临近预测地方经济:使用Yelp数据来衡量经济活动

E. Glaeser, Hyunjin Kim, Michael Luca
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引用次数: 65

摘要

来自在线平台的新数据来源是否有助于衡量当地的经济活动?来自美国人口普查局等机构的政府数据集提供了地方经济活动的标准衡量标准。然而,这些统计数据通常在多年后才出现,面向公众的版本汇总到县或邮政编码级别。相比之下,来自Yelp等在线平台的众包数据往往是同步的,而且在地理位置上比官方政府统计数据更准确。在本文中,我们提供的证据表明,Yelp的数据可以补充政府的调查,通过测量经济活动接近实时,在颗粒水平上,几乎在任何地理范围。在Yelp上评论的企业和餐馆数量的变化可以预测县商业模式中总体企业和餐馆数量的变化。在未用于生成算法的测试样本中,使用同期和滞后的Yelp数据的算法可以解释29.2%的剩余方差(考虑滞后的CBP数据)。对于人口更密集、更富裕、教育程度更高的邮政编码,该算法更为准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity
Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of local economic activity at the local level. However, these statistics typically appear only after multi-year lags, and the public-facing versions are aggregated to the county or ZIP code level. In contrast, crowdsourced data from online platforms such as Yelp are often contemporaneous and geographically finer than official government statistics. In this paper, we present evidence that Yelp data can complement government surveys by measuring economic activity in close to real time, at a granular level, and at almost any geographic scale. Changes in the number of businesses and restaurants reviewed on Yelp can predict changes in the number of overall establishments and restaurants in County Business Patterns. An algorithm using contemporaneous and lagged Yelp data can explain 29.2 percent of the residual variance after accounting for lagged CBP data, in a testing sample not used to generate the algorithm. The algorithm is more accurate for denser, wealthier, and more educated ZIP codes.
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