DESTINY
PLANNING
AND FORECASTING PROGRAM
DESCRIPTION
OF CAPABILITIES
Reformatted
Joseph
George Caldwell, PhD
Tel.
(001)(864)439-2772, e-mail jcaldwell9@yahoo.com
Internet website http://www.foundationwebsite.org
Copyright
© 1982, 1995, 2005 Joseph George Caldwell. All rights reserved.
Table
of Contents
III. How to Use the DESTINY System
IV. Special Features of the DESTINY System
Although planning and policy analysis are perhaps ten
percent retrospection and ninety percent prospection,
the amount of computer software available to assist these functions is vastly
weighted in favor of analyses of historical data, rather than on forecasting
the future implications of alternative policies or demographic
developments. The software for
statistical analysis is well-known and ubiquitous -- SAS, SPSS, BMDP -- with
powerful versions available for desk-top microcomputers. While recent years have seen the advent of
much-improved time series analysis forecasting packages, the software available
for supporting simulation or projections under alternative assumptions about
demographic or programmatic changes is, by comparison, limited and relatively
little-used.
A major factor is cost -- a single application of a
major microsimulation forecasting program, for
example, can cost thousands of dollars to set up, thousands of dollars in
computer time to implement, and weeks of time may pass before the final results
are available. Furthermore, such runs
require massive amounts of computer core and disk or tape storage on a large mainframe
computer. The exigencies of most
planning situations do not allow for the luxury of such slow, high-cost,
data-intensive and labor-intensive techniques.
Faced with only a few hours to obtain, for example, an estimate of the
budgetary implications of a proposed new program regulation or policy, the
analyst often has to resort to "back-of-the-envelope guesstimates."
Times have changed!
The DESTINY Planning and Forecasting System has been developed to
provide the planner with a low-cost, easy-to-use means for making detailed
forecasts of target populations, the need for services, the requirement for
facilities, equipment, and personnel, and the cost under varying programmatic
and demographic assumptions. The time
required to implement a DESTINY run ranges from a few minutes to a few hours,
and the computer cost is negligible -- a few minutes on a desktop
microcomputer. The DESTINY system is a
powerful new tool for the planner who needs detailed, state-of-the-art
forecasts on a quick-turnaround, low-cost basis.
The essence of planning and policy analysis is the
ability to identify potential demographic and economic developments, to
synthesize alternative responses to those developments, and to evaluate the
impact of those responses. The DESTINY
Planning and Forecasting System has been developed to play a crucial role in
this process -- it offers the user the ability to make fast, detailed
population projections, and to forecast quantities linked to growth or
structural changes in the population.
DESTINY can offer the planner or policy analyst
valuable help in making a wide range of forecasts...
In Population Planning,
DESTINY can be used to forecast the population by age, sex, and race, or by
geographic region and race.
In Education Planning, DESTINY
can forecast local-area school enrollments, based on recent trends in birth
rates and regional migration.
In Health Systems Planning,
DESTINY can project the requirement for health services personnel (nurses,
physicians) and equipment (beds, CAT scanners), by geographic region.
In Social Services Planning,
DESTINY can be used to forecast the levels of need for various social services
(counseling, day care, protective services, chore services), the requirement
for agency personnel (counselors) and for contract services, and the associated
budget. These forecasts can be specified
by geographic region, or broken down by age, sex, and race. The budget estimates may be disaggregated by
service, by resource (e.g., counselor), or by demographic characteristics of
the served population (e.g., age, sex, race, or region).
In Market Research, DESTINY
can be used to estimate demand for products or services whose demand is related
to demographic changes.
In Criminal Justice Planning,
DESTINY can make projections of the prison inmate population, and thereby be
used to assist planning for prison construction requirements under different
sentencing policies.
The preceding are but a few examples of DESTINY
applications. With DESTINY, the user can
make forecasts such as the above quickly and easily. The system can be used to make forecasts at
national, state, or local levels. The
analyst can use DESTINY to answer a wide range of "what-if" type
questions. DESTINY eliminates the
analyst's dependence on "standard" populations projections that may
be too highly aggregated, or may correspond to assumptions that are no longer
reasonable. Detailed forecasts,
corresponding to alternative demographic or programmatic assumptions, may be
developed in a matter of minutes or hours, in most cases using information that
is readily available from standard statistical sources.
The DESTINY Planning and Forecasting System is an
essential technical resource for anyone engaged in planning who needs to be
able to produce detailed demographic-based forecasts quickly and at reasonable
cost.
This brochure describes the DESTINY system and
illustrates its capabilities by means of several examples.
DESTINY is a computer-program system that is used to
forecast the general population, special "target" or
"service" populations of interest, demand for services to these
populations, and the amounts of resources (personnel, facilities, equipment,
supplies) and cost required to provide these services to the target
population. It is designed to provide
fast, detailed forecasts in applications in which the items of interest (target
populations, service populations, services, resources, and cost) vary roughly
in proportion to the sizes of various segments of the general population, such
as age/sex/race/geographic-region categories.
