Benchmark regression
The findings, delineated in Table 2, reveal a statistically significant increase in corporate risk following the RCC restructuring, irrespective of the inclusion of control variables or the decision to use a 3-year rolling range or standard deviation as the measure. Initially, regression results for the entire sample, both with and without firm-specific attributes as controls, are presented in columns (1), (2), (5), and (6). Subsequent adjustments exclude data from the real estate and financial sectors due to the heightened risks associated with these industries and the distinct impact of regional financial decentralization on these firms. The adjusted regression outcomes, displayed in the third, fourth, seventh, and eighth columns of Table 2, demonstrate that the coefficients, which remain significantly positive at the 1% level, have increased in magnitude.
The results from columns four and eight serve as the baseline for further analysis, showing coefficients of 0.060 and 0.056, respectively, indicating that an increase in Rstru by one unit leads to an increase in Risk1 and Risk2 by 0.060 and 0.056 standard deviations, respectively. This suggests that the establishment of RCBs in a region escalates the corporate risk for firms outside the real estate and financial sectors.
These empirical findings support our theoretical claim that greater financial decentralization led by local governments increases corporate risk, thereby confirming Hypothesis H1 of our model. This aligns with Jiang et al. (2020), who documented a negative association between bank deregulation and corporate risk. In contrast, within the context of this study, regional financial decentralization expands the scale of RCBs, reduces banking competition, and channels politically driven capital flows, thereby tightening financing constraints and elevating corporate risk. Moreover, financial decentralization enables local governments to divert financial resources toward preferred sectors such as infrastructure, often at the expense of the real economy. This reallocation worsens firms’ financing environment, intensifying the risks associated with financing constraints. These mechanisms will be examined in greater detail in the subsequent analysis.
The results for the control variables indicate that firm size, tangible asset ratio, and income tax rate are negatively associated with corporate risk, whereas leverage is positively correlated. This suggests that highly leveraged firms face greater operational risks, as debt obligations restrict cash flow and hinder business operations. By contrast, tangible assets can serve as collateral, easing financing constraints and reducing risk. Larger firms generally benefit from more stable markets and greater tangible resources, resulting in lower operational risk. Other variables, including firm age, cash-to-asset ratio, and revenue growth rate, exhibit no significant relationship with corporate risk, likely due to their complex interactions with risk dynamics and inter-variable correlations.
Robustness tests
Parallel trends test
The DID method has strict assumptions, requiring that the control group and the treatment group have consistent trends before the event. This study conducts a parallel trends test using the following model:
$${{risk}}_{i,t}=\beta +\mathop{\sum}\limits_{T=-4}^{-2}{\alpha }_{T}{{Rstru}}_{c,T}+\mathop{\sum}\limits_{T=0}^{4}{\alpha }_{T}{{Rstru}}_{c,T}+\gamma {X}_{i.t}+{\tau }_{{It}}+{\eta }_{c}+{\varepsilon }_{i.t}$$
(13)
where \({{Rstru}}_{c,T}\) is a dummy variable representing the city c in the year T before or after the restructuring of RCCs. For instance, when T = −2, it takes the value of 1 for the 2 periods before the restructuring, and 0 for other years. This parallel trend test includes the entire sample, and periods greater than 4 or less than −4 are grouped into T = 4 and T = −4. To avoid multicollinearity, the study follows the approach of Beck et al. (2010) and takes t = −1 as the base year.
The analysis incorporates consistent variable settings with the baseline regression model. Figures 1 and 2 illustrate the results of the parallel trend tests for Risk1 and Risk2, respectively. Both Figures confirm that, prior to T0, all estimated regression coefficients are statistically insignificant, affirming that the trends between the control and treatment groups were aligned before the restructuring of RCCs, thereby showing no significant pre-existing differences. Post T0, the regression coefficients become significantly positive, indicating a substantial increase in corporate risk within the treatment group relative to the control group. This pattern underscores the significant influences of RCC restructuring on the corporate risk of affected firms.
