When Is Structural Modeling Useful?

What's the difference between structural models and simpler econometric methods like RCTs or difference-in-differences? In this video, I break down when to use each approach in empirical economics research.

Scientific Papers Referenced:
Jardim, Ekaterina, Mark C. Long, Robert Plotnick, Emma van Inwegen, Jacob Vigdor, and Hilary Wething. 2022. "Minimum-Wage Increases and Low-Wage Employment: Evidence from Seattle." American Economic Journal: Economic Policy 14 (2): 263–314.
DOI: 10.1257/pol.20180578

Denning, Jeffrey T., Benjamin M. Marx, and Lesley J. Turner. 2019. "ProPelled: The Effects of Grants on Graduation, Earnings, and Welfare." American Economic Journal: Applied Economics 11 (3): 193–224.
DOI: 10.1257/app.20180100

Key Topics Covered:
The fundamental distinction between policy evaluation and policy design
When simpler methods (RCTs, IV, diff-in-diff) are most appropriate
When structural models become necessary
Understanding mechanisms vs. net effects
General equilibrium and dynamic effects
A practical decision framework for choosing your methodology

Real-World Examples:
Seattle's $15 minimum wage policy
Pell Grant effects on college graduation
Job training program evaluation

Whether you're a grad student choosing a dissertation topic, a policy researcher, or just curious about how economists analyze interventions, this video will help you understand the tradeoffs between methodological complexity and practical applicability.

Decision Checklist Questions:
Are you analyzing an existing or hypothetical policy?
Do you need to understand mechanisms or just net effects?
Are there important equilibrium or dynamic effects?
What's your timeline for analysis?
What are the stakes if you're wrong?

Related Topics: causal inference, econometrics, policy evaluation, structural estimation, randomized controlled trials, difference-in-differences, instrumental variables, labor economics, applied microeconomics

Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods!

Editing credit: @neiljohnmanllios3064

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