The number of public elementary and secondary teachers was projected separately for the elementary and secondary levels. The number of public elementary teachers was projected using the public elementary student/teacher ratio. The ratio was modeled as a function of local education revenue from state sources per student, and the level of elementary and secondary teacher wages relative to the overall economy-level wages. The number of public elementary teachers was obtained by applying the projected public elementary student/teacher ratio to previously projected enrollment in public elementary schools. The number of public secondary teachers was projected using the public secondary student-teacher ratio. The ratio was modeled as a function of local education revenue from state sources per student and public secondary enrollment relative to the 11- to 18-year-old population. The number of public secondary teachers was obtained by applying the projected public secondary student-teacher ratio to previously projected enrollment in public secondary schools.
The models were estimated using the AR1 model for correcting for autocorrelation, and all variables are in log form. Local education revenue from state sources were in constant 2000 dollars.
The equations in this section should be viewed as forecasting rather than structural equations, as the limitations of time and available data precluded the building of a large scale, structural teacher model. The particular equations shown were selected on the basis of their statistical properties, such as coefficients of determination (R²s), the t statistics of the coefficients, the Durbin Watson statistic, and residual plots.
The multiple regression technique will yield good forecasting results only if the relationships that existed among the variables in the past continue throughout the projection period.
The public elementary teacher model is:
ln(RELENRTCHt) = b0 + b1ln(RSALARYt) + b2 ln(RSGRNTELENRt)
RELENRTCHt is the public elementary student/teacher ratio in year t;
RSALARYt is the average teacher wage relative to the overall economy-level wage in year t; and
RSGRNTELENRt is the level of education revenue from state sources deflated by the consumer prices chained-price index in constant 2000 dollars per public elementary student in year t.
Each variable affects the public elementary student/teacher ratio in the expected way. As the average teacher wage relative to the overall economy-level wage increases, schools economize on teachers by increasing the student/teacher ratio as teachers are now more expensive to hire. As the level of real grants per elementary student increases, the class size decreases. The more money being devoted to education, the more teachers are hired, thus decreasing the student/teacher ratio.
The public secondary teacher model is:
ln(RSCENRTCHt) = b0 + b1ln(RSGRNTSCENRt) + b2ln(RSCENRPUt)
RSCENRTCHt is the public secondary student/teacher ratio in year t;
RSGRNTSCENRt is the level of education revenue from state sources deflated by the consumer prices chained-price index in constant 2000 dollars per public secondary student in year t; and
RSCENRPUt is the number of students enrolled in public secondary schools relative to the secondary school-age population in year t.
Each variable affects the public secondary student-teacher ratio in the expected way. As the level of real grants per secondary student increases, the student/teacher ratio decreases. The more money being devoted to education, the more teachers are hired, thus decreasing the student-teacher ratio. As enrollment rates (number of enrolled students relative to the school-age population) increase, the ratio also increases: increases in the enrollment rate are not matched one-for-one in increases in the number of teachers.
Table A17 summarizes the results for the elementary and secondary public teacher models.
Enrollment is by organizational level, not by grade level. Thus, secondary enrollment is not the same as grade 9–12 enrollment because some states count some grade 7 and 8 enrollment as secondary. Therefore, the distribution of the number of teachers is also by organizational level, not by grade span.
Projections of private elementary and secondary teachers were derived in the following manner. From 1960 to 2001, the ratio of private school teachers to public school teachers was calculated by organizational level. These ratios were projected using single exponential smoothing, yielding a constant value over the projection period. This constant value was then applied to projections of public school teachers by organizational level to yield projections of private school teachers. This method assumes that the future pattern in the trend of private school teachers will be the same as that for public school teachers. The reader is cautioned that a number of factors could alter the assumption of constant ratios over the projection period.
The total number of public school teachers, enrollment by organizational level, and education revenue from state sources used in these projections were from the Common Core of Data (CCD) survey conducted by NCES. The proportion of public school teachers by organizational level was taken from the National Education Association and then applied to the total number of teachers from the CCD to produce the number of teachers by organizational level.
An analysis of projection errors from the past 14 editions of Projections of Education Statistics indicated that the mean absolute percentage errors (MAPEs) for projections of classroom teachers in public elementary and secondary schools were 1.0 percent for 1 year out, 1.5 percent for 2 years out, 2.7 percent for 5 years out, and 5.4 percent for 10 years out. For the 2 year ahead prediction, this means that one would expect the projection to be within 1.5 percent of the actual value, on average. For more information on the MAPEs, see table A2.