Trends in and influence of regional federally funded research and development in the US
Name
1051213051-MIT.pdf
Description
Full printable version
Size
3.68 MB
Format
Adobe PDF
Checksum (MD5)
46122a95ea7414015beca69ff0a976f7
Author(s)
Gadgin Matha, Shreyas
Advisor(s)
Jonathan Gruber.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Over the last few decades, although US gross domestic spending on Research and Development (R&D) as a percentage of GDP has risen from around 2.27% in 1981 to 2.74% in 2016, federal funding for R&D has fallen steadily, from 1.19% to o.81% over the same period. These changes reflect a broader shift in the US from a government-driven R&D model to a business-driven model. Towards the goal of identifying the regional economic impacts of federally funded R&D, I first build on previous work to develop a method to obtain federal funding for R&D at granular geographic levels using Natural Language Processing (NLP) methods to automatically classify open data on federal contracts and grants as R&D or non-R&D awards. This method results in a 95% accuracy rate in classifying federal awards, and covers 56% of US federal R&D obligations made in the year 2016. As underreporting issues in the data source are addressed, this method will yield higher coverage rates, thus creating a unique dataset that affords opportunities to study the regional impacts of federally funded R&D. Next, I adapt Hausman, N. (2012). University Innovation, Local Economic Growth, and Entrepreneurship to identify the employment-generation effects of federally funded university R&D and compare impacts of overall R&D funding to the employment-generation arising from R&D funding provided to specific academic disciplines. I find that the employment-generation effects of federally funded computer science R&D are significant and much more pronounced than the corresponding effects of overall federally funded university R&D.
Description
Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 43-46).
Subjects
Institute for Data, Systems, and Society.
Technology and Policy Program.
MIT Department
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Technology and Policy Program
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