Education
- Ph.D. in Economics, Colorado State University (2026) — Advisor: Professor Anita Alves Peña; Dissertation: Poverty Challenges in Pakistan from Three Perspectives: Gendered, Social Protection Programs, and Disaster Recovery
- M.A. in Economics, Colorado State University (2020–2022) — Applied econometrics, micro-econometrics, causal inference
Academic Appointments
- Assistant Professor, SDP Program — Fall 2026–Present
- Research Assistant to Dr. Anita Alves Peña, Colorado State University (Summer 2025)
- Research Associate, Center for CPEC Research, Karakoram International University (2018–2019)
Research Experience
- Feminization of Poverty in Pakistan (Dissertation Chapter 1, In Progress) — Used OLS with household fixed effects and logit models on Pakistan HIES data to examine gendered gaps in poverty incidence and depth; estimated heterogeneous effects across household headship type and region; presented at the Western Economic Association International 100th Annual Conference
- Causal Impact of Benazir Income Support Program (BISP) (Dissertation Chapter 2, In Progress) — Applied RDD and DiD-RDD to nationally representative household microdata to estimate causal treatment effects of a cash transfer program on educational enrollment, dropout, and women’s labor supply; built fully reproducible analytical pipelines in Stata with sensitivity checks, placebo tests, and robustness analyses
- Impact of Earthquake on Economic Activity (Dissertation Chapter 3, In Progress) — Integrated satellite night-time light imagery with macroeconomic indicators and time-series econometric models to estimate regional economic recovery following the 2005 Pakistan earthquake
Teaching
- Graduate Teaching Instructor: Principles of Macroeconomics (3 sections), Economics of Social Issues (2 sections), Economics of International Finance and Policy (Spring 2026) — CSU, 2023–2026
- Graduate Teaching Assistant: Principles of Macroeconomics, Quantitative Methods for Economists — CSU
Honors & Funding
- Fulbright Scholar, 2020–2025 — Competitive award
- Virtual Reality Challenge, Colorado State University, 2022
Biography
Arifa Shabnum is an applied economist whose research examines poverty, gender, and social protection in Pakistan through the lens of causal inference, combining quasi-experimental methods with large household microdata. She is completing her Ph.D. in Economics at Colorado State University under the supervision of Professor Anita Alves Peña, with a dissertation examining poverty challenges in Pakistan from gendered, social-protection, and disaster-recovery perspectives. She also holds an M.A. in Economics from Colorado State University, where she specialized in applied econometrics and micro-econometrics.
Her dissertation research uses regression discontinuity design, difference-in-differences, and panel data methods to estimate the causal effects of Pakistan’s Benazir Income Support Program on schooling and labor supply, to document gendered patterns in poverty incidence, and to trace the economic recovery of earthquake-affected regions using satellite night-time light imagery. A Fulbright Scholar, she has presented her work at the Western Economic Association International Annual Conference and the UW–CSU Joint Seminar Series, and has taught macroeconomics, social issues, and international finance courses at Colorado State University. She joins as Assistant Professor in the SDP Program from Fall 2026.
Publications & Presentations
- Cultural Tourism and Local Wellbeing in Northern Pakistan. IGI Global, 2022 (peer-reviewed book chapter) — Applied econometric modeling and mediation analysis to estimate the effect of cultural tourism on citizen wellbeing, with cultural conservation as a mediating pathway; co-authored with researchers at Karakoram International University
- Feminization of Poverty in Pakistan — Western Economic Association International 100th Annual Conference
- Nutritional Poverty Trap in Pakistan — UW–CSU Joint Seminar Series
Technical Skills
- Stata — Advanced
- Regression discontinuity, DiD / event study designs — Primary method
- Panel data / microdata — Advanced
- Spatial data — Working knowledge
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