A major use of the DESTINY system is to support policy
analysis and program evaluation in service-oriented fields, such as health
care, social services, education, and public safety. DESTINY can be used to develop service
forecasts and budget estimates under a variety of demographic and programmatic
assumptions, and to generate detailed breakdowns (tables and crosstabulations) of these forecasts by age, sex, race, and
geographic-region categories.
Forecasting models based on population projections are
not new. In general, however, the amount
of data and computation required to develop a detailed population-based
forecast is substantial, since to achieve an acceptable level of accuracy the
method requires estimates of the future population broken down by age, sex,
race, and region. While a few
total-population projections may be readily available from national or
state-level agencies, it is usually not possible to obtain detailed projections
(disaggregated by age, sex, race, or region) quickly, corresponding to
arbitrary demographic assumptions, particularly at the local level. One of the major features of the DESTINY
system is its ability to generate detailed population projections rapidly,
corresponding to a wide range of demographic assumptions (concerning fertility,
mortality, and migration).
Except in the field of population planning, most
forecasting applications are not directly concerned with projections of the
general population, but instead with special "target" subpopulations,
such as the physically or mentally ill, the disabled, the student population,
or the prison population. The DESTINY
system can be used to provide forecasts of these special subpopulations, and
forecasts of the resources and cost required to provide specified services to
them. Furthermore, these forecasts can
be broken down, or disaggregated, by detailed demographic
characteristics -- by age, sex, race, and geographic region.
At the local level, planners are often confronted with
the problem of estimating numbers of persons with certain characteristics
(e.g., acute illnesses, chronic health conditions, disabilities, or social
problems) or the number of population-related events (e.g., school or prison
admissions), but there is usually no usable Census or sample survey information
on incidences or prevalences of these characteristics
at the local level. What is often
available, however, are national or regional sample survey data that indicate
the incidence or prevalence, broken down by detailed demographic categories,
such as age, sex, and race. In the
absence of local-level incidence/prevalence data, the planner must use this
national or regional information to develop local-area estimates. In this situation, a recommended procedure
for estimating the numbers of persons having the specified characteristics in
the local area is synthetic estimation.
With this procedure, the national incidences or prevalences
for various age-by-sex-by-race categories are multiplied by the total numbers
of persons of the general local-area population in these categories, to obtain
estimates of the numbers having the specified characteristics in the local
area. DESTINY has the capability for
rapidly computing synthetic estimates.
With this feature, the local planner can quickly construct reasonable
target population estimates which make full use of available regional or
national incidence/prevalence data.
While the DESTINY system can be quickly used to
construct target-population estimates based on available incidence/prevalence
rates (specific to age x sex x race categories), it can also be used in
conjunction with other forecasting procedures to produce high-precision
forecasts. For example, an analyst may
use a multiple regression model, or an autoregressive integrated moving average
(ARIMA or Box-Jenkins) time-series model, or a dynamic systems model to develop
a precise forecast of a future incidence rate for a specific demographic
category (e.g., arrest rates for males aged 20-35). This estimated arrest rate can then be input
to the DESTINY system to forecast total number of arrests for a local area,
taking into account not only trends in the arrest rate, but also anticipated
trends in birth rates, death rates, migration, and population aging.
DESTINY has been designed to enable the planner to
obtain forecasts of the general population, target populations, service
populations, service needs, and the resources and cost required to provide
these services. These projections can
be made under a variety of "what-if" assumptions concerning
demographic and programmatic conditions.
The analyst can make alternative assumptions regarding population
growth, target population incidences or prevalences,
program service ratios, and costs. The
corresponding projections can be computed quickly, and can be disaggregated by
age, sex, race, and geographic region.
DESTINY offers the user the ability to make detailed forecasts quickly
and easily, at substantial savings over manual or partially-automated
procedures.
The DESTINY system was designed to provide a
substantial analytical capability to the user, but it was also designed with
ease-of-use in mind. The DESTINY system
works by setting up a mathematical representation, or "model," of the
population, and using this model to project the future. To use the system, the user needs to set up a
"parameter" file containing the following information:
Demographic Information
o
Total Fertility Rate
o
Fertility Age Distribution
o
Infant Mortality Rate or Expectation of Life at Birth
o
Base-year Population, by age and sex (race optional)
o
External Migration Rates or Amounts
o
Regional Populations (optional)
o Regional Migration Rates or
Amounts (if regions are included in the model)
Target Population Information (Optional)
o
Incidences (rates of occurrence of events) or Prevalences
(proportions of the general population belonging to subpopulations of
interest), either overall or by age and/or sex and/or race and/or region
Service Population Information (Optional)
o
Service Ratios (proportions of the target populations that are served),
either overall or by age and/or sex and/or race and/or region
Service Information (Optional)
o
Average number of service units per year per case served
Resource Information (Optional)
o
Average number of resource units per year required per service unit
expended
Cost Information (Optional)
o
Average cost per resource unit
Names
o
The names of the races, regions, target populations, service
populations, services, resources, and cost categories.
