Predicting doctorate production in the U.S.A. Some lessons for long-range forecasters

The nine major independently conducted forecasts of doctoral degree production for the 1970s shared a consistently high bias, although one was quite accurate. In this paper the authors compare the forecasting models based upon results of these studies and discuss their implications for long-range forecasting. They show that the bias was mainly due to a common core assumption (exemplifying ‘assumption drag’) that students would pursue degrees at close to the existing rates in spite of decreasing traditional demand for graduates. The results also indicate the value of simple models and eclectic forecasting methods, the desirability of robust causal models and the dangers of extrapolative methods (especially with insufficient historical data) when structural changes are anticipated, the usefulness of current expert opinion, the inappropriateness of intensions surveys for time horizons longer than the lead time needed for significant change, and the importance of physical economic limitations and principles (such as substitution effects and interactions between supply and demand) in developing long-range forecasting models.