This study’s sample comprises agricultural enterprises publicly traded on the Shanghai and Shenzhen Stock Exchanges in China from 2002 to 2022. According to Cao et al.’s (2022) examination, the mentioned agricultural companies include agriculture (A01), forestry (A02), animal husbandry (A03), and fishery (A04) within the primary sector; agro-food processing (C13), food manufacturing (C14), and wine, beverage, and refined tea manufacturing (C15) within the secondary sector; and agriculture, forestry, animal husbandry, and fishery services (A05) within the tertiary sector. The sample was refined by (1) excluding companies classified as ST, *ST, and PT; and (2) eliminating outliers and missing values. We applied a shrinkage method to continuous variables within the 1st and 99th percentiles of the sample to ensure the reliability of the regression results and account for extreme values. The screening process resulted in a total of 2,480 valid firm-year observations.

In order to delve into the role of trade credit on the high-quality development of enterprises, this paper uses model (1) to measure the effect of obtaining trade credit on the high-quality development of agricultural enterprises, and model (2) to measure the impact of trade credit provided by agricultural enterprises on the high-quality development of enterprises.

Explained variable (TFP), the current measurement methods of high-quality development are mainly divided into two categories: using a single or comprehensive indicator measurement method. In recent years, total factor productivity (TFP) has emerged as a crucial metric for researchers to assess the development of companies. TFP contains a wealth of information about the company, such as product quality, enterprise business development, and enterprise innovation. In light of this, the study employs total factor productivity as a benchmark for appraising the high-quality development of agricultural enterprises. Nevertheless, the study devises a comprehensive, integrated evaluation system to gage the high-quality development of agricultural enterprises, taking into account the potential bias and constraints associated with relying solely on one indicator of total factor productivity.

Based on Levinsohn and Petrin’s (2003), the LP method to calculate TFP better overcomes the endogeneity problem in the OLS estimation of total factor productivity. Therefore, this article uses the LP method in benchmark regression to calculate the total factor productivity of agricultural enterprises. To make the result more reliable, this article also conducts robustness testing using various enterprise total factor productivity measurement methods, such as the OP and GMM methods.

Explanatory variables are mainly categorized into obtaining trade credit (AP) and providing trade credit (AR), and AP is defined as (accounts receivable + notes receivable + prepayment)/total assets, and AR is defined as (accounts payable + notes payable + prepayment)/total assets.

This paper utilizes control variables to account for various factors that influence the total factor productivity of enterprises, including bank loans (Bank), enterprise scale (Size), cash flow ratio (Cashflow), financial leverage (Lev), return on total assets (Roa), proportion of fixed assets (Fixed), revenue growth rate (Growth), and company listed age (ListAge). The specific calculation method of control variables is in Table 1. Furthermore, the variable λ i in the model denotes the specific fixed effects of each unique business, η t represents the fixed effect of time, and ε i , t stands for the random disturbance term.

This article employs a descriptive statistical analysis to examine each variable within the overall sample (Table 2). The findings indicate that the mean TFP_LP for all enterprises is 7.986, with a maximum of 11.33 and a minimum of 4.507. This indicates a significant difference in the TFP_LP among agricultural businesses. On average, these companies get trade credit of 10.6% of their total assets and extend trade credit of 9.4%. Chinese agricultural firms typically have a Cashflow of 6.3%, Lev of 39.5%, ROA of 4.4%, and a proportion of fixed assets of 27.4%. In this study, we have categorized the data into three sets based on the levels of trade credit acquisition and supply. Subsequently, we have compared the group with the highest degree of trade credit acquisition to the group with the lowest level. Figure 1 shows that the former group, with a considerable level of trade credit acquisition, exhibited a substantially higher average TFP than the lower group. Our preliminary statistical analysis results have shown that the affirmative impact of trade credit on businesses’ overall productivity. Within this context, it is noteworthy that in Figure 2, the TFP of the group with a higher provision of trade credit was significantly lower before 2008 than that of the group with a lower provision. This discrepancy can be attributed to the Chinese government’s implementation of the People’s Republic of China on Property Rights in 2007, which offered a certain level of safeguarding for the provision of trade credit by enterprises. Building upon this critical finding, the paper later undertakes a policy analysis utilizing the double-difference methodology, delving deeper into the effects and implications of this specific policy and occurrence.