If only general population projections are desired,
(i.e., no target population or service projections are desired), the user need
specify only the demographic parameters.
If a detailed program cost estimate is desired, all of the information
must be specified. The demographic data
are available from standard statistical publications, such as national and
state vital statistics annual reports or statistical abstracts. The target population incidence/prevalence
data are available from Census publications, national sample survey reports,
agency publications, and statistical abstracts.
The service, resource, and cost data are generally available from
program administrative records.
The process of setting up the parameter file requires
a little effort, but once the data are input to the computer, they are stored
on disk and are easily updated.
While running the program, the user needs also to
specify the following parameters:
o
how many five-year periods to project
o
what crosstabulations are desired for the
various population, service, resource, and cost projections
o
for which years hard-copy printout are desired
With regard to output, the program computes
projections for the following items:
o
the general population
o
the specified target populations
o
the specified service populations
o
service units
o
resource units
o
cost
The program can generate aggregate projections for
each of the above quantities, and will (at the user's option) provide
disaggregated projections, by age and/or sex and/or race and/or region.
Version 1.0 of the DESTINY system is set up to
accommodate up to three races, fourteen regions, four target populations, ten
services, seven resources, and four cost categories.
The DESTINY system is designed to accommodate a wide
range of detail in both the input and output.
In setting up the parameter file, the user may specify that the same
value of a demographic parameter (such as fertility rates, infant mortality
rates, and migration rates) for the entire population for all future time. On the other hand, quite detailed demographic
or programmatic specifications may be made, to obtain highly
"conditioned" projections.
With regard to specifying incidence/prevalence rates
for the target populations, DESTINY offers the user the choice of ten different
demographic "stratifications."
The user may specify that the same rate applies to the entire general
population, or that the rates vary according to various demographic
classifications, or stratifications, of the general population. Specifically, the user may specify rate
stratifications by age, sex, race, age x sex, age x race, sex x race, age x sex
x race, region, or race x region. With
this flexibility, the analyst may make use of a wide variety of available crosstabulation data from national surveys, to construct
synthetic estimates of target populations for the local area.
In technical terms, the DESTINY system uses the
"cohort-component" method for making its population projections. This method is the most widely-used
analytical method for preparing regional population projections. (Shryock, Siegel,
and Associates, The Methods and Materials of Demography, US Government
Printing Office, Washington, DC, 1980, presents a detailed description of this
method.) For the target population
estimates, the program uses the method of synthetic estimation (also known as
cohort-component participation rates).
The system is set up to enable parameter input and
updating interactively through the video terminal. The specified parameters are stored on disk
for future use. The projections may be
directed to the video screen, to a printer, or to a file.
The user may specify either brief aggregated
projections, or detailed disaggregated projections, in the form of tables or crosstabulations by age, sex, race, or region. In addition to demographic crosstabulations (such as a breakdown of service
utilization by race or region), the user may request crosstabulations
of services by target population; resources by service, or target population;
and cost by resource, service, or target population.
This section presents a number of examples of DESTINY
applications. These examples include:
o
Projection of the General Population
o
Projection of Various Target or Service Populations
o
School enrollments
o Persons with certain health or
disability conditions
o
The elderly in need of social services
o
Prison admissions
o
Projection of Service Needs
o
Social Services
o
Projection of Resource Requirements
o
Teachers
o
Short-term and long-term health care beds
o
Social services counselors
o
Projection of Program Costs
o
Prison operating costs
o
Social service program costs
The following examples illustrate the wide range of
applications of the DESTINY system. They
also illustrate the varying levels of detail which are possible. On the one hand, the user may use the program
to construct a single estimate of the total population; on the other hand, the
user may request a detailed breakdown of a target population (e.g., the blind)
by age, sex, and race, or a breakdown of costs for purchased social services by
service type or geographic region.
While the examples presented here illustrate the
possible levels of detail of DESTINY projections, they do not represent
examples of policy analysis exercises.
In a real planning or policy analysis situation, the user would very
likely make a set of DESTINY runs, under a wide range of alternative
demographic and programmatic assumptions.
DESTINY is ideally suited for such use, since it stored all of the input
data in a "parameter" file, which the user may easily modify with the
touch of a few buttons on the computer keyboard. Hence, while the first DESTINY run requires
some effort (to assemble the needed data and set up the parameter file),
successive runs are easily and quickly accomplished.
The examples are presented in order of increasing
complexity. The examples and the
principal features of each are listed below.
The national projections are for the United States, and the state projections
are for the State of Arizona. The
examples are illustrated using actual program output listings (or extracts from
listings). The projections are made from
a "base year" of 1980 to the year 1990. The user may structure the program output in
a variety of ways, requesting projections for various years and requesting
different tables and crosstabulations for each
year. These examples illustrate the wide
variety of the program output.
