s basic characteristics. This study examines the characteristics of these firms and the economic consequences of the entrusted loan activities. Our results show that credit mismatch, that is, a scenario wherein some firms have more excessive bank loans, is significantly and positively associated with the propensity to engage in entrusted loan activities and the size of the entrusted loans. Additionally, the effect of credit mismatch on entrusted loan activities is closely related to macroeconomic factors such as monetary policy and government intervention, as well as firm characteristics, which include CHINA JOURNAL OF ACCOUNTING STUDIES 251 investment opportunities and property rights. Further analysis of the economic conse- quences of the entrusted loans shows that firms’ engagement in entrusted loan activities harms their primary business activities, despite the additional income from these loans. Therefore, they fail to provide effective and sufficient support for the long-term develop- ment of firms. Our study makes the following contributions to the literature. First, our paper supple- ments the related literature on the re-allocation of funds between enterprises under financial repression. Existing research has focused on the issue of firms’ funds reallocation through channels such as commercial credit and other receivables. This article focuses on the widespread entrusted lending behaviour among enterprises. Compared with other alternative paths of funds transfer hidden in the real transactions, entrusted loans’ transac- tion data is considered as less noisy when compared to alternative paths of funds transfer hidden in the real transactions. Additionally, our study benefits from the manually collected dataset on entrusted loan contracts, comprising the loan amount, the maturity, the interest rate, and the two contracting parties. This enables us to investigate the characteristics of the borrowing firms and provides an anchor point for further research. Although there is some literature on entrusted loans (Allen et al., 2019; Li & Han, 2019; Qian et al., 2017; Yu & Li, 2016), the institutional roots of non-financial enterprise entrusted loans have not been discussed in depth. Based on credit mismatch, this article provides a new explanation for the widely existing entrusted loan behaviours, that is, it is not a spontaneous phenomenon in the market, but there are profound institutional causes. In particular, this paper provides direct empirical evidence at the micro-enterprise level. Second, as aforementioned, there have been studies that have disputed the economic consequences of non-financial com- panies engaging in entrusted loans. This research finds that due to credit mismatch, some enterprises have excess borrowings and engage in entrusted loans. This is a short-term profit-seeking behaviour, as a shadow banking activity, and ultimately damages their primary business activities. This conclusion means that the negative effect of entrusted loans is largely due to the dissimilation of these preferentially favoured banks and the loss of attention to the primary business activities, not just the entrusted loan itself. Meanwhile, this finding helps us to re-understand the economic consequences of non-financial com- panies’ entrusted loans from the institutional level. Finally, this paper expands the related research on entrusted loans through conducting a series of heterogeneity analysis on macro factors such as monetary policy, government intervention, and investment oppor- tunities, and the nature of property rights. The remainder of the paper is organised as follows. Section 2 presents the theoretical analysis and develops our research hypotheses. Section 3 describes the research design. Section 4 analyses the empirical results. Section 5 discusses the robustness tests, and Section 6 concludes. 2. Theoretical analysis and hypotheses development Due to the weak legal enforcement and undeveloped intermediary institutions in China, the credit market lacks a complete credit reporting system, resulting in very high transac- tion costs for loan contracts. The banking sector can only implement strict credit ration- ing, such as whether the company has government guarantees or the firm’s size, to enterprises through non-market means (Allen et al., 2005; Ji et al., 2016; Zhang et al., 252 J. BAI, ET AL. 2013). Given a certain amount of funds and the scarcity of credit resources, credit rationing will inevitably lead to inefficient credit rationing, that is, it is easier for some companies to obtain capital from the credit market and occupy excess bank borrowings. The normal financing needs of other companies cannot be met. It can be seen that under the mismatch of credit, corporate financing capabilities and investment opportunities do not match, which in turn leads to the need for capital adjustment between financing- facilitating companies and financing-rare companies. Lu and Yao (2004) point out that there is a significant ‘financial leakage effect’ among non-financial enterprises, that is, large enterprises that are easy to finance will allocate the cheap funds they obtain to small and medium-sized enterprises (SMEs). However, according to Chinese law, non-financial firms, unlike commercial banks, are prohibited from directly engaging in lending activities. Therefore, shadow banking that allows direct lending poses a high legal risk for the contracting parties. Unlike direct lending, entrusted loans allow companies to refinance their own funds to other compa- nies through financial intermediaries such as banks (Qian et al., 2015, 2017). As a channel of funds reallocation, an entrusted loan enables borrowers and lenders to achieve the retransfer of funds among firms based on their supply and demand. On the one hand, companies with dominant positions tend to engage in entrusted loan activities to make a considerable amount of income, since this special lending mechanism is characterised by a high-interest-rate. The interest rate of entrusted loans is substantially higher than that of bank credit (Allen et al., 2019; Qian et al., 2015; Qian & Li, 2013). On the other hand, when presented with promising investment opportunities, private and small and med- ium-sized enterprises that are discriminated by formal financial institutions are willing to pay higher fees to obtain funds from firms with privileged access to cheap capital. For example, Qian et al. (2017) find that entrusted loans have facilitated cross-regional flow of funds, specifically, from firms in east China that have easy access to funds to the firms in midwestern China that face financial constraints. In summary, as a credit transaction with higher degree of interest rate liberalisation, entrusted loan activities can facilitate the flow of funds, because it not only can meet the rigid demand of firms under