Developing a Projection of
Future Teacher Demand

The projection of a state’s need for science and mathematics teachers, whether short-term or long-term, requires independent projections of teacher supply and teacher demand. The

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A more detailed discussion of state-level teacher supply and demand projection can be found in Reichardt (2003). Our discussion is not directly based on Reichardt’s, but the two are consistent.

guidelines¹² offered here for developing a basic projection of teacher demand consist of five suggested steps:

1. Gather current and, if possible, historical data (preferably for at least the last 5 years) on the identified statewide need each year for secondary science and mathematics teachers.

Elaboration

Although the focus of this specific section is on developing a statewide teacher demand estimate for the future, it is important to start with an estimate of current need. Producing an estimate along the lines outlined in the unit on Establishing a State’s Current Need for Teachers accomplishes the following:

• Encourages a focus on demand at the school and district levels, not only on the aggregate demand statewide. The demand for teachers is ultimately a school-by-school and district-by-district phenomenon, and it is only truly met when it is satisfactorily resolved at the local level
• Pushes states to set teacher demand targets high by suggesting that the total demand estimate should take into account classes not offered in various schools and districts because of an historical inability to find suitable teachers
• Serves as a corrective for estimates of need that may have been made in previous years and thus as a check on the validity and reliability of methods that were used previously to estimate teacher supply and demand
• Helps to identify particularly acute needs for teachers in specific subjects and schools and thereby points to the importance of taking more forceful actions in the future to address them. These actions could then figure into the supply and demand projections to the extent their effect can be anticipated. A number of schools in the state, for example, may not offer high school physics – a situation that officials may decide to address and thereby increase course and teacher demand. Or, there may be too few physics teachers to meet the current demand, let alone a demand adjusted upward, so action may be proposed to increase supply.

Similarly, gathering reliable historical or longitudinal data is invaluable in identifying trends that can serve as the basis for future projections of both teacher demand and supply. It would be impossible, for example, to estimate population growth based on a single year’s population figures; longitudinal data on population changes are required in order to discern any long-term or emerging patterns of growth or decline that can then be used to predict how they may play out in the future. Likewise, it would be indispensable to know whether or not the average number of science and mathematics courses students take has been increasing or decreasing in recent years. Clearly, trends are not necessarily linear; an increase in course-taking patterns may have leveled off over time, or new graduation requirements may have intervened to increase course taking and thereby disrupt a previous trend. Barring disruptive events like new course requirements, economic cataclysms, or sudden changes in the population, however, the longer back historical data can be traced, the more reliable a future projection can be made on the basis of those data because anomalous peaks and valleys are more readily averaged out.

Close Elaboration

2. Determine current statewide student enrollment by specific subject (e.g., Algebra, Physics, and Biology) and course difficulty level (basic, college preparatory, Advanced Placement) in secondary science and mathematics courses, and if possible track historical enrollments over at least the past 5 years

Elaboration

Since accurate statewide enrollment data rely on local data, student enrollment data in science and mathematics courses for individual schools and districts must be available as the starting point for the statewide computation. Although districts should routinely collect and record this data, we noted in the previous discussion of The State of State Data that in the absence of a strong centralized statewide data system the quality and compatibility of district data are far from assured.

Close Elaboration

3. Project future statewide student enrollment over the next
   

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   Enrollment can be projected more than 5-10 years into the future at a state’s discretion. The reliability of estimates deteriorates the further into the future they aim, however. The National Center for Education Statistics limits its projections to ten years and notes the significant decrease in accuracy of those projections over the 10-year period. (See, in particular, the “Accuracy of Projections” discussion on p. 7. )
   5-10 years¹³ for secondary science and mathematics

Elaboration

It is not particularly difficult for states to project total student enrollment in the different grade levels K-12, though this calculation is always approximate because enrollments are a moving target due to population migration. Indeed, virtually all



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See, for example, the statewide projections for Maryland. The National Center for Education Statistics also projects ten-year student enrollment figures by state in its annual Projections of Education Statistics. The NCES projections are not by individual grade level, however.

states¹⁴ undertake or have access to projections of the school-age population by grade – generally both statewide and by county or district. And many individual



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See, for example, the projections for Broward County, Florida and Eau Claire, Wisconsin

districts¹⁵ also develop their own, more detailed school-by-school projections.

It is much more difficult to forecast total student enrollment in specific mathematics and science courses and subjects, but this is ultimately necessary in order to accurately project the demand for teachers in those subjects. With adequate data as described in 2. above, a

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As an example, suppose that the percentage of all students taking high school physics courses in a state has increased from 21% to 32% over the last five years, with enrollment among upper income students growing from 35% to 45%, among median income students from 25% to 34%, and among low-income students from 17% to 22%. Similar data might be obtained for students from different ethnic groups. Projecting these same trends into the future, taking into account the anticipated growth rates of these different populations, it becomes possible to derive a reasonable estimate of the student demand for physics courses into the future assuming all other factors (e.g., high school graduation requirements) remain the same.
serviceable projection¹⁶ of enrollment in specific science and mathematics subjects could be constructed by projecting the trend in actual current and historical enrollments in science and mathematics courses into the future and adjusting for significant anticipated changes in the total student population in future years, including changes in the proportion of students from various ethnic or socio-economic backgrounds that might be associated with course-taking patterns.

Close Elaboration

4. Derive a first-order projection of teacher demand for each science and mathematics subject over the next five years by multiplying
   

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   The target class size may be a state-mandated or district-mandated upper limit. Target ratios may differ from one district to another and these may differ from the state target, complicating the statewide computation of demand.
   the target class size¹⁷ in science and mathematics subjects by the projected student enrollments in the subjects for the next five years.
This will actually yield a projection of the number of classes that must be covered in the various science and mathematics subjects. To project the number of teachers required, further divide the number of classes to be covered by the

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This will yield an approximation that likely underestimates the actual number of qualified individual science and mathematics teachers required because some schools and districts will not be able to give teachers a full-time course load in these disciplines. This means that a FTE load in the demand estimate would in actuality sometimes have to be split among more than one teacher. On the other hand, some of those teachers may be doubly qualified in science and mathematics, thus reducing the actual number of individual teachers needed overall.
average teacher course load.¹⁸
5. Refine the first-order projection of teacher demand
• Adjust the student enrollment projections in light of any new high school graduation requirements or other curricular changes (both statewide and district-specific) that are likely to be implemented and to have an impact on the number or kinds of courses students will take.
• To the extent possible, develop district-by-district projections for the entire state. The aggregate statewide current need estimate for teachers already should reflect district needs as a baseline (see 1. and 2. above), but future projections of student population for individual districts over the next 5-10 years, which drives teacher demand, will not necessarily be readily available district by district. District-by-district projections also should be adjusted to reflect total demand – i.e., the addition of science and mathematics classes schools may have not been able to offer historically because of their inability to find suitable teachers.