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Cited by 185

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

High School Curriculum and Financial Outcomes: The Impact of Mandated Personal Finance and Mathematics Courses

Cole, Shawn, Paulson, Anna, Shastry, Gauri Kartini·2016·Journal of Human Resources

Abstract

Financial literacy and cognitive capabilities are convincingly linked to the quality of financial decision-making. Yet, there is little evidence that education intended to improve financial decision-making is successful. Using plausibly exogenous variation in exposure to state-mandated personal finance and mathematics high school courses, affecting millions of students, this paper answers the question “Can high school graduation requirements impact financial outcomes?” The answer is yes, although not via traditional personal finance courses, which we find have no effect on financial outcomes. Instead, we find additional mathematics training leads to greater financial market participation, investment income, and better credit management, including fewer foreclosures.

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Metadata

Authors
Cole, S., Paulson, A., & Shastry, G. K.
Venue
Journal of Human Resources
Published
2015-11-30
Volume
51
Issue
3
Pages
656-698
ISSN
  • 0022-166X (ISSN-L)
  • 1548-8004
OpenAlex
W62189294
Cited by
185
References
7
FWCI
44.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: High School Curriculum and Financial Outcomes: The Impact of Mandated Personal Finance and Mathematics Courses. https://app.cochat.ai/papers/10.3368/jhr.51.3.0113-5410r1
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Cole, S., Paulson, A., & Shastry, G. K. (2016). High School Curriculum and Financial Outcomes: The Impact of Mandated Personal Finance and Mathematics Courses. Journal of Human Resources. https://doi.org/10.3368/jhr.51.3.0113-5410r1

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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.