Abstract
Empirical evidence shows that disadvantaged students tend to have less-qualified and less-effective teachers than their more-advantaged peers. These teacher quality gaps (TQGs), which have existed for decades and across many measures of student disadvantage and teacher quality, are an important factor explaining student achievement gaps between advantaged and disadvantaged students. Research by Dan Goldhaber, Vanessa Quince, and Roddy Theobald — focusing on the sources of TQGs across different states and measures of teacher quality — suggests that policy makers should consider both the setting and the type of gap they wish to prioritize when designing policies to address TQGs.
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.
Background
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.
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).
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.
Proportion of novice teachers by underrepresented minority (URM) status
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.
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.
Simulated sources of gaps in exposure to novice teachers by levels of economic advantage
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.
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).