Teacher Quality and Students’ Post-Secondary Outcomes

Abstract

Research suggests teachers’ observable characteristics are inequitably distributed across schools, leaving minoritized students with less exposure to experienced and credentialed teachers. While prior research focuses on how the teacher sorting explains disparities in students’ test scores, little research explores how teacher quality gaps might implicate students’ post-secondary outcomes. To fill this gap, we analyze administrative data on high schools in Georgia and explicate how variations in teachers’ human capital correlate with the percentage of students who enroll and persist in college. Our study confirms prior research accentuating widespread disparities in access to highly qualified teachers, and finds students attending schools with more experienced and credentialed teachers are more likely to have positive post-secondary outcomes.

Keywords

teacher sorting teacher quality gaps equity post-secondary access descriptive analyses

Introduction

Recent Federal legislation, such as Race to the Top, has focused on bolstering teacher quality to improve students’ academic outcomes (Darling-Hammond et al., 2016; Glazerman & Max, 2011; Lankford et al., 2002). These policies have risen in response to well-documented literature positioning teachers as the most critical educational inputs inside K-12 schools (Hanushek & Rivkin, 2010; Rivkin et al., 2005; Rockoff, 2004). Additional evidence indicates teachers’ characteristics, such as years of experience, educational attainment, and credentials, are disparately sorted across schools (Clotfelter et al., 2005; Goldhaber et al., 2015; Jackson, 2009; Lai et al., 2020). Teacher sorting might partly explain stagnant academic achievement disparities, and growing opportunity gaps since low-income, urban, and lower-achieving students are consistently exposed to less credentialed and experienced teachers (Desimone & Long, 2010; Goldhaber et al., 2015, 2018).

While research on the inequitable distribution of teacher quality between and within schools is well documented, evidence concerning the implications of students’ disproportionate access to well-credentialed and experienced teachers is less clear. Some scholars suggest teachers’ observable characteristics, such as licensure status, the selectivity of college attended, experience, and test scores, might explain variations in students’ academic achievement (Adnot et al., 2017; Clotfelter et al., 2011; Hill et al., 2019; Wiswall, 2013). Other scholars, however, have found evidence to the contrary and postulate that teachers’ observable characteristics account for little variance in students’ testing performance (Chingos & Peterson, 2011; Hanushek & Rivkin, 2010; Rockoff et al., 2011). Similarly, literature on the extent to which teachers’ educational attainment influences students’ outcomes remains inconclusive (Clotfelter et al., 2007; Harris & Sass, 2011; Wayne & Youngs, 2003).

With existing sorting literature focusing almost exclusively on the relationship between teachers’ observable characteristics and students’ test scores (Hanushek & Rivkin, 2010; Hill et al., 2019; Wiswall, 2013), the current scholarship provides educational stakeholders with an incomplete understanding of the potential consequences related to students’ disparate access to quality teachers. Scholars have begun to address this gap by exploring a more comprehensive range of student outputs teachers affect, including high school graduation and college enrollment (Chetty et al., 2011; Jackson, 2018). Such studies broaden the conception of outputs teachers affect and present essential considerations about how disparities in students’ access to credentialed and experienced teachers may widen opportunity gaps. For instance, to the extent that teachers improve students’ postsecondary outcomes, labor markets wherein highly qualified teachers sort out of urban, under-resourced, and, consequently, lower-achieving schools likely constrain students’ college going and staying opportunities. Therefore, underscoring how teacher quality gaps might hinder students’ access to higher education has relevant equity implications.

We examine the intersection of teacher sorting patterns and students’ post-secondary outcomes to elucidate whether, how, and to what extent changes in teacher quality are associated with variations in students’ college enrollment and persistence rates. In so doing, the present study contributes to and extends prior literature by: (1) highlighting the degree to which teachers are inequitably sorted between schools, across a large, statewide, and a longitudinal sample of Georgia high schools and (2) expanding the literature’s focus beyond students’ test scores to understand better how teachers’ background characteristics are related to students’ college going and staying behaviors. Further, we also extend the current literature by examining differential access to quality teachers between Black and Hispanic students, who researchers often combine into a single group (e.g., unrepresented minority students) in other relevant studies (Goldhaber et al., 2015, 2018). Finally, our unique focus on high school students, where implications for access to higher education is likely most pronounced, is another important contribution.

In our study, we exploit within school variations in multiple measures of teacher quality—including an indexed measure that combines teachers’ experience and educational credentials—and explore them in conjunction with students’ college going and staying behaviors. We classify experience and credentials as human capital and consider these metrics as proxies for teacher quality. Using Georgia’s school level data on enrollment patterns, sociodemographic variables, college enrollment/persistence rates, and teacher quality, this study explores three research questions:

(1) To what extent is teacher quality disparately sorted across school contexts?

(2) To what extent is within school variation in teacher quality associated with changes in the percentage of high school graduates who enroll in college?

(3) To what extent is within school variation in teacher quality associated with changes in the percentage of high school graduates who persist in college?

The subsequent sections of the manuscript proceed as follows. We begin by presenting literature on the distribution of teacher quality across schooling contexts, followed by a summation of scholarship examining the extent to which teachers’ observable characteristics influence students’ test scores and longer-term outcomes. We then present our data and measures, followed by an overview of our methods. Next, we present the findings related to our research questions. Lastly, we discuss the policy implications of our manuscript.

Background Literature

Inequitable Distribution of Teacher Quality

Mounting evidence suggests focusing on teacher quality as means to close opportunity gaps between advantaged and socially minoritized groups is warranted, as teachers play as critical a role in the production of education as any within school factor (Chetty et al., 2011; Rivkin et al., 2005; Rockoff, 2004). Disparities in teacher quality exist across schooling contexts, such that the least experienced, qualified, and effective teachers are concentrated in underserved schools (Goldhaber et al., 2015). Lankford et al. (2002) document inequitable teacher sorting patterns in experience, degree attainment, licensure status, and selectivity of college, such that across each of these measures, schools with greater proportions of poor, Black and Hispanic, and low-performing students in New York consistently had lower levels of teacher quality. Clotfelter et al. (2005) corroborated these findings in North Carolina, suggesting gaps in teacher quality exist within schools. They found that novice teachers were significantly more likely to teach Black students than White students and accentuated the importance of examining variations in teacher quality between and within schools.

Goldhaber et al. (2015) bolster this research by assessing teacher effectiveness using value-added measures, in addition to years of experience and licensure exam scores. Goldhaber et al. find that each teacher quality measure they observed was inequitably distributed across all indicators of societal disadvantage. Specifically, the least experienced and effective teachers consistently taught low-income and under-achieving students and students of color. Similar to Clotfelter et al. (2005), Goldhaber et al.’s (2015) findings underscore the importance of examining gaps in teacher quality at multiple levels, as within and between classroom, school, and district factors explain substantial variations in teacher quality. Notably, Clotfelter et al. (2005) find that 37% of teacher sorting occurs across schools and within districts, compared to 38% across districts, suggesting that districts’ hiring practices, as well as teachers’ preferences for particular communities, are vital to consider in advancing the equitable distribution of teacher quality.

Mechanisms Driving Inequitable Teacher Sorting

Inequities that occur within district level hiring practices do not remain there, as schools appear to reinforce how teachers are assigned or preferential to certain classrooms in ways that buttress teacher sorting’s detrimental ramifications (Cohen-Vogel et al., 2013). For instance, recent evidence suggests that even within schools, teacher sorting remains problematic, as classes with higher proportions of students who stand to gain the most from equitable access to quality teachers are most likely to be taught by novice educators (Kalogrides & Loeb, 2013; Ronfeldt, Reininger et al., 2013). Additionally, more recent evidence most clearly accentuates the inequities in teacher assignment by focusing explicitly on the types of classrooms schools assign teachers to for their initial appointment. An analysis of one large, urban school district by Kalogrides et al. (2013) finds that less experienced, female, and minority teachers are most likely to be initially assigned to classrooms with lower-achieving students, relative to White, male teachers. Bruno et al. (2020) also provides compelling evidence underscoring how inexperienced teachers are too commonly asked to serve in school settings with high levels of concentrated disadvantage, which can have harmful effects.

