1. Introduction
The teacher shortage and its spatial and temporal dimensions have been a persistent problem in the education sector (
Aragon 2016;
Amanti 2019;
Cowan et al. 2016). It is not just a problem of a shortage of educational resources in the labor market but also a problem of the spatial and temporal distribution of these resources, which affects student-teacher Rates and student achievement (
Sutcher et al. 2019). Investigations at different scales, such as school, school district, county, state, and national levels, have shown spatial heterogeneity in the student-teacher rate, exposing various secular local education management deficiencies (
Aragon 2016).
Reichardt et al. (
2020) mentioned that specific subject areas, grade level, and geographic location were the three main aspects of solving teacher shrinkage (
Reichardt et al. 2020). In this research, we focused on visualizing the geographic heterogeneity of teacher shortages, which refers to mapping the trajectory of teacher shortages over time in counties and states. The student-teacher rate is used as a key indicator to measure the spatiotemporal disparity in teacher supply and demand (i.e., the number of students enrolled divided by the number of teachers from all sources who are willing and able to teach). By using geographic spatial visualization and a spatial-temporal mismatch model, this research aims to highlight the uneven distribution of educational resources and provide a new interdisciplinary paradigm known as educational geography. The spatial-temporal distribution of teacher supply and demand is useful to deepen the understanding of educational inequality between students and teachers at the national level, to identify potential trends in teacher vacancies, and to induce labor markets to reallocate teachers to more efficient uses while mitigating teacher turnover.
Compared to teacher attrition, retention, and turnover in subject areas (i.e., STEM subjects) and teacher credentials or qualifications (
Zweig et al. 2021), research on student-teacher Rates is not well documented. It dates back to the late 19th century (
Lewit and Baker 1997) and focuses on class size reduction (
Peers 2016;
Jensen 2021;
Solheim and Opheim 2019;
Wang and Eccles 2016;
Waasdorp et al. 2011;
Finn et al. 2008) and teacher burnout (
Jensen 2022;
Borman and Dowling 2008;
Jensen and Solheim 2020). Recent literature has highlighted high poverty in specific states such as Missouri (
Reichardt et al. 2020), Mississippi Delta (
Curran 2017), Arkansas (
TNTP 2021), New York (
Zweig et al. 2021), and professional teacher shortages such as music teachers (
Hash 2021) and STEM teachers (
Ridley-Kerr et al. 2020;
Gross 2018;
Woo 1985). Spatial-temporal descriptions of student-teacher rates are merely rare, not to mention macro-spectrum predictions of teacher shortages. Although
Sutcher et al. (
2019) proposed teacher mismatch between supply and demand as education reports, they did not mention their methods and navigate on large-scale assessment (
Sutcher et al. 2019). Moreover, spatial mismatch originally refers to the phenomenon that the spatial allocation of production factors deviates from Pareto optimality due to various reasons, resulting in the loss of economic benefits (
Kicsiny and Varga 2022;
Li et al. 2022,
Marrero-Vera et al. 2022). Previous spatial mismatch analysis intensively focuses on minor educator labor workforce allocation (
Holzer 1991;
Wasmer and Zenou 2002;
Gobillon et al. 2007;
Hsieh and Moretti 2019;
Kicsiny and Varga 2022;
Logan et al. 2020;
Li et al. 2016;
Silva et al. 2021;
Wang et al. 2022;
Yang et al. 2022). The spatial mismatch hypothesis is not employed in high-need school districts in the U.S. This paper took advantage of the theory of spatial mismatch and explored the components of student-teacher spatial mismatch so that teachers are optimally allocated.
Public school poverty, teacher comparable wages, the distance between school districts and highways, cities near school districts, and air quality were host factors related to student-teacher rate. Public school poverty is the free and reduced lunch program enrollment divided by the number of students enrolled in the same school, which directly reflects the household income of the students. Teacher-comparable wages are a critical factor in teachers’ willingness to work in schools. The distance between school districts and highways is used to measure the degree of transportation convenience. The urban-rural factor is represented by the shortest distance of cities to which school districts are close, which is the core of the spatial mismatch hypothesis (i.e., teachers residing in inner cities face adverse labor market outcomes). (
Zhang et al. 2007;
Lau 2011;
Li and Chu 2022). Air quality is an environmental factor that reveals the impact of the environment on the student-teacher rate. Five factors include socio-economic, environmental, and transportation comprehensive impacts on teacher shortage. There is insufficient evidence to support claims of a growing teacher shortage at the national level. If the teacher labor market is tight, it is more important than ever to ensure that students have access to quality education and achieve educational success. This study intends to either pinpoint the spatial mismatches areas, such as states and counties or suggest local education policymaking regarding increasing and decreasing teacher supply. By using a multi-criteria evaluation to calculate teacher demand, the mismatch index with the SMI model would automatically examine the coupling degree between the current supply student-teacher Rate and the integrated teacher demand degree.
