Resume

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Contact Information

Name Daisoon Kim
Professional Title PhD Economist
Email kim.daisoon.kr@gmail.com
Location Vienna, Virginia

Professional Summary

PhD economist and researcher with 7+ years of experience transforming large, granular datasets, building end-to-end data pipelines, and developing structural economic models to inform policy and business strategy. Proven track record of leading funded research programs (e.g., NSF co-PI, 459K; Caterpillar PI, 86K), publishing in top field journals (e.g., JME, JIE), and delivering research used by central banks and international institutions (e.g., the Federal Reserve, IMF, and UN).

Experience

  • 2019 - current

    Raleigh, NC

    Assistant Professor of Economics
    Poole College of Management, NC State University
  • 2023 - current

    Cambridge, MA

    Research Economist
    National Bureau of Economic Research
  • 2023 - current

    Providence, RI

    Affiliate Professor
    Global Linkages Lab, Brown University
  • 2018 - 2019

    London, UK

    Research Fellow in Economics
    London Business School
  • 2012 - 2012

    Sejong, Korea

    Research Analyst
    Korea Institute of Public Finance

Education

  • 2013 - 2019

    Seattle, WA

    PhD in Economics
    University of Washington

Skills

Professional Skills: Project Management, Data-Driven Decisions, Quantitative Research
Quantitative Modeling: Heterogeneous Firms/Households Dynamics Models, Monte Carlo Simulation, Structural Econometrics
Econometric Analysis: Causal Inference (DID, Synthetic Controls), Dynamic Factor Models, Panel Local Projections
Data: Complex Data Integration, Panel Data Construction, Unstructured Data
Dataset: CRSP, Compustat, FactSet, ORBIS, EPFR, FT fDi Market, LCA, Nielsen Scanner, KLIPS, UN Comtrade

Interests

Fields: International Economics, Macroeconomics, Industrial Organization
Topics: Firm Resilience, Firm Dynamics, Business Cycles, Market Structure, Systemic Risk, Uncertainty, Spillover, Network Effects, Sanctions, Global Value Chains and Investment

Projects

  • Granular Data and Firm Dynamics (Isolating Synchronized Idiosyncratic Shocks)

    Aggregate Fluctuations from Firm Comovement (R&R JME)

    • Data Structure. Constructed a comprehensive 40-year longitudinal dataset covering the U.S. public firms (Compustat Fundamentals), cleaning and harmonizing sector classifications across decades to ensure consistency.
    • Identification Strategy. Developed a non-parametric approach to decompose aggregate volatility. Unlike standard demeaning methods which wash out heterogeneity, this method isolates contribution of firm-specific shock spillovers.
    • Economic Insight. Demonstrated that 10-15 percent of aggregate volatility stems from correlated idiosyncratic shocks, challenging standard representative firm assumptions.
  • Multi-Source Data Integration and Supply Chain Network Analysis

    Global Connectedness and Diversification during the Pandemic (R&R JMCB)

    • Data Engineering. Engineered a high-dimensional linked panel by merging FactSet Revere (supply chain relationships), Compustat (balance sheets), and CRSP (daily stock returns).
    • Identification Strategy. Exploited the exogenous timing of COVID-19 lockdowns across different countries to create a Difference-in-Differences (DID) design.
    • Analysis. Mapped the propagation of supply chain shocks through specific firm-to-firm linkages.
    • Result. Proved that while global diversification buffers domestic shocks, it exposes firms to specific foreign supplier risks—a finding only visible through granular network mapping.
  • Quasi-Experimental Design and Policy Evaluation

    Public Information on Overseas Buyers for Export Promotion

    • Data Engineering. Digitized and structured historical archives of KOTRA buyer contact releases to create a unique dataset of information provision. Merged with establishment and industry level datasets.
    • Identification Strategy. Utilized a Quasi-Experimental Design, exploiting variation in the timing and industry-targeting of government information releases to establish causality.
    • Economic Insight. Found that reducing information frictions significantly increases export volumes at the extensive margin (new buyers), particularly for small-to-medium enterprises (SMEs).
  • High-Dimensional Scanner Data and Dynamic Discrete Choice

    Consumer Learning and Package Size in Dynamic Brand Choice

    • Data Structure. Analyzed Nielsen Scanner Data, handling millions of transaction-level observations to track household purchasing histories over multiple years.
    • Econometric Challenge. Disentangling true preference learning from spurious state dependence (habit formation) in consumer panel data.
    • Result. Quantified how information friction drives brand-purchasing behavior, providing micro-evidence that consumers use small package sizes to test new products before committing to bulk purchases.