Predictive effects of teachers and schools on test scores, college attendance, and earnings

Abstract

I studied predictive effects of teachers and schools on test scores in fourth through eighth grade and outcomes later in life such as college attendance and earnings. For example, predict the fraction of a classroom attending college at age 20 given the test score for a different classroom in the same school with the same teacher and given the test score for a classroom in the same school with a different teacher. I would like to have predictive effects that condition on averages over many classrooms, with and without the same teacher. I set up a factor model that, under certain assumptions, makes this feasible. Administrative school district data in combination with tax data were used to calculate estimates and do inference.education production function | unmeasured inputs | teacher effects T he outcome data are based on elementary and middle school classrooms, grades four through eight. For a classroom, there is an average score based on a math or reading test given near the end of the school year. There are also later outcome measures for that classroom. These measures include the fraction of the classroom that is attending college at age 20 and the average earnings of the classroom at age 28. The classrooms can be grouped by schools, and, within a school, can be grouped by teacher.The goal of the paper is to provide predictive effects of teachers and schools on these outcomes. For example, predict the fraction of a classroom attending college at age 20 given the test score for a different classroom in the same school with the same teacher and given the test score for a classroom in the same school with a different teacher. Or predict the fraction of a classroom attending college at age 20 given the fraction attending college for a different classroom with the same teacher and given the fraction attending college for a classroom in the same school with a different teacher. I would like to have predictive effects that condition on averages over many classrooms, with and without the same teacher. I set up a factor model that, under certain assumptions, makes this feasible. Then I can define teacher and school factors based on test score data and measure the predictive effect of the teacher factor on college attendance. More directly, I can define teacher and school factors based on the college attendance data and measure the predictive effect of the teacher factor on college attendance.These predictive effects can be based on residuals, where first we form predictions based on observed variables (X) such as class size, years of teacher experience, lagged test scores, and parent characteristics. The residuals are the prediction errors. Then the teacher and school effects that I measure in these residuals correspond to unmeasured (latent) variables or, more precisely, to the parts of those latent variables that are not predictable using the observed variables in X. I am interested in these latent variables because they may be related to unmeasured characteristics of teachers that have a causal effect on outcomes,...

What Do Test Scores Miss? The Importance of Teacher Effects on Non–Test Score Outcomes

“…However, I present some feasible policy uses. The results provide an explanation for why Chamberlain (2013) finds that value-added estimates may reflect less than one-fifth of the total effect of teachers. Also, consistent with Heckman, Pinto, and Savelyev (2013), teacher effects on proxies for noncognitive skills offers an explanation for why teacher test score effects fade over time (Jacob, Lefgren, and Sims 2010) despite having meaningful effects on long-run outcomes.…”

Non-Cognitive Ability, Test Scores, and Teacher Quality: Evidence from 9th Grade Teachers in North Carolina

“…This source of variation is robust to student selection to school tracks and is valid as long as students do not select to teachers within schooltrack-year cells. Tests in section IV.2 show that the findings are not driven by student selection within school-track-years 12.…”

“…If teachers improve skills not captured by test scores, then excellent teachers who improve long-run outcomes may not raise test scores, and the ability to raise test scores may not be the best predictor of effects on long-run outcomes. Indeed, Chetty, Friedman, and Rockoff (2011) note that teachers may have important effects on longer-run outcomes that are not reflected in their test score value-added and, using their same data, Chamberlain (2013) finds that test score effects may account for less than one quarter of the overall effect of teachers on college entry. This paper speaks to this second critique by being the first to investigate (a) whether teachers affect skills not captured by test scores, and (b) whether, and to what extent, teacher effects on a proxy for non-cognitive skills predict effects on long-run outcomes (that are missed by effects on test scores).…”

“…These results provide explanations for three seemingly conflicting findings in the recent literature. First, the importance of teacher effects on non-cognitive skills helps explain the Chamberlain (2013) finding that the effects of test score value-added on college-going presented in Chetty et al (2011) reflect less than one-fifth of the total effect of teachers. Second, the relative importance of non-cognitive effects for English teachers can explain the finding that English teachers have larger effects on long-run outcomes than math teachers despite smaller test score effects.…”

“…In these models, the teacher effects are teacher-level means of the outcome after adjusting for incoming student characteristics, school-by-year level shocks, and school-by-track effects. For test score outcomes, this model is a standard value-added model with covariate adjustments 12. To compare variation within school tracks during the same year to variation within school tracks across years (cohorts), I computed the number of teachers in each non-singleton school-track-year-cell for both Algebra I and English I (AppendixTable A2).…”