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John Nunley Ph.D.
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John Nunley Ph.D.

John Nunley is an applied microeconomist. He is an expert in data management, visualization, and analysis and has extensive experience designing experiments and collecting original data. John’s research has appeared in high-quality academic journals, including the Journal of Economic Behavior and Organization, Demography, Labour Economics, Industrial and Labor Relations Review, Contemporary Economic Policy, and B.E. Journal of Economic Analysis and Policy, and has been featured in leading non-academic publications and news outlets, such as the Wall Street Journal, Time Magazine, Fortune Magazine, NPR’s Hidden Brain, The Conversation, and Wisconsin Public Radio.

Experience

  • Instructed undergraduate courses in microeconomics, econometrics, and labor economics
  • Published peer-reviewed research in top economics journals, including Industrial and Labor Relations Review,
  • Journal of Economic Behavior and Organization, Labour Economics, Demography, and B.E. Journal of Economic Analysis and Policy.
  • Research featured in major outlets, including Time Magazine, Wall Street Journal, NPR, Market Watch, and Wisconsin Public Radio.

Research methodologies employed include:

  • Designed, implemented, and managed large-scale field experiments in product and labor markets
    • Led the design of four experiments in collaboration with coauthors and managed research teams to execute the studies.
    • Categorized job advertisement text into detailed occupations using machine learning algorithms, enhancing data quality by integrating external datasets.
    • Performed regression analysis to test hypotheses and conduct sensitivity analyses.
  • Identified causal relationships with survey and administrative data
    • Leveraged secondary-data sources to build econometric models assessing the impact of state policy changes on child, education, and labor-market outcomes.
    • Analyzed the impact of automation-technology exposure on indirect outcomes, such as SSDI applications and business quality/quantity, by linking government, proprietary, and secondary data sources and applying causal-inference methods (e.g., instrumental variables).
  • Time-Series Analysis
    • Used time-series estimation techniques to analyze changes in aggregate economic indicators and generate forecasts.
    • Performed univariate and multivariate analyses, including modeling and trend decomposition.
  • Survey Design
    • Collaborated with 40+ partners to design and implement the Global COVID-19 Student Survey across 26 universities in seven countries.
    • The survey assessed five key areas of student impact during the pandemic: health awareness, online learning, mental health, social behavior, and financial challenges.

Led organizational efforts to design a model to enhance pay equity among 450+ employees, evaluate effects of pricing strategy on demand and make recommendations to upper management, and revise the organization’s compensation schedule associated with completion of tasks outside of normal workload.

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