Revising the Economic Imperative for US Stem Education
Abstract/Contents
- Abstract
- Over the last decade macroeconomic studies have established a clear link between student achievement on science and math tests and per capita gross domestic product (GDP) growth, supporting the widely held belief that science, technology, engineering, and math (STEM) education are important factors in the production of economic prosperity. We critique studies that use science and math tests to predict GDP growth, arguing that estimates of the future economic value of STEM education involve substantial speculation because they ignore the impacts of economic growth on biodiversity and ecosystem functionality, which, in the long-term, limit the potential for future economic growth. Furthermore, we argue that such ecological impacts can be enabled by STEM education. Therefore, we contend that the real economic imperative for the STEM pipeline is not just raising standardized test scores, but also empowering students to assess, preserve, and restore ecosystems in order to reduce ecological degradation and increase economic welfare.
Description
Type of resource | text |
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Date created | January 2014 |
Creators/Contributors
Author | Donovan, Brian |
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Subjects
Subject | STEM education |
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Subject | environmental economics |
Subject | PISA |
Subject | Ecosystem services |
Subject | natural capital |
Genre | Article |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
Preferred citation
- Preferred Citation
- Donovan BM, Moreno Mateos D, Osborne JF, Bisaccio DJ (2014) Revising the Economic Imperative for US STEM Education. PLoS Biol 12(1): e1001760. doi:10.1371/journal.pbio.1001760
Collection
Stanford Research Data
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- Contact
- briand79@stanford.edu
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