The Paradox of Institutional Trust and Entrepreneurship in Transitional Countries
Bibliographic record
Abstract
The relationship between institutional trust and entrepreneurship is not straightforward but is intertwined with social context. This study explores this relationship by estimating the relationship between entrepreneurship and institutional trust together with a set of individual social demographics and the country of residence in 27 transitional countries in Eastern Europe and countries of the former Soviet Union using the data of the 2016 Life in Transition Survey (LiTS). The analytical framework in this study is that individuals make their decisions in choosing the type of employment by weighing the level of institutional trust in their communities, a set of democratic factors and social indicators. The results of our 2SLS estimations indicate a consistent negative association between institutional trust and entrepreneurship for all the sub-datasets. However, this cannot be interpreted as evidence for the negative effect of institutional trust on entrepreneurship. Given our analytical framework, this counter common-sense phenomenon would be interpreted as when the institutional trust was high, individuals would rather choose to have a paid job instead of running their own business in these transitional countries. This study provides evidence of how far these countries have gone on the path of transition three decades after the transition.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".