Luke Catalano
Economist Apprentice, Amazon Ads Economics
I work on empirical industrial organization, auction theory, causal inference, and platform and marketplace economics. At Amazon, I focus on the calibration and validation of demand models used in advertising systems.
I hold an M.S. in Quantitative Economics and Finance (2026) and a B.A. in Economics with Honors (2025), both from the University of California, Santa Cruz. I plan to apply to Ph.D. programs in economics in the Fall 2027 cycle.
Curriculum vitae
Research
Calibration and validation of demand models in advertising systems
Calibration and validation of production price-elasticity of demand models used in advertising systems, using randomized pricing experiments across international marketplaces. The calibration coefficient is identified with an IV/Wald estimator using randomized treatment assignment as an instrument for the endogenous, mismeasured price shock, and product-category-level coefficients are pooled via a hierarchical Bayesian model. Calibration via RCTs increases production model accuracy by 36pp against observational demand responses and 32pp against held-out experimental demand responses.
Spatial heterogeneity in the effects of NYC Congestion Pricing on collisions
Estimated spatially heterogeneous causal effects of NYC's January 2025 Congestion Pricing Act on weekly motor vehicle collisions using Local Linear Causal Forests. Constructed a 62-week post-treatment panel from NYPD collision data across 3,598 hexagonal spatial units; one hex reached the 95% level after honest calibration, providing evidence against a measurable citywide collision effect.
NYC Congestion Pricing and vehicle collisions: a difference-in-differences analysis
Applied causal analysis of NYC's January 2025 Congestion Pricing Act using a difference-in-differences framework. Estimated a ~17% reduction in motor vehicle collisions during the first 10 weeks post-implementation; validated identifying assumptions (parallel trends, no anticipation) and discussed policy implications.
Injunctive norms and information about labor practices in a duopoly market
Designed and implemented a laboratory experiment (oTree/Python) testing how information about unethical labor practices and injunctive social norms affect consumer choice in a duopoly market. On pilot data (n=24), labor-practice information raised the probability of choosing the ethical seller by 57.5 percentage points, and adding an injunctive-norm treatment raised it by 61.5 percentage points, relative to a 12.5% baseline.