Teacher Supply, Demand, and Quality Policy Issues, Models, and Data Bases (1992) / Chapter Skim
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PART IV MODELS
PART IV MODELS
Pages 127-224
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From page 127... ...
One purpose is to identify, review, analyze, and compare the most promising of such models that have been developed at the state, regional, and national levels, as well as to review research relevant to modeling teacher supply and demand variables. The range of alternative approaches is described, and the strengths and limitations of the best examples of alternative types are analyzed.
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From page 129... ...
Policy makers' questions about prospects for staffing the schools have stimulated efforts over the years to generate better information on the outlook for teacher supply and demand. Many of these efforts have focused on creating the data bases on which supply and demand analysis necessarily depends data on the size and makeup of the teaching force, on teacher assignments and career patterns, on persons trained and certificated to teach, 129
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From page 130... ...
At the same time, other efforts have focused on creating, and then applying, the analytical tools needed to make the data meaningful and to provide policy makers and other users with the information they need-not just the facts, but estimates, inferences, and judgments as to what the facts imply. Prominent among these tools are the teacher supply-demand projection models reviewed in this paper.
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From page 131... ...
Purpose and Scope The general purpose of this assessment is to determine whether the current teacher supply-demand projection models and methods (and some now under development) are well conceived, technically sound, and most important capable of satisfying policy makers' information needs.
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From page 132... ...
The state-level models to be examined were chosen semisystematically from among models cited in the literature, models submitted by states to the National Research Council in response to a general request for teacher supply-demand studies, and models suggested by experts in the field. The coverage of state models is neither comprehensive nor necessarily representative; almost certainly, it is skewed in favor of the more elaborate and sophisticated modeling efforts.
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From page 133... ...
Likewise, disaggregated projections of demand by field or subject specialty provide some of the basic information needed to judge whether serious problems are likely to arise, as some have alleged, in finding enough teachers in such areas as science, mathematics, and special and bilingual education. Ideally, demand projection models should also be instruments for assessing the effects of future economic, fiscal, and demographic changes on the size and makeup of the teaching force; examining connections between teacher staffing and teacher compensation; and even exploring tradeoffs between numbers of teachers and teacher quality; but as will be seen, major advances in the state of the modeling art will be needed before any of these broader ambitions can begin to be realized.
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From page 134... ...
projection models, in which, typically, ratios of teachers to pupils (or trends therein) are assumed to be fixed and projections of numbers of teachers are driven by enrollment forecasts.
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From page 135... ...
To respond to these concerns, many state models offer disaggregated, subject-specific demand projections. The standard approach to disaggregation is to apply to each subject area exactly the same method as is used to project demand in the aggregate: namely, to multiply projected enrollment in the subject area by an extrapolated, subject-specific teacher-pupil ratio.
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From page 136... ...
National studies of course-taking behavior based on samples of high school transcripts may also be useful for projecting subject-specific demands.5 This particular unexplored area of demand modeling appears to be ripe for substantial progress. The Demand for Teacher Quality Even the most sophisticated projections of numbers of teachers likely to be wanted in the future would not meet the needs of those concerned with issues of teacher quality.
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From page 137... ...
Two related aspects of the demand for teacher quality that merit investigation (and that could conceivably be reflected in a future generation of demand projection models)
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From page 138... ...
Thus, there is much to sort out before even beginning to model or project the demand for teacher quality. National and State Demand Projection Models The Standard Demand Projection Model With few exceptions, projections of the demand for teachers are made according to a simple, mechanical, standard model.
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From page 139... ...
, and NCES provides regularly updated national enrollment projections and, of late, enrollment projections by state.7 The accuracy of demand projections naturally depends on the accuracy of the underlying projections of enrollment. Whether the enrollment projection methods deal adequately with migration, dropping out, and private school enrollment are particularly sensitive concerns.
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From page 140... ...
Thus, like the Nebraska model, it reflects the assumption of simple proportionality between the number of teachers at each level and the number of pupils enrolled. (Interestingly, the same Wisconsin study also offers more detailed projections of future numbers of newly hired teachers, covering 16 subject-area categories of regular education teachers and 8 categories of special education teachers, but these are not based on projections of numbers of teachers demanded but rather on direct extrapolations of numbers of new hires in previous years.)
