The Context of Future Need Projections

Future Need and Public Policy

Although the starting point for states’ estimates of their need for science and mathematics teachers should be the assessment of 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.

need,⁶ it is the projection of their need for the future that is most important in shaping related policies and practices. There is only a very limited range of actions a state or district can undertake to address a current shortfall of teachers because what’s required in response are very immediate “fixes” that cannot await the lengthy deliberations of the policy-making process. Responses to immediate needs for teachers can involve adjustments in the assignments of current teaching staff, consolidation or cancellation of classes, emergency hires that may involve requests for licensure waivers, perhaps the offer of financial incentives, but little more.

If, however, there is a shortfall (or significant surplus) of teachers that has to be addressed not only this year but over the next five years or longer, then it becomes relevant and important to develop policies and enact other more systemic changes in response. Addressing the factors that may contribute to problematic rates of teacher attrition, responding to the labor market for teachers by adjusting compensation or reducing opportunity costs (like lengthy preparation and certification requirements), implementing more aggressive teacher recruitment efforts, pursuing appropriate adjustments in the production capacity of the state’s teacher preparation programs – all of these and similar longer-term strategies may be appropriate for addressing a need for teachers projected into the future.

Such policy options make it clear that in responding to the projected need for teachers it becomes important to focus not only on the demand for teachers throughout the state but also on the supply. The available supply is not a fixed and unalterable constant, however, but a variable that can be manipulated. There may be limitations to how extensively it can be altered in the span of a few years; an ambitious new effort to recruit greater numbers of recent high school graduates into teaching, for example, would only begin to have impact after the first recruits begin to graduate college four years later. Moreover, economic uncertainties limit our ability to predict accurately what the supply will be at a future point in time; the economic downturn of 2007-2010, for example, has had a significant impact on teacher labor markets across the U.S. Nevertheless, teacher supply is malleable and responsive to our efforts to influence it – however inadequate or ill-timed those efforts ultimately may be.

Teacher demand, of course, is also not fixed. It is subject, on the one hand, to the fluctuating economic circumstances that influence birth and immigration rates and thus have an impact on the size and make-up of the student population that must be served. But it is also a function of deliberate and voluntary policy decisions, such as changes in class size limits or in the number of science and mathematics credits required for high school graduation or college admission, which have an impact on the number and kinds of courses to be made available for secondary school students and, consequently, on the number and expertise of the science and mathematics teachers who will be required to staff them. Beyond that, teacher demand is a function of the nature of schooling in the U.S., which may seem like an unalterable given but is in reality the product of decisions that more and more educators believe need to be revisited. It goes well beyond the 5-10 year timeline that is the assumed need target here to think about a fundamental restructuring of our public schools that may change the relationship between teachers and students, rely more heavily on technology to encourage greater student self-instruction and reduce dependence on the physical classroom, eliminate grade levels in favor of student competencies, and in other ways greatly alter the way we think about and calculate our need for teachers. But such possibilities certainly loom on the long-term education horizon and have already been realized to an extent in isolated schools and districts throughout the U.S.

The Complexity of Future Need Projections

All of this points to the fact that making reliable projections of a state’s future need for science and mathematics teachers is a much more complicated affair than developing current need estimates. And it is more complicated not only because of the additional factors that have to be considered and because of the ability of state educators and policymakers to influence the projections through their actions, but also because there are far more data to gather and digest and more complex statistical methods that can be brought to bear in generating forecasts from these data. Simply to project changes in the population over the next decade requires attention to the validity and reliability of the method for doing it. To project the student population and the impact on of its changing demographic make-up on the demand for courses in science and mathematics requires yet more sophisticated calculations. And to estimate the probable impact on the teacher supply of various economic factors, whether compensation incentives that may be under consideration or recession-related changes in the entire labor market, is more methodologically complex still.

