Measuring Learning on the Job
Name
Liu-jiageng-SMMR-Management-2024-thesis.pdf
Description
Thesis PDF
Size
1.63 MB
Format
Adobe PDF
Checksum (MD5)
18220a41a50f69877af2c218e49b5e64
Author(s)
Liu, Jiageng
Advisor(s)
Schoar, Antoinette
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
I study on-the-job learning at IT firms. Using detailed online activity data of 144,000 employees matched with 25,000 firms across the globe, I measure the intensity and the direction of technology acquisition, a key input to innovation. A standardized measure shows that employee-entrepreneurs who join small, young firms spend more time learning about new software than similar employees who join large incumbent firms. They engage with more diverse and rarely combined topics, behaviors that are found to be associated with more radical innovations. Within firms, more actively learning employees work on more projects and start reviewing others' code sooner. The results are consistent with channels of firm-employee matching and job security at incumbent firms. They complement Akcigit and Goldschlag (2023) that finds inventors apply for fewer patents and receive higher wages after joining incumbent firms. A heterogeneous supply of unobserved learning cannot explain all results. I also document life-cycle patterns of learning behavior that are consistent with predictions of the standard labor theory. Such predictions had been challenging to test past formal education.
MIT Department
Sloan School of Management
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