Seungmin Lee, University of Notre Dame
3150 Jenkins Nanovic Halls
Pulte Institute for Global Development
University of Notre Dame
Notre Dame, IN 46556
(574) 631-2940
slee76@nd.edu
How to pronounce my name:
Seungmin = “suhng-min” listen

Seungmin Lee

Postdoctoral Research Associate

Seungmin Lee is an applied economist and postdoctoral research associate at the Pulte Institute for Global Development, University of Notre Dame. His research develops and applies causal inference and machine learning methods to measure household welfare and evaluate the policies that shape it. He has applied these tools across development, agricultural, and environmental economics - including poverty dynamics, food security, SNAP, and payments for ecosystem services - using each context as a proving ground for empirical methods.

Before coming to Pulte, he worked as a research assistant at Innovations for Poverty Action (IPA) and Yale University, and as a consultant at Development Impact Evaluation (DIME) at the World Bank.

Education

  • PhD Applied Economics and Management

    Cornell University, 2024

    Applied Econometrics Development Economics Agricultural Economics Public Policy
  • BSc (Mathematics, Economics)

    University of Michigan in Ann Arbor, 2014

Job Market Paper

Estimating Long Run Welfare Outcome in Rotating Panel with Grouped Fixed Effects: Application to Poverty Dynamics in Peru - with Hongdi Zhao

Household welfare dynamics are often difficult to investigate due to a lack of long-term panel data. Existing methods, such as pseudo-panel and synthetic panel, are widely used approaches based on repeated cross-sectional designs, but they impose ex ante assumptions that often do not hold. This paper introduces an empirical method to estimate welfare dynamics using a rotating panel design. The paper employs the grouped fixed effects (GFE) estimator to exploit within-household variation from short-term observations and to estimate longer-term welfare and its dynamics using time-varying group-level heterogeneity, thereby relaxing a few assumptions in the previous literature. The paper applied this method to Peru's National Household Survey on Living Conditions and Poverty (ENAHO) from 2007 to 2019 and examined long-term poverty and its dynamics during this period. The new method estimates household-level poverty status and its transitions reasonably well, suggesting it as a new approach to studying household welfare dynamics at the group level and providing an interpretable grouping structure that supports richer descriptions of poverty persistence and mobility.