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From page 141... ...
As in the models described previously, no allowance is made for the possibility that the mix of courses taken by pupils at a given grade level will change during the projection period. Massachusetts A teacher supply and demand analysis and projection model for Massachusetts has been produced by a group at the Massachusetts Institute for Social and Economic Research (MISER)
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From page 142... ...
The analysts at MISER have been engaged for some time in a much more ambitious project intended to produce teacher supply and demand information, including projections, for a multistate region encompassing the New England states and New York. This project, known as the Northeast Educator Supply and Demand study (NEDSAD)
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From page 143... ...
Specifically, NCES, as of 1985, produced so-called low, intermediate, and high projections of numbers of elementary and secondary teachers by multiplying the projected enrollment at each level by teacher-pupil ratios determined as follows: · For the low projection, assuming that the teacher-pupil ratio would remain constant at its value in the most recent year for which data were available;
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From page 144... ...
General Assessment of Projections Based on the Standard Model The limitations of the demand projections described above are for the most part self-evident and have been discussed in earlier reviews, so I summarize them only briefly here. First, although the mechanical projection models reflect no explicit assumptions about future conditions affecting teacher demand, they implicitly reflect the assumption that all major influences on demand will either remain constant or continue to change at the rates at which they have been changing in the past.
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From page 145... ...
This regression model, as it now stands, is a very simple one with some serious conceptual and technical flaws; yet it represents an important first step, and one to be encouraged, away from the traditional mechanical projections and in the direction of behavioral models. Model Description The NCES regression equations for elementary and secondary teachers both relate the number of teachers demanded to enrollment, per capita income, and state education aid per pupil.
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From page 146... ...
Projections of the two enrollment variables, ELENR and SCENR, are obtained from the NCES enrollment projection models described in the same annual Projections volumes. Projections of disposable per capita income (PCI)
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From page 147... ...
Although it is certainly true that state aid is a major determinant of spending by local school districts, the objective in this instance is to explain the overall level of spending, and hence teacher demand, in the nation. Treating the largest component of school spending, state aid, as exogenous and projecting it mechanically merely avoids the issue.
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From page 148... ...
There is considerable practical motivation, therefore, for efforts to improve and refine teacher attrition/retention models. When I and others reviewed teacher supply and demand projection methods for the National Research Council in 1986 (Barro, 1986; Cavin, 1986; Popkin and Atrostic, 1986)
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From page 149... ...
Accordingly, I deal in this review with selected research on teacher attrition as well as with explicit state and national projection models. First, however, I comment on some general considerations pertaining to attrition rates and their uses in projecting the supply of retained teachers.
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From page 150... ...
In practice, however, most teacher supply-demand models pertain only to the public schools, which means that attrition from private schools is not taken into account and transfers from private to public schools are treated as a component of the supply of "new" public school teachers. The implications of the transfer phenomenon for modeling are that (1)
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From page 151... ...
But developing importation projections, it turns out, is one of the more difficult and least explored tasks in modeling teacher supply. The distinction between temporary and permanent (or between shortterm and long-term)
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From page 152... ...
Voluntary versus Involuntary Attrition In most supply-demand modeling efforts, the supply of retained teachers is taken as synonymous with the number who actually remain in their jobs from one year to the next, the implicit assumption being that all prioryear teachers who want to continue teaching are retained by their employers. The possibility that the supply of would-be retainees might be greater than the number actually employed is rarely, if ever, allowed for in projections, even though the number supplied and the number employed are two different concepts.
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From page 153... ...
According to Coelen and Wilson (199lb) , the average Massachusetts teacher holds 3.3 certifications.~4 To the extent that teachers are multiply certificated or multiply qualified, the sum of teacher supply by field will exceed teacher supply in the aggregate.
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From page 154... ...
Again, the major unresolved issuethis time in the context of field-specific rather than aggregate attrition is whether it is possible to distinguish analytically between demand-side and supply-side determinants of the attrition rate. Attrition Rates in Relation to Attributes of Teachers A major characteristic that distinguishes more refined projections of the supply of retained teachers from crude ones is disaggregation of attrition rates by age, subject specialty, and perhaps other attributes of teachers.
