图书情报工作 ›› 2020, Vol. 64 ›› Issue (20): 96-105.DOI: 10.13266/j.issn.0252-3116.2020.20.011

• 情报研究 • 上一篇    下一篇

政务大数据政策的技术创新效应分析——基于PSM-DID方法的估计

陈玲1, 段尧清1,2   

  1. 1 华中师范大学信息管理学院 武汉 430079;
    2 湖北省数据治理与智能决策研究中心 武汉 430079
  • 收稿日期:2020-04-21 修回日期:2020-06-27 出版日期:2020-10-20 发布日期:2020-10-20
  • 作者简介:陈玲(ORCID:0000-0003-0379-3512),博士研究生,E-mail:2471685835@qq.com;段尧清(ORCID:0000-0002-8991-5842),教授,博士生导师。
  • 基金资助:
    本文系国家社会科学基金重点项目"基于全生命周期的政府开放数据整合利用机制与模式研究"(项目编号:17ATQ006)和中央高校基本科研业务费资助(优博培育项目)(项目编号:2019YBZZ098)研究成果之一。

Technological Innovation Effect Analysis of Government Big Data Policy——Estimation Based on PSM-DID Method

Chen Ling1, Duan Yaoqing1,2   

  1. 1 School of Information Management, Central China Normal University, Wuhan 430079;
    2 Hubei Data Governance and Intelligent Decision Research Center, Wuhan 430079
  • Received:2020-04-21 Revised:2020-06-27 Online:2020-10-20 Published:2020-10-20
  • Supported by:
     

摘要: [目的/意义] 技术创新是经济发展和转型的重要推动因素,实证分析我国政务大数据政策实施的技术创新效应,有助于推动国家创新驱动发展战略,促进数据要素与技术要素的融合发展,实现联动创新和开放创新。[方法/过程] 基于《促进大数据发展行动纲要》的发布和国家级大数据综合试验区的设立,以我国31个省份2000-2019年的面板数据作为研究样本,运用PSM方法对实验组和控制组样本进行倾向得分匹配,在此基础上运用DID方法进行双重差分,并通过变量替换方法进行稳健性检验,以此探究政务大数据政策与技术创新两者之间的因果关系。[结果/结论] 通过解决公共政策的内生性问题和虚拟事实的不可观测性,研究发现政务大数据政策可以推动技术创新。

 

关键词: 政务大数据, 技术创新效应, 政策评估, 倾向得分匹配, 双重差分

Abstract: [Purpose/significance] Technological innovation is an important driving factor for economic development and transformation. We attempt to empirically analyze the technological innovation effect of China government big data policy, which will help to promote the national innovation driven development strategy and the deep integration of data elements and technical elements,realizing linkage innovation and open innovation.[Method/process] Based on the action plan for big data development and the establishment of the national comprehensive test area for big data, this paper took the panel data of 31 provinces in China from 2000 to 2019 as the research sample, and used Propensity Score Matching method to analyze the tendency score matching between the experimental group and the control group. In addition, this paper utilized the Difference in Differences method to double difference the matched samples, and used the method of variable replacement to test the robustness, so as to explore the relationship between the government big data policy and technological innovation.[Result/conclusion] By solving the endogenous problem of public policy and the non observability of virtual facts, we have found that government big data policy can promote technological innovation.

Key words: government big data, technological innovation effect, policy evaluation, propensity score matching, difference in difference

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