Innovation has been widely accepted as one of the driving forces of corporate success and long-term economic growth. However, R&D activities may be under-invested due to the problem of asymmetric information (Hall, 2005) and limited access to external finance (Rajan & Zingales, 2001). Therefore, to promote corporate innovation activities, governments provide support for firms to mitigate the risk of market failure (Hong et al., 2016).

The main rationale of such a policy is to address market inefficiency, specifically the underinvestment of private funds in R&D activities (Arrow, 1972), because of the nature of R&D, which involves three major issues (Link & Scott, 2013). First, there are positive externalities, which refer to the situation that the benefits of a firm's R&D can be spilled over to its competitors fairly easily (Haskel & Westlake, 2018); as a result, the investing firm cannot fully capture the potential return on its R&D investment. Consequently, the market investment in R&D as a whole will become less than socially desirable. Second, there is information asymmetry between the firm and its financers, which could lead to problems of moral hazard and adverse selection (Bakker, 2013), creating barriers for the firm from accessing commercial funding. Third, the R&D activity inherently involves uncertainty and risk, from technical, strategic, and market to profit aspects, which increase the difficulty and costs of private financing (Bakker, 2013). To address these issues, it is important that governments provide support for private R&D investment.

Successful public support should enable recipients to increase investment in innovation (Cin, Kim, & Vonortas, 2017; Lach, 2002), resulting in lower costs of innovation and better access to external finance (Takalo, Tanayama, & Toivanen, 2013). R&D subsidies are an input-driven policy for supporting firms’ innovation capacity, reducing the costs of R&D, improving market success rate (Dimos & Pugh, 2016; Guo, Zou, Zhang, Bo, & Li, 2020), and increasing margin (Chen & Xu, 2021). First of all, R&D subsidies can help the firm reduce the investment risk in R&D, allowing the firm greater flexibility in leveraging the financial resources to explore and exploit new opportunities from the R&D endeavors (Gao, Hu, Liu, & Zhang, 2021).

Secondly, there is an additionality effect, i.e., the subsidies motivate the firm to increase R&D investment from their own resources (Guo et al., 2020). Moreover, R&D subsidies create signaling effects (Wu, 2017), attenuating the information asymmetry problem, helping the subsidy recipient firm to gain access to external funding (Yan & Li, 2018), and to attract collaborations from both academic and corporate partners (Bianchi et al., 2019). Strategic collaboration is particularly encouraged for promoting knowledge transfer in the high-tech sector and, consequently, innovation performance (Abbas, Avdic, Xiaobao, Zhao, & Chong, 2018; Zhao, Jiang, Peng, & Hong, 2020). Recently, Ahn et al. (2020) have provided evidence that government subsidy helps alleviate R&D externalities’ concern for business. They find that increased innovation collaborations were statistically significant between the recipients of R&D subsidies, confirming the role of such subsidies in stimulating firm collaboration and reducing market inefficiency.

Different effects can occur simultaneously (Ahn et al., 2020). Ultimately, it is expected that recipient firms have more resources to commercialize their innovations and, consequently, improve the outputs of their R&D and overall firm performance (Guo et al., 2020). Nevertheless, not all R&D subsidies achieve their desired effects. A well-documented negative effect is crowding-out, i.e., the recipient firms use the grants to replace their own R&D investments, undermining the expected additionality effect (Gao et al., 2021; Marino, Lhuillery, Parrotta, & Sala, 2016).

Empirical studies have suggested that the effects of subsidies vary across industries or firms, signifying that there are moderating variables in the effectiveness of the policy Hong et al. (2016). suggest the subsidies have positive effects on innovations in some industries, but negative effects in others. Bianchi et al. (2019) find that R&D subsidies’ effect on technological partnership is stronger for smaller than larger firms. Examination of heterogeneity in firm characteristics is important for understanding the differences in the outcome of a public policy (Dimos & Pugh, 2016) Gao et al. (2021). suggest that central R&D schemes are not as effective as local ones in facilitating firm innovation. However, the extant literature seems to have neglected the role played by the accounting information, which could potentially moderate the ex-post effectiveness of government subsidies.

