Dr. Muhammad Ali is a Research Scientist, He did Ph.D. Economics with a concentration in Health Economics Area of Interest (Obesity, Abdominal Bloating, Mental Health, Stress Management, Healthcare Management, Healthcare Financing & Evaluation. He wrote many research papers on health economics, Development & international economics, and Political economics. Economist Financial & Business Consultant working in different research areas of economics, especially health economics regional & development economics & comparative study of Islamic & conventional economic systems.
EDUCATION
Ph.D. Economics
RESEARCH INTERESTS
Health Economics, Aging, Obesity, Health economics & Financing, Development & Trade Economics, International economics, Managerial economics, Panel data economics, Data economics, Comparative study of Islamic & conventional economics
8
Scopus Publications
333
Scholar Citations
10
Scholar h-index
12
Scholar i10-index
Scopus Publications
Machine learning vs. traditional logistic regression: predictive performance and risk factor identification for child nutritional outcome in Pakistan Muhammad Shahid, Muhammad Ali Yahya, Jiayi Song, Hafiz Muhammad Naveed, Serhat Yuksel, Hasan Dincer, Muhammad Ali BMC Public Health, 2026 Logistic regression (LR) has long been the standard econometric tool for modeling child nutritional outcomes in public health research. However, conventional econometric LR (CE-LR) faces limitations in predictive accuracy, reliance on restrictive assumptions, and handling high-dimensional data. Machine learning-enhanced LR (ML-LR)-which relaxes the strict statistical assumptions of traditional models to better capture complex patterns-combined with Shapley Additive Explanations (SHAP), offers a promising alternative, improving both prediction and interpretability of risk factors. This study presents the first Pakistan-specific application and comparison of ML-LR (with SHAP analysis) against CE-LR, introducing a novel hybrid framework that combines predictive power with interpretability for policy-relevant insights using nationally representative data from Pakistan's 2017-2018 Demographic and Health Survey (n = 4,098 children under five). Results indicate persistent malnutrition rates: stunting (38.13%), underweight (23.04%), and wasting (8.05%). The ML-LR model identified all 13 hypothesized risk factors as significant, while CE-LR detected only six. Crucially, ML-LR captured key predictors missed by CE-LR, such as maternal BMI, employment, and dietary diversity. The SHAP analysis further revealed nuanced relationships: child age, low maternal BMI, unemployment, and unimproved water increased malnutrition risk, while higher birth order, adequate dietary diversity (if children were given ≥ 5 food items), maternal education, and male gender had protective effects. Crucially, ML-LR + SHAP uncovered context-dependent relationships invisible to CE-LR. For example, dietary diversity operated bidirectionally-low diversity was a risk factor, while adequate diversity was protective-a distinction CE-LR failed to capture. These findings demonstrate ML-LR's superior ability to model complex, heterogeneous determinants of child malnutrition. The study advocates for integrating ML techniques with explainable AI (e.g., SHAP) in econometric analyses to enhance policy-relevant insights in public health.
AI-Driven Financial Solutions for Climate Resilience and Geopolitical Risk Mitigation in Low- and Middle-Income Countries Abdelrahman Mohamed Mohamed Saeed, Muhammad Ali Economies, 2026 Climate change disproportionately threatens low- and middle-income countries, yet integrated assessments combining socio-economic fragility with physical hazards remain limited. This study quantifies multi-dimensional climate vulnerability and derives optimized adaptation policies for six representative nations (Bangladesh, Colombia, Kenya, Morocco, Pakistan, Vietnam) by fusing socio-economic indicators with climate risk data (2000–2024). A computational framework integrating unsupervised learning, dimensionality reduction, and predictive modeling was employed. Principal Component Analysis synthesized eight indicators into a Compound Vulnerability Score (CVS), while K-Means and DBSCAN identified distinct vulnerability regimes. XGBoost quantified driver importance, and Graph Neural Networks captured systemic interconnections. XGBoost identified projected drought risk (31.2%), precipitation change (18.1%), and poverty headcount (14.3%) as primary drivers. Graph networks demonstrated significant risk amplification in African nations (Morocco SRS: 0.728–0.874; Kenya SRS: 0.504–0.641) versus damping in Asian countries. A Reinforcement Learning (RL) agent was trained using Deep Q-Networks with experience replay to optimize intervention portfolios under budget constraints. The RL policy achieved a 23% reduction in systemic risk compared to uniform allocation baselines, generating context-specific priorities: drought management for Morocco (score 50) and Pakistan (40); poverty alleviation for Kenya (40); coastal protection for Bangladesh (40); agricultural resilience for Vietnam (35); and institutional capacity building for Colombia (50). In conclusion, socio-economic fragility non-linearly amplifies climate hazards, with poverty and drought risk constituting critical vulnerability multipliers. The AI-driven framework demonstrates that targeted interventions in high-sensitivity systems maximize systemic risk reduction. This integrated approach provides a replicable, evidence-based foundation for strategic adaptation finance allocation in an increasingly uncertain climate future.