The coefficient value and confidence interval of \({{Rstru}}_{c,T}\) in Model (13) establishing \({Risk}1\) as the dependent variable.
The coefficient value and confidence interval of \({{Rstru}}_{c,T}\) in Model (13) establishing \({Risk}2\) as the dependent variable.
The lack of significance in the coefficients for the T0 time period, as shown in Figs. 1 and 2, can be attributed to two primary factors. First, several instances of RCC restructuring in the sample occurred towards the year’s end, minimizing their immediate impact on corporate risk within that fiscal year. Second, the influence of RCC restructuring on corporate risk predominantly transpires through financial mechanisms, such as the provisioning of loans, which inherently exhibit a temporal delay. Companies typically perceive the effects only after their existing loans reach maturity. Consequently, the impact of RCC restructuring on corporate risk may not manifest until after the restructuring year, indicating a potential lag in the effect. This delayed response highlights the temporal dynamics at play in the financial repercussions of RCC restructuring on corporate risk.
5.2.2 Placebo test: shifting the event impact beforehand
This study implements a placebo test by adjusting the timeline of RCC restructurings. It hypothesizes that these restructurings took place 1–6 years earlier than they actually did and assesses the potential impact on corporate risk. The robustness of the DID methodology is tested by this approach; specifically, a robust baseline regression would exhibit insignificant coefficients when the timing of the restructuring is artificially advanced. The regression outputs, detailed in columns (1–6) of Table 3, confirm that after adjusting the timing of the restructuring event, the coefficients for proxies related to financial decentralization shocks remain statistically insignificant. This outcome supports the integrity of the baseline model, indicating that actual temporal changes associated with the restructuring events drive the observed variations in corporate risk. The above results indicate that the fabricated policy shock cannot explain the changes in firm risk, ruling out the possibility that the baseline effect is driven by other policy shocks.
Placebo test: replacing the dependent variable
Corporate risk indicators encompass both market data-based and financial data-based risks. This study focuses on indicators derived from financial data. Benchmark regression analysis reveals that the restructuring of RCCs positively influences local business risk due to alterations in the local financial environment prompted by these restructuring activities. In contrast, market data-based risks, such as stock return volatility, are generally less sensitive to local policies that do not directly relate to businesses and are predominantly influenced by corporate strategies, industry developments, and similar factors.
Consequently, it is posited that the restructuring of RCCs primarily affects operational risk rather than the financial market risk of businesses. To validate this hypothesis, the paper substitutes the previous business risk indicators with stock return volatility. If the explanatory variables’ estimated coefficients remain significant, it would imply that the observed increase in business risk might be attributable to factors other than the restructuring of RCCs, possibly indicating a broader increase in business risk. The pertinent regression results, presented in Column (1) of Table 4, demonstrate that the coefficient estimates for key variables are not significant, suggesting that the restructuring of RCCs does not influence the market risk levels of businesses. This outcome substantiates that the conclusions drawn in this paper are not influenced by extraneous variables.
Placebo test: randomly selecting the treatment group
In empirical studies, fully isolating all factors influencing corporate risk is a formidable task, and random variables can introduce estimation biases. To mitigate these issues, this study employs a placebo test involving the random selection of an experimental group.
The methodology applied in this analysis includes several key steps: Initially, each prefecture-level city is assigned a unique identifier (ID), and these IDs are preserved alongside the actual year of RCC restructuring. The restructuring year is maintained, while a new variable, FN, is generated randomly. Utilizing the FN sequence, a fictitious variable, Fake_id, is created and subsequently integrated with the original dataset to generate Fake_year, which represents an imagined restructuring timeline. This procedure is replicated 1000 times, and each instance undergoes regression analysis using the fictitious restructuring year while maintaining all other variables constant.
Figures 3 and 4 display the probability density distribution of the regression coefficients, with a dashed line indicating the position of the benchmark regression coefficient. The distribution of coefficients from the random sampling is approximately normal, centered around zero, and most coefficients are notably distinct from the benchmark regression coefficient. This pattern in the distribution confirms that the findings of this study are not artifacts of random factors, thereby validating the robustness of the regression results.