4. Discussion
Based on the aforementioned mismatch between teacher supply and demand at multiple scales, we found several compelling implications. First, although the proportion of mismatched areas at the school district and county levels was the same at 1%, meaning that teacher supply satisfied potential teacher demand at two levels, there was apparently spatial heterogeneity: NV, IN, VT, MA, and FL were mismatched at the state level but had good match at the county and school district levels. In other words, the differentiation was masked at the inter-county level. Second, the spatial mismatch index model in this research, which examines the uncoupled relationship between student-teacher ratios and a weighted linear combination of five factors: school poverty, air quality, comparable teacher salaries, teacher transportation, and teacher proximity to urban areas, provided a new sight for state-level analysis. Thirdly, in light of five selected factors, we failed to find an imbalance between teacher supply and teacher demand. There may be other unpredictable factors that trigger teacher shortages, such as workload, school rankings, and teacher vacancies. These should be considered for further investigation in the next research plans.
In fact, the government is sparing no effort to improve the treatment of teachers and alleviate the potential shortage of teachers via proper policy direction and perfecting incentives. For example, some significant funding has been provided by nonprofit educational institutions, such as the American Association for the Advancement of Science (AAAS), which is dedicated to promoting and recruiting STEM majors and professionals to become K–12 teachers. The Robert Noyce Teacher Scholarship Program is reportedly designed to increase the number of K–12 teachers with strong STEM backgrounds who are willing to teach in high-need school districts (
http://nsfoyce.org, accessed on 31 January 2020). Throughout 2021, the Noyce Project has supported 1083 Noyce Scholarship programs with a total award amount of
$1,251,686,486.00. In Montana, for example, Salish Kootenai College, Montana State University, the University of Montana, and the University of Providence received
$9,506,316.00 in awards for six programs from 2009 to 2019, aligning faculty resources and alleviating some of the pressure on the faculty labor market. At the same time, the existing hierarchical education system pays more attention to advanced education (college education or above) and ignores general education (senior high school or below). This may cause a temporary dynamic imbalance between teachers and students. In general, general education has a greater impact on culture and society as students navigate life beyond their undergraduate experience (
Smith and Tarantino 2019).
Moreover, school reputation is seen as a key barrier that explains how public schools are affected by school choice and competition (
Jenkins 2020). All teachers really believe that their benefits are closely related to the school’s reputation. There are two polarizations between enrollment above demand and enrollment below demand. Some prestigious urban schools have excellent teachers and students with intense competition, but rural schools have extreme shortages of teachers based on population. In addition, school administration has an impact on teacher turnover. A teaching environment is a blend of social, emotional, and instructional elements that stimulate teachers’ subjective willingness and career aspirations. Creating a welcoming teacher education program is essential to teacher retention (
Menzies 2023).
Beyond the spatial mismatch analysis, we are also aware that the deep root of the mismatch is insufficient financial security and political competition. Financial security is not enough to estimate the workload and salary increase in teachers, which caused the mass exodus. First, there are intangible extra burdens that interfere with teachers’ normal routines, such as reading academies, grading, and extra reading training. Teachers are expected to sacrifice their leisure time to perform these extra tasks. Second, teachers’ benefits lack an inflationary budget. In almost all school districts, teachers’ salaries are only budgeted at a minimum of 3% of income. When the inflation rate exceeds 3%, the cost of living rises, which eats up the teacher salary increase. If there is no proper teacher financial stimulation regulation to guarantee teacher development, it is hard to solve the teacher shortage naturally. As for policy issues, some states have addressed the teacher shortage in a positive way, but some states have hidden it. For example, in Texas, in order to slow down the deterioration of the teacher shortage. On 11 March 2022, the government established a task force to address the teacher shortage. On 25 July 2022, new laws will be constructed to help Texas schools unintentionally contribute to the teacher shortage. In contrast, ten other states are missing data in the CCD. One of the main reasons may be that state education agencies are unwilling to share their teacher and student information to avoid negative repercussions.
Some limitations should be noted as a result of modeling the spatial mismatch between teachers and students. First, this research does not account for data imputation. Inevitably, there is missing data in several states. In order to ensure the authenticity of the data, we did not use data imputation methods to fill in the missing data, so the missing data does not affect our results. Second, the statistical unit in this research is a school district, not an individual school. Since school-level computation is too large for traditional computers to perform smoothly, we chose the school district as the research unit after empirical school-level data merging failure in Python. To refine our data further, delving into individual schools should examine our results.
5. Conclusions
The research identified the impact of teacher salary, school poverty, transportation, and environmental factors on the student-teacher ratio but did not consider student-learning outcomes. It does not mean that student learning outcomes are insignificant; it is a key criterion for assessing educational equity. However, the student learning outcome is the final result that several factors related to teachers contributed and depends on individual learning preference, practice time, and intelligence development. Herein, student learning outcomes were not selected in this research. Furthermore, it also measured the presence of geographic disparities in the U.S. in terms of teacher-student mismatch across school districts, counties, and states. We developed a spatial mismatch model to quantify these disparities with spatial mismatch indices. It could be valuable in addressing and reducing educational disparities by providing a more transparent and data-driven understanding of how educational resources are distributed geographically. In addition, the research suggests that these disparities are shaped by factors such as state and local regulation and social service provision rather than federal policy. By identifying areas of greatest disparity, policymakers and educators may be able to target resources and interventions where they are most needed. It is, therefore, a good reference for local policymakers.