Example 1. National Population Projection,
Single-Race Model
Demographic representation: single
race, single region
Service-system representation:
none
Example 2. National Population Projection, Two-Race
Model
Demographic representation: two
races (white, other), single region
Service-system representation:
none
Example 3. State Population Projection, Single-Race
Model
Demographic representation: single
race, single region
Service-system representation:
none
Example 4. State Population Projection, Three-Race,
14-Region Model
Demographic representation: three
races (white, Indian, other), 14 regions (counties)
Service-system representation:
none
Example 5. Projection of the Hispanic Population
Demographic representation: two
ethnic groups (Hispanic, nonHispanic), 14 regions
(counties)
Service-system representation:
none
Example 6. Rehabilitation Services, Projection of the
Work-Disabled
Demographic representation: three
races (white, Indian, other), 14 regions (counties)
Service-system representation: two
target populations (severe and partially work-disabled)
Example 7. Education, Projection of School Enrollment
Demographic representation: three
races (white, Indian, other), 14 regions (counties)
Service-system representation: one
target population (students), one service population (elementary and secondary school
students) one resource (teachers), one cost (teachers' salaries)
Example 8. Criminal Justice,
Projection of Prison Admissions and Operating Cost
Demographic representation: three
races (white, Indian, other), 14 regions (counties)
Service-system representation: one
target population (admissions), one service population (sentence-years), one
service (incarceration), one resource (cell), one cost (operating cost)
Example 9. Health Care,
Projection of Short-term and Long-term Health Care Beds
Demographic representation: three
races (white, Indian, other), 14 regions
Service-system representation:
four target populations (beds in non-federal short-stay hospitals, nursing
homes, and long-term care institutions for psychiatric and mentally handicapped
patients)
Example 10. Projection of Social Services for the
Elderly
Demographic representation: three
races (white, Indian, other), 14 regions
Service-system representation: one
target population (the elderly), seven services, three resources, and three
costs
Example 1. National
Population Projection by Age and Sex
This example illustrates the most basic application of
DESTINY -- projection of a general population by age and sex. In this example, the resident population of
the United States is projected from the base year of 1980 to the
"projection year" of 1990. The
program output includes all possible tables and crosstabulations
involving age and sex: a table of the population by age, a table by sex, and a crosstabulation by age and sex.
The data on which this projection is based were taken
from Statistical Abstract of the United States, 1981, US Government
Printing Office, Washington, DC. The
accuracy of the projection may be assessed by comparing the projected 1990
population estimates to the 1990 Census values (in Statistical Abstract of
the United States, 1994). For
example, DESTINY projected a resident US population of 250,026,462 for 1990;
the 1990 Census value is 248,701,000.
This is an error of about one-half of one percent. DESTINY projected 9,229,281 males under the
age of five for 1990, compared to the 1990 Census value of 9,392,409 (an error
of less than two percent).
Listing 1. PROJ
Run for Example 1 (National Population Projection, Single-Race Model)
DESTINY PLANNING
AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS): 11:39: 2
PARAMETER FILE
NAME: US801.DAT
GENERAL
POPULATION DESCRIPTION:
UNITED STATES
RESIDENT POPULATION
BASE YEAR: 1980
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 250026462.
DISTRIBUTION OF
POPULATN
BY AGE
0‑4 18150140.
5‑9 18340630.
10‑14 16961798.
15‑19 17341700.
20‑24 18916165.
25‑29 21907018.
30‑34 22038919.
35‑39 20150596.
40‑44 18063934.
45‑49 14262631.
50‑54 11763287.
55‑59 10935458.
60‑64 11141584.
65‑69 10411490.
70‑74 8156979.
75+ 11484134.
DISTRIBUTION OF
POPULATN
BY SEX
MALE 122714472.
FEMALE 127311990.
CROSSTABULATION
OF POPULATN
BY AGE AND SEX
MALE FEMALE
0‑4 9229281. 8920859.
5‑9 9321578. 9019052.
10‑14 8668490. 8293308.
15‑19 8858743. 8482957.
20‑24 9644578. 9271587.
25‑29 11108700.
10798317.
30‑34 11004406.
11034512.
35‑39 10003094.
10147502.
40‑44 8909808. 9154126.
45‑49 6987165. 7275467.
50‑54 5721375. 6041912.
55‑59 5251952. 5683506.
60‑64 5234019. 5907564.
65‑69 4745989. 5665501.
70‑74 3592512. 4564467.
75+ 4432781. 7051353.
Example 2. National
Population Projection by Age, Sex, and Race
This example extends the population model detail to
include representation of two races in the model -- white and other. In this case, the projection may be
disaggregated by age, sex, race, age-by-sex, age-by-race, sex-by-race, and
age-by-sex-by-race.
The projection is based on data from the 1981 edition
of the Statistical Abstract of the United States, and may be compared to
1990 Census values in the 1994 edition.
For example, DESTINY projects 197,443,844 whites and 53,294,702 others
in 1990. The corresponding 1990 Census
figures are 199,686,000 for whites and 49,024,000 for others -- errors of or
-1.1% and 8.7%, respectively.
