数字基础设施对要素市场配置的影响研究

田新豹，王欣然

（山西大学 经济与管理学院，山西 太原 030006）

摘要：提升要素市场配置效率是推动经济高质量发展与建设全国统一大市场的关键。本文以“宽带中国”示范城市（城市群）创建为准自然实验，选取2012—2023年中国275个地级及以上城市的面板数据，运用双重机器学习方法，实证考察了数字基础设施建设对要素市场配置效率的影响及其作用机制。研究发现：数字基础设施建设能够提升要素市场配置效率，有效缓解资本、劳动及整体市场的扭曲程度；其作用主要通过增强城市创新能力、优化就业结构与促进数字金融发展三条路径实现。此外，数字基础设施建设的影响效应在非资源型城市、非老工业基地城市以及经济发达地区更为明显。本研究为理解数字技术如何优化要素市场配置、促进区域协调发展提供了经验证据与政策启示。

基金项目：国家社会科学基金一般项目“‘双碳’目标与数字经济双驱动下煤炭资源型区域能源转型的机制与路径研究”（23JLB00225）

关键词：数字基础设施；要素市场配置效率；“宽带中国”；双重机器学习；要素扭曲

作者简介：田新豹，山西大学经济与管理学院副教授；王欣然，山西大学经济与管理学院硕士研究生。

引用格式：田新豹,王欣然.数字基础设施对要素市场配置的影响研究[J].首都经济贸易大学学报,2026,28(3):58-70.

The Impact of Digital Infrastructure on Factor Market Allocation

TIAN Xinbao, WANG Xinran

（Shanxi University, Taiyuan 030006）

Abstract: For a long time, China's factor markets have been characterized by pronounced allocation distortions and regional segmentation. These distortions hinder the efficient reallocation of production factors from low-productivity to high-productivity sectors, resulting in substantial efficiency losses due to resource misallocation. This has become a fundamental constraint on the development of a unified national market and high-quality economic growth. Against this backdrop, the rapid expansion and diffusion of digital infrastructure provide new technological pathways for alleviating factor market distortions and optimizing resource allocation.

This study takes the “Broadband China” pilot city policy as a quasi-natural experiment. Using panel data from 275 prefecture-level and above cities in China from 2012 to 2023, and applying a double machine learning approach, this study systematically examines the impact of digital infrastructure development on factor allocation efficiency and its underlying mechanisms. Factor allocation efficiency and distortion are measured across three dimensions-capital, labor, and the overall market-while the transmission channels and heterogeneous effects are further explored.

The results show that digital infrastructure development significantly improves factor allocation efficiency and effectively alleviates distortions in capital, labor, and the overall market. Mechanism analysis indicates that digital infrastructure promotes factor allocation efficiency through three main channels. First, it enhances urban innovation capacity, including overall innovation output and green technology innovation. Second, it optimizes the labor market structure by expanding employment scale and improving job quality. Third, it facilitates the development of digital finance, thereby broadening the scope of financial services and improving capital allocation efficiency. The regression coefficients for all three channels are significantly positive, confirming the effectiveness of the transmission mechanisms.

Heterogeneity analysis further shows that the effects of digital infrastructure on factor allocation are more pronounced in non-resource-based cities, non-traditional industrial bases, and economically developed regions, indicating that the effects are significantly moderated by urban resource endowments, institutional environments, and technological absorption capacity.

This study integrates digital infrastructure and factor market allocation into a unified analytical framework. Based on a nationwide city-level sample and a quasi-natural experiment design, it provides robust micro-level evidence for understanding how digital technologies alleviate factor distortions and promote coordinated regional development. In addition, this study identifies three key transmission channels, namely, innovation capacity, labor market structure, and digital finance, and clarifies the boundary conditions under which these effects operate, offering important policy implications for advancing market-oriented factor reforms and designing a differentiated policy framework.

Keywords: digital infrastructure; factor market allocation efficiency; Broadband China; double machine learning; factor distortion

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