Corporate Financialization and Agricultural Supply Chain Resilience: Evidence from Agricultural Listed Companies

Abstract

Against the backdrop of heightened global economic uncertainty and increasingly frequent risks in agricultural supply chains, enhancing agricultural supply chain resilience has become a critical issue for safeguarding national food security and promoting high-quality agricultural development. As key actors within agricultural supply chains, the impact of financialization—defined as the shift of resources to non-core financial assets—among agricultural listed firms on supply chain resilience warrants systematic examination. Using panel data from 165 Chinese agricultural listed firms (2010–2022), this study empirically investigates the impact of corporate financialization on agricultural supply chain resilience and its underlying mechanisms. An entropy-weighted composite index based on 16 parameters is used to assess agricultural supply chain resilience. It is composed of three dimensions: resistance capability, recovery capacity, and renewal capacity. The results show that: Financialization significantly undermines supply chain resilience, with the most substantial negative effect on recovery capacity, followed by renewal capacity, and the weakest on resistance capacity. Heterogeneity analyses show more pronounced negative effects among non-state-owned enterprises, non-primary sector firms, and capital-intensive enterprises. Financing constraints and capital expenditures partially mediate the negative relationship between financialization and resilience, while profitability persistence exacerbates the crowding-out effect. These findings suggest that policymakers should strike a compromise between reducing excessive financialization and strengthening agricultural supply chains. While prudently guiding agricultural firms’ financial asset allocation, greater emphasis should be placed on developing a diverse and coordinated industrial support system, thereby diverting financial capital away from crowding out core operations and toward effectively serving the real economy, ultimately contributing to national food security and agricultural modernization.

1. Introduction

Corporate financialization—the tendency of non-financial firms to channel funds into financial assets rather than their core productive activities [1]—has become a defining feature of the global economy in recent years. As financial markets have expanded and returns in the real economy have come under pressure, firms across countries and industries have increasingly turned to financial investments to hedge risks or enhance short-term earnings. Whether this shift ultimately weakens firms’ long-term productive capacity—and, by extension, the stability and resilience of supply chains—remains a deeply contested issue.

The issue is especially consequential in agri-food supply chains. Agriculture and food production are marked by long investment horizons, high exposure to uncertainty, and a strong dependence on continuity and reliability, making them particularly sensitive to changes in how firms allocate resources [2]. At the same time, as agricultural companies become more closely integrated into capital markets, their financialization may ripple through supply chains—crowding out real investment [3], reshaping risk incentives, and altering relationships with upstream and downstream partners. These spillovers can ultimately determine how well an entire supply chain withstands external shocks. Viewed from an industrial chain perspective, the impact of corporate financialization on agricultural supply chain resilience therefore carries both theoretical importance and clear real-world relevance.

China provides a revealing setting in which to examine these dynamics. In recent years, amid slower economic growth and tightening profit margins in the real economy, China’s listed agricultural firms have displayed a pronounced shift toward financial assets. According to recent data, the average financial assets of Chinese agricultural listed firms surged from RMB 180 million in 2010 to RMB 1.02 billion in 2022, reflecting an average annual growth rate of 15.6%. In some leading firms, financial assets account for over 50% of total assets, indicating a pronounced trend of “de-agriculturalization.” As pivotal players within agricultural supply chains, these firms’ capital allocation decisions influence not only their own operational performance but may also transmit financialization effects to upstream and downstream segments, thereby amplifying systemic risks across the entire chain [4].

This trend unfolds at a time when agricultural supply chains worldwide are under growing strain from climate change, natural disasters, public-health emergencies, geopolitical tensions, and trade frictions. Strengthening the resilience of agricultural supply chains has become a critical issue for the international community. Against this backdrop, a systematic assessment of how corporate financialization affects the resilience of agricultural supply chains—and through what mechanisms—can sharpen understanding of the broader economic consequences of financialization. It can also offer practical insights for policymakers seeking to limit the excessive diversion of capital away from the real economy while bolstering the robustness of agricultural and food systems.

Existing studies have primarily explored the underlying motivations and economic consequences of corporate financialization. Regarding its driving forces, research can be broadly categorized into two levels. At the macro-level, scholars have identified several institutional and environmental determinants that directly influence firms’ financialization behaviors. These include policy tools [5,6,7], policy uncertainty [8,9], government regulation [10], and climate risk [11]. At the micro-level, attention has been paid to firm-specific and managerial characteristics. Studies have found that executive traits [12,13], digital transformation [14,15], customer concentration [16], equity-based incentives [17], and corporate social responsibility [18] significantly affect the degree of financialization within firms.

Another strand of the literature focuses on the economic consequences of financialization. Scholars have examined its impact on income inequality [19], labor market [20], profit growth [21], technological innovation [22,23], production efficiency [24], investment efficiency [25,26], ESG performance [27], and firm valuation [28]. Two dominant theoretical perspectives have emerged. The first is the “crowding-out effect,” which suggests that financialization diverts resources away from core business activities. In this view, financial and industrial investments are substitutes. When returns on financial assets significantly exceed those in the real sector, profit-maximizing managers are incentivized to reduce investments in equipment upgrades, research and development (R&D), and other productive activities. This substitution can ultimately lead to industrial hollowing and a shift away from real economic value creation [3]. The second is the “reservoir effect,” which views financialization as a strategic liquidity management tool. Firms engage in financial activities to mobilize idle capital, enhance asset liquidity, and mitigate financing constraints [29]. Especially under conditions of high market uncertainty, financialization may serve to stabilize core operations and support long-term industrial development.

Overall, the existing literature provides valuable insights into the nature and economic consequences of corporate financialization. However, several important research gaps remain. First, few studies have systematically examined the impact of corporate financialization on the agricultural supply chain, leaving theoretical uncertainty regarding whether financialization induces a crowding-out effect or a reservoir effect in this context. Second, most assessments of supply chain resilience are conducted at the macro-level (e.g., provincial or national), with limited attention to the resilience of firms as micro-level actors. Yet, firm-level resilience is a critical component that reflects the micro-foundations of supply chain robustness. Third, current research on corporate financialization is predominantly focused on listed firms at the national level, often lacking industry-specific investigations. However, industry heterogeneity may significantly alter the outcomes and implications of financialization.

In response to these gaps, this study employs panel data from 165 agricultural listed firms in China over the period 2010–2022 to investigate the impact and mechanisms of corporate financialization on agricultural supply chain resilience. This study makes several marginal contributions. First, departing from existing literature that predominantly addresses supply chain resilience from a macro-level or a single-dimensional viewpoint, this study introduces a firm-level micro-perspective by defining agricultural supply chain resilience as an integrated construct encompassing resistance, recovery, and renewal capacities, and develop corresponding measurement indicators accordingly. This provides a new approach for the quantitative evaluation of agricultural supply chain resilience. Second, methodologically, this paper employs a fixed-effects model to systematically identify the impact of corporate financialization on supply chain resilience among agricultural listed firms, and further examines heterogeneous effects based on ownership nature, industry attributes, and factor intensity. This advances knowledge of the context-dependent economic effects of corporate financialization. Third, in terms of mechanisms, this paper investigates the transmission pathways through which corporate financialization affects agricultural supply chain resilience, focusing on financing constraints and capital expenditure, and examines the moderating role of business performance persistence. By constructing a relatively complete framework of the underlying mechanisms, it provides empirical evidence for exploring pathways to strengthening, extending, and supplementing the agricultural supply chain from a micro-level perspective.

