Predictors and Extent of Institutional Trust in Government, Banks, the Media and Religious Organisations: Evidence from Cross-Sectional Surveys in Six Asia-Pacific Countries
Bibliographic record
Abstract
BACKGROUND: Building or maintaining institutional trust is of central importance in democratic societies since negative experiences (potentially leading to mistrust) with government or other institutions may have a much more profound effect than positive experiences (potentially maintaining trust). Healthy democracy relies on more than simply trusting the national government of the time, and is mediated through other symbols of institutional power, such as the legal system, banks, the media and religious organisations. This paper focuses on institutional trust-the level and predictors of trust in some of the major institutions in society, namely politics, the media, banks, the legal system and religious organisations. We present analyses from a consolidated dataset containing data from six countries in the Asia Pacific region-Australia, Hong Kong, Japan, South Korea, Taiwan and Thailand. METHODS: Cross-sectional surveys were undertaken in each country in 2009-10, with an overall sample of 6331. Analyses of differences in overall levels of institutional trust between countries were undertaken using Chi square analyses. Multivariate binomial logistic regression analysis was undertaken to identify socio-demographic predictors of trust in each country. RESULTS: Religious institutions, banks and the judicial system had the highest overall trust across all countries (70%, 70% and 67% respectively), followed by newspapers and TV (59% and 58%) and then political leaders (43%). The range of levels of higher trust between countries differed from 43% for banks (range 49% in Australia to 92% in Thailand) to 59% for newspapers (28% in Australia to 87% in Japan). Across all countries, except for Australia, trust in political leaders had the lowest scores, particularly in Japan and South Korea (25% in both countries). In Thailand, people expressed the most trust in religious organisations (94%), banks (92%) and in their judicial/legal system (89%). In Hong Kong, people expressed the highest level of trust in their judicial/legal system (89%), followed by religious organisations (75%) and banks (77%). Australian respondents reported the least amount of trust in TV/media (24%) and press/newspapers (28%). South Korea put the least trust in their political leaders (25%), their legal system (43%) and religious organisations (45%). The key predictors of lower trust in institutions across all countries were males, people under 44 years and people unsatisfied with the health and standard of living. CONCLUSION: We interpreted our data using Fukuyama's theory of 'high/low trust' societies. The levels of institutional trust in each society did not conform to our hypothesis, with Thailand exhibiting the highest trust (predicted to be medium level), Hong Kong and Japan exhibiting medium trust (predicted to be low and high respectively) and Australia and South Korea exhibiting low trust (predicted to be high and medium respectively). Taiwan was the only country where the actual and predicted trust was the same, namely low trust. Given the fact that these predictors crossed national boundaries and institutional types, further research and policy should focus specifically on improving trust within these groups in order that they can be empowered to play a more central role in democratic vitality.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".