The 14th International German Socio-Economic Panel User Conference (SOEP2022) will be held in Berlin from June 30 to July 1, 2022 at DIW Berlin. The conference provides researchers who use the SOEP (including the SOEP part of the Cross-National Equivalent File (CNEF) and LIS/LWS data) with the opportunity to present and discuss their work with their peers. Researchers of all disciplines (e.g.,...
Using data on 4.1 million apps at the Google Play Store from 2016 to 2019, we document that GDPR induced the exit of about a third of available apps; and in the quarters following implementation, entry of new apps fell by half. We estimate a structural model of demand and entry in the app market. Comparing long-run equilibria with and without GDPR, we find that GDPR reduces consumer surplus and...
Wie unterscheiden sich Menschen in ihren sozialen Beziehungen? Mit Hilfe von Smartphones geht dieser Frage ein Team von WissenschaftlerInnen des SOEP am DIW Berlin, der Universität Heidelberg und der Ludwig-Maximilians-Universität (LMU) München nach. Dabei setzen die PsychologInnen Smartphones einerseits als „mobile Fragebögen“ ein. Andererseits untersuchen sie die alltägliche ...
In der Folge des ntv Podcast "Wieder was gelernt" vom 4. Februar 2022 "Tech-Riesen, Onlineshops, Reiche - Das sind die Gewinner der Pandemie" spricht Caroline Amme mit Alexander Kritikos darüber, welche Branchen von der Corona-Krise profitieren und ob der Erfolg anhält. Hier geht es zum Podcast
Nonprobability online panels are commonly used in the social sciences as a fast and inexpensive way of collecting data in contrast to more expensive probability-based panels. Given their ubiquitous use in social science research, a great deal of research is being undertaken to assess the properties of nonprobability panels relative to probability ones. Much of this research focuses on selection bias, ...
About 80 million people were displaced worldwide at the end of 2020. To support this highly vulnerable group, in recent years, local bottom-up initiatives proliferated to support refugee integration in hosting communities. This study examines a network intervention for refugees in collaboration with a social start-up whose mission is to match refugees and local volunteers to form friendships. We apply ...
We quantify the value of data for the prediction policy problem of reducing antibiotic prescribing to curb antibiotic resistance. Using varying combinations of administrative data, we evaluate machine learning predictions for diagnosing bacterial urinary tract infections and the outcomes of prescription rules based on these predictions. Simple patient demographics improve prediction quality substantially ...