Targeted Poverty Alleviation and Enterprise Innovation: The Mediating Effect of Talent and Financing Constraints
-
Dong Guo
,
Xiaoyu Sun
,
Mo Yang
Abstract
Targeted poverty alleviation (TPA) conducted by the Chinese government has set a tremendous example for fighting against poverty. Using Chinese firm-level panel data, we design a quasi-natural experiment to investigate the relationship between TPA participation and enterprise innovation. We find that enterprises’ participation in TPA can promote innovation by attracting more talent and alleviating financing constraints. Further analysis reveals that the innovation-promoting effect of TPA is more pronounced for firms with higher media exposure, those located in regions with weaker intellectual property rights protection, areas with lower levels of merger and acquisition activity, regions with less developed financial markets, and non-state-owned enterprises. Enterprise participation in TPA further enhances total factor productivity and innovation efficiency. These findings provide insightful references for managers and policymakers regarding the effect of government-led corporate social responsibilities on corporate governance.
1 Introduction
Poverty remains a persistent global challenge despite economic development and human progress. China’s strategy of targeted poverty alleviation (TPA) provides a very promising response to the call for poverty eradication. To boost listed firms’ TPA engagement, the Shanghai and Shenzhen Stock Exchanges made participation disclosure mandatory in 2016. Since then, the number of enterprises participating in TPA has increased significantly from 569 in 2016 to 1,249 in 2020, and the active participation of enterprises has accelerated the process of achieving final victory in the fight against poverty in China. By participating in TPA, enterprises not only receive recognition and support from the government, but also send positive signals to the market that the enterprises operate well and actively fulfill social responsibility through the disclosure of TPA information, thus improving their social reputation and helping them attract various social and financial resources (Jing et al., 2023). Existing literature has explored the economic consequences of TPA participation, and primarily examined the effects on firm risks (Chen & Li, 2021), bond credit spreads (Zhen et al., 2022), financing cost (Zhao et al., 2022), financing constraints (Huo et al., 2023), stock performance (Qiao et al., 2021) and corporate value (Jing et al., 2023). However, there is limited scholarly focus on how TPA participation influences corporate innovation.
Innovation is essential for boosting a company’s competitive edge and fostering enduring economic growth (Chatzoglou & Chatzoudes, 2017). In recent years, corporate R&D investments in emerging economies have garnered significant attention. The report of the 20th National Congress of the Communist Party of China states that innovation is the first driving force, and China has entered a new development stage: Technological advancements are increasingly driving high-quality development and fostering shared prosperity. As such, enterprises take improving technological innovation capability as their priority, which is not only essential for enhancing core competitiveness but also contributes to the national strategy of innovation-driven development. As talent and financial resources are crucial factors in driving enterprise innovation (Lodh et al., 2014), the ways to enhance the attractiveness of enterprises to skilled individuals and venture capitalists have become hot topics that are of theoretical and practical significance.
Previous studies have explored the effects of corporate social responsibility (CSR) on firm innovation (Lu et al., 2024; Masulis & Reza, 2023), among which charitable donation is more relevant to this study. According to Masulis and Reza (2023), charitable donations impose substantial costs on shareholders, potentially leading to distorted corporate financing and investment choices. Different from conventional CSR, TPA is a government-led CSR (Chang et al., 2021). Government-led CSR provides support for enterprise innovation through policy coordination and resource redistribution. Enterprises participating in government-led social responsibility (e.g., TPA) are more likely to receive government support, such as access to government grants, tax incentives, and low-interest loans, which directly support their R&D investment. However, voluntary CSR relies more on market-driven incentives (e.g., brand differentiation and stakeholder trust). The long-term financial stability of corporate voluntary CSR is insufficient and is greatly affected by market volatility, for example, public welfare projects may face the risk of capital chain breakage if they do not form a commercialization model. Government-led CSR can promote cross-sectoral cooperation among enterprises (e.g., “industrial poverty alleviation” in the context of TPA), facilitate the sharing of resources between enterprises and local governments and research institutions, and accelerate innovation. In contrast, voluntary corporate CSR may not have an advantage in sharing resources with local governments and research organizations. An increasing number of studies investigate the government’s role in fostering CSR engagement among enterprises (e.g., Yin & Quazi, 2018; Yin & Zhang, 2012).
In addition, disclosure requirements for listed companies participating in TPA differ significantly from those for other CSR activities. Since 2016, stock exchange regulations have mandated that listed companies disclose specific TPA-related information in their annual reports, including detailed accounts of poverty alleviation fund allocations and material contributions, following standardized reporting formats. In contrast, participation in other types of CSR initiatives faces no such compulsory disclosure requirements or prescribed reporting standards, which indicates that listed companies can voluntarily choose whether and how to disclose their participation in CSR activities other than TPA. However, the disclosure of CSR information – including whether it is disclosed, how it is disclosed, and the quality of disclosure – can be influenced by multiple factors (Ali et al., 2017), meaning there may be inconsistencies between CSR disclosures and actual CSR performance. Consequently, previous literature on corporate social responsibility may encounter data accuracy issues due to this discretionary disclosure practice. The mandatory disclosure of TPA information by listed companies generates more precise data for examining CSR effects on firms, thereby contributing to the advancement of CSR-related research.
For enterprises, if the active implementation of CSR can help them obtain key resources from the government, then they will have fewer financial constraints and can spend more on innovative activities. Consequently, we conjecture that enterprises’ participation in TPA will affect their innovation, which, surprisingly, has drawn very limited attention. To our understanding, Zhou and Wei (2023) are the only ones who have examined the impact of TPA on enterprise innovation, discovering a positive effect but without delving into the underlying mechanism. From the perspectives of corporate governance and policymaking, it is more important to understand the mechanism. Based on a quasi-experimental design, we identify both the attraction of talent and the alleviation of financing constraints as the main influencing channels. We further examine the heterogeneity in the effect through different influencing channels.
Our initial dataset comprises listed Chinese A-share companies covering the period from 2016 to 2020. We find that enterprises’ participation in TPA can promote innovation by attracting talent and alleviating financing constraints. To evaluate the reliability of the empirical findings, we conduct a series of analyses including adopting alternative measures as the dependent variable and performing the parallel trend test and placebo test, which lead to consistent results.
The main contributions of this article are as follows: First, although the economic consequences of enterprises’ participation in TPA have been examined from various aspects, few studies have explored the effect on enterprise innovation. To our knowledge, this study is the first to examine the underlying mechanisms linking enterprises’ participation in TPA with their innovation from a new perspective, which provides valuable references for corporate governance and policymaking to motivate enterprises’ participation in achieving rural revitalization and common prosperity. This article finds that enterprises’ participation in TPA can promote enterprises’ innovation by attracting more talent and alleviating financing constraints, and hence identifies the main influencing channels. Second, we identify the factors that influence the relationship between TPA participation and firm innovation, and find that TPA participation has a more pronounced effect on firm innovation for firms with high media attention, as well as for firms located in regions with low intellectual property rights protection, low mergers and acquisitions activity, low levels of financial development, and non-state-owned enterprises. Third, our findings indicate that enterprise involvement in TPA enhances not only innovation but also total factor productivity and innovation efficiency. These findings provide insights for the government to improve its role in motivating enterprises’ participation in CSR and promoting enterprise innovation.
2 Literature Review and Hypothesis Development
2.1 CSR and Enterprise Innovation
Enterprises in developing countries may have less incentive to improve their CSR performance because of fewer market regulations and more intention on profitability (Azmat & Samaratunge, 2009; Marquis & Qian, 2014), and face more challenges. However, proponents of the stakeholder theory argue that fulfilling CSR in developing countries can indeed promote enterprise development by sending positive signals to the market. Previous studies have found that enterprises’ participation in CSR can increase consumer willingness to buy (Ramesh et al., 2019), improve employee relations (Bénabou & Tirole, 2010) and managers’ reputations (Masulis & Reza, 2015), improve equity market liquidity (Egginton & McBrayer, 2019), reduce the cost of financing (Desender et al., 2020), and improve company performance (Bebchuk et al., 2009). Recent evidence further underscores that robust CSR disclosure mitigates information asymmetry and stabilizes market access during crises, particularly for firms facing greater informational challenges (Clancey-Shang & Fu, 2024). Notably, firms from developing markets strategically enhance CSR disclosure, particularly in environmental and social dimensions, when operating in stringent regulatory environments like the USA, to overcome informational disadvantages and improve market outcomes such as liquidity and institutional ownership (Chowdhury et al., 2021). Besides, enterprises’ active participation in CSR can effectively improve their competitiveness and innovation capability (Mellahi & Harris, 2016). In recent years, investigations into the connection between CSR and innovation have progressively extended to specific domains within the ESG framework. For example, Khalil et al. (2024) found that environmental innovations not only significantly enhance firms’ financial performance but also improve environmental performance by reducing carbon emissions.
Enterprises’ engagement in CSR positively contributes to corporate growth, particularly by fostering innovation. For example, a good corporate reputation can promote corporate innovation (Ou & Hsu, 2013). Furthermore, a positive association is observed between philanthropic contributions and innovation outcomes (Jiang et al., 2018). Meanwhile, enterprise innovation is also affected by other factors such as financing constraints (Savignac, 2008).