The subsequent sections of this study are organized as follows: Section 2 presents the theoretical analysis and research hypotheses; Section 3 introduces the research design, including the sample, variables, and model; Section 4 conducts the empirical analysis, covering baseline regressions, robustness tests, heterogeneity analysis, and further discussion; Section 5 summarizes the research conclusions, proposes policy implications, and highlights limitations and future directions.

2. Theoretical Analysis and Research Hypothesis

2.1. Corporate Financialization and Agricultural Supply Chain Resilience

Corporate financialization refers to the practice of non-financial firms allocating capital to financial assets with relatively high levels of virtuality [1]. From the perspective of motivation, financialization can be driven by speculative arbitrage or precautionary saving [30], and the resulting financial asset allocation can have contrasting effects on the resilience of agricultural supply chains.

On the one hand, when agricultural listed firms pursue financialization for strategic and precautionary purposes, it can create a “reservoir effect,” whereby financial asset allocation enhances the supportive role of financial services in real economic activities. Compared to physical assets, financial assets offer advantages in terms of liquidity and adjustability. According to the precautionary savings theory, short-term financial holdings strengthen corporate liquidity buffers, reducing dependence on external financing. Concurrently, financial returns serve as an alternative funding source that enhances capital acquisition efficiency, thereby providing robust financial backing for technological innovation and ultimately reinforcing core business development and supply chain resilience [31]. Moreover, the flexible use of financial derivatives enables firms to hedge against uncertainties in production and operations [32,33]—particularly risks arising from fluctuations in upstream raw material prices and exchange rate volatility in the agricultural supply chain. Such financial assets serve as practical tools for hedging, transferring, and diversifying risks, thereby strengthening the supply chain’s shock-absorption capacity and fostering stable operations. Ultimately, the portfolio diversification effect of financialization serves as an effective mechanism to smooth earnings volatility during cyclical industry contractions. Such risk-hedging functionality not only diminishes systemic exposure but also reinforces corporate sustainability and promotes supply chain resilience [29]. When leading firms act as “reservoirs” by holding high-quality financial assets, they can ease financing constraints, enhance their own risk-bearing capacity, and ultimately improve the overall resilience of the agricultural supply chain.

On the other hand, financialization driven by speculative motives may undermine agricultural supply chain resilience through “crowding-out effects” [34]. According to the resource constraint theory, firms face hard budget constraints when allocating resources between financial investments and real operations, with excessive financial asset allocation directly crowding out capital earmarked for agricultural technological innovation and capacity upgrading [29]. This resource misallocation manifests primarily as an “attention dilution effect”—where management’s preference for high returns from financial speculation creates a behavioral finance phenomenon of “myopic bias” [15], leading firms to reinvest short-term financial gains into the virtual economy rather than core R&D, thereby forming a vicious cycle of “real-to-virtual” deviation. Furthermore, when financial returns persistently exceed agricultural ROE, this accelerates strategic drift away from core operations, resulting in declining R&D investment and delayed equipment renewal—classic symptoms of innovation deficiency [35]. Most critically, such speculative financialization includes systemic risks from financial markets with cyclical risks inherent to agricultural operations. Through industrial linkages, this creates risk multiplier effects that significantly impair the supply chain’s capacity to recover from external shocks, ultimately jeopardizing the stability of the entire industrial chain.

H1a: 

Corporate financialization exerts a crowding-out effect on agricultural supply chain resilience.

H1b: 

Corporate financialization generates a reservoir effect on agricultural supply chain resilience.

2.2. Mechanism of Action

Financing constraints pose a significant challenge to firm development, particularly for core firms, known as “chain leaders,” in agricultural supply chains under external shocks such as disruptions or systemic risks. In such contexts, rebuilding broken links often intensifies reliance on external financing. However, information asymmetry and agency problems remain the root causes of financing difficulties. Therefore, the way corporate financialization affects the financing conditions of businesses determines how it affects supply chain resilience.

On the one hand, agricultural listed firms that hold high-quality financial assets can enhance capital liquidity and generate a “reservoir effect,” thereby alleviating financing constraints. Financial assets that exhibit significant marketability, minimal adjustment costs, ease of trading, and high liquidity [24], facilitate intertemporal resource allocation, improve payment efficiency, and optimize capital structures. These advantages enhance internal financing capacity [36]. In addition, firms strategically hold financial assets as a precautionary measure to buffer against potential liquidity shortages and external uncertainties. Returns from financial investments can also enhance short-term financial performance, reduce dependence on external borrowing, and provide resources for core operations and innovation activities—ultimately reinforcing supply chain resilience.

On the other hand, according to agency theory, management may shift resources from real investment to financial speculative activities due to short-term performance motives or risk preferences, thereby intensifying agency conflicts. Moreover, excessive investment in high-risk, high-return financial markets may lead to regulatory restrictions and tightened credit access. For instance, the China Securities Regulatory Commission (CSRC) prohibits refinancing for non-financial firms holding large, long-term financial assets. Meanwhile, banks and other financial institutions tend to restrict lending to agricultural enterprises that not only operate in high-risk sectors but also engage heavily in speculative financial investments. This dual risk perception weakens creditworthiness and limits access to funding, especially for long-term innovation activities that require sustained capital input. As a result, financialization may exacerbate financing difficulties and undermine resilience by crowding out investment in core operations. Accordingly, the following hypothesis is proposed:

H2: 

Corporate financialization affects agricultural supply chain resilience through its impact on financing constraints.

Capital expenditure serves as a fundamental pillar for sustainable corporate growth, playing a pivotal role in enhancing core competitiveness and maintaining stability in the industrial chain for agricultural enterprises. Strategic capital investments by “chain leader” firms in critical segments are particularly crucial for mitigating supply chain disruption risks. Corporate financialization exerts indirect influence on agricultural supply chain resilience through its impact on both the scale and allocation of capital expenditures.

The existing literature reveals a dual-path mechanism through which financialization affects capital expenditures. On one hand, moderate financialization may facilitate capital expenditure via a “reservoir effect.” As demonstrated by Kliman and Williams [37], reasonable financialization not only preserves real investment efficiency but also provides essential funding for critical capital expenditures. Grounded in precautionary savings theory, financial asset allocation enables firms to broaden financing channels and alleviate external constraints [38], while the liquidity advantage of financial assets creates a monetary buffer that safeguards long-term operational funding through hedging mechanisms. Furthermore, financial investment returns can supplement internal cash flows to support real capital expenditures [39], thereby contributing to the stability of the industrial chain.

On the other hand, excessive financialization may reduce capital expenditures through a “crowding-out effect.” According to the capital arbitrage theory, firms tend to favor short-term, high-return financial investments. The yield differential between financial and industrial assets may drive enterprises to “substitute virtual for real” investments under resource constraints [40], thereby undermining equipment upgrades, technology adoption, and market responsiveness—ultimately impairing industrial chain stability and operational efficiency. Concurrently, the high profitability of financial sectors may exacerbate managerial myopia, leading to an excessive focus on short-term gains at the expense of long-term capital investments crucial for sustainable development [41]. Such misallocation of capital expenditure priorities resulting from excessive financialization significantly elevates the risk of industrial chain disruptions. Based on this theoretical framework, the following is proposed:

H3: 

Corporate financialization influences agricultural supply chain resilience through its impact on capital expenditure.