Another plausible mechanism of teacher sorting is the turnover rates in schools serving higher shares of Black and low-income students (Marinell & Coca, 2013; Redding & Henry, 2018, 2019; Ronfeldt, Loeb et al., 2013; Williams, Swain, & Graham, 2021). Constant turnover makes it challenging for schools to attract and retain effective teachers and results in schools leaving positions vacant, or alternatively, accepting applicants on a first-come, first-serve basis (Simon & Johnson, 2015). Federal accountability policies throughout much of the 21st century also instituted harsh sanctions for schools with lower scores on mandated tests, which may exacerbate retention challenges and disincentivize teachers from working in schools in marginalized contexts (Clotfelter et al., 2004; Rodriguez et al., 2020). Research shows teachers describe accountability-based initiatives as leading to the “deprofessionalization” of teaching (Milner, 2013) and others postulate that they create test-driven cultures that teachers view as unfavorable and frustrating (Byrd-Blake et al., 2010).

Given that the elementary and secondary education profession largely operates in local labor markets (Engel & Cannata, 2015), geography might also be an increasingly important mechanism by which inequitable teacher sorting patterns form. Scholars have come to understand the geographic scope of teacher labor markets as oftentimes small and highly localized due to teachers’ preferences to teach either where they grew up or in areas demographically resembling their hometowns (Boyd et al., 2005a, 2005b; Cannata et al., 2010). Teachers’ fixed spatial preferences mirror wider inequities because labor markets’ demographic compositions are functions of existing attendance boundaries that serve to residentially segregate on the basis of students’ race and income (Frankenberg, 2013; Owens et al., 2016; Reardon et al., 2009). Furthermore, teachers’ preferences to complete their preparation programs close to where they live, and to teach in close proximity to the pre-service programs (Krieg et al., 2016) might further segregate them from students of color and low-income students (Knight, 2020).

Consequently, Black teachers might live closer to schools serving higher proportions of Black students than White teachers, and White teachers might live closer to affluent neighborhoods with schools serving more high-income students than Black teachers (Buddin & Zamarro, 2009; Hanushek & Rivkin, 2007). With highly credentialed and experienced teachers sorting out of lower-achieving and economically under resourced schools (Clotfelter et al., 2005; Goldhaber et al., 2015; Lankford et al., 2002), the resulting labor market available to such schools might consist of less effective teachers who have either remained in or returned to the district (Boyd et al., 2011). Therefore, researchers regard teachers’ draw to home as a sorting mechanism that potentially further disadvantages the neediest schooling contexts and exacerbates existing educational inequities (Hanushek et al., 2004; Lankford et al., 2002).

Effects of Teachers’ Observable Characteristics on Student Outcomes

Effects on Cognitive Outcomes

While researchers and policymakers agree that teachers have important influences on students’ outcomes, there is less clear empirical support for the precise teacher characteristics that explain variations in students’ outcomes. For instance, although the research synthesized above indicates that teacher quality, as measured by available proxies, is unevenly distributed across schools and student subgroups, educational attainment, experience, and licensure status may be only weakly correlated with student test score achievement (Goldhaber, 2008; Goldhaber & Brewer, 2000; Rivkin et al., 2005). Clotfelter et al. (2007) examined the extent to which teachers’ experience, graduate degree status, and licensure exam scores influenced students’ test scores and concluded significant effects for only experience and licensure exam scores. More recently, some researchers have shown that teaching experience is vital for bolstering students’ outcomes (Ladd & Sorensen, 2017; Papay & Kraft, 2015), suggesting that disparate access to positive professional environments is a plausible mechanism for the inequitable sorting of experienced teachers across schools (Kraft & Papay, 2014), which might explain test-score differences across student subgroups.

Using panel data that matches students to different teachers over time, Rockoff (2004) disentangled how variations within and between teachers’ effectiveness ratings impact students’ reading and math scores. In a fixed effects framework, Rockoff (2004) was able to separate variation in teacher quality from variation in students’ cognitive abilities, concluding that reading achievement improves by roughly 0.17 SD between novice teachers and those with 10 or more years of experience. These results provide suggestive evidence that experience and, to a lesser degree, credentials may improve students’ short-term outcomes.

Effects on Students’ Non-Cognitive Outcomes

Notably missing from prior research is how teachers impact a wider range of skills that test scores might not capture. Some researchers have begun to address the gap, concluding teachers affect a broad range of non-cognitive outcomes (Blazar & Kraft, 2017; Gershenson, 2016; Jackson, 2012). For instance, Jackson (2018) found teachers’ effects on students’ non-cognitive skills (e.g., attendance, behavior, and graduation) are more pronounced than those on test scores. Similarly, Kraft (2019) exploited the random assignment of teachers to classrooms to accentuate the causal impacts of teachers’ effects on students’ grit, growth mindset, and class effort, concluding consistent and positive effects. Importantly, recent research suggests that teachers impact non-cognitive and cognitive skills in different ways, indicating various aspects of teacher quality might influence student outcomes in unique ways. Gershenson (2016), who used longitudinal data from North Carolina to examine how the same teacher impacts different student outcomes her/his students, found significant and positive effects on both attendance and test scores. However, he found that the same teachers who boost test scores do not necessarily improve attendance. Furthermore, evidence from Blazar and Kraft (2017) highlights how teachers’ effects have indirect influences on non-cognitive skills. They found that variations in teachers’ effectiveness explain differences in students’ math self-efficacy beliefs and self-reported happiness, which are related to academic outcomes.

While this literature addresses gaps around how distributions in teacher quality might affect more than test scores, there are still questions concerning the extent to which disparate access to quality teachers might exacerbate opportunity gaps in postsecondary outcomes. Jackson (2018) provides the most substantial evidence that this relationship might exist by showing that teachers who raise students’ non-cognitive skills have important impacts on their later outcomes. Specifically, a teacher who raises a student’s non-cognitive skills by 1 SD increases the student’s likelihood of graduating from high school by 1.47% points. Additionally, he found that quality teachers improve students’ SAT test-taking rates, senior year grade point average (GPA), and intentions to attend a 4-year college. This work is foundational to understanding how the “teacher quality gap” (Goldhaber et al., 2015; Knight, 2020; Steele et al., 2015) may have further reaching implications for educational equity than the current literature recognizes.

In summary, extant literature makes clear that students of color have inequitable access to experienced and credentialed teachers due to hiring practices and teacher preferences. The present manuscript contributes to and expands upon the extant research in important ways. First, situating Georgia at the center of this study allows us to examine educational disparities within a demographically diverse state that presents relevant and historical implications but its largely underexamined among scholars. Georgia’s demographic context allows us to deepen our understanding of the extent to which students’ access to experienced and credentialed teachers varies across racial-ethnic groups. Georgia’s diverse student population lends itself to our empirical strategy that reverts away from aggregating non-White students into a singular group, thereby positioning our analysis to better identify important within racial-ethnic group educational experiences (Clotfelter et al., 2005; Knight, 2020). Second, our study underscores the potential implications of inequitable teacher sorting patterns on students’ college going and staying behaviors. In examining students’ post-secondary attainment and persistence, our study uniquely focuses on high school where implications for access to higher education is likely most pronounced.

Data and Measures

Independent Variables: To add to the growing body of scholarship exploring the intersections of teacher quality, school characteristics, and students’ postsecondary outcomes, we use data from multiple sources to examine the distribution of teacher quality within and across districts in Georgia. We construct a 7-year panel (2011–2012 through 2017–2018, varied by availability) of statewide and school level data. We begin with de-identified, school level data provided by the Georgia Department of Education (GaDOE). Using Georgia’s Certified Personnel Information (CPI) data, we construct a teacher quality measure, which is the primary independent variable in our models. CPI data include information on teachers’ degree attainment, years of experience, certification levels (e.g., professionally licensed or provisionally licensed), and employment status (e.g., part-time or full-time). We use these indicators to document the magnitude of teacher quality gaps across high schools in Georgia in degree attainment, experience, employment, and licensure status. However, in our predictive models, we focus solely on how teachers’ degree attainment and experience are associated with schools’ college enrollment rates and persistence.

Notably, these data are recorded as totals at the school level, such that each school reports the number of teachers in their school who have a bachelor’s, master’s, specialist, or doctoral degree, are full-time or part-time, and are professionally or provisionally licensed. Schools also report teachers’ total years of experience (the number of first-year teachers, 1–10, 11–20, 21–30, and 30+ years of experience). Using these data, we construct a composite measure of teacher quality—human capital—which results from indexing teachers’ years of experience and attainment. We focus specifically on these two measures because as teachers gain experience and credentials, they accrue skills that are transferrable to their students and can have significant effects on their outcomes.

