大数据征信与平台流量:基于共享单车免押骑行的经验研究

Big-Data Credit Investigation and Platform User Traffic:An Empirical Study of Deposit-Free Bike-Sharing

基于大数据技术的个人征信服务是构建社会信用体系的一项重要内容。由于中国目前主要的大数据征信服务供应商是互联网平台企业的内嵌板块而非独立主体,其业务蓬勃发展的同时也引发了关于用户流量向头部平台集中的担忧。本文以芝麻信用分达到650分即可免押金骑行某品牌共享单车这一消费情境为例,利用消费者个体层面数据考察了大数据征信对互联网平台流量的影响。我们使用断点回归的识别策略发现,免押骑行政策导致用户在支付宝端口使用共享单车的比例提高了约12个百分点,而其主要竞争对手平台的流量均有不同程度的下滑。本文首次从计量上发现,互联网平台企业提供的大数据征信服务可能影响市场竞争结构,这为今后一个时期中国互联网平台企业反垄断规制和社会信用体系建设提供了参考。

Personnel credit investigation services based on big-data technology play a key role in the construction of the social credit system. However, the current major providers of big-data credit investigation services in China are mostly integrated into internet platform companies rather than independent entities, and therefore their rapid growth has also raised concerns regarding the concentration of user traffic on major platforms. This paper uses the policy adopted by a leading bike-sharing platform where a consumer can use its shared bikes without any cash pledge if his/her Zhima Credit score reaches650 points. Using individual consumer-level data aided by a regression discontinuity identification strategy, the paper finds that the credit-based deposit-free policy increases the proportion of bike-sharing usage through the Alipay APP by about 12%, while all other competing platforms experience decreases in user traffic. The paper provides novel empirical evidence showing that internet platform companies could go so far as to leverage big-data credit investigation technology to affect the competitive structure of the market. This insight offers rich policy implications for anti-trust regulation of internet platforms and the construction of the social credit system in China.