The capacity for performance growth, representing the evolutionary path of primary business operations, stands as a fundamental measure of dynamic corporate economic performance [42]. For agricultural listed companies, sustained growth in primary business revenues not only guides financialization decisions but also plays a pivotal role in supporting the modernization of agriculture and ensuring supply chain stability. Given substantial heterogeneity in development stages, industry attributes, production conditions, and local policy environments, firms exhibit considerable variation in the continuity of performance, which in turn affects the nuanced relationship between corporate financialization and agricultural supply chain resilience [40].

On the one hand, strong and stable business performance can alleviate external financing constraints, particularly those imposed by the banking sector. One of the primary motivations for financialization among agricultural firms is the need to diversify financing channels and reduce dependency on external funds. When firms demonstrate consistent earnings growth, they are more likely to receive favorable credit support from banks, diminishing their reliance on financial assets. This shift facilitates greater capital flow into core operations, thereby enhancing supply chain resilience.

On the other hand, profitability persistent may also trigger speculative behavior. Due to the natural limitations of agricultural expansion—such as land constraints and diminishing returns to scale—firms that maintain profits but face barriers to further industrial growth may redirect excess capital toward high-return financial investments. While such actions may enhance short-term financial metrics, they risk diverting resources from core agricultural activities, potentially hollowing out the real economy and undermining the long-term resilience of the agricultural supply chain. Based on this analysis, the following hypothesis is proposed:

H4: 

Profitability persistence plays a moderating role in the relationship between corporate financialization and agricultural supply chain resilience.

3. Study Design

3.1. Sample Selection

This study identifies agricultural listed firms based on the latest industry classification standards issued by the China Securities Regulatory Commission, supplemented by an analysis of firms’ primary business activities using the WIND and Choice databases. An initial sample of 192 agricultural firms was selected. After excluding companies with significant financial risk (ST, *ST) and those with missing data for three consecutive years, the final panel dataset comprises 165 agricultural listed firms spanning the period from 2010 to 2022. Missing values were addressed using mean imputation and linear interpolation methods. To mitigate the impact of outliers and enhance the robustness of the analysis, continuous variables underwent a 1% winsorization process. Financial data were obtained from the China Stock Market & Accounting Research Database (CSMAR), while patent application data were sourced from the China National Research Data Services Platform (CNRDS).

3.2. Definition of Variables

3.2.1. Dependent Variable

Agricultural supply chain resilience (ASCR). ASCR refers to a firm’s ability to maintain the stable functioning of the supply chain through internal organizational mechanisms when faced with external adverse shocks [43]. Such resilience enables firms to quickly adapt and recover from disruptions, thereby supporting the sustained development of the supply chain. Some scholars define supply chain resilience in two dimensions: resistance and recovery [44,45], with a focus on a system’s ability to continue operations during disruptions and restore functionality afterward. This perspective is expanded upon by other researchers, who contend that industrial chain resilience includes proactive capabilities for structural adaptation and capability upgrading in uncertain situations, in addition to reactive capacities to endure shocks. Consequently, resilience may be more comprehensively characterized through a multidimensional framework that includes absorption capacity, adaptive capacity, and renewal capacity [46]. Building on this foundation and synthesizing prior work, this study defines the supply chain resilience of agricultural listed firms across three key dimensions—resistance, recovery, and renewal—operationalized through a set of 16 indicators. Resistance capacity (RC1) captures a firm’s ability to maintain operational stability in the face of external shocks. It is measured through three components: Production Efficiency, which captures operational efficiency and productivity performance [47]; Profitability, which reflects firms’ earning capacity and cost control [45]; and Fundamentals, which represent the stability of firms’ asset structure and financial foundations. Recovery capacity (RC2) reflects the firm’s dynamic adaptability to current uncertainties. This dimension is characterized by two aspects: Internal Factors, which include firms’ financial flexibility and operational resilience [45], and External Support, which captures the level of support received from government policies and financial institutions [48]. Renewal capacity (RC3) represents its innovation and transformation capability. It is measured through Innovation Input, representing investments in R&D resources, and Innovation Output, capturing the tangible results of innovative activities, such as patents or new product introductions [49]. Details of the index construction are presented in Table 1.

Table 1. ASCR Evaluation Index System.

First-Level Indicators	Second-Level Indicators	Third-Level Indicators	Description of Indicators	Attribute
Resistance Capacity (RC1)	Production Efficiency	Total Factor Productivity	calculated using the OP method	Positive
	Profitability	Cost-to-Revenue Ratio	(Operating Cost/Revenue) × 100 (yuan)	Negative
		Return on assets	Total Profit/Average Total Assets (%)	Positive
		Operating Margin	Operating Profit/Revenue (%)	Positive
	Fundamentals	Fixed Assets	Net Fixed Assets Amount (yuan)	Positive
		Equity Multiplier	Total Assets/Shareholders’ Equity (%)	Negative
Recovery Capacity (RC2)	Internal Factors	Debt-to-Asset Ratio	Total Liabilities/Total Assets (%)	Positive
		Quick Ratio	(Cash + Receivables)/Current Liabilities (%)	Negative
		Operating Cash Flow	Net Cash Flow from Operating Activities (yuan)	Positive
		Asset Turnover	Revenue/Average Total Assets (%)	Positive
		Net Profit	After-tax Profit (yuan)	Positive
	External Support	Government Subsidies	Subsidies from “Other Income” and “Non-operating Income” accounts (yuan)	Positive
		Financing Scale	Annual Loan Amount (yuan)	Positive
Renewal Capacity (RC3)	Innovation Input	R&D Expenditure	R&D Investment Amount (yuan)	Positive
		R&D Personnel Ratio	R&D Staff/Total Employees (%)	Positive
	Innovation output	Number of patent applications	Number of independent and joint patent applications (items)	Positive

3.2.2. Core Independent Variable

Corporate Financialization (Fin): Existing literature typically measures corporate financialization from two perspectives: investment behavior and profit generation channels. The investment behavior perspective views financialization as a resource allocation strategy, focusing on balance sheet components, while the profit channel perspective emphasizes the sources of earnings—particularly the distinction between operating profits and returns from financial investments. Given this study’s focus on the impact of financialization on supply chain resilience and the “de-agriculturalization” trend, the investment behavior perspective is adopted to measure financialization. Specifically, financialization is defined as the ratio of financial assets to total assets [22,29]. A higher ratio indicates a higher degree of financialization. Financial assets include interest receivables, dividend receivables, trading financial assets, derivative financial instruments, held-to-maturity investments, available-for-sale financial assets, long-term equity investments, and investment real estate. Total assets are measured using the book value reported in the firm’s consolidated balance sheet.

3.2.3. Control Variables

Beyond financialization, firm organizational structure and governance mechanisms may also significantly affect the resilience of agricultural supply chains. Drawing on prior literature [22], this study incorporates the following eight control variables: (1) Firm Size (Size): Measured as the natural logarithm of total assets. Larger firms typically have a stronger resource base and greater capacity to withstand external shocks. (2) Firm Age (Age): Calculated as the natural logarithm of (1 + the number of years since establishment). Older firms tend to have accumulated more experience and resilience. (3) Asset Growth Rate (Asset): Measured by the annual growth rate of total assets. This reflects the firm’s ability to accumulate capital and adjust its business strategy. (4) Equity Structure (Top5): Reflects corporate governance structure. Defined as the shareholding ratio of the top five shareholders. (5) Board Size (Board): Measured by the total number of board members, reflecting the scale of corporate governance and potential decision-making efficiency. (6) Industrial-Financial Integration (FinInst): A binary variable equal to 1 if the firm holds equity in financial institutions, and 0 otherwise. This captures the extent of integration between industrial and financial capital. (7) Enterprise Value (TobinQ): Calculated as the ratio of the firm’s market value to the replacement cost of its assets. A higher Tobin’s Q indicates stronger speculative incentives and capital reallocation tendencies. (8) Profitability Status (Loss): A dummy variable equal to 1 if the firm reports a net loss, and 0 otherwise.

