Overview

The urgency of present problems that schools may be having in adequately staffing their science and mathematics classes makes states’ assessments of their current

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We use the term “need” throughout this report to reflect the extent to which the supply of teachers meets the demand. “Supply” is used in this report to denote the available pool of teachers, and “demand” is used to denote the number of teachers required to staff the classes offered – and ideally, the number of classes that would be offered were the supply adequate to staff them.

need¹ for science and mathematics teachers the obvious first step in addressing the inability of teacher supply to satisfy teacher demand. A current teacher shortage may not simply be a reflection of present realities, however, but also may be an indication of persistent trends or emerging patterns that need to be understood and confronted. In addition, the current status of teacher need reflects the impact of various state and district policies and practices on the teacher supply and demand situation.

The repertoire of responses available to address an identified immediate need for teachers is extremely limited, however. There is simply not time to make changes in policy, or even significant changes in practice, which might ameliorate a shortage problem. Moreover, effective policies cannot be designed only with an eye to current realities; they must look to the future and take into consideration anticipated changes in the situation the policies are intended to address. Thus, projections of the future need for science and mathematics teachers are the necessary basis for effective policy responses.

Projections of the future need for teachers are significantly more challenging, however, than assessments of current need. First, they face the inherent uncertainty of the future. Estimates of current need and identified differences in the profile of teachers between schools and districts (e.g., in age, experience, certification status, and attrition rates) are important in alerting educators and state officials to current staffing problems and inter-district inequities in teacher quality that may be chronic and likely to persist into the future. If current need data are consistent with historical trends, the data can be taken as an indication that the situations they describe can be expected to continue into the future. But demographic, economic, political, and other realities may in fact conspire to make future needs substantially different.

The attempt to account for such contingencies in projections of the future need for teachers introduces a level of complexity into the calculation that becomes the second challenge faced by future need forecasts. The effort to collect and synthesize more data about the future in and of itself exposes the resulting estimates to greater sources of error and inaccuracy. For example, the relatively straightforward identification of classes without adequately qualified teachers – the essential data point for estimates of current need – must be supplemented in projections of future need by an estimate of future teacher demand both statewide and, ideally, by district.

This future demand forecast is complex and contingent upon several uncertain and constantly-changing variables. In particular, it involves tricky demographic projections of the number of students who can be expected to enroll in science and mathematics courses in various subjects. Such projections require data on historical course-taking trends among the population of students, trends which are not linear but reflect the changing socioeconomic composition of students and differing (and evolving) course-taking patterns among the various student sub-groups. Furthermore, enrollment projections for science and mathematics need to consider the impact of any impending curricular revisions or changes in graduation requirements.

A projection of future teacher need, however, requires not only a forecast of future teacher demand but also an estimate of future teacher supply – i.e., of the number of adequately qualified teachers available to fill the courses to be offered. And this involves the synthesis and collection of still more often-slippery data. Some of these data, such as the age distribution and the attrition and retirement rates of teachers in a state are comparatively easy to collect. But other data, such as the number of teachers in the teacher “reserve pool” who are available to fill needed positions, are much more elusive and may require calculations based on complex econometric models that are idealized and thus imperfect approximations of human behavior. Moreover, some relevant factors, such as the labor market for teachers, are much more volatile than the factors on the demand side and thus much more difficult to predict with confidence.

Finally, projections of future teacher need integrally involve not only forecasts of supply and demand but also the complicated consideration of teacher quality. Truly satisfactory estimates must address any shortcomings in the qualifications of the current and/or projected supply of science and mathematics teachers – whether statewide or local. And they ideally should address not only the qualifications appropriate to the current requirements of the secondary school curriculum but also the qualifications necessary to handle what could well be more rigorous science and mathematics requirements and courses in the future.

These significant challenges should not be taken to imply, however, that efforts to predict the future need for science and mathematics teachers are fruitless or that the inherent unreliability of such estimates is so great that one forecasting method cannot be judged more trustworthy or useful than another. States willing to employ high quality data and sound statistical methods to undertake a rigorous and thorough analysis of their current and future need for teachers can be confident both that their projections have reasonable reliability and that policies developed in response to those projections have that much greater chance of success.

The express purpose of Projecting a State’s Future Need for Science and Mathematics Teachers is to offer guidance in generating rigorous forecasts of a state’s near-term and longer-term need for science and mathematics teachers. Although the guidance includes suggested steps that should be taken, it is as much an orientation to the theoretical and practical challenges involved as it is any sort of recipe. The assumed focus of the discussion is on need forecasts for a state’s public schools because it is a state’s or district’s direct responsibility to address the need for teachers in those schools. At a time, however, when many districts increasingly rely upon private schools – especially private charter schools – to provide educational services, the inclusion of those schools in a supply and demand forecast might be

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The inclusion of the private school sector would require appropriate modifications of both supply and demand estimates. Private school teachers, for example, could no longer be considered part of the “reserve pool” for the public schools, even though there would continue to be migration of private school teachers into public schools. Similarly, teachers who leave public schools to teach in private schools would not properly be counted in state attrition rates if both public and private education is included in the analysis of teacher supply. Another issue if private schools are included involves the appropriate definition and count of the state’s science and mathematics teachers. Not all private schools require their teachers to have the credentials that would be required to teach in the state’s public schools.

worth considering². The focus here is also limited to secondary science and mathematics because the need for teachers at the secondary level is more acute and the related policy issues are distinct from those in elementary education.