Adding to the complexity of estimates of future teacher need, whether short term over the next five years or longer term over the next ten to twenty, is the fact that the consideration of teacher quality is even more in play than it is in estimates of current need. Not only must estimates of future need consider shortcomings in the qualifications of the current supply of teachers, both statewide and local. In addition, they should consider the qualifications that will be required of teachers in order to respond appropriately to future demand, which may include a greater need for teachers capable of teaching more sophisticated science and mathematics courses. And adequate responses to projected shortages go beyond finding ways to help ensure that the best available teachers will fill the vacancies identified; ideally, they involve efforts to improve the quality of the entire science and mathematics teacher workforce so that all available teachers will be well-qualified and effective and especially so that any imbalances in teacher quality between individual schools and districts in the state will be addressed and largely eliminated.

Finally, as we shall discuss in more detail in the Guide to Teacher Data, good projections of future teacher supply and demand have more sophisticated data requirements than estimates of current need. On the supply side, for example, longitudinal data that track teachers over time as they move through their careers are invaluable in estimating attrition and retention rates. These are even more helpful if they also include information about teachers’ salaries and job placements that can then be used, for example, to generate hypotheses about the impact of proposals to increase compensation or improve working conditions. Likewise, data about the production capacity and attrition rates of programs that prepare science and mathematics teachers are essential.

On the demand side, we previously noted the need to track population trends over time. In addition, historical data would be very helpful. It would be useful to have data, for example, on the effect of any previous increases in high school science and mathematics course requirements on the demand for teachers in order to forecast the impact of any new changes in requirements that may be in the offing. Similarly, historical data on course-taking patterns among

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This is not intended to imply that we should rest content with the status quo, in which African-American and Hispanic students tend to take significantly fewer and less rigorous courses in science and mathematics than Anglo and Asian-American students. This pattern will not change easily, however, and it is virtually certain to persist to some degree into the short-term future.

different populations of students⁷ could be helpful in projecting how predicted changes in the make-up of the student population will affect the demand for teachers.

The State of State Data

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Florida and Virginia are two states that have very sophisticated teacher data warehouses with information on teachers from the beginning of their preparation program through their certification and employment history.

A fortunate few states⁸ have, or come very close to having, the ideal sets of data required to undertake sophisticated teacher supply and demand projections or the capacity to generate these data readily. These states have invested heavily in education data systems and have a robust data “warehouse” that centralizes different kinds of longitudinal (historical) data on teachers, including their education history, certification status and history, course and school assignment history, and retirement status. Centralized state data warehouses also hold detailed information on the educational histories of K-12 students, which makes it possible to identify historical enrollment trends in science and mathematics and the course-taking patterns of different student sub-groups. Moreover, a primary purpose of establishing a state data warehouse is to ensure the maximum accuracy of the data and the uniformity and compatibility of data from different sources.

Although there has been significant progress among states

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This progress can be attributed to the emphasis in No Child Left Behind on measuring student achievement and to the specific efforts of the Data Quality Campaign and others, including, most recently, encouragement from the U.S. Department of Education’s Race to the Top.

nationwide⁹ over the last several years in establishing high-quality centralized databases that hold detailed information on students’ educational careers from kindergarten through college, there is no similar national groundswell to strengthen states’ data on teachers. In most states, the data on teachers are of variable quality depending upon their source, with state-generated data (such as teacher certification and retirement information) tending to be more reliable and district and school-generated data (such as teacher assignment and shortage information) tending to be both less reliable and less uniform between districts. Data from different sources within a state may use different database platforms (i.e., basic database architecture) and different data definitions, thus limiting the state’s ability to aggregate data confidently and make accurate inter-district comparisons. Districts may differ, for example, in the course titles they use; a “pre-calculus” course in one district may be a close equivalent to a combined Algebra III and Trigonometry course offered in another district. Similarly, data sources may have different technical definitions of “out-of-field” teaching or different criteria for what constitutes a shortage of teachers in a particular subject. In addition, school and district administrative data can be tainted as the result of incentives, such as sanctions for non-compliance with No Child Left Behind, which encourage school and district administrators to mis-report particular data like the number of classes not taught by appropriately qualified teachers. This means that in states without a well-centralized data system it may be difficult to track the movement of teachers between districts or tabulate statewide student enrollment in specific science or mathematics subjects – in other words, difficult to derive any reliable statewide picture of teacher supply and demand.

