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Quantifying the Economic Costs of Financial Corruption in Pakistan: An Integrated Econometric and Machine Learning Approach

Abdelrahman Mohamed Mohamed Saeed et al.

Economies2026https://doi.org/10.3390/economies14030082article
AJG 1ABDC C
Weight
0.37

What the paper says

This study investigates the macroeconomic impact of financial corruption and institutional weakness on Pakistan’s economy from 1996 to 2023, addressing a critical research gap in quantifying the simultaneous effects of shadow economy operations and poor governance on economic growth. Grounded in institutional economics theory, the research tested hypotheses that weak control of corruption and a large shadow economy negatively affect GDP growth, while also examining the roles of tax revenue, inflation, trade openness, and foreign direct investment. Utilizing a dual-methodological approach, this study employed multiple regression analysis with stationary testing to ensure robust inference, complemented by Random Forest machine learning with Leave-One-Out Cross-Validation for predictive accuracy and variable importance ranking. The econometric results identified shadow economy size and inflation rate as the most statistically significant barriers to growth, with a one percentage point increase in each associated with 0.32 and 0.08 percentage point reductions in GDP growth, respectively (p < 0.05). Control of corruption and institutional quality showed positive but statistically weaker effects. The machine learning analysis corroborated these findings, ranking shadow economy (31.8%) and inflation (24.5%) as the dominant predictors of GDP growth, with the Random Forest model achieving superior predictive performance (R2 = 0.68) compared to traditional linear regression (R2 = 0.45). Both techniques converged on the conclusion that formalizing informal activity and stabilizing prices represent the most impactful policy levers for growth enhancement, while institutional quality improvements operate through indirect channels. The findings underscore the urgent need for policymakers to prioritize inflation control through credible monetary policy and to formalize informal economic activity via simplified regulations and anti-corruption measures. This research provides a replicate dual-methodology framework for analyzing institutional economic issues in developing nations with limited data.

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https://doi.org/https://doi.org/10.3390/economies14030082

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@article{abdelrahman2026,
  title        = {{Quantifying the Economic Costs of Financial Corruption in Pakistan: An Integrated Econometric and Machine Learning Approach}},
  author       = {Abdelrahman Mohamed Mohamed Saeed et al.},
  journal      = {Economies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3390/economies14030082},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.