The Effects of Online Social Identity Signals on Retailer Demand
Bibliographic record
Abstract
Recent shifts in societal discourse have led digital platforms to support equity, inclusivity, and diversity by introducing identity signaling features, e.g., indicators of owner race or gender. In this work, we explore whether, when, and how using those features may impact retailer demand. We tackle this question via a multi-method study. We begin by conducting a controlled experiment on Prolific.co, presenting subjects' with actual Google Places business profiles for a set of Black-owned restaurants in Chicago. We randomly vary the presence of Black-owned and Women-owned labels in these profiles and assess subjects' expectations of popularity and quality along various restaurant dimensions. Our results demonstrate that the Black-owned label, in particular, drives significant increases in all outcomes, with the effects arising primarily from Black and democratically liberal subjects. Next, we conduct an archival analysis of the effect label adoption has on the physical foot traffic that retailers receive, based on SafeGraph's mobile patterns database. Our difference-in-differences estimations yield consistent results; we find evidence of a positive average effect on foot traffic volumes. Further, we show that a rise in visitors from liberal-leaning geographies drives these effects. We discuss implications for digital platform operators and for retailers.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".