Quantifying the Economic Costs of Financial Corruption in Pakistan: An Integrated Econometric and Machine Learning Approach Abdelrahman Mohamed Mohamed Saeed, Muhammad Ali Husnain, Muhammad Ali Economies, 2026 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.
The law of diminishing marginal returns in health insurance: evidence on child health outcomes from Pakistan’s Sehat Sahulat program Muhammad Shahid, Hafiz Muhammad Naveed, Jiayi Song, Serhat Yuksel, Hasan Dincer, Muhammad Ali Frontiers in Public Health, 2026 Background Child malnutrition remains a serious public health challenge in Pakistan. The national health insurance initiative, the Sehat Sahulat Program (SSP), aims to improve access to health care for low-income families. This study examines whether the effectiveness of SSP follows the Law of Diminishing Marginal Returns (LDMR), whereby the marginal health benefits of insurance are greatest for the poorest households and decline with increasing wealth. Methods We analyzed data on 4,499 children under 5 years of age in the Pakistan Demographic and Health Survey 2017–18. To address endogeneity and wealth-based heterogeneity, we employed an IV-Probit model and an IV-Quantile Regression (IV-QR) across wealth quintiles, using community-level internet access and distance to the nearest health facility as instrumental variables. Logistic regression was applied as a baseline model. Results Analysis reveals a strong gradient in the effectiveness of SSP program. Insurance coverage is associated with reduced stunting and marked declines in diarrhea rates among the poorest households. These benefits have diminished in the overall distribution of wealth and have become statistically insignificant for the richest quintile. Econometric tests have confirmed a clear pattern of declining marginal returns. Conclusion The Sehat Sahulat project is a very effective tool for reducing malnutrition among poor children in Pakistan. The observed pattern of diminishing returns suggests that SSP provides the greatest health benefits to the poorest households. These findings support a pro-poor targeting strategy in Pakistan to maximize the program’s impact within resource constraints.
The Role of Institutional Quality in Chinese Outward Foreign Direct Investment and Domestic Investment’s Impact on Economic Stability Waqar Ameer, Aulia Luqman Aziz, Muhammad Ali, Mochammad Fahlevi, Arfendo Propheto Economies, 2025 Capital flow, integral to the global economy, is significantly influenced by business potential and institutional environments. As one of the world’s largest economies, China’s outflow plays a crucial role in the rapid development of its economy. This study examines domestic investment into public and private components to avoid aggregation bias, whether China’s outward foreign direct investment (OFDI) serves as a substitute or complement to local investments, and how local institutional quality mediates this relationship. We employed Dynamic Autoregressive Distributed Lag model ARDL simulation methods for the period of 1996–2021 in order to control endogeneity, auto-correlation, cross-sectional bias, as well as heteroscedasticity issues, which normally arise in time-series datasets. Our findings reveal that OFDI has a dual impact on local economies. Firstly, OFDI has a generally positive effect on private and public investment, but this relationship is nonlinear. Furthermore, institutional quality significantly influences private investment more than public investment. Additionally, higher interest rates are shown to adversely affect both private and public investments by increasing borrowing costs. These results offer valuable insights for policymakers aiming to optimize investment flows and economic stability. Specifically, fostering institutional quality can amplify the positive spillovers of OFDI on private investment, while mitigating its crowding-out effects on public investment.
