Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 73 tok/s
Gemini 2.5 Pro 42 tok/s Pro
GPT-5 Medium 26 tok/s Pro
GPT-5 High 34 tok/s Pro
GPT-4o 96 tok/s Pro
Kimi K2 191 tok/s Pro
GPT OSS 120B 454 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Characterizing Measurement Error in the German Socio-Economic Panel Using Linked Survey and Administrative Data (2501.03015v1)

Published 6 Jan 2025 in econ.GN and q-fin.EC

Abstract: This paper exploits the linkage of German administrative social security data (GER: Integrierte Erwerbsbiografien) and survey data from the socio-economic panel (GER: Sozio-\"okonomisches Panel, SOEP) for the characterization of measurement error in metrics quantifying individual-specific labor earnings in Germany. We find that survey participants' decision whether to consent to linkage is non-random based on observables. In that sense, the studied sample does not constitute a random sample of SOEP. Measurement error is not classical: we observe underreporting of income on average, autocorrelation, and non-zero correlation with the true signal and other observable characteristics. In levels, calculated reliability ratios above 0.94 hint at a relaitvely small attenuation bias in simple linear univariate regressions with earnings as the explanatory variable. For changes in income, i.e. first differences, the bias from measurement error is exacerbated.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 2 posts and received 0 likes.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube