Charles Teddlie, E. Raymond Hackett, and James L. Morrison|
[Note: This is a re-formatted manuscript that was originally published in
Society Bulletin, 1982, 16(6), 25-30.]
a current planning problem faced by five Louisiana parishes, the authors
illustrate step by step how policy impact analysis techniques and the advisory
guidance of a state educational planner can be used to develop policies for
attaining desirable futures for education on a region-wide basis.
decade of the 1980s promises to be characterized by shifting demographics,
changing enrollment patterns, changing program demands, and fiscal restraints.
Given these conditions, identifying, analyzing and evaluating educational
policy options at the local and state levels take on increased importance.
It is our contention that the policy impact analysis model as described
by William Renfro (1980) in an earlier issue of WFS Bulletin provides a
framework within which a variety of futures research techniques may be used to
develop and assess specific educational policies.
purpose in this article is to briefly review the policy impact model, describe a
current situation in the state of Louisiana amenable to the application of this
model, and illustrate the role that a planner at the state level could play by
using the model to provide assistance to local school districts.
Policy Impact Analysis Model
are four stages in the policy impact analysis model: monitoring, forecasting,
goal setting, and policy analysis and implementation.
Monitoring refers to the identification and selection of issues of
concern to policy makers. The
second stage of the model, forecasting, involves using a variety of futures
research techniques to forecast probable futures and their relationship to
selected issues. In response to the
projected trends and probable futures, policy makers then establish goals: the
third stage of the model.
the fourth stage, a variety of possible policies are analyzed in order to
determine their probable impact, and are ranked on those characteristics deemed
important; e.g., relative costs versus benefits. Those policies ranked at the top are then implemented.
Evaluation occurs when the stages of the model are repeated using
additional analyses and further refinements.
This model, illustrated in Figure 1, will be described in more detail
Policy Impact Analysis Model
Louisiana: An Example
Louisiana State Department of Education (LSDE) was recently contacted by local
school boards and state legislators from five parishes in the northwest region
of the state, a section in which the mining of extensive lignite coal resources
will begin in the near future. Local
school officials were concerned about what demographic or socioeconomic trends
in their area they might expect since these would, of course, affect the
validity of their enrollment projections and facilities planning for the next
ten to fifteen years.
must be noted that the role of the state educational planner in Louisiana is a
sensitive one, since the current constitution (written in 1974) delimits the
state's role in local school business matters.
However, the planner can do monitoring and forecasting for the local
school board and can aid the board in goal-setting and policy implementation by
coordinating local efforts with regional and state activities.
This coordinating role with regard to goal-setting and policy
implementation is particularly important in an area such as the northwest region
of Louisiana, where the needs of five parishes must be taken into consideration.
the following sections, the role of the state education planner as the doer of
the monitoring and forecasting stages and as the facilitator for the
goal-setting and policy implementation stages will be delineated using the
northwest Louisiana example. It
should be noted at the outset that this project is in the planning stages, and
that none of the proposed activities by the LSDE has yet been accomplished.
It is hoped that working through the application of the policy impact
analysis model to this problem area in advance will aid the LSDE if the project
Stage 1: Monitoring
three components of monitoring are: 1) identifying the area of study; 2)
selecting appropriate indicators (or variables) for study; and 3) developing and
plotting the historical data. For
the state education planner in Louisiana, the identification of an appropriate
area of study could come from several sources: 1) requests from the state
Superintendent of Education; 2) requests from the Board of Elementary and
Secondary Education; 3) requests from local school boards; 4) requests from
state legislators; and 5) personal identification of an area of study by the
the case of the northwest Louisiana project, requests for technical assistance
were received from local school boards and from state legislators.
These requests reflect growing concern about orderly growth and
development in the lignite impacted area. During
1979, ten corporations requested permits from the State of Louisiana for the
exploration, mining, and use of lignite in the area.
It is estimated that over one billion tons of recoverable lignite exists
in Louisiana, comprising 17.4 percent of the state's known energy resources
(Louisiana State Department of Natural Resources, 1980).