Corporate accounting information is known to play a pricing function in determining the costs of external finance and stock prices (Christensen, Nikolaev, & Wittenberg-Moerman, 2016), while it also has a governance function, which receives less attention in the literature. The governance function refers to its role in alleviating information asymmetries and restraining the opportunistic behavior of the management team (Zhai & Wang, 2016). In China, the governance role is particularly important where the acceptance of R&D subsidies is viewed as the course of a contract between the supplier (i.e., government) and the recipient firm.

According to the contracting theory in accounting (Lambert, 2001; Sunder & Cyert, 1997), accounting information plays a key role in determining the contractual mechanism. On one hand, by alleviating the problem of asymmetric information, the provision of high AIQ facilitates the fulfillment of contracts. On the other hand, high AIQ also alleviates the problem of adverse selection by reducing the costs of information collection so that a contract could be reached at lower costs. The economic consequences of providing high AIQ have been widely documented in terms of its favorable effects on reducing the cost of capital (Chen & Zhu, 2013; Hsieh, Shiu, & Chang, 2019) and debt agency costs (Billett, King, & Mayer, 2007), improving access to loans (Kim & Yasuda, 2019; Palazuelos, Crespo, & del Corte, 2018), investment efficiency (Chen et al., 2011; Hidayat & Mardijuwono, 2021) and reducing bid-ask spreads (Zhou, 2007).

The AIQ is important for the success of an R&D subsidy, because corporate R&D activities carry the nature of uncertainties and information opaqueness, while the government may lack strong incentives to scrutinize private information possessed by applicant firms. Due to such an asymmetric information problem, it is difficult for policymakers to assess the effectiveness of public policies (Butler, 2012). At a micro-level, the critical question is whether the government has the ability to identify the right R&D projects that private firms are reluctant to fund but those projects have the potential to generate high social returns. However, government authorities often face an information disadvantage that comes from the uncertainties of R&D projects, such as the variation of project returns over time (David, 2011) and the difficulty of measuring social returns (Hubbard, 2011). Information disadvantage could increase the risk of adverse selection in grants distribution and, consequently, result in government failure in subsidy policy implementation.

Studies have shown that Chinese government subsidy decisions are more reliant on ‘hard’ corporate information (e.g., assets and R&D investment) than the quality of financial and accounting information (Lee, Walker, & Zeng, 2014) Chen, Wang, Hu, and Zhou (2020). find government even uses some external indicators, such as whether a firm is listed on both Shanghai and Hong Kong Stock Markets, as an information agent to make a subsidy decision. Their empirical results show that the firms targeted by the Shanghai-Hong Kong Stock Connect are more likely to receive R&D subsidies from the government. This indicates that there are severe information asymmetry problems between government and subsidy applicants. The application for R&D subsidies could be taken as a self-selection behavior at the firm level to reduce risks and the cost of financing R&D activities (Takalo et al., 2013). In an attempt to obtain a larger subsidy, business managers tend to exaggerate their demand for R&D investment and to engage in earnings management in the application process. Opportunistic business managers are likely to provide incomplete or even distorted accounting information if the AIQ is not taken into consideration in the process of allocating public R&D subsidies. Moreover, public choice theory (Butler, 2012) suggests that government may also take opportunistic actions to prioritize certain firms, give credit to their agency managers, and gain a short-term reputation for the ‘effectiveness’ of its programs.

The importance of accounting information has been well captured in theoretical frameworks, such as the theory of incomplete contract to help information users make more informed decisions (Christensen et al., 2016). Empirical evidence from China has shown that accounting information exerts a significant favorable effect on the efficiency of resource allocation with capital flowing to the best industries (Zhai & Wang, 2016). Based on the data of Spanish small and medium enterprises (SMEs), Palazuelos et al. (2018) show that AIQ is an important factor for firms to access external loans. Based on the data of Japanese SMEs, Kim and Yasuda (2019) reveal that accounting information is effectively used in the guaranteed loans screening process, highlighting the importance of AIQ for firms. Furthermore, Hsieh et al. (2019) indicate that AIQ helps firms to reduce the cost of capital Hidayat and Mardijuwono (2021). examine the effect of AIQ on investment efficiency on Indonesian manufacturing firms and find that those with high AIQ are associated with high efficiency of investment decisions and performance.