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From page 155... ...
There is also evidence of an age-gender interaction effect, wherein the early attrition rates of young women are higher than those of young men, but attrition patterns of older male and female entrants are essentially the same (Murnane et al., 1988, 1989; Murnane and Olsen, 1989J. Other things being equal, jurisdictions that hire different mixes of male and female teachers are likely to experience different rates of attrition a finding that has not yet been reflected in supply projection models.
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From page 156... ...
Moreover, it is only with multivariate models that one can progress from mechanical projection models to behavioral models that allow, for example, for the effects of salaries, working conditions, and other policy variables on teacher attrition. Variations in Attrition Rates Over Time Even if all the difficulties of measuring and disaggregating attrition rates were overcome, the problem would remain of projecting attrition rates into the future.
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From page 157... ...
The NCES Projection Model The NCES national projection model uses extrapolated national-average attrition rates to project numbers of retained teachers in the future. The projection methodology has evolved but remains very simple, as the following comparison of the method circa 1985 and the method today will indicate.
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From page 158... ...
The underlying BLS estimates of the separation rate, which are for 1983-84, are 4.9 and 5.6 percent for elementary and secondary teachers, respectively, employed full time in public schools. Apart from the elementary-secondary distinction, there is no differentiation of attrition rates by subject or by any teacher characteristic.
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From page 159... ...
estimated that the overall rate of attrition of public school teachers from the profession was 4.1 percent between fall 1986 and fall 1987 (4.2 percent among elementary teachers and 4.0 percent among secondary teachers)
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From page 160... ...
containing initial attrition rate estimates based on the aforementioned SASS individual teacher baseline and follow-up surveys. According to this report, 5.6 percent of public school teachers left the profession between 1987-88 and 1988-89 (5.5 percent of public elementary teachers and 5.6 percent of public secondary teachers)
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From page 161... ...
Results are expected in the latter half of 1992. State Attrition Models State models of teacher attrition (as of teacher supply and demand in general)
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From page 162... ...
The implicit assumption is that the net attrition rate in each subject area will remain fixed during the five-year projection period at the average rate observed during the five-year base period. The state's data base, it turns out, contains information on every teacher in the state from 1981-82 to the present, which means that annual attrition estimates could have been developed; also, the data base would apparently support breakdowns by age and, presumably, other teacher characteristics.~7 However, these features have not yet been incorporated into the South Carolina analysis.
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From page 163... ...
More Advanced Models Three state models that I have had the opportunity to review those for Massachusetts, New York, and Connecticut project the supply of retained teachers on the basis of detailed attrition rates, disaggregated both by the teacher's age and the teacher's subject specialty or field of certification. I provide brief explanations of how each state model develops and uses the disaggregated attrition-rate figures as well as comments on selected methodological points.
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From page 164... ...
Connecticut The Connecticut model (Connecticut Board of Education, 1988) projects the supply of retained teachers on the basis of age-specific attrition rates for six subject-area categories of classroom teachers plus two categories of staff other than classroom teachers.
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From page 165... ...
The Connecticut model projects numbers of retained teachers for all school years through 2000-2001 by applying the aforesaid attrition rates, year by year, to data on the composition of the teaching force by category and age. (There is some ambiguity in the Connecticut report as to whether the attrition rates used for this purpose pertain to individual years of age or to five-year age brackets; however, the latter seems more likely because tables in the report present attrition rates for five-year intervals from 20-24 to 65-69.~9)
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From page 166... ...
The extended analysis makes it possible, among other things, to distinguish differences in attrition rates associated with subject specialty per se from differences due to the varying age and gender composition of different fields. In addition, it demonstrates significant malefemale differences in attrition patterns, confirming a finding that has emerged in recent econometric studies of teacher attrition.
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From page 167... ...