Probability density distribution of the coefficients of \({Rstru}\) in Model (8) establishing \({Risk}1\) as the dependent variable when randomly selecting the treatment group.
Probability density distribution of the coefficients of \({Rstru}\) in Model (8) establishing \({Risk}2\) as the dependent variable when randomly selecting the treatment group.
In summary, our DID model has passed the necessary prerequisite assumption tests, including the parallel trend test and the placebo test. Furthermore, we conducted a series of robustness checks, including adding more control variables, using local government debt as an alternative measure of financial decentralization, employing the industrial enterprise database, adjusting combinations of fixed effects and clustering levels, and applying machine learning analysis. The results of the robustness checks (see Tables A2–A6 in the Appendix) consistently support the validity of the baseline conclusions of this study.
Mechanism analysis
Financial institution crowding out effect
This paper argues that local governments gain effective control over financial resources through the restructuring of RCCs, thereby achieving implicit financial decentralization. The baseline regression results show that RCC restructuring increases the risk borne by local enterprises, indicating that this form of decentralization influences corporate risk. However, the underlying mechanisms remain unclear. In Section “Benchmark regression,” drawing on Jiang et al. (2020), we propose that the increase in corporate risk arises because RCC restructuring expands the scale of RCBs, crowds out other banks, and reduces banking competition, which we term the “financial institution crowding-out effect.”
The crowding-out of central banks and the reduction in competition affect corporate risk in several ways. First, reduced bank competition exacerbates financing constraints for enterprises (Jiang et al., 2020). Easing financing constraints allows firms to borrow more readily in the face of short-term shocks, thereby reducing risk. Alleviating financing constraints also mitigates moral hazard and adverse selection, encouraging firms to undertake lower-risk investments (Chen and Jiang, 2024). In contrast, financial decentralization reduces competition by crowding out other banks, thereby tightening financing channels and increasing risk. Second, bank competition promotes financial innovation, broadening access to financing methods such as supply chain finance and intellectual property pledge financing. This diversification reduces dependence on a single financing channel, lowering financial vulnerability. A decline in competition, however, weakens the alignment between corporate assets and financial services, heightening mismatches between investment and financing and raising operational risks. Third, stronger competition compels banks to improve service quality, including providing specialized risk management and hedging strategies for market and exchange rate risks. The absence of such services under a crowding-out effect deprives firms of valuable support, increasing operational risks. Finally, competition enhances fund settlement efficiency. Rapid and accurate settlement improves cash flow management, lowers transaction costs, and reduces the risk of cash flow interruptions. Conversely, reduced competition undermines settlement efficiency, further aggravating operational risks. Overall, the financial institution crowding-out effect induced by RCC restructuring contributes to higher corporate risk by constraining financing, reducing innovation, limiting risk management support, and impairing cash flow stability.
In this section, we investigate the financial institution crowding out effect at the city level. The study uses the percentage of a specific type of financial branch institution exiting, relative to the total number of all financial institutions, as the dependent variable. This approach allows for the examination of the impact of RCC reforms on other financial branch institutions within the region. This study considers the crowding out effects of RCCs reform on state-owned large banks (\({Quit}1\)) and joint-stock commercial banks (\({Quit}2\)), with the models set as follows:
$${{Quit}}_{c,t}=\alpha +\beta {{Rstru}}_{c,t}+\gamma {X}_{c,t}+{\tau }_{t}+{\eta }_{c}+{\varepsilon }_{c.t}$$
(14)
where \({{Quit}}_{c,t}\) represents the percentage of a specific type of financial branch institution exiting in city \(c\) at time \(t\), \({{Rstru}}_{c,t}\) indicates whether city \(c\) has undergone the reform of RCCs at year \(t\), \({X}_{c,t}\) represents relevant control variables for city \(c\), including per capita GDP (\({PerGDP}\)), total fixed asset investment (\({Fixed}\)), and the logarithm of total population (\({PoP}\)), \({\tau }_{t}\) denotes time fixed effects, \({\eta }_{c}\) signifies city fixed effects, and \({\varepsilon }_{c.t}\) stands for the error term.