financial constraints, but also enable fund providers to earn spreads with almost no cost. Based on this, the discussion above leads to the first hypothesis H1: Ceteris paribus, the higher the degree of credit mismatch, the higher would be the motivation of firms to issue entrusted loans and the larger would be the size of these loans. The impact of mismatched credit on corporate entrusted loans is potentially constrained by the macroeconomic environment. To begin with, the monetary policy, as one of the macroeconomic policies of the central bank, directly affects the total amount of credit in the entire market. Many empirical studies have shown that the volatility of monetary policy has a significant impact on firms’ financial decisions (Rao & Jiang, 2013). When the monetary policy is tight, it is more difficult to obtain bank loans and costlier to finance, leading to conflicts between obtaining funds for working capital and capital investment expenditures. Yu et al. (2015) find that when monetary policy restricts the scale of desirable loans, banks tend to ignore the potential investment opportunities of enter- prises, which leads them to prefer large companies with good mortgages. This has allowed large enterprises to have excess borrowings beyond normal demand, while other companies face financing constraints. In this scenario, in order to seize potential CHINA JOURNAL OF ACCOUNTING STUDIES 253 investment opportunities, companies restricted by financing need to seek other shadow banking channels to obtain development funds. Allen et al. (2019) document a significant upward trend in entrusted loans between firms when the interbank rate rises. Therefore, under credit mismatch, the tightening of monetary policy will inevitably increase the financing needs of the capital-scarce parties, thus providing opportunities for fund pre- ferential parties to engage in entrusted loans as a shadow banking activity. In addition, the government has the motivation and ability to exercise appropriate regulation on the market to achieve the corresponding goals. There are various forms of government intervention in the market, such as the control of credit volume and interest rates (Ji et al., 2016), and the implementation of industrial policies. This can promote the flow of low-cost credit funds to government-supported enterprises and industries. Obviously, the higher degree of government intervention in the market, the easier for the supported enterprises to obtain bank loans (Wang et al., 2017) becoming the fund- advantage parties in the credit market. Meanwhile, the funding gap of companies that suffer from financing discrimination will also widen. However, due to the limited informa- tion held by the government, the resources allocated through intervention methods do not always match the financing needs of enterprises. As a result, the incentives for reallocation of credit resources between favoured parties and disadvantaged parties have increased. Based on this, we develop the second hypothesis H2: Ceteris paribus, the tighter the monetary policy or the higher the degree of government intervention, the stronger the positive relationship between the degree of credit mismatch and the firm’s entrusted loans. In fact, firms with credit advantages engaged in entrusted loans may also be significantly related to the micro-level characteristics of companies. First of all, with the slowdown in the growth of investment and consumption, investment opportunities faced by enter- prises are gradually decreasing. In order to get rid of the constraints of these development difficulties, some companies, in addition to building their own capabilities, will actively seek government help (Yang, 2011). Despite the lack of good investment opportunities, a large number of companies can still rely on external ‘blood transfusions’ and never die (Huang & Chen, 2017). Existing research confirms the economic phenomenon of mis- match between corporate financing and investment opportunities in terms of investment crowding out, innovation crowding out, and tax distortion (Li et al., 2018; Tan et al., 2017). Liu (2011) also points out that with the deterioration of investment opportunities, relatively inefficient companies tend to invest the low-cost credit funds in high-yield companies. Similarly, based on data from corporate consolidated statements, Shin & Zhao (2013) and Wang et al. (2015) find that due to scarce investment opportunities, companies are more inclined to provide funds to other companies. It can be seen that the lack of investment opportunities will cause non-financial companies with financing advantages to engage in more entrusted loans. Secondly, state-owned enterprises (SOEs), as undertaking multiple social goals (such as employment and public facilities), are closely related to state-owned banks that dominate the credit market. Coupled with the imperfect corporate information disclosure and bond protection systems in China, banks generally have ‘ownership discrimination’ against enterprises (Shao, 2010; Song et al., 2011; Zhang et al., 2013). That is, SOEs have great financing advantages that can easily obtain a large amount 254 J. BAI, ET AL. of low-cost credit funds, while private enterprises are faced with greater financing constraints. Yu et al. (2014) show that SOEs enjoyed preferential financing signifi - cantly crowds out the credit financing of non-state-owned enterprises (non-SOEs), resulting in non-SOEs’ investment efficiency lower than that of SOEs’. Besides, this phenomenon is even more obvious under the shock of monetary policy. To obtain more scarce credit resources, the latter often adopts a series of coping strategies, and SOEs are undoubtedly more likely to be providers of funds. Based on this, we put forward the third hypothesis H3: Ceteris paribus, the relationship between the degree of credit mismatch and the company’s external entrusted loans are related to the poor investment opportunities and the nature of state-owned property rights. Then, under credit mismatch, when firms with financing advantages engage in entrusted loan activities, what kind of impact on a firm’s production and operation? Intuitively, if the privileged companies use the excess bank borrowings for external entrusted loans, it seems to make full use of the loans beyond company’s normal operating financing, which will have a positive effect on corporate performance. However, it should be noted that, to begin with, it is a general phenomenon that certain types of enterprises have excessive borrowing, but for a specific enterprise, rent-seeking activities also need to face competi- tion. Therefore, to maintain the short-term benefits of entrusted loans, corporate manage- ment has to spend a lot of time and energy to maintain relations with the government in order to continuously obtain excess borrowings, which