Without attempting to go into detail, the basic problem seems to be that data on actual subject-area assignments of individual teachers were not available, making it necessary to draw on teacher certification files and aggregate staffing data rather than teacher assignment data and to rely on various assumptions and imputations to disaggregate the teaching force by both age and subject.20 But in principle, if not in practice, the method of measuring retention is the same as in New York and Connecticut: annual retention rates are obtained by determining what percentage of teachers in each age-subject category employed in a given year are still employed in the following year. The Massachusetts study differs from the New York and Connecticut analyses in that its attrition rate estimates are based on multiyear attrition data rather than only on data for a single base year but, unfortunately, the potential advantages of having multiyear data seem not to have been realized.
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From page 168... ...
According to the preliminary model specifications (Coelen and Wilson, l991b) , only state-aggregate data on attrition rates within teacher categories (defined by age, sex, and subject specialty)
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From page 169... ...
that occur naturally, without any change in teacher or employer behavior, simply as a result of the changing age composition of the teaching force. Disaggregation by age requires data at the individual teacher level, but not all states with individual-teacher files have made the transition to working with age-specific attrition rates.
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From page 170... ...
Finally, although the strong connection between age and attrition has been taken into account in some state projection models, there has been no such recognition of the relationships of attrition rates to other personal characteristics of teachers. In particular, none of the models that I have examined reflects the now well-established findings that attrition rates vary by gender and race and perhaps by marital status and full-time or part-time employment.
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From page 171... ...
Cross-Sectional Analyses The disaggregated state modeling efforts described above are all crosssectional attrition analyses of a sort, in that they yield information on how teacher attrition rates vary with age and subject specialty, but they are very limited analyses in several respects. They do not take into account other factors associated with differences in attrition rates, do not examine the dynamics of attrition, and do not apply the multivariate tools needed to sort out the marginal effects of, and interactions among, the various determinants of attrition.
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From page 172... ...
The Grissmer-Kirby theoretical framework draws on theories of human capital, career progression, and imperfect information in the labor market. It explains, among other things: · The many factors that produce U-shaped age-attrition profiles high attrition rates for the youngest and oldest teachers and much lower attrition rates for those in between; · Why the U-shaped curves are likely to have different shapes for men and women, with higher attrition rates for women in the early years of teaching; · How differences in opportunities outside teaching (opportunity costs)
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From page 173... ...
These findings reinforce the conclusion that using a fixed set of attrition rates to project numbers of retained teachers 5 or 10 years into the future is not a satisfactory procedure. The most important contribution of the Grissmer-Kirby work on Indiana is not this crosssectional analysis, however, but rather a multivariate analysis of longitudinal data on entering cohorts, described separately below.
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From page 174... ...
and how long they remain in the teaching force the second time, or subsequent times, around. The combination of longitudinal data bases with the recently developed statistical techniques of survival and hazard modeling (see below)
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From page 175... ...
Although the findings in their present form are not directly usable for projections, this line of work could eventually lead to a new class of behavioral projection models. In a study of Michigan public school teachers who entered teaching in 1972 or 1973, Murnane et al.
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From page 176... ...
In addition, the North Carolina analysis yields the striking finding that teachers who score higher on the NTE tend to leave sooner-that is, higherquality teachers (to the extent that NTE score signifies quality) are less likely to remain in the retained teaching force.26 This demonstration, in a multivariate framework, that NTE scores are negatively associated with retention rates appears to be the closest that anyone has yet come to dealing with teacher quality in an analysis of teacher supply.
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From page 177... ...
They also find that declining female attrition rates are associated with the increasing labor market participation rate for women. They are not able, however, to demonstrate an effect of earnings outside teaching, possibly because their median income variable is too crude an indicator of the opportunity wage.
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From page 178... ...
· Findings about male-female differences in attrition patterns, which are not currently reflected in projection models, can easily be incorporated into models based on individual-teacher data. · The strong indications from the research that attrition rates vary over time should help convince model developers to abandon the assumption that rates will remain constant and to introduce time-varying rates into their projections, even if, initially, these are based on nothing more than trend extrapolations.
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From page 179... ...
More state studies now take account of multiple sources of teacher supply, including the supply of re . turning former teachers and other hires from the "reserve pool"; fewer focus solely on the flow of new graduates from teacher training institutions, as most studies did in the past.
