Does Information Help or Hinder Job Applicants from Less Developed Countries in Online Markets?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Online markets reduce certain transaction costs related to global outsourcing. We focus on the role of verified work experience information in affecting online hiring decisions. Prior research shows that additional information about job applicants may disproportionately help or hinder disadvantaged populations. Using data from a major online contract labor platform, we find that contractors from less developed countries (LDCs) are disadvantaged relative to those from developed countries (DCs) in terms of their likelihood of being hired. However, we also find that although verified experience information increases the likelihood of being hired for all applicants, this effect is disproportionately large for LDC contractors. The LDC experience premium applies to other outcomes as well (wage bids, obtaining an interview, being shortlisted). Moreover, it is stronger for experienced employers, suggesting that learning is required to interpret this information. Finally, other platform tools (e.g., monitoring) partially substitute for the LDC experience premium; this provides additional support for the interpretation that the effect is due to information about experience rather than skills acquired from experience. We discuss implications for the geography of production and public policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it