Corporate social responsibility fulfilment, product-market competition and debt risk: Evidence from China

This paper reviews the research achievements of domestic and foreign scholars in recent years. It is found that there are four main reasons that can explain why the fulfilment of social responsibility has a negative impact on debt risk: reducing the cost of debt financing, extending the debt maturity and reducing the strictness of debt contract, and improving the credit rating so as to have a restraining effect on debt risk. First, in terms of reducing the cost of debt financing: Goss and Roberts [2] conducted a sample survey of 3996 corporate loans in the United States, and found that the loan interest rate of firms with better CSR is 7–18 basis points lower than that of firms only focusing on CSR. Second, in terms of extending debt maturity and reducing the strictness of debt contracts, Shi and Sun [3] found that the more firms invest in CSR, the less restrictive the clauses in their debt contracts. Third, in recent years, studies have focused more on the relationship between CSR and credit rating. Attig et al. [4] showed that credit rating agencies tend to give higher credit ratings to firms that perform well in social responsibility. The studies mentioned above all lend credence to the view that firms playing an active part in CSR can obtain the benefit of reduced debt risk owing to their good corporate image, lower debt financing cost, more relaxed credit conditions and higher credit rating. According to this inference, we deduce the first research hypothesis:

H1: The fulfilment of CSR can inhibit the debt risk faced by firms.

With the advent of the ‘new normal’, the degree of industry competition and the firm behaviour in China’s product market show new characteristics. On the one hand, product-market competition urges firms to seek better internal governance; a highly competitive market will force firms to reduce product costs, seek lower financing costs, improve internal governance [5] and reduce the degree of information asymmetry [6]. On the other hand, fierce competition in the product market will aggravate firm risk. Wu Haomin et al. [7] show that the significant increase of idiosynthetic risk of listed companies in China is attributable to the intensification of product-market competition. Han Zhongxue and Zhou Tingting [8] found that for all manufacturing listed companies in China, product-market competition has a significant predatory risk effect on cash holdings. Competition is one of the most urgent problems for firms to face when they are established in the market. To some extent, firms’ fulfilment of CSR is affected by the competitors’ CSR input level. When stakeholders evaluate the fulfilment of CSR, they will also refer to the competitors’ degree of CSR fulfilment in the same industry. Thus, we propose the second hypothesis of this paper:

H2: The degree of product-market competition regulates the relationship between CSR fulfilment and debt risk.

This paper extracts data for the Chinese A-share listed companies in the Shanghai and Shenzhen stock exchanges from 2010 to 2019 as research samples, and carries out the following processing steps:

Remove the samples of listed companies with abnormal trading status such as ST, *ST, Pt, etc.

Through the steps outlined above, annual observations of 23,660 companies were finally obtained. The CSR data used in this paper were obtained from the CSR rating database Hexun, and the financial data from the CSMAR database.

In order to test hypothesis 1, we design model 1, which is represented as follows: (1) FDRi,t=α0+α1CSRi,t+α2Controlsi,t+∑Industry+∑Year+εi,t {\rm{FDR}}_{i,t} = {\alpha _0} + {\alpha _1}{\rm{CSR}}_{i,t} + {\alpha _2}{\rm{Controls}}_{i,t} + \sum {\rm{Industry}} + \sum {\rm{Year}} + {\varepsilon _{i,t}}

In the above model, the main explanatory variable is CSR, which is the comprehensive score of CSR published by Hexun from 2010 to 2019. The rating system integrates the CSR fulfilment in five aspects: shareholder, employee, rights and interests of suppliers, customers and consumers, environment and social of listed Companies. The full score is 100; the higher the score, the better is the fulfilment of CSR.

