测量中产阶级化:使用Yelp数据量化邻里变化

E. Glaeser, Hyunjin Kim, Michael Luca
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引用次数: 19

摘要

我们证明,来自Yelp等数字平台的数据有可能提高我们对中产阶级化的理解,既可以提供接近实时的数据(即临近预报和预测),也可以提供有关当地经济如何变化的额外背景。结合Yelp和人口普查的数据,我们发现,以教育程度、年龄和种族构成的变化来衡量的中产阶级化,与杂货店、咖啡馆、餐馆和酒吧数量的增加密切相关,几乎没有证据表明其他类别的企业会被挤出市场。我们还发现,当地商业环境的变化是房价变化的领先指标,星巴克(以及更普遍的咖啡店)进入一个社区预示着士绅化。每增加一家星巴克,房价就会上涨0.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change
We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e. nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a ZIP code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes, and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.
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