Economic analysis of corruption: evidence from Pakistan
Bibliographic record
Abstract
Misuse of public power is generally known as corruption, and this issue becomes an international dilemma in the economic world. However, different levels of corruption exist in developing and developed countries. This study attempts to identify the economic determinants of corruption in Pakistan and investigate the impact of trade openness on corruption. This study empirically inspects these impacts by using data for the period 1987–2017. This study is based on time series analysis; augmented dickey fuller test (ADF), Tobit Model of Censored Regression, and Autoregressive Distributed Lag (ARDL) are used to estimate significant economic determinants of corruption in Pakistan. It is found that literacy rate, GDP growth, and economic integration have a negative effect on corruption, whereas Inflation is found to have a positive effect on corruption in Pakistan. However, investment, an extensive index, public spending, and income distribution are statistically insignificant as economic determinants in the case of Pakistan.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| 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) | 0.002 | 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".