Network Referrals and Self-Presentation in the High-Tech Labor Market
Bibliographic record
Abstract
The practice of recruiting job candidates sourced through social contacts (i.e., referrals) is pervasive in the labor market. One reason employers prefer to recruit through referrals is that these candidates often present resumes that are perceived to be a better fit for the role. Whereas existing research attributes this pattern to how individuals who make referrals (i.e., referrers) select individuals to refer, we propose a new mechanism: differences in self-presentation. We argue that referral ties increase the candidates’ propensity to engage in self-presentation work, motivating and assisting candidates in presenting their backgrounds to convey fit. We examine this claim by utilizing unique data from an applicant-tracking system containing job applications for positions at U.S.-based high-tech firms between 2008 and 2012. A candidate fixed-effects specification reveals that when a candidate applies to a firm via a referral, he or she tends to showcase a rendition of his or her career history that better matches the target job than when the candidate pursues positions without such ties. Several mechanism checks, combined with supplementary survey evidence, further indicate that the presence of referral ties to the target firm is associated with greater motivation to engage in self-presentation work as well as the provision of different forms of assistance in that work. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.16674 .
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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 | 0.005 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".