Signaling and Employer Learning with Instruments Gaurab Aryal Manudeep Bhuller Fabian Lange NBER Working Paper No. 25885

Issued in May 2019, Revised in February 2020

NBER Program(s):Economics of Education, Labor Studies

The social and the private returns to education differ when education can increase productivity, and also be used to signal productivity. We show how instrumental variables can be used to separately identify and estimate the social and private returns to education within the employer learning framework of Farber and Gibbons [1996] and Altonji and Pierret [2001]. What an instrumental variable identifies depends crucially on whether the instrument is hidden from, or observed by, the employers. If the instrument is hidden then it identifies the private returns to education, but if the instrument is observed by employers then it identifies the social returns to education. Interestingly, however, among experienced workers the instrument identifies the social returns to education, regardless of whether or not it is hidden. We operationalize this approach using local variation in compulsory schooling laws across multiple cohorts in Norway. Our preferred estimates indicate that the social return to an additional year of education is 5%, and the private internal rate of return, aggregating the returns over the life-cycle, is 7.2%. Thus, 70% of the private returns to education can be attributed to education raising productivity and 30% to education signaling workers' ability. You may purchase this paper on-line in .pdf format from SSRN.com ($5) for electronic delivery. Access to NBER Papers You are eligible for a free download if you are a subscriber, a corporate associate of the NBER, a journalist, an employee of the U.S. federal government with a ".GOV" domain name, or a resident of nearly any developing country or transition economy. If you usually get free papers at work/university but do not at home, you can either connect to your work VPN or proxy (if any) or elect to have a link to the paper emailed to your work email address below. The email address must be connected to a subscribing college, university, or other subscribing institution. Gmail and other free email addresses will not have access. E-mail:

Acknowledgments Machine-readable bibliographic record - MARC, RIS, BibTeX Document Object Identifier (DOI): 10.3386/w25885