Quantifying the influence of informal payments on self-rated health: evidence from 26 post-communist countries
Bibliographic record
Abstract
In contrast to previous studies that have focused on proximal outcomes such as access to and the utilization of healthcare, this study establishes and quantifies the influence of informal payments (IP) directly on population self-rated health, which can be considered the ultimate outcome. More specifically, we examine how making informal payments influences self-rated health by testing several theoretically grounded explanations of the influence of making IP. Using the quasi-experimental instrumental variable technique increases the likelihood that our findings are not the result of reverse causality, omitted variable problem and measurement error. Our main finding is that overall, making informal payments have a negative influence on self-rated health. However, this influence is higher for men, those who are poorer, live in rural areas, have a university education and have lower social capital. Theoretical approaches that have stood out in explanations regarding the effects of making IP on self-rated health are Public Choice Theory, Institutional Theory, and Sociological Theories of Differences in Life Opportunities, Social Determinants of Health and Social Capital.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".