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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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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Wirtschaft im Wandel
Wirtschaft im Wandel Die Zeitschrift „Wirtschaft im Wandel“ unterrichtet die breite Öffentlichkeit über aktuelle Themen der Wirtschaftsforschung. Sie stellt wirtschaftspolitisch…
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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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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 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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Guiding Theme and Research Profile
Tasks of the IWH Guided by its mission statement , the IWH places the understanding of the determinants of long term growth processes at the centre of the research agenda. Long…
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Organisation of Research
Tasks of the IWH Guided by its mission statement , the IWH places the understanding of the determinants of long term growth processes at the centre of the research agenda. Long…
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