The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The (Perceived) Returns to Education and the Demand for Schooling*
Abstract
Economists emphasize the link between market returns to education and investments in schooling. Though many studies estimate these returns with earnings data, it is the perceived returns that affect schooling decisions, and these perceptions may be inaccurate. Using survey data for eighth-grade boys in the Dominican Republic, we find that the perceived returns to secondary school are extremely low, despite high measured returns. Students at randomly selected schools given information on the higher measured returns completed on average 0.20–0.35 more years of school over the next four years than those who were not.
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.
Integrity signals
Registry
Cites
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.
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?
In the citing paper
“Higher perceived learning affects choices (Jensen 2010) and in the case of LLMs may thus affect adoption.”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Jensen 2010
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
Duflo, E. (2001) · American Economic Review · doi:10.1257/aer.91.4.795 · Cited by 1,940
Cited by
Women Empowerment and Economic Development
Duflo, E. (2012) · Journal of Economic Literature · doi:10.1257/jel.50.4.1051 · Cited by 2,978
Allcott, H., & Rogers, T. (2014) · American Economic Review · doi:10.1257/aer.104.10.3003 · Cited by 1,340
Determinants of College Major Choice: Identification using an Information Experiment
Wiswall, M., & Zafar, B. (2015) · The Review of Economic Studies · doi:10.1093/restud/rdu044 · Cited by 533
Thinking, fast and slow? Some field experiments to reduce crime and dropout in Chicago
Heller, S. B., Shah, A. K., Guryan, J., Ludwig, J., Mullainathan, S., & Pollack, H. A. (2017) · The Quarterly Journal of Economics · doi:10.1093/qje/qjw033 · Cited by 493
“The possibility that youth growing up in high-poverty neighborhoods may lack role models who can demonstrate the returns to schooling has been of long-standing concern to social scientists (Wilson 1987; Manski 1993; Jensen 2010).”
Damgaard, M. T., & Nielsen, H. S. (2018) · Economics of Education Review · doi:10.1016/j.econedurev.2018.03.008 · Cited by 321
Information and College Access: Evidence from a Randomized Field Experiment*
Oreopoulos, P., & Dunn, R. (2013) · The Scandinavian Journal of Economics · doi:10.1111/j.1467-9442.2012.01742.x · Cited by 192
Behavioral development economics
Kremer, M., Rao, G., & Schilbach, F. (2019) · Handbook of Behavioral Economics: Applications and Foundations 1 · doi:10.1016/bs.hesbe.2018.12.002 · Cited by 188
Shocking behavior: random wealth in antebellum georgia and human capital across generations
Bleakley, H., & Ferrie, J. (2016) · The Quarterly Journal of Economics · doi:10.1093/qje/qjw014 · Cited by 125
Role Models in Movies: The Impact of Queen of Katwe on Students’ Educational Attainment
Riley, E. (2024) · Review of Economics and Statistics · doi:10.1162/rest_a_01153 · Cited by 117
Bursztyn, L., & Yang, D. Y. (2022) · Annual Review of Economics · doi:10.1146/annurev-economics-051520-023322 · Cited by 117
Fertility restrictions and life cycle outcomes: evidence from the one-child policy in China
Huang, W., Lei, X., & Sun, A. (2021) · The Review of Economics and Statistics · doi:10.1162/rest_a_00921 · Cited by 106
Behavioral Economics of Education
Lavecchia, A. M., Liu, H., & Oreopoulos, P. (2016) · Handbook of the Economics of Education · doi:10.1016/b978-0-444-63459-7.00001-4 · Cited by 89
Returns to education in developed countries
Gunderson, M., & Oreopolous, P. (2020) · The Economics of Education · doi:10.1016/b978-0-12-815391-8.00003-3 · Cited by 88
Is Information Enough? The Effect of Information about Education Tax Benefits on Student Outcomes
Bergman, P., Denning, J. T., & Manoli, D. (2019) · Journal of Policy Analysis and Management · doi:10.1002/pam.22131 · Cited by 75
Can Neighbor Attributes Predict School Absences?
Gottfried, M. A. (2014) · Urban Education · doi:10.1177/0042085913475634 · Cited by 62
Ou, D., & Hou, Y. (2019) · Research in Higher Education · doi:10.1007/s11162-018-9514-2 · Cited by 53
King, N., Dewey, C., & Borish, D. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138362 · Cited by 42
Mass media and public education: The effects of access to community radio in Benin
Keefer, P., & Khemani, S. (2014) · Journal of Development Economics · doi:10.1016/j.jdeveco.2014.03.010 · Cited by 42
The Returns to College(s): Relative Value-Added and Match Effects in Higher Education
Mountjoy, J., & Hickman, B. (2020) · doi:10.2139/ssrn.3537773 · Cited by 41
Can perceived returns explain enrollment gaps in postgraduate education?
Boneva, T., Golin, M., & Rauh, C. (2022) · Labour Economics · doi:10.1016/j.labeco.2021.101998 · Cited by 35
Behavioral economics and nudging in education: evidence from the field
Damgaard, M. T., & Nielsen, H. S. (2020) · The Economics of Education · doi:10.1016/b978-0-12-815391-8.00002-1 · Cited by 18
AI Meets the Classroom: When Do Large Language Models Harm Learning?
Lehmann, M., Cornelius, P. B., & Sting, F. J. (2024) · arXiv · doi:10.48550/arxiv.2409.09047 · Cited by 11
Voter Preferences, Electoral Promises, and the Composition of Public Spending
Keefer, P., Scartascini, C., & Vlaicu, R. (2020) · doi:10.18235/0002434 · Cited by 10
Metadata
- Authors
- Jensen, R.
- Venue
- Quarterly Journal of Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-05
- Volume
- 125
- Issue
- 2
- Pages
- 515-548
- ISSN
- 0033-5533 (ISSN-L)
- 1531-4650
- OpenAlex
- W2073400560
- Cited by
- 1,048
- References
- 1
- FWCI
- 250.16
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.