Different from charitable donations and other forms of CSR, TPA is led by the government and has a significant political attribute. Especially for SOEs, TPA is a political task. On the one hand, enterprises’ active participation in TPA can help achieve the government’s political target and thus receive the government’s support. Also, as TPA attracts high attention from the whole society after enterprises disclose TPA information, investors tend to have positive expectations (Zhao et al., 2022), which will help enterprises improve their reputation and attract more talent and other resources. As such, the underlying mechanism of enterprises’ participation in TPA on enterprise innovation can be fundamentally different from conventional CSR, such as charitable donations, which is to be examined in this study.
2.2 Hypothesis Development
Over the last decade, China has achieved significant progress in reducing poverty through TPA, in which the participation of enterprises has made irreplaceable contributions. The existing literature indicates that enterprises’ engagement in TPA has been demonstrated to significantly lower the expenses associated with equity financing (Zhao et al., 2022), boost stock performance (Qiao et al., 2021).
Different from conventional CSR, China’s TPA is led by the government and has attracted significant attention from society. The regulatory mandates imposed by the Shanghai and Shenzhen Stock Exchanges regarding TPA disclosure have established a standardized framework for corporate transparency. This disclosure mechanism enables stakeholders to comparatively evaluate organizational TPA implementation, thereby facilitating corporate reputation enhancement and brand visibility expansion among listed entities (Baron, 2008; Kothari et al., 2009). Fu et al. (2025) demonstrate that sophisticated investors, characterized by superior information processing and complex modeling techniques, significantly influence asset pricing and market dynamics, highlighting the critical role of investor interpretation in translating corporate signals into market outcomes. An enterprise’s strategic allocation of resources to TPA can send the signal to external investors that the enterprise’s financial condition is sound (Ferrell et al., 2016), which can potentially attract more investment and thus alleviate financing constraints. Also, by actively participating in TPA, enterprises can improve their industrial reputation so as to attract more talent.
On the one hand, enterprises that actively participate in TPA can gain government support. From the perspective of the government, enterprises’ participation in TPA can effectively reduce the population living in poverty, promote economic development in poor areas, and thus reduce the pressure on local governments, thereby helping enterprises to obtain government support in resources and policies (Manchiraju & Rajgopal, 2017; Marquis & Qian, 2014). In addition, participating in TPA can send positive signals that the enterprise is developing well and is dedicated to fostering long-term social sustainability, thus establishing a good reputation for the enterprise (Chang et al., 2021). By participating in TPA, enterprises can enhance their external financing and attract more talent, which is critical for improving innovation activities.
On the other hand, involvement in TPA by enterprises can bolster their competitive edge, thereby stimulating innovation within the organization. Charitable giving has become one of the essential ways for enterprises to gain competitive advantages (Xu et al., 2022). According to the resource-based theory, the donation behavior of enterprises can effectively build the trust of consumers and stakeholders, and obtain intangible strategic assets such as government support (Petrenko et al., 2016), bringing enterprises sustainable competitive advantages. As TPA is a social responsibility led by the Chinese government and widely participated in by society, enterprises’ participation in TPA can receive extensive attention and provide enterprises with huge competitive advantages. By consolidating its competitive advantage, an enterprise obtains an advantageous position in the industry, thereby obtaining a higher level of profits, which provides strong support for innovation activities. Given the analysis conducted previously, this article formulates the subsequent hypothesis:
H1. Enterprises’ participation in TPA can improve their innovation level.
Talent is at the core of enterprise innovation. According to existing literature, enterprises that actively engage in CSR are more appealing to employees (Turban & Greening, 1997). On the one hand, according to the theory of signal transmission, an enterprise’s active performance of CSRs is conducive to improving the image of the enterprise, thus enhancing talent’ sense of identity with the enterprise (Kim et al., 2010) and attracting more innovative talent. On the other hand, enterprises with government support generally have good prospects for development, which can send positive signals to talent and improve the attractiveness of enterprises. More talent provides more innovative power for enterprises to improve their innovation level. Given the analysis conducted previously, this article formulates the subsequent hypothesis:
H2. Enterprises’ participation in TPA improves the degree of corporate innovation by increasing the talent population.
Enterprise innovation largely relies on continuous capital investment (Aghion & Howitt, 1992; Hamm et al., 2021), and most enterprises receive financial support from external sources such as shares and bonds. In other words, financial constraints can hinder enterprises from participating in innovation activities. Previous research has indicated that the proactive fulfillment of CSRs has a notable effect on alleviating financing constraints (Bebchuk et al., 2009). As a government-led CSR, enterprises’ participation in TPA can attract the attention of both the government and society, which can be important for alleviating financing constraints. First, the government not only has the power to allocate substantial resources but also has a certain policy orientation towards resource allocation (Li & Shi, 2022). By actively participating in TPA, companies are more inclined to gain backing from the government, thus easing financing constraints. Second, enterprises’ active participation in TPA can also attract more external investors for financial support. With sufficient financial investment, enterprise innovation can be effectively promoted. Therefore, this article puts forward the subsequent hypothesis:
H3. Enterprises’ participation in TPA improves the level of enterprise innovation by mitigating corporate financing constraints.
3 Methodology and Data
3.1 Variables
3.1.1 Dependent Variable
Following the literature (Hirshleifer et al., 2012), this study takes the annual number of invention patent applications, which reflects innovation output, as a proxy for firm innovation. Recognizing that patent application activity may not occur annually for all firms, we take the natural logarithm of an enterprise’s annual invention patent applications plus 1 as the measure of innovation to reduce heteroscedasticity and sample loss.
3.1.2 Independent Variable
We have established a staggered DID design for the empirical analysis. One dummy variable, namely, Treat, is set to 1 if an enterprise has participated in TPA and 0 otherwise. The other dummy variable, namely, Post, is set to 1 for the second and subsequent years of an enterprise’s TPA participation, and 0 otherwise. The independent variable of interest is the interaction of Treat and Post.
3.1.3 Mediator Variables
The population of talent, measured by the natural logarithm of the number of corporate R&D personnel (denoted as Talent) and the financing constraints measured by the WW index (denoted as WW), are the mediator variables. The WW index is calculated by reference to Whited and Wu (2006), and the method is depicted in equation (1):
where CF represents the ratio of cash flow to total assets. DivPos serves as an indicator, assigning a value of 1 if cash dividends are paid in the current period and 0 otherwise. Lev indicates the ratio of long-term debt to assets. Size is calculated as the natural logarithm of total assets. ISG stands for the industry-average sales growth rate, while SG signifies the sales revenue growth rate.
3.1.4 Control Variables
Enterprise innovation is affected by various enterprise characteristics. We control some control variables. Detailed definitions of all variables are reported in Table 1.
Definitions of variables
| Variables | Definitions |
|---|---|
| Independent variable | |
| Treat | If the enterprise has participated in TPA, the assignment is 1, otherwise it is 0 |
| Post | The second and subsequent years of an enterprise’s TPA participation are assigned a value of 1, and all other years are assigned 0 |
| Dependent variable | |
| Innovation | The natural logarithm of the number of annual invention patent applications plus 1 |
| Mediator variables | |
| Talent | The natural logarithm of the number of corporate R&D personnel |
| WW | WW index is calculated by reference to Whited and Wu (2006) |
| Control variables | |
| Size | The natural logarithm of total assets |
| Lev | The ratio of total liabilities over total assets |
| Growth | Operating income growth rate |
| Balance | The ratio of the number of shares held by the largest shareholder to the number of shares held by the top ten shareholders |
| Cash | The ratio of monetary funds to total assets at the end of the period |
| Pro | The ratio of corporate net profit to operating income |
| Lmc | The natural logarithm of the sum of the cash compensation of the top three executives |
| Dual | A dummy variable indicating whether the chairman and general manager of the enterprise are the same person (taking 1) or not (taking 0) |
| Soe | A dummy variable indicating whether the enterprise is a SOE (taking 1) or not (taking 0) |
This table provides a detailed definition of the key variables utilized in our analysis.
3.2 Empirical Design
Considering that enterprises participating in TPA and those not participating may have pre-existing differences before they participate in TPA, merely comparing their TPA participation horizontally or vertically will lead to the neglect of these pre-existing differences, which will lead to biased estimation of the impact of enterprises participating in TPA on enterprise innovation, so we use double difference model to control the pre-existing differences between the two groups of samples. In addition, because the years in which enterprises in China participate in TPA are not fixed, this article uses the staggered DID to study the impact of participating in TPA on enterprise innovation. The equation is as follows:
where Treat × Post is the independent variable and Controls are the control variables, as defined in Table 1. Moreover, we further control for the individual firm fixed effect (denoted as Firm i ) and year fixed effect (denoted as Year t ).
To further verify the mediating effect, we examine the mediating effects of corporate talent and financing constraints by constructing the following models:
where MV represents the mediating factors, the number of corporate R&D personnel (Talent), and financing constraints (WW).
3.3 Data and Descriptive Statistics
Our sample covers Chinese enterprises listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2016 to 2020. To ensure the accuracy of the data, we also manually collect the TPA data from listed enterprises’ annual reports (accessible from www.cninfo.com.cn) for comparison and supplement. To eliminate the influence of extreme values, we winsorize every continuous variable at the 1st and 99th percentiles. Next, we make the following treatments: excluding the data of enterprises in the financial industry, excluding special treatment (ST and *ST) enterprises, excluding the data with missing values for control variables in the model. The final sample size is 15,227. We obtain the data mainly from the CSMAR database.
