Problem
Peace Corps lacked a standardized, data-driven method to diagnose the specific recruitment challenges faced by its diverse country Posts. This prevented targeted resource allocation, as the “why” behind a Post being “hard to fill” was unclear and often based on anecdotal evidence.
Methodology
- Data Collection & Analysis: Conducted a comparative cohort analysis of the full applicant lifecycle for over 50 country Posts using administrative data from the agency’s internal database (DOVE). Processed and cleaned large datasets using Python (pandas) to generate key performance indicators.
- Modeling & Technique: Developed a composite Recruitability Index by standardizing four key metrics (Invitation Acceptance Rate, EOD Yield Rate, Application Share, and Medical Supportability) into Z-scores using Excel and statistical methods. This approach normalized disparate metrics and adjusted for known selection biases, such as the systematic placement of medically complex applicants at better-supported Posts.
- Communication & Strategy: Created a series of data visualizations (heatmaps, scatterplots) in Python (matplotlib) to distill complex findings into actionable “Post Profiles” (e.g., ‘Leaky Bucket’ vs. ‘Niche Appeal’). Presented a strategic framework to senior leadership that reframed the “hard to fill” problem into a set of distinct, diagnosable operational challenges.
Outcome
- Strategic Impact: Informed strategic planning by providing the first quantitative, multi-dimensional framework for comparing Post-level recruitment challenges. This shifted the agency’s approach from generic solutions to targeted interventions tailored to specific funnel weaknesses.
- Operational Impact: Created a diagnostic tool that enables the Offices of Recruitment, Placement, and Health Services to identify specific bottlenecks (e.g., low initial interest vs. high post-acceptance attrition) and align resources more effectively.
- Knowledge Impact: Delivered a novel analytical asset that quantifies the implicit operational cost of recruitment, providing a critical complement to traditional financial “Post Cost” analysis mandated by the Kate Puzey Act.