Exploring the Gender Preferences for Healthcare Providers and Their Influence on Patient Satisfaction Felician Andrew Kitole, Zaiba Ali, Jiayi Song, Muhammad Ali, Mochammad Fahlevi, Mohammed Aljuaid, Petra Heidler, Muhammad Ali Yahya, Muhammad Shahid Healthcare Switzerland, 2025 Background: Patient satisfaction is a key indicator for improving healthcare delivery, yet the influence of gender preferences on healthcare providers remains underexplored. Cultural norms and gender perceptions often shape the patient preferences, affecting access to care, patient–provider relationships, and overall satisfaction. Thus, this study investigates the patients’ gender preferences and their impact on satisfaction in Tanzania. Methods: The study utilized a cross-sectional design, collecting data from five health centres: Mikongeni, Konga, Mzumbe, Tangeni, and Mlali. A total of 240 randomly selected respondents participated in the study. Gender preferences were categorized as male, female, and both, and determinants were analyzed using a multivariate probit model (MPM), while satisfaction was analyzed using an ordered logit model (OLM). Results: Results reveal that female providers were preferred for empathy (58.30%), intimate care (50.00%), and receptionist roles (50.00%), while males were favored for surgery (50.00%), professionalism (0.86), and IT roles (41.70%). Professionalism (0.75) and communication (0.70) had the strongest positive effects on very high satisfaction. Male provider preference was strongly linked to higher satisfaction (0.84), while female preference showed a mild effect (0.23). Insurance (0.32) and care at Tangeni Health Centre (0.70) boosted satisfaction, while consultation fees (−0.26) reduced it. Conclusions: The study recommends that healthcare systems address gender stereotypes by equipping all providers with both technical and relational care skills, regardless of gender. It also highlights the need for culturally and religiously sensitive care practices that acknowledge how societal norms shape patient preferences and satisfaction. To enhance patient-centered care, policies should promote affordability, broaden insurance coverage, and integrate patient feedback on gender preferences into healthcare delivery models.
Factors Influencing Expatriate Employees’ Commitment to the Private Sector in Qatar Muhammad Irfan, Aso Kurdo Ahmed, Saeideh Shariati Najafabadi, Fariba Azizzadeh, Muhammad Ali, Mohammad Shahidul Islam, Umara Noreen International Journal of Interdisciplinary Social and Community Studies, 2024 A scholarly article by authors Muhammad Irfan, Aso Kurdo Ahmed, and Saeideh Shariati Najafabadi, Fariba Azizzadeh, Muhammad Ali, Mohammad Shahidul Islam, Umara Noreen published in The International Journal of Interdisciplinary Social and Community Studies
Machine learning vs. traditional logistic regression: predictive performance and risk factor identification for child nutritional outcome in Pakistan M Shahid, MA Yahya, J Song, HM Naveed, S Yuksel, H Dincer, M Ali BMC public health 26 (1), 667 , 2026 2026 Citations: 3
AI-Driven Financial Solutions for Climate Resilience and Geopolitical Risk Mitigation in Low-and Middle-Income Countries AMM Saeed, M Ali Economies 14 (4), 134 , 2026 2026
Quantifying the economic costs of financial corruption in Pakistan: An integrated econometric and machine learning approach AM Mohamed Saeed, MA Husnain, M Ali Economies 14 (3), 82 , 2026 2026 Citations: 1
Energy Poverty, Trade Openness and Foreign Direct Investment: Evidence from Latin America MA Husnain, M Ali, MK Bhatti, W Ameer Journal of Hunan University Natural Sciences 53 (1) , 2026 2026
Unveiling Smoke Signals: An Empirical Examination of the Escalating Smoking Trend in Pakistan and Its Dual Impact on Health and Economy M Ali, W Ameer, MH Danish Journal of Research in Social Sciences 14 (1), 1-15 , 2026 2026
