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.
I develop methods for using data to improve economic 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
Better targeting needs better decisions, not just better income predictions.
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. The standard plug-in rule, which treats predictions as exact, is inadmissible. I develop a nonparametric empirical Bayes rule that allocates the budget using posterior mean poverty gaps in place of estimated gaps. Its Bayes regret is bounded by the mean squared difference between its posterior mean gaps and the oracle's, so the budget and nonnegativity constraints do not slow its convergence to the oracle. The approach extends to an objective that penalizes only remaining poverty and to fixed benefit menus, with weaker guarantees.
In simulations using household surveys from nine African countries, relative to plug-in OLS targeting:
as many poor people reached, for the same budget
less spending to achieve the same average poverty-gap reduction
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