| 王弟海,徐弘毅,党燕宇,陈诗一.政府共享数据对产业结构变迁和经济增长的影响[J].数量经济技术经济研究,2026,(7):107-132 | | 政府共享数据对产业结构变迁和经济增长的影响 | | The Impact of Government Data Sharing on Industrial Structural Transformation and Economic Growth | | | | DOI： | | 中文关键词: 数据要素 数据共享 产业结构 经济增长 | | 英文关键词: Data Factor Data Sharing Industrial Structure Economic Growth | | 基金项目: | | | 中文摘要: | | 政府数据共享是数据要素融入实体经济发展的重要方式之一，本文主要研究政府共享数据对产业结构变化和经济增长的影响机制和效应。本文首先分析了中国政府共享数据的发展规律和特征事实，研究发现，服务业部门的政府共享数据产出弹性显著大于工业部门。基于这一特征事实，本文构建了一个两部门的动态一般均衡模型，从理论上分析政府共享数据对产业结构和经济增长的影响。理论研究发现，政府共享数据会通过直接的数据深化效应和间接的结构调整效应影响产业结构变化；数据在促进增长方面则具有直接增长效应、结构变化交叉效应、加速资本积累的间接增长效应和产业结构调整效应等四种效应，其中第一和第三种效应为正，第四种效应为负，而第二种效应同经济结构变化的方向有关。最后，本文基于中国经济进行了数值模拟分析，研究发现，政府共享数据增长能部分抵消资本深化所带来的去工业化趋势，这使得整体产业结构变化速度放缓，并且政府共享数据通过这一机制能够提高经济增速。本文为理解数据要素驱动的结构调整与经济增长提供了机制解释，并为数据要素共享制度发展和改革提供了理论依据。 | | 英文摘要: | | The emergence of data as a production factor has the potential to reshape both the pace and the direction of economic development. In China, recent policy initiatives have accelerated the construction of government data-sharing platforms and substantially expanded the stock of publicly accessible datasets. However, the understanding of the macroeconomic mechanisms linking this expansion to industrial transformation and aggregate growth remains insufficiently developed in the theoretical literature. Although existing empirical studies documented the correlations between public data availability and micro-level outcomes such as firm innovation and productivity, they did not systematically account for the way in which sectoral heterogeneity in data utilization shapes the trajectory of structural change or determines the magnitude of the growth. This paper addresses this gap by developing a unified framework to analyze the dynamic consequences of government data sharing when the output elasticity of data differs across sectors.
First, we document a set of stylized facts concerning the evolution of government data provision in China. The evidence suggests that the output elasticity of data is unlikely to be uniform across sectors, with services exhibiting a higher elasticity than manufacturing.
To examine the macroeconomic implications of this heterogeneity, we construct a multi-sector dynamic general equilibrium model, in which data enters the production function of each sector with a distinct elasticity. This model yields several analytical insights. On the structural transformation dimension, government data sharing operates through two distinct channels. The first is a direct data-deepening effect: because data are more productive in one sector and consumer demand is not perfectly substitutable across goods, the optimal allocation of capital and labor shifts toward the sector with lower data elasticity, thereby altering the composition of output. The second is an indirect structural-adjustment effect: the data-induced change in the current industrial composition modifies the baseline from which future structural change unfolds, generating a dynamic feedback that dampens the overall speed of transformation. The results of the theoretical analysis also reveal four main mechanisms through which data influences economic growth: the direct growth effect of data; the structural change cross effect; the indirect growth effect; and the industrial structure adjustment effect. The direct growth effect is positive, and refers to the role of data supply growth in driving economic growth. The structural change cross effect is uncertain in terms of both magnitude and direction, and captures the role of the current industrial structure in mediating data-driven growth. The indirect growth effect is positive, and refers to the role of data accumulation in accelerating capital accumulation and promoting economic growth. The industrial structure adjustment effect refers to the impact on economic growth resulting from structural changes induced by data.
We calibrate the model to Chinese data and conduct numerical simulations to quantify these mechanisms. The baseline results and the counterfactual analysis indicate that data would slow down the pace of tertiarization and foster economic growth. The counterfactual analysis reveals that the difference of data-output elasticity could reduce the service share change by 1.1%. Overall, the shared government data increase annual output growth by approximately 0.31%, accounting for nearly 12% of the total growth rate observed over the simulation interval. The difference of data-output elasticity and capital-output elasticity, as well as the data sharing ratio, are analyzed to strengthen this conclusion.
This paper makes four primary contributions to existing literature. First, it performs a theoretical investigation of a new question, i.e., how government?shared data affect industrial structure, and proposes a novel mechanism of influence, i.e.,the differences in data output elasticity across industries. We find that when data elasticity in industry is lower than in services, data growth reallocates the economy toward industry, dampening the shift to services. Second, we expand the scope of existing theories, which considered only productivity or innovation channels, to examine also the structural?change channel. Data not only raise productivity, but also interact with structural change to affect growth, and enhance the capital’s growth effect by inhibiting the shift to services. Third, we uncover a new stylized fact: the output elasticity of government?shared data differs significantly across industry and services. Fourth, our numerical simulations quantify the direction and magnitude of each mechanism, offering practical implications for the reform of China’s government data?sharing platform. | | 查看全文 相关附件： 下载数据代码附录 |
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