Exploring the Landscape of Teacher Applications

Despite widespread concern about teacher shortages, there is little evidence on when teaching positions are posted and how many people apply for them. Using detailed job-posting and application data on nearly 57,000 applicants from 19 traditional school districts and 24 charter organizations, we provide descriptive evidence on the timing of job postings, the supply of applicants, and the characteristics of applicants, emphasizing how each varies across organizations and between the district and charter sectors. We find that late hiring is less prevalent than prior single-district studies suggest—about 11% of openings are posted late—but varies widely across organizations. Applicant supply differs widely across organizations and by sector: districts average 3.3 applicants per opening, versus 6.1 for charters. We also find sectoral differences in applicant pool composition; compared to charters, district applicant pools have higher percentages of White applicants and applicants with prior teaching experience.

This version: August 2026. This paper is a revised version of "Exploring the Landscape of Teacher Applications" (CALDER Working Paper No. 329-1025 / CEDR Working Paper 07222025-1, October 2025) and supersedes it. The earlier version remains available at  
https://caldercenter.org/publications/exploring-landscape-teacher-applications for reference. 

The revised version was reorganized following peer review and differs in several respects from the October 2025 version: (1) the analysis of application volume over the hiring season has been removed, as applications-per-opening is not well suited to cross-organization comparison; (2) a new analysis of applicant characteristics (demographics, experience, education, and measures of applicant quality) has been added; (3) the paper has been reorganized to foreground the comparison between traditional school districts and charter organizations; (4) inference is now based on the delete-one-cluster (CV3) jackknife variance estimator (MacKinnon, Nielsen, and Webb, 2023), given the small number of clusters. Under this approach some relationships reported in the earlier version — most notably the positive salary–applicant supply association among districts — are no longer statistically significant, though point estimates are essentially unchanged; (5) the small number of observations from 2019 are excluded from the analytic sample.