To create this indexed measure of teacher quality, we run a principal component analysis (PCA), which allows us to perform a dimensionality reduction procedure that tests for the presence of underlying construct(s), known as principal component(s). A PCA attempts to create a linear combination of new variables that retain the maximum amount of variation from the original variables (Abdi & Williams, 2010; Awrejcewicz et al., 2021; Jolliffe, 2002). This PCA process is useful because it allows us to determine the extent to which a set of variables related to teacher quality (e.g., credentials and years of experience) are representative of the underlying construct we call human capital. In short, we use PCA to create a school level index of teacher quality by testing for the presence of an underlying construct by examining the number of returned components after entering each level of the experience and credential teacher quality measures.

Our PCA returned two components with eigenvalues greater than 1—a typical threshold of an interpretable component. The first component’s eigenvalue is 5.37 (see Supplemental Appendix 1), highlighting that the underlying construct of human capital retains a substantial amount of the variance from the original measures of teacher quality. The PCA results—coupled with extant literature—provide us statistical and conceptual justification to reduce the initial set of experience and educational attainment measures to our human capital index. In Supplemental Appendix 2, we examine eigenvalues representing the unique influence of each level of the experience and attainment variables on the latent construct, showing values for each individual measure ranged from 0.23 to 0.40. To produce the human capital construct, we weighted all measures of teachers’ experience and credentials by their unique influence on the latent variable of human capital. For each school and year, we summed these weighted values to produce a single value of human capital, examine it descriptively across contexts, and analyzed its association with students’ postsecondary outcome.

Dependent Variables: In terms of the outcome measures, we incorporate data from several places. First, college enrollment data come from GA AWARDS, a data gathering institution connected to the Governor’s Office of Student Achievement (GOSA). These data include information on all Georgia high schools and documents the number of students who enroll in postsecondary institutions within 16 months of graduating high school. We use this variable to measure schools’ college enrollment rates by converting the totals to percentages (e.g., the number of college enrollees divided by the number of high school graduates). These data are available for the full 7-year panel. Secondly, college persistence data also come from GA AWARDS but includes those students who not only enrolled in a postsecondary institution within 16 months of graduation but who also completed at least 1 year of credits within the first 2 years of enrollment. We use this measure as a proxy for college persistence. These data are currently available for only 5 years of our panel (2011–2012 through 2014–2016). We also convert persistence into a percentage by dividing the number of students who persist in college by the number who enroll.

Control Variables: Lastly, we include school covariates for sociodemographic factors. To identify students experiencing poverty in our sample, we use the economically disadvantaged indicator provided by the GaDOE. Additionally, we merge school level covariates using unique identifiers from the GaDOE and GOSA. School level variables include: (a) student demographics (e.g., race and ethnicity), (b) school characteristics (e.g., the percentage of students classified as gifted and remedial), and (c) teacher and school leader characteristics (e.g., salary, principal experience, and retention rates). Supplemental Appendix 3 displays correlation coefficients between all independent variables included in the regression models. With few exceptions, correlations between variables tend to be moderate to low, mitigating concerns about multicollinearity. The strongest correlations we observe are between the share of Black and economically disadvantaged students (r = .63) and between the percentage of students recognized as gifted and economically disadvantaged students (r = −.67). These relatively high correlations are consistent with other empirical studies and common understandings of existing education iniquities. Nonetheless, the variance inflation factor for each variable included in the fully specified models range from 1.06 to 3.26, with the average score observed as 1.66.

Methods

For research question one, we create terciles of levels for common categories related to student disadvantage. We group schools based on high, medium, and low levels of student poverty, and the share of Black and Hispanic students. We proxy for student poverty by using the percentage of students classified as economically disadvantaged. We then run a series of one-way analyses of variances (ANOVA) to test for mean differences among these stratified groups. With the terciles stratification strategy, we used Tukey’s post hoc comparison to test the null hypothesis that mean differences across these three groups are not significantly different from 0 at the .05 alpha level.

For the second and third research questions, we employ our measure of teacher quality in a series of regressions within increasingly controlled models. In Model 1, we include only human capital as a predictor of the outcomes of interest (e.g., college enrollment or persistence rates). Model 2 adds sociodemographic controls (schools’ racial and socioeconomic compositions). Finally, Model 3 includes additional controls for school contextual factors (e.g., the percentage of students in remedial courses and labeled as gifted, and relevant information on teachers and leaders). We include controls for sociodemographic variables because students of color and low-income students may have lower college enrollment and persistence rates and may also attend schools with lower levels of observed teacher quality. Controlling for these variables allows us to examine the relationship between teacher quality and postsecondary outcomes among schools with similar student demographics while accounting for essential characteristics that may confound the relationship between our primary predictor and outcome(s) variables. Lastly, we respectively run our fully specified models on college enrollment and persistence outcomes for Black, Hispanic, White, and economically disadvantaged students.

Analytic Strategy

Before fitting out main regression models, we bolster our descriptive results by conducting ancillary analyses to examine the extent to which gaps in exposure to teacher quality change after accounting for differences across districts. To do so, we first calculate the intraclass correlation (ICC) across the pooled panel years to investigate the amount of variance in teacher quality gaps attributable to differences within districts (see Supplemental Appendix 4). A higher ICC suggests that within district factors, such as hiring practices, poverty levels, and within district segregation, appear to explain more of the variation in teacher quality than differences in these factors across districts. We also regress all teacher quality measures (e.g., experience, educational attainment, and human capital) on our indicators of disadvantage (e.g., either high, medium, or low percent economically disadvantaged, Black, and Hispanic students served). These regression results provide the statewide average difference in teacher quality between moderately or highly disadvantaged schools (e.g., in the upper 2/3 distribution of societal disadvantage) compared to those in the bottom third (Panel A). In a subsequent model (Panel B), we include a district fixed effect to observe how the teacher quality gaps change after accounting for differences across districts (see Supplemental Appendix 4).

Predicting Students’ Outcomes

Next, we examine the association between human capital and students’ post-secondary outcomes. Our regression model is expressed in the equation below:

\begin{matrix} Y_{s t} = β 0 + β 1 T e a c h e r Q u a l i t y_{s (t - 1 o r t - 3)} + β 2 P e r B l a c k_{s (t - 1 o r t - 3)} + β 3 P e r H i s_{s (t - 1 o r t - 3)} \\ + β 4 P e r E c o n D i s a d v_{s (t - 1 o r t - 3)} + β 5 P e r G i f t e d_{s (t - 1 o r t - 3)} + β 6 P e r R e m e d i a l_{s (t - 1 o r t - 3)} \\ + β 7 R e t e n t i o n_{s (t - 1 o r t - 3)} + β 8 T c h S a l a r y_{s (t - 1 o r t - 3)} + β 9 A d m E x p e r i e n c e_{s (t - 1 o r t - 3)} \\ + α_{s} + λ t + e_{s t} \end{matrix}

where $Y_{s (t - 1 o r t - 3)}$ represents students’ post-secondary outcomes—either schools’ college enrollment or college persistence. We include lagged indicators for all predictor variables (e.g., $t - 1$ for enrollment and $t - 3$ for persistence) to reflect the fact that college enrollment numbers are recorded to the GaDOE 1 year after students graduate, and persistence is reported 3 years after students graduate. We regress these outcomes on our primary predictor, which is an indexed measure of teacher quality (β1) at the school level. We interpret the coefficient on our human capital measure as representing how variations in the share of high school graduates who enroll or persist in college in year t are associated with a 1 SD increase in teacher quality at times $t - 1$ and $t - 3$ , respectively.

Sociodemographic controls are increasingly included in models to account for potentially confounding factors. For instance, lagged indicators for the percent Black (β2), Hispanic (β3), and low-income (β4) students represent demographic controls at school s in year $t$ . These coefficients represent how a 1-unit change in the percentage of schools’ Black, Hispanic, or economically disadvantaged student body population relates to variations in the outcomes of interest. Next, we add controls for school contextual factors, which include the share of students classified as gifted (β5) or students taking remedial courses (β6), as well as the teacher retention rate (β7), average teacher salary (β8), and administrator experience (β9) at school $s$ in year $t - 1 o r t - 3$ .

All models include a school ( $α$ ) and year ( $λ t$ ) fixed effect to account for differences in school quality across school and time, as well as any trends across the state in a given year. Including school and year fixed effects account for invariant factors influencing post-secondary outcomes that may vary across one dimension while remaining invariant across the others. These factors, such as hiring practices, management practices, and economic shocks, may vary systematically with our measure of human capital, and thus introduce a spurious relationship between teacher quality and students’ post-secondary outcomes. All of our models include robust standard errors that are clustered at the school level. Finally, to account for the nesting of schools within districts, we re-fit all our analyses using hierarchal linear modeling (HLM). In so doing, we find coefficients remain comparable in magnitude and direction (these results are available upon request).

