Job market paper · 2026
The rural mobilization gap in a U.S. place-based tax credit
Does the rural private-leverage gap persist when rural and urban projects are compared within the same Community Development Entity?
2026–27 economics job market · August 2026
Independent economist since 2024 · PhD in Economics, Georgia Tech, 2024
I am an applied economist working across place-based policy, trade and spatial measurement, labor and AI, and quantitative education.
Job market paper · sole author and originator
Latest version: August 2026
Pooled across intermediaries, rural New Markets Tax Credit projects carry less additional capital per dollar of subsidized investment than urban projects (−0.262); within the same Community Development Entity the gap is −0.047 (95% CI −0.245 to +0.152; median −0.001), and 86% of the explained movement is associated with which CDEs originate the deals.
The New Markets Tax Credit gives private investors a 39% federal credit for routing capital through certified Community Development Entities (CDEs) into projects in low-income census tracts. Over FY2001–FY2022 the program placed $66.6 billion of subsidized investment in 8,024 projects worth $120.9 billion. Non-metropolitan tracts received 19.6% of dollars, close to the program’s 20% administrative target, yet project-level leverage was lower there (median 1.07× against 1.19× metro). The raw rural gap is a precise −0.262. With CDE fixed effects, the mean within-CDE estimate is −0.047 and the median-regression analog is −0.001; neither the extensive- nor the intensive-margin estimate is statistically distinguishable from zero within intermediary. Of the explained movement, −0.185, or 86%, is associated with observed CDE composition. Deployment shows no bunching at the 20% target, although compliance may operate through allocation agreements that the public data do not record.
I assembled the transaction and project record, documented its provenance, estimated the pooled and within-intermediary comparisons, and decomposed the movement between them. The within-CDE estimates use CDE fixed effects identified from the 163 of 343 CDEs that deploy on both sides of the rural line; an order-invariant Gelbach decomposition divides the movement between the pooled and within-CDE estimates; and a cross-CDE excess-mass test looks for bunching at the 20% non-metropolitan target.
This is descriptive, noncausal evidence. The mean interval is wide; the design does not identify why intermediaries differ or resolve the mechanism.
Letters of recommendation
Professor, School of Economics
Georgia Institute of Technology
Associate Professor, School of Economics
Georgia Institute of Technology
Georgia Institute of Technology
Selected research
Job market paper · 2026
Does the rural private-leverage gap persist when rural and urban projects are compared within the same Community Development Entity?
Preprint · 2025
Does a gravity model fit better when bilateral distance reflects where population and measured economic activity are located within countries?
Public working paper · 2024
Under what conditions can economic equilibrium be represented as an optimal-transport problem in an infinite-dimensional Hilbert space?
Published dissertation · 2024
How do network position, within-country spatial heterogeneity, and optimal transport change the representation of international trade?
Teaching record
Instructor of record for three semesters at Georgia Tech; teaching support at Duke and Indiana; online instruction in microeconomics and macroeconomics; independent instruction and research-design coaching.
An introduction to econometrics in twelve chapters. It opens with identification and moves through probability, projection, uncertainty, research designs, measurement, and dependence to decisions, with worked problems, three browser experiments, Python and R reproductions, and a teaching guide with a twelve-week schedule.
Fifteen lectures from stationary processes and ARMA models to state-space models, cointegration, and conditional volatility, with complete derivations, lecture summaries, problem sets with worked answers, three semester projects, and code in R, Julia, and Python.
Foundation chapters on economic questions and interventions, measurement, and observation clocks. Passages are marked at four reading levels, from concrete orientation to complete proofs, and each chapter's calculations run independently in Python, Julia, and R.
Documents and contact