Economist
I study how institutional rules and market structure shape incentives, bargaining power, and firm strategy in professional sports and banking. Assistant Professor at North Carolina A&T State University.
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Brazilian-born, U.S.-trained, fascinated by how institutions, risk, and incentives affect market outcomes
In professional sports, I study labor markets, contracts, and the local economic impact of facilities. A current project with Brad Humphreys and Candon Johnson finds that banks in metropolitan areas that open a new stadium or arena take in about 13 percent more deposits. Published work covers contract and wage determinants in the WNBA, career continuity under collective bargaining, pay and discrimination in the NBA, and the returns to tanking. Ongoing projects estimate what WNBA rookies would earn without the rookie scale, and how betting markets price roster news.
In banking, I study how regulation and market structure shape lending, performance, and risk-taking, using FDIC and other banking data. Published work covers what makes community banks unique and how cost efficiency relates to the Dodd-Frank Act, and current work asks what sovereign wealth investment does to bank risk.
A third line of research examines how people learn quantitative material, including work with communication colleagues on instructor listening, math anxiety, and burnout in quantitative courses.
I use AI-augmented workflows in my research. Automated checks validate merges, weights, and constructed variables before any estimate is produced. A co-author and I also use a shared database that catalogs our sports datasets and records where each one came from and what it covers.
I earned my PhD in Economics from West Virginia University in 2021 and my B.A. summa cum laude from Winthrop University. I also work one-on-one with students and early-career professionals; past mentees have moved on to Harvard Law School, the Federal Reserve Bank of Boston, and national fellowships. As an educator, I emphasize AI integration and active learning experiences in the classroom.
Industrial organization · Labor economics · Financial economics
Minuci, E. G., & Rodriguez, Z. (2026). Journal of Sports Economics. Advance online publication. https://doi.org/10.1177/15270025261474143
Minuci, E. G., & Rubenstein, K. (2026). Eastern Economic Journal, 52, 539–561. https://doi.org/10.1057/s41302-026-00329-9
Minuci, E. G., Cardazzi, A., & Johnson, C. (2026). Contemporary Economic Policy, 44(2), 408–425. https://doi.org/10.1111/coep.12705
Minuci, E. G., & Johnson, C. (2025). Applied Economics, 57(5), 543–546. https://doi.org/10.1080/00036846.2024.2305608
Johnson, C., & Minuci, E. (2020). Southern Economic Journal, 87(2), 517–539. https://doi.org/10.1002/soej.12461
Minuci, E., & Schuh, S. (2026). Contemporary Economic Policy, 44(2), 426–461. https://doi.org/10.1111/coep.70014
Minuci, E. G. (2025). Economic Notes, 54, e70008. https://doi.org/10.1111/ecno.70008
Minuci, E. G., & Rodriguez, Z. (2024). Journal of Economics and Finance, 48, 947–977. https://doi.org/10.1007/s12197-024-09684-9
McCannon, B. C., & Minuci, E. (2020). Journal of Behavioral and Experimental Finance, 26, 100279. https://doi.org/10.1016/j.jbef.2020.100279
Minuci, E. G., Kelly, S., Burkey, M. L., & Ducking, J. (2025). Communication Reports, 38(3), 153–167. https://doi.org/10.1080/08934215.2025.2451895
Kelly, S., Minuci, E. G., Ducking, J., & Romero, A. (2025). Communication Quarterly, 73(1), 115–132. https://doi.org/10.1080/01463373.2024.2438835
Minuci, E., Ferreira Neto, A. B., & Hall, J. (2019). Heliyon, 5(7), e01990. https://doi.org/10.1016/j.heliyon.2019.e01990
Backward course design, active learning, and assessment built around student judgment
Students critique published studies, develop testable hypotheses, and work with real-world datasets, building analytical skills that transfer beyond the classroom.
From econometrics to data visualization, courses emphasize hands-on application of quantitative tools to economic questions.
Sports leagues and banks both operate under explicit rules, which makes them good settings for students to see how institutional design shapes behavior and outcomes.
