A Method for Forecasting the Demand for Scientists and Engineers
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A Method for Forecasting the Demand for Scientists and Engineers:
It Works for Weather and Retirement Planning – Sometimes
Whether it is the weather, the stock market, or the demand for scientists and engineers, forecasting is still an inexact science. The National Institutes of Health (NIH), the National Science Foundation (NSF), as well as many other government and industry agencies have struggled to develop credible and accurate forecasts for the demand for graduate scientists and engineers.
NIH, NSF, and other agencies use these projections to establish budgets for grants for graduate and postdoctoral students. In the past, demand projections have been based on short-term economic indicators, projected government spending in research and development (R&D), and intuitive estimates. For example, demand forecasts generated during the country’s emergence from the 1993 recession tended to project lower growth rates than had been experienced in the 1980s. These forecasts did not foresee the relative economic boom that has characterized the last five years of the 1990s,
In comments presented at the 2000 AAAS Annual Meeting in Washington, D.C., Mike Finn, a senior economist at the Oak Ridge Institute of Science and Education, highlighted the problem. He said, "The simple fact is that responsible people in Washington are still very skittish about using demand projections to justify any policy to expand educational funding in science and technology."
Finn, however, remains optimistic. "I continue to think that employment projections have the potential to be used in this constructive way for proposed changes in human resource investments. Of course, to do so we would have to have a model that incorporates supply and demand, and we would have to have some authoritative source that could be trusted to test policy changes. We aren’t there yet."
Another reality that Finn points out is that "as long as congress, universities, corporations, and graduate students make long-term investments, they will feel the need to make some kind of assumptions about future labor market conditions for scientists and engineers."
Until more exact forecasting tools are available, Finn suggests techniques similar to those used to forecast income from investment growth and the weather might also be applied to project trends for the future demand for graduate scientists and engineers.
Finn suggests that those interested in forecasting the trends in demand for scientists and engineers should follow the example of financial planners who assist in retirement planning. They use market trends based on historical market data that span 30, 40, or more years to project trends. Over longer periods of time, these projections have demonstrated reasonable accuracy in market trends.
For example, research and development spending has been used as an indicator for the demand for scientists and engineers. Finn points out that the total U.S. increase in R&D expenditures in the decade of the 1990s is very close to the average for increases in spending since 1959. Had the projections for the 1990s been based on an average of the previous 30 years, the projections would have seemed optimistic during the first half of the decade, but would have proven reasonably accurate over the full span of the 1990s.
This example also illustrates, as Finn points out, that it may not be possible to anticipate spikes and slumps in demand, but the average growth over the past 40 years should offer some insight into the trends that can be anticipated for the future.
According to Finn, "The point of this analogy is that we can and should use historical statistics not only to project future behavior, but also to ask about worst-case scenarios. With regard to the labor market for scientists there are two distinctly different worst-case scenarios. First, for government agencies and other employers, the worst case is that degree awards stay constant while the demand for scientists grows at the highest rate we have seen over any ten-year period in the last 50 years. For new Ph.D.s, that is the best-case scenario; the worst case is the lowest rate of growth over any ten-year period in the last 50 years."
Finn continued, "I am suggesting we calculate the historical odds of each case when we do projections based on extrapolations of past growth. And while we are at it, we should calculate the odds of less severe—but significant—deviations from that trend."
Using projections based on extrapolated historical data, Finn believes that it is possible: (1) to make reasonable long-term forecasts, (2) to project best-case and worst-case forecasts, and (3) to assign a level of probability or confidence to each forecast.
Finn might also warn that, when there is an 80% chance of sun, there is still a 20% chance of clouds and rain. Forecasting is still an inexact science.
The Oak Ridge Institute for Science and Education (ORISE) is operated by ORAU for the U.S. Department of Energy. ORISE undertakes national and international programs in education, training, health, and the environment. Established in 1946, Oak Ridge Associated Universities (ORAU) is a consortium of 87 doctoral-granting colleges and universities. As a consortium, ORAU carries out active programs with and for its members. ORAU, a private, not-for-profit corporation, serves the government, academia, and the private sector in important areas of science and technology.
