What is failing us? An examination of diversity initiatives in financial planning
Bibliographic record
Abstract
Abstract This paper examines the lack of diversity in the financial planning profession by expanding the variable focus to include both the household side (i.e., consumer demand for financial advice) and the aspiring and existing financial planner professional side (i.e., the supply of financial advice). This study uses data from 13,507 households from the 2004 to 2016 waves of Survey of Consumer Finances (SCF) and data from 37,665 students enrolled at the College for Financial Planning for 2005 to 2018. We found that any amount of racial bias against non‐White financial planners could substantially reduce their market. Previous studies have focused almost exclusively on macro‐demographic comparisons. Therefore, reliable information on the extent of racial bias and satisfaction among racially discordant financial planner‐client relationships is not available. Black and Latinx households lag White and other households to attain high school diplomas and undergraduate degrees, partially explaining the lack of a diverse pipeline into the profession. We argue that there continues to be a need to eliminate survivorship bias when attempting to understand the realities faced by non‐White financial planning professionals by surveying those who were not successful in becoming financial planners.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".