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Reposted by Marco Schmandt

Berlin School of Economics @bsoeberlin.bsky.social · 10/07/2026

🌍 Thank you to everyone who joined the 2nd Berlin PhD Conference in Economics! Proud to see this international conference organized by PhD students from the Berlin School of Economics. Thanks to all who contributed! #economics

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Reposted by Marco Schmandt

Stefan Bach @sbachtax.bsky.social · 09/06/2026

„Historisch gesehen waren Kriegs- und Krisenzeiten meist Momente, in denen Reichen & Vermögenden mehr Solidarität abverlangt wurde. Zumal es sich um jene Klasse handelt, die schon bald jeden zehnten Euro aus dem Haushalt in Form von Zinszahlungen erhält.“ www.faz.net/aktuell/feui...

faz.net

Reformen des Sozialstaats: Warum Widerstand berechtigt ist

Wer sich gegen Sparmaßnahmen wehrt, gilt aktuell als Reformverweigerer und Besitzstandswahrer. Doch der Umbau des Sozialstaats ist mehr als eine Standortfrage. Der deutsche Gesellschaftsvertrag steht ...

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Reposted by Marco Schmandt

Berlin School of Economics @bsoeberlin.bsky.social · 27/01/2026

🎯 Does random assignment really guarantee unbiased results? 👥 A new study by @marcodavis94.bsky.social, Constantin Tielkes, and Felix Weinhardt shows why this common assumption can be misleading. Read more 📑 berlinschoolofeconomics.de/about-us/new... #economicresearch #evidencebasedpolicy

When Random Assignment Is Not Enough for Causal Evidence
by Marco Schmandt, Constantin Tielkes, and Felix Weinhardt

Focus and Research Question

The paper examines whether studies that rely on random placement — the random assignment of people to places or groups — can still produce biased results when estimating the effects of local conditions or group characteristics. This matters because random placement is often treated as a gold standard for causal evidence in economics.

Core Idea

The authors show that random placement alone does not guarantee unbiased estimates of local factors, because people are assigned to places, not to specific local characteristics like unemployment or social attitudes. As a result, estimates can mix causal effects with hidden biases.

Data and Setting

The framework is tested using administrative data on more than 69,000 refugees in Germany, who were initially assigned to counties under a random dispersal policy. The data track individuals over time and capture all later moves, which is crucial for studying mobility bias.

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