Trust and Total Factor Productivity: What Do We Know About Effect Size and Causal Pathways?
Bibliographic record
Abstract
This article explores what is known about the relationship between trust and total factor productivity (TFP). Generalized interpersonal trust is widely considered the best summary measure for social capital, and if this is the case the impact of trust should be reflected in estimates of TFP. A systematic review of the literature on trust, incomes, growth, and TFP finds relatively few articles on the latter despite a developed literature on trust, income, and growth. Using a development accounting framework, a simple model of the relationship between trust and TFP is set out and the size of the impact of trust on TFP is estimated empirically using a cross-country panel dataset based on the European Social Survey (ESS). Despite the limitations of the ESS, estimates of the magnitude of the impact of trust on TFP are broadly similar to those from the only other similar study identified (Bjornskov and Meon, 2015), which is based on the World Values Survey. A counterfactual estimate of TFP is used to illustrate the magnitude of the effect of trust on TFP, highlighting that the impact of trust is non-trivial in real terms, even for high-trust countries.
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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 | 0.019 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".