While the research has controlled the crucial aspects that might impact the overall productivity of firms, it is still possible that the model may overlook essential variables. In addition, the development of enterprises will expand their access to finance from banks, increasing the trade credit supply. Thus, trade credit may reversely affect enterprises’ total factor productivity.

This study incorporates an instrumental variable, AP/AR Industry, which derives from the average of past trade credit acquisition and provision within the firm’s industry, to mitigate the impact of endogeneity on the study findings. Mei and Cheng (2021) suggest re-evaluating the model regression results using the two-stage least squares (2SLS) technique. Regarding correlation, enterprises’ business and operating features are comparable, implying that their supply and obtaining of trade credit are similar to those of other enterprises in the same sector. Regarding exogeneity, the industry mean refers to a variable at the industry level. The industry average of company credit has less association with the firm’s micro-level factors, thereby fulfilling the exogeneity principle.

During the preliminary phase, we observed that the estimated coefficients of the instrumental and corresponding explanatory variables have a remarkable significance level, with a p-value lower than 0.01. This serves as evidence that the instrumental variables effectively elucidate the endogenous variables. The F-value exceeds 10, indicating that the instrumental variables fulfill the necessary criteria and that there are no weak instrumental variables. The research results in Table 5 show that providing and obtaining trade credit has a positive impact on the total factor productivity of enterprises, which is consistent with the results of the baseline regression of this study.

Amidst the widespread economic upheaval of 2008, businesses across the globe were reeling from the ramifications of the financial crisis. The adverse effects of this crisis were profound, manifesting in reduced investments and depleted production rates (Shuai et al., 2023). As such, our study exclusively focuses on post-2010 data. Notably, our empirical findings reveal that even during periods free of financial crises, trade credit remains a crucial catalyst for enhanced total factor productivity in firms.

As a growing market nation, China has implemented and revised several regulations to enhance firms’ external business environment. Specifically, the Property Law was officially announced in March 2007. China’s Law on Property Rights has established comprehensive regulations on security rights, mortgages, and pledges, resulting in a well-structured and comprehensive property rights framework. As seen in Figure 2 above, the group with a higher provision of trade credit had a lower TFP than the group with a lower provision of trade credit before 2007, which may be mainly because the reform of security rights marked by the enactment of the Property Law in 2007 has taken measures to protect the funds provided by enterprises for trade credit. The Chinese law clearly states that inventory (Article 108) and accounts receivable (Article 223) can be mortgaged and pledged. The law explicitly states inventories (Article 108) and receivables (Article 223) can be mortgaged and pledged. According to research by Jun et al. (2017), the Property Law provides appropriate safeguards for creditors and encourages firms to enhance their innovative capabilities. This article employs the Property Law for quasi-natural experiment analysis to investigate the alterations in firms’ total factor productivity level. By using the difference-in-differences model and analyzing the disparities between the experimental and control groups, it is possible to mitigate the endogeneity problem to some degree and obtain a more accurate measurement of the causal link between the two. The results from applying the double differencing method in column 3 of Table 5 demonstrate that the productivity level is more significant in the group with a higher provision of trade credit. Additionally, this paper conducts a placebo test on the policy time in advance, and the results in the 4th column of Table 6 are not statistically significant, which confirms the reliability of the outcomes. Adopting the difference-in-differences model also further overcomes the interference of endogeneity concerns, confirming the robustness of the benchmark regression results.

Firms’ innovation achievements are strongly linked to the enhancement of overall efficiency. However, there is a delay between the inputs made toward innovation and the actual outcomes of innovation. Notably, the production of high-quality innovations requires a longer duration. As a result, there might be a temporal mismatch between the availability of trade credit funding and the firms’ overall efficiency. Consequently, we conduct a regression analysis on the whole factor productivity of businesses using a one-period lag. The results in rows 5 and 6 of Table 6 show that trade credit still positively affects enterprises’ total factor productivity. This reinforces the robustness and dependability of the study’s findings.

The enterprise life cycle hypothesis pertains to the dynamic trajectory of a business’s development and expansion, encompassing three distinct stages: growth, maturity, and decline. According to the hypothesis, firms at different phases have distinct financial demands and resource usage rates. The effect of trade credit on company growth may differ based on the business’s life cycle stage.

