数据要素如何影响消费韧性?

How do Data Factors Affect Consumption Resilience?

刘新愿,盛鹏飞

2026 第 52 卷 第 09 期 页码 139-153

DOI: 10.16538/j.cnki.jfe.20260720.402

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摘要

在外部不确定性上升和扩大内需战略深化推进的双重背景下,数据要素正深刻改变消费模式与运行机制,为增强消费韧性发挥了重要作用。文章基于2009—2023年全国252个城市面板数据,实证探究了数据要素对消费韧性的影响及作用机制,并分析不同政策环境下数据要素的边际影响效应。文章利用城市面对外部不确定时消费的波动程度测度消费韧性,并结合数据要素供给基础与利用能力,采用省级数据要素发展水平与市级数据要素利用指数的交互项测度城市数据要素发展水平,并以此表示城市数据要素发展状况。实证结果表明,数据要素能够显著增强消费韧性,该结论在经过一系列稳健性检验后仍然成立。机制分析表明,数据要素通过两条路径增强消费韧性:一是通过提升收入水平增强居民消费倾向,进而增强消费韧性;二是通过优化消费结构增强消费韧性。进一步分析表明,不同政策下数据要素的作用效果存在差异:“宽带中国”政策显著削弱数据要素的边际影响,公共数据开放政策提升数据要素的边际影响,而数据交易平台政策并未改变数据要素的边际影响。异质性分析表明数据要素对消费韧性的作用在非省会城市、外贸依存度较低、数据应用吸收能力强的城市更明显。文章的结论对数据要素供给体系建设、政策协同转化以及城市差异化治理等方面提供了政策参考。

As the digital economy advances, data factors are becoming more deeply embedded in household consumption through digital platforms, mobile payments, and related applications, making the extent to which their value can be translated into stronger consumption resilience a key issue in current efforts to promote consumption and expand domestic demand. A systematic examination of the effects and mechanisms of data factors on consumption resilience not only deepens the theoretical understanding of the relationship between data factors and household consumption, but also provides empirical evidence for strengthening consumption resilience and reinforcing domestic economic circulation.

Using panel data from 252 Chinese cities from 2009 to 2023, this paper examines the impact of data factors on consumption resilience. The results show that data factors significantly enhance consumption resilience. Mechanism testing identifies two principal channels: One is to raise residents’ income levels and average propensity to consume; the other is to promote the upgrading of the consumption structure. The effect varies across policy environments, as the Broadband China policy weakens the marginal effect of data factors, public data openness strengthens it, and data trading platform policies generate no significant moderating effect.

The contributions of this paper are as follows: First, it measures consumption resilience by the fluctuation of consumption relative to external uncertainty, providing a more comprehensive representation of its theoretical meaning. Second, it combines provincial data-factor development with municipal utilization capacity, capturing both the supply foundation of data factors and the process through which they are absorbed and transformed into economic value. Third, it clarifies the mechanisms through which data factors enhance consumption resilience from the perspectives of income and consumption structure. Fourth, it examines policy environments related to digital infrastructure, public data sharing, and data-factor marketization, further revealing the institutional conditions under which data factors exert their effects.

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