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dc.contributor.advisorErik Brynjolfsson.en_US
dc.contributor.advisorErik Brynjolfsson.en_US
dc.contributor.authorRock, Daniel Ian.en_US
dc.contributor.otherSloan School of Management.en_US
dc.date.accessioned2020-01-23T16:58:04Z
dc.date.available2020-01-23T16:58:04Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123582
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractThis dissertation contains four essays concerning the economics of information technology, intangible capital, and artificial intelligence. In the first essay, "Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence" I describe how firms can appropriate some of the value of their employees' human capital by assigning firm-specific tasks. I then use a database of employment records to document dynamics in the valuation of publicly traded firms as they relate to different types of employment, focusing especially on AI skills. The second essay, "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies" (coauthored with Erik Brynjolfsson and Chad Syverson) addresses the concern that new technologies with wide applicability throughout the economy can cause both underestimation and overestimation of total factor productivity.en_US
dc.description.abstractAs capital is accumulated, intangible investment output, and therefore productivity growth, will be underestimated only to later generate a yield (at which point productivity growth will be overestimated). Presenting a theoretical description of how to use corporate valuations to recover hidden investment value, we discuss how productivity growth and levels can be adjusted to accommodate these changes. Implications for research and development, computer hardware, and computer software investments are considered. The third essay, "Machine Learning and Occupational Change" (coauthored with Erik Brynjolfsson and Tom Mitchell), develops and implements a method to measure the labor market impact potential of machine learning technologies. Tasks are evaluated for their Suitability for Machine Learning (SML). We find that few occupations can be fully automated with machine learning, but many occupations will potentially be redesigned.en_US
dc.description.abstractThe final essay, "Do Labor Demand Shifts Occur Within Firms or Across Them? Non-Routine-Biased Technological Change 2000-2016" (coauthored with Seth Benzell and Guillermo Lagarda) decomposes labor share shifts of occupational groups into changes between firms, within firms, and due to entry and exit. We find that within-firm compositional shifts are an important component of changes in the overall labor market. We also find that the rate of within-firm shifts has declined in the period from 2000 to 2016. Together, these essays offer insights into how artificial intelligence technologies, particularly machine learning, will impact the U.S. economy.en_US
dc.description.statementofresponsibilityby Daniel Rock.en_US
dc.description.tableofcontentsChapter 1. Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence -- Chapter 2. The Productivity J-Curve: How Intangibles Complement General Purpose Technologies -- Chapter 3. Machine Learning and Occupational Change -- Chapter 4. Do Labor Demand Shifts Occur Within Firms or Across Them? Non-Routine Biased Technological Change, 2000-2016.en_US
dc.format.extent216 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.titleEssays on information technology, intangible capital, and the economics of artificial intelligenceen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentSloan School of Managementen_US
dc.identifier.oclc1135802300en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Sloan School of Managementen_US
dspace.imported2020-01-23T16:58:03Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentSloanen_US


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