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C1c Online Learning in the Face of Unemployment
Short Description
Shortly after the onset of the COVID-19 pandemic the unemployment rate reached 14.7%-- the highest rate since the Great Depression. We use COVID-19 as an event study to compute the average treatment effect (ATE) of both voluntary and involuntary job exits on online educational content using augmented inverse propensity-weighted (AIPW) estimators. We found that following the pandemic, individuals who were involuntarily laid off increased their consumption of online educational content compared to people who were not laid off. We argue that this subgroup engages in online educational content in order to increase their human capital and have higher job prospects. Our significant findings are critical for policymakers when planning workforce initiatives during periods of mass layoffs.
Type of presentation
Accepted Oral Presentation