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From page 180... ...
Definitions and Concepts of Supply The supply of entering teachers in a state or in the nation is the number of persons (not already teaching) who are available and willing to take teaching jobs.
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From page 181... ...
In contrast, not all applicants for teaching jobs are able to obtain them. The number of teachers employed is smaller than total teacher supply, and the number of entering teachers hired each year usually falls short of the number seeking teaching positions.
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From page 182... ...
· Newly trained teachers (new graduates of teacher training programs) versus teachers who graduated in earlier years, · Teachers who have never taught in the state versus teachers previously employed in the same state (the latter usually described as reentering teachers)
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From page 183... ...
It seems important not to make the teacher supply seem less flexible than it is, which means that the capability of teachers and prospective teachers to teach multiple subjects should not be ignored. Thus far, however, none of the models I have seen takes the possibilities of substitution and mobility across fields adequately into account.
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From page 184... ...
The latter approach has, in fact, been implemented in a recent study of new teachers in North Carolina (Murnane and Schwinden, 1989) , but the results are not directly transferable, for rea sons to be explained, to teacher supply projection models.
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From page 185... ...
An overall assessment of the models is provided at the end. Projections of Numbers of Certificated Teachers Two models that I have examined, those for Nebraska and South Carolina, reflect the notion that the stock or flow of persons with teaching certificates constitutes a state's teacher supply.
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From page 186... ...
Maryland The Maryland model of teacher supply (Maryland State Department of Education, 1988) recognizes four categories of entrants: (1)
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From page 187... ...
Wisconsin The Wisconsin report on teacher supply and demand (Lauritzen and Friedman, 1991) presents unusually detailed descriptive information on numbers and characteristics of entrants into teaching but only a rudimentary and incomplete model for projecting the entrant component of supply.
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From page 188... ...
Again, it appears that no characteristics of certificated persons other than time since certification are taken into account in estimating the entry probabilities; that is, the projected entry rates are not differentiated by age, sex, or subject specialty. The number of entrants in the final category, teachers hired under waivers of certification, is projected as a fixed percentage of the combined number of teachers hired in the previous two categories.
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From page 189... ...
The Coelen-Wilson model differs from other state models in that it does not yield separate demand and supply estimates but instead offers projections of teacher hiring and emnlovment r ~ that are supposed to reflect both demand and supply factors. The employment projections depend, however, on the simplistic assumption that the number of teachers employed in each future year will be the lesser of the projected number demanded or the projected number supplied.30 Specifically, if the supply projections, based on the previously described sets of projected entry rates, fall short of the demand projections, the model "adjusts" by resetting teacher-pupil ratios at levels consistent with projected supply.
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From page 190... ...
First, at the conceptual level, the MISER framework does not distinguish clearly between influences on teacher supply and influences on entry into teaching. Although Coelen and Wilson recognize that the effects of demographic factors, salary levels, and other influences on supply behavior may or may not be reflected in entry rates, depending on what is happening on the demand side of the market, this awareness is not reflected in the proposed modeling technique.
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From page 191... ...
Survey-Based Supply Projections: The Connecticut Approach The Connecticut teacher supply-demand study (Connecticut Board of Education, 1988) provides an unusually rich body of descriptive information on sources and characteristics of entering teachers but offers only a simple and incomplete set of projections of future supply.
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From page 192... ...
The problem is that certification is only weakly and indirectly related to teacher supply. The connection between the two breaks down in two respects: first, many people with teaching certificates are not in the supply that is, they are not interested in applying for or accepting teaching positions.
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From page 193... ...
. Under normal circumstances, therefore, entry rates per se convey little information about teacher supply.
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From page 194... ...
Significantly, their work seems to imply that bringing the quality dimension of teacher supply into the model is critical-that is, the possibility of estimating a supply function hinges on being able to observe the quality-related characteristics of the teachers hired at different times or in different places. Thus, two key deficiencies of existing models, the lack of a sound method of quantifying teacher supply and neglect of teacher quality, prove to be logically related.
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From page 195... ...
