Data Science

Supply, Demand, and Price Elasticity: Lessons from the Fulton Fish Market

Analyzing 150 years of NYC's fish market data to understand how weather shocks reveal true price elasticity and market dynamics using causal inference

20 juillet 2024
Partager
9 min de lecture

The Fulton Fish Market, operating for over 150 years as NYC's seafood trading hub, provides a unique natural experiment in market dynamics. By analyzing Kathryn Graddy's groundbreaking research with modern Python tools, we uncover how weather-driven supply shocks reveal true price elasticity and competitive behavior in real-world markets.

Key findings: - Stormy weather reduces supply by 2,371 pounds (average) while increasing prices by $0.32/pound - Demand is relatively inelastic with elasticity of -0.57 under normal conditions - Weather shocks serve as instrumental variables for identifying causal relationships

Traditional economic analysis struggles with endogeneity—prices and quantities are simultaneously determined, making it difficult to isolate causal effects. The Fulton Fish Market offers a solution through natural experiments: weather conditions affect supply (fishermen can't go out in storms) but not demand (restaurants still need fish), providing clean identification of market dynamics.

This case study demonstrates how to: - Use instrumental variables for causal inference - Estimate price elasticity in real markets - Identify price discrimination patterns - Apply econometric methods to business problems

À propos de l'auteur

Cyril Noirot

Cyril Noirot

Lead Data Scientist

Data scientist freelance. Je conçois et déploie des systèmes de décision — prévision, pricing, marketing measurement, optimisation.

Newsletter

Articles techniques sur la prévision, le pricing et les systèmes de décision. Aucune fréquence imposée.

Enter your email
Subscribe