The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood

Chetty, Raj; Friedman, John N.; Rockoff, Jonah E.

doi:10.3386/w17699

Cited by 324 publications

(352 citation statements)

References 55 publications

(110 reference statements)

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“…We instead compare track attendance in middle school of students born earlier (i.e., between January and April) and later (i.e., between May and December) during the year (instead of June/July). In Section 5.1 , we provide evidence that this is likely to lead to an underestimate in the …rst-stage estimates, which causes bias away from zero in 12 Our results remain almost unchanged if we exclude these control variables from the regressions. 13 We cannot do this for the …rst-stage e¤ect because our data sets provide information only on birth month or whether the pupil was born earlier or later during the year; see Sections 4.2 and 4.3 for details.…”

Section: First-stage Estimatesmentioning

confidence: 71%

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The Long‐Term Effects of Early Track Choice

Dustmann

Puhani

Schönberg

2017

The Economic Journal

127

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.IZA Discussion Paper No. 7897 January 2014 ABSTRACTThe Long-Term Effects of Early Track Choice * Despite its efficiency in tailoring education to the needs of students, a tracking system has the inherent problem of misallocating students to tracks because of incomplete information at the time of the tracking decision. This paper investigates the effects of attending a more advanced track in middle school on long-term education and labor market outcomes for Germany, a country with a very rigorous tracking system where the risk of misallocating students to tracks is, due to the early age at which tracking takes place, particularly high. Our research design exploits quasi-random shifts between tracks induced by date of birth, and identifies the long-term effects of early track attendance for a group of marginal students most at risk of misallocation. Remarkably, we find no evidence that for these students, attending a more advanced track leads to more favorable long-term outcomes. We attribute this result to the up-and downgrading of students between tracks after middle school when more information about their potential is available. Overall, our findings underscore that flexibilities built into a tracking system, which allow students to revise initial track choices at a later stage, effectively remedy even a prolonged exposure to a less advanced school environment.JEL Classification: I21, J10

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Section: First-stage Estimatesmentioning

confidence: 71%

“…12 We report heteroscedasticity-consistent (robust) standard errors. We also report regression discontinuity estimates that exploit the student's exact date of birth.…”

Section: Reduced-form Estimatesmentioning

confidence: 99%

The Long‐Term Effects of Early Track Choice

Dustmann

Puhani

Schönberg

2017

The Economic Journal

127

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“…This point has been emphasized in Rothstein (16). The issue has been addressed in Kane and Staiger (6), using a dataset with random assignment of teachers to classrooms, and in Chetty et al (15), who look at effects based on changes in teaching staff.…”

Section: Resultsmentioning

confidence: 99%

“…The work of Chetty et al (15) is the first to measure teacher effects on later outcomes such as college attendance and earnings. They combine two databases: administrative school district records and information on those students and their parents from US tax records.…”

Section: Resultsmentioning

confidence: 99%

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

Chamberlain

2013

Proc. Natl. Acad. Sci. U.S.A.

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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,...

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“…Ineffective treatments maintain or exacerbate harmful behaviors (Lilienfeld 2002), and poor graduate training creates practitioners who are likely to be ineffective. Analyses conducted in other disciplines suggest that the very worst practitioners -to invoke a round number, perhaps the least skilled 10 % or so -cause most of this damage (e.g., Chetty et al 2011;Hanushek 2011). Given a distribution of levels of practitioner expertise (Fig.…”

Section: Some Reasons To Monitor Graduate Training Qualitymentioning

confidence: 99%