Industry Mix, Local Labor Markets, and the Incidence of Trade Shocks
Steffen Müller, Jens Stegmaier, Moises Yi
Journal of Labor Economics,
Nr. 3,
2024
Abstract
We analyze how skill transferability and the local industry mix affect the adjustment costs of workers hit by a trade shock. Using German administrative data and novel measures of economic distance we construct an index of labor market absorptiveness that captures the degree to which workers from a particular industry are able to reallocate into other jobs. Among manufacturing workers, we find that the earnings loss associated with increased import exposure is much higher for those who live in the least absorptive regions. We conclude that the local industry composition plays an important role in the adjustment processes of workers.
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Medienecho
Medienecho Juli 2025 Oliver Holtemöller: Wir versus Wirtschaft in: taz Die Tageszeitung, 01.07.2025 Juni 2025 Oliver Holtemöller: Eine Frage der Ideologie in: nd DER TAG,…
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Archiv
Medienecho-Archiv 2021 2020 2019 2018 2017 2016 Dezember 2021 IWH: Ausblick auf Wirtschaftsjahr 2022 in Sachsen mit Bezug auf IWH-Prognose zu Ostdeutschland: "Warum Sachsens…
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People
People Doctoral Students PhD Representatives Alumni Supervisors Lecturers Coordinators Doctoral Students Afroza Alam (Supervisor: Reint Gropp ) Annika Backes (Supervisors: Simon…
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DPE Course Programme Archive
DPE Course Programme Archive 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2025 Causal Machine Learning Martin Huber April 10-11, 2025 (IWH) Microeconomics…
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CompNet
Welcome to CompNet CompNet Data Provider Forum & TSI Workshop The upcoming CompNet Data Providers Forum and TSI Workshop, taking place in Amsterdam on November 18-19, 2024,…
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CompNet Database
The CompNet Competitiveness Database The Competitiveness Research Network (CompNet) is a forum for high level research and policy analysis in the areas of competitiveness and…
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Risky Oil: It's All in the Tails
Christiane Baumeister, Florian Huber, Massimiliano Marcellino
NBER Working Paper,
Nr. 32524,
2024
Abstract
The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.
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The Long-term Legacy of the Liberation of the Sudetenland by the Red and US Armies
Jakub Grossmann, Štěpán Jurajda
IDEA CERGE EI Studie,
Nr. 7,
2022
Abstract
Forced migration results in trauma to the millions of people displaced from their homes, but very little is known about the fate of those who avoided expulsion and became a minority in the new society. This analysis reveals how and to what degree the manner and extent of the post-war expulsion of the German population from the Sudetenland influenced the country’s long-term social development. (This publication is written in czech language.)
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Lecturers
Lecturers at CGDE Institutions Jordan Adamson Assistant Professor at Institute for Empirical Economic Research, Leipzig University. Website Course: Econometrics (winter term…
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