Forecasting Demand and Supply of Doctoral Scientists and Engineers Report of a Workshop on Methodology (2000) / Chapter Skim
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Executive Summary
Executive Summary
Pages 1-8
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Models may be based on incorrect assumptions about overall structure, included variables, lag structure, and error structure. Data used for estimation may be flawed or aggregated at an inappropriate level.
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The committee has therefore directed most of its recommendations to SRS, since this division should be able to encourage improvements in the construction and use of forecasting models for highly trained scientists and engineers, even when SRS should not carry out the work itself. Recommendation 1.
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l hose qualitative projections require timely data, but they need not be based on broad surveys or censuses. Research funding agencies and Congress need relatively long-term projections of supply and demand factors by specific discipline that can be used to guide policy on training support and institutional development.
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A clear organizational separation should be made between data collection and modeling/forecasting activities undertaken for NSF's own policy use or for use by federal agencies. For example, convert the SRS into a National Center for Science Statistics on the model of the National Center for Health Statistics (NCHS)
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In addition, it should coordinate definitions and categories across agencies to facilitate a consistent picture of the different stages in the market, from student training and degree choice to mid-career transitions across and out of science and engineering fields. Moreover, sample sizes have been reduced since the late 1970s, which makes modeling difficult for small fields, specific employment sectors, and for rare events (such as mid-career changes)
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This would enhance modeling of individual and institutional behavior in response to changes in funding, demographically driven demand, compensation, etc. Standard coding of vitae would permit collection of more detailed data than the Survey of Doctorate Recipients form.
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Finally, the NSF's SRS website currently focuses almost exclusively on providing relatively simple tabulations that are useful for casual policy analysis but not very useful for either career planning by students or for research on the science and engineering market. The data management program and website should be redesigned to service these neglected user communities (or in the case of students, the public and private organizations and associations that provide career guidance)
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