Against the backdrop of China’s bank-dominated credit allocation system, the structural tilt in credit allocation, and the persistent difficulties and high costs of financing faced by SMEs, evaluating the impact of industrial credit allocation solely at the level of a focal industry will systematically understate the overall policy effects. This is because credit allocation can propagate across industries through multiple rounds of transmission along supply chains and trade credit linkages, generating significant network spillovers. This paper examines how industrial credit allocation affects economic output, with a particular focus on the pre- sence of two intertwined and superimposed linkage structures among economic agents.

Based on the OECD input-output table data, this paper builds an industry-year panel for China using Chinese macro-industry and financial statistics, focusing on 44 industries from 2010 to 2020. It measures economic output by real value added, industrial credit by end-of-year industry loan balances, and the supply-credit network weights using input-output coefficients and trade credit intensity, constructing upstream and downstream industrial credit allocation indicators. The empirical results show that industrial credit allocation has a statistically significant direct effect on the focal industry’s growth, but the network effects are economically larger. In the baseline estimates, a 1% increase in the focal industry’s loan balance is associated with about a 0.044% increase in its value added, while a 1% increase in upstream weighted loans and downstream weighted loans is associated with roughly 0.078% and 0.384% higher focal-industry value added, respectively, highlighting that downstream network transmission dominates the direct channel in magnitude. Mechanism testing supports the model’s propagation logic: Credit expansion changes trade credit supply and demand along the chain, and upstream credit reduces the focal industry’s input-output coefficient. Policy effectiveness is strongly moderated by network topology and by the type of financial friction. Industries with lower upstreamness or lower weighted outdegree display stronger direct policy effects and larger aggregate impacts, implying that allocating credit toward these nodes can yield more pronounced economy-wide gains.

This paper has the following policy implications: First, industrial credit policy should be designed with a network perspective, coordinating bank credit with supply-chain finance instruments rather than treating industries in isolation. Second, policymakers should tailor tools to frictions, using quantity and collateral-easing instruments when constraints are primarily collateral-based, and using price-based tools such as interest subsidies when the financing premium dominates. Third, improving receivables pledgeability, standardizing trade-credit governance, accelerating confirmation and discount channels, and reducing payment arrears can strengthen the liquidity reallocation channel and magnify policy multipliers. Fourth, targeting credit toward industries with lower upstreamness or lower weighted outdegree may deliver larger aggregate output gains, while still indirectly supporting upstream sectors through network spillovers.