Culture compensates for weak institutions: Determinants of attitudes toward free-riding across the world
Bibliographic record
Abstract
This study investigates the factors contributing to cross-cultural variation in the disapproval of free-riding behaviors such as tax evasion, fare dodging, and benefits fraud. While some cross-cultural researchers contend that attitudes toward these behaviors exhibit minimal variance due to a universal value of fairness, others adopting an institutional perspective argue that significant differences can be explained by factors like government effectiveness and the rule of law. We aim to replicate the pivotal role of institutional quality and propose that its impact is further moderated by cultural dimensions of individualism-collectivism and tightness-looseness. Utilizing a diverse dataset from the World Values Survey and European Values Study, encompassing 92 countries and over 158,000 individuals, we employ multilevel modeling to explore global and contextual patterns. Our findings reveal that while institutional quality reduces tolerance for free riding globally, its effect is not uniform and varies significantly based on cultural context. Specifically, whereas attitudes to free riding in individualistic and loose cultures depend heavily on just and effective governance, in collectivist and tight societies this reliance diminishes as group-centric values and low tolerance toward deviance help sustain compliant attitudes even when institutional quality is low.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".