Identifying the causal impact of teacher turnover is a difficult task. The main threat to a causal interpretation of the association between teacher entry rates and student performance is, of course, the self-selection or assignment of incoming teachers to student groups that are already lower or higher performing. To address this concern, we focus on the ‘intent to treat’ effect of teacher entry into subject groups, across all grades in a school in a given year, on the final school qualifications of students taking their exams in that subject in that year, while controlling for time varying school and subject specific shocks in a fixed effects regression design. There is an advantage of this approach, over, say comparing the performance of students in a year when they are allocated a new teacher with those who are not (Atteberry et al. 2017), or comparing the performance of students experiencing different rates of teacher entry in specific grades (Ronfeldt et al. 2013). The advantage is that it is hard to reallocate specialised secondary school teachers across subjects, mitigating concerns about selective allocation of new teachers to lower or higher performing students or student groups within a school. The improvement over using a single subject (Hanushek et al. 2016), is that we can control more effectively for school-by-year shocks using fixed effects estimation. Thus, our regression methods identify the causal impacts from the variation in entry rates in school-subject year groups (akin to school departments), conditional on combinations of fixed effects at school-by-year, school-by-subject, subject-by-year level, and finally, fixed effects at student level. The identifying assumption is that turnover between subjects within schools, or over time in school-by-subject groups is likely driven by random shocks, or by exits of teachers based on personal preferences, rather than any factors directly linked to poor student performance. An array of robustness checks strongly supports our findings: we demonstrate through a range of placebo, balancing, and other tests that we can treat turnover as random, conditional on these fixed effects.

A further advantage of our work over existing studies that analyse the effects of grade-specific variation in turnover is that, in these studies, students move between grades, typically experiencing a change in teachers every year, regardless of levels of turnover. Therefore, any estimates of turnover based on this type of design will omit effects due to disruption in the continuity of teaching experienced by students, which appears to be playing an important role, as shown in Henry and Redding (2020). Our study, in contrast, looks at turnover in subject groups during a two-year period where students are preparing for their crucial end of school exams, and where disruption is often thought to be particularly important. Usually, students are taught by the same teachers over this period.

The second important contribution of our study is to look at various potential mechanisms through which teacher turnover can affect student learning. A likely reason why entry of teachers affects student achievement is that incoming teachers lack specific knowledge about the school and its students i.e. they lack school-specific human capital. But these teachers may also lack general teaching experience if they are new to teaching, i.e. industry specific human capital, or lack general experience in the labour market. We investigate these channels by comparing the effects from entry of experienced and less experienced teachers (measured by years of teaching experience), and by comparing the effects of length of school tenure, age and experience amongst teaching staff.3 Although the importance of general and specific forms of human capital has been examined widely in labour economics since the seminal work of Becker (1962), we have little evidence in relation to teaching. An exception is Ost (2014) who finds that grade-specific and general human capital do matter for teacher productivity. Like Ost (2014), our data set linking student performance to the characteristics of their teachers provides an opportunity to investigate these questions with direct measures of teachers’ success in improving student achievement. These metrics are better than wages for measuring teacher productivity, because wages in the state-sector teaching professions – like those in the public sector generally – are carefully regulated. We also provide important insights on some other potential channels that might drive the disruptive impact of turnover such as teacher quality and workload.

To examine how schools respond to mitigate the impacts of turnover, we use information on the grade allocation of teachers to show to what extent schools/departments assign incoming teachers away from the critical final year of compulsory schooling (Year 11). If schools take direct actions to mitigate the disruption effect of turnover, then our ‘intent to treat’ estimates based on school-subject-year turnover may understate the impacts of teacher entry, if new teachers are assigned to students in grades other than that for which we measure student outcomes (i.e. there is non-compliance with the treatment). This in itself is very important as it sheds light on the extent to which re-organisation may lead to underestimation of the impact of many types of interventions or shocks in schools, or more widely in the public sector. This is a pervasive concern throughout public policy evaluation as it implies that estimates of policy interventions on school performance, or other public sector institutions, might be lower than what policy makers and researchers might expect unless they allow for this kind of organizational re-adjustment.4