Influence of interpersonal and institutional trusts on welfare state support revisited
Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on the effects of interpersonal and institutional trust on welfare state support in the countries of Eastern Europe and the former Soviet Union (FSU). Design/methodology/approach The authors use micro-data from two rounds of a multinational survey conducted in these countries in 2010 and 2016. The outcome variable of interest is the willingness to pay more taxes to support the welfare state. The authors define the welfare state broadly, and focus on support for three main domains of the welfare state, namely, support for the needy, public healthcare and public education. Binomial regression is used to establish influence of interpersonal and institutional trust on welfare state support. Findings The authors find that both interpersonal and institutional trust have positive influences on strengthening support for the welfare state against a number of alternative explanations for public support for the welfare state. These positive effects remain the same for all three domains under investigation, namely, helping the needy, public healthcare and public education. Furthermore, these positive effects were observed both in the relatively less developed countries of the FSU and in the more developed Eastern European countries. Moreover, the positive effects of interpersonal and institutional trust on support for the needy, public healthcare and public education were found to grow over time. Research limitations/implications The findings indicate that the benefits of nurturing social capital will likely be substantial. Decision-makers, politicians, welfare state administrators and multinational founders (e.g. the UN and World Bank) should acknowledge the role played by trust in influencing the citizenry’s support for the allocation of financial resources toward the development and maintenance of the welfare state. The findings imply that welfare state reforms could prove be more effective within a social context where levels of trust are high. Thus, special attention should be paid to initiatives aimed at developing strategies to build trust. Practical implications Social welfare reforms in post-communist transitional countries may fail without active strategies aimed at nurturing institutional trust. One way to nurture institutional trust is through making additional efforts at enhancing the levels of accountability and transparency within a society as well as through increasing citizen engagement. Another way to build increased levels of trust is to take part in a variety of initiatives in good governance put forth by multinational initiatives. Originality/value As far as the authors know, this is the first paper which studies effect of interpersonal and institutional trust on support of the welfare state using a large and diverse sample of 27 countries over the period of five years. This is the first study which focuses on post-communist countries where trust is inherently low.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".