3.2.4. Mechanism Variables

Based on the theoretical framework, this study introduces three mediating mechanisms: financing constraints, capital expenditure, and performance persistence. (1) Financing Constraints (SA): Measured using the SA index [50], where a greater absolute value indicates more severe external financing constraints faced by the firm. (2) Capital Expenditure (Capital): Defined as cash outflows for the acquisition of fixed assets, intangible assets, and other long-term assets [51]. This variable reflects capital investment in core operations. (3) Profitability Persistence (Growth): Measured by the growth rate of operating revenue, serving as a proxy for the firm’s long-term profitability [52]. Higher values indicate stronger and more sustained profit-generating capacity within the supply chain. Table 2 and Table 3 present the definitions and descriptive statistics of all variables.

Table 2. Variable Definitions.

Category	Variable	Symbols	Variable Definition
Dependent variable	Agricultural supply chain resilience	ASCR	Calculated through the entropy weighting method
independent variable	Corporate Financialization	Fin	financial assets/total assets
Control variables	Firm Size	Size	Natural logarithm of total assets
	Firm Age	Age	Natural logarithm of (firm age + 1)
	Asset Growth Rate	Asset	Annual growth rate of total assets = (Total assetst/Total assetst−1) − 1
	Equity Structure	Top5	Ownership concentration: Shareholding ratio of the top five shareholders
	Board Size	Board	Board size: Total number of directors on the board
	Industrial-Financial Integration	FinInst	Financial-industrial integration: 1 if the firm holds equity in financial institutions; otherwise 0
	Enterprise Value	TobinQ	Tobin’s Q = (Market value of tradable shares + Non-tradable shares × Net asset per share + Book value of liabilities)/Total assets
	Profitability Status	Loss	Loss indicator: 1 if net profit is negative in the current year; otherwise 0
Mechanism variables	Financing Constraints	SA	SA index = −0.737 × Size + 0.043 × Size2 − 0.040 × Age
	Capital Expenditure	Capital	Capital expenditures: Cash paid for the acquisition of fixed assets, intangible assets, and other long-term assets
	Profitability Persistence	Growth	Revenue growth = (Operating revenuet/Operating revenuet−1) − 1

Table 3. Descriptive Statistical Analysis.

Variable	Obs	Mean	SD	Min	Max
ASCR	1604	0.0785	0.0602	0.0198	0.6857
Fin	1604	0.0641	0.0883	0.0000	0.5961
Size	1604	22.1488	1.0603	19.4463	25.7017
Age	1604	2.9336	0.3200	1.3863	3.6636
Asset	1604	0.1513	0.2667	−0.3989	3.4158
Top5	1604	52.4869	14.6187	15.4147	85.1802
Board	1604	2.0990	0.2036	1.6094	2.7081
FinInst	1604	0.0380	0.1913	0.0000	1.0000
TobinQ	1604	2.1078	1.1667	0.8332	11.4238
Loss	1604	0.1253	0.3312	0.0000	1.0000
SA	1604	−3.8544	0.2311	−4.6630	−3.1463
Capital	1604	0.0704	0.0562	0.0005	0.3757
Growth	1604	0.1643	0.4862	−0.8817	11.8426

3.3. Model Construction

To examine the impact of financialization in agricultural listed firms on the resilience of the agricultural supply chain, the following baseline model is constructed:

$${ASCR}_{it}={\alpha }_0+{\alpha }_1{Fin}_{it}+{\sum }_{j=2}^9{{\alpha }_{j}X}_{it}+{\mu }_i+{\lambda }_t+{ϵ}_{it}$$

(1)

where ${ASCR}_{it}$ represents the supply chain resilience of firm i in year t, while ${Fin}_{it}$ captures the degree of financialization of agricultural listed firms in the same year. $X_{it}$ represents a collection of control variables, μ_i signifies individual fixed effects, and λ_t indicates time fixed effects.

3.3.1. Mediation Effect

Following Du and Zhang [53], this study employs a mediation model to examine whether financing constraints and capital expenditures serve as a mediating mechanism in the relationship between corporate financialization and agricultural supply chain resilience.

$$M_{it}={\beta }_0+{\beta }_1{Fin}_{it}+{\sum }_{j=2}^9{{\beta }_{j}X}_{it}+{\mu }_i+{\lambda }_t+{ϵ}_{it}$$

(2)

$${ASCR}_{it}={\gamma }_0+{\gamma }_1{Fin}_{it}+{\gamma }_2{M}_{it}+{\sum }_{j=3}^{10}{{\gamma }_{j}X}_{it}+{\mu }_i+{\lambda }_t+{ϵ}_{it}$$

(3)

Here, M denotes the mediating variables: financing constraints (SA) and capital expenditures (Capital). Equation (3) incorporates these mediators into the baseline regression model. If both coefficients β₁ and γ₂ are statistically significant, it indicates a partial mediation effect, and the size of the mediation effect is given by the product β₁ × γ₂. If γ₂ is significant while γ₁ becomes insignificant upon inclusion of the mediator, it implies a full mediation effect [54].

3.3.2. Moderating Effect

Building on Tu et al. [52], this study constructs the following model to examine the moderating effect of profitability persistence (Growth) on the relationship between corporate financialization and agricultural supply chain resilience:

$$\begin{gathered} {ASCR}_{it}={\delta }_0+{\delta }_1{Fin}_{it}+{{\delta }_1{Fin}_{it}\times {Growth}_{it}+\delta }_3{Growth}_{it}+ \\ {\sum }_{j=4}^{11}{{\delta }_{j}X}_{it}+{\mu }_i+{\lambda }_t+{ϵ}_{it} \end{gathered}$$

(4)

In this equation, Growth represents the persistence of a firm’s operating performance, reflecting how profitability persistence moderates the impact of corporate financialization on supply chain resilience.

4. Empirical Analysis

4.1. Baseline Regression

All variables were subjected to a Variance Inflation Factor (VIF) test in order to reduce any estimation bias that can result from multicollinearity. The findings show that all VIF values are below 2, with a mean value of 1.08, well below the conventional threshold of 10, indicating that multicollinearity is not a concern. Subsequently, the F-test, Lagrange Multiplier (LM) test, and Hausman test were employed to compare pooled OLS, fixed effects, and random effects models to identify the optimal specification (Table 4). The F-test rejects the null hypothesis of no individual heterogeneity at the 1% significance level, supporting the use of the fixed effects model over the pooled OLS model. Similarly, the LM test rejects the null hypothesis of no random effects at the 1% level, favoring the random effects model over the pooled OLS model. Finally, after confirming the presence of heteroskedasticity, a robust Hausman test was conducted, which rejects the null hypothesis at the 5% significance level. Taken together, these results support the fixed effects model as the most appropriate specification.

Table 4. Results of F-test, LM-test, and Hausman Test.

