Measurement error in income and schooling, and the bias of linear estimators
(2017) In Journal Labor Economics p.1117-1148- Abstract
- We propose a general framework for determining the extent of measurement error bias in OLS and IV estimators of linear models, while allowing for measurement error in the validation source. We apply this method by validating Survey of Health, Ageing and Retirement in Europe (SHARE) data with Danish administrative registers.
Contrary to most validation studies, we find measurement error in income is classical, once we account for imperfect validation data. We find non-classical measurement error in schooling, causing a 38 percent amplification bias in IV estimators of the returns, with important implications for the program evaluation literature.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c0656edb-bfd7-48f6-bb9b-ad3150d18553
- author
- Bingley, Paul and Martinello, Alessandro LU
- organization
- publishing date
- 2017
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal Labor Economics
- pages
- 1117 - 1148
- publisher
- University of Chicago Press
- external identifiers
-
- scopus:85029767095
- ISSN
- 0734-306X
- DOI
- 10.1086/692539
- language
- English
- LU publication?
- yes
- id
- c0656edb-bfd7-48f6-bb9b-ad3150d18553
- date added to LUP
- 2016-08-16 11:20:10
- date last changed
- 2022-04-24 17:00:39
@article{c0656edb-bfd7-48f6-bb9b-ad3150d18553, abstract = {{We propose a general framework for determining the extent of measurement error bias in OLS and IV estimators of linear models, while allowing for measurement error in the validation source. We apply this method by validating Survey of Health, Ageing and Retirement in Europe (SHARE) data with Danish administrative registers.<br/>Contrary to most validation studies, we find measurement error in income is classical, once we account for imperfect validation data. We find non-classical measurement error in schooling, causing a 38 percent amplification bias in IV estimators of the returns, with important implications for the program evaluation literature.}}, author = {{Bingley, Paul and Martinello, Alessandro}}, issn = {{0734-306X}}, language = {{eng}}, pages = {{1117--1148}}, publisher = {{University of Chicago Press}}, series = {{Journal Labor Economics}}, title = {{Measurement error in income and schooling, and the bias of linear estimators}}, url = {{http://dx.doi.org/10.1086/692539}}, doi = {{10.1086/692539}}, year = {{2017}}, }