A reasonable conjecture would be that accounting information helps to enhance R&D subsidy effectiveness via both effective selection and effective implementation (Fig. 1). On one hand, by reducing the chance of adverse selection due to the ex-ante information asymmetry, high AIQ improves the effectiveness of subsidy selection (Dimos & Pugh, 2016). On the other hand, high AIQ alleviates the moral hazard problem caused by ex-post information asymmetries in financing R&D activities. The implementation effectiveness of subsidy covenants is subject to the problem of the moral hazard of subsidy recipients, due to agency conflicts in corporate governance mechanisms and contract incompletion, such as the possibility of misusing or deferred use of subsidized funds. For example, recipient firms may shirk, transfer subsidies to other usages, or reduce their own R&D investment. Improved AIQ could reduce the possibility of subsidy recipients taking such actions and, hence, the moral hazard problem is alleviated.

Fig. 1.

Effect of AIQ on the implementation of R&D subsidies.

(0.24MB).

Based on these above-mentioned two mechanisms, we posit that R&D subsidy recipients with a higher AIQ would have a stronger self-funded R&D investment motivation and better R&D outputs. Our rationales are threefold. First, high AIQ signals a good governance mechanism, which reduces the likelihood of the subsidy being diverted to activities other than those agreed (Dimos & Pugh, 2016). Second, high AIQ is verifiable, which serves as an effective measuring tool and enhances the willingness of interested parties to monitor the implementation of a subsidy contract (Hope et al., 2017). Third, high AIQ is transparent, which may increase the government's effective information load and enables the government to dynamically monitor contractual performance.

We collect relevant financial data and basic information of firms from the database of China Stock Market & Accounting Research (CSMAR) for all listed firms in China, including firms in the Growth Enterprise Board (GEM), Small and Medium Enterprise (SME) Board, and the Mainboard between 2007 and 2015. CSMAR is widely used in the literature as the data source of Chinese listed company research (e.g., Kwak, Chang, & Jin, 2021; Tian, Kou, & Zhang, 2020; Zhang, Wang, & Chen, 2021), which collects, codes and cleans all listed companies’ financial and operational activity information from their compulsory or voluntary disclosures. We capture the effectiveness of government R&D subsidies based on both the input (e.g., stimulating further R&D investment by recipient firms) and output (e.g., the number of patents obtained) of R&D investment. We exclude non-subsidized firm samples1 for the following reasons. First, the allocation of subsidies has been widely examined (e.g., Boeing, 2016). Second, existing empirical materials do not allow us to apply a two-stage approach to examine both subsidy allocation and effectiveness. This is because, on one hand, allocation error may exist, and some applicants were mistakenly rejected. On the other, the current empirical data do not allow us to make a distinction between rejected non-subsidized firms or non-applicant firms. Third, subsidized firms and non-subsidized firms may have different accounting practices and pooling the samples may generate selection bias.We hand-collect R&D subsidy and patent information of sample firms from WIND, which is the most popular data service platform for investors in China that provides a wide range of detailed information useful for investment decisions, including detailed financial, operational information of firms, macro-economic data, government policy, and other business-related news. It has also been used as a data source in literature for case studies and other studies that require in-depth information about firms (e.g., Ali, Qiang, & Ashraf, 2018; Duan & Jin, 2014; Yang, Orzes, Jia, & Chen, 2021). WIND collects information about the detailed subsidy items a company has received from the government for any reason. We exclude subsidies that are not related to an R&D purpose, identified by keywords in the subsidy item description, and then summarize the R&D-related subsidy value to firm-year panel data. WIND also collects companies’ innovation-related information, such as patent applications and grants, research spending, and so on. Following Daim, Monalisa, Dash, and Brown (2007) and Lahr and Mina (2016), we choose the number of patents granted in a year as the R&D output. To fully capture the effects of R&D subsidies on corporate R&D performance, we exclude samples with information less than two years, industries with little R&D investment and sample firms without R&D activities or R&D subsidies2. Overall, we have 1561 sample firms and 11,853 firm-year observations.

As presented in Table 1, corporate self-funded R&D investment3 accounted for about 2.9% of revenue on average and increased by 0.5% annually. Total R&D investment, including government subsidies, was about 3.1% of total revenue and, on average, sample firms received an amount equivalent to 0.2% revenue from the government as R&D subsidies. In terms of R&D output, on average, the sample firms acquired 69 patents annually. The sample firms had discretionary accruals at -0.3% of revenue and 20% of the sample firms amended their financial statements.