Our empirical evidence indicates that the reform of RCCs does not significantly affect the exit of regular joint-stock commercial bank branches. However, it has a significant positive effect on the exit of state-owned large bank branches. The regression results, presented in columns (2), (3), (5), and (6) of Table 5, show coefficients of 0.0528, 0.0489, 0.0481, and 0.0455, respectively. Specifically, after the completion of RCC reforms, an average of 8.16% of state-owned large bank branches exit the region. This finding supports the use of RCC reform as a proxy for implicit financial decentralization. However, when the dependent variable is Quit2, the estimated coefficient for Rstru is not significant, suggesting that the RCC reform did not negatively impact the market share of joint-stock banks.
To explain this phenomenon in the context of China’s financial development, we propose two potential reasons. Historically, China’s banking market—and, more broadly, the financial market—has been dominated by the five major state-owned banks. Consequently, the financial institutions most affected by financial decentralization were the branches of state-owned banks, which were forced to exit local markets due to increased competition. In contrast, joint-stock banks initially had fewer branches. During the sample period, the CBRC relaxed restrictions on the cross-regional establishment of bank branches, which enabled some joint-stock banks to expand rapidly. As a result, the number of joint-stock bank branches remained largely unaffected by the effects of financial decentralization. Another important reason lies in the differing responses of various types of banks to competition. Joint-stock banks, being smaller and more flexible, have leaner organizational structures and shorter decision-making processes. In response to financial decentralization, they optimize cost management and improve service quality. Conversely, state-owned banks, due to their larger scale and redundant organizational structures, often suffer from low management efficiency. Cost-control strategies, such as reducing branch networks and streamlining non-core personnel, are more effective for state-owned banks in coping with financial competition. Therefore, state-owned bank branches are more likely to exit local markets as a result of financial decentralization.
In summary, we find that the reform of RCCs crowds out central control over local finance, thereby increasing local control over regional finance. This mechanism is one way in which the reform of RCCs affects enterprise risk. Importantly, this effect does not extend to regular joint-stock commercial banks.
Corporate loan crowding out effect
Section “Fiscal decentralization” shows that RCC reform influences corporate risk by crowding out large state-owned bank branches. The relationship between financial institutions and enterprises is largely defined by borrowing and lending, meaning the most direct effect on firms stems from shifts in borrowing capacity. Financial decentralization also enables local governments to capture financial resources. With a relatively fixed financial supply, more funds are directed toward government-preferred sectors such as infrastructure and real estate, leaving fewer resources available for the real economy. This crowding effect worsens the financing environment and amplifies financing constraints.
The corporate loan crowding-out effect affects corporate risk in several ways. First, a reduction in bank loans decreases the external funds available to firms. Since operations such as purchasing raw materials and paying wages require steady cash flow, diminished financing can disrupt production processes, raising operational risk. Second, corporate expansion often demands substantial capital for building facilities, acquiring equipment, or entering new markets. A decline in bank loans deprives firms of critical financing, constraining their ability to expand production, seize market share, and enhance competitiveness (Zhou et al., 2023). Third, many firms rely on long-term investments for technological upgrades and innovation. When bank loans shrink, enterprises struggle to sustain these projects, potentially falling behind competitors and facing higher operational risks. Finally, without sufficient financing, firms cannot pursue scale expansion, weakening their price competitiveness and making them more vulnerable to losing market share. In sum, by restricting access to bank loans, RCC reform heightens corporate risk through reduced liquidity, constrained expansion, stifled innovation, and diminished competitiveness.
In this section, we examine the mechanisms through which reforms in RCCs influence corporate risk via corporate bank lending practices. We operationalize the volume of newly issued loans in the current period by calculating the difference between the current and previous total loan amounts. Given the strong association between changes in a firm’s loan amount and its inherent characteristics, we employ the ratio of newly issued loans to the previous total loan amount (nloantl) as our analytical variable. The regression findings are presented in columns (1–3) of Table 6.
