Behavioral economics and monetary wisdom: A cross‐level analysis of monetary aspiration, pay (dis)satisfaction, risk perception, and corruption in 32 nations
Bibliographic record
Abstract
Abstract Corruption involves greed, money, and risky decision‐making. We explore the love of money, pay satisfaction, probability of risk, and dishonesty across cultures. Avaricious monetary aspiration breeds unethicality. Prospect theory frames decisions in the gains‐losses domain and high‐low probability. Pay dissatisfaction (in the losses domain) incites dishonesty in the name of justice at the individual level. The Corruption Perceptions Index, CPI, signals a high‐low probability of getting caught for dishonesty at the country level. We theorize that decision‐makers adopt avaricious love‐of‐money aspiration as a lens and frame dishonesty in the gains‐losses domain (pay satisfaction‐dissatisfaction, Level 1) and high‐low probability (CPI, Level 2) to maximize expected utility and ultimate serenity. We challenge the myth: Pay satisfaction mitigates dishonesty across nations consistently. Based on 6500 managers in 32 countries, our cross‐level three‐dimensional visualization offers the following discoveries. Under high aspiration conditions, pay dissatisfaction excites the highest‐ (third‐highest) avaricious justice‐seeking dishonesty in high (medium) CPI nations, supporting the certainty effect. However, pay satisfaction provokes the second‐highest avaricious opportunity‐seizing dishonesty in low CPI entities, sustaining the possibility effect—maximizing expected utility. Under low aspiration conditions, high pay satisfaction consistently leads to low dishonesty, demonstrating risk aversion—achieving ultimate serenity. We expand prospect theory from a micro and individual‐level theory to a cross‐level theory of monetary wisdom across 32 nations. We enhance the S‐shaped Curve to three 3‐D corruption surfaces across three levels of the global economic pyramid, providing novel insights into behavioral economics, business ethics, the environment, and responsibility.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".