Table 2 presents the descriptive statistics for all variables, revealing an average innovation value of approximately 2.211 between 2016 and 2020. The range, from a minimum of 0 to a maximum of 6.387, underscores considerable variation in the level of innovation among enterprises.
Descriptive statistics
| Variables | Mean | Std. Dev. | Min | Median | Max |
|---|---|---|---|---|---|
| Innovation | 2.211 | 1.563 | 0.000 | 2.197 | 6.387 |
| Treat | 0.357 | 0.479 | 0.000 | 0.000 | 1.000 |
| Post | 0.176 | 0.381 | 0.000 | 0.000 | 1.000 |
| Talent | 5.387 | 1.425 | 0.000 | 5.447 | 8.743 |
| WW | −1.030 | 0.071 | −1.245 | −1.027 | −0.878 |
| Size | 22.208 | 1.272 | 19.966 | 22.042 | 26.171 |
| Lev | 0.408 | 0.201 | 0.060 | 0.396 | 0.906 |
| Growth | 0.165 | 0.400 | −0.608 | 0.104 | 2.473 |
| Balance | 0.564 | 0.186 | 0.198 | 0.554 | 0.945 |
| Cash | 0.179 | 0.123 | 0.017 | 0.146 | 0.610 |
| Pro | 0.057 | 0.236 | −1.454 | 0.076 | 0.496 |
| Lmc | 14.630 | 0.652 | 13.154 | 14.586 | 16.535 |
| Dual | 0.318 | 0.466 | 0.000 | 0.000 | 1.000 |
| Soe | 0.292 | 0.455 | 0.000 | 0.000 | 1.000 |
This table presents the summary statistics. All continuous variables are winsorized at the 1 and 99 percentiles to alleviate the effects of outliers. All variables are defined in Table 1.
4 Empirical Results
4.1 Baseline Regression Analysis
Table 3 presents the baseline regression results, with both columns controlling for year and industry fixed effects. Column (1) shows the results without including control variables, while Column (2) shows the results after adding control variables. As can be seen from Column (1), the coefficient of Treat × Post is significantly positive at the 1% level, confirming that enterprises’ participation in TPA can significantly improve the innovation output. In Column (2), the coefficient of Treat × Post is still significantly positive at the 1% level after controlling for potential influencing factors. Regarding the economic significance of the impact of TPA on firms’ innovation, the coefficient of Treat × Post is 0.083, which means that the innovation level of firms participating in TPA increases by about 8.65% (e0.083) ‒ 1 compared to firms not participating in TPA. This result demonstrates that TPA not only exhibits statistical significance but also carries substantial economic significance, effectively promoting corporate innovation. Hypothesis 1 is supported.
Baseline regression results
| Variables | (1) | (2) |
|---|---|---|
| Innovation | Innovation | |
| Treat × Post | 0.112*** | 0.083*** |
| (4.42) | (3.32) | |
| Size | 0.550*** | |
| (19.80) | ||
| Lev | −0.426*** | |
| (−4.66) | ||
| Growth | 0.011 | |
| (0.65) | ||
| Balance | 0.059 | |
| (0.56) | ||
| Cash | −0.188** | |
| (−2.09) | ||
| Pro | −0.002 | |
| (−0.07) | ||
| Lmc | 0.031 | |
| (1.29) | ||
| Dual | 0.014 | |
| (0.56) | ||
| Soe | −0.019 | |
| (−0.33) | ||
| Constant | 1.941*** | −10.433*** |
| (145.79) | (−16.16) | |
| Firm FE | YES | YES |
| Year FE | YES | YES |
| Observations | 15,227 | 15,227 |
| R-squared | 0.075 | 0.114 |
This table reports the results of the baseline regression. The dependent variable Innovation is the natural logarithm of the number of annual invention patent applications plus 1. Treat is a dummy variable indicating whether the enterprise has participated in TPA (taking 1) or not (taking 0). Post is a dummy variable, the second and subsequent years of an enterprise’s TPA participation are assigned a value of 1, and all other years are assigned 0. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. *** and ** indicate significant at the level of 1 and 5%, respectively, and t statistics are reported in brackets.
4.2 Mechanism Analysis
The participation of enterprises in TPA helps to reduce information asymmetry (Zhao et al., 2023), so that external stakeholders can have a more accurate understanding and prediction of the enterprises’ business conditions. In addition, since TPA is led by the government, the government itself has a strong appeal, and enterprises’ participation in TPA is more likely to be noticed by society. As such, participation in TPA can help enhance enterprises’ reputation and hence attract more talent, which is an important factor in promoting enterprise innovation (He & Tian, 2018).
Enterprise innovation requires a large amount of upfront investment, and the easing of financing constraints is conducive to improving the intensity of enterprises’ R&D investment (Brown et al., 2012), while talent is a key determinant of innovation success. Therefore, enterprises’ participation in TPA may promote enterprise innovation by easing financing constraints and attracting talent.
To further verify this conjecture, we examine the mediating effects of corporate talent and financing constraints by model (3) and model (4). The results are shown in Table 4.
Results of the mechanism analysis
| Variables | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| Talent | Innovation | WW | Innovation | |
| Treat × Post | 0.039** | −0.002* | ||
| (2.00) | (−1.84) | |||
| Talent | 0.120*** | |||
| (8.88) | ||||
| WW | −0.750** | |||
| (−2.16) | ||||
| Controls | YES | YES | YES | YES |
| Constant | −9.536*** | −10.070*** | 0.130*** | −9.549*** |
| (−18.55) | (−14.30) | (5.03) | (−12.34) | |
| Firm FE | YES | YES | YES | YES |
| Year FE | YES | YES | YES | YES |
| Observations | 13,656 | 13,656 | 10,851 | 10,851 |
| R-squared | 0.156 | 0.122 | 0.627 | 0.126 |
This table reports the relevant results of mechanism test. The dependent variable Talent is the natural logarithm of the number of corporate R&D personnel. The dependent variable WW is calculated by reference to Whited and Wu (2006). Innovation is the natural logarithm of the number of annual invention patent applications plus 1. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. ***, **, and * indicate significant at the level of 1, 5, and 10%, respectively, and t statistics are reported in brackets.
In Table 4, Column (1) and Column (2) show the test results of the talent mechanism, and it can be seen that the Treat × Post coefficient in Column (1) is significantly positive, indicating that firms’ participation in TPA significantly attracts more talent. The Talent coefficient in Column (2) is significantly positive, indicating that the increase in the number of talents in the enterprise significantly improves the innovation level of the enterprise. Columns (3) and (4) show the results of the financing constraints mechanism, and it can be seen that the coefficient of Treat × Post in column (3) is significantly negative, indicating that the participation of enterprises in TPA can significantly reduce the financing constraints of enterprises. The coefficient of WW in column (4) is significantly negative, indicating that the lower the financing constraints of enterprises, the higher the innovation level of enterprises.
4.3 Robustness Checks
4.3.1 Alternative Measures of Enterprise Innovation
To test the robustness of the empirical results, we draw on Brown et al. (2009) and use the natural logarithm of total R&D investment at the end of the year as the measure of enterprise innovation (denoted as Innovation2). In addition, under the new accounting standards in China, intangible assets mainly consist of patents and non-proprietary technologies, which can reflect information about the innovation progress of enterprises. We use the ratio of corporate intangible assets to total corporate assets multiplied by 100 as another proxy for enterprise innovation (denoted as Innovation3). In addition to this, the quality of corporate patents is also a crucial indicator reflecting corporate innovation. Chinese firms place more emphasis on quantity than quality in innovation. Too much focus on quantity may lead to stagnation of technological innovation, while high-quality innovation is more conducive to long-term competitiveness and sustainable development of firms (Pang et al., 2024). Therefore, we use the natural logarithm of the citation count of corporate inventive patents plus 1 to represent the innovation level of enterprises (denoted as Innovation4). The regression results based on the alternative measures of enterprise innovation are shown in Table 5.
Results based on alternative measures of innovation
| Variables | (1) | (2) | (3) |
|---|---|---|---|
| Innovation2 | Innovation3 | Innovation4 | |
| Treat × Post | 0.041** | 0.055*** | 0.001** |
| (1.97) | (3.44) | (2.00) | |
| Controls | YES | YES | YES |
| Constant | −6.146*** | 0.081 | 0.076*** |
| (−11.51) | (0.19) | (5.05) | |
| Firm FE | YES | YES | YES |
| Year FE | YES | YES | YES |
| Observations | 15,227 | 13,643 | 15,153 |
| R-squared | 0.110 | 0.368 | 0.028 |
This table reports the regression results after replacing the dependent variables. Innovation2 is the natural logarithm of total R&D investment at the end of the year. Innovation3 is the ratio of corporate intangible assets to total corporate assets multiplied by 100. Innovation4 is the natural logarithm of the citation count of corporate inventive patents plus 1. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. *** and ** indicate significant at the level of 1 and 5%, respectively, and t statistics are reported in brackets.
Table 5 presents the regression results using alternative measurement methods for enterprise innovation. In each column, the coefficient of Treat × Post is significantly positive, which yields the same result that firms’ participation in TPA can improve their innovation capabilities.