Even so, they are likely to come much closer to the truth than would estimates based on the rates at which teachers are actually hired. ~ ~ it, , ~ Research on the Supply of Entrants I concluded in a previous section that the most promising work on patterns of teacher attrition and retention has come from academic research rather than from efforts to project the future supply of retained teachers.
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From page 196... ...
It should also be recognized, however, that although multivariate models of entry behavior are preferable in several respects, the estimates of entry and reentry rates from such models are no more indicative of supply behavior than are the average entry and reentry rates used in state projection models. In both cases, rates of entry and reentry into teaching may be determined more by school systems' hiring decisions than by the supply behavior of potential teachers.
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From page 197... ...
The Former NCES Supply Projections The classroom teacher sections of NCES's annual Projections of Education Statistics included, as of 1985, not only projections of teacher demand and teacher attrition but also projections of one element of teacher supply, the annual production of new graduates of teacher training programs. These projections were linked to the NCES projections of numbers of recipients of bachelor's degrees.
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From page 198... ...
The usefulness of SASS for analyzing teacher supply from the reserve pool is particularly limited because neither SASS itself nor any other national data base provides data on the size or composition of the pool. However, one aspect of supply from the reserve pool, namely the rate at which teachers who leave teaching subsequently return, may eventually become analyzable.
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From page 199... ...
OVERVIEW: THE STATE OF THE ART AND PROSPECTS FOR IMPROVEMENT The State of the Art in General The main attributes, capabilities, and limitations of current models for projecting teacher supply, demand, and quality can be summarized, in capsule form, as follows: 1. Teacher supply-demand projection models remain mechanical rather than behavioral, which means that they can project future teacher employment and future numbers of new hires only under the implicit assumption that all influences on demand and attrition (or on trends therein)
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From page 200... ...
An important implicit assumption built into all the current demand projection models is that the teacher market, both in the aggregate and in each subject specialty, is and has been in a condition of excess supply. This assumption is reasonable in the aggregate and for most subject areas, but it may be wrong for certain critical fields, such as science, mathematics, and special education, that are of particular interest to policy makers.
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From page 201... ...
Projections of the Supply of Retained Teachers Attrition/retention modeling has become the bright spot in the supplydemand projection field. The more sophisticated state retention models now project numbers of continuing teachers by applying age-specific attrition rates to age distributions of teachers, differentiating also by level of education and subject specialty.
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From page 202... ...
Consequently, the current teacher supply cannot be measured, and the future supply cannot be projected, from data on employment and hiring. Additional information is needed to estimate how many teachers would be available to take teaching jobs if hiring were not limited by demand.
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From page 203... ...
1986 The State of the Art in Projecting Teacher Supply and Demand. Paper prepared for the Panel on Statistics on Supply and Demand for Precollege Science and Mathematics Teachers, Committee on National Statistics, National Research Council.
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From page 204... ...
Cavin, Edward S 1986 A Review of Teacher Supply and Demand Projections by the U.S.
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From page 205... ...
National Bureau of Economic Research. Maryland State Department of Education 1988 Teacher Supply and Demand in Maryland, 1988-1991.
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From page 206... ...
South Carolina Department of Education 1990 Teacher Supply and Demand for South Carolina Public Schools, 1989-90 Update. December.
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From page 207... ...
14. The significance of the number of certificates per teacher depends strongly on how finely subjects of instruction are classified and how the certification categories correspond to the subject fields taken into account in projecting teacher supply.
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From page 208... ...
18. Strictly speaking, estimates of the age distribution of entering teachers should be among the outputs of models for projecting the supply of entrants rather than of the models of teacher attrition, but given the undeveloped state of models of entrants (see below)
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From page 209... ...
This is because some teacher supply-demand studies offer no projections of the supply of entering teachers. In particular, the Ohio and New York analyses circumvent the supply-of-entrants issue by projecting numbers of new hires as fixed or extrapolated percentages of the total number of teachers employed.
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From page 210... ...
Adopting an idealistic perspective, he downplays mechanical and demographic models that are "capable only of estimating what will happen in the future if established patterns or trends continue" in favor of behavioral models that can allow for the "effects of hypothetical changes in circumstances on teacher supply and demand." Since examples of the latter do not yet exist, Barro devotes most of his discussion to modeling desiderata laced with provocative critiques of recent efforts to provide the basic information about teachers needed to gauge the breadth and severity of school staffing problems today and in the future. The italicized terms above are often controversial when used to advocate some methods and denounce others.