is clearly different from the surplus operating funds of their own. Obviously, rent-seeking activities are more difficult to sustain than production activities, and will largely crowd out the resources invested in the main business activities. Besides, Jensen (1986) shows that due to agency problems within the company, management tends to use free cash flow for on-the-job consumption, or to build a corporate empire through investment, thereby reducing the operating efficiency and the value of the enterprise. It can be seen that because the utility functions between management and shareholders are not consistent, abundant resources are not a sufficient condition for management to enhance the long-term value of the enterprise. Under financial repression, the cost of credit financing is significantly lower than the market level (Ji et al., 2016). Therefore, when companies are able to obtain excess borrowings, management facing pressure for performance appraisal is more prone to opportunistic behaviours, which will distort the company’s business decisions. Yu & Li (2016) also document that in order to pursue the high interest brought by entrusted loans in the short term, management will abandon R&D projects with long-term and uncertain returns. Hence, the level of innovation will decline significantly in the future. Similarly, the short-term excess returns obtained through entrusted loans are likely to cause management to spend less effort to run the business, that is, resulting in slack behaviour and less motivation for doing main business activities. A firm’s long-term development still falls back on frontier technology and high-quality products and services, and it is hard to achieve sustain- able development by merely relying on generating profits via entrusted loans. To sum up, it is not difficult to see that although a company engaged in entrusted loans can obtain short-term returns for the company under credit mismatch, it will CHINA JOURNAL OF ACCOUNTING STUDIES 255 ultimately be detrimental to stable development of the company in the long term. For example, crowding out its primary business activities. Based on this, we propose the last hypothesis H4: Ceteris paribus, the higher the degree of credit mismatch, the more the company’s entrusted loans are not conducive to the devel- opment of its main business activities. 3. Research design 3.1. Model design First, to test how credit mismatch affects entrusted loans, that is, H1, we construct the following econometric model based on the research of Qian et al. (2017) and Allen et al. (2019). EL ¼ α þ α CM þ α NLoan þ α Control þ Yearþ Industryþ ε (1) i;t 0 1 i;t 2 i;t 3 i;t 1 i;t where t indexes time, and i indexes firm. The dependent variable EL represents entrusted loans, which is measured by two indicators, EL_dum and EL_size. EL_dum is a dummy variable that equals one if a firm engages in entrusted loan activities, and zero otherwise. EL_size represents the size of the firm’s entrusted loans; it is measured by the ratio of the size of entrusted loans to total assets. The independent variable CM represents credit mismatch, which is measured by two indicators, OLoan1 and OLoan2. Different from prior research, this study attempts to estimate the degree of credit mismatch at the firm level. In China’s institutional setting, the credit mismatch phenomenon is characterised by the scenario wherein some firms can obtain borrowings more than they need for operations (excessive bank loans), while other firms face financial constraints. On this basis, we follow the research of Flannery & Rangan (2006), Lu & Yang (2011), and Deng et al. (2016) to measure the firm-level credit mismatch. First, bank loans obtained by firms are decomposed into two parts – normal bank loans and excessive bank loans. Subsequently, excessive bank loans are used to measure the degree of credit mismatch at the firm level. To avoid the measurement bias caused by using a single method, this study employs two methods. One measure- ment involves dynamic adjustment to capital structure (Deng et al., 2016; Flannery & Rangan, 2006), where we get the first group of indicators for normal bank loans NLoan1 and excess borrowing OLoan1. The other measurement is the industry average method (Deng et al., 2016; Jiang & Liu, 2005); it is employed to calculate the second set of indicators for normal bank loans NLoan2 and excessive bank loans OLoan2. In the robustness test, this study also uses the regression analysis method to obtain the third group of indicators for normal bank loans NLoan3 and excessive bank loans OLoan3. The above calculation methods are shown in the Appendix. Based on prior research (Deng et al., 2016; Qian et al., 2017), we control for the vector Control comprising other control variables that could affect a firm’s entrusted loans in period t-1. Specifically, Control includes the following variables: firm’s size, Size, measured by the natural logarithm of total assets; capital structure, 256 J. BAI, ET AL. Lev, measured by the ratio of total debt to total assets; profitability, Roa, measured by the ratio of net income to total assets; fixed assets, Tangible, measured by the ratio of net-fixed assets to total assets; cash flow from operations activities, Cfo, measured by ratio of net cash flow from operating activities to total assets; cash holding, Cashhold, measured by the ratio of cash and cash equivalents to total assets; property rights, SOE, an indicator defined based on firm’s ultimate controller that equals 1 if it is SOE, and 0 otherwise; operation risk, Risk, measured by the standard deviation of the return on total assets within three years. Year and Industry represent year and industry fixed effects, respectively; ε is the residual of the model. To further test the impact of the macro-institutional environment and micro-individual characteristics on the firm’s entrusted loans in the credit mismatch scenario, that is, H2 and H3, we construct a model as follows: EL ¼ β þ β CM þ β Dþ β CM � Dþ β Control þ Yearþ Industryþ ε (2) i;t i;t i;t i;t 1 i;t 0 1 2 3 4 Based on the model (1), this model further adds moderator D, which specifically includes four indicators: monetary policy, MP; government intervention, FM; investment opportu- nity, TobinQ; and property rights nature, SOE. Among them, the monetary policy dummy variable MP is set to one for 2009, 2010, 2012, 2013, 2015, and 2016, and zero for other years; the government intervention, FM, which is the Chinese marketisation index devel- oped by Wang et al. (2016), when the government intervention index is less than the annual average, FM takes one, and zero otherwise; the investment opportunity, TobinQ, if the investment opportunity of the enterprise is lower than the annual average of the industry, TobinQ is equal to one, and zero otherwise. We examine which characteristics will