Limitations

The present study’s primary limitation is selection bias, wherein teachers sort into and out of schools based on many variables. As we show in this study, teachers with more experience and credentials are more likely to teach in schools with more societally advantaged students who are likely to attend and persist in college at high rates regardless of their exposure to qualified teachers. While we cannot overcome this selection bias challenge, we control for observable characteristics related to teacher sorting patterns to bolster the precision of our estimates. Another limitation we face is that our data analysis is at the school level, which raises concerns about aggregation bias. Scholars attempt to account for this bias using clustered standard errors and HLMs. In the present study, we do both, and the results of the HLM analyses do not change substantively from our two-way fixed effects models (results available upon request). Finally, due to our study’s data and design, we cannot draw causal conclusions about the relationships we observe. Therefore, we position our study as primarily elucidating disparities in access to quality teachers and how such inequities might have policy relevant implications for students’ college going and staying rates.

Apart from the design, data, and unit of analysis, a more conceptual limitation of our study includes the inherently deficit-oriented nature of examining mean differences between groups (e.g., racial-ethnic backgrounds and poverty status). While mean differences can identify substantial disparities between groups, they may simultaneously obscure variations within subgroups by positioning them as monolithic. We also contend that normative uses of words such as high or low “quality” can further negate the work of many teachers in schools whose aggregate metrics relegate them into a “low-performing” classification. As such, we transparently acknowledge that what constitutes quality educators extends far beyond the level of education and experience they have. We contend that the data and measures to which we have access offer as complete a story as we can tell, duly noting its inherent limitations.

Findings

Describing the Contexts of Students’ Demographics and College Enrollment and Persistence Rates Across Georgia’s High Schools

Figure 1 depicts trends in schools’ college enrollment and persistence rates across all high schools in Georgia. In investigating these postsecondary outcomes, we observe college enrollment trending downward across all student subgroups in Georgia. Each line represents the share of high school graduates from each subgroup in year t to enroll in college within 16 months of their high school graduation (i.e., year $t + 1$ ). The state’s average college enrollment rate fluctuates from about 65% to slightly above 70% across the panel years, with consistent declines across time. This reduction in college enrollment rates differs across subgroups. For instance, Black students saw their rates decrease from around 74% in 2011 to 65% in 2017, while White students’ only dropped by about 5% across those same years. Students from low-income backgrounds and Hispanic students have the lowest college enrollment rates across the panel and experienced reductions across time that mirrored the statewide trend.

Figure 1.

Trends in college enrollment and persistence by student subgroup.

While college persistence data only correspond with three of the panel years, we still show the share of high school graduates in year t who enrolled in and completed at least 1 year of college coursework in year $t + 3$ . Given the much shorter panel, it is difficult to draw clear conclusions about college persistence trends across subgroups. However, it seems that higher proportions of the Hispanic students who enroll in college persist through at least 1 year over time. This is evidenced by the fact that nearly 75% of Hispanic college enrollees complete at least 1 year of college credit within 2 years of enrolling in college. Black students appear to be least likely to persist, as only between 50% and 55% of Black college enrollees complete their first year. Finally, students classified as economically disadvantaged have experienced a fairly stable incline in college persistence rates over time. To fully unpack these college persistence rates, however, more data are needed to establish more apparent trends.

Table 1 shows descriptive statistics for the teachers and students in all Georgia high schools across different poverty thresholds for the 2015 to 2016 academic year, which is the most recent year for which complete data. On average, schools in the upper third of the distribution for school level poverty, as measured by the share of students classified as economically disadvantaged, are concentrated with far higher levels of Black (65%), low-income (91%), and remedial students (13%) than schools in the bottom two-thirds of the poverty distribution. Hispanic students, who made up 11% of the student body population across all Georgia high schools in 2015, appear in more socioeconomically mixed schools (13% for medium poverty) than schools in either tail of the poverty distribution. High poverty schools have significantly fewer students scoring above proficiency thresholds on state exams in math (17%) and language arts (22%) than schools in the medium (30% and 35%, respectively) and low (32% and 37%, respectively) poverty thresholds.

Table 1.

School Summary Statistics by School Poverty.

Variables	All schools		Low poverty		Medium poverty		High poverty
Variables	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Student characteristics
% Black	40.51	30.83	21.33	17.54	31.46	25.64	65.34	27.55
% Hispanic	10.71	12.18	9.81	7.28	12.63	13.00	10.06	14.79
% White	43.09	28.92	60.25	21.31	50.81	26.12	21.18	22.73
% Economically disadvantaged	61.50	27.29	30.89	12.51	59.95	5.61	91.07	9.86
% Students with disabilities	10.83	3.20	9.52	2.87	11.26	2.67	11.70	3.48
Limited English proficiency	2.53	4.21	1.90	1.97	2.76	3.62	2.93	5.82
% Remedial	10.26	12.06	7.99	8.52	9.31	9.10	13.11	15.82
% Gifted	13.10	9.18	20.46	10.40	11.34	4.23	7.13	4.89
Algebra achievement	31.54	20.79	48.29	19.97	30.07	14.81	16.62	12.20
Literature achievement	36.74	18.28	54.49	15.12	34.92	8.61	21.71	10.73
School enrollment	1,285.52	708.14	1,698.43	683.29	1,221.69	601.93	952.73	612.57
High school graduates	265.88	146.58	367.50	147.00	248.04	111.76	185.51	110.59
College enrollment	185.99	126.08	286.43	136.34	156.95	81.97	113.23	71.73
College persistence	108.84	81.56	177.79	87.26	87.00	49.40	57.73	38.93
% Graduated and enrolled	65.85	13.71	76.04	12.08	62.58	9.49	58.72	12.22
% College persisted	58.22	16.00	65.15	10.23	59.77	19.89	49.75	13.02
Personnel characteristics
Average teacher exp	13.51	2.32	14.04	1.95	14.06	2.02	12.58	2.58
Teacher salary	55,464	3,872	57,763	3,519	55,174	2,830	53,544	3,783
Teacher retention	79.15	13.33	84.23	7.37	81.73	7.31	72.40	17.79
Average admin exp	20.55	4.35	20.56	3.96	21.08	3.92	20.12	4.97
Administrator salary	85,859	9,110	89,921	8,241	83,379	7,676	83,997	9,651
N =	366		126		105		135

Note. Descriptive statistics represent means and standard deviations for all high schools in Georgia from school year 2015 to 2016; the final year for which all sources of data are available. Thresholds for poverty status are determined by terciles.

Stark differences emerge when examining students’ college going and staying behaviors across poverty dimensions. Regarding the share of high school graduates who enroll in college within 16 months of graduation, schools with the lowest level of poverty see 76% of their graduates enroll in college, which is significantly and substantively higher than those from medium (63%) and high (59%) poverty schools. Of those college enrollees from high poverty schools, only around 50% complete at least 1 year of college coursework within 24 months of enrollment, which is a significantly lower rate than those from low (65%) and medium (60%) poverty schools.

We also observe similar disparities for our indicators on the personnel differences between schools across our poverty thresholds. On average, schools classified as low and medium poverty have slightly more experienced teachers (14 years of average experience) than those with the highest levels of low-income students (13 years). The average salary differences are stark across these schools, with teachers educating the most financially secure students earning about $4,000 more annually than those serving more economically disadvantaged students. Salary differences across poverty thresholds are even more pronounced for principals, with those in low poverty schools earning about $6,000 more annually than those in high poverty schools. Finally, teachers are sorting out of high poverty schools at alarmingly high rates, such that low-income schools retain only 72% of their teachers, which is much lower than that of low (84%) and medium (82%) poverty schools.

Teacher Sorting and Access to Credentialed and Experienced Educators

Research Question 1: To what extent is teacher quality disparately sorted across school characteristics? Consistent with extant literature, we find that teacher quality is not equitably distributed across schools. Table 2 shows that, relative to low poverty schools (28% and 21%), schools with the highest concentrations of student poverty have significantly more teachers with bachelor’s degrees (33%), which are the minimum qualification required for employment, and considerably fewer teachers with specialist degrees (16%). Across poverty thresholds, we find a small but statistically significant difference in the share of teachers with master’s degrees, such that medium poverty schools have slightly fewer, and high poverty schools have marginally more. We find no significant differences with regard to the share of teachers with doctorate degrees, which is not particularly surprising considering so few teachers across the state attain that advanced degree status. In terms of teachers’ experience, schools serving the largest proportion of low-income students also have the highest percentage of teachers with less than 1 year of experience (7%). This value is significantly higher than schools in the low poverty group (5%). Additionally, high poverty schools have more teachers with experience ranging from only 1 to 10 years (41%) than middle and low poverty schools (about 34% in both). This trend continues in every stratification we use for experience, except for teachers having between 21 and 30 years of experience, who are slightly more common in the moderately low-income schools, and the few teachers who have 30 or more years of experience, who appear relatively evenly distributed across school contexts.