Listing 2. PROJ
Run for Example 2 (National Population Projection, Two-Race Model)
DESTINY
PLANNING AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS): 12:17: 5
PARAMETER FILE
NAME: US802.DAT
GENERAL
POPULATION DESCRIPTION:
UNITED STATES
RESIDENT POPULATION BY RACE (W/O)
BASE YEAR: 1980
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 250738546.
DISTRIBUTION OF
POPULATN
BY AGE
0‑4 18669617.
5‑9 18806048.
10‑14 17101239.
15‑19 17460667.
20‑24 19000300.
25‑29 21967328.
30‑34 22058888.
35‑39 20138388.
40‑44 18001916.
45‑49 14192279.
50‑54 11705971.
55‑59 10862325.
60‑64 11041520.
65‑69 10298760.
70‑74 8064410.
75+ 11368890.
DISTRIBUTION OF
POPULATN
BY SEX
MALE 123028940.
FEMALE 127709605.
DISTRIBUTION OF
POPULATN
BY RACE
WHITE 197443844.
OTHER 53294702.
CROSSTABULATION
OF POPULATN
BY AGE AND SEX
MALE FEMALE
0‑4 9492151. 9177466.
5‑9 9556684. 9249364.
10‑14 8735127. 8366112.
15‑19 8915115. 8545552.
20‑24 9682667. 9317633.
25‑29 11135944.
10831384.
30‑34 11007309.
11051579.
35‑39 9985651.
10152737.
40‑44 8867066. 9134850.
45‑49 6942907. 7249371.
50‑54 5685799. 6020171.
55‑59 5209615. 5652710.
60‑64 5180874. 5860647.
65‑69 4692481. 5606280.
70‑74 3550310. 4514099.
75+ 4389239. 6979651.
CROSSTABULATION
OF POPULATN
BY AGE AND
RACE
WHITE OTHER
0‑4 13386385. 5283232.
5‑9 13621659. 5184390.
10‑14 12648435. 4452804.
15‑19 13055499. 4405168.
20‑24 14468046. 4532254.
25‑29 16941640. 5025688.
30‑34 17251096. 4807792.
35‑39 15930166. 4208222.
40‑44 14547949. 3453967.
45‑49 11604716. 2587563.
50‑54 9575698. 2130273.
55‑59 9022988. 1839336.
60‑64 9362446. 1679074.
65‑69 8906125. 1392635.
70‑74 7060025. 1004385.
75+ 10060972. 1307918.
CROSSTABULATION
OF POPULATN
BY SEX AND
RACE
MALE FEMALE
WHITE 97116269.
100327575.
OTHER 25912671. 27382030.
CROSSTABULATION
OF POPULATN
BY AGE, SEX,
AND RACE
WHITE
MALE
FEMALE
0‑4 6808785. 6577600.
5‑9 6925470. 6696189.
10‑14 6486648. 6161786.
15‑19 6690797. 6364702.
20‑24 7400519. 7067527.
25‑29 8607548. 8334093.
30‑34 8649989. 8601107.
35‑39 7966863. 7963303.
40‑44 7239716. 7308232.
45‑49 5738483. 5866234.
50‑54 4701144. 4874554.
55‑59 4377281. 4645708.
60‑64 4439947. 4922499.
65‑69 4080277. 4825848.
70‑74 3124926.
3935098.
75+ 3877875. 6183096.
OTHER
MALE FEMALE
0‑4 2683366. 2599866.
5‑9 2631214. 2553175.
10‑14 2248479. 2204325.
15‑19 2224318. 2180851.
20‑24 2282148. 2250106.
25‑29 2528396. 2497292.
30‑34 2357320. 2450472.
35‑39 2018788. 2189434.
40‑44 1627350. 1826618.
45‑49 1204425. 1383138.
50‑54 984655. 1145617.
55‑59 832334. 1007002.
60‑64 740927. 938147.
65‑69 612203. 780432.
70‑74 425384. 579001.
75+ 511364. 796554.
Example 3. State
Population Projection by Age and Sex
This example illustrates the use of DESTINY to make
projections of the resident population of the State of Arizona from the base
year of 1980 to 1990. For this
projection, all of the population is combined, i.e., the model involves a
single "race" (consisting of the total state population) and a single
"region" (consisting of the entire state).
The data were obtained from the 1981 Statistical
Abstract of the United States and Arizona 1980 Vital Health Statistics,
and may be compared to 1990 Census values in the 1990 Census of Population
or the 1994 Statistical Abstract.
From the base year of 1980, DESTINY projects a state population of
3,694,625 for 1990, compared to the 1990 Census value of 3,665,000 -- and error
of less than one percent.
