Cited by 793School Choice and PerformanceTeacher Education and AssessmentsTeacher Professional Development and Motivation

Generalizations about Using Value-Added Measures of Teacher Quality

Hanushek, Eric A, Rivkin, Steven G·2010·American Economic Review

Abstract

The extensive investigation of the contribution of teachers to student achievement produces two generally accepted results. First, there is sub stantial variation in teacher quality as measured by the value added to achievement or future aca demic attainment or earnings. Second, variables often used to determine entry into the profession and salaries, including post-graduate schooling, experience, and licensing examination scores, appear to explain little of the variation in teacher quality so measured, with the exception of early experience. Together these findings underscore explicitly that observed teacher characteristics do not represent teacher quality.

Integrity signals

Registry

Crossref · 10.1257/aer.100.2.267

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • The 0.06σ average treatment effect is roughly one-third of a standard deviation in teacher performance, which is typically estimated at 0.15-0.20σ in math and somewhat smaller in reading.

    “Gains of 0.06σ represent roughly one-third of a standard deviation in teacher performance, which is typically estimated at 0.15-0.20σ in math and somewhat smaller in reading (Hanushek and Rivkin 2010, Jackson, Staiger, and Rockoff 2014).”
  • The estimated within-school teacher effects are similar to those from other studies using large administrative datasets.

    “Notably, these findings are similar to those from other studies that also estimate within-school teacher effects in large administrative datasets (Hanushek & Rivkin, 2010).”

    From Teacher and Teaching Effects on Students’ Attitudes and Behaviors (2017) · cites this paper as Hanushek & Rivkin, 2010

  • A 1 SD difference in the distribution of teacher effectiveness represents approximately a 0.18 SD difference in student test scores (Table 1, column 1).

    “Consistent with prior estimates (e.g., Hanushek & Rivkin, 2010), we find that a 1 SD difference in the distribution of teacher effectiveness represents approximately a 0.18 SD difference in student test scores (see Table 1, column 1).”
  • It is much harder for teachers to improve students' achievement in English than in math.

    “Hence, the returns to better performance in English are especially large, although it is much harder for teachers to improve students' achievement in English (e.g., Hanushek and Rivkin 2010; Kane et al. 2013).”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Hanushek, E. A., & Rivkin, S. G.
Affiliations
  • Hoover Institution (US)
  • Amherst College (US)
Venue
American Economic Review
Published
2010-05-01
Volume
100
Issue
2
Pages
267-271
ISSN
0002-8282 (ISSN-L)
OpenAlex
W2061572779
Cited by
793
References
3
Citations by year
2026: 32 · 2025: 51 · 2024: 33 · 2023: 50 · 2022: 46 · 2021: 48 · 2020: 53 · 2019: 63 · 2018: 52 · 2017: 49 · 2016: 66 · 2015: 60 · 2014: 64 · 2013: 61 · 2012: 26
FWCI
182.77
Type
journal article
Language
en
Open access
closed
Locations
  • American Economic Review (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Education › School Choice and Performance
  • Social Sciences › Education › Teacher Education and Assessments
  • Social Sciences › Education › Teacher Professional Development and Motivation
Keywords
teacher quality, value-added measures, academic performance, teacher salaries, teacher experiences, teacher characteristics, teacher selection

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