Students work through individual cases with real data, use AI as part of the work, verify what it returns, and defend their conclusions out loud. I use a growth-mindset approach that treats difficulty as part of learning.
I teach sports economics, managerial economics, and money and banking, along with principles of macroeconomics and microeconomics, and I deliberately run material between the upper-level courses. In sports economics, students work through financial statements, the valuation of assets and liabilities, and what an organization in the sports industry is actually worth under competing methods. Money and banking runs the same material in the other direction, using firms from that industry as worked examples when we reach financial instruments and capital structure.
How AI actually shows up in my own teaching and research
Every student works a different real case: their own company, their own data, their own recommendation, defended live on video. Preparing that many separate projects used to cap how many I could run. Now the limit is the number of students in the room, not my prep hours.
Students tell me in the first week where they want to end up. I talk with the firms, franchises, and institutions that use the tools we cover, so what students practice matches how the work is actually done. Rebuilding a week of examples around both takes me an afternoon.
Before I build a slide, a structured AI interview forces me to state the course’s learning objectives first. Every weekly example is either cited to a real source or labeled illustrative.
I design assessment around a gap. Models are strong on recall and structure, and weak on owning a contested call. So my assignments run students through concepts, math, original research, data analysis, and graphical work. Then the question turns on judgment. Do you cut one job or lower everyone’s wage? Do you match a rival’s price cut or hold and lose share? A model will argue either side. It will not own the decision. Students carry the analysis all the way to a conclusion they can defend.
Course slides are built to pass the LMS accessibility check, and the conversion is scripted rather than fixed deck by deck.
Before any estimate, a check runs on the merges, the weights, the filters, and the plausibility of every constructed variable. It fails the build rather than printing a warning. Errors surface before they reach a table, not after a referee finds them.
Every paper I read gets split, read closely, and catalogued with its findings, methods, and exhibits. Different literatures use different words for the same idea, so I search on the idea. The index runs locally, so copyrighted papers and unpublished work never leave my machine. A new project starts from what I already know instead of rebuilding it.
Separate passes audit what I cannot see. A bibliography check verifies that every citation resolves to the paper I think it does. Another looks for the problem hiding in plain sight in my methodology and output. Catching it myself is cheaper than catching it at referee stage.
The tools behind this are ones I built and tested myself, each matched to a specific task rather than bought off the shelf. Designing and refining them is applied methods work, and it is the part that transfers to other people’s problems.
One-on-one guidance for students and early-career professionals
Mentees have earned Harry S. Truman Scholarship (first in NC A&T history) RFK John Lewis Young Leaders Fellowship (1 of 16 nationally) Harvard Law School Federal Reserve Bank of Boston BLK Capital Management AEA Summer Program at Howard
Career planning works best when it starts with what you actually value, not with what others say you should value. We explore your highest values and use them as the foundation for academic and career decisions. The work centers on you: what fits your priorities, what builds the life you want, what sustains your motivation past the first hard week.
Considering economics PhD programs, public policy, banking, finance, law school, or industry research roles? We map where you want to go, what you’d need to get there, and how each path fits the priorities you’ve identified.
Causal inference, applied econometrics, and empirical work with real datasets across sports, labor, finance, policy, and education. Sessions are hands-on. You build the skills by working through actual problems, not by watching someone else solve them.
Working on an undergrad thesis, independent project, or first conference paper? Together we find the gap in the literature, conduct a critical literature review, evaluate feasibility, scope the question, choose the right methods, and shape the writing.
Applying to PhD or master’s programs in economics, public policy, or law school? You’ll move through school selection, drafting personal statements, choosing writing samples, and working through the application process, with feedback at each step.
Truman Scholarship, RFK John Lewis Young Leaders Fellowship, AEA Summer Program, and other competitive national programs. We look at which programs fit your goals and what a thoughtful, competitive application looks like.
How to use AI tools across research, writing, and job-prep work without shortcutting the learning. Practical workflows for undergrads and early-career professionals.
Ready to work together? Let’s talk.
Get in TouchGet in touch
Assistant Professor of Economics
North Carolina A&T State University
Greensboro, NC
Academic: egminuci@ncat.edu
Personal: minucieg@gmail.com