Overview
Sendhil Mullainathan is a professor at the University of Chicago Booth School of Business (formerly Harvard and MIT). He works at the intersection of economics, behavioral science, and machine learning, with applications to discrimination, poverty, and healthcare. His methodological work includes foundational contributions to DiD inference and racial discrimination in hiring.
Key Contributions / Features
- Serial correlation in DiD (Bertrand, Duflo, and Mullainathan 2003): Co-established the serial correlation problem in multi-period DiD and the clustered standard errors solution.
- Racial discrimination in hiring (Bertrand and Mullainathan 2004, AER): Resume audit study showing 50% more callbacks for White-sounding names; established the audit methodology in discrimination economics.
- Scarcity (Mullainathan and Shafir 2013): Behavioral-economic theory of how scarcity (of money, time) captures mental bandwidth and impairs decision-making, with applications to poverty traps.
- Machine learning in economics: Research using algorithmic tools to detect discrimination in lending and healthcare decisions.
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