Exploring the Severity of Local Teacher Shortages With a National Forecast Model

• In Event: A Profession in Crisis? New Data on the Challenges of Recruitment and Retention of the Educator Workforce

Abstract

Motivation

Concerns about teacher shortages have been commonplace for years but have recently surged due to the Coronavirus pandemic. Unfortunately, there are no national data sources that policymakers and school leaders can access to understand the size and scope of teacher shortages with enough granularity and timeliness to allow for appropriately targeted teacher shortage policy. It is unclear the degree to which teacher shortages are worse in high-demand grades or subjects and in schools that serve marginalized communities. Understanding the differences in the concentration of shortages is integral to districts developing teacher recruitment strategies. There is a particular lack of understanding about the size of pandemic era teaching shortages in schools that serve marginalized communities. The goal of this project is to leverage publicly available data to estimate the size of teacher shortages and forecast shortages for the whole country. We aim to investigate the characteristics of school systems that face the more acute teacher recruitment challenges.

Data and Methods

Data

We are in the process of collecting teacher supply and demand data from the 2016-17 school year to the 2021-22 school year. Teacher supply data will be collected from the Title II teacher certification reports and from IPEDS. We will then use the American Community Survey to estimate the number of individuals with teacher certifications in specific metropolitan areas. Teacher demand data is available from states and federal survey data. 14 states have publicly available school or district level teacher turnover data (Author, 2022), which we will supplement with federal data (e.g., OCR, SASS, NTPS, ASEC, QCEW). We observe teacher demand data (i.e., teacher turnover) in each metropolitan area in the country. In the future we intend to collect data from 2000-01 to present.

Estimating Missing Data

We estimate teacher turnover for metropolitan areas and years in which we do not observe data using Structural Equation Modeling with Full-Information Maximum Likelihood (Allison, 1987; Graham, 2009). This flexible approach eschews case-wise deletion and produces turnover estimates for all metropolitan areas using available data (e.g., school, district, county, state). We will use a similar approach to estimate teacher shortages in rural areas.

The supply and demand data we observe allows us to estimate both new teachers and teacher leavers for every metropolitan area for the 5 years from 2016-17 to 2021-22. We adapt the model from Reichardt and colleagues (2020) which demonstrates that shortages are a function of the number of new teachers and teachers who left their school. We will then forecast local shortages using an ARIMA model.

Preliminary Results

Our preliminary estimates suggest there is considerable variation in teacher shortages across the United States. That result is consistent with prior research (Edwards et al., 2022) and suggests that future shortages will also reflect localized dynamics. In future work we will endeavor to develop local teacher shortage forecasts for specific subjects (e.g., math, science), school levels (e.g., elementary, middle, high), and race/ethnicity (e.g., Black, Hispanic).

Authors

• Joshua Bleiberg, University of Pittsburgh

• Patricia Saenz-Armstrong, National Council on Teacher Quality

• Tuan D Nguyen, University of Missouri