About Me
I am an applied economist who earned a Ph.D. in Resource Economics from the University of Massachusetts Amherst in 2026. My research focuses on how competition, market structure, and public policy shape firm behavior, product offerings, and consumer outcomes.
My work lies at the intersection of applied microeconomics, industrial organization, and health economics and policy. My dissertation, Essays on Competition, Public Policy, and Firm Behavior, combines spatial competition models, large-scale product- and firm-level data, and causal inference methods to study restaurant location decisions, firm responses to nutrition policy, and the role of local competition in shaping policy outcomes.
I also hold an M.S. in Computer Science from the Georgia Institute of Technology. This interdisciplinary training supports my work with large-scale datasets, econometric and computational methods, spatial analysis, data visualization, and machine learning.
I am currently seeking opportunities in applied economics, economic consulting, policy research, and data-driven research roles.
Research
US Restaurants’ Quality and Spatial Competition
Job Market Paper
This paper examines how quality differentiation, population concentration, and local market conditions shape restaurant location patterns across major U.S. cities. Using city-level, pairwise, and multilevel spatial analyses, I find systematic differences in the spatial behavior of high- and low-quality restaurants. In particular, high-quality restaurants tend to be more spatially dispersed within their own quality group, while low-quality restaurants tend to be more concentrated.
Reactions to FDA Sodium Reduction Guidance: Demand and Supply
Selected Paper, 2023 AAEA Annual Meeting
This paper evaluates restaurant responses to the FDA’s voluntary sodium reduction guidance using a longitudinal panel of more than 400,000 chain-restaurant menu-item observations from 2016 to 2019. Using fixed-effects and event-study specifications, I find evidence of selective rather than broad-based reformulation, with stronger sodium reductions concentrated among limited-service restaurants and specific menu categories.
Local Competitive Density and Voluntary Policy Compliance
This paper examines how local market structure conditions firm and consumer responses to voluntary nutrition policy. I combine longitudinal menu data, store-level Yelp reviews, and spatial measures of restaurant competition. The results show that chains with greater exposure to dense urban markets experienced larger sodium reductions, while consumer-response associations with reformulation were stronger in more competitive local markets, particularly among limited-service restaurants.
Work in Progress
- Reactions to NYC’s Calorie Labeling Rule for Food Service Establishments
- Investigating the Role of Entry Barriers in Shaping Competition among Food Companies
Teaching
I have served as instructor of record for:
- RES-ECON 323: Financial Analysis for Consumers and Firms, University of Massachusetts Amherst (Fall 2024, Spring 2025)
- RES-ECON 213: Intermediate Statistics for Business and Economics, University of Massachusetts Amherst (Summer 2023)
I have also taught at Smith College and served as a teaching assistant for courses including Industrial Organization, Introductory Econometrics, Managerial Economics, Public Policy in Private Markets, Price Theory, Decision Analysis, and Statistics for Social Sciences.
I received the Vijay Bhagavan Teaching Assistant of Distinction Award from the Department of Resource Economics at UMass Amherst in 2022.
Details are available on my Teaching page.
Skills
- Econometric & Quantitative Methods: Panel data econometrics, causal inference, structural IO modeling, discrete choice models, multilevel/hierarchical models, spatial competition modeling, heterogeneity analysis
- Data & Computational Methods: Large-scale data collection and cleaning, web scraping, record linkage, spatial analysis, text analysis, sentiment analysis, machine learning
- Programming & Software: R, Python, Stata, SQL, MATLAB, Java, C++, LaTeX