The Role of Institutional Quality in Chinese Outward Foreign Direct Investment and Domestic Investment’s Impact on Economic Stability W Ameer, AL Aziz, M Ali, M Fahlevi, A Propheto Economies 13 (12), 344 , 2025 2025 Citations: 1
Socioeconomic Disparities in Healthcare Access and Outcomes in Pakistan: A Pre-and Post-Pandemic Analysis of the Public-Private Divide M Ali, SBB Zainol, W Ameer, IH Khan Pakistan Journal of Social Sciences 45 (3), 251-271 , 2025 2025
Healthcare Financing, Economic Growth in Pakistan: COVID-19 Impacts and Policy Pathways M Ali, SB Zaino, W Ameer, A Aljounaidi, AH Ateik Qlantic Journal of Social Sciences and Humanities 6 (3), 273-285 , 2025 2025 Citations: 1
Economic Challenges In Pakistan: A Comparative Analysis of Political Regimes and Policy Implications M Ali, IH Khan, A Aljounaidi3a, MS Islam, AH Ateik3b Social Sciences & Humanity Research Review 3, 3 , 2025 2025 Citations: 5
Exploring the gender preferences for healthcare providers and their influence on patient satisfaction FA Kitole, Z Ali, J Song, M Ali, M Fahlevi, M Aljuaid, P Heidler, MA Yahya, ... Healthcare 13 (9), 1063 , 2025 2025 Citations: 18
Empowering Lives: Assessing the Impact of Zakah Payments on the Well-being of Payees in Pakistan M Ali, IH Khan, W Ameer, A Ateik, A Aljounaidi Journal of Political Stability Archive 3 (1), 998-1014 , 2025 2025 Citations: 2
Unpacking public value destruction through solid waste management: A case study of Pakistan Z Ahmad, P Esposito, M Ali Qlantic Journal of Social Sciences and Humanities 6 (1), 145-162 , 2025 2025 Citations: 10
Unraveling the economic quagmire: A comprehensive analysis of rule of law violations in Pakistan M Ali, W Ameer, Z Ahmad Research Journal of Social Sciences and Economics Review 6 (1), 1-12 , 2025 2025 Citations: 9
The risk factors and problems of waste management in developing countries as hurdles Z Ahmad, P Esposito, M Ali Qlantic Journal of Social Sciences 6 (1), 130-144 , 2025 2025 Citations: 7
The law of diminishing marginal returns in health insurance: evidence on child health outcomes from Pakistan’s Sehat Sahulat program M Shahid, HM Naveed, J Song, S Yuksel, H Dincer, M Ali Frontiers in Public Health 13, 1729440 , 2025 2025 Citations: 2
Informal Labor Market in Pakistan: Evaluating the Effectiveness of Government Relief Programs using Econometrics and Machine Learning Approach M Ali, SB Zainol, W Ameer, MA Husnain Journal of Economic Impact 7 (3), 231-244 , 2025 2025
Exploring the quality of care and the nursing practice environment in Saudi Arabia M Ali, W Ameer, Z Ahmad, A Aljounaidi, AH Atiek Qlantic Journal of Social Sciences 5 (4), 182-196 , 2024 2024 Citations: 5
Ethical deployment of cognitive biases in marketing a framework for responsible influence MS Islam, F Azizzadeh, M Ali Applied Psychology Research 3 (2), 1363-1363 , 2024 2024 Citations: 11
Consumer decision-making processes in digital environments—A psychological perspective MS Islam, M Ali, F Azizzadeh Applied Psychology Research 3 (1), 1362-1362 , 2024 2024 Citations: 23
Weighty Matters: Exploring the Economic Ramifications of Obesity, Abdominal Bloating, and Spinal Deformities in Pakistan M Ali, Z Ahmad sjesr 7 (1), 20-29 , 2024 2024 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Assessing the impact of technology advancement and foreign direct investment on energy utilization in Malaysia: An empirical exploration with boundary estimation AR Ridzuan, NHA Rahman, KSJ Singh, H Borhan, M Ridwan, LC Voumik, ... International conference on business and technology, 1-12 , 2023 2023 Citations: 35
Consumer decision-making processes in digital environments—A psychological perspective MS Islam, M Ali, F Azizzadeh Applied Psychology Research 3 (1), 1362-1362 , 2024 2024 Citations: 23
