Michael Best
Columbia University, NY·Social Science
Governments need money and honest officials to provide public services, but tax evasion and corruption drain both. Enforcement capacity is scarce, so a key question is where to aim limited audits and oversight.
- Fields
Social Science
Mathematics & Statistics
- Topics
tax evasion
tax enforcement
production networks
- Grants
- 2 known grants, through Mar 2027
What they work on
- Extend the standard tax-evasion model to production networks, where firms report trades with each other, and derive optimal rules for targeting enforcement to minimize total evasion.
- Measure direct and spillover effects of electronic invoicing and discrepancy-focused desk audits on firms, suppliers and clients through a randomized controlled trial using administrative tax data.
- Test a calibrated optimal enforcement targeting rule against status-quo and random targeting in a second randomized trial with tax authorities.
- Determine how best to delegate monitoring of public works projects to citizens, accounting for differences in their motivation and ability to perform audit tasks.
- Develop machine learning and AI tools that combine citizen and whistleblower reports to predict corruption and help governments triage reports.
More about the research
Two projects tackle this question with economic theory, administrative data, field experiments and machine learning. One focuses on firms evading taxes. The other focuses on corruption in government contracting.
Why it matters
Tax authorities could use evidence-based targeting rules and electronic invoicing to collect revenue more fairly and efficiently. Anti-corruption agencies could gain tools to sort citizen reports and focus scarce investigators. The findings aim to inform tax administration in the US and policy decisions in other countries.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
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Michigan State University, MI·Social Science
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Nicole Pashley
Rutgers University-New Brunswick, NJ·Mathematics & Statistics
Develop randomization-based methods to analyze incomplete block designs, where each site runs only a random subset of the treatments, using the potential outcomes framework that relies on fewer assumptions than model-based approaches.
The school
Columbia University: 89 labs on PI Pages, $1.4B a year on research, 625 research doctorates a year.
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