It is typically less profitable for an opportunistic borrower to divert inputs than to divert cash. Therefore, suppliers may lend more liberally than banks. This simple argument is at the core of our contract theoretic model of trade credit in competitive markets. The model implies that trade credit and bank credit can be either complements or substitutes. Among other things, the model explains why trade credit has short maturity, why trade credit is more prevalent in less developed credit markets, and why accounts payable of large unrated firms are more countercyclical than those of small firms.

中小企业财务困境会给自身带来一系列负面影响，这种负面影响可能在供应链上蔓延。已有研究大多关注前者，忽略了中小企业财务困境对上游供应商造成的信用风险传导效应。为此，本文基于供应链视角，构建传导模型来分析中小企业财务困境对上游供应商信用风险的影响机制；进一步，基于2010-2018年间的中小板上市公司样本对上述机制进行实证检验。理论模型与实证研究均表明，中小企业财务困境对上游供应商的信用风险存在传导过程；上游供应商自身因素对其信用风险的影响较中小企业财务困境因素更大；这种传导过程存在两年的时间效应。此外，高规模、国有性质、高客户集中度供应商的风险抵御能力更强；分行业和产业后发现，制造业、第一二产业供应商的风险抵御能力更弱。本文研究结果，一方面为中小企业以及整个供应链系统的信用风险防控提出参考；另一方面也为增强供应链竞争力提供理论基础。

The financial distress of small and medium-sized enterprises（SMEs）will bring a series of negative effects to themselves， from its startup to its decline. Multiple theories and much empirical evidence have been proposed to interpret this phenomenon. At the same time， the distress events can lead to negative wealth and profitability outcomes for supply chain members in the short time and long time. In fact， the financial distress of SMEs has become a major concern in disruptions to normal activities， but only a few contributions in the literature have focused on the transmission effect of SMEs’ financial distress on the credit risk of suppliers. Credit risk refers to the risk caused by the borrower's possible default and failure to repay the loan and interest. The credit risk may propagate further， sometimes even with amplifications， leading to a negative performance outcomes for the members， reducing the overall operational efficiency and competitiveness of the supply chain. To this end， a transmission model is constructed to analyze the impact of SMEs’ financial distress on the credit risk of suppliers. During modeling， classical Jarrow–Turnbull model is used as reference to denote the intensity of default risk $\alpha$and the probability of default risk $F(t)$. Furthermore， the above mechanism is tested by the sample of Small and Medium-sized Board from 2010 to 2018. The results are as follows based on the theoretical model and empirical research： Firstly， there is a positive relationship between the financial distress of SMEs and the credit risk of suppliers. Secondly， the suppliers’ own factors have a greater impact on their credit risk than that of SMEs’ financial distress. Additionally， this process has a time effect of two years， and suppliers of high scale， high-customer-concentration and state-owned nature are more resistant to credit risk. On the contrast， manufacturing， primary and secondary industry suppliers have weaker risk resilience. The findings of this paper， on the one hand， provide reference for the credit risk prevention and management of SMEs and the supply chain system. On the other hand， promoting the stability and competitiveness of supply chain node-enterprises.

We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data—an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers’ data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions.

在百年变局、逆全球化等多重影响下，供应链韧性优化已然成为企业险中求生、化危为“绩”的关键。本文基于2013—2020年沪深A股上市公司数据构建三重差分模型，从供应链韧性优化视角考察了智慧供应链建设对企业绩效的影响及其传导机制。研究发现，智慧供应链建设对企业绩效提升具有积极作用。机制检验结果表明，智慧供应链建设不仅能通过提升供应链主动能力、供应链反应能力、供应链设计质量来优化动态能力，还能发挥专业分工、成本管控、资金融通、创新协同的多维驱力，切实提升企业绩效。进一步分析表明，智慧供应链建设对成长期企业、非国有企业、劳动密集型和技术密集型企业的绩效提升作用更大，且在商品市场分割程度和劳动力市场分割程度较低、隶属东部地区的情形下政策效应更佳。本文结论为政府加快智慧供应链转型升级、稳固产业链供应链安全基础提供了政策启示。

Under the background of China’s increased focus on the stability and security of its industrial and supply chains， as well as the growing challenges faced by Chinese enterprises in a dynamic market environment， there is a pressing need to systematically evaluate the impact of smart supply chain construction on supply chain resilience optimization and enterprise performance improvement. Employing the panel data of Shanghai and Shenzhen A-share listed companies over 2013-2020， the implementation of supply chain innovation and application policies is approached as a quasi-natural experiment in this study. To estimate the impact of constructing a smart supply chain on enterprise performance， a robust DDD model is utilized.The empirical analysis results demonstrate that the construction of a smart supply chain has significant influences on enterprise performance. Firstly， it significantly enhances enterprise performance， thereby indicating that the intelligent transformation and upgrading of the supply chain can assist enterprises in achieving sustainable performance improvement. Secondly， supply chain resilience optimization serves as a key mechanism through which a smart supply chain enhances enterprise performance. This mechanism facilitates the development of dynamic capabilities， enabling professional division of labor， control of transaction costs， internal and external financing， and innovation synergy. Thirdly， the policy effect exhibits significant heterogeneity. The construction of smart supply chains has a greater impact on the performance improvement of growing period enterprises， non-state-owned enterprises， labor-intensive enterprises， and technology-intensive enterprises. Moreover， the policy effect is more favorable in situations where there is a lower degree of market segmentation for goods and labor， and when the enterprises are located in the eastern region. The findings of this study have important policy implications for expediting the intelligent transformation and upgrading of supply chains， ensuring the continuous enhancement of supply chain resilience， and accelerating the high-quality development of enterprises.

供应链气候风险作为气候风险的一种重要表现形式, 直接关乎产业链条的稳定. 现有文献已经注意到企业自身气候风险的经济金融后果, 却尚未关注到外部供应链气候风险同样可能会对企业产生一些潜在的影响, 而商业信用契约作为企业供应链金融的核心组件, 是否会受到外部供应链气候风险的影响? 基于2013--2020年沪深A股上市公司数据, 研究发现, 供应链气候风险越高, 企业越能获得有利的商业信用契约, 在经过工具变量回归、 熵平衡法匹配等一系列稳健性检验之后, 上述正向影响仍然显著. 机制检验表明, 企业往往会出于融资约束动因与短视套利动因接受气候风险较高的供应商以获取优厚的商业信用契约, 而横截面测试则表明, 在信贷可获得性较低、 成本转嫁能力较高以及外部制度环境较差的企业样本中, 上述影响将更为显著. 从经济后果测试来看, 尽管能藉此获取一定的商业信用优势, 但过高的供应链气候风险却会对企业的ESG发展、 绿色创新水平以及全要素生产率产生显著的负面影响, 不利于企业的高质量发展.