Understanding the reasons for gaps in teacher quality will help school leaders determine how best to reduce them.
We know that the public education system plays an important role in exacerbating or ameliorating inequality. Court and legislative actions designed to eliminate historical inequities in spending have been a feature of the educational landscape for more than 50 years, going back at least as far as the landmark Brown v. Board of Education ruling. Yet, despite these ongoing efforts, empirical evidence has shown that the public education system has continually failed to level the playing field between advantaged and disadvantaged students, in part because disadvantaged students tend to have less-qualified and less-effective teachers than their more-advantaged peers. Given that teacher effectiveness has been found to be the most important schooling resource linked to student achievement, addressing these “teacher quality gaps” (TQGs) can help close gaps in outcomes between advantaged and disadvantaged students.
Much of the evidence on the existence of TQGs is not new; indeed, as we discuss below, empirical evidence on the inequitable distribution of teacher qualifications across different types of students and schools goes back nearly two decades. But several recent research and policy initiatives have put these findings into sharper focus. First, the availability of administrative data measuring multiple elements over the long term has allowed researchers to better quantify the effect of teachers on students. Research shows that teachers influence short-run test-based measures of achievement (Rivkin, Hanushek, & Kain, 2005); non-test outcomes like attendance (Jackson, 2018); and longer-run outcomes such as college going and labor market earnings (Chetty, Friedman, & Rockoff, 2014). Second, there is new evidence that TQGs exist for outcome-based measures of teacher effectiveness and performance (Goldhaber, Quince, & Theobald, 2018a; Isenberg et al., 2016). Dan Goldhaber, Roddy Theobald, and Danielle Fumia (2018) estimate that gaps between advantaged and disadvantaged students on test performance and advanced coursework would decrease by about 5% to 10% if TQGs were eliminated for grades 4 to 8.
This greater awareness of the importance of teachers has led to growing interest in creating policies that address TQGs. For example, the U.S. Department of Education recently directed states to quantify inequities in teacher quality across their public schools and develop plans to make the distribution of high-quality teachers more equitable (see Elizabeth Ross’s article in this issue of Kappan). This policy interest has, in turn, spurred research that goes beyond simply documenting the existence of TQGs and addresses the history, sources, and implications of these gaps. This new empirical evidence points toward potential priorities for policy makers looking to close TQGs.
Decades of research have documented large and persistent gaps between advantaged and disadvantaged students, as measured by race and socioeconomic status, in outcomes like test scores, high school graduation rates, and college completion rates (e.g., Bailey and Dynarski, 2011; Hedges & Nowell, 1998; Reardon, 2011). Efforts to close these gaps in outcomes have been at the heart of U.S. education policy since at least the Coleman Report (Coleman, 1966). Given the crucial role that teachers play in student outcomes, it is no surprise that previous research has investigated whether the distribution of teacher quality contributes to these gaps.
Empirical evidence on the inequitable distribution of teacher qualifications across different types of students and schools goes back nearly two decades.
Much of the existing research documents the existence of gaps in teacher qualifications. In a seminal study using data from New York, Hamilton Lankford, Susanna Loeb, and James Wyckoff (2002) found that lower-quality teachers as measured by experience, degree level, certification, and college attendance are more likely to teach in schools with low-performing minority students. Likewise, in North Carolina, Charles Clotfelter, Helen Ladd, and Jacob Vigdor (2005) reported that Black students are more likely to be taught by a novice teacher than their White counterparts. Follow-up studies have documented that these TQGs can be attributed to student and teacher sorting across districts, across schools within a district, and across classrooms within a school (Kalogrides & Loeb, 2013; Kalogrides, Loeb, & Béteille, 2013).
Recent studies have documented TQGs in terms of teacher effectiveness, using value-added measures (e.g., Goldhaber, Lavery, & Theobald, 2015; Isenberg et al., 2016; Mansfield, 2015; Sass et al., 2012). While the magnitudes of the estimated TQGs differed across these studies and were somewhat sensitive to the way that value added is estimated (e.g., Goldhaber, Quince, & Theobald, 2016), all found that disadvantaged students are, on average, taught by less effective teachers. Research on teacher labor markets and mobility provides some explanations for how these TQGs emerge. Several studies, for instance, have demonstrated that teachers in disadvantaged schools are more likely to leave the workforce (e.g., Boyd et al., 2008; Hanushek, Kain, & Rivken, 2004) or move from schools serving higher proportions of disadvantaged students into schools with lower proportions (e.g., Goldhaber, Gross, & Player, 2011; Scafidi, Sjoquist, & Stinebrickner, 2007).
