Does More Corruption Lead to Higher Prices? An Empirical Analysis
Bibliographic record
Abstract
This paper examines the existing relationship between inflation and corruption in a country-level panel data analysis from 2012 to 2021. Our findings are consistent with the existing literature, indicating a positive relationship between corruption and inflation. Specifically, inflation rates related to the real estate market were sensitive to increases in perceived corruption, indicating that this sector is subject to illicit practices and is an essential component of the countries’ inflationary basket. Furthermore, when we examine the behavior of past economic freedom on current inflation, we see similar and even more significant increases in inflation, which indicates corruption has a long-term effect. Finally, there is evidence that a country’s income level is related to perceived corruption levels, with poorer countries experiencing higher levels of corruption and richer countries experiencing lower levels. A country’s income level is frequently related to its cultural background and the regional subgroup it belongs to. According to this metric, Northern and Western Europe, North America, Australia, and New Zealand had the lowest levels of perceived corruption.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".