A comprehensive analysis of recent flood disaster & their economic impact on Pakistan Economy & its causes M Ali, ZMM Mohammed, FMO Al-Shaghdari SSRN , 2022 2022 Citations: 22
Current political crisis impacts on Pakistan’s public life: An economic case study regarding the present situations in Pakistan M Ali, AH Ateik, SB Zainol, F Azizzadeh, A Aljounaidi, M Subhan, ... Sarc. Jr. Eco. Bus. Man 1 , 2022 2022 Citations: 21
Exploring the gender preferences for healthcare providers and their influence on patient satisfaction FA Kitole, Z Ali, J Song, M Ali, M Fahlevi, M Aljuaid, P Heidler, MA Yahya, ... Healthcare 13 (9), 1063 , 2025 2025 Citations: 18
Stagnate economic analysis of regime change & administration shuffling Impact on Pakistan economy M Ali, MW Naeem, Z Ahmed, MH Iftikhar Pakistan Journal of Economic Studies (PJES) 6 (1), 1-19 , 2023 2023 Citations: 18
Economic Growth, Trade, and Foreign Direct Investment Interrelationships in South Asian Countries. AH Ateik, M Ali, MH Danish, A Aljounaidi, F Azizzadeh Journal of Applied Economics & Business Studies (JAEBS) 7 (3) , 2023 2023 Citations: 15
Al Harath Atiek and Fariba Azizzadeh. The devestation of COVID-19 & Its economic effects on developing countries: a global analysis M Ali Journal of economics & Management Sciences 3 (2), 29-41 , 2022 2022 Citations: 14
Investigative nexus of depression-anxiety disorder in Pakistan pre and post-pandemic COVID-19: Coro-nomic analysis M Ali, A Aljounaidi, RR Marzo, AH Ateik, M Fahlevi, AR Ridzuan, ... Journal of Coastal Life Medicine 11 (1), 1514-1524 , 2023 2023 Citations: 12
Ethical deployment of cognitive biases in marketing a framework for responsible influence MS Islam, F Azizzadeh, M Ali Applied Psychology Research 3 (2), 1363-1363 , 2024 2024 Citations: 11
Unpacking public value destruction through solid waste management: A case study of Pakistan Z Ahmad, P Esposito, M Ali Qlantic Journal of Social Sciences and Humanities 6 (1), 145-162 , 2025 2025 Citations: 10
Economic impacts of the Russia-Ukraine conflict on developing countries: A focus on agriculture, industry, and food security A Aljounaidi, M Ali, S Shabbir Research Journal of Social Sciences and Economics Review 5 (1), 1-15 , 2024 2024 Citations: 10
Unraveling the economic quagmire: A comprehensive analysis of rule of law violations in Pakistan M Ali, W Ameer, Z Ahmad Research Journal of Social Sciences and Economics Review 6 (1), 1-12 , 2025 2025 Citations: 9
Factors Influencing Expatriate Employees’ Commitment to the Private Sector in Qatar M Irfan, AK Ahmed, S Shariati Najafabadi, F Azizzadeh, M Ali, MS Islam, ... The International Journal of Interdisciplinary Social and Community Studies … , 2024 2024 Citations: 9
The covid-19 pandemic and economy –A study on Bangladesh F Azizzadeh, SM Basir, MS Islam, M Ali Jurnal Aplikasi Manajemen, Ekonomi dan Bisnis 7 (2), 19-26 , 2023 2023 Citations: 9
Prevalence of obesity consequences its impacts on health & working performance evidence from Saudi Arabia A Ateik, A Aljounaidi, M Ali, SMBM Jafre, F Azizzadeh, S Zupok Int J Econ Stud Manag 3, 1106 , 2023 2023 Citations: 8
Benefits of Islamic Economic System and its fruits in real life: a comparative analysis M Ali, A Aljounaidi, F Azizzadeh, G Tavassoli Zenodo (CERN European Organization for Nuclear Research) 26 , 2022 2022 Citations: 8
The risk factors and problems of waste management in developing countries as hurdles Z Ahmad, P Esposito, M Ali Qlantic Journal of Social Sciences 6 (1), 130-144 , 2025 2025 Citations: 7
Knowledge and awareness of Zakah among the people of Pakistan: Implications for Islamic economics A Aljounaidi, M Ali, Z Ahmad, AH Atiek, IH Khan Qlantic Journal of Social Sciences 4 (4), 346-356 , 2023 2023 Citations: 7
Is the current situation of Pakistan responsible for Inflation, Unemployment & economic collapse? A Thematic analysis M Ali A Thematic Analysis (January 9, 2023) , 2023 2023 Citations: 7