The general procedure for Projections was to express the variable to be projected as a percent of a "base" variable. These percents were then projected and applied to projections of the "base" variable. For example, the number of 18-year-old college students was expressed as a percent of the 18-year-old population for each year from 1972 through 1999. This enrollment rate was then projected through the year 2011 and applied to projections of the 18-year-old population from the Bureau of the Census.
Enrollment projections are based primarily on population projections. Projections of elementary and secondary teachers, high school graduates, earned degrees conferred, and expenditures are based primarily on enrollment projections.
Exponential smoothing and multiple linear regression are the two major projection techniques used in this publication. Single exponential smoothing is used when the historical data have a basically horizontal pattern. On the other hand, double exponential smoothing is used when the time series is expected to change linearly with time. In general, exponential smoothing places more weight on recent observations than on earlier ones. The weights for observations decrease exponentially as one moves further into the past. As a result, the older data have less influence on these projections. The rate at which the weights of older observations decrease is determined by the smoothing constant selected.
P = projected value
constant (0 <
Xt = observation for time t
This equation illustrates that the projection is a weighted
average based on exponentially decreasing weights. For a high
smoothing constant, weights for earlier observations decrease
rapidly. For a low smoothing constant, decreases are more moderate.
Projections of enrollments and public high school graduates are
based on a smoothing constant of
The farther apart the observations are spaced in time, the more likely it is that there are changes in the underlying social, political, and economic structure. Since the observations are on an annual basis, major shifts in the underlying process are more likely in the time span of just a few observations than if the observations were available on a monthly or weekly basis. As a result, the underlying process tends to be unstable from one observation to the next. Another reason for using high smoothing constants for some time series is that most of the observations are fairly accurate, because most observations are population values rather than sample estimates. Therefore, large shifts tend to indicate actual changes in the process rather than noise in the data.
Multiple linear regression is also used in making projections, primarily in the areas of elementary and secondary teachers, earned degrees conferred, and expenditures. This technique is used when it is believed that a strong relationship exists between the variable being projected (the dependent variable) and independent variables. However, this technique is used only when accurate data and reliable projections of the independent variables are available.
The functional form primarily used is the multiplicative model. When used
with two independent variables, this model takes the form:
This equation can easily be transformed into the linear form by taking the
natural log (ln) of both sides of the equation:
The multiplicative model has a number of advantages. Research has found that
it is a reasonable way to represent human behavior. Constant elasticities
are assumed, which means that a 1 percent change in lnX will lead
to a given percent change in lnY. This percent change is equal
to b1. And the multiplicative model lends itself easily
to "a priori" analysis because the researcher does not have to
worry about units of measurement when specifying relationships.
In fact, the multiplicative model is considered the standard in
economic analyses. For additional information, see Long-Range
Forecasting: From Crystal Ball to Computer by J. Scott Armstrong
(John Wiley and Sons, 1978, pp. 180-181).
Because projections are subject to errors from many sources, alternative
projections are shown for some statistical series. These alternatives
are not statistical confidence intervals, but instead represent
outcomes based on alternative growth patterns. Alternative projections
were developed for college enrollment, earned degrees conferred,
elementary and secondary teachers, and expenditures in public elementary
and secondary schools.
All projections are based on underlying assumptions, and these
assumptions determine projection results to a large extent. It
is important that users of projections understand the assumptions
to determine the acceptability of projected time series for their
purposes. Descriptions of the primary assumptions upon which the
projections of time series are based are presented in table
For most projections, low, middle, and high alternatives are shown. These alternatives reveal the level of uncertainty involved in making projections, and they also point out the sensitivity of projections to the assumptions on which they are based.
Many of the projections in this publication are demographically based on Bureau of the Census middle series projections of the population by age, but are not adjusted for the 1990 net undercount of 4 to 5 million. The population projections developed by the Bureau of the Census reflect the incorporation of the 1999 estimates and middle series assumptions for the fertility rate, net immigration, and a declining mortality rate.
These middle series population projections are based on the estimated population as of January 1, 1999 and the estimated base population as of April 1, 1990. The future fertility rate assumption, which determines projections of the number of births, is one key assumption in making population projections.
The middle series population projections assume an ultimate complete cohort fertility rate of 2.13 births per woman by the year 2011. Yearly net migration is assumed to increase from 970,368 in 2000 to 980,425 in 2001 and then decrease to 724,192 by 2011. This assumption plays a major role in determining population projections for the age groups enrolled in nursery school, kindergarten, and elementary grades. The effects of the fertility rate assumption are more pronounced toward the end of the projection period, while the immigration assumptions affect all years.
For enrollments in secondary grades and college, the fertility
assumption is of no consequence, since all students enrolled at
these levels were already born when the population projections
were made. For projections of enrollments in elementary schools,
only middle series population projections were considered. Projections
of high school graduates are based on projections of the percent
of grade 12 enrollment that are high school graduates. Projections
of associate's, bachelor's, master's, doctor's, and first-professional
degrees are based on projections of college-age populations and
college enrollment, by sex, attendance status and level enrolled
by student, and by type of institution. Projections of college
enrollment are also based on disposable income per capita and
unemployment rates. The projections of elementary and secondary
teachers are based on education revenue receipts from state sources
and enrollments. The projections of expenditures of public elementary
and secondary schools are based on enrollments and projections
of disposable income per capita and various revenue measures of
state and local governments. Projections of disposable income
per capita and unemployment rates were obtained from the company,
Therefore, many additional assumptions made in projecting disposable
income per capita and unemployment rates apply to projections
based on projections of these variables.
Limitations of Projections
Projections of time series usually differ from the final reported data due to errors from many sources. This is because of the inherent nature of the statistical universe from which the basic data are obtained and the properties of projection methodologies, which depend on the validity of many assumptions. Therefore, alternative projections are shown for most statistical series to denote the uncertainty involved in making projections. These alternatives are not statistical confidence limits, but instead represent judgments made by the authors as to reasonable upper and lower bounds. The mean absolute percentage error is one way to express the forecast accuracy of past projections. This measure expresses the average value of the absolute value of errors in percentage terms. For example, the mean absolute percentage errors of public school enrollment in grades K-12 for lead times of 1, 2, 5, and 10 years were 0.2, 0.5, 1.2,
and 2.9 percent, respectively. On the other hand, mean absolute
percentage errors for doctor's degrees for lead times of 1, 2,
and 5 years were 2.0, 2.8, and 3.7 percent respectively. For more
information on mean absolute percentage errors, see table
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