Test Type	Statistic	p-Value	Model Evaluation Conclusion
F-test	10.710	0.0000	Fixed effects model preferred over pooled OLS
LM test	1778.250	0.0000	Random effects model preferred over pooled OLS
Hausman test	21.013	0.0126	Fixed effects model preferred over random effects

Table 5 presents the estimation results of corporate financialization (Fin) on agricultural supply chain resilience (ASCR) under different model specifications. In Model 1, which includes only the core explanatory variable and omits controls and fixed effects, the coefficient on Fin is negative but statistically insignificant. As control variables and firm and year fixed effects are progressively introduced in Models 2–4, the coefficient of Fin becomes significantly negative at the 1% level. This suggests that after adequately controlling for observable firm characteristics and unobserved heterogeneity, corporate financialization exerts a significant crowding-out effect on supply chain resilience, thus supporting Hypothesis 1a. Furthermore, the adjusted R-squared values show a notable increase with the addition of controls and fixed effects across specifications, reflecting gains in explanatory power. The joint significance of all extended models underscores the robustness of our baseline results.

Table 5. Impact of Corporate Financialization on ASCR.

Variable	(1)	(2)	(3)	(4)
	ASCR	ASCR	ASCR	ASCR
fin	−0.0207	−0.0805 ***	−0.0551 ***	−0.0620 ***
	(0.0160)	(0.0150)	(0.0137)	(0.0138)
Size			0.0437 ***	0.0424 ***
			(0.0024)	(0.0024)
Age			−0.0012	−0.0220
			(0.0064)	(0.0141)
Asset			−0.0111 ***	−0.0104 ***
			(0.0033)	(0.0034)
Top5			0.0004 ***	0.0004 ***
			(0.0001)	(0.0001)
Board			0.0052	0.0079
			(0.0071)	(0.0072)
FinInst			−0.0207 ***	−0.0200 ***
			(0.0050)	(0.0051)
TobinQ			0.0028 ***	0.0032 ***
			(0.0009)	(0.0010)
Loss			−0.0052	−0.0055 **
			(0.0027)	(0.0027)
Constant	0.0798 ***	0.0564 ***	−0.9161 ***	−0.8418 ***
	(0.0014)	(0.0044)	(0.0456)	(0.0593)
Year FE	No	Yes	No	Yes
Firm FE	No	Yes	No	Yes
Observations	1604	1604	1604	1604
R-squared	0.0012	0.1896	0.3370	0.3495
Number of id	165	165	165	165

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

Unlike previous research that emphasizes the positive or resilience-enhancing effects of corporate financialization [55], our results show that financialization significantly undermines the resilience of agricultural supply chains. This contrast reveals strong sectoral heterogeneity in the economic outcomes of financialization and implies that insights derived from manufacturing or high-tech industries may not directly extend to agriculture. This finding may be attributed to a structural shift from core agricultural operations toward financial investment, leading to reduced capital allocation to real-sector activities. Such diversion diminishes a firm’s capacity to sustain and expand its operational activities. Additionally, excessive financial asset holdings increase firms’ exposure to market volatility, exacerbating operational uncertainty. In recent years, global commodity prices have experienced significant fluctuations due to geopolitical tensions and prolonged disruptions caused by the COVID-19 pandemic. Given China’s heavy reliance on imported agricultural inputs such as feedstock, this further compromises upstream chain stability. Lastly, compared to firms in the manufacturing sector, agricultural “chain leader” firms tend to lack the financial sophistication and diversification capacity necessary to effectively leverage financial capital, which remains a key constraint on resilience enhancement.

The results in Column (3) reveal that firm size (Size), ownership concentration (Top5), and investment opportunities (TobinQ) all exert significant positive effects on agricultural supply chain resilience. Larger firms benefit from economies of scale, enabling better resource integration and allocation, which in turn enhances the overall efficiency and resilience of the supply chain. Higher ownership concentration improves governance efficiency, facilitates faster decision-making, and strengthens a firm’s ability to respond and adapt to external disruptions. Moreover, a higher Tobin’s Q denotes market optimism about a firm’s future growth prospects. Such optimism helps attract vital resources (e.g., investment, talent) to the agricultural supply chain, which in turn strengthens its resilience.

Conversely, the negative impact of asset growth (Asset) may stem from short-termism, whereby firms prioritize immediate asset expansion at the expense of innovation and technological upgrading, undermining long-term adaptability. The negative relationship between industrial-financial integration (FinInst) and resilience highlights a “financialization effect,” where excessive capital allocation to financial markets erodes resources available for core operations, thus weakening internal coordination and adaptive capacity across the supply chain. Lastly, the negative coefficient of the loss indicator (Loss) suggests that firms in financial distress are more likely to prioritize short-term liquidity and loss mitigation, diverting managerial attention away from building strategic resilience and operational stability.

4.2. Robustness Tests

4.2.1. Instrumental Variable Approach

To address potential endogeneity arising from reverse causality between corporate financialization and agricultural supply chain resilience, this study employs an instrumental variable (IV) approach. Following Shah et al. [56], the lag of the financialization variable (L.fin) is used as the instrument, and a two-stage least squares (2SLS) estimation is applied.

As shown in Column (1) of Table 6, the coefficient of L.fin in the first stage is 0.5442 and is statistically significant at the 1% level, indicating strong explanatory power. In Column (2), the coefficient on financialization remains significantly negative at the 1% level, suggesting that after addressing endogeneity, financialization of agricultural listed firms continues to undermine supply chain resilience. Compared to the baseline regression results, except for the loss of significance in the Top5 variable, the coefficient signs and significance levels of the remaining control variables remain consistent with the regression results of Model (4) in Table 5. This indicates that the estimates obtained using the 2SLS method generally support the baseline conclusions in Table 5. Furthermore, the Kleibergen-Paap LM statistic is 70.703 with a p-value below 0.1, rejecting the null hypothesis of under-identification. The F-statistics from weak instrument tests exceed the 10% critical value, confirming the instrument’s strength. These findings validate the robustness of Hypothesis 1a and reinforce the conclusion that corporate financialization negatively affects the resilience of the agricultural supply chain.

Table 6. Robustness of the Test Results.

	(1)	(2)	(3)	(4)
Variable	Fin	ASCR	ASCR	ASCR
	IV–I	IV–II		Subsample
Fin		−0.0995 ***		−0.0435 ***
		(0.0282)		(0.0109)
L.fin	0.5442 ***
	(10.2421)
Fin2			−0.0592 ***
			(0.0138)
Size	−0.0026	0.0451 ***	0.0428 ***	0.0262 ***
	(−0.7235)	(0.0069)	(0.0024)	(0.0020)
Age	0.0176	−0.0377	−0.0225	0.0070
	(0.9401)	(0.0240)	(0.0141)	(0.0103)
Asset	−0.0150 ***	−0.0146 **	−0.0105 **	−0.0045 **
	(−2.6039)	(0.0071)	(0.0034)	(0.0023)
Top5	0.0002	0.0001	0.0004 ***	0.0006 ***
	(1.3833)	(0.0002)	(0.0001)	(0.0001)
Board	0.0297 ***	0.0123	0.0079	0.0027
	(2.7925)	(0.0082)	(0.0072)	(0.0054)
FinInst	0.0149 **	−0.0112	−0.0201 ***	−0.0060
	(2.4879)	(0.0064)	(0.0051)	(0.0031)
TobinQ	0.0053 ***	0.0059 ***	0.0033 **	0.0010
	(3.0033)	(0.0012)	(0.0010)	(0.0008)
Loss	−0.0051	−0.0064 **	−0.0055	−0.0059 ***
	(−1.0051)	(0.0027)	(0.0027)	(0.0020)
Year FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
_cons			−0.8489 ***	−0.5615 ***
			(0.0594)	(0.0474)
Kleibergen-Paap rk LM	70.703 (0.0000)
Cragg-Donald Wald F	572.76 (16.38)
Kleibergen-Paap rk Wald F	104.901 (16.38)
N	1211		1604	1604
R2	0.325		0.349	0.349