For firms in the growth stage, the findings indicate that having access to commercial finance significantly impacts productivity. From the standpoint of obtaining trade credit, businesses in the growth phase face limited access to external financial resources. Using trade credit financing may effectively reduce funding limits and provide “incentive effects” that promote high-quality firm development. From the perspective of providing trade credit, enterprises face a shortage of funds during the growth stage. To quickly expand their market, they tend to offer more trade credit to clients. However, this practice hampers the allocation of funds toward research and development (R&D), thereby hindering the overall improvement of the enterprises’ total factor productivity.

For mature firms, both obtaining and supplying trade credit significantly increase their TFP, and mature firms have a relatively stable market share and substantial profits relative to growth-phase firms. On the one hand, acquiring trade credit provides mature firms with funds for innovation. On the other hand, mature firms are more capable of lowering transaction costs based on stable markets and sales channels, making the provision of trade credit more stable and profitable, and promoting firms’ high-quality development.

For declining firms, formal financing is relatively difficult. It relies more on trade credit funding from business transactions, so that trade credit financing funds for declining firms may support firms in innovation and improve their total factor productivity. In addition, declining firms’ provision of trade credit further deteriorates firms’ financial flows, which is not conducive to high-quality growth.

Stable and adequate funding is crucial for the growth of an enterprise in China, as the formal finance sector is not well developed. The small scale of agricultural enterprises makes it challenging to obtain sufficient financial support, leading to financing constraints for many agricultural enterprises. Enterprise innovation requires consistent financial backing. Insufficient financing can lead to interruptions in innovation, increase sunk costs for businesses, diminish their passion for innovation, and perhaps disrupt routine operations. Guo (2023) revealed that financial restrictions hinder the enhancement of total factor productivity in Chinese fishing firms.

This research utilizes the methods developed by Petersen and Rajan (1997) to create a thorough index. The KZ index gages the degree of financing constraints faced by agribusiness. Several factors, such as net cash flow from operations, payout level, dividend holdings, degree of indebtedness, and growth index, contribute to its calculation. We adjust these factors for normalization based on the total assets at the start of the period. A higher KZ index indicates more significant financing constraints. The data presented in Table 12 demonstrates a negative correlation between trade credit financing and the KZ index. This suggests that access to trade credit offers alternative financing options for agribusinesses, which contributes to easing agribusiness financing constraints. Previous studies by Allen et al. (2005) and Cull et al. (2009) support this finding.

The distortion of capital allocation is a fundamental issue that restricts the high-quality development of companies. Neoclassical economic theory emphasizes that the demand for factors of production by a firm is determined by the marginal cost and marginal benefit associated with their use. However, in practice, the marginal cost and marginal benefit of using factors of production often differ, leading to inefficient investments that significantly hinder the enterprise’s pursuit of high-quality development. In their study, Xing et al. (2023) discovered that enhancing the effectiveness of resource allocation can boost firms’ overall factor productivity. Efficiently addressing the issue of inadequate investment in agricultural firms holds significant practical importance in enhancing their innovative capacity, improving TFP, and achieving high-quality development.

In this paper, we refer to Richardson (2006) and Li and Huang (2020) to measure the investment efficiency of the company to build the model (3).

where I n v t is the firm’s actual new investment expenditures in year t; Growt h t − 1 denotes the firm’s growth capacity in year t-1, as measured by Tobin’s Q; A g e t − 1 stands for denotes the age of the firm at year t-1; L e ν t − 1 denotes the firm’s financial leverage in year t-1, as measured by gearing; C a s h t − 1 denotes the firm’s cash flow in year t-1; S i z e t − 1 denotes the size of the firm’s assets in year t-1; R e t t − 1 denotes the firm’s stock return in year t-1; In ν t − 1 denotes t − 1 year’s new investment expenditures; ∑ Industry denotes the industry dummy variable; and ∑ Year denotes the year dummy variable. Further, perform OLS regression on the model (3) to get the model’s residuals. As estimated by the model, the magnitude of these residuals represents the extent of the company’s inefficiency investment. A higher absolute value of the residuals indicates more significant inefficiency in the investment. Results in Table 12, column 2, show that the provision of trade credit can curb inefficient investment, which is conducive to increased efficiency in the use of funds and the promotion of TFP by enterprises.