Listing 3. PROJ
Run for Example 3 (State Population Projection, Single-Race Model)
DESTINY
PLANNING AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS):
PARAMETER FILE
NAME: AZ801.DAT
GENERAL
POPULATION DESCRIPTION:
ARIZONA
RESIDENT POPULATION
BASE YEAR: 1980
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3694625.
DISTRIBUTION OF
POPULATN
BY AGE
0‑4 308528.
5‑9 309834.
10‑14 265533.
15‑19 262196.
20‑24 272382.
25‑29 312138.
30‑34 326384.
35‑39 291639.
40‑44 255842.
45‑49 199194.
50‑54 163155.
55‑59 147819.
60‑64 144704.
65‑69 142762.
70‑74 121020.
75+ 171493.
DISTRIBUTION OF
POPULATN
BY SEX
MALE 1827212.
FEMALE 1867413.
CROSSTABULATION
OF POPULATN
BY AGE AND SEX
MALE FEMALE
0‑4 156914. 151614.
5‑9 157507. 152327.
10‑14 135307. 130226.
15‑19 133601. 128596.
20‑24 138207. 134175.
25‑29 158591. 153548.
30‑34 164894. 161490.
35‑39 146991. 144649.
40‑44 128908. 126934.
45‑49 97980. 101214.
50‑54 80273. 82882.
55‑59 71925. 75893.
60‑64 67382. 77322.
65‑69 63623. 79139.
70‑74 53404. 67616.
75+ 71705. 99788.
Example 4. Population
Projection by Age, Sex, Race, and Region
This example illustrates application of DESTINY to
project the Arizona population from 1980 to 1990, by age, sex, race, and
region. For this example, three racial
groups were selected -- white, American Indian, and other. The regions of the model are the 14 Arizona
counties. Population data on these
racial groups and counties is presented in the 1980 Census of Population,
and vital statistics data are available from the 1991 Statistical Abstract
of the United States and Arizona 1980 Vital Health Statistics.
This projection is based on the assumption that
fertility and mortality rates continue at 1980 levels, and that the state
experiences a net annual immigration of approximately 70,000 persons per year.
The projected values may be compared to 1990 Census
figures presented in the 1990 Census of Population. For example, DESTINY projects a 1990 white
population of 2,886,323 for 1990, compared to a 1990 Census value of 2,963,186
-- an error of -2.6%. The
projected population of
Listing 4. PROJ
Run for Example 4 (State Population Projection, Three-Race, 14-Region Model)
DESTINY PLANNING
AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS):
PARAMETER FILE
NAME: AZ803C.DAT
GENERAL
POPULATION DESCRIPTION:
BASE YEAR: 1980
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3735219.
DISTRIBUTION OF
POPULATN
BY RACE
WHITE 2886323.
AMERIND 228240.
OTHER 620656.
DISTRIBUTION OF
POPULATN
BY REGION
APACHE 72497.
COCHISE 97683.
COCONINO 97171.
GILA 40759.
GRAHAM 26383.
GREENLEE 11039.
MARICOPA 1893992.
MOHAVE 83827.
NAVAJO 84127.
PIMA 649633.
PINAL 101590.
STA.CRUZ 24075.
YAVAPAI 95609.
CROSSTABULATION
OF POPULATN
BY REGION AND
RACE
WHITE AMERIND OTHER
APACHE 15397. 55256. 1844.
COCHISE 78373. 692. 18618.
COCONINO 59977. 29599. 7595.
GILA 30676. 7197. 2886.
GRAHAM 17766. 3880. 4737.
GREENLEE 7876. 324. 2839.
MARICOPA 1582814. 32267.
278911.
MOHAVE 80313. 2070. 1444.
NAVAJO 34020. 45483. 4623.
PIMA 513643. 21069.
114920.
PINAL 57469. 12017. 32104.
STA.CRUZ 17931.
81. 6063.
YAVAPAI 91349. 1412. 2848.
Example 5. Projection
of the Hispanic Population
This example presents a projection of the population
by Spanish origin ("Hispanic" vs. nonHispanic)
and region (county), from 1980 to 1990.
Data on Hispanic status by county are available from the 1980 Census
of Population (or the County and City Data Book).
The projected values may be compared to 1990 Census
figures presented in the 1990 Census of Population or the County and
City Data Book 1994. From the base
year of 1980, DESTINY projects 1990 Hispanic and nonHispanic
populations of 647,662 and 3,054,470, compared to 1990 Census values of 688,388
and 2,976,840 -- errors of -5.9% and 2.6%, respectively.
(Note: The
total-population estimate for 1990 for this example differs slightly from the
total-population estimate for 1990 for Examples 3 and 4, since the demographic
parameter specifications are not equivalent.)
Listing 5. PROJ
Run for Example 5 (Projection of the Hispanic Population)
DESTINY
PLANNING AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS):
PARAMETER FILE
NAME: AZ80HC.DAT
GENERAL
POPULATION DESCRIPTION:
BASE YEAR: 1980
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3702132.
