Modeling the Biomedical/Behavioral Workforce: Strengths and Limitations

Although demographic models and associated life tables are typically applied to total populations, the panel examined the applicability of these techniques to a particular subset of the population—those employed in biomedical or behavioral sciences (hereafter referred to as the biomedical or behavioral workforce5). These models have both strengths and limitations. Among the possible strengths of such models is their ability to project salient characteristics as well as the size of these workforces. Attributes such as their distribution by age, gender, and type of work activity (e.g., research vs. nonresearch) have policy significance.

The models are capable of addressing the following issues:

  • How many new entrants would be required to support a given rate of workforce growth?
  • How many members of these workforces can be expected to retire over the next five years?
  • How will alternative inflows of new Ph.D.s affect the age distribution of these workforces?
  • How long will members of these workforces remain in research careers?

Accurate projections can be generated by these models if the following occur:

  • All transitions are made analytically explicit—either by assumption or with estimated values.
  • The estimates of the base workforce and the transition rates are derived from a representative sample.
  • The estimated transition rates are either stable or vary over time in some predictable way.

These types of models can probably produce more accurate short-term projections than alternative models because most of the future workforce already exists as part of the base period workforce, and information about that workforce is used as a basis for estimation of the model parameters. For a population in which there is relatively little turnover, errors arising from the projections will be small.6 In

5  

Throughout the rest of the paper, the term "biomedical/behavioral workforce" is used to refer to those Ph.D.s who are employed as biomedical or behavioral scientists.

6  

The magnitude of such errors will vary directly with the size of projected flows relative to the size of the workforce. It will also vary directly with the interval of the projection period.



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Modeling the Biomedical/Behavioral Workforce: Strengths and Limitations Although demographic models and associated life tables are typically applied to total populations, the panel examined the applicability of these techniques to a particular subset of the population—those employed in biomedical or behavioral sciences (hereafter referred to as the biomedical or behavioral workforce5). These models have both strengths and limitations. Among the possible strengths of such models is their ability to project salient characteristics as well as the size of these workforces. Attributes such as their distribution by age, gender, and type of work activity (e.g., research vs. nonresearch) have policy significance. The models are capable of addressing the following issues: How many new entrants would be required to support a given rate of workforce growth? How many members of these workforces can be expected to retire over the next five years? How will alternative inflows of new Ph.D.s affect the age distribution of these workforces? How long will members of these workforces remain in research careers? Accurate projections can be generated by these models if the following occur: All transitions are made analytically explicit—either by assumption or with estimated values. The estimates of the base workforce and the transition rates are derived from a representative sample. The estimated transition rates are either stable or vary over time in some predictable way. These types of models can probably produce more accurate short-term projections than alternative models because most of the future workforce already exists as part of the base period workforce, and information about that workforce is used as a basis for estimation of the model parameters. For a population in which there is relatively little turnover, errors arising from the projections will be small.6 In 5   Throughout the rest of the paper, the term "biomedical/behavioral workforce" is used to refer to those Ph.D.s who are employed as biomedical or behavioral scientists. 6   The magnitude of such errors will vary directly with the size of projected flows relative to the size of the workforce. It will also vary directly with the interval of the projection period.

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addition, important flow parameters (e.g., death and retirement rates) are expected to be reasonably stable. This strengthens the expectation of reliable results. Among the limitations of these models is their reliance on longitudinal databases for estimation of the flows. Such databases are expensive to compile; hence they are relatively scarce. Moreover, such databases are vulnerable to possible response and selectivity biases, which could bias parameters derived from them. For example, if people who change fields are less likely to answer the questionnaire than those who remain in a field, a field mobility parameter estimated from the sample will be smaller than the true parameter. Further, the degree of reliable disaggregation such databases will allow will be limited by their sample sizes. This means that fields that display disparate behavior may be lumped together, and important aspects of behavior for separate fields may be missed. In addition, parameter estimates may vary due to changes in the composition of the heterogeneous sample, even though they might be stable for a particular field taken separately. Another possible limitation arises from the assumption usually made by such models that their estimates of transition rates are given and not sensitive to market conditions and/or other factors that could affect flows into, out of, and within these workforces.7 If transition rates are influenced by factors not included in the description of the demographic system, projections using them are likely to prove inaccurate. Demographic models can, however, provide "baseline" analyses that reflect the demographic characteristics of the population being modelled. Adjustment to market conditions can then be incorporated in the demographic model. For example, salaries, which adjust to excess supply or demand, will affect transition rates. If the magnitude of this effect is estimated, transition rates that are assumed fixed in the demographic model can then be made endogenous in a model that incorporates this adjustment to market conditions. Despite the limitations of demographic models, the committee felt that it could illuminate a number of aspects of adjustment in the biomedical/behavioral workforce that were muddied or improperly specified in the earlier models constructed for NIH. 7   With sufficient data, it might be possible to estimate how transition rates depend on market conditions.