affect the relationship between credit mismatch and entrusted loans by focusing on interaction terms between D and credit mismatch, CM. The other variables are the same as the model (1). Finally, this article further explores the economic consequences of entrusted loans, under the backdrop of credit mismatch. We refer to the research design of Zhu et al. (2015) and Yu & Li (2016) and construct the following regression model to conduct empirical tests on the above issues: OUTCOME ¼ γ þþγ EL þ γ CM þ γ EL � CM þ γ Control þ Year þ Industryþ ε i;t i;t i;t i;t i;t i;t 0 1 2 3 4 (3) where t indexes time, and i indexes firm. The dependent variable OUTCOME includes profits from entrusted loans and performance of primary business activities (measured by the change of operating income and the growth rate of sales). Profits from entrusted loan (Shouyi ) equals the amount of the external entrusted loans multiplied by the annualised i,t interest rate and the terms of these loans. The change of operating income (D_Profit ) i,t+1 equals operating income of the next period minus that of current period and then divided by total assets. And the growth rate of sales (Growth ), which is calculated as sales of i,t+1 the next period minus that of the current period, and then divided by that of the current period. The independent variable is interaction term EL*CM, which is the interaction between the amount of entrusted loans and credit mismatch. Control variables include Size, Lev, Risk, Cfo, SOE, Roa, Age and MB. The definition of all the above variables are presented in Table 1. CHINA JOURNAL OF ACCOUNTING STUDIES 257 Table 1. Variable definitions. Variables Definition EL The propensity of issuing entrusted loans, EL_dum, is a dummy variable that equals 1 if the enterprise engages in entrusted loan activities, and 0 otherwise Size of entrusted loans, EL_size, is calculated as the ratio of the size of entrusted loans to total assets CM Credit mismatch, OLoan1, is estimated by using the dynamic adjustment method to calculate excessive bank loans. The specific calculation is in Appendix Credit mismatch, OLoan2, is estimated by using the industry average method to calculate excessive bank loans. The specific calculation is in Appendix NLoan Normal bank loans, NLoan1, is calculated by using the dynamic adjustment method. The specific calculation is available in Appendix Normal bank loans, NLoan2, is calculated by using industry average method. The specific calculation is available in Appendix MP Tight monetary policy is an indicator that equals one if the year is 2009, 2010, 2012, 2013, 2015 and 2016, and 0 zero otherwise FM Government intervention, the government intervention index in the China’s marketisation index developed by Wang et al. (2016), if it is lower than the annual average, the value is 1, and 0 otherwise TobinQ Investment opportunities, a dummy variable that equals 1 if a firm’s investment opportunity is lower than that of the industry average, and 0 otherwise SOE Property rights, an indicator defined on the basis of the firm’s ultimate controller that equals 1 if it is SOE, 0 otherwise Shouyi Profit from entrusted loan income, which is equal to the amount of the external entrusted loan multiplied by the annualised interest rate and the terms of these loans D_Profit Change of operating profits, which is equal to operating income of the next period minus that of the current period and then divided by total assets Growth Sales growth rate, which is equal to the sales of the next period minus that of the current period, and then divided by that of the current period Size Firm size, which is measured by the natural logarithm of total assets Lev Capital structure, which is measured by the ratio of total debt to total assets Roa Profitability, which is measured by the ratio of net income to total assets Cfo Cashflow from operations activities, which is measured by the ratio of net cash flow from operating activities to total assets Cashhold Cash holding, which is measured by the ratio of cash and cash equivalents to total assets Risk Operation risk, which is measured by the standard deviation of the return on total assets within 3 years 3.2. Sample selection and summary statistics We use Chinese A-share listed companies from 2008 to 2016 as our sample. We treat the original data as follows. First, we exclude observations in finance and insurance industries; we also exclude ST and *ST with leverage ratio greater than 1 or with missing values. This yields a sample of 13,242 observations. Second, all continuous variables are winsorised at the 1% and 99% levels. We also manually sort each entrusted loan contract to obtain data on loan amount, maturity, interest rate, and the borrower’s basic characteristics, and finally get 2,429 observations. The above financial data are obtained from the China Stock Market & Accounting Research (CSMAR) database, and the entrusted loans’ contract data are obtained from the Juchao website. The statistical analysis software is Stata15.0. Table 2 reports the summary statistics. Panel A presents the descriptive statistics, and it shows that the average propensity of issuing entrusted loans (EL_dum) is 0.070. It means that about 7% of the listed firms are engaged in entrusted loan activities; the average size of entrusted loans (EL_size) is 0.003, and the minimum and maximum sizes show that the size difference between sample firms is ST means that the company has suffered losses for two consecutive years, and *ST means that the company has suffered losses for three consecutive years. http://www.cninfo.com.cn/new/index. 258 J. BAI, ET AL. significant. The averages of firms’ excessive bank loans (OLoan1, OLoan2) calculated by using different methods are all positive, indicating that the existence of exces- sive bank loans is prevalent. Furthermore, the standard deviation shows that the variance in excessive bank loans between different firms is distinctive, showing signs of credit mismatch. Panel B compares the degree of credit mismatch between SOEs and non-SOEs and between small firms and large firms. The results of this comparison show that SOEs (large firms) have more excessive bank loans than non- SOEs (small firms) and indicates that credit mismatch brings financing advantages for SOEs and large firms. Panel C presents the difference test for entrusted loans in the credit mismatch scenario. It also shows that firms with excessive bank loans are significantly different from those without surplus loans in terms of the propensity to engage in entrusted loan activities and the size of entrusted loans. This finding suggests that credit mismatch makes firms more likely to get involved in entrusted loans activities. Table 2. Summary statistics. Panel A:Descriptive statistics Variables N Mean Std. Min Median Max EL_dum 13,242 0.070 0.255 0.000 0.000 1.000 EL_size 