Bibliographic record
Abstract
Purpose – Empirical evidence on the relation between happiness (life satisfaction) and corruption is barely perceptible in the literature. The purpose of this paper is to contribute to closing this gap by presenting some estimates using a large cross-section of countries over the period 1996-2010. Design/methodology/approach – The empirical model allows both corruption and per capita income to enter as arguments of a happiness “production function”. The correlation between happiness and corruption is presumed to be non-linear. Findings – While the results do not support the existence of a Kuznets-type trajectory, the study finds that the level of per capita income determines whether happiness and corruption are related and in what way. The authors estimate cutoff income levels at which corruption has a discernible effect on happiness. The results show that corruption reduces happiness, but only for high-income countries – roughly the upper half of the income range in the sample. Practical implications – Results nullify the oft-asserted statement that happiness is negatively linked to corruption in all countries. The nature of correlation is more complex. Originality/value – The paper goes beyond simply testing whether happiness is related to corruption. It conjectures that the relationship between the two variables is non-monotonic. Thus, the analysis considers the notion that the association between happiness and probity is income dependent. A novel feature of the empirical model is that the estimated income cutoff levels are endogenously determined. That is, income thresholds are not pre-determined. The authors also test for the robustness of the results by addressing the issue of potential endogeneity of corruption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".