Table 2.

Distribution of Teacher Quality by School Poverty.

Variables	Low poverty		Medium poverty		High poverty
Variables	Mean	SD	Mean	SD	Mean	SD
Teacher characteristics
% Bachelor’s	28.48	6.65	29.84	6.79	32.71	10.39
% Master’s	45.88	7.76	43.58	8.45	45.80	10.63
% Specialists	21.38	7.85	21.68	9.06	16.43	8.51
% Doctoral	3.45	2.49	3.40	2.80	3.48	3.39
% Other degree status	0.81	1.14	1.52	1.73	1.60	2.14
% <1 year exp	4.81	3.52	5.38	3.73	7.00	5.42
% 1–10 years exp	34.28	8.62	34.23	9.48	41.40	10.57
% 11–20 years exp	37.15	7.19	35.48	8.46	30.61	9.55
% 21–30 years exp	19.38	6.38	20.21	8.19	17.03	8.77
% >30 years exp	4.37	3.80	4.69	4.03	3.95	3.67
Average teacher exp	14.04	1.95	14.06	2.02	12.58	2.58
% Part time	6.34	6.46	8.81	8.43	12.99	21.51
% Full time	93.67	6.47	91.20	8.44	87.05	21.52
% Professional license	95.95	3.50	94.74	3.86	91.44	5.94
% Provisional license	4.05	3.50	5.26	3.86	8.57	5.93
Human capital	0.57	0.98	−0.07	0.87	−0.47	0.89
Student characteristics
% Black	21.33	17.54	31.46	25.64	65.34	27.55
% Hispanic	9.81	7.28	12.63	13.00	10.06	14.79
% White	60.25	21.31	50.81	26.12	21.18	22.73
% Economically disadvantaged	30.89	12.51	59.95	5.61	91.07	9.86
N =	126		106		136

Note. Descriptive statistics represent means and standard deviations for all merged high schools in Georgia from school year 2015 to 2016; the final year for which all sources of data are available. Thresholds for poverty status are determined by terciles, which evenly split the sample into three group (e.g., Low, Medium, and High). Human capital refers to our standardized indexed measure of teacher quality. Bold values represent statistically significant mean differences at the .05 alpha level, with low poverty as the reference group.

We find similar trends among other variables related to teacher characteristics, which further compounds inequitable access to quality teachers. Georgia high schools with the highest percentage of students experiencing poverty have, on average, significantly more part-time teachers (13%) relative to those schools serving the most advantaged students (6%). Considering the importance of establishing positive relationships with teachers and its effects on students’ outcomes (McGrath & Van Bergen, 2015; Wang et al., 2015), this finding has problematic implications. Further, the comparatively higher share of part-time teachers might also represent increased turnover levels and difficulty in staffing schools with concentrations of students from low-income backgrounds.

Our analyses also suggest that Georgia high schools educating more economically disadvantaged students may be responding to teacher shortages and vacancies, spurred by sorting preferences and turnover, by hiring non-certified teachers. High poverty schools have over twice as many provisionally licensed teachers (9%) than low poverty schools (4%) and significantly more than medium poverty schools (5%). Despite the mixed evidence on the extent to which teachers’ licensure status affects student outcomes (Buddin & Zamarro, 2009; Clotfelter et al., 2007; Goldhaber & Brewer, 2000), policymakers should be concerned with whether or not schools overly rely on teachers who are not fully credentialed. Finally, our indexed measure of human capital further shows disparities in teacher quality, as teachers in high poverty schools are 0.47 SD lower in quality than the typical school (which is 0 on this standardized measure).

In addition to school poverty, we examine how teacher quality distribution varies as the proportion of Black students in a school increases (see Table 3 below). Stratifying the data this way reveals nearly all of the same inequalities related to teacher quality gaps, with many becoming more pronounced for Black students. These schools have roughly 7% points fewer teachers with specialist degrees, relative to those serving the fewest proportion of Black students. Consistent with what we find within high poverty schools, schools with the largest proportion of Black students also have significantly more provisionally licensed teachers (9%) than the middle (5%) and low poverty (4%) schools. Further, they also tend to have teachers with nearly 2 fewer years of experience relative to the schools in the bottom two-thirds of the distribution.

Table 3.

Distribution of Teacher Quality by % Black Students Served.

Variables	Low black		Medium black		High black
Variables	Mean	SD	Mean	SD	Mean	SD
Teacher characteristics
% Bachelor’s	29.95	7.60	30.16	8.16	31.19	9.47
% Master’s	42.78	8.66	45.58	8.57	47.24	9.64
% Specialists	23.04	10.25	19.75	7.52	16.07	6.74
% Doctoral	3.03	2.48	3.33	2.66	3.98	3.48
% Other degree status	1.20	1.75	1.19	1.43	1.53	2.05
% <1 year exp	4.79	3.67	5.67	3.96	6.91	5.35
% 1–10 years exp	32.75	9.00	34.62	8.56	43.34	9.72
% 11–20 years exp	37.51	7.72	34.91	7.75	30.31	9.71
% 21–30 years exp	20.37	7.50	20.36	7.72	15.55	7.70
% >30 years exp	4.58	3.29	4.44	4.22	3.89	3.92
Average teacher exp	14.32	1.97	13.97	2.09	12.22	2.34
% Part time	8.78	10.96	8.90	9.23	10.84	20.79
% Full time	91.24	10.96	91.10	9.23	89.20	20.80
% Professional license	96.48	2.75	94.73	3.36	90.58	6.23
% Provisional license	3.54	2.75	5.27	3.36	9.42	6.23
Human capital	0.09	0.94	0.23	1.17	−0.31	0.84
Student characteristics
% Black	9.30	6.28	34.01	9.01	78.59	15.44
% Hispanic	13.68	15.24	12.10	12.18	6.35	5.97
% White	70.55	16.99	47.26	16.27	11.11	11.95
% Economically disadvantaged	42.60	23.12	58.13	21.74	84.00	18.65
N =	125		120		123

Note. Descriptive statistics represent means and standard deviations for all merged high schools in Georgia from school year 2015 to 2016; the final year for which all sources of data are available. Thresholds for percent Black students served are determined by terciles, which evenly split the sample into three group (e.g., Low, Medium, and High). Bold values represent statistically significant mean differences at the .05 alpha level, with low Black as the reference group.

In terms of the share of teachers with specialist degrees, provisional licensure, and experience, teacher quality gaps are larger in magnitude when stratifying by the percentage of Black students than when doing so by the share of low-income students. High schools with lower shares of Black students have slightly fewer part-time teachers than schools with the highest shares of Black students served, and they hire over three times as many provisionally licensed teachers, compared to those with the lowest shares. Finally, our indexed measure of teacher quality reveals that schools in the upper third distribution for the percentage of Black students served have significantly lower average teacher quality (−0.31 of a standard deviation) relative to schools in the bottom third of the distribution for the share of Black students served.

In other studies, teacher quality gaps tend to be much more consistently similar when examined across race and poverty thresholds (Goldhaber et al., 2018; Knight, 2020) than suggested in our study. That our findings are more pronounced for Black students may reflect the ways class and race intersect to further compound the effects of societal disadvantage on Black students’ schooling outcomes in Georgia (Crenshaw, 1989). We present additional standardized observations of variables related to teacher quality across thresholds of poverty and race in Supplemental Appendix 4 and how these magnitudes change after accounting for district fixed effects. Across both models, gaps in exposure to experienced teachers are significantly larger when stratifying by Black students compared to low-income students, suggesting more pronounced disparities for Black students.

These trends are mostly unnoticeable when we stratify by the proportion of Hispanic students enrolled in a school, as shown in Table 4 below. For instance, in terms of the share of teachers with master’s or specialist degrees, we find no statistically significant differences between those schools serving the highest and lowest percentages of Hispanic students. The same is true when we examine the proportion of teachers with less than 1 year of experience. Across all levels, increases in the percentage of Hispanic students served are not associated with diminished access to experienced and educated teachers. Additionally, the trend reverses when considering the proportion of teachers with part-time employment and provisional licenses. In schools serving the highest proportions of Hispanic students, significantly fewer teachers are provisionally licensed (5%) or part-time (7%) in comparison to schools with the lowest proportions (13% part-time and 7% provisional licensed), suggesting that these schools may have fewer hiring challenges.

