The Importance of Institutional Trust in Explaining Life-Satisfaction: Lessons From the 2008 Global Financial Crisis
Bibliographic record
Abstract
We theorize that greater institutional trust leads to higher levels of life satisfaction and test our hypothesis empirically on a diverse sample of 27 countries for the period of the 2008 Global Financial Crisis and its aftermath. We used country-level data with a time-series cross-sectional design to examine how changes in institutional trust over the period of the crisis and beyond affected changes in life satisfaction vis-à-vis well-established predictors of institutional trust. We found that institutional trust, together with a subjective assessment of the economic situation, is the only significant predictors explaining life satisfaction during the crisis and its aftermath. The magnitude of the effect of institutional trust demonstrates resilience and did not change due to the crisis. The results of individual-level analysis confirmed this conclusion. The individual-level analysis also highlights that the effect of institutional trust is relatively stronger than the effect of many well-established individual-level characteristics. The main theoretical conclusion of our study is that institutional trust is one of the most important determinants of life satisfaction. Policy makers and international donors should focus on nurturing institutional trust through institutional reforms. From the research standpoint, our findings suggest that institutional trust should be included as one of the covariates in studies on life satisfaction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".