Forecasting Demand and Supply of Doctoral Scientists and Engineers Report of a Workshop on Methodology (2000) / Chapter Skim
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6 Summary and Recommendations
6 Summary and Recommendations
Pages 49-60
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From page 49... ...
funding agencies, such as NSF and NIH, that must decide how to allocate funds for traineeships and research assistantships. Other clients include universities that face decisions on the size of research and teaching programs and faculty recruitment and members of Congress who make policy decisions that affect research and the labor market.
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From page 50... ...
The level of detail required in forecasts of industrial employment of scientists and engineers has not been resolved. For example, is it useful to produce separate forecasts of demand for biologists in the agricultural and pharmaceutical industries, or does a forecast of overall industrial demand suffice?
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From page 51... ...
In addition, what new data are needed? What are the critical gaps in the current data collection programs?
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From page 52... ...
Users of gap models sometimes make the defacto assumption that equilibrating mechanisms are very slow. Orthodox models often assume that equilibration is rapid without articulating the specific mechanisms that produce equilibrating adjustments or determining that they do indeed work quickly.
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From page 53... ...
Could this division then facilitate the independence of the agencies involved or maintain the integrity of the data collection efforts? In one model, for example, NSF would not make an official forecast, but similar to blue-chip indicators would present a variety of forecasts prepared by others.
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From page 54... ...
Students need qualitative projections of likely career outcomes and probabilities of success, with particular attention to the state of the job market in a few years when they will seek employment. These qualitative projections require timely data, but they need not be based on broad surveys or censuses.
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From page 55... ...
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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From page 56... ...
Recommendation 3: Undertake a comprehensive review of data collection in the light of forecasting needs. NSF's SRS should undertake a comprehensive review of its data management program, preferably in coordination with Bureau of Labor Statistics (BLS)
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From page 57... ...
production of timely, descriptive statistics on employment and salaries by field of training, occupation, and sector, in a consistent time-series format that permits tracking and projections of trends; (2) production of an individual-level Public Use Sample, containing a consistent time series of cross-sections of doctoral recipients, and when available, nondoctoral recipients; and (3)
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From page 58... ...
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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From page 59... ...
This work should be conducted through the ordinary peer-reviewed research support process already in place at NSF. SRS should facilitate the dissemination of results of these studies but should stop short of sponsoring or endorsing specific forecasts or methods.
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