Table 4.

Distribution of Teacher Quality by % Hispanic Students Served.

Variables	Low Hispanic		Medium Hispanic		High Hispanic
Variables	Mean	SD	Mean	SD	Mean	SD
Teacher characteristics
% Bachelor’s	31.87	9.44	29.45	8.38	30.03	7.19
% Master’s	44.63	9.95	45.91	9.07	44.97	8.34
% Specialists	18.92	8.71	19.89	8.59	20.09	9.10
% Doctoral	3.09	3.47	3.48	2.52	3.77	2.71
% Other degree status	1.50	1.85	1.27	1.95	1.14	1.41
% <1 year exp	5.95	4.77	5.14	4.28	6.34	4.29
% 1–10 years exp	38.02	10.51	35.69	10.18	37.08	9.82
% 11–20 years exp	32.13	9.49	36.26	8.78	34.23	8.05
% 21–30 years exp	18.92	8.10	19.04	7.26	18.26	8.55
% >30 years exp	4.98	4.66	3.88	3.07	4.08	3.53
Average teacher exp	13.46	2.45	13.74	2.24	13.29	2.28
% Part time	12.97	19.43	8.90	14.07	6.60	6.41
% Full time	87.07	19.45	91.10	14.07	93.42	6.42
% Professional license	92.53	6.47	94.67	4.32	94.57	3.60
% Provisional license	7.47	6.47	5.33	4.32	5.44	3.59
Human capital	−0.56	0.69	0.12	0.87	0.45	1.17
Student characteristics
% Black	55.43	34.97	35.34	27.05	30.84	23.92
% Hispanic	2.31	1.18	7.22	1.61	23.28	14.79
% White	38.99	33.87	50.44	26.07	39.16	24.68
% Economically disadvantaged	74.87	24.85	50.47	27.21	59.94	23.83
N =	121		130		117

Note. Descriptive statistics represent means and standard deviations for all merged high schools in Georgia from school year 2015 to 2016; the final year for which all sources of data are available. Thresholds for percent Hispanic students served are determined by terciles, which evenly split the sample into three group (e.g., Low, Medium, and High). Bold values represent statistically significant mean differences at the .05 alpha level, with low Hispanic as the reference group.

Further, after accounting for district fixed effects, we observe no statistically significant differences across all of our teacher quality measures between schools in the upper third of the distribution of Hispanic students served and those in the bottom third (see Supplemental Appendix 4, Panel B). Concerning human capital, we show reversed gaps, wherein schools with more Hispanic students have teachers who score significantly higher on our indexed measure of teacher quality. The fact that we observe statistically insignificant or reversed gaps in teacher quality when examining the share of Hispanic students in schools should prompt researchers studying other contexts to consider the utility of combining Black and Hispanic students into a single group. Aggregating these student subgroups into one group is problematic and may obscure the magnitude of the teacher quality gap, particularly for Black students. At least in Georgia, poverty is tied to race in far more produced ways for Black students than Hispanic students. For instance, schools in the upper third of the distribution for Black students served also have about 84% of their students classified as economically disadvantaged. In comparison, this number is only around 60% for the students with the most Hispanic students. Thus, to the extent that this is true in other contexts, which might be challenging to assess because of insufficient shares of Hispanic or Black students, extant research might substantially under-weight the size of the teacher quality gap for Black students. Thus, we argue our results accentuate the need to analyze these subgroups separately, despite the common forms of marginalization society subjects them to, as Black students, especially in the Deep South, have unique histories with racism and inequities.

In addition to showing disparities in access to credentialed and experienced teachers, we also seek to underscore the extent to which between or within district factors account for most of the variation in teacher quality. We show that within district factors appear to contribute more to variations in teacher quality than across district factors. For instance, the intraclass correlation of teacher quality shows that 68% of the variance in human capital is explained by differences within districts (see Supplemental Appendix 5). Further, when we regress our measure of teacher quality on thresholds of school level poverty, we find statistically significant and substantive disparities in access to teacher quality between high $(β = - . 99)$ and middle $(β = - . 61)$ poverty schools and low poverty schools. After accounting for district fixed effects, these teacher quality gaps reduce to −0.05 for middle poverty schools, which is not statistically significant, and −0.24 for the high poverty schools, which remains significant (see Supplemental Appendix 4). This same trend, while not as pronounced, also holds when regressing teacher quality on our thresholds for the share of Black students served. The teacher quality gap between the middle group and the lowest group is null after accounting for differences between districts $(β = . 04, p > . 05)$ , although schools in the upper distribution for Black students served still have statistically significantly lower teacher quality compared to those in the bottom distribution $(β = - . 21, p < . 01)$ . We also fit this regression based on schools’ percentages of Hispanic students and find that teacher quality is significantly higher in schools with higher shares of Hispanic students, but this reduces after accounting for district fixed effects.

Teacher Quality and Students’ Enrollment in College

Research Question 2: To what extent does variation in teacher quality predict college enrollment? To better understand whether variation in teacher quality predicts college enrollment rates, we fit a series of increasingly controlled regression models using school and year fixed effects. In Model 1, we include only the primary independent and dependent variables. In Model 2, we add controls for sociodemographic variables, and in Model 3, we add controls for schools’ contextual characteristics. Using our composite measure of teacher quality, we find that within school changes in teachers’ human capital development explain statistically significant variation in the share of students enrolling in college. In Model 1 of Table 5, we find that a one standard deviation increase in human capital is associated with a 2% point increase in the share of high school graduates who enroll in college $(b = 2.038 p < . 01)$ . Including the controls in Model 2 decreases the coefficient $(b = 1.885 p < . 01)$ , but the percentage of graduates who enroll in college remains positively associated with increased human capital after accounting for sociodemographic controls. We find a comparably small uptick in the patterns we observe between human capital and college enrollment after accounting for personnel variables $(b = 1.887 p < . 01)$ , suggesting that our fully specified model reflects positive correlation between teacher quality and college enrollment.

Table 5.

Effect of Teacher Quality on % College Enrollment.

Variables	Model 1	Model 2	Model 3
Human capital	2.038*** (0.640)	1.885*** (0.631)	1.887*** (0.628)
% Black		0.028 (0.077)	0.072 (0.065)
% Hispanic		0.069 (0.089)	0.067 (0.090)
% Economically disadvantaged		−0.044* (0.025)	−0.042* (0.025)
% Gifted		0.075 (0.068)	0.082 (0.068)
% Remedial		−0.028 (0.022)	−0.029 (0.022)
Teacher retention			−0.001 (0.018)
Teacher salary			0.050 (0.066)
Average admin exp			−0.034 (0.046)
R ²	0.26	0.27	0.27
N	2,526	2,382	2,378

Note. This table displays coefficients for the effect of teacher quality on the percentage of high school graduates who enroll in college within 16 months of graduation. All models include school and year fixed effects and robust standard error clustered at the school level.

p < .1. **p < .05. ***p < .01.

Predicting college enrollment by high school graduation rate ignores seniors who fail or drop out of school, which might upwardly bias schools’ college enrollment rates. By ignoring seniors who drop out or fail, who likely differ dramatically in unobserved and observed ways from graduates, our estimates may be biased because it centers on a sample of potentially more advantaged students (i.e., graduates). To test for this potential source of bias, we construct college enrollment rates based on the total number of students in the 12th grade at the start of the fall semester in each year, rather than from the number of students who graduate. When expanding our sample this way, we observe a noticeable decline in the magnitude of the relationship between teacher quality and college enrollment ( $b = 1.24 p < . 01$ ). However, this result remains statistically and substantively significant, suggesting that the patterns we observe hold even after accounting for high school seniors who do not make it to graduation (see Supplemental Appendix 6).

To further explore the effects of school level human capital development among teachers, we run our fully specified model on the same college enrollment outcome across student subgroups. In Table 6 we find that for each subgroup, except for Hispanic students, variations in teacher quality predict college enrollment at statistically significant levels. Results appear to be most pronounced among economically disadvantaged students, as a 1 SD increase in teacher quality corresponds with a 7% point increase in the share of low-income students enrolling in college ( $b = 7.140 p < . 05$ ). For Black and White students, increases in college enrollment rates appear to rise in tandem with changes in teacher quality, though coefficients are smaller in magnitude. For every 1 SD increase in schools’ average teacher quality in year $t - 1$ , Black (2.6% points) and White (1.6% points) high school graduates are more likely to enroll in college in year $t$ . We do not find statistically significant results for Hispanic students, but this should be interpreted cautiously given the smaller sample size for this group.

Table 6.

Effect of Teacher Quality on College Enrollment by Subgroup.

