Job Market Candidates
Job Market Candidates Marius Fourné Marius Fourné is a PhD candidate in Economics at the Halle Institute for Economic Research (IWH) and Martin Luther University of…
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From Shares to Machines: How Common Ownership Drives Automation
Joseph Emmens, Dennis Hutschenreiter, Stefano Manfredonia, Felix Noth, Tommaso Santini
IWH Discussion Papers,
Nr. 23,
2024
Abstract
Does increasing common ownership influence firms’ automation strategies? We develop and empirically test a theory indicating that institutional investors’ common ownership drives firms that employ workers in the same local labor markets to boost automation-related innovation. First, we present a model integrating task-based production and common ownership, demonstrating that greater ownership overlap drives firms to internalize the impact of their automation decisions on the wage bills of local labor market competitors, leading to more automation and reduced employment. Second, we empirically validate the model’s predictions. Based on patent texts, the geographic distribution of firms’ labor forces at the establishment level, and exogenous increases in common ownership due to institutional investor mergers, we analyze the effects of rising common ownership on automation innovation within and across labor markets. Our findings reveal that firms experiencing a positive shock to common ownership with labor market rivals exhibit increased automation and decreased employment growth. Conversely, similar ownership shocks do not affect automation innovation if firms do not share local labor markets.
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Forecast Combination and Interpretability Using Random Subspace
Boris Kozyrev
IWH Discussion Papers,
Nr. 21,
2024
Abstract
This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.
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Alumni IWH provides guidance and support in job placement after graduation, including letters of recommendation and career advice. Graduates have found placements in academia…
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