利用社交媒体衡量劳动力市场流动

Dolan Antenucci, Michael J. Cafarella, Margaret C. Levenstein, C. Ré, M. Shapiro
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引用次数: 118

摘要

社交媒体为测量经济活动和分析经济行为提供了有前景的新方法,使用的信息独立于标准调查和行政来源,可以实现高频率和实时的分析。本文使用Twitter上的数据来创建失业、求职和招聘的索引。信号是通过计算twitter上与工作相关的短语(如“丢了工作”)得出的。社交媒体指数是由这些信号的主成分构成的。密歇根大学的社交媒体失业指数以中等和高频率跟踪首次申请失业保险的人数,并预测首次申请失业保险的共识预测的预测误差方差的15%至20%。社交媒体指数提供了飓风桑迪和2013年政府关闭等事件的实时指标。将失业指数与搜索和招聘指数进行比较,可以发现贝弗里奇曲线自2011年以来一直在向内移动。密歇根大学社交媒体失业指数每周更新,可在http://econprediction.eecs.umich.edu/上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Social Media to Measure Labor Market Flows
Social media enable promising new approaches to measuring economic activity and analyzing economic behavior at high frequency and in real time using information independent from standard survey and administrative sources. This paper uses data from Twitter to create indexes of job loss, job search, and job posting. Signals are derived by counting job-related phrases in Tweets such as "lost my job." The social media indexes are constructed from the principal components of these signals. The University of Michigan Social Media Job Loss Index tracks initial claims for unemployment insurance at medium and high frequencies and predicts 15 to 20 percent of the variance of the prediction error of the consensus forecast for initial claims. The social media indexes provide real-time indicators of events such as Hurricane Sandy and the 2013 government shutdown. Comparing the job loss index with the search and posting indexes indicates that the Beveridge Curve has been shifting inward since 2011. The University of Michigan Social Media Job Loss index is update weekly and is available at http://econprediction.eecs.umich.edu/.
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