It Works for Weather and Retirement Planning – Sometimes
Whether it is the weather, the stock market, or the demand for scientists and engineers, forecasting is still an inexact science. The National Institutes of Health (NIH), the National Science Foundation (NSF), as well as many other government and industry agencies have struggled to develop credible and accurate forecasts for the demand for graduate scientists and engineers.
NIH, NSF, and other agencies use these projections to establish budgets for grants for graduate and postdoctoral students. In the past, demand projections have been based on short-term economic indicators, projected government spending in research and development (R&D), and intuitive estimates. For example, demand forecasts generated during the country’s emergence from the 1993 recession tended to project lower growth rates than had been experienced in the 1980s. These forecasts did not foresee the relative economic boom that has characterized the last five years of the 1990s,
In comments presented at the 2000 AAAS Annual Meeting in Washington, D.C., Mike Finn, a senior economist at the Oak Ridge Institute of Science and Education, highlighted the problem. He said, "The simple fact is that responsible people in Washington are still very skittish about using demand projections to justify any policy to expand educational funding in science and technology."
Finn, however, remains optimistic. "I continue to think that employment projections have the potential to be used in this constructive way for proposed changes in human resource investments. Of course, to do so we would have to have a model that incorporates supply and demand, and we would have to have some authoritative source that could be trusted to test policy changes. We aren’t there yet."
Another reality that Finn points out is that "as long as congress, universities, corporations, and graduate students make long-term investments, they will feel the need to make some kind of assumptions about future labor market conditions for scientists and engineers."
Until more exact forecasting tools are available, Finn suggests techniques similar to those used to forecast income from investment growth and the weather might also be applied to project trends for the future demand for graduate scientists and engineers.
Finn suggests that those interested in forecasting the trends in demand for scientists and engineers should follow the example of financial planners who assist in retirement planning. They use market trends based on historical market data that span 30, 40, or more years to project trends. Over longer periods of time, these projections have demonstrated reasonable accuracy in market trends.
For example, research and development spending has been used as an indicator for the demand for scientists and engineers. Finn points out that the total U.S. increase in R&D expenditures in the decade of the 1990s is very close to the average for increases in spending since 1959. Had the projections for the 1990s been based on an average of the previous 30 years, the projections would have seemed optimistic during the first half of the decade, but would have proven reasonably accurate over the full span of the 1990s.
This example also illustrates, as Finn points out, that it may not be possible to anticipate spikes and slumps in demand, but the average growth over the past 40 years should offer some insight into the trends that can be anticipated for the future.
According to Finn, "The point of this analogy is that we can and should use historical statistics not only to project future behavior, but also to ask about worst-case scenarios. With regard to the labor market for scientists there are two distinctly different worst-case scenarios. First, for government agencies and other employers, the worst case is that degree awards stay constant while the demand for scientists grows at the highest rate we have seen over any ten-year period in the last 50 years. For new Ph.D.s, that is the best-case scenario; the worst case is the lowest rate of growth over any ten-year period in the last 50 years."
Finn continued, "I am suggesting we calculate the historical odds of each case when we do projections based on extrapolations of past growth. And while we are at it, we should calculate the odds of less severe—but significant—deviations from that trend."
Using projections based on extrapolated historical data, Finn believes that it is possible: (1) to make reasonable long-term forecasts, (2) to project best-case and worst-case forecasts, and (3) to assign a level of probability or confidence to each forecast.
Finn might also warn that, when there is an 80% chance of sun, there is still a 20% chance of clouds and rain. Forecasting is still an inexact science.
The Oak Ridge Institute for Science and Education (ORISE) is operated by ORAU for the U.S. Department of Energy. ORISE undertakes national and international programs in education, training, health, and the environment. Established in 1946, Oak Ridge Associated Universities (ORAU) is a consortium of 87 doctoral-granting colleges and universities. As a consortium, ORAU carries out active programs with and for its members. ORAU, a private, not-for-profit corporation, serves the government, academia, and the private sector in important areas of science and technology.
