Public Concern About Immigration and Customer Complaints Against Minority Financial Advisors
Bibliographic record
Abstract
We examine the relation between public concern about immigration and customer complaints against minority financial advisors in the United States. We find that minority advisors are more likely to receive complaints in periods of high public concern about immigration than in other periods, relative to their white colleagues from the same firm, at the same office location, and at the same point in time. This result holds for both complaints with merit and dismissed complaints and is more pronounced in counties where residents likely hold stronger anti-immigration views. We also find that minority advisors are more likely to face regulatory actions or leave their firms after customer allegations in periods of high public concern about immigration than in other periods. Overall, our study provides descriptive evidence of a positive relation between public concern about immigration and customer dissatisfaction with minority advisors. This paper was accepted by Suraj Srinivasan, accounting. Funding: K. K. F. Law acknowledges financial support from a Nanyang Technological University’s Start-Up Grant. L. Zuo acknowledges financial support from the Cornell SC Johnson College of Business and fromUniversity of Toronto RogerMartin Award for Emerging Leaders. Supplemental Material: Code is available at https://doi.org/10.1287/mnsc.2021.4283 .
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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 | 0.002 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".