1979, Governor Edwin Edwards designated the previously mentioned parishes as an
Energy Impacted Area pursuant to Section 601 of the Powerplant and Industrial
Fuel Use Act of 1978 (PL 95620). A
proposal by Senator Don Kelly of Natchitoches Parish during the 1981 Louisiana
Legislative Session to earmark 20% of the state's Enhanced Mineral Income Trust
Fund for development of government facilities in the area was defeated, but
debate over the proposal focused more attention on the needs of the area.
One result of this debate was the realization that more detailed
information was needed on the real impact that lignite coal development would
have on governmental services in the area.
an area of study has been identified, the next step is to select appropriate
variables for study. The sequence
of events that might lead to an increased need for school facilities in the
region is as follows: industrial growth produces an increase in population,
which leads to an increase in school enrollment, which may necessitate increased
of interest may be broken down into the following broad categories: 1) indices
of industrial growth, particularly employment data; 2) population data on a ten
year annual basis for the municipalities and
parishes in the region, broken down by age groups; 3) school enrollment data by
parish for the past ten years; 4) current number of students and classroom
teachers for all schools in the five parishes; 5) current number of schools and
size of sites plus number of classrooms and conditions of school' buildings; 6)
current information on school transportation systems in the parishes, including
the number of buses and the number of students transported annually; and 7)
current plans for expansion by local school boards plus information about the
indebtedness and fiscal condition of each school district.
the variables have been selected, the next stage in monitoring is to develop a
database. This involves locating
the appropriate sources for the data and determining the number of years of data
that one needs. Renfro (1980)
proposed that the planner have as many years of historical data as the time
horizon of the study. Since the
impact of the lignite coal resource development will be felt most severely in
the next ten years, ten years of historical data will be required for most of
the variables. Data more than ten
years old will have marginal utility for the study.
of data for the industrial indices include: 1) the Federal Economic Development
Agency (EDA) for the region; 2) the Louisiana Association of Business and
Industry; and 3) annual business data for the region compiled by James R.
Michael of the Louisiana Tech University College of Administration and Business.
Additional information on the industries that have applied for permits
for lignite exploration may be obtained through the Department of Natural
Resources Geological Survey team. This
information was gathered as part of Federal reporting requirements for Section
601 of the Powerplant and Industrial Fuel Use Act of 1978 mentioned above.
These data include estimates of the number of construction, operations
and secondary employees currently in the area plus projections of the number of
employees that will enter the area over the next fifteen years.
data from the 1980 United States Census is available through the Louisiana State
Planning Office; and annual estimates of the population for each year from 1970
to 1982 are available on the parish and municipality level from Louisiana Tech
University. These estimates will
provide the most accurate picture of annual population growth in the region for
the past ten to fifteen years.
enrollment figures, by parish, are available through the Bureau of Research at
the LSDE for the past fifteen years. The
data for the past three years are available on computer tape; and the data for
previous years could easily be computerized.
The current number of students and classroom teachers for all schools in
the five parishes will be available through Principal Summary Session Reports,
sent annually to the Bureau of Research. Information
on the number of Classrooms and condition of buildings is available through Part
II-A of the Annual Financial and Statistical Report.
This part of the annual report has been expanded for the 1981-82
school year, such that data for that year will be the most thorough ever
collected by the LSDE.
on school transportation systems is also collected as part of the Bureau of
Research's Annual Report process. Plans
for expansion of existing facilities can be obtained by a special survey of the
school board in each parish. Extensive
information about the indebtedness and fiscal condition of each school district
is available through Part II-A of the Annual Report.
Four years of this data is on computer tape; the variables of interest
could be easily computerized for previous years.
the information required for the monitoring stage of the project can be easily
obtained and entered into a computerized database.
The state education planner can collect and organize this data on a
regional basis for the local school boards and state legislators more thoroughly
and accurately than they could themselves.
This database will then be extensively used in the second stage of the
policy impact analysis-, model-forecasting.
