Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/237351 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
ECONtribute Discussion Paper No. 106
Verlag: 
University of Bonn and University of Cologne, Reinhard Selten Institute (RSI), Bonn and Cologne
Zusammenfassung: 
We study response behavior in surveys and show how the explanatory power of selfreports can be improved. First, we develop a choice model of survey response behavior under the assumption that the respondent has imperfect self-knowledge about her individual characteristics. In panel data, the model predicts that the variance in responses for different characteristics increases in self-knowledge and that the variance for a given characteristic over time is non-monotonic in self-knowledge. Importantly, the ratio of these variances identifies an individual's level of self-knowledge, i.e., the latter can be inferred from observed response patterns. Second, we develop a consistent and unbiased estimator for self-knowledge based on the model. Third, we run an experiment to test the model's main predictions in a context where the researcher knows the true underlying characteristics. The data confirm the model's predictions as well as the estimator's validity. Finally, we turn to a large panel data set, estimate individual levels of self-knowledge, and show that accounting for differences in self-knowledge significantly increases the explanatory power of regression models. Using a median split in self-knowledge and regressing risky behaviors on self-reported risk attitudes, we find that the R2 can be multiple times larger for above- than below-median subjects. Similarly, gender differences in risk attitudes are considerably larger when restricting samples to subjects with high self-knowledge. These examples illustrate how using the estimator may improve inference from survey data.
Schlagwörter: 
survey research
rational inattention
lab experiment
non-cognitive skills
preferences
JEL: 
C83
C91
D83
D91
J24
Dokumentart: 
Working Paper

Datei(en):
Datei
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