13,242 0.003 0.012 0.000 0.000 0.090 OLoan1 13,242 0.016 0.107 −0.199 0.003 0.306 NLoan1 13,242 0.178 0.071 0.022 0.174 0.358 OLoan2 13,242 0.028 0.130 −0.210 0.014 0.373 NLoan2 13,242 0.165 0.057 0.065 0.159 0.367 Size 13,242 22.035 1.263 19.478 21.855 25.820 Lev 13,242 0.491 0.196 0.050 0.496 0.895 Roa 13,242 0.036 0.049 −0.156 0.033 0.197 Cfo 13,242 0.040 0.075 −0.194 0.041 0.256 Cashhold 13,242 0.168 0.117 0.012 0.138 0.665 SOE 13,242 0.515 0.500 0.000 1.000 1.000 Risk 13,242 0.235 0.232 0.014 0.172 1.470 Panel B:Difference test of entrusted loans under credit mismatch OLoan1 < 0 OLoan1 > 0 Mean Median Mean Median Mean test Median test EL_dum 0.059 0.000 0.073 0.000 −0.014*** −0.000*** EL_size 0.002 0.000 0.003 0.000 −0.001*** −0.000*** OLoan2 < 0 OLoan2 > 0 Mean Median Mean Median Mean test Median test EL_dum 0.058 0.000 0.074 0.000 −0.016*** −0.000*** EL_size 0.002 0.000 0.003 0.000 −0.001*** −0.000*** We use t-test in mean, and z-test in median test. *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. To further examine the listed companies’ entrusted loans, Table 3 reports detailed information about each entrusted loan contract, including the amount of the entrusted loans, the interest rate, and other basic characteristics of the borrowers. As shown in Table 3, the average size of entrusted loans, Esize, is about 166 (in millions of RMB), ranging from half a million to 6,000 million RMB. On average, the maturity of entrusted loans is around 17 months, which is classified as short-term financing. The average interest rate of entrusted loans, Rate, is about 7%, and the maximum is 24.5%, significantly higher than the banks’ CHINA JOURNAL OF ACCOUNTING STUDIES 259 benchmark lending rates in the same period. It indicates that the financing cost of entrusted loans is higher. Concerning guarantees, Ensure, its average value is approximately 34%, which indicates that about 34% of the sampled companies must provide loan guarantees when obtaining entrusted loans. Concerning the characteristics of the borrowers of the funds, statistics show that about 29% of the entrusted loans are between firms located in the same city, and it is presumed that these firms are familiar with each other. With regard to property rights, 48% of the sampled firms are SOEs, and the rest are non-SOEs. In terms of the establishment age, firm size, debt level and profitability, the overall endowment of these enterprises, whether listed or not listed, is lower than that of the large listed companies. Table 3. Basic characteristics of entrusted loans. Variable N Mean Std. Min Median Max Esize 2,429 166.013 312.092 0.500 70.000 6000.000 Term 2,241 16.718 13.846 1.000 12.000 144.000 Rate 2,169 7.186 3.861 0.000 6.000 24.500 Ensure 2,429 0.339 0.473 0.000 0.000 1.000 R_Citydum 2,429 0.287 0.452 0.000 0.000 1.000 R_State 2,429 0.485 0.500 0.000 0.000 1.000 R_List 2,429 0.011 0.105 0.000 0.000 1.000 R_Age 2,376 8.011 6.374 0.000 6.000 30.000 R_Size 2,329 18.421 1.611 14.509 18.421 22.291 R_Lev 1,359 0.596 0.328 0.000 0.597 1.878 R_Roa 1,181 0.023 0.104 −0.236 0.004 0.500 R_Growth 532 0.081 0.731 −1.000 0.000 6.116 Esize is the amount of each entrusted loan (in millions); Term is the maturity of each entrusted loan (in months); Rate is the annualised rate of each entrusted loan; Ensure is an indicator that equals 1 if the entrusted loan needs guarantee, and 0 otherwise; R_Citydum is a dummy variable that equals 1 if the borrower and the listed company are in the same city, and 0 otherwise; R_State is an indicator that equals 1 if the borrower is a SOE, and 0 otherwise; R_List is an indicator that equals 1 if the borrower is listed company, and 0 otherwise; R_Age is the age of the borrower; R_Size is the natural logarithm of the borrower’s registered capital; R_Lev is the borrower’s debt-to-asset ratio; R_Roa is the borrower’s return on total assets, and R_Growth is the growth rate of the borrower’s sales. 4. Empirical analyses 4.1. Baseline results Table 4 reports the results based on model (1), that is, the baseline regression results between credit mismatch and entrusted loans. In column (1) and column (2), the coeffi - cient estimates of credit mismatch, CM (OLoan1, OLoan2), are significantly positive, regardless of whether the dependent variable is the propensity to engage in entrusted loans activities (EL_dum) or the size of entrusted loans (EL_size). This implies that the higher the degree of credit mismatch, the higher would be the tendency of firms with excessive bank loans to engage in entrusted loan activities and the larger would be the size of these entrusted loans. In contrast, the coefficient estimates of NLoan (NLoan1 and NLoan2, corresponding to OLoan1, OLoan2 in credit mismatch) are not significant. This findings suggest that the normal bank loans that firms borrowed have not been used for entrusted loans, and credit mismatch has raised excessive bank loans beyond the firms’ normal demand, which has led to firms’ engagement in entrusted loan activities. The above results support our H1. 260 J. BAI, ET AL. Table 4. Credit mismatch and entrusted loans: baseline results. Dependent variables EL_dum EL_size i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t (1) (2) (3) (4) CM 1.144** 0.915* 0.005*** 0.004*** (2.361) (1.950) (4.183) (4.299) NLoan −0.778 0.963 0.002 0.005 i,t (−0.512) (0.415) (0.675) (0.728) Size 0.235*** 0.218*** 0.000** 0.000** i,t-1 (4.665) (4.526) (2.069) (1.979) Lev −0.283 −0.468 −0.002** −0.002*** i,t-1 (−0.648) (−1.179) (−2.119) (−2.578) Roa −1.617 −1.729 −0.007** −0.007*** i,t-1 (−1.470) (−1.559) (−2.563) (−2.599) Cfo −0.083 0.428 0.001 0.002 i,t-1 (−0.112) (0.721) (0.672) (1.188) Cashhold −0.331 −0.199 −0.002 −0.001 i,t-1 (−0.755) (−0.455) (−1.575) (−1.470) SOE 0.438*** 0.435*** 0.001*** 0.001*** i,t-1 (3.616) (3.585) (3.661) (3.652) Risk 0.587*** 0.537*** 0.002*** 0.002*** i,t-1 (3.784) (3.647) (3.594) (3.555) Constant −7.778*** −7.662*** −0.003 −0.003 (−7.166) (−6.696) (−1.161) (−1.052) Fixed-Effects Year and Year and Year and Year and Industry Industry Industry Industry N 13,242 13,242 13,242 13,242 Chi 195.770 194.690 194.730 194.210 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respec- i,t i,t tively; (2) z-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. 