开始:VCALENDAR版本:2.0 PRODID: / /学院Labor Economics//Zope//EN METHOD:PUBLISH CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Berlin BEGIN:DAYLIGHT TZOFFSETFROM:+0100 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU DTSTART:19810329T020000 TZNAME:CEST TZOFFSETTO:+0200 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU DTSTART:19961027T030000 TZNAME:CET TZOFFSETTO:+0100 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:33491606777200@conference.iza.org LOCATION;CHARSET=UTF-8:Zoom Seminar DESCRIPTION:We implement a generalized random forest (Athey et al., 2019) to a differencein-difference setting to identify substantial heterogeneity in earnings losses across displaced workers. Using administrative data from Austria over three decades we document that a quarter of workers face cumulative 11-year losses higher than 2 times their pre-displacement annual income, while almost 10% of individuals experience gains. Our methodology allows us to consider many competing theories of earnings losses. We find that the displacement firm�s wage premia and the availability of well paying jobs in the local labor market are the two most important factors. This implies that earnings losses can be understood by mean reversion in firm wage premia and losses in match quality, rather than by a destruction of firm-specific human capital. We further show that 94% of the cyclicality of earnings losses is explained by compositional changes of displaced workers over the business cycle. SEQUENCE:1 X-APPLE-TRAVEL-ADVISORY-BEHAVIOR:AUTOMATIC SUMMARY:IZA Seminar: Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach by Andreas Gulyas (University of Mannheim) DTSTART;TZID=Europe/Berlin:20201201T140000 DTEND;TZID=Europe/Berlin:20201201T151500 END:VEVENT END:VCALENDAR