4.3.2 Assessing TPA’s Impact with t + 1/t + 2 Innovation
Considering innovation outcomes, particularly high-quality patents, often require extended R&D cycles, meaning the effects of TPA may not be immediately observable in the same year of policy implementation. By analyzing lagged patent outputs, we account for this inherent time delay in the innovation process. In addition, in order to mitigate the potential problem of reverse causation, we use t + 1 and t + 2 Innovation to examine the impact of TPA policies on enterprise innovation.
Table 6 presents the impact of TPA on firms’ innovation in periods t + 1 and t + 2. The significantly positive coefficients of Treat × Post suggest robust empirical support for our findings.
TPA’s impact with t + 1/t + 2 innovation
| Variables | (1) | (2) |
|---|---|---|
| Innovation t+1 | Innovation t+2 | |
| Treat × Post | 0.053* | 0.065* |
| (1.85) | (1.81) | |
| Controls | YES | YES |
| Constant | −4.919*** | 0.698 |
| (−5.74) | (0.60) | |
| Firm FE | YES | YES |
| Year FE | YES | YES |
| Observations | 11,576 | 8,390 |
| R-squared | 0.060 | 0.018 |
This table presents regression results where the dependent variables are the natural logarithm of (annual invention patent applications plus 1) at t + 1 and t + 2. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. *** and * indicate significant at the level of 1 and 10%, respectively, and t statistics are reported in brackets.
4.3.3 Using TPA Amount as Explanatory Variable
While the main analysis employs a DID approach to identify the average treatment effect of TPA on corporate innovation, replacing it with the TPA amount provides a more nuanced perspective, capturing the intensity of a firm’s efforts. This substitution not only validates the main findings but also reveals whether a larger investment in poverty alleviation has a more significant impact on enterprise innovation, enhancing the study’s robustness and explanatory power. Specifically, we measure a firm’s input into TPA by taking the natural logarithm of the sum of its financial contributions and the monetary value of material donations plus 1 (denoted as Fund).
Table 7 presents the results of the regression after replacing the explanatory variable with the TPA amount. It can be seen that the coefficient between Fund and Innovation is significantly positive, indicating that the more the enterprise spends on TPA, the more the enterprise’s innovation outcomes.
Using TPA amount as explanatory variable
| Variables | (1) |
|---|---|
| Innovation | |
| Fund | 0.003* |
| (1.76) | |
| Controls | YES |
| Constant | −10.443*** |
| (−16.14) | |
| Firm FE | YES |
| Year FE | YES |
| Observations | 15,227 |
| R-squared | 0.113 |
This table presents the regression results using the total corporate TPA funding as the explanatory variable. Innovation is the natural logarithm of the number of annual invention patent applications plus 1. Fund is the natural logarithm of the sum of its financial contributions and the monetary value of material donations plus 1. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. *** and * indicate significant at the level of 1 and 10%, respectively, and t statistics are reported in brackets.
4.3.4 Parallel Trend Test
The parallel trend assumption is essential for estimating the treatment effect using the DID method. Following Bertrand and Mullainathan (2003), we augment our baseline specification (equation (2)) with a series of interaction terms between the treatment indicator (Treat) and year-specific dummies for both pre- and post-treatment periods. Specifically, we include: Treat × Beforeₖ (k = 1, 2, 3) to capture dynamic effects in the kth year prior to TPA participation; Treat × Afterₘ (m = 1, 2, 3, 4) to measure dynamic effects in the mth year following TPA participation. Treat × Before4 was omitted and used as the baseline period.
The coefficients of Treat × Before3, Treat × Before2, and Treat × Before1 before the firms’ participation in TPA are not significant, indicating that the differences in firms’ innovations before the firms’ participation in TPA are not significant. The parallel trend assumption is deemed reasonable. In contrast, the coefficients of Treat × Current, Treat × After1, Treat × After2, Treat × After3, and Treat × After4 are significantly positive. This result indicates that the participation of enterprises in TPA has continuously improved their innovation capacity (Figure 1, Table 8).
Graphical representation of the parallel trend test. The coefficients of the relative time dummy variables before the TPA participation are all insignificant and small in value, indicating that before the TPA participation, there is no significant difference in the level of innovation between the treatment group and the control group, which satisfies the parallel trend hypothesis.
Results of the parallel trend test
| Variables | Innovation |
|---|---|
| Treat × Before3 | 0.017 |
| (0.20) | |
| Treat × Before2 | 0.024 |
| (0.28) | |
| Treat × Before1 | 0.134 |
| (1.60) | |
| Treat × Current | 0.142* |
| (1.67) | |
| Treat × After1 | 0.210** |
| (2.35) | |
| Treat × After2 | 0.213** |
| (2.33) | |
| Treat × After3 | 0.257*** |
| (2.72) | |
| Treat × After4 | 0.306*** |
| (3.06) | |
| Controls | YES |
| Constant | −10.408*** |
| (−16.07) | |
| Firm FE | YES |
| Year FE | YES |
| Observations | 15,227 |
| R-squared | 0.115 |
This table shows regression results for the parallel trend assumption test. The dependent variable Innovation is the natural logarithm of the number of annual invention patent applications plus 1. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. ***, **, and * indicate significant at the level of 1, 5, and 10%, respectively, and t statistics are reported in brackets.
4.3.5 Placebo Test
Drawing on the approach of Bradley et al. (2017), we carry out a bootstrapping placebo test. We randomly select firms from the full sample, randomize the impact of firms’ participation in TPA on these firms, and fit a baseline model to it. Figure 2 shows the kernel density of the estimated coefficient based on 500 simulations, implying a normal distribution that does not deviate significantly from zero. The robustness of the findings is further confirmed.
Distribution of placebo test coefficients. This figure shows the result of a placebo test that randomly assigns value to participation in TPA. This procedure is repeated 500 times, and the distribution of the estimated coefficients on Pseudo_TPA from the placebo test is reported in this figure.
4.3.6 PSM-DID Estimation
Although the DID model identifies the average treatment effect of corporate participation in TPA, the TPA initiative is not a strict natural experiment. Selection bias may arise due to systematic differences in firm characteristics prior to TPA participation, which could undermine the credibility of the DID results. To resolve this issue, we employ a 1:1 nearest-neighbor matching method using the firm-level control variables from the baseline regression as matching covariates. We then re-estimate the effect using a staggered DID approach on the matched sample.
Columns (1) and (2) in Table 9 show the results of the post-matching regressions with no control variables added and with control variables added, respectively. It can be seen that the DID coefficients are still both significantly positive and not substantially different from the baseline results. This confirms the robustness of our main findings.
The estimation of PSM-DID
| Variables | (1) | (2) |
|---|---|---|
| Innovation | Innovation | |
| Treat × Post | 0.107*** | 0.074** |
| (2.92) | (2.07) | |
| Controls | NO | YES |
| Constant | 1.931*** | −11.530*** |
| (86.39) | (−10.18) | |
| Firm FE | YES | YES |
| Year FE | YES | YES |
| Observations | 6,732 | 6,732 |
| R-squared | 0.098 | 0.144 |
This table reports the regression results after PSM matching. Column (1) reports the results without control variables. Column (2) reports the results with control variables. The dependent variable Innovation is the natural logarithm of the number of annual invention patent applications plus 1. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. *** and ** indicate significant at the level of 1 and 5%, respectively, and t statistics are reported in brackets.
5 Further Analysis
5.1 Heterogeneity Analysis
5.1.1 Media Attention
Enterprises can improve their image and obtain external resource support by fulfilling their CSRs and making their activities known to society. That is, enterprises should disclose CSR-related information to stakeholders through various channels in a timely manner. In this regard, the media play a vital role: the media can prevent enterprises from overstating their contributions to TPA, while the media can also motivate external stakeholders and society to provide resources and support of various kinds for enterprises that actively participate in TPA. Therefore, for enterprises with high media attention, participation in TPA may have more influence on enterprise innovation.
To explore the influence of varying media focus on the connection between TPA and enterprise innovation, we classify enterprises into two groups based on the annual median level of media attention: strong media attention (above the median) and weak media attention (below the median). The data related to media attention are obtained from the online media attention (denoted as Media) in the CNRDS database. The results are shown in Columns (1) and (2) of Table 10. As can be seen, the regression coefficient between TPA and enterprise innovation for the group with high media attention is significant at the 1% level, indicating that enterprises’ participation in TPA can promote enterprise innovation. In contrast, for the group with low media attention, the corresponding regression coefficient is not significant. Hence, media exposure emerges as a contributing factor in the relationship between TPA engagement and enterprise innovation.