- Podcast with Dan Goldhaber on Research Minutes
New research on sources of TQGs
Our own research has considered the history, sources, and implications of TQGs. For example, when we used longitudinal data from North Carolina and Washington State to investigate how TQGs have changed over the past several decades according to two measures of student disadvantage (race and poverty) and three measures of teacher quality (experience, licensure test scores, and value added), we found find that disadvantaged students in both states were more likely to have low-quality teachers in every year of available data and under every definition of student disadvantage and teacher quality (Goldhaber, Quince, & Theobald 2018a). However, we also found some important variations in both the magnitude and decompositions of these TQGs over time. As shown in Figure 1, TQGs in North Carolina, inequities in the proportion of novice teachers appear to be more attributable to inequities across different schools within the same district. On the other hand, in Washington State, exposure to novice teachers has grown substantially, with almost all of this growth due to increased inequity across different school districts.
While there is generally limited evidence about specific causes of TQGs, simulation evidence from North Carolina and Washington (Goldhaber, Quince, & Theobald, 2018b) attempts to disentangle the extent to which four processes in the teacher labor market — the attrition of teachers from the workforce, the movement of teachers within a district, the movement of teachers between districts, and the hiring of teachers into open positions — contribute to gaps between advantaged and disadvantaged schools.
Disadvantaged students were more likely to have low-quality teachers in every year of available data and under every definition of student disadvantage and teacher quality.
The simulation results suggest that all four processes contribute to gaps in each measure of teacher quality (experience, licensure test scores, and value added). For example, the simulation summarized in Figure 2 shows that TQGs with respect to experience increase as a function of all four processes. It also suggests that teacher attrition and mobility contribute more to TQGs in North Carolina than in Washington, while teacher hiring contributes more to TQGs in Washington than in North Carolina. Furthermore, there is little evidence that cross-district teacher mobility is an important factor in either state, a finding that is consistent with the localness of teacher labor markets (e.g., Boyd et al., 2005) and, more generally, the fact that there are often large transaction costs associated with moving across district boundaries.
How to respond
If most of the existing research on TQGs serves primarily as an autopsy of the failure of U.S. public education to provide equal access to effective teachers, our simulation evidence (Goldhaber, Quince, & Theobald, 2018b) and other research perhaps serve as a step toward identifying cures. Specifically, the simulations show that we need to address multiple sources of TQGs.
One obvious policy lever is to provide teachers with greater financial inducements to serve in disadvantaged schools. Indeed, studies suggest that financial incentives, such as retention bonuses (e.g., Cowan & Goldhaber, 2018; Clotfelter et al., 2008; Dee & Wyckoff, 2015); incentives to move to disadvantaged schools (e.g., Glazerman et al., 2012); and teacher loan forgiveness programs (Feng & Sass, 2015) can influence teachers’ decisions about where to teach and whether to stay in particular positions. Using such incentives, however, may present political challenges, such as institutional inertia and opposition from teachers unions (e.g., Goldhaber, 2010; Prince, 2002).
We also know that teachers make employment decisions based on the working conditions in schools (e.g., Ladd, 2011). Therefore, one way to address distributional issues is to improve working conditions in disadvantaged schools. This is complicated, however, as the working conditions that teachers care most about (especially the quality of school leadership) may be difficult to influence directly through policy (Dee & Goldhaber, 2017). However, our research on TQGs can help district leaders prioritize different types of interventions. For example, given that we found extensive inequities across districts in Washington and within districts in North Carolina (Goldhaber, Quince, & Theobald, 2018a), state-level policies are likely to be more effective at closing TQGs in Washington, while district-level policies may be a better policy response in North Carolina.
We found extensive inequities across districts in Washington and within districts in North Carolina.
