{"title":"大流行病的裁员与留守订单的作用","authors":"Marianna Kudlyak, Erin L. Wolcott","doi":"10.2139/ssrn.4894607","DOIUrl":null,"url":null,"abstract":"We compile a novel high-frequency, detailed geographic dataset on mass layoffs from U.S. state labor departments. Using recent advances in difference-in-difference estimation with staggered treatment, we find that locally-mandated stay-at-home orders issued March 16–22, 2020 triggered mass layoffs equal to half a percent of the population in just one week. Our findings contribute to explanations for why job loss in 2020 was synchronous and catastrophic, yet temporary.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":"106 9","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pandemic Layoffs and the Role of Stay-at-Home Orders\",\"authors\":\"Marianna Kudlyak, Erin L. Wolcott\",\"doi\":\"10.2139/ssrn.4894607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We compile a novel high-frequency, detailed geographic dataset on mass layoffs from U.S. state labor departments. Using recent advances in difference-in-difference estimation with staggered treatment, we find that locally-mandated stay-at-home orders issued March 16–22, 2020 triggered mass layoffs equal to half a percent of the population in just one week. Our findings contribute to explanations for why job loss in 2020 was synchronous and catastrophic, yet temporary.\",\"PeriodicalId\":21855,\"journal\":{\"name\":\"SSRN Electronic Journal\",\"volume\":\"106 9\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SSRN Electronic Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.4894607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SSRN Electronic Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.4894607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pandemic Layoffs and the Role of Stay-at-Home Orders
We compile a novel high-frequency, detailed geographic dataset on mass layoffs from U.S. state labor departments. Using recent advances in difference-in-difference estimation with staggered treatment, we find that locally-mandated stay-at-home orders issued March 16–22, 2020 triggered mass layoffs equal to half a percent of the population in just one week. Our findings contribute to explanations for why job loss in 2020 was synchronous and catastrophic, yet temporary.
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