2026–27 Economics Job Market Candidate
Juan C. Yamin
I am a Ph.D. candidate in the Department of Economics at Brown University, with primary interests in applied econometrics and development.
I develop methods that help policymakers and researchers use data to make better decisions. My work brings together statistical decision theory, causal inference, and experimental design.
Dissertation committee: Toru Kitagawa, Soonwoo Kwon, and Jonathan Roth
Job Market Paper
Poverty Targeting with Imperfect Information
Is it enough to target the households that look poorest?
How should antipoverty programs allocate transfers when household income is known only through noisy predictions? I formulate this as a statistical decision problem in which a policymaker chooses nonnegative transfers within a fixed budget to minimize squared deviations of post-transfer income from the poverty line. I show that the standard plug-in rule, which treats predictions as exact, is inadmissible. I then develop a nonparametric empirical Bayes allocation rule that replaces the estimated gaps with posterior mean poverty gaps. The rule's Bayes regret vanishes at least as fast as the mean squared error in estimating posterior mean poverty gaps, despite the budget and nonnegativity constraints. The approach extends, with weaker guarantees, to a policymaker who cares only about how far households remain below the poverty line after transfers and to programs that pay a fixed set of benefit amounts. In simulations using household surveys from nine African countries, the empirical Bayes rule reaches about 1.8 times as many poor people as plug-in OLS with the same budget, and it achieves the same average poverty gap reduction with 6.1% less spending or a 42% smaller training sample.
Other Research
When and How to Pilot
A pilot can help researchers choose a more efficient split between treatment and control, but a small pilot can also mislead. I develop a method that guards against overreacting to limited evidence and approaches the optimal split as the pilot grows.
Birds of a Feather Collude Together
Using close elections in Colombia, we find that partisan alignment between mayors and governors increases the fabrication of “ghost” students to obtain education transfers. Actual enrollment and student performance do not improve.
Software
cmrdesign
cmrdesign is an R and Python package that implements the design rules from When and How to Pilot. Given pilot outcomes and treatment assignments, it recommends how to split the main experiment between treatment and control. It also reports a finite-sample bound on how much precision that split can lose relative to the best allocation.
R
install.packages("cmrdesign") library(cmrdesign) fit <- cmr_two_arm(y, d, alpha = 0.05, method = "auto") fit$pi # share of the main wave to treat fit$U_CMR # bound on precision lost vs. best split
Python
pip install cmrdesign
import cmrdesign as cmr
fit = cmr.cmr_two_arm(y, d, alpha=0.05,
method="auto")
fit.pi # share of the main wave to treat
fit.U_CMR # bound on precision lost vs. best split