Heterogeneity analyses
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
|---|---|---|---|---|---|---|---|---|---|---|
| High media attention | Low media attention | High IPR protection | Low IPR protection | High M&A activity | Low M&A activity | High financial development | Low financial development | SOEs | NSOEs | |
| Innovation | Innovation | Innovation | Innovation | Innovation | Innovation | Innovation | Innovation | Innovation | Innovation | |
| Treat × Post | 0.107*** | 0.039 | 0.059 | 0.099*** | 0.060 | 0.101*** | 0.066 | 0.096*** | 0.048 | 0.078** |
| (2.85) | (0.99) | (1.51) | (2.99) | (1.50) | (3.04) | (1.34) | (2.97) | (1.30) | (2.28) | |
| Controls | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES |
| Constant | −11.312*** | −9.385*** | −10.266*** | −10.576*** | −10.407*** | −10.519*** | −8.847*** | −11.710*** | −10.240*** | −11.008*** |
| (−11.33) | (−9.06) | (−10.62) | (−11.77) | (−10.75) | (−11.48) | (−5.99) | (−13.37) | (−8.10) | (−14.07) | |
| Firm FE | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES |
| Year FE | YES | YES | YES | YES | YES | YES | YES | YES | YES | YES |
| Observations | 7,569 | 7,658 | 7,596 | 7,631 | 7,833 | 7,394 | 6,554 | 8,673 | 4,443 | 10,784 |
| R-squared | 0.130 | 0.103 | 0.107 | 0.118 | 0.105 | 0.126 | 0.091 | 0.123 | 0.166 | 0.101 |
This table presents the results of subgroup analyses examining how the relationship between TPA participation and enterprise innovation varies across five dimensions: media attention, IPR protection, M&A activity, financial development, and ownership type. Columns (1)–(10) report the regression results for these subgroups. The dependent variable Innovation is the natural logarithm of the number of annual invention patent applications plus 1. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. *** and ** indicate significant at the level of 1 and 5%, respectively, and t statistics are reported in brackets.
5.1.2 IPR Protection
Intellectual property rights (IPR) protection possesses an exclusivity function, which can largely prevent imitators from infringing upon innovators (Glaeser & Landsman, 2021), and high-level protection of IPR can increase the incentive for firms to disclose their innovative information (Fang et al., 2017; Sweet & Maggio, 2015). In regions with weaker IPR protection, firms face heightened risks of innovation imitation or theft, and their willingness to disclose information related to innovation is even weaker, exacerbating information asymmetry. This asymmetry can hinder firms’ ability to attract innovative talent and secure external financing. In these environments, TPA participation can serve as a compensatory mechanism, fostering innovation by providing firms with additional resources, enhancing their reputation, or bolstering social capital. In regions with weaker IPR regimes, TPA participation may play a more substantial role in promoting innovation, effectively substituting for strong IPR protection. Therefore, we conjecture that the effect of TPA participation on enterprise innovation is likely to be more significant in regions with weak IPR protection. To examine how the level of IPR protection affects the relationship between TPA participation and firm innovation, we matched the IPR protection levels of the respective regions with the sample of this study, subsequently calculated the annual median values, and categorized the sample into high IPR protection and low IPR protection groups based on the annual median. Following Ginarte and Park (1997) and Park (2008), we measure the level of IPR protection from two aspects: administrative law enforcement and judicial protection. The specific calculation method is as follows: we take the average of the number of intellectual property infringement cases and the number of lawyers in each region and divide it by the total population, the logarithm of which is taken as the measure of the regional IPR protection level. The results are shown in Columns (3) and (4) of Table 10.
It can be seen that in regions with high-level protection of IPR, the regression coefficient between TPA participation and enterprise innovation is not significant. In contrast, in regions with low-level protection of IPR, the regression coefficient between TPA and enterprise innovation is significant at the level of 1%, confirming that the level of IPR protection is an influencing factor.
5.1.3 M&A Activity
Many enterprises carry out mergers and acquisitions (M&A) activity for the purpose of enhancing their innovation capabilities (Bena & Li, 2014). Through M&A, firms are able to sustain their innovation outputs over time (Cefis & Marsili, 2015). As a result, companies in regions with more M&A activities tend to be more innovative. In regions with lower M&A activity, firms have fewer opportunities to acquire external resources, technologies, or knowledge through market-based transactions like mergers or acquisitions. TPA can serve as an alternative channel by fostering collaborations with external entities, such as government bodies, or other firms, thereby providing access to the resources and knowledge necessary for innovation. Therefore, the effect of TPA participation on enterprise innovation may be more significant in regions where the M&A market is less active, because the TPA participation can make up for the adverse impact of inactive merger and acquisition activities on enterprise innovation. To verify this conjecture, we matched the regional M&A activity levels with the sample in this study, calculated the annual median values, and then classified the sample into high M&A activity and low M&A activity groups based on the yearly median. The results are presented in Columns (5) and (6) of Table 10.
In regions with more vibrant M&A markets, the regression coefficient linking TPA to firm innovation is insignificant. In contrast, the regression coefficient between TPA and firm innovation is significant at the 1% level in regions with less active M&A markets. This indicates that the participation of enterprises with a low degree of activity in the M&A market in TPA will have a more obvious role in improving enterprise innovation.
5.1.4 Financial Development
The innovation activities of enterprises need sufficient financial support, and the location’s financial development impacts the financing activities of the enterprise (Hsu et al., 2014). It means that if an enterprise is located in a region with a higher financial development level, it can carry out financing activities more conveniently. Therefore, enterprises in areas with higher financial development levels tend to have higher levels of innovation. In regions with less developed financial systems, firms often struggle to secure the funding required for innovation due to limited access to capital markets or credit. TPA can mitigate this constraint by improving a firm’s social reputation and trustworthiness, which may facilitate access to external financing, or by directly providing financial support through government subsidies or social contributions tied to TPA participation. So we expect that the role of TPA participation in improving enterprise innovation is more effective in regions with low financial development levels, because enterprise innovation in these regions is more restricted by financing constraints, and the TPA participation of enterprises is more likely to play a mitigating effect on financing constraints in these regions. We matched the regional financial development levels with the sample in this study, computed the annual median values, and then classified the observations into high and low financial development groups based on the yearly median. Drawing on Hsu et al. (2014), we measure financial development as the ratio of total deposits and loans to GDP in a province. The regression outcomes are displayed in Columns (7) and (8) of Table 10.
The regression coefficient between TPA and firm innovation is insignificant in regions with high financial development, but significant at the 1% level in regions with lower development. This suggests that TPA participation has a more pronounced impact on enterprise innovation in less financially developed areas.
5.1.5 Ownership Type
The innovation behavior of firms is moderated by multiple contextual factors. Huo and Li (2024) found that the negative impact of gender-diverse leadership structures on innovation is weaker in state-owned enterprises (SOEs), which stems from SOEs’ policy support and resource guarantee mechanisms. Similarly, as a policy-driven social responsibility initiative, TPA may exhibit differential innovation effects between SOEs and NSOEs. Unlike SOEs, which often prioritize policy mandates and social objectives, NSOEs face greater market competition and thus have stronger incentives to leverage TPA engagement for strategic innovation gains. Additionally, NSOEs typically operate under tighter resource constraints, making them more efficient in repurposing TPA-related government subsidies, and stakeholder networks into innovation inputs. NSOEs tend to face more severe financing constraints, and thus NSOEs may benefit more from alleviating financing constraints through TPA. Therefore, the enhancement effect of firms’ participation in TPA on innovation may be more significant among NSOEs.
Columns (9) and (10) of Table 10 show the regression results. It can be seen that the regression coefficient between TPA and firm innovation is not significant among SOEs. On the contrary, the regression coefficient between TPA and firm innovation is significant at the 5% level among NSOEs. This suggests that the effect of TPA participation on firms’ innovation capability is more significant in NSOEs.
5.2 Economic Consequence Analysis
The Chinese economy has entered a new development stage characterized by quality improvement rather than quantitative expansion. The Chinese government’s ultimate goal in calling on enterprises to participate in TPA is to achieve common prosperity through high-quality development. To achieve this goal, China has abandoned the economic development mode that relies on large-scale material capital investment and instead takes innovation as the core motivation for economic development. Therefore, we assume that the participation of enterprises in TPA will ultimately promote the high-quality development of enterprises by promoting enterprise innovation. We built the following model to examine the economic consequences:
where Tfp_LP is the total factor productivity calculated by the LP method (Levinsohn & Petrin, 2003). The term Treat × Post serves as the explanatory variable, consistent with its definition in the baseline regression, while Controls represent a set of control variables, also aligned with their definitions in the baseline regression. The regression results are shown in Table 11.
Economic consequence and innovation efficiency test
| Variables | (1) | (2) |
|---|---|---|
| Tfp_LP | InnoEff | |
| Treat × Post | 0.021** | 0.005*** |
| (2.01) | (3.00) | |
| Controls | YES | YES |
| Constant | −2.095*** | −0.369*** |
| (−8.19) | (−8.33) | |
| Firm FE | YES | YES |
| Year FE | YES | YES |
| Observations | 12,907 | 13,585 |
| R-squared | 0.446 | 0.084 |
This table reports the impact of enterprises’ participation in TPA on high-quality development and innovation efficiency. Tfp_LP is the total factor productivity calculated by the LP method (Levinsohn & Petrin, 2003). InnoEff is measured by the ratio of firms’ innovation output to firms’ R&D input. All other control variables are described in Table 1. Fixed effects of firm and year are controlled. *** and ** indicate significant at the level of 1 and 5%, respectively, and t statistics are reported in brackets.
Column (1) of Table 11 presents the regression results. It can be seen that the coefficient between Treat × Post and Tfp_LP is significantly positive at the 5% level, indicating that firms’ participation in TPA enhances total factor productivity.
