Summary, Conclusions, and Recommendations

Summary

The possibility of using demographic models and techniques to project the size and characteristics of the future biomedical and behavioral workforce (and/or the number of new entrants required to support these workforces) was explored. These models and techniques are feasible if the following occur:

  • The longitudinal database used to estimate the parameters of the model is adequate.
  • The parameters derived from analysis of such a database are stable or vary in some predictable manner.

Conclusions

The SDR is a reasonably adequate data base for estimating model parameters. The sample sizes are adequate, the estimated parameters are reasonably precise, and response bias does not appear to be a major problem.30 In addition, the parameters derived from the SDR data are reasonably stable over time.

The major flaw in the database is that it does not adequately account for immigration—specially of biomedical or behavioral scientists who earned their degree from foreign institutions. These scientists constitute a nontrivial share of these workforces. It may be possible to supplement SDR data with other data collected by NSF and the Immigration and Naturalization Service to remedy this deficiency.

An additional shortcoming of the empirical analysis may lie in the taxonomy of these fields. In particular, the fields included as part of biomedical and behavioral sciences appear to be too heterogeneous, yet parameter estimates based on small SDR sample sizes for subfields will likely be unreliable. Further study should include a look at feasible disaggregation. Finally, an analytic flaw in these models is that they fail to address the role of market feedback. This shortcoming is not unique to these models, but it is a generic problem in practically all such simulation models.

Recommendations

The findings from this initial exploration of demographic models and techniques is encouraging, and, based on them, the panel believes that further effort to refine and extend these models and

30  

SDR sample sizes are probably not large enough to estimate parameters for underrepresented minorities, although they are large enough to estimate parameters separately by gender.



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Summary, Conclusions, and Recommendations Summary The possibility of using demographic models and techniques to project the size and characteristics of the future biomedical and behavioral workforce (and/or the number of new entrants required to support these workforces) was explored. These models and techniques are feasible if the following occur: The longitudinal database used to estimate the parameters of the model is adequate. The parameters derived from analysis of such a database are stable or vary in some predictable manner. Conclusions The SDR is a reasonably adequate data base for estimating model parameters. The sample sizes are adequate, the estimated parameters are reasonably precise, and response bias does not appear to be a major problem.30 In addition, the parameters derived from the SDR data are reasonably stable over time. The major flaw in the database is that it does not adequately account for immigration—specially of biomedical or behavioral scientists who earned their degree from foreign institutions. These scientists constitute a nontrivial share of these workforces. It may be possible to supplement SDR data with other data collected by NSF and the Immigration and Naturalization Service to remedy this deficiency. An additional shortcoming of the empirical analysis may lie in the taxonomy of these fields. In particular, the fields included as part of biomedical and behavioral sciences appear to be too heterogeneous, yet parameter estimates based on small SDR sample sizes for subfields will likely be unreliable. Further study should include a look at feasible disaggregation. Finally, an analytic flaw in these models is that they fail to address the role of market feedback. This shortcoming is not unique to these models, but it is a generic problem in practically all such simulation models. Recommendations The findings from this initial exploration of demographic models and techniques is encouraging, and, based on them, the panel believes that further effort to refine and extend these models and 30   SDR sample sizes are probably not large enough to estimate parameters for underrepresented minorities, although they are large enough to estimate parameters separately by gender.

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techniques would be productive. In the ideal world, an obvious next step would be to update the parameters of the model based on more recent SDR data (1993). Unfortunately, this will not be possible because of radical changes that were made in the 1993 survey instrument, creating problems for comparisons with earlier years, although work is under way to assess the extent to which this lack of comparability presents problems. Despite this barrier, efforts to refine the taxonomy, improve the estimation of immigration, and deal more adequately with the estimation of reentrants to the workforce can be expected to improve the performance of these models. Finally, the sensitivity of the estimated transitions to differences in gender needs to be explored. In addition, efforts to expand the model ought to be undertaken. In particular, the effects of market feedback on the transition rates ought to be incorporated into the model. Both an assessment of the feasibility of transforming the transition estimates from parameters to functions that relate them to indicators of market conditions and efforts to expand the sensitivity of model outcomes to alternative market scenarios and variables need to be undertaken. Life-table estimates can inform science policy by exploring the implications of very different rates of change in variables of interest (e.g. net migration, Ph.D. production, R&D funding), but they can only give a very rough estimate, especially if the data on which they are based are problematic or lack comparability over time. They are, however, one useful approach to the construction of estimates of future need for biomedical research personnel.