IWH Doctoral Programme in Economics
IWH Doctoral Programme in Economics The Halle Institute for Economic Research (IWH) offers doctoral positions in economics that lead to a PhD at a German university under the…
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Non-Standard Errors
Albert J. Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Koetter, Markus Kirchner, Sebastian Neusüss, Michael Razen, Utz Weitzel, Shuo Xia, et al.
Journal of Finance,
No. 3,
2024
Abstract
In statistics, samples are drawn from a population in a datagenerating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidencegenerating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
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Advanced Technology Adoption: Determinants and Labor Market Effects of Robot Use
Verena Plümpe
Otto-von-Guericke-Universität Magdeburg, PhD Thesis,
2024
Abstract
The recent advances in automation technology, robotics in particular, have sparked a heated debate over the future of labor and human society at large. The ongoing process of robotization may engender profound impacts on various segments of the labor market. Given the far-reaching implications of robots, it is thus very important to understand the scale and scope of robot use and characteristics of robot users. However, the main challenge is the limited availability of robot data at the microeconomic level (Raj and Seamans, 2018). Due to the data constraint, the bulk of the existing literature relies on cross-country industry-level data from the International Federation of Robotics (IFR). The lack of micro-level robot data makes it difficult to paint a comprehensive picture of robotization in industrial settings, and perhaps more importantly, to assess how within-industry firm level heterogeneity manifests itself in robot use and adoption.
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Centre for Business and Productivity Dynamics
Centre for Business and Productivity Dynamics (IWH-CBPD) The Centre for Business and Productivity Dynamics (CBPD) was founded in January 2025 and works with policy and research…
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Research Data Centre
Research Data Centre (IWH-RDC) Direct link to our Data Offer The IWH Research Data Centre offers external researchers access to microdata and micro-aggregated data sets that…
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Application Barriers and the Socioeconomic Gap in Child Care Enrollment
Henning Hermes, Philipp Lergetporer, Frauke Peter, Simon Wiederhold
Abstract
Why are children with lower socioeconomic status (SES) substantially less likely to be enrolled in child care? We study whether barriers in the application process work against lower-SES children — the group known to benefit strongest from child care enrollment. In an RCT in Germany with highly subsidized child care (N = 607), we offer treated families information and personal assistance for applications. We find substantial, equity-enhancing effects of the treatment, closing half of the large SES gap in child care enrollment. Increased enrollment for lower-SES families is likely driven by altered application knowledge and behavior. We discuss scalability of our intervention and derive policy implications for the design of universal child care programs.
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
No. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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Green Transition
Green Transition Research and Policy Advice for Structural Change in the German Economy Dossier, 18.06.2024 Green Transition The green transition is a key topic of our time. In a…
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Management Buyouts
Management Buyouts in Eastern Germany The study on management buyouts (MBOs) examines an important group of East German companies and their development: companies which, in the…
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Research Clusters
Three Research Clusters Research Cluster "Economic Dynamics and Stability" Research Questions This cluster focuses on empirical analyses of macroeconomic dynamics and stability.…
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