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Data Analyst – Estimate Workers Affected by a Minimum Wage Increase in a Major Urban Area
We are seeking an experienced data analyst to estimate the number of employed workers (excluding tipped occupations) in a major U.S. urban area who would be affected by a potential increase in the minimum wage above its current level. The results will inform a policy research project focused on labor market impacts. The analysis must be methodologically sound, transparent, and based on publicly available microdata. The ideal candidate will have experience working with wage and employment data from sources such as the American Community Survey (ACS) or Current Population Survey (CPS) and be comfortable performing regional-level filtering and wage-band analysis. Responsibilities: • Extract and prepare individual-level wage and employment data for a large U.S. metropolitan area using datasets such as ACS PUMS or CPS MORG • Filter the data to focus on currently employed, non-tipped workers • Convert income to hourly wages, where needed, based on hours worked • Identify the number and proportion of workers earning below specific hourly wage thresholds • Apply appropriate survey weights to generate population-level estimates • Produce visualizations (e.g., wage distribution histograms) and summary tables • Provide a clearly written explanation of methodology, data sources, and assumptions Required Skills: • Proficiency in R, Python (pandas, NumPy, statsmodels), or Stata • Experience using ACS PUMS or CPS microdata for labor market analysis • Familiarity with survey weighting and data filtering techniques • Knowledge of occupational classification systems (e.g., SOC codes) and geographic identifiers (e.g., PUMAs, metro area codes) • Strong analytical and data visualization skills • Background in labor economics or public policy is a plus but not required Deliverables: 1. Cleaned dataset or well-documented script for data processing and analysis 2. Table summarizing the estimated number and share of workers earning below the target wage threshold 3. One or more basic visualizations (e.g., hourly wage histogram) 4. A brief memo or markdown file describing the data, filters, assumptions, and methodology Timeline and Budget: • Timeline: 1–2 weeks preferred • Budget: Open to proposals; please include your rate and estimated hours To Apply: Please include: • A short note on your experience with labor force or wage analysis • A sample of similar work (if available) • Your preferred analytical tool (R, Python, Stata, etc.) and how you would approach the project