Variables	% Black enrollment	% White enrollment	% Hispanic enrollment	% Econ disadvantaged enrollment
Human Capital	2.601*** (0.901)	1.586* (0.825)	−0.586 (1.518)	7.140** (3.285)
R ²	0.22	0.11	0.06	0.12
N	2,008	1,959	1,114	2,274

Note. This table displays coefficients for the effect of teacher quality on the percentage of college enrollees by student subgroups. Columns regresses within school variations in college enrollment rates (measured as the share of high school graduates who enroll in college, on the primary independent variable—teacher quality—and the full set of covariates). Additionally, models include school and year fixed effects.

p < .1. **p < .05. ***p < .01.

Teacher Quality and Students Persistence Through College

Research Question 3: To what extent does variation in teacher quality predict college persistence? Next, we examine the relationship between variations in teacher quality and rates of college persistence among students. In this study, we use the proportion of students who complete 1 year’s worth of college credits, 3 years after graduating high school, as a proxy for college persistence. As such, college persistence at time $t$ is predicted by teacher quality at time $t - 3$ , holding constant the same sociodemographic and school contextual indicators outlined above. Consistent with models discussed earlier, Table 7 shows positive and significant relationships between teachers’ human capital development and students’ college persistence rates when using the full sample. Specifically, schools that increased their teacher quality by 1 SD saw, on average, an increase in the share of students who persist in college by roughly 6% points students $(b = 6.316 p < . 05)$ , as shown in Model 1 of Table 7.

Table 7.

Effect of Teacher Quality on % College Persisted.

Variables	Model 1	Model 2	Model 3
Human capital	6.316** (2.658)	5.606** (2.634)	5.826** (2.788)
% Black		−0.207 (0.364)	−0.211 (0.363)
% Hispanic		0.402 (0.506)	0.384 (0.525)
% Economically disadvantaged		−0.144 (0.130)	−0.147 (0.129)
% Gifted		−0.069 (0.476)	−0.063 (0.471)
% Remedial		−0.044 (0.076)	−0.044 (0.077)
Teacher retention			0.039 (0.098)
Teacher salary			−0.038 (0.372)
Average admin exp			0.102 (0.227)
R ²	0.12	0.13	0.13
N	1,028	991	989

Note. This table displays coefficients for the effect of teacher quality on the percentage of high school graduates who completed 1 year of college within 24 months of enrollment. We use this measure as a proxy for college persistence. All models include school and year fixed effects.

p < .1. **p < .05. ***p < .01.

Our primary coefficient representing the amount of variance in students’ college persistence rates does not change substantially after accounting for sociodemographic factors and school contextual factors (as shown in Models 2 and 3 of Table 7). The relatively small amount of change in the variance in college persistence explained by our model is not surprising, as students’ college persistence rates are much more likely to be proximally associated with individual and university level variables, of which we do not have access to in this study. Previously observed patterns, however, continue to indicate that schools with higher shares of students who persist in college also tend to have more experienced and credentialed teachers. This trend does not appear to be fully explained by sociodemographic factors, as our fully specified model, which includes controls for the shares of Black, Hispanic, and low-income students, and additional school contextual factors, still shows a statistically significant correlation between human capital and college persistence $(b = 5.826 p < . 05)$ .

As with our construction of college enrollment, we also test for the potential source of bias stemming from calculating college persistence rates based on the total number of students who graduate, rather than using all high school seniors. When examining this more complete sample of high school seniors, we again observe similar results $(b = 5.154 p < . 01)$ to our original fully specified model. This robustness check suggests that students’ educational attainment levels do not solely drive the patterns we observe. However, reductions in our coefficient’s magnitude may imply that our estimates are more pronounced for students who graduate, although we need better data to understand our main results’ heterogeneity across students’ background characteristics.

When running our fully specified model across student subgroups, we find the relationship between teacher quality and college persistence to be inconsistent. For instance, while we observe positive associations for White and Hispanic students, these results are not statistically significant. However, we find that a one standard deviation increase in teachers’ human capital is marginally related to an increase in the percentage of Black students $(b = 9.258, p < . 10)$ who persist in college. Our subgroup analyses imply that the primary patterns we see in Table 8 appear to be driven by the relationship between teacher quality and Black students’ college persistence trends. While we cumulatively find inconsistent evidence that teacher quality is associated with persistence, we find some evidence that greater exposure to well credentialed and experienced teachers may improve Black students’ college persistence rates. This apparent relationship might partly contextualize is Figure 1, wherein we observe a slight increase in Black students’ college persistence rates across panel years—from around 50% in the first year of the panel to 55% in the final year.

Table 8.

Effect of Teacher Quality on College Persistence by Subgroup.

Variables	% Black persist	% White persist	% Hispanic persist	% Econ disadvantaged persist
Human capital	9.258* (5.116)	1.887 (6.407)	10.759 (9.907)	−7.305 (5.985)
R ²	0.07	0.07	0.10	0.07
N	711	809	283	813

Note. This table displays coefficients for the effect of teacher quality on the percentage of high school graduates who completed 1 year of college within 24 months of enrollment by student subgroups. We use this measure as a proxy for college persistence. All models include school and year fixed effects.

p < .1. **p < .05. ***p < .01.

Finally, we examine how the coefficients on the association between teacher quality and college enrollment and persistence vary across different thresholds of student poverty. With respect to college enrollment, wherein we regress the proportion of high school graduates who enroll in college at time $t$ on schools’ average human capital at time $t - 1$ , we find evidence contrary to the hypothesis explained above. For instance, as shown in Figure 2, we see the association between teacher quality and college enrollment becomes increasingly pronounced as the share of economically disadvantaged students in a school increases. This association is null across the lower strata of student poverty, suggesting that schools with more financially advantaged students do not experience increased rates of college enrollment rates when their teacher quality increases. For schools with higher concentrations of low-income students, however, the association between teacher quality and college enrollment is more evident.

Figure 2.

Main effect of human capital on college enrollment.

The results of the analysis suggest that the patterns we observe in Table 5 are almost entirely driven by variations in teacher quality in schools with more students experiencing poverty. This is evidenced by the fact that the coefficient on the relationship between human capital and college enrollment across the full sample using our fully specified model is 1.887, suggesting that a 1-SD increase in teacher quality is associated with a 2% point increase in the percentage of high school graduates who enroll in college. For schools with above 65% of their student body population classified as economically disadvantaged, the coefficient on this relationship hovers around 1.8. Conversely, schools with lower levels of student poverty have coefficients that range from negative to around 0, which implies that the relationship we observe between teacher quality and students’ college enrollment rates is not nearly as pronounced in these more well-resourced settings.

For college persistence, the picture is less clear, but generally consistent, as the relationship between college persistence and teacher quality is more steadily pronounced at higher levels of school poverty (see Figure 3). Nonetheless, we do see some evidence that the relationship is strongest at lower levels of school poverty, which suggests students across the income distribution, particularly Black students, benefit in important ways from increases in teachers’ experience and educational attainment. These results also imply that the story we present here is not as simple as more qualified teachers educating more advantaged students that attend college at higher rates. These same trends hold and become more pronounced when stratifying by the share of Black students a school serves (results are available upon request).

Figure 3.

Main Effect of Human Capital on College Persistence.

These robustness checks do not imply that teacher quality does not hold important implications for schools with lower levels of poverty, but rather accentuates how experienced and credentialed teachers can bolster the college going rates of high school graduates who are classified as economically disadvantaged. Cumulatively, the results of our study illuminate the following: (1) marginalized students, namely low-income and Black, continue to have less access to highly experienced and credentialed teachers, and (2) these disparities in access should be of keen interest to policymakers, as our results imply teachers may hold important implications for students’ post-secondary outcomes and success. This is particularly relevant for Black students, who experience the brunt of disparities related to quality teacher access and whose post-secondary outcomes correspond most consistently and positively with increased teacher quality in their schools.

Discussion and Conclusion

A fundamental challenge facing education policymakers is meaningfully addressing students’ disparate access to highly qualified teachers. While prior research elucidates that students of color, lower-achieving students, and students in urban settings have less access to highly effective teachers (Clotfelter et al., 2005; Goldhaber et al., 2015; Jackson, 2009), evidence on the extent to which teachers’ sorting patterns influence students’ schooling outcomes is mixed (Chingos & Peterson, 2011; Clotfelter et al., 2007; Hanushek & Rivkin, 2010; Harris & Sass, 2011; Rockoff et al., 2011). Therefore, lawmakers may find it difficult to use evidence to drive policies around advancing teachers’ human capital, as they may be unable to draw definitive conclusions from the available research. Further complicating this is the comparably small amount of literature examining how teachers’ observable characteristics affect non-test score outcomes. Nevertheless, teacher sorting is related to the trajectory of students’ exposure to highly qualified educators and subsequent academic achievement. As such, teacher sorting patterns and characteristics present relevant policy implications for K-12 education and further indicate our study’s utility.

