The U.S. Scientific and Technical Workforce
Improving Data for Decisionmaking
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Concerns about the size and adequacy of the U.S. scientific, technical, engineering, and mathematics workforce have grown amid fears of a dwindling labor pool and concern that this may erode U.S. leadership in science and technology and could complicate mobilization of appropriate manpower for homeland security. In the past, such fears have failed to materialize, and surpluses have been more common than shortages. But this should not be grounds for complacency. Fundamentally, available data are inadequate for valid predictions. The RAND Corporation organized a conference to identify the limitations of the available data and explore potential improvements. The event brought together leading researchers, science agency policymakers, and statistical agency experts together to address ways to improve the data system for decisionmaking with respect to this workforce. This volume contains the proceedings of that conference, consisting of the papers delivered and discussed at the workshop, as well as RAND's synthesis of workforce data needs and opportunities for meeting those needs.
Table of Contents
Introduction PART II: Contributed Papers
Do We Need More Scientists?
What Will It Take for the United States to Maintain Global Leadership in Discovery and Innovation?
Does America Face a Shortage of Scientists and Engineers?
Data! Data! My Kingdom for Data! Data Needs for Analyzing the S&E Job Market
What Data Do Labor Market Researchers Need? A Researcher's Perspective
What Data Do Science, Technology, Engineering and Mathematics (STEM) Agency Policymakers Need?
What Data Do STEM Agency Policymakers Need? Workforce Planning for the Future: The NASA Perspective
Meeting the Data Needs: Opportunities and Challenges at the National Science Foundation
Opportunities and Challenges at the Bureau of Labor Statistics
U.S. Census Bureau Data and the Science and Technology Workforce
Opportunities and Challenges at the National Center for Education Statistics
Summary and Conclusions
Priority Data Improvements
Biographical Notes on Contributors