FDR is used as an explained variable to measure the debt risk of firms. According to previous studies, there are mainly two ways to estimate the debt risk of firms. The first approach is to use accounting data to measure the debt risk, and the second way is to measure the debt risk by combining market data and accounting data. Z_score [9] and O_score [11] are among the commonly used accounting data estimation methods. Estimation methods based on the combination of market data and accounting data include the default option model mentioned by Black and Scholes [12], the KMV model used by Merton [13] to measure default distance and a simple risk model mentioned in the study of Shumway [14]. In the past, a large number of studies in the literature used the combination of market data and accounting data to predict the debt risk of firms [15, 16]. However, when Agarwal and Taffler [17] conducted cross-border analysis, they found that the Z_score model was superior to risk-based and market-based prediction models in the accuracy of debt risk prediction. In a recent study, Altman et al. [18] confirmed the effectiveness of Z_score as a predictor of debt risk. Based on the above inferences drawn from the literature, this paper uses a forecasting model based on accounting data to estimate the debt risks faced by firms.

In model 1, FDR is the explained variable to measure the debt risk of firms. We mainly use two accounting measurement methods to predict the debt risk of firms, namely Z_score [19] as calculated by model 2 and O_score [11] as calculated by model 3. (2) Z_score=1.2×(Operating funds/Total assets)+1.4×(Retained earnings/Total assets)+3.3×(Earnings before interestand tax/Total assets)+0.6×(Value of equity/Value of liability)+1×(Sales revenue/Total assets) \matrix{{{\rm{Z}}\_{\rm{score}}} \hfill & {= 1.2 \times ({\rm{Operating}}\,{\rm{funds}}/{\rm{Total}}\,{\rm{assets}}) + 1.4 \times ({\rm{Retained}}\,{\rm{earnings}}/{\rm{Total}}\,{\rm{assets}})} \hfill \cr {} \hfill & {+ 3.3 \times ({\rm{Earnings}}\,{\rm{before}}\,{\rm{interest}}{\kern 1pt} {\rm{and}}\,{\rm{tax}}/{\rm{Total}}\,{\rm{assets}}) + 0.6 \times ({\rm{Value}}\,{\rm{of}}\,{\rm{equity}}/{\rm{Value}}\,{\rm{of}}\,{\rm{liability}})} \hfill \cr {} \hfill & {+ 1 \times ({\rm{Sales}}\,{\rm{revenue}}/{\rm{Total}}\,{\rm{assets}})} \hfill \cr}

The higher the calculated value of Z_score, the better the financial status and the lower the debt risk of the firm. For the sake of explanation, Z_score has been negative in the follow-up empirical test. (3) O_scorei,t=−1.32−0.407×logTAi,t+0.63×(TLi.t/TAi,t)−1.43×(WCi.t/TAi,t)+0.076×(CLi.t/CAi,t)−1.72×(OEMEGi,t)−2.37×(NLi,t/TAi,t)−1.83×(OIBDi.t/TAi,t)+0.285×(EBITTWOi,t)−0.521×(NIi,t−NIi,t−1)/(|NIi,t|−|NIi,t−1|) \matrix{{{\rm{O}}\_{\rm{score}}_{i,t}} \hfill & {=- 1.32 - 0.407 \times {\log}{{\rm{TA}}_{i,t}} + 0.63 \times ({\rm{TL}}_{i.t}/{\rm{TA}}_{i,t}) - 1.43 \times ({\rm{WC}}_{i.t}/{\rm{TA}}_{i,t})} \hfill\cr{} \hfill & {+ 0.076 \times ({\rm{CL}}_{i.t}/{\rm{CA}}_{i,t}) - 1.72 \times ({\rm{OEMEG}}_{i,t}) - 2.37 \times ({\rm{NL}}_{i,t}/{\rm{TA}}_{i,t}) - 1.83 \times ({\rm{OIBD}}_{i.t}/{\rm{TA}}_{i,t})} \hfill\cr{} \hfill & {+ 0.285 \times ({\rm{EBITTWO}}_{i,t}) - 0.521 \times ({\rm{NI}}_{i,t} - {\rm{NI}}_{i,t - 1})/(|{\rm{NI}}_{i,t}| - |{\rm{NI}}_{i,t - 1}|)} \hfill\cr}

