Does Online Search Crowd Out Traditional Search and Improve Matching Efficiency? Evidence from Craigslist*
Bibliographic record
Abstract
Ever since the seminal work of Stigler (1962), economists have recognized that information in markets is costly to acquire and can lead to “search frictions”. The remarkable growth in online search has substantially lowered the cost of information acquisition. Despite this, there is little evidence concerning the extent to which this has altered the search process and raised overall matching efficiency. To address this issue, we analyze the expansion of the website "Craigslist", which allows users to post job ads and apartment and housing rental ads at virtually no cost. Exploiting the sharp geographic and temporal variation in the availability of online search, induced by Craigslist, we produce three key findings: Craigslist significantly lowered classified job advertisements in newspapers, caused a significant reduction in the apartment and housing rental vacancy rate, and had no effect on the unemployment rate. Contact Kroft at
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".