Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement*
Abstract
Growing concerns over the inadequate achievement of U.S. students have led to proposals to reward good teachers and penalize (or fire) bad ones. The leading method for assessing teacher quality is “value added” modeling (YAM), which decomposes students' test scores into components attributed to student heterogeneity and to teacher quality. Implicit in the VAM approach are strong assumptions about the nature of the educational production function and the assignment of students to classrooms. In this paper, I develop falsification tests for three widely used VAM specifications, based on the idea that future teachers cannot influence students' past achievement. In data from North Carolina, each of the VAMs' exclusion restrictions is dramatically violated. In particular, these models indicate large “effects” of fifth grade teachers on fourth grade test score gains. I also find that conventional measures of individual teachers' value added fade out very quickly and are at best weakly related to long-run effects. I discuss implications for the use of VAMs as personnel tools.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Teachers' impacts on test scores fade out very rapidly in subsequent grades.
“Our conclusion that teachers have long-lasting impacts may be surprising given evidence that teachers' impacts on test scores fade out very rapidly in subsequent grades (Rothstein 2010; Carrell and West 2010; Jacob, Lefgren, and Sims 2010).”
From Measuring the Impacts of Teachers II: Teacher Value-Added and Student Outcomes in Adulthood (2014) · cites this paper as Rothstein 2010
Cites
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293 · see citing works
Teachers, Schools, and Academic Achievement
Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005) · Econometrica · doi:10.1111/j.1468-0262.2005.00584.x · Cited by 4,501 · see citing works
On the Specification and Estimation of the Production Function for Cognitive Achievement
Todd, P. E., & Wolpin, K. I. (2003) · The Economic Journal · doi:10.1111/1468-0297.00097 · Cited by 1,514 · see citing works
A teacher like me: Does race, ethnicity, or gender matter?
Dee, T. S. (2005) · American Economic Review · doi:10.1257/000282805774670446 · Cited by 1,078 · see citing works
Teacher-Student Matching and the Assessment of Teacher Effectiveness
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2006) · Journal of Human Resources · doi:10.3368/jhr.xli.4.778 · Cited by 739 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Measuring the Impacts of Teachers II: Teacher Value-Added and Student Outcomes in Adulthood
Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014) · American Economic Review · doi:10.1257/aer.104.9.2633 · Cited by 1,717 · see citing works
Kraft, M. A., & Papay, J. P. (2014) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373713519496 · Cited by 433 · see citing works
The Effect of Evaluation on Teacher Performance
Taylor, E. S., & Tyler, J. H. (2012) · American Economic Review · doi:10.1257/aer.102.7.3628 · Cited by 405 · see citing works
Darling-Hammond, L., Amrein-Beardsley, A., Haertel, E., & Rothstein, J. (2012) · Phi Delta Kappan · doi:10.1177/003172171209300603 · Cited by 402 · see citing works
Self-citationPapay, J. P., & Kraft, M. A. (2015) · Journal of Public Economics · doi:10.1016/j.jpubeco.2015.02.008 · Cited by 370 · see citing works
Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables
Rothstein, J. (2009) · Education Finance and Policy · doi:10.1162/edfp.2009.4.4.537 · Cited by 332 · see citing works
Self-citationTeacher Effects and Teacher-Related Policies
Jackson, C. K., Rockoff, J. E., & Staiger, D. O. (2014) · Annual Review of Economics · doi:10.1146/annurev-economics-080213-040845 · Cited by 300 · see citing works
Dynamic effects of teacher turnover on the quality of instruction
Hanushek, E. A., Rivkin, S. G., & Schiman, J. C. (2016) · Economics of Education Review · doi:10.1016/j.econedurev.2016.08.004 · Cited by 200 · see citing works
Singh, A. (2015) · Journal of Development Economics · doi:10.1016/j.jdeveco.2014.10.004 · Cited by 187 · see citing works
Refocusing the Debate: Assessing the Purposes and Tools of Teacher Evaluation
Papay, J. (2012) · Harvard Educational Review · doi:10.17763/haer.82.1.v40p0833345w6384 · Cited by 155 · see citing works
Steinberg, M. P., & Garrett, R. (2016) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373715616249 · Cited by 150 · see citing works
Online Course-taking and Student Outcomes in California Community Colleges
Hart, C. M. D., Friedmann, E., & Hill, M. (2018) · Education Finance and Policy · doi:10.1162/edfp_a_00218 · Cited by 136 · see citing works
Taylor, E. (2014) · Journal of Public Economics · doi:10.1016/j.jpubeco.2014.06.002 · Cited by 74 · see citing works
Not Too Late: Improving Academic Outcomes among Adolescents
Guryan, J., Ludwig, J., Bhatt, M. P., Cook, P. J., Davis, J. M. V., Dodge, K., Farkas, G., Fryer, R. G., Mayer, S., Pollack, H., et al. (2023) · American Economic Review · doi:10.1257/aer.20210434 · Cited by 77 · see citing works
Value-Added Modeling and Educational Accountability: Are We Answering the Real Questions?
Everson, K. C. (2017) · Review of Educational Research · doi:10.3102/0034654316637199 · Cited by 68 · see citing works
Characteristics of Schools Successful in STEM: Evidence from Two States’ Longitudinal Data
Hansen, M. (2014) · The Journal of Educational Research · doi:10.1080/00220671.2013.823364 · Cited by 48 · see citing works
The distributions of teacher qualification: A cross-national study
Qin, L., & Bowen, D. H. (2019) · International Journal of Educational Development · doi:10.1016/j.ijedudev.2019.102084 · Cited by 19 · see citing works
Making Inferences About Teacher Observation Scores Over Time
Briggs, D. C., & Alzen, J. L. (2019) · Educational and Psychological Measurement · doi:10.1177/0013164419826237 · Cited by 14 · see citing works
- Authors
- Rothstein, J.
- Affiliations
- National Bureau of Economic Research (US)
- Corresponding author
- Rothstein, Jesse
- ORCID
- Rothstein, Jesse
- Venue
- Quarterly Journal of Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-02
- Volume
- 125
- Issue
- 1
- Pages
- 175-214
- ISSN
- 0033-5533 (ISSN-L)
- 1531-4650
- OpenAlex
- W2153163719
- Cited by
- 735
- References
- 5
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 17 · 2023: 21 · 2022: 29 · 2021: 26 · 2020: 40 · 2019: 40 · 2018: 48 · 2017: 50 · 2016: 67 · 2015: 66 · 2014: 89 · 2013: 70 · 2012: 64
- FWCI
- 308.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Quarterly Journal of Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Education › School Choice and Performance
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Social Sciences › Education › Teacher Education and Assessments
- Keywords
- teacher quality, value-added modeling, education production function, academic performance, falsification tests, exclusion restriction, teacher evaluation, student assignments, test score gains
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