5.3 Impact on Innovation Efficiency
For enterprises, innovation efficiency is also a very important indicator. Higher innovation efficiency means that an enterprise produces more innovation output with less innovation input. Therefore, it is also important to examine the impact of enterprises’ participation in TPA on their innovation efficiency. Talent is a very important factor for enterprise innovation, and we have verified earlier that enterprises’ participation in TPA can improve enterprise innovation by attracting more talent, who can also improve the input-output ratio of enterprise innovation. Therefore, we conjecture that the participation of enterprises in TPA can effectively enhance the efficiency of enterprise innovation. To examine this, we construct the following model:
where innovation efficiency (denoted as InnoEff) is measured by the ratio of firms’ innovation output to firms’ R&D input. The term Treat × Post serves as the explanatory variable, consistent with its definition in the baseline regression, while Controls represent a set of control variables, also aligned with their definitions in the baseline regression.
Column (2) in Table 11 shows the results, and it can be seen that the regression coefficient between Treat × Post and InnoEff is positive and significant at the 1% level, indicating that firms’ participation in TPA improves firms’ innovation efficiency.
6 Conclusion and Implications
This research investigates the association between enterprises’ participation in TPA and corporate innovation, and confirms that enterprises’ participation in TPA can promote enterprise innovation. We also examine the intrinsic mechanism and find that enterprises’ participation in TPA can promote their innovation by attracting more talent and alleviating financing constraints. Further analysis reveals that the innovation-promoting effect of TPA is more pronounced for firms with higher media exposure, those located in regions with weaker intellectual property rights protection, areas with lower levels of merger and acquisition activity, regions with less developed financial markets, and non-state-owned enterprises. In addition, we also find that enterprise participation in TPA further enhances total factor productivity and innovation efficiency.
Based on the findings of this article, we propose the following policy recommendations: First, enterprises should actively respond to national strategies and take the initiative to participate in CSR practices such as rural revitalization. While serving the overall situation of national development, enterprises can effectively enhance their social image by fulfilling CSR, so as to obtain more development resources and support, enhance the attractiveness of talents, and ultimately improve the level of innovation and the quality of development. The research in this article shows that enterprises actively fulfill the CSR advocated by the government to help them obtain key resources and achieve development goals, so enterprises should take the initiative to incorporate the practice of CSR into long-term development strategic planning. Second, enterprises should attach great importance to the key role of talents in innovation efficiency, and enhance the attractiveness of high-end talents by optimizing talent policies and improving incentive mechanisms. Research has shown that the element of talent has a significant role in promoting the innovation and development of enterprises, so enterprises should continue to increase the investment in the introduction and cultivation of talent. Third, enterprises should disclose non-financial information such as CSR in a timely manner to reduce the information asymmetry with external investors. Standardized information disclosure not only helps enterprises alleviate financing constraints but also wins more support for innovation and development. The findings suggest that an effective information disclosure regime contributes positively to enterprises’ ability to secure innovation resources, prompting the need for firms to develop a well-structured disclosure system that improves both information quality and transparency.
-
Funding information: This work was supported by the National Natural Science Foundation of China (72172029, 71971046, 72403033), National Social Science Foundation of China (21FJYB032), Humanities and Social Science Fund of Ministry of Education of China (24YJAZH192), and Major Program of National Social Science Foundation of China (23&ZD048).
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. DG: writing – original draft, data curation, software, writing – review and editing; XS: writing – original draft, data curation, software, writing – review and editing; MY: funding acquisition, methodology, writing – review and editing; HZ: funding acquisition, validation, supervision, writing – review and editing; JL: validation, writing – review and editing. All authors contribute equally to the article.
-
Conflict of interest: The authors state no conflict of interest.
-
Data availability statement: The data used in this study are available from the corresponding author on reasonable request.
-
Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.
References
Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323–351.10.2307/2951599Suche in Google Scholar
Ali, W., Frynas, J. G., & Mahmood, Z. (2017). Determinants of corporate social responsibility (CSR) disclosure in developed and developing countries: A literature review. Corporate Social Responsibility and Environmental Management, 24(4), 273–294.10.1002/csr.1410Suche in Google Scholar
Azmat, F., & Samaratunge, R. (2009). Responsible entrepreneurship in developing countries: Understanding the realities and complexities. Journal of Business Ethics, 90(3), 437–452.10.1007/s10551-009-0054-8Suche in Google Scholar
Baron, D. P. (2008). Managerial contracting and corporate social responsibility. Journal of Public Economics, 92(1–2), 268–288.10.1016/j.jpubeco.2007.05.008Suche in Google Scholar
Bebchuk, L., Cohen, A., & Ferrell, A. (2009). What matters in corporate governance? The Review of Financial Studies, 22(2), 783–827.10.1093/rfs/hhn099Suche in Google Scholar
Bena, J., & Li, K. (2014). Corporate innovations and mergers and acquisitions. The Journal of Finance, 69(5), 1923–1960.10.1111/jofi.12059Suche in Google Scholar
Bénabou, R., & Tirole, J. (2010). Individual and corporate social responsibility. Economica, 77(305), 1–19.10.1111/j.1468-0335.2009.00843.xSuche in Google Scholar
Bertrand, M., & Mullainathan, S. (2003). Enjoying the quiet life? Corporate governance and managerial preferences. Journal of Political Economy, 111(5), 1043–1075.10.1086/376950Suche in Google Scholar
Bradley, D., Kim, I., & Tian, X. (2017). Do unions affect innovation? Management Science, 63(7), 2251–2271.10.1287/mnsc.2015.2414Suche in Google Scholar
Brown, J. R., Fazzari, S. M., & Petersen, B. C. (2009). Financing innovation and growth: Cash flow, external equity, and the 1990s R&D boom. The Journal of Finance, 64(1), 151–185.10.1111/j.1540-6261.2008.01431.xSuche in Google Scholar
Brown, J. R., Martinsson, G., & Petersen, B. C. (2012). Do financing constraints matter for R&D? European Economic Review, 56(8), 1512–1529.10.1016/j.euroecorev.2012.07.007Suche in Google Scholar
Cefis, E., & Marsili, O. (2015). Crossing the innovation threshold through mergers and acquisitions. Research Policy, 44(3), 698–710.10.1016/j.respol.2014.10.010Suche in Google Scholar
Chang, Y., He, W., & Wang, J. (2021). Government initiated corporate social responsibility activities: Evidence from a poverty alleviation campaign in China. Journal of Business Ethics, 173(4), 661–685.10.1007/s10551-020-04538-wSuche in Google Scholar
Chatzoglou, P., & Chatzoudes, D. (2017). The role of innovation in building competitive advantages: An empirical investigation. European Journal of Innovation Management, 21(1), 44–69.10.1108/EJIM-02-2017-0015Suche in Google Scholar
Chen, S., & Li, Y. (2021). Targeted poverty alleviation and stock price crash risk: Evidence from China. The Singapore Economic Review, 68(4), 1271–1301.10.1142/S0217590821440033Suche in Google Scholar
Chowdhury, R. H., Fu, C., Huang, Q., & Lin, N. (2021). CSR disclosure of foreign versus US firms: Evidence from ADRs. Journal of International Financial Markets, Institutions and Money, 70, 101275.10.1016/j.intfin.2020.101275Suche in Google Scholar
Clancey-Shang, D., & Fu, C. (2024). CSR disclosure, political risk and market quality: Evidence from the Russia-Ukraine conflict. Global Finance Journal, 60, 100938.10.1016/j.gfj.2024.100938Suche in Google Scholar
Desender, K. A., LópezPuertas-Lamy, M., Pattitoni, P., & Petracci, B. (2020). Corporate social responsibility and cost of financing- The importance of the international corporate governance system. Corporate Governance: An International Review, 28(3), 207–234.10.1111/corg.12312Suche in Google Scholar
Egginton, J. F., & McBrayer, G. A. (2019). Does it pay to be forthcoming? Evidence from CSR disclosure and equity market liquidity. Corporate Social Responsibility and Environmental Management, 26(2), 396–407.10.1002/csr.1691Suche in Google Scholar
Fang, L. H., Lerner, J., & Wu, C. (2017). Intellectual property rights protection, ownership, and innovation: Evidence from China. The Review of Financial Studies, 30(7), 2446–2477.10.1093/rfs/hhx023Suche in Google Scholar
Ferrell, A., Liang, H., & Renneboog, L. (2016). Socially responsible firms. Journal of Financial Economics, 122(3), 585–606.10.1016/j.jfineco.2015.12.003Suche in Google Scholar
Fu, C., Jacoby, G., Lin, N., & Lu, L. (2025). The value of investor sophistication. Financial Management. doi: 10.1111/fima.12497.Suche in Google Scholar
Ginarte, J. C., & Park, W. G. (1997). Determinants of patent rights: A cross-national study. Research Policy, 26(3), 283–301.10.1016/S0048-7333(97)00022-XSuche in Google Scholar
Glaeser, S. A., & Landsman, W. R. (2021). Deterrent disclosure. The Accounting Review, 96(5), 291–315.10.2308/TAR-2019-1050Suche in Google Scholar
Hamm, S. J. W., Jung, M. J., & Park, M. (2021). Corporate venture capital, disclosure, and financial reporting. Corporate Governance: An International Review, 29(6), 541–566.10.1111/corg.12379Suche in Google Scholar
He, J., & Tian, X. (2018). Finance and corporate innovation: A survey. Asia-Pacific Journal of Financial Studies, 47(2), 165–212.10.1111/ajfs.12208Suche in Google Scholar
Hirshleifer, D., Low, A., & Teoh, S. H. (2012). Are overconfident CEOs better innovators? The Journal of Finance, 67(4), 1457–1498.10.1111/j.1540-6261.2012.01753.xSuche in Google Scholar
Hsu, P. H., Tian, X., & Xu, Y. (2014). Financial development and innovation: Cross-country evidence. Journal of Financial Economics, 112(1), 116–135.10.1016/j.jfineco.2013.12.002Suche in Google Scholar
Huo, M., & Li, C. (2024). “Work is easy or very tired”: The impact of the heterosexual leadership structure on enterprise innovation investment. China Finance Review International, 14(2), 390–417.10.1108/CFRI-06-2021-0107Suche in Google Scholar