4.2. Heterogeneity analysis To further investigate the impact of the macro-institutional environment on the relation- ship between credit mismatch and entrusted loans, this study uses two different macro- indicators, that is, government intervention and monetary policy. First, we define a dummy variable, MP, to measure whether the monetary policy is tight, and thereby examine how the monetary policy affects the relationship between credit mismatch and entrusted loans. Column (1) to column (4) of Table 5 show that the coefficient estimates of the interaction term between credit mismatch and monetary policy, CM *MP, are positive and significant at least at the 10% level. This finding suggests the tightening of the monetary policy will result in a decline in the bank credit resources available to firms, which, in turn, will strengthen the demand for funds transfer between firms and thus exacerbate the entrusted loans between firms. Second, column (5) to column (8) of Table 5 report the impact of government intervention on the relationship between credit mismatch and entrusted loans. We can see that the coefficient of the interaction term between credit mismatch and government intervention, CM *FM, are positive and statistically significant at least at the 5% level. This suggests that when financial regulation is tightened by the government, the degree of credit mismatch becomes higher and the funds reallocation of non-financial firms becomes predominant. The above conclusions support H2. In other words, the macro-institutional environment in which the enterprise is located is an important factor influencing a firm’s engagement in entrusted loan activities. CHINA JOURNAL OF ACCOUNTING STUDIES 261 Table 5. Credit mismatch and entrusted loans: the influence of the macro-institutional environment. D = Monetary policy (MP) Government intervention (FM) Dependent variables EL_dum EL_size EL_dum EL_size i,t i,t i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t i,t i,t i,t i,t (1) (2) (3) (4) (5) (6) (7) (8) CM*D 1.652*** 1.076** 0.004* 0.003* 1.387** 1.513*** 0.007*** 0.007*** (2.599) (2.196) (1.695) (1.760) (2.071) (2.806) (3.487) (4.080) CM 0.100 0.100 0.002 0.002 0.458 0.141 0.001 0.001 (0.155) (0.180) (1.298) (1.350) (0.896) (0.318) (0.954) (0.923) D 0.014 −0.012 0.000 0.000 −0.046 −0.083 −0.000** −0.001** (0.206) (−0.178) (0.381) (0.091) (−0.626) (−1.096) (−2.040) (−2.410) Constant −8.585*** −7.315*** −0.004** −0.002 −7.676*** −7.538*** −0.002 −0.002 (−8.651) (−6.426) (−1.998) (−0.753) (−10.713) (−9.593) (−0.845) (−0.805) Control Yes Yes Yes Yes Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry Year and Industry N 13,242 13,242 13,242 13,242 13,242 13,242 13,242 13,242 Chi 185.660 189.650 186.110 186.770 382.560 385.260 209.330 213.380 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respectively; (2) z-statistics are reported in parentheses, and *, ** and *** denote i,t i,t significance at the 10%, 5%, and 1% level, respectively. 262 J. BAI, ET AL. Under credit mismatch, the behaviour associated with entrusted loan activities may also be affected by firm-level characteristics. Therefore, this study further examines the impact of investment opportunities and the nature of property rights on the company’s external entrusted loan behaviour from the perspective of micro-enterprises. First, companies that lack investment opportunities are more likely to invest their credit resources via shadow banking activities to maximise their profits, that is, to earn benefits through lending. Column (1) to column (4) of Table 6 present the impact of poor corporate investment opportunities on the relationship between credit mismatch and corporate external entrusted loans. It can be seen that the coefficient estimates of credit mismatch and investment opportunity, CM *TobinQ, are positive and statistically signifi - cant at least at the 10% level. It implies that firms with excessive bank loans are more inclined to issue entrusted loans when lacking attractive investment opportunities. Second, columns (5) to column (8) of Table 6 report the impact of the nature of property rights on the relationship between credit mismatch and corporate entrusted loans. The results show that the coefficient estimates of credit mismatch and property rights, CM *SOE, are positive and significant at least at the 5% level. Overall, compared with private enterprises, SOEs can obtain more excessive credit funds from banks and transfer them to other firms through entrusted loans. The above conclusions verify H3. In other words, the micro-individual characteristics (insufficient investment opportunities, property rights, and the relative position of the firm in the industry chain) are important considerations for firms to engage in entrusted loan activities. 4.3. Economic consequences Table 7 provides the regression results of the model (3). Columns (1) and (2) show that engaging in entrusted loan activities can bring additional benefits to firms. However, column (3) and (4) reveal that, when using the change of operating income (D_Profit ) i,t+1 as the dependent variable, the coefficient on the interaction term, EL_size*CM, is signifi - cantly negative. The above results show that engaging in entrusted loans under credit mismatch is short-term profit-seeking behaviour, which is detrimental to the firm in the long run. Similarly, when using sales growth rate (Growth ) as the dependent variable, i,t+1 the results are as shown in columns (5) and (6), and the coefficient on the interaction term, EL_size*CM, is still negative, which shows that engaging in entrusted loans is harmful to the improvement of overall profitability. This finding is consistent with Zhang & Zhang (2016) that firms’ financialization leads to a decline in real economic investment. 5. Robustness tests To ensure the reliability of our empirical results, this study also conducts several robust- ness tests to address concerns about endogeneity, sample selection, variable definitions, and regression methods. Untabulated results of using EL_dum to measure external entrusted loans are similar. CHINA JOURNAL OF ACCOUNTING STUDIES 263 Table 6. Credit mismatch and entrusted loans: the influence of micro-individual characteristics. D = Investment opportunities (TobinQ) Property rights (SOE) Dependent variables EL_dum EL_size EL_dum EL_size i,t i,t i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t i,t i,t i,t i,t (1) (2) (3) (4) (5) (6) (7) (8) CM*D 0.548** 0.393* 0.005** 0.004** 1.763** 1.271** 0.008*** 0.007*** (2.198) (1.848) (2.375) (2.349) (2.406) (2.181) (4.078) (4.055) CM 0.481 0.361 0.001 0.001 −0.072 0.018 −0.000 0.000 (0.951) (0.829) (0.675) (0.752) (−0.114) (0.035) (−0.081) (0.254) D −0.145*** −0.111*** 0.001** 0.001** 0.395*** 0.384*** 0.001*** 0.001*** (−3.647) (−2.940) (2.570) (2.453) (4.611) (4.401) (3.072) (2.759) Constant −6.239*** −6.454*** −0.000 −0.000 −7.785*** −7.636*** −0.003 −0.003 (−7.096) (−6.770) (−0.147) (−0.104) (−10.942) (−9.783) (−1.155) (−0.996) Control Yes Yes Yes Yes Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Year and Year and Year and Year and Industry Year and Industry Year and Industry Year and Industry Industry Industry Industry Industry N 13,242 13,242 13,242 13,242 13,242 13,242 13,242 13,242 Chi 390.360 384.050 207.290 206.120 382.440 385.430 186.110 186.770 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respectively; (2) z-statistics are reported in parentheses, and *, ** and *** denote i,t i,t significance at the 10%, 5%, and 1% level, respectively. 