In model 3, logTA_i,t is natural logarithm of total assets; TL_i.t/TA_i,t is total liabilities divided by total assets; WC_i.t/TA_i,t is working capital divided by total assets; CL_i.t/CA_i,t is current liabilities divided by current assets; OEMEG_i,t is a dummy variable, and if the total liabilities of the firm are greater than the total assets, this takes the value 1, and otherwise 0; NL_i,t/TA_i,t is net profits divided by total assets; OIBD_i.t/TA_i,t is operating income before depreciation divided by total assets; EBITTWO_i,t is a dummy variable, and if firm net profit has been negative for 2 consecutive years, the index takes the value 1, and otherwise 0; NI_i,t and NI_i,t−1 represent the firm’s net profit for the current year and the previous year, respectively. The higher the value of O_score, the greater the risk of bankruptcy faced by the firm.

Based on model 1, model 4 is designed to test hypothesis 2, as under: (4) FDRi,t=β0+β1CSRi,t+β2HHIi,t+β3CSRi,t×HHIi,t+β4Controlsi,t+∑Industry+∑Year+εi,t {\rm{FDR}}_{i,t} = {\beta _0} + {\beta _1}{\rm{CSR}}_{i,t} + {\beta _2}{\rm{HHI}}_{i,t} + {\beta _3}{\rm{CSR}}_{i,t} \times {\rm{HHI}}_{i,t} + {\beta _4}{\rm{Controls}}_{i,t} + \sum {\rm{Industry}} + \sum {\rm{Year}} + {\varepsilon _{i,t}}

In model 4, the explanatory variable is the cross product of CSR and HHI, where HHI is the degree of market competition. Based on the method suggested by Peress [20], the present study uses the Herfindahl–Hirschman index (HHI) to portray the degree of industry competition. Among others, one calculation formula used for HHI is the following: (5) HHI=∑i=1N(XiX)2 {\rm{HHI}} = \sum\limits_{i = 1}^N {\left({{{{X_i}} \over X}} \right)^2} where N is the total number of firms in the industry, X_i is the annual sales revenue of firm i and X is the total annual sales revenue of firms in the market. The lower the HHI, the stronger the competitiveness is in the industry, and vice versa. For the sake of explanation, the HII has been allowed to assume a negative value in the empirical tests that follow in this paper.

In models 1 and 4, we also include the following sets of control variables, which probably affect FDR. Firm’s characteristics variables: firm’s scale (Size), firm’s age (Age). Firm’s financial characteristics variables: profitability (ROA), cash flow volatility (VCF), management expense ratio (Overhead), non-debt tax shield (NDT), market-to-book ratio of equity (MTB), whether there is a dividend payout during the current year (Div), noncurrent assets to total assets ratio (PPE), shareholding ratio of the top five shareholders (Lncon), growth rate of operating income (Growth) and stock returns (RET). Board characteristic variables: Ratio of independent directors (Indratio), CEO duality (Duality). We also control for ∑Industry (∑Year), which is a set of industry (year) dummy variables. Definitions of other relevant main variables are shown in Table 1.

Table 2 presents descriptive statistics of the main variables. Altman [19] pointed out that when the Z_score is >2.99, it means that the financial status of the firm is in good health and the probability of debt risk is low. When Z_score is <1.81, it indicates that the firm has latent debt risk. When Z_score is between 1.81 and 2.99, it is called the ‘grey area’, which indicates that the financial situation of firm is in an unstable state. The main variable that is explained in this paper, Z_score, has a mean value of 6.7777, the maximum value is 55.5422 and the minimum value is 0.1785; further, the 25% percentile is 2.0313, the median value is 3.7837 and the standard deviation is 8.8820. O_score’s mean value is −11.5968, the maximum value is −5.0486 and the minimum value is −27.3073; further, the 25% percentile is −13.3983, the median value is −10.7945 and the standard deviation is 3.9466. This shows that the debt risk faced by different firms is quite different, and that at least 25% of firms are faced with debt risk. The full score of CSR fulfilment published in the Hexun database is 100. According to these data, the average and median value of CSR are 24.8365 and 22.1300, respectively, with the maximum value of 74.5200 and the minimum value of −3.1500, indicating that the degree of CSR fulfilment in China needs to be further improved. With regard to firm characteristics, the mean value of Size is 22.1602, the mean value of ROA is 0.0394, the mean value of VCF is 0.0453, the mean value of Growth is 0.1967 and the mean value of PPE is 0.2186, which indicates that the firms in this sample have a large proportion of fixed assets, low asset profitability and weak solvency, and face a certain level of default risk.