Stage 2: Forecasting
is possible to categorize forecasting techniques as exploratory or normative.
Exploratory techniques are used in Stage 2 to analyze trends and cycles
to determine the most likely future developments.
Normative techniques are used in Stage 3 (goal setting) to define the
most desirable future developments and help identify a course of action that
should lead to them. In the
northwest Louisiana example, the purpose of the forecasting stage is to make the
most accurate projections of school enrollment for each of the five parishes
over a ten year period (say, 1982-92). These
enrollment projections would then be used in the goal setting stage to help
define the most desirable future for each parish with regard to additional
school facilities development.
quantitative and qualitative techniques will be used in Stage 2 to define the
expected future most accurately. Quantitative
techniques employ mathematical models to predict the future, while qualitative
techniques use judgments by experts to predict the future.
Various quantitative mathematical models, each with a different set of
assumptions, may be used to project enrollment figures.
It will be necessary to check these projections, however, against expert
opinion before proceeding to the goal-setting stage.
It may be that the assumptions of the underlying mathematical models do
not conform to the actual situation in each parish.
For example, a mathematical model using a linear projection technique may
be appropriate for Nathcitoches Parish, but inappropriate for Red River Parish,
where extensive construction operations will have an immediate impact on school
enrollment that radically alters the linear pattern.
Expert opinion regarding the likelihood and impact of extraordinary
events can be used to alter the mechanical projections made by quantitative
quantitative techniques that may be used to project school enrollments in
northwest Louisiana may be either pattern-based models (time-series models) or
casual models. Pattern-based models
vary in complexity from simple averaging and ratio methods to cohort survival,
Markov-chain, and various curve fitting formulas.
Causal models develop projections on the basis of the numerical values of
indicator variables which have been demonstrated through multiple regression to
have a strong relationship to enrollment levels.
In the northwest Louisiana example, these variables would most probably
be measures of industrial growth attributable to lignite coal resource
development in the area. Unfortunately,
indicators of industrial growth associated with this lignite development are
tentative at this time, primarily because no coal will be mined until 1984.
Some indicators of industrial growth have appeared in the area, but
preliminary examination of the relationship between these indicators and
enrollment levels indicate that the relationship is not strong enough at this
time to warrant the development of detailed causal models.
it seems more appropriate to examine pattern-based models as they are presently
being employed in Louisiana to project school enrollments and to determine how
these models may be improved. The
Board of Regents and the Louisiana State University Division of Institutional
Research currently use cohort survival techniques to project elementary and
secondary enrollments. These
projections are then used to project numbers of freshmen entering colleges and
universities in the state. This
method is adequate for projecting the size of incoming freshmen classes, but
does not accurately project elementary and secondary enrollments, particularly
in areas where extensive in- or out-migration occurs.
For example, previous projections of school enrollment by the Board of
Regents in some of the parishes in the lignite-impacted area have indicated
enrollment decreases, whereas enrollments have actually increased.
alternative model has been developed by the Department of Natural Resources (DNR).
This model projects the number of new students entering the area directly
as a result of construction, operation, and support services for the coal mines.
Under Section 601 reporting requirements, DNR gathered aggregate
estimates of the number of employees that will be needed by the mines in either
construction, operation, or support capacities.
DNR then calculated how many of these employees would migrate into the
area during a three year period (1981-83).
Estimates of the age distribution of children for the in-migrating
workers with families present were then made.
These calculations allowed DNR to estimate the in-migrating school-aged
population for this three year period. Unfortunately,
these predictions have not proven to be very accurate, primarily because of
delays in the schedules for constructing and operating the mines.
summarize, then, the cohort survival technique method of forecasting school
enrollments is inadequate because it does not allow for the effect of
in-migration, while the DNR technique has failed thus far because of faulty
industrial-estimates of the number of in-migrating workers.
However, several alternative pattern-based techniques are available to
the state education planner. The
planner must choose from among these techniques the one most appropriate for
each parish. At present, the most
attractive alternative may be double exponential smoothing (Gardner, 1980).