The source of the gap also matters. For instance, initial hiring explains most of the gaps in licensure, test performance, and value-added measures, which suggests that policies targeting the hiring and recruiting of teachers into disadvantaged schools could be the best way to address these gaps. One understudied but potentially promising policy lever — given that it is highly malleable and affects teacher candidates before they enter the profession — is student-teaching placements. Recent research (Krieg, Goldhaber, & Theobald, 2018; Krieg, Theobald, & Goldhaber, 2016) shows that, in general, teacher candidates tend to do their student teaching in more advantaged settings than where they begin their teaching careers. Future research could investigate whether placing more student teachers (particularly the most effective ones) in disadvantaged schools could attract those skilled candidates to disadvantaged schools once they enter the profession. On the other hand, if the policy priority is to close TQGs in terms of teacher experience, our simulation research (Goldhaber, Quince, & Theobald, 2018b) suggests that the best policies may actually center on interventions designed to retain higher-quality teachers in disadvantaged schools (Carver-Thomas & Darling Hammond, 2017).
The bottom line, however, is that among our various public institutions, our school system is best positioned to help ameliorate the inequality that tends to persist intergenerationally in families (Chetty et al., 2014). We now have good evidence about how schools contribute to this problem — specifically in regards to inequities in teacher quality across schools and districts — but we need much more evidence and testing of interventions designed to address the challenges of inequality associated with these gaps (Gamoran, 2014).
- Also by Dan Goldhaber and Roddy Theobald: Why we need a diverse teacher workforce
Bailey, M.J. & Dynarski, S.M. (2011). Gains and gaps: Changing inequality in US college entry and completion (Working Paper No. 17633). Cambridge, MA: National Bureau of Economic Research.
Boyd, D., Lankford, H., Loeb, S., & Wyckoff, J. (2005). The draw of home: How teachers’ preferences for proximity disadvantage urban schools. Journal of Policy Analysis and Management, 24 (1), 113-132.
Boyd, D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2008). Who leaves? Teacher attrition and student achievement (Working Paper No. 14022). Cambridge, MA: National Bureau of Economic Research.
Carver-Thomas, D. & Darling-Hammond, L. (2017). Teacher turnover: Why it matters and what we can do about it. Palo Alto, CA: Learning Policy Institute.
Chetty, R., Friedman, J.N., & Rockoff, J.E. (2014). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review, 104 (9), 2633-2679.
Chetty, R., Hendren, N., Kline, P., Saez, E., & Turner, N. (2014). Is the United States still a land of opportunity? Recent trends in intergenerational mobility. American Economic Review, 104 (5), 141-147.
Clotfelter, C., Glennie, E., Ladd, H., & Vigdor, J. (2008). Would higher salaries keep teachers in high-poverty schools? Evidence from a policy intervention in North Carolina. Journal of Public Economics, 92 (5-6), 1352-1370.
Clotfelter, C.T., Ladd, H.F., & Vigdor, J. (2005). Who teaches whom? Race and the distribution of novice teachers. Economics of Education Review, 24 (4), 377-392.
Coleman, J.S. (1966). Equality of educational opportunity. Washington, DC: National Center for Educational Statistics.
Cowan, J. & Goldhaber, D. (2018). Do bonuses affect teacher staffing and student achievement in high poverty schools? Evidence from an incentive for National Board certified teachers in Washington State. Economics of Education Review, 65, 138-152.
Dee, T.S. & Goldhaber, D. (2017). Understanding and addressing teacher shortages in the United States. Washington, DC: Brookings Institution, The Hamilton Project.
Dee, T.S. & Wyckoff, J. (2015). Incentives, selection, and teacher performance: Evidence from IMPACT. Journal of Policy Analysis and Management, 34 (2), 267-297.
Feng, L. & Sass, T.R. (2015). The impact of incentives to recruit and retain teachers in” hard-to-staff” subjects: An analysis of the Florida Critical Teacher Shortage Program (Working Paper 141). Washington, DC: National Center for Analysis of Longitudinal Data in Education Research.
Gamoran, A. (2014). The future of educational inequality in the United States: What went wrong and how can we fix it? New York, NY: William T. Grant Foundation.
Glazerman, S., Protik, A., The, B-R., Bruch, J., & Seftor, N (2012). Moving high-performing teachers: Implementation of transfer incentives in seven districts. Princeton, NJ: Mathematica Policy Research.
Goldhaber, D. (2010). Teacher pay reforms: The political implications of recent research (Working Paper No. 2010-4.0). Seattle, WA: Center for Education Data & Research.
