Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 249

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Do Value-Added Estimates Add Value? Accounting for Learning Dynamics

Open access
Andrabi, Tahir, Das, Jishnu, Ijaz Khwaja, Asim, Zajonc, Tristan·2011·American Economic Journal: Applied Economics
DOI 10.1257/app.3.3.29
Open this paper in CoChat — free account

Abstract

This paper illustrates the central role of persistence in estimating and interpreting value-added models of learning. Using data from Pakistani public and private schools, we apply dynamic panel methods that address three key empirical challenges: imperfect persistence, unobserved heterogeneity, and measurement error. Our estimates suggest that only one-fifth to one-half of learning persists between grades and that private schools increase average achievement by 0.25 standard deviations each year. In contrast, value-added models that assume perfect persistence yield severely downward estimates of the private school effect. Models that ignore unobserved heterogeneity or measurement error produce biased estimates of persistence. (JEL I21, J13, O15)

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1257/app.3.3.29

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 13 references

Cited by

See all 249

Metadata

Authors
Andrabi, T., Das, J., Ijaz Khwaja, A., & Zajonc, T.
Venue
American Economic Journal: Applied Economics
Publisher
American Economic Association
Published
2011-07-01
Volume
3
Issue
3
Pages
29-54
ISSN
  • 1945-7782 (ISSN-L)
  • 1945-7790
DOI
10.1257/app.3.3.29
OpenAlex
W2134656453
Cited by
249
References
13
FWCI
102.09
Type
journal article
Language
en
Open access
green

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Do Value-Added Estimates Add Value? Accounting for Learning Dynamics. https://app.cochat.ai/papers/10.1257/app.3.3.29
The paper
Andrabi, T., Das, J., Ijaz Khwaja, A., & Zajonc, T. (2011). Do Value-Added Estimates Add Value? Accounting for Learning Dynamics. American Economic Journal: Applied Economics. https://doi.org/10.1257/app.3.3.29

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.