If states want to be able to carry out more accurate statewide estimates of student enrollment in science and mathematics courses – both current and projected – it would be helpful to undertake an inventory of district course offerings in those subjects throughout the state in an effort to ascertain the true congruence of courses. Likewise, it would behoove states to implement some sort of unitary course titling system at the secondary level and to standardize other student-related and teacher-related data definitions.

States must weigh the costs and benefits of improving data systems to provide more sophisticated and useful data and facilitate reliable data analyses related to teacher supply and demand. Given the inherent uncertainty attaching to projections of future need – both estimates of demand and especially estimates of supply – there may be reluctance on the part of some states to invest in the data systems and expert personnel required to generate high-quality projections. We believe that the benefits far exceed the costs in the long-run, however, and we hope the discussion here helps to illustrate the advantages of good-quality data and a thorough supply and demand analysis.

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Several of these studies can be accessed via the Internet. See, for example, the studies from Illinois; Kansas; andTexas.

A number of states¹⁰ have commissioned studies of their current and projected workforce needs with a view toward strengthening their position in the increasingly technological economy of the future. These studies typically identify various indicators of the science, technology, engineering, and mathematics (STEM) capacity of their current workforce and discuss the adequacy or inadequacy of their K-12 and post-secondary educational systems to meet the future STEM education needs they have projected. Such reports are not so much rigorous efforts to estimate long-term future needs for STEM-proficient workers (including teachers) as they are attempts to persuade policymakers and the public to strengthen the science and mathematics proficiency of a state’s students. We would hope states would be interested in augmenting their capacity to generate the high-quality data and the need projections based upon them that would facilitate efforts to ensure that their teacher workforce is adequate to the task.

Purpose of this Unit

It is the specific purpose of Projecting a State’s Future Need for Science and Mathematics Teachers to provide direction in developing reliable forecasts that can ground appropriate policies and practices to address an imbalance between teacher supply and demand. At their best, however, such projections cannot be as confident as estimates of current need. The future is inherently uncertain, and that uncertainty

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It is instructive to look at the ten-year Projections of Education Statistics released annually by the National Center for Education Statistics. NCES notes(see, for example, p. 18 of the 2017 Projections) that the accuracy of its predictions declines significantly the further into the future they project.. And the accuracy of projections of teacher supply declines more than twice as much as that of projections of student population – the main driver of teacher demand. Compare the “Accuracy of Projections” discussions on pp. 7 and 18.

increases¹¹ the farther into it one attempts to venture; even a five-year timeline invites the intervention of all sorts of unforeseen turns and unanticipated events that can wreak havoc on any forecast one might develop. Moreover, whereas current need estimates depend principally on descriptive data, projections into the future require inferences from that data. And the statistical methods used to produce those inferences or projections employ calculations that assume the continuity of various contingent historical trends and on mathematical models that make reasonable but not ironclad assumptions about human choices and behavior. Projections into the future also are more reliable over large populations than over smaller ones, meaning that statewide supply and demand estimates generally have a higher confidence level than local projections.

The guidance here is offered notwithstanding the very complex methodological nuances and issues that attend estimates of teacher supply and demand. We note some of these complexities in the ensuing discussion, but we ignore others in the interest of simplicity and the desire to provide some sort of reasonable, if ultimately imperfect, direction to those individuals who have the responsibility to develop or employ the kinds of estimates discussed. A more detailed discussion of methodological issues can be found in the Research Analysis unit of this project, which was written by Stephen Raphael and provides much of the theoretical underpinning for many of comments that follow below. And a still more rigorous discussion of the state of the art in teacher supply and demand assessments, which is illuminating even though somewhat dated, can be found in the 1992 National Research Council publication Teacher Supply, Demand and Quality: Policy Issues, Models, and Databases.