The analysis in the first column reveals that RCC reform exerts a significant negative impact on the volume of corporate newly issued loans. Results from the second and third columns illustrate that the variation in newly issued loans mediates the effect of RCC reform on corporate risk, as indicated by the risk proxy variables Risk1 and Risk2. Notably, relative to the baseline regression, the absolute value of the estimated coefficient for the restructuring variable Rstru decreases significantly, along with a reduction in its statistical significance. This trend is particularly pronounced when Risk2 is the dependent variable, where the coefficient for Rstru remains positive but becomes statistically non-significant. The diminished significance of this coefficient implies that the effect of financial decentralization on pronounced fluctuations in corporate revenue is predominantly channeled through changes in bank loan allocations. This observation suggests a partial mediation effect, though it may not be complete. Collectively, these results indicate that RCC reform impacts corporate credit channels to a notable extent, subsequently influencing corporate operational risk.
Bank loans are typically classified into four categories based on their nature: mortgage loans, pledge loans, guarantee loans, and credit loans. Our dataset comprises current balances of mortgage loans, pledge loans, and unsecured loans. We measure changes in the shares of these loan types by using the ratio of the difference between the current and previous balances to the total loan amount. Unsecured loans, which lack collateral and guarantees, represent a higher risk for banks. Consequently, during periods of reduced lending willingness, banks often curtail the provision of credit loans to enterprises. In contrast, while pledge and mortgage loans are deemed higher-quality loans by banks, they entail higher default costs for enterprises. Thus, amidst constrained credit availability, banks tend to decrease credit loan issuance, whereas enterprises are likely to lessen their applications for pledge and mortgage loans.
The regression results are displayed in columns (4–6) of Table 6, analyzing the ratios of changes in mortgage loan amounts (Dyloantl), unsecured loan amounts (Cloantl), and pledge loan amounts (Zyloantl). These results indicate that RCC reform significantly negatively impacts both mortgage and unsecured loans, suggesting shifts in both banks’ lending behaviors and enterprises’ borrowing intentions due to increased uncertainty about future borrowing conditions.
It is important to note that the regression coefficient for Rstru is not significant when Zyloantl is the dependent variable. This result suggests that financial decentralization does not negatively affect the scale of collateralized loans. The finding is consistent with the trajectory of China’s financial development, in which traditional commercial banks have long dominated the market. Given the underdeveloped state of financial technology and the limited range of financial instruments in China, commercial banks have historically relied heavily on collateralized assets to mitigate lending risks. This reliance underscores the crucial role of tangible assets in easing financing constraints for Chinese firms. When liquidity in the financial market tightens, credit loans—based on weaker credit relationships—are typically the first to be reduced, whereas collateralized loans, secured by tangible assets, remain relatively stable. The persistence of collateralized lending, even under conditions of increasing financial decentralization, illustrates its robustness and highlights the continued importance of tangible assets in China’s evolving financial landscape.
In summary, this section confirms the crowding-out effect of financial decentralization on corporate loans; however, it only applies to credit.
Heterogeneity analysis
Fiscal decentralization
Research conducted by Qian and Roland (1998) underscores that fiscal decentralization significantly influences local economic development. The extent of fiscal decentralization mirrors a region’s demand for fiscal autonomy; regions with a robust desire for autonomy are more inclined to pursue higher levels of fiscal decentralization. We hypothesize that the influence of RCC reform on corporate risk is primarily attributable to local governments’ aspirations to enhance their financial authority via such reforms. So, whether the motivation for this financial control is driven by fiscal conditions is the question we need to address in this section.