Total factor productivity (TFP) refers to the portion of economic growth that cannot be accounted for by the increase in inputs from production factors. Technological innovation has a crucial role in enhancing the overall productivity of organizations. Schumpeter’s theory of economic growth posits that firms’ innovations can result in transferring factors of production from less productive companies to more productive companies. This process compels less productive firms to exit the market, ultimately enhancing the industry’s overall productivity (Bartelsman et al., 2013). Karafillis and Papanagiotou (2011) examined the correlation between innovation and organic agriculture. In a study conducted by Karafillis and Papanagiotou (2011), the connection between innovation and total factor productivity in organic agriculture was examined. Their research revealed a positive correlation between innovation and TFP in this field.

This research delves deeper into the influence of the supply and acquisition of trade credit on corporate innovation. This study measures innovation using company patent applications as a proxy variable. We can classify the patents granted to Chinese enterprises into three categories: innovation, utility model, and design. Invention patents are considered significant inventions aimed at advancing a company’s technical development and obtaining a competitive edge. Enterprises seeking to prioritize “quantity” and “speed” of invention may employ strategic innovation by obtaining design and utility model patents, which are comparatively less challenging.

The findings in Table 12 demonstrate that acquiring trade credit has a significant impact on promoting firms’ substantive innovation. In contrast, it does not significantly influence enterprises’ strategic innovation. Trade credit supply fosters substantial company innovation and significantly impeding strategic innovation. When combined with the prior theoretical research, it is further validated that businesses that get trade credit may mitigate financial limitations, enhance investment in innovation, enhance the quality of enterprise innovation, and foster high-quality business development.

The study performed a benchmark test and discovered that trade credit notably influences the overall factor productivity of agricultural businesses. To address the endogeneity issue in the model, the study replaced the variables and conducted a further robustness test using the least squares method. The study’s ultimate conclusion underscores the potential of trade credit accessibility and supply to boost enterprises’ TFP significantly. These results align with those of the benchmark regression and are consistent with the conclusions drawn by Detthamrong and Chansanam (2023).

Furthermore, this paper aims to examine the mechanism through how trade credit impacts businesses’ high-quality growth, using theoretical and empirical perspectives.

We further investigate the impact of trade credit on enterprise financing constraints, inefficient investment, and innovation. Research has shown that trade-credit financing can help companies overcome financial constraints. Additionally, offering trade credit can prevent companies from making inefficient investments (Karakoç, 2023), stimulate innovation, and improve TFP.

There is a deficiency in the current state of affairs regarding evaluating companies’ high-quality development, as there is a lack of a comprehensive index system and measuring procedures. Additionally, utilizing a single indicator to gage the quality of enterprise development has its limitations, as it may not accurately depict the influence of each contributing factor. To address this issue, this article employs the entropy value technique to appraise the high-quality growth of agricultural firms. This method, distinguished by six primary and 13 innovative indicators, has proven efficient in empirical testing. Moreover, the study has revealed that trade credit plays a significant role in enhancing firms’ high-quality development index. This finding further solidifies the credibility and robustness of the conclusions presented in this research.

Government policies should be strengthened to support agribusinesses and mitigate their long production cycles and financing constraints. Implementing the Property Law has significantly improved productivity and corporate profits. At the same time, a well-functioning financial system can help alleviate financing constraints and improve corporate governance, leading to increased agricultural productivity and modernization. The reform of the domestic financial market should also be promoted, including improving the informal financial market and establishing an enterprise information disclosure system. Supply chain finance should be actively developed to enhance financial service levels for the agricultural industry.

At the enterprise level, businesses should leverage bank and trade credit to attract talent and promote technological progress, resulting in improved productivity. Additionally, internal governance mechanisms, such as improving information disclosure, should be enhanced to obtain more financial support and increase competitiveness. The utilization of supply chain finance is also encouraged to improve overall financial services for agricultural enterprises.

China’s agricultural development faces challenges such as overconsumption of resources, reliance on inputs, and poor management. This has hindered sustainable development and led to a lack of competitiveness. To improve, China must adopt a new model, enhance sustainability, and promote transformation and upgrading for long-term growth. The concept of extensive agriculture sees the sector as an interconnected system and argues that trade credit can boost high-quality growth by addressing financing constraints, curbing inefficient investments, and promoting innovation.