Consumer Protection in an Online World: An Analysis of Occupational Licensing
summary
Uses eight months of platform data (millions of bids, all 50 states) plus a 5,000-person survey and an event‑study that exploits the 5.5‑day average lag between license submission and verification; instruments price with geographic distance, uses a jackknife instrument for ratings, builds a principal‑component license‑stringency index from Institute for Justice rules and applies machine‑learning / double‑ML for task complexity. Finds license badges do not raise hiring probability, while stricter licensing cuts bids by ~2.4% and raises quoted prices ~3.2% (up to +17% for jobs >$1,000).
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chapters (20)
- 0:00Intro
- 0:51Occupational Licensing is very common
- 3:31Occupational Licensing in an Online World
- 4:34Research Questions
- 6:49Online Platform for Home Improvement Services
- 9:03Platform License Validation
- 10:15Most Common Licenses in Home Services
- 14:47Data
- 15:321. Setting / Descriptive Stats
- 15:46Event Study: License and First Review
- 17:16Does Hire Rate Change around Verification?
- 23:07Does Hire Rate Change around First Review?
- 24:21Choice Regression
- 26:51Beyond Our Sample
- 29:38Are Consumers in Favor of Licensing Regulation?
- 30:17Effects of Licensing on Market Outcomes
- 31:54Measuring Licensing Stringency at State-Occupation Level
- 32:47Dimensionality Reduction
- 37:07Baseline Results
- 40:17Results Broken Down by Price Point
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