264 J. BAI, ET AL. Table 7. Credit mismatch and entrusted loans: economic consequence. Dependent variables Shouyi D_Profit Growth i,t i,t+1 i,t +1 CM = OLoan1 OLoan2 OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t i,t i,t (1) (2) (3) (4) (5) (6) EL_size *CM 0.019*** 0.023*** −0.537*** −0.484** −2.471* −0.660 i,t (3.107) (4.504) (−2.580) (−2.040) (−1.780) (−0.417) EL_size −0.000 −0.001 −0.005 0.021 −0.133 −0.077 i,t (−0.541) (−1.292) (−0.160) (0.632) (−0.640) (−0.358) CM 0.000** 0.000** 0.019*** 0.016*** 0.058** 0.037 (1.997) (2.195) (5.287) (3.960) (2.456) (1.393) Constant 0.011*** 0.011*** −0.002 0.004 0.319*** 0.340*** (25.627) (25.737) (−0.273) (0.525) (5.706) (6.195) Control Yes Yes Yes Yes Yes Yes i,t Fixed-Effects Year and Year and Year and Year and Year and Year and Industry Industry Industry Industry Industry Industry N 8,350 8,350 10,598 10,598 10,598 10,598 Adj_R 0.259 0.260 0.027 0.025 0.079 0.078 F 82.056 82.544 9.489 9.077 27.723 27.532 (1) Due to the missing of some variables, the number of observations has decreased;(2) t-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. 5.1. Endogeneity concerns We use a two-stage regression method based on instrumental variables to alleviate the endogenous concerns. Specifically, we use industry average (OLoan1_indmean, OLoan2_indmean) of corporate credit mismatch (OLoan1, OLoan2) in the same year as instrumental variables of enterprise excess bank loans. Many prior studies use the mean value of industry average of the explanatory variables in the same year as the instrumental variables of the explanatory variable, such as Lu (2014), Yang et al. (2015). The main reasons are as follows: first, the industry average of credit mismatch in the same year is closely related to the corporate’s credit mismatch, which satisfies the correlation require- ment of instrumental variables; second, the industry average of credit mismatch in the Table 8. Credit mismatch and entrusted loans: instrumental variable. First stage Second stage Dependent OLoan1 OLoan2 EL_dum EL_size i,t i,t i,t i,t variables (1) (2) (3) (4) (5) (6) CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t FB1 0.837*** (10.573) FB2 0.954*** (12.553) CM 0.211* 0.406*** 1.649* 3.020*** (1.851) (3.398) (1.879) (3.694) Constant 0.255*** 0.147*** −0.456*** −0.390*** −5.115*** −4.802*** (6.000) (4.075) (−9.071) (−8.795) (−16.347) (−14.966) Control Yes Yes Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Year and Year and Year and Year and Year and Industry Industry Industry Industry Industry Industry N 13,242 13,242 13,242 13,242 13,242 13,242 Adj_R 0.428 0.181 F 141.143 70.392 (1) The first stage uses OLS regression, with t values in parentheses; (2) the second stage takes into account the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respectively, with z-statistics i,t i,t in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. CHINA JOURNAL OF ACCOUNTING STUDIES 265 same year only affects the entrusted loan behaviour by influencing firms’ credit funds, satisfying the exogeneity requirement of instrumental variables. Table 8 shows the results of the first-stage regression, and the coefficients of the instrumental variables (OLoan1_indmean, OLoan2_indmean) are significantly positive, and the model’s F value is large, meaning that the credit mismatch at the industry level is an appropriate instru- mental variable. In the second stage of regression, the coefficient of credit mismatch is still significantly positive, indicating that after controlling for endogenous problems, the credit mismatched company’s entrusted loan behaviour still has a significant effect, further supporting the research hypothesis of this article. 5.2. Other robustness tests First, since not all listed firms obtain excessive bank loans, and those without excessive bank loans cannot engage in the entrusted loan activities. We rerun our analysis only using the sample with excessive positive loans. The regression results in Table 9 show that the excessive bank loans are significantly and positively correlated with the entrusted loans, indicating that credit mismatch makes these firms have more excessive bank loans Table 9. Credit mismatch and entrusted loans: firms with excessive bank loans. Dependent variables EL_dum EL_size i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t (1) (2) (3) (4) CM 2.910*** 1.755** 0.011*** 0.009*** (3.525) (2.323) (5.002) (5.284) NLoan −1.761 −1.696 −0.002 0.005 i,t (−0.836) (−0.561) (−0.316) (0.532) Control Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry N 6,017 7,142 6,044 7,142 Chi 145.950 168.610 138.100 196.220 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit i,t i,t respectively; (2) z-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. Table 10. Credit mismatch and entrusted loans: alternative measures. Alternative measures of dependent variable Alternative measures of independent variable Dependent variables EL_size (scaled by sales) EL_dumi,t EL_sizei,t i,t CM = OLoan1i,t OLoan2i,t OLoan3 i,t (1) (2) (3) (4) CM 0.016*** 0.016*** 0.822* 0.004*** (5.349) (5.860) (1.748) (4.129) NLoan 0.018* 0.028 3.669** 0.007 i,t (1.844) (1.457) (2.198) (1.445) Control Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry N 13,242 13,242 13,242 13,242 Chi 328.520 328.350 194.460 193.270 (1) Considering the characteristic of dependent variables (EL_dum , EL_size ), regressions use Logit and Tobit respec- i,t i,t tively; (2) z-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. 266 J. BAI, ET AL. Table 11. Credit mismatch and entrusted loans: alternative regression method. Dependent variables EL_dum EL_size i,t i,t CM = OLoan1 OLoan2 OLoan1 OLoan2 i,t i,t i,t i,t (1) (2) (3) (4) CM 0.072** 0.059* 0.005*** 0.004*** (2.252) (1.897) (2.806) (2.721) NLoan −0.034 0.104 0.002 0.005 i,t (−0.341) (0.654) (0.499) (0.655) Control Yes Yes Yes Yes i,t-1 Fixed-Effects Year and Industry Year and Industry Year and Industry Year and Industry N 13,242 13,242 13,242 13,242 Adj_R 0.026 0.026 0.012 0.012 F 4.342 4.351 2.391 2.390 All regressions were adjusted for heteroskedasticity and clustered at the company level. t-statistics are reported in parentheses, and *, ** and *** denote significance at the 10%, 5%, and 1% level, respectively. and that the more excessive bank loans they have, the more likely they will engage in entrusted loan activities. Second, alternative proxies are used for the dependent and independent variables. At first, we use sales to scale the dependent variables. The results presented in columns (1) and (2) of Table 10 imply that the baseline results are robust to these alternative measures. Additionally, following Deng et al. (2016), we use the regression method to obtain the third group of proxies for normal bank loans, NLoan3, and excessive bank