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

4.2.2. Alternative Variable Specification

In accordance with the latest Chinese Accounting Standards, investment real estate is classified as an operational asset rather than a financial asset due to its low liquidity and long disposal cycle. As such, it does not effectively serve as a buffer for short-term cash flow needs. Therefore, investment real estate is removed from the computation of financial assets, and an alternative measure of financialization (Fin2) is constructed to test the robustness of our findings. As reported in Column (4) of Table 6, the coefficient of Fin2 is −0.0592 and remains statistically significant at the 1% level. This result confirms that the negative impact of corporate financialization on agricultural supply chain resilience holds under the revised specification.

4.2.3. Subsample Regression

To further assess the robustness of the results, subsample regressions are conducted. Given the substantial external shocks caused by the COVID-19 outbreak starting in 2020, the study excludes data from 2020 onwards and re-estimates the model using only the pre-pandemic period (2010–2019). The results, presented in Column (5) of Table 6, show that the financialization of agricultural listed firms continues to exert a significantly negative effect on supply chain resilience. The direction and significance of control variables remain largely consistent, lending further support to the robustness of the findings.

4.3. Heterogeneity Analysis

4.3.1. Dimensions of Agricultural Supply Chain Resilience

In addition to constructing a composite index for agricultural supply chain resilience, this study decomposes the resilience measure into three dimensions: resistance capacity, recovery capacity, and renewal capacity. To enhance the robustness of the analysis, separate regressions are conducted using each of these dimensions as the dependent variable (see Columns (1)–(3) in Table 7).

Table 7. Heterogeneity Test (A).

Variable	(1)	(2)	(3)
	RC1	RC2	RC3
Fin	−0.0154 ***	−0.0268 ***	−0.0198 ***
	(0.0030)	(0.0070)	(0.0065)
Size	0.0112 ***	0.0162 ***	0.0150 ***
	(0.0005)	(0.0012)	(0.0011)
Age	−0.0060 *	−0.0090	−0.0069
	(0.0031)	(0.0071)	(0.0067)
Asset	−0.0029 ***	−0.0038 **	−0.0038 **
	(0.0007)	(0.0017)	(0.0016)
Top5	0.0001 ***	0.0002 ***	0.0002 ***
	(0.0000)	(0.0001)	(0.0001)
Board	0.0015	0.0061	0.0003
	(0.0016)	(0.0036)	(0.0034)
FinInst	−0.0024 **	−0.0105 ***	−0.0070 ***
	(0.0011)	(0.0026)	(0.0024)
TobinQ	0.0007 ***	0.0006	0.0019 ***
	(0.0002)	(0.0005)	(0.0005)
Loss	−0.0013 **	−0.0026	−0.0017
	(0.0006)	(0.0014)	(0.0013)
_cons	−0.2210 ***	−0.3203 ***	−0.3005 ***
	(0.0131)	(0.0300)	(0.0280)
Year FE	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes
N	1604	1604	1604
R2	0.377	0.212	0.315

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

The regression results reveal two key findings. First, the coefficient on financialization for agricultural listed firms is significantly negative across all three dimensions, indicating that increased financialization has an adverse effect on every aspect of supply chain resilience, thereby reinforcing the robustness of our main findings. Second, the magnitude of the effect varies across dimensions: a 1% increase in the degree of financialization is associated with the largest negative effect on recovery capacity (approximately −2.68%), followed by renewal capacity, while the impact on resistance capacity is the smallest.

4.3.2. Heterogeneity Analysis of Enterprise Property Rights

The ownership structure significantly shapes corporate decision-making frameworks and investment strategies, which in turn influence how financialization affects the resilience of agricultural supply chains. The baseline model is re-estimated for each subgroup of state-owned and non-state-owned agricultural listed enterprises in order to investigate this heterogeneity. The regression results (Columns 1–2 in Table 8) indicate that the coefficient for financialization of state-owned enterprises (SOEs) is −0.0551. However, it lacks statistical significance, whereas the coefficient for non-state-owned enterprises (NSOEs) is −0.0511 and statistically significant at the 1% level. Although the absolute value is greater for SOEs, the negative effect of financialization on supply chain resilience is more pronounced for NSOEs. This suggests that firm ownership type introduces significant heterogeneity in the mechanism through which financialization affects supply chain resilience.

Table 8. Heterogeneity Test (B).

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	ASCR	ASCR	ASCR	ASCR	ASCR	ASCR	ASCR
	SOEs	NSOEs	Primary Agriculture	Agri-Related Industries	Labor- Intensive	Asset- Intensive	Technology- Intensive
Fin	−0.0551	−0.0511 ***	−0.0160	−0.0742 ***	−0.0548 ***	−0.0879 ***	−0.0358
	(0.0304)	(0.0160)	(0.0297)	(0.0156)	(0.0164)	(0.0283)	(0.0766)
Size	0.0311 ***	0.0550 ***	0.0604 ***	0.0358 ***	0.0494 ***	0.0252 ***	0.0602
	(0.0038)	(0.0033)	(0.0055)	(0.0028)	(0.0029)	(0.0047)	(0.0367)
Age	−0.0035	−0.0182	0.0272	−0.0246	0.0040	−0.0284	−0.7376 **
	(0.0242)	(0.0177)	(0.0360)	(0.0151)	(0.0193)	(0.0215)	(0.3130)
Asset	−0.0125 **	−0.0113 ***	−0.0238 ***	−0.0065	−0.0117 ***	−0.0081	−0.0106
	(0.0053)	(0.0043)	(0.0084)	(0.0036)	(0.0043)	(0.0053)	(0.0493)
Top5	0.0002	0.0007 ***	−0.0003	0.0005 ***	0.0007 ***	0.0004 **	−0.0012
	(0.0002)	(0.0002)	(0.0003)	(0.0001)	(0.0002)	(0.0002)	(0.0026)
Board	0.0161	0.0020	−0.0191	0.0125	−0.0048	0.0121	−0.0125
	(0.0124)	(0.0092)	(0.0176)	(0.0077)	(0.0092)	(0.0117)	(0.0534)
FinInst	−0.0047	−0.0478 ***	−0.0244 **	−0.0225 ***	−0.0239 ***	−0.0147	0.0179
	(0.0059)	(0.0099)	(0.0100)	(0.0060)	(0.0067)	(0.0080)	(0.0260)
TobinQ	0.0041 **	0.0025 **	0.0035	0.0030 ***	0.0033 ***	0.0077 ***	−0.0099
	(0.0018)	(0.0012)	(0.0026)	(0.0011)	(0.0011)	(0.0026)	(0.0173)
Loss	−0.0075 **	−0.0019	−0.0112 **	−0.0035	−0.0064 *	−0.0077	0.0034
	(0.0038)	(0.0038)	(0.0049)	(0.0033)	(0.0033)	(0.0051)	(0.0171)
_cons	−0.6596 ***	−1.1167 ***	−1.2514 ***	−0.7088 ***	−1.0517 ***	−0.4732 ***	0.8363
	(0.0990)	(0.0800)	(0.1396)	(0.0661)	(0.0777)	(0.0974)	(1.2957)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	636	968	393	1211	1105	451	48
R2	0.358	0.382	0.340	0.386	0.364	0.382	0.772