Data Checklist

To develop a solid projection of a state’s future need for science and mathematics teachers requires that the state have reliable data on both teacher supply and teacher demand – ideally both for individual districts and for the state as a whole. The “Basic Data” are the minimum kinds of data required to develop a reliable first-order estimate of the state’s current unfilled need for teachers. The “Bonus Data” are data that, if available and reliable, will enable states to refine that first-order estimate. The purpose of collecting much of the data suggested here is to establish historical trends and baselines for future projections. Thus, any additional information (e.g., about emerging economic conditions, changes in student educational requirements, or significant initiatives to increase the production and retention of science and mathematics teachers) that suggests future deviations from historical patterns is extremely important to bring into the supply and demand calculations. Clearly, states and districts ultimately must make a need determination with the best data available, even if they do not meet the ideal for quality or scope.

1. To Determine Teacher Demand:
   Basic Data:
   • A current need estimate for science and mathematics teachers to provide a basis for identifying and addressing specific local needs
   • Historical data (at least 5 years back) on teacher demand in science and mathematics
   • Current and historical student enrollment data, including the total number of students and the percentage taking secondary science and mathematics courses (by specific subject, if possible)
   • Student enrollment projections that match the desired timeline for the teacher need forecast (likely 5-10 years)
   • 

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     The target class size may be a state-mandated or district-mandated upper limit. Target limits may differ from one district to another, and these may differ from the state target, thus complicating the statewide computation of demand.
     Target class size limit³ for science and mathematics courses – district-by-district if this is locally determined
   Bonus Data:
   • Anticipated course enrollment impact of any changes in high school graduation requirements or curricular changes (e.g., in the sequence of biology, chemistry, and physics courses) in science or mathematics
   • 

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     If minority students in a particular state or district, for example, tend to take fewer science courses than white, non-Hispanic students, then we could anticipate a change in the demand for science courses – other things being equal – if the proportions of these groups in the total student population are projected to change.
     Differences⁴ in course taking patterns in science and mathematics among the various ethnic and socioeconomic groups
2. To Determine Teacher Supply:
   Basic Data:
   • Baseline data on the teachers who are currently teaching science and mathematics courses throughout the state: their number, licensure or certification status, age, gender, ethnicity, and years of teaching experience
   • Historical rates of teacher attrition and retirement in the state over the last 5-10 years – by age, gender, ethnicity, and years of experience and specifically for teachers in the sciences and mathematics if possible
   • Historical numbers of newly licensed science and mathematics teachers produced each year by the various teacher preparation programs in the state (including alternate route programs)
   • Numbers of science and mathematics teachers over the last 5 years who have received new licenses each year as transfers from out of state
   • Data on other potential sources of science and mathematics teachers who may be enticed into active teaching positions by appropriate policies and incentives, especially the
     

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     It is virtually impossible to know the exact number of licensed teachers in a state, as many who have licenses on record will have moved away and others no longer in teaching are likely to be lost track of. In any event, the importance of data on the number of all licensed science and mathematics teachers is not to provide a hard figure that can be included in a calculation of total teacher supply but to gain an order of magnitude estimate of the “reserve pool” of licensed teachers on the assumption that, if the pool is large enough, effective policies and incentives might draw more of them into (or back into) teaching than those who currently become delayed entrants and re-entrants.
     total numbers⁵ of licensed science and mathematics teachers in the state, including retirees
   Bonus Data:
   • Data on the delayed entry and re-entry rates of individuals either who take time out after licensure prior to their initial entry into teaching or who take a break between their initial entry and their subsequent return. This would include the percentages and/or number who delay entry or who re-enter, identified if possible as a function of age and prior years of experience and ideally calculated specifically for science and mathematics teachers
   • The total number of individuals who applied during the current year and each of the past several years for the science and mathematics positions available in each district
   • The number of individuals who applied for science and mathematics teaching positions in the state and districts during each of the past several years and were not hired by any school or district
   • District-level data that include the number and preparation programs of all newly hired science and mathematics teachers in each district
   • Data from the largest teacher preparation programs in the state that tracks the placement of their graduates over the last 5 years
   • Data on the impact of compensation and other incentives – and of any other labor market considerations – on science and mathematics teacher recruitment and attrition in the state and for specific schools and districts

Early Warning Data

In addition to the data related to developing rigorous forecasts of teacher supply and demand, a good analysis of a state’s currently employed teacher workforce can provide a few key data points that offer a preliminary indication (an “early warning”) of any emerging need for science and mathematics teachers that is likely to be particularly acute either across the state or in particular districts. One important caveat, however, is that data on a single year may be aberrant and not necessarily a sign of a trend; it is advisable, if possible, to collect these same data points over the past 3-5 years:

• Proportion of science and mathematics teaching slots that were filled during the current year by new hires
If a high proportion of teaching slots are filled with new hires – especially as a multi-year trend – this may be an indication of a serious turnover problem. This indicator is especially useful when comparing districts to one another or individual schools within a district.
• Proportion of employed teachers in a given science or mathematics subject area who are novice, under-certified, or teaching out-of-field
This is a proxy measure of the quality of the teacher workforce. The higher the proportion of teachers with limited qualifications, the greater the concern about their quality. As with the proportion of new hires, this indicator is especially valuable in comparing the quality of teachers between districts or schools.
• Proportion of employed science and mathematics teachers who are within 5-10 years of or have passed the average teacher retirement age in the state or district
The greater the proportion of teachers nearing retirement in a state or district, the more the state or district needs to be concerned about recruiting and developing new teachers to fill the impending departures.