AEFP 45th Annual Conference

Toward a Meaningful Impact through Research, Policy & Practice

March 19-21, 2020

Revisiting Teacher Quality Gaps: Geographic Disparities in Access to Highly Effective Teachers Across Tennessee

Luis A. Rodriguez, New York University,

Background and Purpose:
Previous research documents the income achievement gap is widening, while racial and ethnic achievement gaps, though lessening, are nevertheless persistent (e.g., Reardon, 2011). Many look to public interventions as a means to close existing achievement gaps that exist between students of different racial, ethnic, and socioeconomic backgrounds. However, one barrier to closing gaps in achievement is the inequitable distribution of teacher quality across students.
A growing body of work indicates there are substantial inequities in teacher quality according to test-score-based “value-added” measures (VAMs) of teaching effectiveness (Goldhaber, Lavery, & Theobald, 2015; Goldhaber, Quince, & Theobald, 2018; Isenberg et al., 2013; Sass et al., 2010). Some recent studies, however, call into question this body of work and suggest the distribution of teacher quality may be more equitable than previously thought (Isenberg et al., 2016), but the findings from these studies are primarily derived from large, urban districts where the teacher labor markets may be substantially different than rural districts. The purpose of this study is to more thoroughly explore the presence of teacher quality gaps using both VAM and observational measures of teaching effectiveness and assess whether similar distributional patterns exist across the broader teaching workforce and diverse geographical contexts. More specifically, this study seeks to answer:
(1) To what extent are there gaps in teacher quality by student racial/ethnic and socioeconomic disadvantage background?
a. Do these gaps vary descriptively across urban, suburban and rural geographical settings?
b. What other district-, school-, and neighborhood-level factors predict the presence of teacher quality gaps?
Data and Methods:
The project will primarily rely on rich, longitudinal data on public school students, educators, and schools in Tennessee collected and processed under the partnership between the Tennessee Department of Education (TDOE) and the Tennessee Education Research Alliance (TERA) at Vanderbilt. The analysis for this study will follow the approach of Goldhaber, Lavery, & Theobald (2015) and Goldhaber, Quince, & Theobald (2018) to calculate teacher quality gaps (TQGs) in each school year, representing school-level and district-level exposure rates to high quality teachers across student racial/ethnic and socioeconomic subgroups in each year. Using a combination of descriptive data analyses and regression-based methods, we explore which observable geographic as well school and district-level factors predict variation in teacher quality gaps.
Preliminary findings show the existence of teacher quality gaps that are similar in size to those found in previous studies of other state settings (Goldhaber, Lavery, & Theobald, 2015; Goldhaber, Quince, & Theobald, 2018). Figure 1 shows the evolution of teacher quality gaps over time, both across schools within the same district and across districts. The magnitudes of gaps are consistent for different measures of disadvantage; marginalized students are between 3.5 and 6 percentage points less likely to be exposed to a high-quality teacher. Further, a little over half of these gaps are associated with differences in exposure occurring across rather than within districts.
We also find notable variation in teacher quality gaps across districts and geographic regions. Figure 2 plots the exposure rate to high quality teachers (defined as top-decile-VAM within state in previous year) for 4th–8th grade students by under-represented minority status and by economically disadvantaged status for all districts in Tennessee. In about half of all districts, marginalized students are disproportionately under-exposed to high quality teachers (below the 45-degree line) while in others marginalized students are disproportionately over-exposed to high quality teachers compared to their more advantaged peers. Similar to Figure 1, Figure 3 shows the change of teacher quality gaps broken down by urbanicity. As shown, gaps tend to be larger in urban areas, slightly smaller in suburban/towns and markedly smaller in rural settings. However, we find that the smaller gaps in teacher quality in rural areas reflect less variation in teacher quality in those settings.
Contribution and Next Steps:
The results of this study would be of great benefit to education policymakers and administrators to inform whether policies designed to remedy the inequitable distribution of teacher quality across students (e.g., bonuses, in-service training) should be targeted to certain segments of the teacher workforce or applied more broadly. Though prior work documents teacher quality gaps, particularly in North Carolina, the proposed study will more thoroughly explore differences across urban and non-urban education contexts. As a set of next steps, we will explore the extent to which teacher quality gaps are related to geographic/localized economic factors versus district and school resources and characteristics using regression-based methods.
(References/Figures Attached)



I found this work to be very interesting, Luis. Why do you think moderate-sized schools have less TQG than large schools? Is it just chance? I'm not sure how teacher class assignments work in TN. In PA, where I taught, seniority played a large role in who taught what. In a large school, novice teachers always had classes with more economically-disadvantaged students. In the smaller school where I later taught, the same relationship held, but because there were not as many sections of any one subject, all teachers taught a more diverse group of students. Is this TQG a reason we should be thinking about more moderately-sized schools?

Hi Greg, sorry for the delayed response. Just looking at poster comments now that I'm making edits to this paper once again. The pattern you describe is our current working theory, though we have yet to rigorously explore this in the data as of yet.

Thank you for sharing your interesting work. I was struck by the finding where you identify teachers quality gaps between rural and non-rural school districts, generally. This seems like a really important finding, but not one that you highlight in your discussion. Although nationwide more students attend urban and suburban schools, there are actually more rural school districts. And, we know that the teacher staffing constraints in rural districts are very different than in urban areas. I t would be great to see you unpack/highlight this finding further in your work.. Thanks! Tammy

Thank you, Tammy! I agree. This was a pattern that struck me as well, and one that I don't believe that has been thoroughly discussed in the current literature.

Nice work! Wondering about the overlap or intersection of costly neighborhoods where distribution is more inequitable and higher resourced districts where distributions is more equitable. And, why you think there may be a difference there?

This is an interesting study--I appreciated you showing the between and within district variation. I wonder what this would look like if you added the within-school level as well. Obviously, that would play to a different set of decisions (student-teacher matching and tracking vs. where teachers are taking jobs), but I wonder how that dimension looks as well. I also really enjoyed your paper in the School Climate/Discipline session, Luis--I look forward to seeing how that that work evolves as well!

Thank you, Cassie!

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