Goldhaber, D., Gross, B., & Player, D. (2011). Teacher career paths, teacher quality, and persistence in the classroom: Are public schools keeping their best? Journal of Policy Analysis and Management, 30, 57–87.
Goldhaber, D., Lavery, L., & Theobald, R. (2015). Uneven playing field? Assessing the teacher quality gap between advantaged and disadvantaged students. Educational Researcher, 44 (5), 293-307.
Goldhaber, D., Quince, V., & Theobald, R. (2016). Reconciling different estimates of teacher quality gaps based on value-added. Washington, DC: National Center for the Analysis of Longitudinal Data in Education Research.
Goldhaber, D., Quince, V., & Theobald, R. (2018a). Has it always been this way? Tracing the evolution of teacher quality gaps in US public schools. American Educational Research Journal, 55 (1), 171-201.
Goldhaber, D., Quince, V., & Theobald, R. (2018b). How did it get this way? Disentangling the sources of teacher quality gaps across two states. Washington, DC: National Center for the Analysis of Longitudinal Data in Education Research.
Goldhaber, D., Theobald, R., & Fumia, D. (2018). Teacher quality gaps and student outcomes: Assessing the association between teacher assignments and student math test scores and high school course taking. Washington, DC: National Center for the Analysis of Longitudinal Data in Education Research.
Hanushek, E., Kain, J., & Rivken, S. (2004). Why public schools lose teachers. Journal of Human Resources, 39, 326–354.
Hedges, L.V. & Nowell, A. (1998). Black-White test score convergence since 1965. In C. Jencks & M. Phillips (Eds.), The Black-White test score gap (pp. 149-181). Washington, DC: Brookings Institution Press.
Isenberg, E., Max, J., Gleason, P., Johnson, M., Deutsch, J., & Hansen, M. (2016). Do low-income students have equal access to effective teachers? Princeton, NJ: Mathematica Policy Research.
Jackson, C.K. (2018). What do test scores miss? The importance of teacher effects on non–test score outcomes. Journal of Political Economy, 126 (5), 2072-2107.
Kalogrides, D. & Loeb, S. (2013). Different teachers, different peers: The magnitude of student sorting within schools. Educational Researcher, 42 (6), 304-316.
Kalogrides, D., Loeb, S., & Béteille, T. (2013). Systematic sorting: Teacher characteristics and class assignments. Sociology of Education, 86 (2), 103-123.
Krieg, J.M., Goldhaber, D., & Theobald, R. (2018). Teacher candidate apprenticeships: Assessing the who and where of student teaching (Working Paper 206-1118-1). Washington, DC: National Center for the Analysis of Longitudinal Data in Education Research.
Krieg, J.M., Theobald, R., & Goldhaber, D. (2016). A foot in the door: Exploring the role of student teaching assignments in teachers’ initial job placements. Educational Evaluation and Policy Analysis, 38 (2), 364-388.
Ladd, H.F. (2011). Teachers’ perceptions of their working conditions: How predictive of planned and actual teacher movement? Educational Evaluation and Policy Analysis, 33 (2), 235-261.
Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban schools: A descriptive analysis. Educational Evaluation and Policy Analysis, 24 (1), 37-62.
Mansfield, R.K. (2015). Teacher quality and student inequality. Journal of Labor Economics, 33 (3), 751-788.
Prince, C.D. (2002). Higher pay in hard-to-staff schools: The case for financial incentives. Arlington, VA: American Association of School Administrators.
Reardon, S.F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G.J. Duncan & R.J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 91-116). New York, NY: Russell Sage Foundation.
Rivkin, S.G., Hanushek, E.A., & Kain, J.F. (2005). Teachers, schools, and academic achievement. Econometrica, 73 (2), 417-458.
Sass, T.R., Hannaway, J., Xu, Z., Figlio, D.N., & Feng, L. (2012). Value added of teachers in high-poverty schools and lower poverty schools. Journal of Urban Economics, 72 (2-3), 104-122.
Scafidi, B., Sjoquist, D.L., & Stinebrickner, T.R. (2007). Race, poverty, and teacher mobility. Economics of Education Review, 26, 145–159.
Citation: Goldhaber, D., Quince, V., & Theobald, R. (2019) Teacher quality gaps in U.S. public schools: Trends, sources, and implications. Phi Delta Kappan, 100 (8), 14-19.