This time-series technique allows one to selectively weight historical
data points on the basis of the assumed relative importance of more recent
information. For example, the
enrollment figures in Red River Parish could be collected for the last twelve
years (since 1970). A formula could
then be derived to predict enrollment for the next three years based on the
twelve-year trend with heavier weights given to the enrollment figures for the
last years of the historical data. As
more in-migrating students enter schools and elevate recent enrollment figures,
projections for the future will increase at a greater rate using this technique.
a quantitative model has been used to project enrollments for a given period of
time, the projections should be checked against qualitative forecasts generated
by experts from each parish. The
delphi technique could be used to develop a combined forecast of enrollment for
each parish based on the opinions of a group of experts within those parishes.
Thus, the double exponential smoothing technique could be utilized and
then separately checked in each parish using the delphi technique.
If there are discrepancies between the quantitative and qualitative
estimates, then a set of alternative enrollment projections (high, medium, low)
based on alternative assumptions could be developed for the policy makers to
utilize when setting their goals.
Stage 3: Goal Setting
the goal-setting stage of the policy-impact analysis model, likely futures
derived from exploratory forecasting are converted into desirable futures
through normative forecasting. For
example, let us suppose that the state education planner forecast an increase of
500 students in De Soto Parish for the 1982-84 period and also knew that the
current school facilities in De Soto were at maximum capacity.
The likely future is that the schools would be severely overcrowded by
1984; the desirable future might be that a new K-12 school be constructed by
1984. If this were the desirable
future, the specific goals could be set up for the following two years to
accomplish the desired future by 1984.
forecasting techniques are, by necessity, decision-making procedures.
The decision to construct a new school must be made by the local school
board and superintendent with input from others concerned-such as state
representatives and local citizens. It
is at this point that the sensitive nature of the state education planner's role
in policy-impact analysis becomes apparent.
the monitoring and forecasting stages, the state education planner organized
data and developed likely forecasts to meet information requests from local
school boards and legislators. As
noted above, the planner is the doer during these stages.
When goal setting and policy analysis/implementation begin, however, the
planner must assume the role of facilitator.
The state education planner provides likely futures based on
quantitative/qualitative methods, but it is the local policy makers who must
define their desirable future and set goals for making that future happen.
the northwest Louisiana example, the state education planner would first report
back to the school boards and legislators who had requested information.
The planner would present to each parish the likely
future for that parish and for other parishes in the region.
During these separate meetings, the local school boards might see the
need for a coordinated approach to education planning and budgeting.
They might then ask the planner to coordinate a meeting of all affected
authorities, including members of the school board from each parish, the school
superintendents of each parish and local state legislators.
It is incumbent upon the local school boards to request the state
education planner to assume this facilitating role; the planner should not
assume the role without the request of the local authorities.
the first joint meeting of these local school representatives, the state
education planner should provide matrices of likely futures for the region.
One such matrix might have parish on one dimension and high/low
enrollment projections on the other dimension.
This matrix would thus have ten cells that could be collapsed into five
high enrollment futures or five low enrollment futures, which in turn could be
further collapsed into one high enrollment or one low enrollment future for the
region as a whole. Of course this
matrix would be accompanied by other relevant quantitative/qualitative
information such as current condition of buildings, current maximum capacity of
buildings, school transportation patterns, plans for expansion, and fiscal
conditions in all the school districts.
state education planner would, therefore, provide the policy makers with
exploratory forecasts and aid them in generating future scenarios.
The representatives from each parish would, of course, come into the
meeting with some general idea of a desirable future for their parish.
These desirable futures could be combined and restated using delphi
procedures. For example, the
parishes might generate a common desirable future that included earmarking 10%
of the state's Enhanced Mineral Income Trust Fund for school construction in
their region. Planning for the most
efficient and timely use of the funds could be accomplished by considering each
parish's desirable future and compromising on a future most desirable for the
region as a whole.
process of normative forecasting will necessarily involve a great deal of
discussion, debate, and several rounds of forecasting in a delphi-like process.
