Data and Code for: "Measuring Science: Performance Metrics and the Allocation of Talent"

Principal Investigator(s): View help for Principal Investigator(s) Sebastian Hager, University of Oxford; Carlo Schwarz, Bocconi; Fabian Waldinger, University of Munich

Version: View help for Version V1

Name	File Type	Size	Last Modified
Reproduction Upload			10/11/2024 11:15:AM

Project Citation:

Hager, Sebastian, Schwarz, Carlo , and Waldinger, Fabian . Data and Code for: “Measuring Science: Performance Metrics and the Allocation of Talent.” Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-10-23. https://doi.org/10.3886/E205281V1

Project Description

Summary: We study how performance metrics affect the allocation of talent by exploiting the introduction of the first citation database in science. For technical reasons, it only covered citations from certain journals and years, creating quasi-random variation: some citations became visible, while others remained invisible. We identify the effects of citation metrics by comparing the predictiveness of visible to invisible citations. Citation metrics increased assortative matching between scientists and departments by reducing information frictions over geographic and intellectual distance. Highly-cited scientists from lower-ranked departments (“hidden stars”) and from minorities benefited more. Citation metrics also affected promotions and NSF-grants, suggesting Matthew effects.

Scope of Project

Subject Terms: economics of science

JEL Classification:
J62 Job, Occupational, and Intergenerational Mobility; Promotion
O31 Innovation and Invention: Processes and Incentives

Geographic Coverage: United States

Time Period(s): 1956 – 1969

Universe: Scientists in the United States in 1956 and 1969

Data Type(s): observational data

Methodology

Unit(s) of Observation: Individuals

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Published Versions

V1 [2024-10-23]

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