Abstract
Are teachers' impacts on students' test scores (value-added) a good measure of their quality? This question has sparked debate partly because of a lack of evidence on whether high value-added (VA) teachers improve students' long-term outcomes. Using school district and tax records for more than one million children, we find that students assigned to high-VA teachers are more likely to attend college, earn higher salaries, and are less likely to have children as teenagers. Replacing a teacher whose VA is in the bottom 5 percent with an average teacher would increase the present value of students' lifetime income by approximately $250,000 per classroom. (JEL H75, I21, J24, J45)
Highlights
Are teachers’ impacts on students’ test scores a good measure of their quality? This question has sparked debate partly because of a lack of evidence on whether high value-added (VA) teachers improve students’ long-term outcomes
We model the relationship between earnings residuals and teacher VA in school year t using the following linear specification: (4) Yit= a + κgmjt+ ηit, where mjt= μjt/σμdenotes teacher j’s normalized value-added (i.e., teacher quality scaled in standard deviation units of the teacher VA distribution)
This paper has shown that the same VA measures are an informative
Summary
We first present a simple statistical model of students’ long-term outcomes as a function of their teachers’ value-added. We show how we estimate the impacts of teacher VA on long-term outcomes given that each teacher’s true value-added is unobserved. Interpretation of Reduced-Form Treatment Effects.—The parameter κgin (4) represents the reduced-form impact of a one standard deviation increase in teachers’ test-score VA in a given school year t on earnings. _ CoVva(r Y(mit , jmt) jt)= κg+ _ C oVva(r η(mit , jmt) jt) It follows that we can identify the impact of a one standard deviation increase in a teacher’s true VA mjtfrom an OLS regression of earnings residuals Yiton teacher VA estimates mjt,. We provide descriptive statistics and report correlations between test scores and long-term outcomes as a benchmark to interpret the magnitude of the causal effects of teachers. Note that the dataset we use in this paper is identical to that used in our first paper, except that we restrict attention to the subset of students who are old enough for us to observe outcomes in adulthood by 2011
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