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

This divergence may stem from two key factors. On the one hand, SOEs tend to allocate financial assets with a stronger emphasis on policy and strategic considerations, and their financial investments are typically subject to more stringent regulatory oversight. In addition, SOEs benefit from implicit government guarantees and fiscal support during periods of financial stress [57], which creates a buffer against financial risk. As a result, their “passive financialization” may exert negative effects, but these are relatively moderate in scope [58]. On the other hand, NSOEs, facing greater profit pressure and tighter financing constraints, are more inclined to engage in “active financialization” aimed at short-term returns. Such behavior often leads to a sustained crowding-out of investment in core operations, thereby significantly weakening the firm’s capacity to withstand external shocks and ultimately undermining the overall resilience of the agricultural supply chain. The findings of this study are broadly consistent with existing research [55,58].

4.3.3. Heterogeneity Based on Industry Type

Agricultural listed firms can be categorized into two sectors: primary agriculture (farming, forestry, animal husbandry, and fisheries) and agri-related industries (agri-food processing, pesticide manufacturing, agricultural machinery, etc.). The latter group tends to be more capital-intensive and industrialized. As shown in Table 8, financialization exhibits a negative but insignificant effect on resilience in the primary agriculture group. In contrast, the effect is significantly negative in the agri-related industries group (coefficient = −0.0742, p < 0.01).

This difference can be attributed to distinct capital accumulation capabilities. Firms in agri-related sectors hold significantly larger financial assets (CNY 606 million vs. CNY 360 million on average), leading to faster capital reallocation toward financial markets. Their stronger financial capacity also increases the likelihood of excessive financialization, which crowds out real investment and weakens supply chain adaptability and recovery capacity. Conversely, the limited capital and low value-added nature of primary agricultural firms reduce their exposure to such financial risks.

4.3.4. Heterogeneity Based on Factor Intensity

The factor intensity of an industry significantly influences firms’ resource allocation strategies and may moderate the effect of financialization on supply chain resilience. Based on the China Securities Regulatory Commission’s 2012 industry classification guidelines, agricultural listed firms are categorized into three groups: labor-intensive industries, asset-intensive industries, and technology-intensive industries ((1) Labor-intensive industries, including agriculture, forestry, animal husbandry, fisheries, related services, agro-product processing, and food manufacturing; (2) Asset-intensive industries, mainly pesticide manufacturing; (3) Technology-intensive industries, specifically agricultural machinery manufacturing). The regression results, presented in Table 8, show that in labor- and asset-intensive sectors, the coefficients of financialization on supply chain resilience are significantly negative (−0.0548 and −0.0879, respectively; p < 0.01). These findings suggest that factor intensity acts as a key moderator of the negative impact of financialization on resilience, with asset-intensive sectors experiencing the strongest adverse effects.

This can be attributed to the following: labor-intensive firms rely primarily on human capital and maintain relatively low capital intensity, which constrains both their capacity and incentive for financial investment—thus mitigating the negative effects of financialization. Conversely, asset-intensive firms require substantial fixed and working capital and are more likely to engage in financial activities to accelerate capital accumulation and pursue higher returns. This reallocation of capital from industrial operations to financial markets can erode supply chain coordination and adaptability, thereby undermining resilience. Although the coefficient for technology-intensive sectors is also negative, it is statistically insignificant—possibly due to the limited sample size. Nevertheless, the result supports the overall robustness of the study’s conclusion that financialization tends to weaken agricultural supply chain resilience.

4.4. Further Analysis

Based on the preceding theoretical framework, the financialization of agricultural listed companies may indirectly affect the resilience of agricultural supply chains through two primary mechanisms: financing constraints and capital expenditure. Meanwhile, business profitability persistence may also serve as a moderating factor in this relationship. Therefore, this section conducts a mechanism-based analysis to further examine the transmission pathways linking financialization and supply chain resilience.

4.4.1. Analysis of Mediating Effects

As shown in Column (1) of Table 9, the coefficient for financialization on financing constraints is −0.0667 and statistically significant at the 1% level, indicating that a higher degree of financialization is associated with more severe financing constraints (smaller SA values) [59]. In Column (2), the coefficient of the SA is significantly positive at the 1% level, implying that weaker financing constraints (larger SA values) enhance agricultural supply chain resilience. Furthermore, the negative effect of corporate financialization on resilience remains significant, though its absolute magnitude is smaller than that reported in column (4) of Table 5. These results suggest that financing constraints partially mediate the relationship between corporate financialization and supply chain resilience, thereby validating Hypothesis 2.

Table 9. Mechanism analysis.

Variable	(1)	(2)	(3)	(4)	(5)
	SA	ASCR	Capital	ASCR	ASCR
Fin	−0.0667 ***	−0.0247 ***	−2.8879 ***	−0.0332 ***	−0.0582
	(0.0186)	(0.0092)	(0.7217)	(0.0119)	(0.0239)
SA		0.5597 ***
		(0.0131)
Capital				0.0100 ***
				(0.0004)
Fin × Growth					−0.0188 **
					(0.0062)
Growth					0.0031
					(0.0028)
Size	0.0235 ***	0.0293 ***	1.6224 ***	0.0262 ***	0.0420 ***
	(0.0032)	(0.0016)	(0.1253)	(0.0022)	(0.0115)
Age	−0.0433 **	0.0022	−1.4228	−0.0078	−0.0212
	(0.0190)	(0.0094)	(0.7377)	(0.0121)	(0.0339)
Asset	−0.0207 ***	0.0011	0.2037	−0.0125 ***	−0.0123
	(0.0045)	(0.0022)	(0.1753)	(0.0029)	(0.0063)
Top5	0.0011 ***	−0.0002 ***	−0.0098	0.0005 ***	0.0004
	(0.0002)	(0.0001)	(0.0062)	(0.0001)	(0.0003)
Board	−0.0053	0.0109 **	−0.1428	0.0094	0.0078
	(0.0096)	(0.0047)	(0.3731)	(0.0061)	(0.0104)
FinInst	−0.0295 ***	−0.0035	−0.9447 ***	−0.0105 **	−0.0193
	(0.0068)	(0.0034)	(0.2646)	(0.0044)	(0.0097)
TobinQ	0.0051 ***	0.0004	0.1236 **	0.0020 **	0.0031
	(0.0013)	(0.0007)	(0.0517)	(0.0008)	(0.0014)
Loss	−0.0027	−0.0040 **	0.0192	−0.0057 **	−0.0055
	(0.0036)	(0.0018)	(0.1396)	(0.0023)	(0.0031)
_cons	−4.0267 ***	1.4118 ***	−30.2610 ***	−0.5402 ***	−0.8338 ***
	(0.0796)	(0.0657)	(3.0950)	(0.0523)	(0.2060)
Year FE	Yes	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes	Yes
N	1604	1604	1604	1604	1604
R2	0.920	0.716	0.175	0.527	0.352

Note: Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

This phenomenon primarily stems from the following mechanism: the financialization of agricultural enterprises sends a signal of “shifting from the real economy to the virtual economy” to the external market, which deteriorates their credit environment. As a result, productive capital is crowded out, and liquidity becomes constrained, thereby weakening key capabilities such as technological upgrading, collaborative stability, and risk response within the industrial chain. Ultimately, this indirectly undermines the overall resilience of the industrial chain through the financing constraints channel.