Huo, Z., Xia, Y., Liu, D., & Zhang, M. (2023). The impact of listed companies’ participation in targeted poverty alleviation on financing constraints: Mediating effect based on policy resources. Frontiers in Environmental Science, 11, 1115819.10.3389/fenvs.2023.1115819Suche in Google Scholar
Jiang, S., Mi, J., Tao, X., & Hu, W. (2018). Corporate philanthropy and innovation performance. International Journal of Business and Management, 13(4), 173–182.10.5539/ijbm.v13n4p173Suche in Google Scholar
Jing, J., Wang, J., & Hu, Z. (2023). Has corporate involvement in government-initiated corporate social responsibility activities increased corporate value? – Evidence from China’s Targeted Poverty Alleviation. Humanities and Social Sciences Communications, 10(1), 1–16.10.1057/s41599-023-01869-7Suche in Google Scholar
Khalil, M. A., Khalil, R., & Khalil, M. K. (2024). Environmental, social and governance (ESG)-augmented investments in innovation and firms’ value: A fixed-effects panel regression of Asian economies. China Finance Review International, 14(1), 76–102.10.1108/CFRI-05-2022-0067Suche in Google Scholar
Kim, H. R., Lee, M., Lee, H. T., & Kim, N. M. (2010). Corporate social responsibility and employee company identification. Journal of Business Ethics, 95(4), 557–569.10.1007/s10551-010-0440-2Suche in Google Scholar
Kothari, S. P., Li, X., & Short, J. E. (2009). The effect of disclosures by management, analysts, and business press on cost of capital, return volatility, and analyst forecasts: A study using content analysis. The Accounting Review, 84(5): 1639–1670.10.2308/accr.2009.84.5.1639Suche in Google Scholar
Levinsohn, J., & Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. The Review of Economic Studies, 70(2), 317–341.10.1111/1467-937X.00246Suche in Google Scholar
Li, T. W., & Shi, L. (2022). Donation, political connection and credit access: The case of Chinese small and medium enterprises. Finance Research Letters, 46, 102269.10.1016/j.frl.2021.102269Suche in Google Scholar
Lodh, S., Nandy, M., & Chen, J. (2014). Innovation and family ownership: Empirical evidence from India. Corporate Governance: An International Review, 22(1), 4–23.10.1111/corg.12034Suche in Google Scholar
Lu, Q., Deng, Y., Wang, X., & Wang, A. (2024). The impact of China’s green credit policy on enterprise digital innovation: Evidence from heavily-polluting Chinese listed companies. China Finance Review International, 14(1), 103–121.10.1108/CFRI-11-2022-0224Suche in Google Scholar
Manchiraju, H., & Rajgopal, S. (2017). Does corporate social responsibility (CSR) create shareholder value? Evidence from the Indian Companies Act 2013. Journal of Accounting Research, 55(5), 1257–1300.10.1111/1475-679X.12174Suche in Google Scholar
Marquis, C., & Qian, C. (2014). Corporate social responsibility reporting in China: Symbol or substance? Organization Science, 25(1), 127–148.10.1287/orsc.2013.0837Suche in Google Scholar
Masulis, R. W., & Reza, S. W. (2015). Agency problems of corporate philanthropy. The Review of Financial Studies, 28(2), 592–636.10.1093/rfs/hhu082Suche in Google Scholar
Masulis, R. W., & Reza, S. W. (2023). Private benefits of corporate philanthropy and distortions to corporate financing and investment decisions. Corporate Governance: An International Review, 31(3), 464–490.10.1111/corg.12476Suche in Google Scholar
Mellahi, K., & Harris, L. C. (2016). Response rates in business and management research: An overview of current practice and suggestions for future direction. British Journal of Management, 27(2), 426–437.10.1111/1467-8551.12154Suche in Google Scholar
Ou, Y. C., & Hsu, L. C. (2013). How does corporate reputation affect innovative performance? International Business Research, 6(12), 46–59.10.5539/ibr.v6n12p46Suche in Google Scholar
Pang, S., Hua, G., & Yan, Z. (2024). Why do Chinese corporate innovations emphasize quantity over quality? A new perspective on R&D capital market distortions. China Finance Review International. doi: 10.1108/CFRI-04-2024-0160.Suche in Google Scholar
Park, W. G. (2008). International patent protection: 1960–2005. Research Policy, 37(4), 761–766.10.1016/j.respol.2008.01.006Suche in Google Scholar
Petrenko, O. V., Aime, F., Ridge, J., & Hill A. (2016). Corporate social responsibility or CEO narcissism? CSR motivations and organizational performance. Strategic Management Journal, 37(2), 262–279.10.1002/smj.2348Suche in Google Scholar
Qiao, T., Han, L., & Liu, Y. (2021). Does targeted poverty alleviation disclosure improve stock performance? Economics Letters, 201, 109805.10.1016/j.econlet.2021.109805Suche in Google Scholar
Ramesh, K., Saha, R., Goswami, S., Sekar, & Dahiya, R. (2019). Consumer’s response to CSR activities: Mediating role of brand image and brand attitude. Corporate Social Responsibility and Environmental Management, 26(2), 377–387.10.1002/csr.1689Suche in Google Scholar
Savignac, F. (2008). Impact of financial constraints on innovation: What can be learned from a direct measure? Economics of Innovation and New Technology, 17(6), 553–569.10.1080/10438590701538432Suche in Google Scholar
Sweet, C. M., & Maggio, D. S. E. (2015). Do stronger intellectual property rights increase innovation? World Development, 66, 665–677.10.1016/j.worlddev.2014.08.025Suche in Google Scholar
Turban, D. B., & Greening, D. W. (1997). Corporate social performance and organizational attractiveness to prospective employees. Academy of Management Journal, 40(3), 658–672.10.2307/257057Suche in Google Scholar
Whited, T. M., & Wu, G. (2006). Financial constraints risk. The Review of Financial Studies, 19(2), 531–559.10.1093/rfs/hhj012Suche in Google Scholar
Xu, L., Guo, X., Liu, Y., Sun, X. & Ji, J. (2022). How does corporate charitable giving affect enterprise innovation? A literature review and research directions. Sustainability, 14(23), 15603.10.3390/su142315603Suche in Google Scholar
Yin, J., & Quazi, A. (2018). Business ethics in the greater China region: Past, present, and future research. Journal of Business Ethics, 150(3), 815–835.10.1007/s10551-016-3220-9Suche in Google Scholar
Yin, J., & Zhang, Y. (2012). Institutional dynamics and corporate social responsibility (CSR) in an emerging country context: Evidence from China. Journal of Business Ethics, 111(2), 301–316.10.1007/s10551-012-1243-4Suche in Google Scholar
Zhao, L., Wu, X., & Wang, Z. (2022). Targeted poverty alleviation information disclosure and equity financing cost. Emerging Markets Finance and Trade, 58(8), 2181–2190.10.1080/1540496X.2021.1964950Suche in Google Scholar
Zhao, W., Liang, Y., & Li, P. (2023). Can enterprises’ participation in targeted poverty alleviation reduce the cost of debt financing? Evidence from China. Finance Research Letters, 58, 104551.10.1016/j.frl.2023.104551Suche in Google Scholar
Zhen, H., Wang, S., Lian, M., & Zheng, S. (2022). Do bond investors attend to corporate targeted poverty alleviation? China Journal of Accounting Studies, 10(1), 95–119.10.1080/21697213.2022.2084008Suche in Google Scholar
Zhou, L., & Wei, F. (2023). Does engaging in government initiated corporate social responsibility activities improve corporate innovation? Evidence from China. Economic Research-Ekonomska Istraživanja, 36(1), 2119426.10.1080/1331677X.2022.2119426Suche in Google Scholar
© 2025 the author(s), published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
Artikel in diesem Heft
- Research Articles
- Research on the Coupled Coordination of the Digital Economy and Environmental Quality
- Optimal Consumption and Portfolio Choices with Housing Dynamics
- Regional Space–time Differences and Dynamic Evolution Law of Real Estate Financial Risk in China
- Financial Inclusion, Financial Depth, and Macroeconomic Fluctuations
- Harnessing the Digital Economy for Sustainable Energy Efficiency: An Empirical Analysis of China’s Yangtze River Delta
- Estimating the Size of Fiscal Multipliers in the WAEMU Area
- Impact of Green Credit on the Performance of Commercial Banks: Evidence from 42 Chinese Listed Banks
- Rethinking the Theoretical Foundation of Economics II: Core Themes of the Multilevel Paradigm
- Spillover Nexus among Green Cryptocurrency, Sectoral Renewable Energy Equity Stock and Agricultural Commodity: Implications for Portfolio Diversification
- Cultural Catalysts of FinTech: Baring Long-Term Orientation and Indulgent Cultures in OECD Countries
- Loan Loss Provisions and Bank Value in the United States: A Moderation Analysis of Economic Policy Uncertainty
- Collaboration Dynamics in Legislative Co-Sponsorship Networks: Evidence from Korea
- Does Fintech Improve the Risk-Taking Capacity of Commercial Banks? Empirical Evidence from China
- Multidimensional Poverty in Rural China: Human Capital vs Social Capital
- Property Registration and Economic Growth: Evidence from Colonial Korea
- More Philanthropy, More Consistency? Examining the Impact of Corporate Charitable Donations on ESG Rating Uncertainty
- Can Urban “Gold Signboards” Yield Carbon Reduction Dividends? A Quasi-Natural Experiment Based on the “National Civilized City” Selection
- How GVC Embeddedness Affects Firms’ Innovation Level: Evidence from Chinese Listed Companies
- The Measurement and Decomposition Analysis of Inequality of Opportunity in China’s Educational Outcomes
- The Role of Technology Intensity in Shaping Skilled Labor Demand Through Imports: The Case of Türkiye
- Legacy of the Past: Evaluating the Long-Term Impact of Historical Trade Ports on Contemporary Industrial Agglomeration in China
- Unveiling Ecological Unequal Exchange: The Role of Biophysical Flows as an Indicator of Ecological Exploitation in the North-South Relations
- Exchange Rate Pass-Through to Domestic Prices: Evidence Analysis of a Periphery Country
- Private Debt, Public Debt, and Capital Misallocation
- Impact of External Shocks on Global Major Stock Market Interdependence: Insights from Vine-Copula Modeling
- Informal Finance and Enterprise Digital Transformation
- Wealth Effect of Asset Securitization in Real Estate and Infrastructure Sectors: Evidence from China
- Consumer Perception of Carbon Labels on Cross-Border E-Commerce Products and its Influencing Factors: An Empirical Study in Hangzhou
- How Agricultural Product Trade Affects Agricultural Carbon Emissions: Empirical Evidence Based on China Provincial Panel Data
- The Role of Export Credit Agencies in Trade Around the Global Financial Crisis: Evidence from G20 Countries
- How Foreign Direct Investments Affect Gender Inequality: Evidence From Lower-Middle-Income Countries
- Big Five Personality Traits, Poverty, and Environmental Shocks in Shaping Farmers’ Risk and Time Preferences: Experimental Evidence from Vietnam
- Academic Patents Assigned to University-Technology-Based Companies in China: Commercialisation Selection Strategies and Their Influencing Factors
- Review Article
- Bank Syndication – A Premise for Increasing Bank Performance or Diversifying Risks?