During this process, the state planner will serve at the pleasure of the
local representatives. The planner
may be asked to further facilitate the discussion or may be asked to leave as
the policy decisions are being made.
Stage 4: Policy Analysis
to Renfro (1980), the fourth stage seeks to determine which policy options and
responses are available to decision makers to transform the most probable future
into the most desirable future. The
initial task in this stage is to identify those events that could have positive
or negative effects on trends related to the goals set.
Once these positive or negative events have been identified, policies
designed to bring about the positive events or decrease the probability of the
negative events can be implemented.
the role of the state education planner is a sensitive one.
The planner is not a policy maker, yet the local school boards or
legislators may continue to seek technical assistance from the planner.
The major contribution that the planner can make at this point is to aid
the policy makers in assessing the impact that potential policies may have on
events that will affect trends.
planner should take each potential policy and set up a policy-to-event impact
matrix. This procedure may become
very complex, but it is absolutely necessary for the policy-impact analysis
model to work. A simple example may
make this part of the model easier to understand.
that only two parishes (Red River and De Soto) were involved in lignite coal
resource development. Further
assume that the two local school boards
and superintendents met and established two policy options: 1) Red River Parish
will pursue the immediate construction of a new elementary school in the western
part of the parish across the Red River from De Soto Parish; 2) De Soto Parish
will pursue the construction of a new elementary school in three years in the
eastern part of the parish across the Red River from Red River Parish.
What impact could these policies have on events that might affect
noted in Figure 2, the immediate construction of a new elementary school in Red
River might influence newly arriving construction and operation workers with
families to settle in Red River Parish rather than De Soto Parish, which only
has plans for a new elementary school.
This would rapidly increase the number of school children in Red River
Parish and decrease the number of school children migrating into De Soto Parish.
These events in turn could stimulate a still greater demand for school
construction in Red River Parish and severely depress the demand for more school
construction in De Soto Parish. Thus
a cooperative effort by the two parishes involving slightly different time-lines
for school construction might be needed to produce a balanced situation in the
two school systems to accommodate new in-migrating students.
Red River Parish pursues the immediate construction of a new elementary
Number of students in Red River Parish
Building of future schools in Red River Parish
De Soto Parish pursues the construction of a new elementary school
in three years
Number of students in De Soto Parish
Building of future schools in De Soto Parish
that Figure 2 plots only the effect of P1 on E1, T1, E2, and T2.
If the state education planner is involved in the policy
analysis/implementation stage, the planner must create realistic
policy-event-trend matrices and then aid the local school boards in evaluating
both new and old policies. Again,
the planner must be careful not to overstep the advisor role, which must be
confined to helping local authorities create the most realistic
policy-event-trend matrices using delphi and cross-impact analysis.
This final stage leads back to the monitoring stage, for which the
planner has a primary responsibility. Moreover,
beginning the process of monitoring anew enables the planner to evaluate the
effectiveness of policies by comparing actual impacts with those forecasted. This, of course, requires that the data base be updated and
maintained in order to evaluate the forecasts and policies and to add important
new trends as they are identified. Implementation
of the model also requires that current and past events be reevaluated, and that
probabilistic forecasts be updated in order to enable goals to be redefined and
This process allows the education community
a maximum hand in defining its place within the evolving social environment.
To those familiar with long-range planning techniques, the policy-impact
analysis model may offer nothing new or startling.
However, this model ties together current forecasting and planning
techniques and provides a guide for their systematic use in the policy-making
process. We hope that the example offered here lends substance to our
assertion that the policy-impact analysis model is a useful tool for state-level
article is based on material originally presented at the 1982 annual meeting of
the American Educational Research Association.
D. (1980). Enrollment forecasting with double exponential smoothing: two methods
for objective weight factor selection. Portland, OR: Portland State University.
State Department of Natural Resources. (1980). Growth management plan for
lignite impacted area. Baton Rouge, LA: DNR Geological Survey.
W. L. (1980). Policy impact analysis: a step beyond forecasting. World future
society bulletin, XIV, 4.