Does Information Help or Hinder Job Applicants from Less Developed Countries in Online Markets?
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".