loans, OLoan3, and the latter proxy of an alternative measure of credit mismatch. The regression results are shown in columns (3) and (4) of Table 10. Our results remain robust to these alternative measures. Third, the results in Table 11 indicate that our results are also robust if the ordinary least squares (OLS) regression is used. 6. Conclusions Based on the phenomenon that non-financial firms are engaged in shadow banking activities. we use Chinese A-share listed firms’ data and entrusted loans’ data, including the loan amount, the maturity, the interest rate, and the borrower’s basic characteristics collected from each entrusted loan contract from 2008 to 2016 as our sample, and investigate the characteristics of a firm’s entrusted loan activities and the economic consequences of such activities, under a credit mismatch scenario. It shows that the higher the number of excessive loans of non-financial firms, the higher would be their tendency to lend entrusted loans and the larger would be the size of these loans. Given the degree of credit mismatch, the size of the firm’s entrusted loans is closely associated with macro-institutional factors, such as monetary policy and government intervention; the entrusted loan’s size is also associated with firm-level characteristics, such as invest- ment opportunities and property rights. We also analyse the economic consequences of a firm’s entrusted loan activities under credit mismatch and find that although engaging in entrusted loan activities can bring additional benefits, it can also significantly damage the firm’s main business activities. The policy implications of this study are as follows. First, the operational mechanism and institutional reform of financial markets are closely related to the behavioural choices of microeconomic entities. The conclusion of this study indicates that the credit mismatch problem caused by financial repression induces enterprises to engage in shadow banking CHINA JOURNAL OF ACCOUNTING STUDIES 267 activities such as entrusted loans activities, which may explain the slow development of the real economy. Second, we find that the interest rate of entrusted loans is high, and the risks are relatively high. Hence, the government should actively promote the construction of a market-oriented financial market and the reform of financial systems, such as banks, accelerate the reform of interest rate liberalisation, and extend multi-level financing channels for small and medium-sized firms, thereby improving firms’ external financing environment. Finally, this study can help regulatory authorities to further alleviate non- financial firms’ financialization. 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First, we select main factors that affect firm’s bank loans, and, subsequently, use model (1-a) to estimate the regression coefficient of the period t-1 on the period t, that is: Loan ¼ α þ α Size þ α Tangible þ α Liquidity þ α Growth þ i;t 0 1 i;t 1 2 i;t 1 3 i;t 1 4 i;t 1 α MB þ α Roe þ α CFO þ α Lev þ α TC þ α Risk þ α Age þ 5 i;t 1 6 i;t 1 7 i;t 1 8 i;t 1 9 i;t 1 10 i;t 1 11 i;t 1 α Dividend þ α Bond þ βLoan þ Yearþ Industryþ ε 12 i;t 1 13 i;t 1 i;t 1 i;t where Loan represents firm’s actual bank loans. We control the following variables: firm’s size, Size; the ratio of tangible assets, Tangible; the liquidity of assets, Liquidity; firm’s growth, Growth; market-to- book ratio, MB; return on equity, Roe; cash flow from operating activities, CFO; leverage, Lev; trade credit, TC; operational risk, Risk; establishment age, Age; whether to issue cash dividends, Dividend; whether to issue corporate bonds, and Bond; year fixed effects (Year) and industry fixed effects (Industry). Subsequently, we substitute the coefficient estimate, β, into the following model (1-b): NLoan ¼ Loan β� Loan ε =ð1 βÞ i;t i;t i;t i;t Subsequently, we get the firm’s target bank loans (NLoan1). 2. The calculation of firms’ normal and excessive bank loans (NLoan2, OLoan2) Drawing on the industry average method of Jiang & Liu (2005) and Deng et al. (2016), we estimate the company’s target bank loan. The rationale of this method is to use weights to control the large capital structure differences between different industries, that is, to construct the weighted average bank loan in the industry as the company’s target bank loans. The calculation model is as follows (2-a): n n X X NLoan ¼ Size � Loan = Size i;t i;t i;t i;t i¼1 i¼1 Then, we get the firm’s target bank loans (NLoan2). 3. The calculation of firms’ normal and excessive bank loans (NLoan3, OLoan3) Drawing on the regression method by Deng et al. (2016), we construct firms’ target capital structure. First, we select the main factors that will affect a firm’s capital structure, and, subsequently, use model (3-a) to estimate the regression coefficient of the period t-1 on the period t, that is: OCS ¼ α þ α Size þ α Tangible þ α NDTS þ α Liquidity þ i;t 0 1 i;t 1 2 i;t 1 3 i;t 1 4 i;t 1 α Growth þ α MB þ α Roe þ α Cashholding þ α Risk þ α Age þ 5 i;t 1 6 i;t 1 7 i;t 1 8 i;t 1 9 i;t 1 10 i;t 1 α Dividend þ Yearþ Industryþ ε 11 i;t 1 i;t where OCS represents firm’s target capital structure. We control the following variables: firm’s size, Size; the ratio of tangible assets, Tangible; the liquidity of assets, Liquidity; firm’s growth, Growth; market to book ratio, MB; return on equity, Roe; cash holding, Cashhold; operational risk, Risk; establishment age, Age; whether to issue cash dividends, and Dividend; year fixed effects (Year) and industry fixed effects (Industry). Then we use model (3-b) and (3-c) to estimate firm’s normal bank loan (NLoan3). CHINA JOURNAL OF ACCOUNTING STUDIES 271 ODebt ¼ OCS � Asset i;t i;t i;t NLoan ¼ ODebt QDebt =Asset i;t i;t i;t i;t where ODebt represents firm’s target debt, QDebt represents firm’s other debt (the median of the difference between total liabilities and debt that includes short-term and long-term debts). Loan is a firm’s actual bank loans, and Asset is a firm’s total assets. Subsequently, we get a firm’s target bank loan (NLoan3). The calculation results of the above three target bank loans (NLoan1, NLoan2, NLoan3) are substituted into the model (4). In other words, by calculating the difference between the actual and the target bank loans of the firm, the company obtains the excessive bank loan (OLoan1, OLoan2, OLoan3). Finally, we substitute the above three estimates of the target bank loan (NLoan1, NLoan2, NLoan3) into the model (4). In other words, we obtain firm’s excessive bank loan by calculating the difference between the firm’s actual and target bank loan. OLoan ¼ Loan NLoan i;t i;t i;t

Journal

China Journal of Accounting Studies – Taylor & Francis

Published: Apr 2, 2020

Keywords: Credit mismatch; excessive bank loans; shadow banking activities; entrusted loans

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