At the firm level, a typical sample firm in our data had a debt ratio of 43%, a cash holding of 18%, a 0.3% growth rate on revenue, and a 26% gross profit margin. In terms of corporate governance, 37.9% of our samples were state-owned enterprises (SOE), 75% of CEOs played a dual role as board directors and the management team held an average of 13.2% ownership.

We commenced our analysis by a baseline model without the interaction term in Eq. (2) and measured the R&D activities by both the value and growth, i.e., self-funded R&D investment (RDrr) and its growth (△RDrr). As Table 2 shows, the self-funded R&D investment in year t was positively associated with R&D subsidies in year t-1 (Model 1) and the incremental R&D investment was also driven by the increasing R&D subsidies a sample firm received from the government (Model 2). Specifically, by controlling for revenue at a constant, every CNY1,000 (ca. USD150) R&D subsidy could encourage sample firms to invest CNY490 (ca. USD73.5) by self-funding per annum and per CNY1,000 increases in government subsidy would stimulate an additional self-funded R&D investment by CNY367 (ca. USD55). Such a result provides evidence of the additionality created by government R&D subsidies, indicating that government R&D subsidy has a positive effect on firms’ R&D investment.

The results also support the model specification where the insignificant AR2 shows that the error terms have no serial correlation. The insignificant Hansen test also ensures the validity of the instruments and confirms that over-identification issues did not exist in the estimations. Additionally, our baseline results show that firm-level characteristics could affect corporate R&D investment which increases over management shareholding, balance shareholding, cash holding, technological asset ratio and profitability margin and decreases with debt ratio and sales growth. There is evidence that state-owned enterprises had a slightly higher propensity to undertake corporate R&D investment.

Similarly, we find governmental subsidies have an additional effect on firms’ R&D output. Due to the lagged effects of R&D investment (input) on patents (output), we considered the effects of lagged R&D investment for two years (t-1 and t-2) for both corporate investment (RDrr) and government subsidies (SUBrr). The results are reported in Table 3, which show that the lagged effect did exist, and corporate R&D investment and governmental subsidy in t-2 have positive impacts on patent obtaining, indicating that government R&D subsidy has a positive effect on firms’ innovation outputs.

We employ a system GMM approach and include an interaction term to capture the moderating effects of accounting information quality as shown by Eq. (3). The significant positive coefficients of interaction terms as shown in Table 4 suggest that the favorable effects of R&D subsidies were positively associated with the AIQ of the subsidy recipient. Meanwhile, the coefficients of subsidy (SUBrr) and its growth (△SUBrr) were no longer significant, which indicates that the effect of the subsidy on corporate R&D investment performance depended heavily on the AIQ of the sample firm. Quantitatively, an improvement of AIQ (DarrN) by one standard deviation (0.155) increases the additionality of the subsidies by about 16% (=1.044 × 0.155) in Model 1 for the value of self-funded R&D investment and about 4% in Model 2 for the growth of corporate R&D investment. In other words, AIQ significantly moderates the effect of government R&D subsidy on firms’ R&D investment.

The test results reported in Table 5 show that AIQ significantly moderates the effect of government R&D subsidy on firms’ R&D output.

We test the robustness of our earlier results by using different empirical approaches and measures. First, instead of using interaction terms, we categorize the samples into groups with low and high AIQ, i.e., samples with greater or lower discretionary accruals than average and samples with or without financial restatement, respectively. More specifically, samples with greater (lower) discretionary accruals and with (without) financial restatement would have low (high) AIQ and, therefore, the favorable effects of R&D subsidies in Table 2 would be weaker (stronger) Table 6. presents the results by using a grouping approach and shows that our earlier results are robust. Similar to the above test, we considered the effects of R&D subsidy (SUBrr) and its growth (△SUBrr) on both the quantity (RDrr) and the growth (△RDrr) of self-funded R&D investment of the sample firms Table 6. and Table 7 show that the favorable effects of R&D subsidies were only statistically significant in those sample groups with high AIQ and such effects were insignificant for those firms with low-AIQ.