We believe that in regions with a higher degree of fiscal decentralization, financial decentralization has a stronger exacerbating effect on corporate risk. According to Qian and Roland (1998), in regions with stronger fiscal decentralization, the government has a stronger motivation to develop the local economy. Under China’s tax-sharing system, the responsibilities and tax rights of local governments are not aligned, making it difficult for local governments to support the economic management tasks assigned by the central government with their own tax revenues. The greater the degree of local fiscal decentralization, the larger the funding gap required for economic development. Therefore, in regions with higher fiscal decentralization, local governments have a stronger motivation to control financial resources to cover fiscal deficits. The crowding-out effect of financial decentralization is stronger, causing more significant negative impacts on business operations.
To explore this hypothesis, we categorized our sample into two groups based on the degree of fiscal decentralization—strong and weak. We then investigated the role of local governments’ fiscal decentralization in modulating the impact of RCC reform on corporate risk. We employed two indicators of fiscal decentralization, calculated from the ratios of prefecture-level cities’ fiscal revenue to the national fiscal expenditure.
The regression outcomes, presented in Table 7, delineate these relationships. Columns (1), (3), (5), and (7) display results from the samples with strong fiscal expenditure decentralization, whereas columns (2), (4), (6), and (8) correspond to those with weak fiscal decentralization. It was observed that the phenomenon of financial decentralization intensifying corporate risk is significant solely in samples with strong fiscal expenditure decentralization and is inconsequential in weaker samples. This pattern persists even when fiscal revenue decentralization metrics are applied to subsamples. The findings in Table 7 reveal that RCC reform significantly elevates corporate risk exclusively in regions characterized by substantial fiscal decentralization, irrespective of whether the focus is on revenue or expenditure decentralization. This variance suggests that the impact of local RCC reform is contingent upon the fiscal environment. The differential impacts likely stem from the diverse objectives pursued by local governments in regions with disparate levels of fiscal decentralization. In jurisdictions with significant fiscal decentralization, local authorities seek to consolidate financial control as a strategy to amplify their fiscal authority. This nuanced understanding of the interplay between fiscal policies and corporate risk highlights the complex dynamics at play in regions undergoing financial and fiscal reforms.
In regions with weak fiscal decentralization—as shown in columns (2), (4), (6), and (8)—local governments have a much weaker incentive to assert control over financial resources, resulting in negligible impacts on corporate risk. This finding is particularly important and warrants further discussion. The analysis so far has demonstrated that the mechanism through which financial decentralization raises corporate risk is rooted in RCC reforms. These reforms have strengthened local governments’ capacity to access and control financial resources, which can crowd out private-sector financing, tighten financing constraints, and heighten operational risks for enterprises. By contrast, in regions with lower levels of fiscal decentralization, local governments have little motivation to capture financial resources for regional development. As a result, even with RCC reforms, there is no substantial shift in local control of financial resources, and corporate risk does not significantly increase. This is reflected in the insignificance of the coefficient estimate for the restructuring variable Rstru. These findings highlight that RCC reforms alone—particularly their institutional improvements—do not inherently impose negative effects on business operations. The critical factor lies in the appropriation of financial resources by local governments. It is this implicit financial decentralization that amplifies corporate risk. This nuanced result underscores the complex interaction between fiscal decentralization, financial reforms, and corporate stability, suggesting that fiscal and financial decentralization must be carefully coordinated and managed to prevent unintended adverse consequences for the regional economy.
Industrial policies
Industrial policies are critical instruments for steering local economic development and transitioning economic growth patterns, as highlighted by Alder et al. (2016). The strategic emphasis on specific industries is especially significant for the developmental agendas of local governments. In this vein, while RCC reform is interpreted as a form of covert financial decentralization, and our baseline regression indicates that such decentralization enhances corporate risk, it is hypothesized that this impact may be mitigated for enterprises operating within industries that are designated as key priorities by local governments. In this section, we will further explain the financial inequality caused by local government intervention by utilizing industry differences.
