[论文解读] Collective Learning in China's Regional Economic Development

Jian Gao, Bogang Jun+3 more|arXiv (Cornell University)|Mar 4, 2017

Regional Economics and Spatial Analysis参考文献 37被引用 15

一句话总结

本文利用1990年至2015年共25年的省级数据，研究了中国区域经济发展中的集体学习机制，表明产业内学习（来自本省相关产业）和区域间学习（来自邻近省份）均推动了产业结构多样化。关键发现是，这两种学习渠道均呈现收益递减，表明其具有替代关系，而高铁网络的扩展为区域间学习在提升产业相似度和生产率方面的作用提供了因果证据。

ABSTRACT

Industrial development is the process by which economies learn how to produce new products and services. But how do economies learn? And who do they learn from? The literature on economic geography and economic development has emphasized two learning channels: inter-industry learning, which involves learning from related industries; and inter-regional learning, which involves learning from neighboring regions. Here we use 25 years of data describing the evolution of China's economy between 1990 and 2015--a period when China multiplied its GDP per capita by a factor of ten--to explore how Chinese provinces diversified their economies. First, we show that the probability that a province will develop a new industry increases with the number of related industries that are already present in that province, a fact that is suggestive of inter-industry learning. Also, we show that the probability that a province will develop an industry increases with the number of neighboring provinces that are developed in that industry, a fact suggestive of inter-regional learning. Moreover, we find that the combination of these two channels exhibit diminishing returns, meaning that the contribution of either of these learning channels is redundant when the other one is present. Finally, we address endogeneity concerns by using the introduction of high-speed rail as an instrument to isolate the effects of inter-regional learning. Our differences-in-differences (DID) analysis reveals that the introduction of high speed-rail increased the industrial similarity of pairs of provinces connected by high-speed rail. Also, industries in provinces that were connected by rail increased their productivity when they were connected by rail to other provinces where that industry was already present. These findings suggest that inter-regional and inter-industry learning played a role in China's great economic expansion.

研究动机与目标

• 探究1990至2015年间中国经济快速扩张背后的机制，特别是集体学习在产业结构多样化中的作用。
• 检验产业内学习（从本省相关产业中学习）和区域间学习（从邻近省份学习）是否促进新产业的出现。
• 评估这两种学习渠道是否相互增强或相互替代，尤其是在收益递减的条件下。
• 利用高铁作为工具变量，解决内生性问题，以分离区域间学习的因果效应。
• 提供实证证据，说明集体学习过程如何塑造中国区域经济复杂性与生产率增长。

提出的方法

• 基于产品共现数据构建产业空间，根据产品空间中的接近程度定义相关产业。
• 通过分析新产业在某省出现的概率随已有相关产业数量的变化，衡量产业内学习。
• 通过评估新产业在某省出现的可能性随邻近省份已生产该产业的数量变化，量化区域间学习。
• 采用双重差分（DID）框架，将高铁开通作为工具变量，以分离区域间学习的因果效应。
• 使用DID方法比较高铁连通与非连通邻近省份在高铁开通前后产业结构相似性与企业生产率的变化。
• 建立产业内学习与区域间学习的交互模型，检验收益递减现象，表明两种渠道之间存在替代效应。

实验结果

研究问题

• RQ1 本省中已有相关产业的数量在多大程度上提高了新产业出现的可能性？
• RQ2 邻近省份的产业存在如何影响某省新产业出现的概率？
• RQ3 产业内学习与区域间学习渠道是相互增强，还是呈现收益递减，表明其为替代关系？
• RQ4 高铁连通是否因果性地提高了邻近省份之间的产业结构相似度与生产率？
• RQ5 在解决内生性问题后，区域间学习在中国区域经济发展中扮演了何种因果角色？

主要发现

• 新产业在某省出现的概率随着已有相关产业数量的增加而上升，为产业内学习提供了有力证据。
• 新产业在某省出现的概率也随着邻近省份已生产该产业的数量增加而上升，支持区域间学习的存在。
• 产业内学习与区域间学习的联合效应呈现收益递减，表明两种渠道更倾向于替代而非互补。
• 高铁开通显著提高了连通省份对之间的产业结构相似度，表明通过改善连通性增强了区域间学习。
• 高铁连通省份中的产业在拥有同行业生产邻近省份时，其生产率更高，证实了区域间学习的因果作用。
• 在控制内生性后结果依然稳健，高铁作为有效工具变量，成功隔离了区域间学习对产业发展因果影响。