Doux Commerces: Does Market Competition Cause Trust?
Bibliographic record
Abstract
This paper documents a strong positive relationship between individual reported trust levels (obtained from the US General Social Survey) and the competitiveness of the sector in which an individual works (obtained from the US census of firms). This correlation is robust to the inclusion of all of the previously studied determinants of individual trust, e.g., income, education, age, sex, marital status, city size, religion, and is large; a one standard deviation increase in sectoral competitiveness makes respondents approximately five percent more likely to answer the canonical trust question with a "usually trust" as opposed to a "usually don’t trust" response. The addition of a rich set of workplace controls shows that this correlation is not likely to be driven by the size of the workplace, the amount of supervision, or related to a congenial work culture. It also appears that it is not due to selection (i.e., trustworthy or trusting individuals selecting into competitive sectors) or risk aversion, but instead seems to be due to individuals becoming more trusting the longer their experience in competitive sectors. We conjecture that trust levels are high when workplaces are characterized by high contributions of discretionary effort, i.e., when co-workers are more likely to be trustworthy. We develop a model which shows that such discretionary efforts are more likely to arise when competition within a sector is high. Competition mitigates incentives for free-riding by imposing costly shut-down on poor performing firms, makes employees more trustworthy, and thus increases trust. The model generates a positive correlation between trust and sectoral competitiveness, displays a threshold effect, suggests a non-monotonic relationship between competition and job security, and predicts patterns for a number of other variables. The data displays a high degree of consistency with these predictions.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".