We believe that the negative impact of financial decentralization on corporate risk may be weaker in key industries. These key industries are not merely sectors of economic focus but are also areas where enterprises receive heightened surveillance and support from local authorities. This protective oversight implies that businesses within these priority sectors are less susceptible to the adverse effects typically associated with financial decentralization. The rationale behind this hypothesis is that local governments, by channeling resources and regulatory favor towards these industries, may effectively shield them from the broader destabilizing influences of financial decentralization. Consequently, the assumption is that while financial decentralization generally escalates corporate risk through mechanisms such as increased competition for financial resources or reduced financial stability, this effect is attenuated in key industries. These sectors likely benefit from more stable financial environments and supportive policies that counterbalance the risks associated with broader financial decentralization. This nuanced interaction suggests that the impact of financial decentralization on corporate risk is not uniform across all sectors but is contingent on the strategic economic priorities of local governments.
To test this hypothesis, we categorize the entire sample into key industry enterprise samples and non-key industry enterprise samplesFootnote 1. The regression results are reported in Table 8. Columns (1) and (3), which present the coefficients for enterprises in key industries, show negative but insignificant values, differing from the baseline regression. In contrast, columns (2) and (4), which report results for enterprises in non-key industries, yield coefficients consistent with the baseline regression. These findings indicate that the increase in corporate risk induced by financial decentralization is concentrated among non-key industry enterprises. This suggests that the risk effect of financial decentralization is selective, exerting limited influence on government-supported industries. The results also validate the effectiveness of using RCC reforms as a proxy for implicit financial decentralization. Enterprises in industries outside the scope of government priorities face tighter financing constraints and, consequently, heightened risks following RCC reforms. By contrast, firms in strategically important sectors remain insulated from such effects, as the government continues to ensure the flow of capital into these industries.
Firm ownership
In Section “Policy background,” we posited that reforms of RCC effectively represent an enhancement in local government control over financial institutions, which can be characterized as covert financial decentralization. This shift implies a deeper involvement of local governments in the financial sector, ostensibly to enhance local financial autonomy and control. In this section, we reveal the differences in the impact faced by enterprises with different ownership structures when dealing with financial decentralization. This analysis allows us to further emphasize the importance of government relations in determining the relationship between financial decentralization and corporate risk.
We believe that the risk effects of financial decentralization are weaker in SOEs. SOEs, which are owned by both central or local governments, typically enjoy a degree of implicit guarantees from these entities. These guarantees often manifest as financial support during downturns or preferential treatment in regulatory and policy frameworks, providing a buffer against market fluctuations and financial instabilities. Given this backdrop, if our conjecture holds—that RCC reform symbolizes an increase in local financial decentralization—then SOEs are likely insulated from the increased corporate risks typically associated with such decentralization. This insulation stems from the implicit guarantees that local governments provide, which serve to stabilize the financial and operational environments of SOEs. In essence, these guarantees act as a risk mitigant, shielding SOEs from the immediate impacts of market and financial pressures that might otherwise be exacerbated by shifts towards more localized financial control. Thus, in the context of RCC reforms leading to increased local financial decentralization, SOEs might not only avoid heightened corporate risk but could potentially leverage this reform to secure more favorable financial and operational conditions facilitated by local government support. Therefore, the relationship between financial decentralization and corporate risk in SOEs illustrates a nuanced dynamic where the typical risks associated with decentralization are counterbalanced by governmental guarantees and support. This dynamic underscore the complex interplay between government policy, financial decentralization, and corporate stability in the state sector.
We divide the entire sample into SOE and non-SOE categories and conduct separate regression analyses for each. The results are presented in Table 9. It is evident that, whether using \({Risk}1\) or \({Risk}2\) as the proxy variable, the regression results for non-SOE samples align with the baseline regression results. In contrast, the regression coefficients for SOE samples are all negative but not significant, marking a significant difference from non-SOE samples. This result validates the hypothesis that, for SOEs benefiting from implicit guarantees provided by local governments, financial decentralization does not affect corporate risk. However, it significantly increases the risk for non-SOEs without such implicit government support. Similarly, this outcome validates that RCC reforms can represent a form of implicit financial decentralization. RCC reforms are able to reflect the financial interventions of local governments, which in turn result in the redirection of funds towards enterprises that are the focal point of local government attention. This redirection leads to reduced financing for enterprises not prioritized by local governments, thereby increasing their risk exposure.