The present manuscript confirms prior literature highlighting the consequential nature of teacher sorting patterns. Specifically, we find widespread inequities in students’ access to highly credentialed and experienced teachers in Georgia’s high schools. Our study also contributes new insights to the growing but smaller body of scholarship seeking to illuminate how teacher quality gaps may exacerbate opportunity gaps in students’ postsecondary outcomes (Chetty et al., 2011; Jackson, 2018). We find teachers’ observable characteristics, such as degree status and years of experience, explain significant and substantial variation in students’ rates of college enrollment and persistence. Therefore, gaps in access to credentialed and experienced teachers may implicate Black, Brown, and low-income students’ access to the promise of higher education.

These findings are particularly timely for at least two reasons. First, the Every Student Succeeds Act (ESSA) has ushered in a new era of accountability, wherein test scores are no longer the sole determinants of schooling effectiveness (Darling-Hammond et al., 2016). While ESSA’s broader focus does not minimalize the importance of targeted achievement efforts, it does heighten the need for scholars to study teachers’ influences on traditionally understudied student outcomes. Our study provides a useful template for other scholars interested in incorporating non-test score outcomes in research on teacher effectiveness. We enrich the potential of those efforts by introducing the postsecondary outcome of college persistence, which to our knowledge, no other researchers have examined.

Our findings also pose profound equity implications in indicating that although traditionally disadvantaged students have disparate levels of access to quality teachers, they indeed benefit from exposure to teachers with higher credentials and more experience. However, it is essential to note that our study suggests that Georgia’s educational inequities do not equally affect all students of color. Specifically, we find that schools with larger shares of Black and poor students have the least exposure to highly qualified teachers, but that this is not the case for schools with large populations of Hispanic students. To the extent that these findings hold across different contexts, we argue that Georgia presents a compelling case study for policy efforts seeking to improve teacher quality through targeted interventions that consider the unique needs of each student subgroup.

Ultimately, our study indicates that students’ race and class are significantly predictive of their access to highly credentialed and experienced teachers in Georgia. Yet, historically marginalized student groups greatly benefit from access to such teachers, especially related to their college going and staying tendencies. We conclude that policies seeking to attenuate such disparities, particularly within the Deep South context, cannot take an ahistorical approach. There is a magnitude of evidence connecting districts’ responses to Brown v. Board of Education to the profound equity challenges facing today’s teaching force (Department of Education, 2016; Ethridge, 1979; Hawkins, 1994; Holmes, 1990; Hudson & Holmes, 1994; Orfield, 1969). For instance, Hudson and Holmes (1994) found the loss of Black teachers following Brown has allowed for misinformed, and oftentimes racist, notions of Black students’ academic inadequacies to prevail amongst educators. Recent literature documents how Black students are more likely to experience disciplinary action and enroll in remedial classes than their White counterparts (Ford et al., 2008; Welsh & Little, 2018, 2019). These within school and societal practices must be redressed to ameliorate historic, longstanding, and present inequities. We suggest that more even distributions of experienced and highly educated teachers can play a crucial role in rectifying such inequities, and we advocate for more research on the unique roles Black teachers play in this process (Duncan, 2020; Gershenson et al., 2018; Lindsay & Hart, 2017).

Barriers to equity become even more apparent when coupling these storied histories with the pernicious schooling realities facing students of color. These students are already more likely to learn in racially and socioeconomically isolated schooling contexts (McNeal, 2009; Owens et al., 2016), which are inadequately funded (Adamson & Darling-Hammond, 2012; Knight, 2017, 2019) and concentrated with less qualified and experienced teachers (Goldhaber et al., 2015; Lankford et al., 2002). Therefore, lawmakers should meaningfully account for these historic and disparate circumstances when devising policies related to teacher sorting patterns and students’ access to quality teachers. Future research should seek to understand better how teachers affect a range of student outcomes, including college enrollment and persistence. Such scholarship is important for redressing trends that emerged in response to Brown, as it could further buttress works emphasizing the critical nature of same race teachers for Black students (Goldhaber et al., 2019; Gottfried et al., 2019) and amplify policies seeking to diversify the teacher labor market.

Directions for Future Research

Our results provide suggestive evidence of a positive relationship between teachers’ accumulation of human capital and students’ post-secondary outcomes. Due to our study’s design and data limitations, we are unable to draw causal inferences about this relationship. Further, we cannot completely rule out omitted variable bias despite our use of fixed effects specification controls for time-invariant factors within schools. As such, future research can meaningfully lend credence to the precision of our estimates by accounting for some of the confounding factors not present in our study. For instance, we do not account for variation in potentially important school-level variables like climate and parental involvement, which could reasonably influence college outcomes. Further, the possibility also exists that policies and initiatives targeting higher education, for which we are also unable to control, could confound the relationship between school context and college outcomes. An example of this is funding variation in the Helping Outstanding Pupils Educationally (HOPE) scholarship, which provides financial aid for many Georgia students attending in-state colleges (Heaton et al., 2017; Long, 2004) and likely has substantial ramifications for college outcomes. Relatedly, at the school-level, time-varying supports for FAFSA and other determinants of college financial aid are also important to consider. Future analyses should incorporate these variables to understand how and to what extent they influence our estimates.

Overall, there is still much work to do in examining the distribution of teacher quality and how it affects postsecondary outcomes across student subgroups. We find scant literature investigating how prepared teachers are to serve in low-income and hyper marginalized school contexts, which might be a factor in current teacher sorting patterns (Carter-Andrews, 2009; Flamini et al., 2020; Freedman & Appleman, 2009; Vagi et al., 2019). To date, policy efforts seeking to solve staffing problems in underserved schools have been primarily informed by scholarship emphasizing financial incentives to recruit (Ingersoll & May, 2011) and retain (Swain et al., 2019) promising teachers in high-poverty schools. While financial incentives are one avenue policymakers can use to redress teacher sorting patterns, more scholarship documenting comprehensive, and targeted interventions that improve school climate and working conditions, such that students gain more equitable access to highly credentialed and experienced teachers, is needed.

In closing, we contend that the effects of inequitable access to quality teachers may be more marked and longstanding than the current literature shows. We advocate for more causal work, which would elucidate whether and to what extent teachers impact students’ postsecondary outcomes. Such work should be of interest to educational stakeholders because higher education, for those who elect to pursue it, can have meaningful and long-term impacts. Thus, better understanding the extent to which highly experienced and credentialed teachers make college more accessible to (especially Black and Brown) students has relevant policy and scholarly implications.

Supplemental Material

sj-pdf-1-epx-10.1177_08959048211049429 – Supplemental material for Teacher Quality and Students’ Post-Secondary Outcomes

Supplemental material, sj-pdf-1-epx-10.1177_08959048211049429 for Teacher Quality and Students’ Post-Secondary Outcomes by Jerome Graham and Monica Flamini in Educational Policy

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jerome Graham

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Jerome Graham is an Assistant Professor in the K-12 Educational Administration Department at Michigan State University. His research interests focus broadly on the interaction of race and class in educational policy and practice. Specifically, Jerome uses mixed-methods research designs to evaluate the effects of education reforms that target minority and disadvantaged students and the mechanisms that drive such reforms. His current research background emphasizes how schools can provide constructive school climates for students, which facilitate the process by which they develop cognitive and socio-emotional skills to improve their short- and long-term outcomes. His theoretical orientation is psychosociological, in that Jerome’s research agenda focuses not only on persons’ individual development (academic, emotional, and otherwise) but also on the social structures that influence their development. For this research presentation, Jerome shares findings from an investigation of the current social and emotional learning reform movement in Georgia, with particular emphasis paid to factors that influence teachers’ classroom practices that build students’ socioemotional well-being.

Monica Flamini is a doctoral candidate completing her final year in the education administration and policy program at the University of Georgia. Her research focuses on school desegregation policies and specifically concerns magnet schools’ propensities to drive equity-oriented student outcomes in today’s socio-political climate. She also studies teachers’ sorting characteristics and students’ climate perceptions within urban schools. During her doctoral studies, Monica has served as a graduate assistant, teaching assistant, and research fellow. She is currently an education research and policy analyst at the Governor’s Office of Student Achievement and will be defending her dissertation in Fall 2021.

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