Optimizing Pricing Delegation to External Sales Forces via Commissions: An Empirical Investigation
Bibliographic record
Abstract
In this paper, using data from indirect auto lending and a structural model of external sales representative (ESR) behaviour, we investigate (1) the role of commissions as a potential tool to influence ESRs’ pricing decisions under limited authority, (2) the impact of optimized commissions on firm profitability, and (3) the implications for customer welfare. The results provide strong evidence for ESRs being strategic (vs. myopic) in their pricing and effort decisions; and in both cases, strategic behaviour is inversely proportional to customer risk. Moreover, once optimized, commissions are an effective tool for firms to bridge the profitability gap between centralized pricing and pricing delegation. Our analyses on social justice and fairness reveal that customer groups along the dimensions of customer risk, income class, and gender, which have been traditionally marginalized in society, suffer from inequities in the indirect-lending ecosystem. While, the optimization of commissions does not intensify these biases, we found females to be the exception, and that the inequities due to gender bias not only persist in the optimized regime, but also deepen. Through counterfactual simulations, we propose two policies for firms to minimize social inequity, which helps them balance immediate profit-maximizing goals with responsible AI initiatives.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".