Further, we use the occurrence of financial re-statement as an alternative measure of AIQ, and run a robustness test again. A sample firm is defined as having high AIQ if it does not have a financial re-statement (restate=0); low AIQ otherwise. The results are reported in Tables 8 and 9.

The study extends the literature on government innovation policy by investigating the effectiveness of government R&D subsidies and the role played by accounting information. Our contributions are twofold. First, the study results help to clarify the inconsistent findings regarding the effectiveness of R&D subsidies (Bellucci et al., 2019; Yi et al., 2021). Our results provide support for the idea that government R&D subsidies can effectively promote innovations in the country in terms of both inputs and outputs (Ahn et al., 2020; Wu et al., 2020). However, unlike Ahn et al. (2020) who focus on the subsidies’ effect on inter-firm collaboration, and Wu et al. (2020) who focus on the subsidies’ role in increasing the chance of being further funded by venture capital, thus boosting the overall investment in renewable energy, we examine the direct effect of both R&D inputs and innovation outputs. Our findings confirm the additionality effects of government R&D subsidies, which are consistent with findings in a provincial survey conducted by Zhai and Wang (2016), which show a positive relation between subsidy amount and the numbers of employees carrying out R&D functions in a firm.

Second, this is the first study to investigate AIQ as a moderator on the effects of R&D subsidy on both innovation inputs and outputs. The findings of this study thus advance our understanding of the mechanism in government R&D subsidy efficiency, by highlighting that the production of high AIQ serves as a governance function for recipients to implement subsidy-related covenants. The study results are in line with the financial investment literature, which suggests that high AIQ helps to alleviate information asymmetries and moral hazards (Lambert, 2001; Sunder & Cyert, 1997), restrain the opportunistic behavior of the management (Dimos & Pugh, 2016; Zhai & Wang, 2016), facilitate contract fulfillment (Hope et al., 2017), access to credit (Kim & Yasuda, 2019; Palazuelos et al., 2018), reduce the cost of capital (Chen & Zhu, 2013; Hsieh et al., 2019) and improve investment efficiency (Chen et al., 2011; Hidayat & Mardijuwono, 2021). However, unlike those financial studies, this study offers the first empirical evidence on whether and how AIQ strengthens the effectiveness of the government's innovation policy.

The study's finding of AIQ's moderation effect implies that firms with higher AIQ will be less opportunistic, i.e., commit more effort to make better use of the subsidy. This is in line with the literature in investment and corporate governance studies. For instance, Chen et al. (2011) find that firms with a higher quality of financial reporting have less incentive to minimize earnings for tax purposes, a sort of earnings management opportunity Elliott, Fanning, and Peecher (2020). find that investors ascribe value to firms that use higher financial reporting quality because they perceive higher financial reporting quality as cooperative behavior, signaling that the firms are more credible. Our results extend the function of AIQ to the information asymmetry problem in the government subsidy context, helping explain the R&D performance inconsistency of subsidy recipients. For example, the studies of Bellucci et al. (2019) using Italy data, and Yi et al. (2021) using China data, both find different, even conflicting, performance results among the subsidy recipients that are similar in location, business, and other aspects.

The findings of this study offer several important policy implications for China and other countries with similar government R&D support programs. Our results suggest that the governing bodies in those countries should place more emphasis on accounting information when screening or monitoring subsidy recipients. First, accounting information is essential in the screening process by determining how much effective information applicants provide. Second, the AIQ reflects the effectiveness of the recipient's corporate governance in controlling the moral hazard problem. As shown in this study, firm-level governance quality is the micro-basis for the effectiveness of a public policy; thus, besides the technical and market indicators, it is crucial that authorities scrutinize firms’ accounting behaviors when implementing or assessing public funding policy aiming at supporting private companies. For firms that intend to apply for government R&D subsidies, they should devote effort to improving their governance system, thus raising the level of AIQ, to enhance their potential of being funded and, consequently, achieve better innovation outputs.

This study does not examine the role of firms’ accounting information in the selection process when government allocates R&D subsidies (i.e., effective selection, Fig. 1), due to data limitation. Future research could investigate such effects and additional determinants of the effectiveness of government R&D subsidies. In addition, future research could examine the impact of subsidies on unsubsidized firms because these firms may increase their R&D activities in response, in order to maintain their competitiveness.