According to Table 3, although we arrive at the conclusion that the fulfilment of CSR will have an inhibitory effect on a firm’s debt risk, according to the CSR scoring system of Hexun, the fulfilment of CSR includes five dimensions: shareholder; employee; rights and interests of suppliers, customers and consumers; environmental; and CSR. In order to test the difference of the inhibitory effect of different dimensions of CSR on corporate debt risk, this paper, based on the inferences drawn by Junshan and Xudong [21], divides the above-mentioned five dimensions of CSR fulfilment into three layers: monetary capital layer (ShareholderR), human capital layer (EmployeeR) and social capital layer (ExternalR). Among them, the monetary capital level is expressed by the score of the shareholder responsibility dimension, the human capital level is expressed by the score of the employee responsibility dimension and the social capital level is expressed by the sum of the three dimensions of supplier, customer and consumer rights responsibility, and environmental responsibility and CSR. Tables 9 and 10 provide the regression results of different layers of CSR fulfilment and debt risk Z_score and O_score, respectively. In columns 1 and 2 (Tables 9 and 10), it can be seen that the fulfilment of CSR in monetary capital layer and human capital layer is negatively correlated with debt risk, and the estimated coefficients are significant at the level of 1%, indicating that the debt risk faced by firms with good CSR fulfilment in these two layers is significantly lower. Debt risk is largely caused by the external financing constraints of firms. From the perspective of monetary capital layer, the fulfilment of CSR at the layer of monetary capital helps firms to better obtain the support of shareholders, creditors and other capital providers, and obtain financing through better channels and more easier credit policies, without regard to the aspect of reducing the cost of equity financing [22] or that of reducing the cost of debt financing. In addition, it also plays a significant role in extending the debt maturity [2], thus alleviating the debt risk faced by firms. From the perspective of human capital layer, employees are one of the key stakeholders of the firm, and human capital is regarded as one of the most critical factors for a firm to succeed in an increasingly competitive environment; to a large extent, these results are in line with the conclusion of Bae et al. [23] that CSR fulfilment in terms of employee treatment fairness has a positive impact on corporate capital structure. From column 3 (Table 9), it can be seen that fulfilment of CSR in social capital layer (ExternalR) and Z_score are negatively correlated, and the estimated coefficient is −0.0067, which is not significant. In column 3, Table 10 displays that the regression results for the fulfilment of CSR in social capital layer (ExternalR) and O_score are also negatively correlated, with an estimated coefficient of −0.0063, although it is significant to the extent of 1%; but it is still remote compared to the coefficient of monetary capital layer CSR fulfilment in column 1 and the coefficient of human capital layer CSR fulfilment in column 2 (Table 10). It can be inferred that the inhibitory effect of CSR fulfilment of social capital on debt risk is significantly weaker than those of monetary capital and human capital. The regression results shown in column 4 (Table 9) also provide direct evidence in support of this conclusion. This shows that the CSR at the social capital layer has little inhibitory effect on the debt risk faced by firms. Column 4 (Tables 9 and 10) shows the regression results of all layers of CSR fulfilment on corporate debt risk, to assess the net impact of CSR fulfilment on corporate debt risk at each level. The regression results are similar to those in columns 1 and 2 (Tables 9 and 10). Similarly, the estimated coefficient of CSR fulfilment of social capital layer is not statistically significant, and this conclusion is consistent with the results of Godfrey et al. [24]. The reason is that the fulfilment of CSR to the main stakeholders (such as fund providers and employees) will create more favourable resource exchange among these groups, which can effectively alleviate the financing constraints of firms, enhance the value of firms and prevent negative economic consequences. It seems that the fulfilment of CSR to the secondary stakeholders cannot create more favourable financing conditions for firms, so as to reduce the debt risk faced by firms [25].