DISTRIBUTION OF
POPULATN
BY RACE
HISPANIC 647662.
NON HISP 3054470.
DISTRIBUTION OF
POPULATN
BY REGION
APACHE 65014.
COCHISE 98260.
COCONINO 92212.
GILA 38996.
GRAHAM 26019.
GREENLEE 11016.
MARICOPA 1871693.
MOHAVE 80664.
NAVAJO 76668.
PIMA 651822.
PINAL 101675.
STA.CRUZ 26575.
YAVAPAI 92273.
CROSSTABULATION
OF POPULATN
BY REGION AND
RACE
HISPANIC NON HISP
APACHE 2867. 62147.
COCHISE 29228. 69032.
COCONINO 10332. 81880.
GILA 9098. 29898.
GRAHAM 6958. 19061.
GREENLEE 5633. 5383.
MARICOPA 281931.
1589762.
MOHAVE 3602. 77062.
NAVAJO 5936. 70732.
PIMA 154500. 497322.
PINAL 33174. 68501.
STA.CRUZ 20544. 6031.
YAVAPAI 6584. 85688.
Example 6. Rehabilitation
Services, Projection of the Work-Disabled
This example illustrates a typical use of the DESTINY
system -- the estimation of a target population, based on available incidence
or prevalence data. This example
illustrates the application of the technique of "synthetic
estimation," by which national incidence/prevalence rates by demographic
category are used to construct state estimates.
This procedure assumes, of course, that the national rates do in fact
apply to the state.
The following table indicates the prevalence of work
disability by age group (the prevalence does not vary markedly by sex or
race). The table rates are proportions
of the total resident population.
Persons with Work Disability (percent)
Age Severe Partial
18-24
2.1 3.9
25-34
2.9 7.0
35-44
6.3 8.3
45-54
11.0 12.4
55-64
24.2 11.8
Source: Derived from Table No.
555, Statistical Abstract of the
In the printout, the acronym WRKDISSV stands for
"Severely Work-Disabled," and the acronym WRKDISPT stands for
"Partially Work-Disabled."
Listing 6. PROJ
Run for Example 6 (Rehabilitation Services, Projection of the Work-Disabled)
DESTINY
PLANNING AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS): 13:18: 6
PARAMETER FILE
NAME: AZ803CD.DAT
GENERAL
POPULATION DESCRIPTION:
BASE YEAR: 1980
TARGET/SERVICE
POPULATION DESCRIPTION:
WORK‑DISABLED
POPULATION (SEVERELY AND PARTIALLY WORK‑DISABLED)
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3735219.
DISTRIBUTION OF
POPULATN
BY AGE
0‑4 338816.
5‑9 338489.
10‑14 268314.
15‑19 263875.
20‑24 272520.
25‑29 312009.
30‑34 326222.
35‑39 290907.
40‑44 253597.
45‑49 196686.
50‑54 161169.
55‑59 145678.
60‑64 142066.
65‑69 139466.
70‑74 117950.
75+ 167454.
DISTRIBUTIONAL
ANALYSIS OF WRKDISSV
TOTAL WRKDISSV
= 169750.
DISTRIBUTION OF
WRKDISSV
BY AGE
0‑4 0.
5‑9 0.
10‑14 0.
15‑19 2217.
20‑24 5723.
25‑29 9048.
30‑34 9460.
35‑39 18327.
40‑44 15977.
45‑49 21635.
50‑54 17729.
55‑59 35254.
60‑64 34380.
65‑69 0.
70‑74 0.
75+ 0.
DISTRIBUTIONAL
ANALYSIS OF WRKDISPT
TOTAL WRKDISPT
= 182943.
DISTRIBUTION OF
WRKDISPT
BY AGE
0‑4 0.
5‑9 0.
10‑14 0.
15‑19 4116.
20‑24 10628.
25‑29 21841.
30‑34 22836.
35‑39 24145.
40‑44 21049.
45‑49 24389.
50‑54 19985.
55‑59 17190.
60‑64 16764.
65‑69 0.
70‑74 0.
75+ 0.
TOTALS OF
TARGET POPULATION(S)
WRKDISSV 169750.
WRKDISPT 182943.
Example 7. Education,
Projection of School Enrollment
This example is similar to the preceding one, since it
deals with the estimation of a specific target population -- in this case,
school enrollments. This example carries
the estimation process a little further, in also estimating the number of
elementary and secondary school teachers.
The number of teachers is estimated for 0-18 year olds, assuming a
student/teacher ratio of 20. The total
salary cost of the teachers is also estimated, assuming an average salary of
$17,200 per year.
The following table presents the school enrollment
rates for 1980, by age. The rates do not
vary appreciably by sex or race.
Enrollment Rate
Age Noninst Civ Pop Base Res Pop Base
3-4 .367 .357
5-6 .957 .930
7-13 .993 .965
14-15 .982 .955
16-17 .890 .865
18-19 .464 .451
20-21 .310 .301
22-24 .163 .158
25-29 .093 .090
30-34 .064 .062
Source: Table No. 225, Statistical
Abstract of the
In the printout, the acronym ELEM/SEC stands for
"Elementary and Secondary School Students." Also, in the distribution of teachers and
teachers' salaries by age, "age" refers to age of students.