Firm characteristics
In this section, we reveal the differences in the impact on businesses with various operating conditions when facing financial decentralization. This analysis allows us to enrich our research conclusions, identify more businesses that need attention and support, and thereby formulate and refine detailed policy recommendations.
We believe that the risk effects of financial decentralization are weaker in larger enterprises and those with higher profit margins. In the mechanism analysis section, we delved into how financial decentralization influences corporate risk, particularly through the mechanism of crowding out enterprises’ access to bank loans. This crowding out effect results from local governments redirecting financial resources towards favored sectors or entities, thus limiting the availability of credit for other enterprises. The bargaining power of a company within the financial market is crucial in this context and is influenced by various firm characteristics, including company size, which is directly associated with financial constraints (Zheng et al., 2025). Companies that possess weaker bargaining power—typically smaller enterprises—are more likely to be adversely impacted under conditions where credit resources are constrained. These companies find it increasingly difficult to secure necessary financing, thereby heightening their risk exposure in scenarios of financial decentralization. Conversely, large enterprises often serve as pillars of local economic development and tend to maintain close relationships with government entities. This proximity to power not only provides them with better access to financial resources but also often ensures their prioritization in government policies. Previous discussions have highlighted that local governments frequently channel financial resources towards supported industries and SOEs. In a similar vein, large enterprises, which play a significant role in local economic stability and growth, often receive favorable treatment from local governments. As a result of these dynamics, the negative impacts of financial decentralization on corporate risk are substantially mitigated for large enterprises. This mitigation arises because these enterprises, due to their size and strategic importance, are less likely to face the brunt of financial resource scarcity. Instead, they benefit from continued support and preferential access to credit, facilitated by their symbiotic relationships with local governments. This differential impact underscores the importance of enterprise size and government relationships in moderating the effects of financial decentralization on corporate risk.
This study employs two indicators, firm size and firm profit margin, to represent firm characteristics and investigates the differential impact of financial decentralization on corporate risk. The regression results are presented in Table 10.
We divide the sample into subsamples based on firm size, measured by total assets. The results of using different size samples are presented in columns (1–4). It is evident that financial decentralization only increases risk for small-scale companies. We believe that the observed outcomes are grounded in practical reasons—listed large enterprises possess a strong local influence, making significant contributions to taxation and employment, which crucially impact local government operations. Consequently, their relationships with the government are notably closer. Driven by performance metrics, local governments may relax financial constraints on these enterprises. Therefore, even with financial decentralization, where local governments possess enhanced influence in financial markets, large listed enterprises face less financial restriction, thus their risk exposure remains minimal. In contrast, small enterprises do not maintain as close relationships with the government as their larger counterparts, leading to more significant financial constraints post-financial decentralization, which in turn increases their risk levels.
This study also categorizes the sample based on enterprise profit margin. Companies with higher profit margins generally have better future repayment capabilities, leading to lower credit constraints. Consistent with the company size grouping, financial decentralization only increases risk for companies with low profit margins. Profit margins reflect a company’s future cash flows, and enterprises with strong profitability levels are less impacted by financial decentralization, primarily for two reasons. First, financial institutions prioritize loan distribution to businesses with robust future repayment capacities. Consequently, enterprises with higher profitability experience less financing constraints compared to those with lower profitability, making them less susceptible to the effects of financial decentralization. Second, companies with higher cash flows have a stronger capacity to cope with adverse shocks. Even in the presence of financing constraints, these companies are unlikely to face significant risks. Therefore, enterprises with high profit levels are minimally affected by financial decentralization.
In summary, this section conducts a heterogeneity analysis and establishes that non-state-owned enterprises, non-priority industries, regions with high fiscal decentralization, low-profitability firms, and small enterprises are more constrained by financial decentralization. This indicates that financial decentralization reduces the efficiency of capital allocation, leading to more capital flowing toward government-preferred sectors, resulting in financial inequality and heightened financial vulnerability.