Nimra Usman | Econometrics and Finance | Best Researcher Award

Bahauddin Zakariya University Multan | Pakistan

Nimra Usman is a Pakistani economist with a strong blend of academic and professional experience in economics, data analysis, and policy advisory. She holds an M.Phil in Economics from the School of Economics at Bahauddin Zakariya University, Multan, where she graduated with distinction, earning with her thesis focusing on “Economic Freedom, Logistics Performance, International Grant & Burden of Debt: Case of Developing Countries.” She also holds an M.Sc in Economics from the same institution. Nimra has contributed to the field through both teaching and consultancy; she served as a Lecturer in Economics at the Institute of Southern Punjab and Nishat Higher Secondary School, where she taught courses including Microeconomics, Macroeconomics, and Development Economics, while also mentoring students and developing curriculum materials. Professionally, she has held leadership and advisory roles, including serving as Chief Operating Officer at Digi Vyze, where she managed operations, team coordination, and strategic execution to enhance growth and efficiency. Most recently, she was a Consultant with the World Bank Group’s Young Economist Immersion Program in Islamabad, where she contributed analytical work for the Pakistan Development Update, supported macro-fiscal meetings, prepared presentations, and aided in report outreach, demonstrating her expertise in fiscal policy and economic analysis. Nimra combines rigorous academic training with practical experience in economic research, policy insights, and organizational leadership, reflecting a commitment to advancing economic understanding and contributing to development-oriented initiatives in Pakistan and beyond.

Profile: Orcid | Scopus

Featured Publications

Javed, A., & Usman, N. (2025). Skill-biased labour market effects of environmental policy and green energy transition in Italy. Energy Policy.

Usman, N., Chaudhry, M. O., & Javed, A. (2025). Navigating debt burden: An analysis of economic freedom, logistics performance and foreign aid using method of moments quantile regression. GCU Economic Journal, 1–41.