The Chief Human Resource Officer in the C-suite: Peer Prevalence and Environmental Uncertainty
David Bendig, Kathrin Haubner, Jonathan Hoke, Sabrina Jeworrek
International Journal of Human Resource Management,
Nr. 11,
2024
Abstract
The chief human resource officer (CHRO) role elevates people-related matters to the apex of the firm. Why do some companies’ leading management teams place so much emphasis on human resources while others do not? The present study argues that CHROs’ presence in the C-suite is driven by firms’ imitation of industry peers’ leadership structures as a response to uncertainty. The investigation also sheds light on the moderating role of environmental factors that can influence mimetic isomorphism in HR leadership. Through a longitudinal analysis of large listed firms between 2006 and 2020, the study shows a positive relationship between the prevalence of the CHRO position among firms’ peers and a focal firm having a CHRO in its top management. The results demonstrate that certain types of uncertainty serve as boundary conditions for such copying actions: Industry growth strengthens mimicking behavior while industry dynamism weakens it. There is no clear evidence for the moderating role of industry competition. The findings contribute a neo-institutional view of human resource structures in the top management and strengthen the bond between the strategy and human resource literature.
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Church Membership and Economic Recovery: Evidence from the 2005 Hurricane Season
Iftekhar Hasan, Stefano Manfredonia, Felix Noth
Economic Journal,
Nr. 664,
2024
Abstract
This paper investigates the critical role of church membership in the process of economic recovery after high-impact natural disasters. We document a significant adverse treatment effect of the 2005 hurricane season in the Southeastern United States on establishment-level productivity. However, we find that establishments in counties with higher rates of church membership saw a significantly stronger recovery in terms of productivity for 2005–10. We also show that church membership is correlated with post-disaster entrepreneurship activities and population growth.
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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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