Full Citation
Title: (Machine) Learning About Immigration's Impact in Local Labor Markets With Classified Ad Text
Citation Type: Miscellaneous
Publication Year: 2019
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Abstract: The decennial Census provides detailed information about local labor markets but at very low-frequencies, making it difficult for researchers to identify short-run effects in local labor markets. Machine learning techniques open the door to studies analyzing local labor markets at high frequency by allowing researchers to analyze large text corpora, such as those found in classified ads. Motivated by Census tabulations showing a Cuban immigration surge in the Miami metro area around 1980, we document a decline in the Help Wanted Index, a monthly count of help-wanted classifieds, for Miami relative to geographic baselines. We conduct a supervised learning exercise demonstrating a statistically significant decline in the fraction of job openings for roles typically associated with less-educated workers using a 1/29 sample of the Miami Herald 's classifieds. We explore these results using topic modeling, which naturally generates occupational and industry categories with minimal researcher degrees of freedom. This exercise uncovers a statistically significant increase in the proportion of job openings advertised for positions in accounting, sales, and engineering and a corresponding decrease in the proportion advertised for positions in the food and automotive industry. Analysis of the Current Population Survey's Annual Social and Economic Supplement confirms the relative importance of job openings for less-educated workers' wage outcomes, but the linkage with employment outcomes is statistically indistinguishable from zero for all workers. We conclude with directions for future research and suggestions for agencies aiming to modernize their statistical releases. 1
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Authors: Anastasopoulos, L Jason; Borjas, George; Cook, Gavin; Lachanski, Michael
Publisher: Peter G Peterson Foundation
Data Collections: IPUMS CPS
Topics: Labor Force and Occupational Structure, Migration and Immigration
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