数据要素价值化是否赋能城市新质生产力？

——基于数据交易平台设立的准自然实验

司增绰¹　魏雨童¹　马克卫²

（1.江苏师范大学；2.山西财经大学）

摘要：数据要素价值化是培育城市新质生产力的重要途径。本文以数据交易平台设立为准自然实验，选取2012—2023年中国288个地级及以上城市面板数据，采用多期双重差分法，实证检验数据要素价值化对城市新质生产力的影响及其作用机制。研究发现，数据要素价值化能够提升城市新质生产力，且经内生性与一系列稳健性检验后结论依然成立。机制分析表明，该影响主要通过技术创新驱动、资源配置优化和数字产业集聚实现。异质性分析发现，政策效应由南向北递减，在非资源型城市中更为明显，且主要集中于大城市和特大城市。门槛效应检验进一步表明，随着城市综合经济活动规模扩大，政策效应呈先增强后减弱的非线性特征，其中中等规模区间促进作用最强。本文为释放数据要素潜能、培育城市新质生产力提供了经验证据与政策参考。

关键词：数据要素价值化；数据交易平台；新质生产力；创新驱动；资源配置；数字产业集聚

作者简介：司增绰，江苏师范大学商学院教授，通信作者，徐州，221116；魏雨童，江苏师范大学商学院硕士研究生；马克卫，山西财经大学统计学院教授，太原，030006。

基金项目：国家社会科学基金重大项目“全球资金流量核算与构建新发展格局研究”（23＆ZD126）；江苏省研究生科研实践创新计划项目“新质生产力培育对生产性服务业扩容提质的需求牵引效应”（26CXJH5043）

引用格式：司增绰，魏雨童，马克卫. 数据要素价值化是否赋能城市新质生产力？：基于数据交易平台设立的准自然实验［J］. 经济与管理研究，2026，47（10）：50-65.

Does Data Factor Valorization Empower Urban New Quality Productive Forces?

—A Quasi-Natural Experiment Based on the Establishment of Data Trading Platforms

SI Zengchuo¹, WEI Yutong¹, MA Kewei²

(1. Jiangsu Normal University, Xuzhou 221116;

2. Shanxi University of Finance and Economics, Taiyuan 030006)

Abstract: Data have become a key input for urban growth, yet raw data do not create value on their own. Value is released only when rules governing ownership, trading, pricing, use, and governance transform data into a usable factor of production. This paper uses the staggered rollout of local data trading platforms in China as a quasi-natural experiment to explore the impact of data factor valorization on urban new quality productive forces (NQPFs).

The sample covers 288 prefecture-level and above cities in China from 2012 to 2023. A multi-period difference-in-differences model is employed to compare changes in NQPFs before and after platform establishment. Urban NQPFs are measured by an entropy index encompassing three dimensions: technological, green, and digital productive forces.

The findings show that data factor valorization enhances urban NQPFs. Mechanism analyses reveal that this impact is primarily achieved through technology-driven innovation, optimized resource allocation, and digital industry agglomeration. First, by expanding access to tradable data and lowering search and matching costs, the platforms improve the conversion of invention patent applications into granted patents. Second, by providing clearer price signals and enabling broader data utilization, the platforms improve the allocation of capital and labor across activities. Although overall resource misallocation declines, labor misallocation rises in the short run, reflecting the cost of skill and job adjustments during digital transformation. Third, by lowering the institutional and transaction costs for digital firms, the platforms support digital industry agglomeration and drive the spread of data, software, and digital services into other sectors. Together, these link data factor valorization to technical change, new input allocation, and industrial upgrading. Heterogeneity analysis reveals that this positive effect is concentrated in large cities, megacities, cities south of the Hu Huanyong line, and non-resource-based cities; cities with insufficient digital networks, skilled talent, market scale, or application scenarios exhibit weaker or even negative estimated effects. A panel threshold model also finds two cutoffs in urban scale: the policy effect first rises and then falls as the night-light measure of scale grows, with the largest gain in the middle range. This pattern suggests that cities need sufficient demand, skills, and scenarios to gain from data trade. However, when they grow too large, they may face higher costs in terms of regulatory coordination and system integration.

This paper provides city-level policy evidence on data markets and NQPFs, highlighting heterogeneous gains across inputs and cities. Policy should improve data-product usability, transaction rules, and platform linkages, while cities with weaker digital foundations should prioritize shared platforms, skills, and application scenarios.

Keywords: data factor valorization; data trading platform; new quality productive forces; technology-driven innovation; resource allocation; digital industry agglomeration

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