{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/117893"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/117893","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Trends in and influence of regional federally funded research and development in the US","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Gadgin Matha, Shreyas"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","school":null,"contributors":[],"advisors":["Jonathan Gruber."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:22:09Z","subjects":["Institute for Data, Systems, and Society.","Technology and Policy Program."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/117893"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Technology and Policy"]},{"key":"dc:title","label":"Title","values":["Trends in and influence of regional federally funded research and development in the US"]}]}],"canonical_facts":{"dc:contributor.advisor":["Jonathan Gruber."],"dc:contributor.department":["Massachusetts Institute of Technology. Institute for Data, Systems, and Society.","Technology and Policy Program."],"dc:contributor.other":["Technology and Policy Program."],"dc:creator":["Gadgin Matha, Shreyas"],"dc:date.accessioned":["2018-09-17T15:48:52Z"],"dc:date.available":["2018-09-17T15:48:52Z"],"dc:date.issued":["2018"],"dc: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)."],"dc:description.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."],"dc:description.degree":["S.M. in Technology and Policy"],"dc:identifier.uri":["http://hdl.handle.net/1721.1/117893"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Institute for Data, Systems, and Society.","Technology and Policy Program."],"dc:title":["Trends in and influence of regional federally funded research and development in the US"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:09Z"}