Misclassification in linear-in-means models: Theory and application to peer effects estimation
Abstract
This paper investigates how misclassification affects the estimation of peer effects in linear-in-means (LIM) models. We formally show that the peer effect estimate is biased by an ”own effect” due to error in the group average and a smearing effect due to misclassification in the individual regressor. The direction of the total bias depends on how misclassification is distributed across groups. We develop a simple heuristic based on the ratio of the estimated peer effect to the estimated individual effect to help researchers identify the likely direction of the total bias. We verify the theoretical predictions in the setting of peer effects of students with learning disabilities on other students’ performance.
Fields of Study & Subjects
Advanced Causal Inference Techniques
Psychometric Methodologies and Testing
School Choice and Performance
Publication details
- Journal / source
- Labour Economics
- Publisher
- Elsevier BV
- Volume
- 102
- Pages
- 102887–102887
- Publication date
- 25 May 2026
- Language
- EN