- Special Issue: The Economics of Green Innovation: Financing And Response To Climate Change
- A Bibliometric Analysis of Digital Financial Inclusion: Current Trends and Future Directions
- Targeted Poverty Alleviation and Enterprise Innovation: The Mediating Effect of Talent and Financing Constraints
- Does Corporate ESG Performance Enhance Sustained Green Innovation? Empirical Evidence from China
- Can Agriculture-Related Enterprises’ Green Technological Innovation Ride the “Digital Inclusive Finance” Wave?
- Special Issue: EMI 2025
- Digital Transformation of the Accounting Profession at the Intersection of Artificial Intelligence and Ethics
- The Role of Generative Artificial Intelligence in Shaping Business Innovation: Insights from End Users’ Perspectives and Practices
- The Mediating Role of Climate Change Mitigation Behaviors in the Effect of Environmental Values on Green Purchasing Behavior within the Framework of Sustainable Development
- Special Issue: The Path to Sustainable And Acceptable Transportation
- Factors Influencing Environmentally Friendly Air Travel: A Systematic, Mixed-Method Review
- Special Issue: Shapes of Performance Evaluation - 2nd Edition
- Redefining Workplace Integration: Socio-Economic Synergies in Adaptive Career Ecosystems and Stress Resilience – Institutional Innovation for Empowering Newcomers Through Social Capital and Human-Centric Automation
- Knowledge Management in the Era of Platform Economies: Bibliometric Insights and Prospects Across Technological Paradigms
- The Impact of Quasi-Integrated Agricultural Organizations on Farmers’ Production Efficiency: Evidence from China
Artikel in diesem Heft
- Research Articles
- Research on the Coupled Coordination of the Digital Economy and Environmental Quality
- Optimal Consumption and Portfolio Choices with Housing Dynamics
- Regional Space–time Differences and Dynamic Evolution Law of Real Estate Financial Risk in China
- Financial Inclusion, Financial Depth, and Macroeconomic Fluctuations
- Harnessing the Digital Economy for Sustainable Energy Efficiency: An Empirical Analysis of China’s Yangtze River Delta
- Estimating the Size of Fiscal Multipliers in the WAEMU Area
- Impact of Green Credit on the Performance of Commercial Banks: Evidence from 42 Chinese Listed Banks
- Rethinking the Theoretical Foundation of Economics II: Core Themes of the Multilevel Paradigm
- Spillover Nexus among Green Cryptocurrency, Sectoral Renewable Energy Equity Stock and Agricultural Commodity: Implications for Portfolio Diversification
- Cultural Catalysts of FinTech: Baring Long-Term Orientation and Indulgent Cultures in OECD Countries
- Loan Loss Provisions and Bank Value in the United States: A Moderation Analysis of Economic Policy Uncertainty
- Collaboration Dynamics in Legislative Co-Sponsorship Networks: Evidence from Korea
- Does Fintech Improve the Risk-Taking Capacity of Commercial Banks? Empirical Evidence from China
- Multidimensional Poverty in Rural China: Human Capital vs Social Capital
- Property Registration and Economic Growth: Evidence from Colonial Korea
- More Philanthropy, More Consistency? Examining the Impact of Corporate Charitable Donations on ESG Rating Uncertainty
- Can Urban “Gold Signboards” Yield Carbon Reduction Dividends? A Quasi-Natural Experiment Based on the “National Civilized City” Selection
- How GVC Embeddedness Affects Firms’ Innovation Level: Evidence from Chinese Listed Companies
- The Measurement and Decomposition Analysis of Inequality of Opportunity in China’s Educational Outcomes
- The Role of Technology Intensity in Shaping Skilled Labor Demand Through Imports: The Case of Türkiye
- Legacy of the Past: Evaluating the Long-Term Impact of Historical Trade Ports on Contemporary Industrial Agglomeration in China
- Unveiling Ecological Unequal Exchange: The Role of Biophysical Flows as an Indicator of Ecological Exploitation in the North-South Relations
- Exchange Rate Pass-Through to Domestic Prices: Evidence Analysis of a Periphery Country
- Private Debt, Public Debt, and Capital Misallocation
- Impact of External Shocks on Global Major Stock Market Interdependence: Insights from Vine-Copula Modeling
- Informal Finance and Enterprise Digital Transformation
- Wealth Effect of Asset Securitization in Real Estate and Infrastructure Sectors: Evidence from China
- Consumer Perception of Carbon Labels on Cross-Border E-Commerce Products and its Influencing Factors: An Empirical Study in Hangzhou
- How Agricultural Product Trade Affects Agricultural Carbon Emissions: Empirical Evidence Based on China Provincial Panel Data
- The Role of Export Credit Agencies in Trade Around the Global Financial Crisis: Evidence from G20 Countries
- How Foreign Direct Investments Affect Gender Inequality: Evidence From Lower-Middle-Income Countries
- Big Five Personality Traits, Poverty, and Environmental Shocks in Shaping Farmers’ Risk and Time Preferences: Experimental Evidence from Vietnam
- Academic Patents Assigned to University-Technology-Based Companies in China: Commercialisation Selection Strategies and Their Influencing Factors
- Review Article
- Bank Syndication – A Premise for Increasing Bank Performance or Diversifying Risks?
- Special Issue: The Economics of Green Innovation: Financing And Response To Climate Change
- A Bibliometric Analysis of Digital Financial Inclusion: Current Trends and Future Directions
- Targeted Poverty Alleviation and Enterprise Innovation: The Mediating Effect of Talent and Financing Constraints
- Does Corporate ESG Performance Enhance Sustained Green Innovation? Empirical Evidence from China
- Can Agriculture-Related Enterprises’ Green Technological Innovation Ride the “Digital Inclusive Finance” Wave?
- Special Issue: EMI 2025
- Digital Transformation of the Accounting Profession at the Intersection of Artificial Intelligence and Ethics
- The Role of Generative Artificial Intelligence in Shaping Business Innovation: Insights from End Users’ Perspectives and Practices
- The Mediating Role of Climate Change Mitigation Behaviors in the Effect of Environmental Values on Green Purchasing Behavior within the Framework of Sustainable Development
- Special Issue: The Path to Sustainable And Acceptable Transportation
- Factors Influencing Environmentally Friendly Air Travel: A Systematic, Mixed-Method Review
- Special Issue: Shapes of Performance Evaluation - 2nd Edition
- Redefining Workplace Integration: Socio-Economic Synergies in Adaptive Career Ecosystems and Stress Resilience – Institutional Innovation for Empowering Newcomers Through Social Capital and Human-Centric Automation
- Knowledge Management in the Era of Platform Economies: Bibliometric Insights and Prospects Across Technological Paradigms
- The Impact of Quasi-Integrated Agricultural Organizations on Farmers’ Production Efficiency: Evidence from China