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From page 211... ...
While few would deny the desirability of having an overarching conceptual framework to guide thinking about the components of a data system and to facilitate bookkeeping associated with codifying data elements, maintaining linkages, and ensuring consistency across subsystems, the need for an allencompassing model is debatable. With respect to Barro's interest in detailed projections of teacher supply and demand, I feel that the task is so highly dependent on the nature of the forecasts and data availability that generalities are academic.
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From page 212... ...
as well as loss categories for nonreturning teachers, the components can be extended to fit any data system, real or imaginary. Alternatively, the equations can serve as overall constraints for sets of underlying relations, such as equations linking stocks of teachers to enrollment levels, or equations relating flow rates into certain loss categories as parametric functions of teacher characteristics, school attributes, and economic factors.
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From page 213... ...
sector and teaching field. =, , _ Hefty salary increases in a large school district or the relaxation of state certification requirements can cause big swings in teacher supply and demand within a state by stimulating the mobility of current teachers or opening up new sources of supply.
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From page 214... ...
Our 1988 RAND report Assessing Teacher Supply and Demand, coauthored with Linda Darling-Hammond and David Grissmer, spells out our rationale for the linked surveys of districts, schools, principals, teachers, and former teachers that were subsequently implemented. Since NCES adopted the survey instruments that we devised almost without change, most of our specifications of data desiderata for a national data base on teachers can be inferred from an examination of the questionnaires themselves.
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From page 215... ...
, but also the information about teachers' characteristics, qualifications, working conditions, family status, future plans, and attitudes that is needed to profile the teaching work force along many other dimensions that bear directly or indirectly on teacher supply, demand, and quality. In my view, the SASS survey instruments were well designed to gather the necessary information to meet these objectives.
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From page 216... ...
Grissmer 1988 Assessing Teacher Supply and Demand.
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From page 217... ...
I think we must begin with that question because it is a very important issue in determining how much to invest in improving the models used in these projections. That is, if these supply and demand projections of teachers are to be used for informa I would like to begin by asking a question.
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From page 218... ...
Consequently, in developing better models for projecting teacher supply and demand, I urge that this be done in a broader context, which I will elaborate on in a moment. In addition, we must decide whether these models of teacher supply and demand are designed primarily for short-term or for long-term projections.
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From page 219... ...
If ultimately you are concerned with the teaching profession, you start out overall, but then you begin to disaggregate the model by industry in order to capture other employment opportunities for those educated as teachers, by type of school, by geography and other important factors, which could affect teacher supply and demand. Although disaggregating the model by industry will yield more precision in its estimating equations, there is a concern, however, because you very likely will have more error in the underlying disaggregated data.
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From page 220... ...
All of my points are made for consideration in improving models for analyzing teacher supply and demand. Even if these recommendations were followed, one could still not develop a model capable of projecting a numerical shortage or surplus of teachers.
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From page 221... ...
By analogy, if the teaching profession had developed like other professions, a teacher would be responsible for 50 or 75 students. However, each teacher would have several paraprofessionals and clerical assistants in the classroom in order to teach such a large group.
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From page 222... ...
Since the Barro paper did not consider theory relevant to TSDQ projection models, a suggestion was made that explication of the theoretical underpinnings of these models would be useful for policy makers who are concerned about teacher supply and demand projections. Although Stephen Barro recognized that such theory was not brought out in the paper, he stated that there is a clear theoretical base for much projection modeling of the teacher force on both the supply side and on the demand side.
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From page 223... ...
Since projections of variables such as teacher attrition rates and enrollment growth in public education are imprecise, so will be projections of teacher supply and demand derived from equations that use these variables. In a world of limited resources, however, projection models will continue to be imperfect.
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From page 224... ...
In the final analysis, the utility of TSDQ projection models depends on the degree to which they inform education policy issues. While there are many problems with models, state and national data independent of models can help clarify a number of policy issues.
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