Columns (3) and (4) of Table 9 show that financialization significantly suppresses capital expenditure (coefficient = −2.8879, p < 0.01). The detrimental effect of financialization on supply chain resilience is still statistically significant when capital expenditure is taken into account as a mediating variable, but capital expenditure has a considerable positive influence. This supports Hypothesis 3 and validates the mediation function of capital expenditure. Capital expenditure is critical for strengthening a firm’s core operations and enhancing its market competitiveness, both of which contribute positively to supply chain resilience. However, in a high-return financial market environment, capital is likely to be diverted away from productive investment, reducing a firm’s ability to respond to shocks and recover from disruptions. Furthermore, the robustness of both mediating mechanisms is confirmed through Bootstrap testing. The confidence intervals for the indirect effects do not include zero, reinforcing the validity of the identified mediation pathways.

4.4.2. Analysis of Moderating Effects

The moderating effect of business profitability persistence on the association between corporate financialization and agricultural supply chain resilience is further examined in Column (5) of Table 9. The results show that when the interaction term is included in the model, the coefficient of Fin changes from −0.0620 (significant) to −0.0582 (insignificant), More importantly, the interaction term between financialization and profitability persistence is significantly negative (−0.0188, p < 0.05), This finding indicates that business profitability persistence plays a crucial role in amplifying the negative impact of corporate financialization on supply chain resilience, thereby supporting Hypothesis 4.

This implies that firms with stable performance are more likely to intensify their allocation of financial assets, crowding out investments in physical production and technological upgrading, thereby further undermining the long-term resilience of agricultural supply chains. A possible explanation lies in the internal capital accumulation driven by consistently strong performance. However, given the agricultural sector’s low profitability and reliance on government support, firms tend to reallocate resources to higher-yield financial markets once they achieve basic operational profitability. This shift reinforces the “financialization over industrialization” trend, thereby exacerbating volatility and weakening the resilience of the agricultural supply chain.

5. Conclusions and Policy Implications

5.1. Conclusions

This study enriches the conceptual framework of agricultural supply chain resilience by integrating three dimensions—resistance, recovery, and renewal—into a composite evaluation system comprising 16 indicators. Anchored in the theories of precautionary savings and financial constraints, a theoretical model is developed to investigate the effect of corporate financialization on supply chain resilience. Using panel data from 165 listed agricultural firms in China spanning 2010–2022, the following are the primary findings: The empirical results show that corporate financialization exerts a significant crowding-out effect on agricultural supply chain resilience, with the most pronounced impact on recovery capacity, followed by renewal, while resistance is least affected. Further heterogeneity analysis reveals that financialization has stronger negative effects in non-state-owned enterprises, non-primary agricultural sectors, as well as asset-intensive and labor-intensive industries. This suggests that firms with lower structural flexibility or higher dependence on capital allocation are more susceptible to resource misallocation triggered by financialization, thereby weakening their adaptability to external shocks and the stability of the industrial chain. Mechanism tests indicate that financialization reduces resilience indirectly by suppressing capital expenditure and intensifying financing constraints. Additionally, the moderating effect analysis finds that stronger business performance persistence amplifies the negative impact of financialization on firm resilience. In conclusion, the findings of this study indicate that while financialization may enhance short-term financial flexibility, its long-term effects on agricultural supply chain resilience are predominantly negative. Therefore, effective policy design should prioritize directing capital toward productive investment and technological upgrading, especially for agricultural enterprises that play a pivotal role in maintaining supply chain stability.

5.2. Policy Implications

In light of the findings presented above, the below policy measures are proposed.

First, corporate financialization in the agricultural sector should be guided in a prudent and differentiated manner, as excessive financialization significantly undermines supply chain resilience—particularly recovery and renewal capacities. Given that financialization crowds out capital expenditure and exacerbates financing constraints, regulatory authorities should establish a dynamic financialization risk assessment system to regularly evaluate and warn against potential risks posed by the financial behavior of listed agricultural firms. Specifically, setting reasonable upper bounds on financialization ratios (e.g., financial assets as a proportion of total assets or revenue) can help prevent excessive diversion from core business operations. Implement differentiated regulatory policies based on ownership and sector characteristics, with stricter oversight of non-state-owned and non-primary agricultural firms. Strengthen disclosure and transparency requirements, mandating regular reports on financial investments, risk exposures, and mitigation strategies.

Second, strengthening the resilience of the agricultural supply chain requires targeted interventions aligned with its multidimensional structure—namely, resistance, recovery, and renewal capacities. To enhance resistance capacity (“stabilizing the chain”), policy efforts should prioritize investment in agricultural infrastructure (e.g., irrigation, logistics, storage), risk-sharing mechanisms, and insurance systems to stabilize production under external shocks. To enhance recovery capacity (“repairing the chain”), support should be directed toward fostering leading enterprises and flexible, multi-tiered supply networks that facilitate rapid adjustment and coordination following disruptions. To strengthen renewal capacity (“reinforcing the chain”), policies should encourage sustained investment in agricultural R&D and technological innovation to facilitate industrial upgrading and long-term competitiveness.

Third, reforms in the financial system should focus on mitigating the distortionary effects of financialization while improving the allocation efficiency of financial resources. Given that financing constraints serve as a key transmission channel through which financialization affects supply chain resilience, financial institutions should develop differentiated financial instruments—such as tailored credit lines, agricultural bonds, and risk-sharing mechanisms—that better match the diverse financing needs across the business lifecycle. At the same time, strengthening information-sharing platforms and credit monitoring systems can reduce information asymmetry, enhance capital allocation efficiency, and prevent excessive financial investment from crowding out productive activities.

5.3. Research Limitations and Future Prospects

This study provides both theoretical and practical insights into how corporate financialization affects the resilience of agricultural industry chains. Based on a systematic empirical analysis. The findings offer an empirical foundation for improving risk management practices and optimizing financial strategies within agricultural supply chains. Nevertheless, several limitations should be acknowledged.

First, the generalizability of the findings is constrained by the sample coverage. Although the dataset includes large listed agricultural enterprises, key actors such as smallholder farmers, rural collective organizations, and small- and medium-sized agricultural enterprises are underrepresented, despite their critical roles in the agricultural financial ecosystem. Future research could expand the sample scope to incorporate a broader range of stakeholders across different segments of the industry chain. Doing so would enable a more comprehensive understanding of the heterogeneous mechanisms through which financialization influences supply chain resilience.

Second, the comprehensiveness of the findings is limited by the indicator framework employed in this study. Due to data availability constraints, the resilience evaluation system focuses primarily on three dimensions—resistance, recovery, and renewal capacities. While this framework captures firms’ ability to maintain stability and adapt internally in response to external shocks, it does not fully reflect the dynamic role of inter-organizational collaboration in promoting supply chain upgrading and long-term development. Future research could adopt multidisciplinary approaches and incorporate data from more diverse sources to enrich the analytical framework. Such efforts would facilitate the development of a more systematic, dynamic, and comprehensive resilience assessment system, thereby better capturing the co-evolutionary dynamics of agricultural industry chains.