Industry Mix, Local Labor Markets, and the Incidence of Trade Shocks
Steffen Müller, Jens Stegmaier, Moises Yi
Journal of Labor Economics,
forthcoming
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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Application Barriers and the Socioeconomic Gap in Child Care Enrollment
Henning Hermes, Philipp Lergetporer, Frauke Peter, Simon Wiederhold
IWH Discussion Papers,
No. 13,
2024
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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12.03.2024 • 8/2024
Risk in the banking sector: four out of ten top supervisors come from the financial industry
Europe's banks realise excess returns on the stock market when their alumni join the boards of national supervisory authorities. A study by the Halle Institute for Economic Research (IWH) shows that this happens more frequently than previously recognised. The findings indicate a risk to financial stability and call for a more merit-based, transparent appointment of senior regulators.
Michael Koetter
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Discrimination in Universal Social Programs? A Nationwide Field Experiment on Access to Child Care
Henning Hermes, Philipp Lergetporer, Fabian Mierisch, Frauke Peter, Simon Wiederhold
IWH Discussion Papers,
No. 12,
2023
Abstract
Although explicit discrimination in access to social programs is typically prohibited, more subtle forms of discrimination prior to the formal application process may still exist. Unveiling this phenomenon, we provide the first causal evidence of discrimination against migrants seeking child care. We send emails from fictitious parents to > 18, 000 early child care centers across Germany, inquiring about slot availability and application procedures. Randomly varying names to signal migration background, we find that migrants receive 4.4 percentage points fewer responses. Replies to migrants contain fewer slot offers, provide less helpful content, and are less encouraging. Exploring mechanisms using three additional treatments, we show that discrimination is stronger against migrant boys. This finding suggests that anticipated higher effort required for migrants partly drives discrimination, which is also supported by additional survey and administrative data. Our results highlight that difficult-to-detect discrimination in the pre-application phase could hinder migrants’ access to universal social programs.
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