Listing 7. PROJ
Run for Example 7 (Projection of School Enrollment)
DESTINY
PLANNING AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS):
PARAMETER FILE
NAME: AZ803CE.DAT
GENERAL
POPULATION DESCRIPTION:
BASE YEAR: 1980
TARGET/SERVICE
POPULATION DESCRIPTION:
ELEMENTARY
& SECONDARY SCHOOL ENROLLMENT; TEACHERS; SALARIES
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL ANALYSIS
OF POPULATN
TOTAL POPULATN
= 3735219.
DISTRIBUTION OF
POPULATN
BY AGE
0‑4 338816.
5‑9 338489.
10‑14 268314.
15‑19 263875.
20‑24 272520.
25‑29 312009.
30‑34 326222.
35‑39 290907.
40‑44 253597.
45‑49 196686.
50‑54 161169.
55‑59 145678.
60‑64 142066.
65‑69 139466.
70‑74 117950.
75+ 167454.
DISTRIBUTION OF
POPULATN
BY REGION
APACHE 72497.
COCHISE 97683.
COCONINO 97171.
GILA 40759.
GRAHAM 26383.
GREENLEE 11039.
MARICOPA 1893992.
MOHAVE 83827.
NAVAJO 84127.
PIMA 649633.
PINAL 101590.
STA.CRUZ 24075.
YAVAPAI 95609.
DISTRIBUTIONAL
ANALYSIS OF STUDENTS
TOTAL STUDENTS
= 925175.
DISTRIBUTION OF
STUDENTS
BY AGE
0‑4 48451.
5‑9 322241.
10‑14 258386.
15‑19 189199.
20‑24 58592.
25‑29 28081.
30‑34 20226.
35‑39 0.
40‑44 0.
45‑49 0.
50‑54 0.
55‑59 0.
60‑64 0.
65‑69 0.
70‑74 0.
75+ 0.
DISTRIBUTION OF
STUDENTS
BY REGION
APACHE 22833.
COCHISE 23786.
COCONINO 26060.
GILA 10332.
GRAHAM 6812.
GREENLEE 2769.
MARICOPA 457268.
MOHAVE 19512.
NAVAJO 24616.
PIMA 159357.
PINAL 26920.
STA.CRUZ 5960.
YAVAPAI 22237.
DISTRIBUTIONAL
ANALYSIS OF ELEM/SEC
TOTAL ELEM/SEC
= 794438.
DISTRIBUTION OF
ELEM/SEC
BY AGE
0‑4 48451.
5‑9 322241.
10‑14 258386.
15‑19 165360.
20‑24 0.
25‑29 0.
30‑34 0.
35‑39 0.
40‑44 0.
45‑49 0.
50‑54 0.
55‑59 0.
60‑64 0.
65‑69 0.
70‑74 0.
75+ 0.
DISTRIBUTION OF
ELEM/SEC
BY REGION
APACHE 19933.
COCHISE 20393.
COCONINO 22522.
GILA 8897.
GRAHAM 5865.
GREENLEE 2377.
MARICOPA 392016.
MOHAVE 16710.
NAVAJO 21394.
PIMA 136725.
PINAL 23184.
STA.CRUZ 5112.
YAVAPAI 19040.
DISTRIBUTIONAL
ANALYSIS OF TEACHING
TOTAL TEACHING
= 794438.
DISTRIBUTION OF
TEACHING
BY AGE
0‑4 48451.
5‑9 322241.
10‑14 258386.
15‑19 165360.
20‑24 0.
25‑29 0.
30‑34 0.
35‑39 0.
40‑44 0.
45‑49 0.
50‑54 0.
55‑59 0.
60‑64 0.
65‑69 0.
70‑74 0.
75+ 0.
DISTRIBUTION OF
TEACHING
BY REGION
APACHE 19933.
COCHISE 20393.
COCONINO 22522.
GILA 8897.
GRAHAM 5865.
GREENLEE 2377.
MARICOPA 392016.
MOHAVE 16710.
NAVAJO 21394.
PIMA 136725.
PINAL 23184.
STA.CRUZ 5112.
YAVAPAI 19040.
DISTRIBUTIONAL
ANALYSIS OF TEACHERS
TOTAL TEACHERS
= 39722.
DISTRIBUTION OF
TEACHERS
BY AGE
0‑4 2423.
5‑9 16112.
10‑14 12919.
15‑19 8268.
20‑24 0.
25‑29 0.
30‑34 0.
35‑39 0.
40‑44 0.
45‑49 0.
50‑54 0.
55‑59 0.
60‑64 0.
65‑69 0.
70‑74 0.
75+ 0.
DISTRIBUTION OF
TEACHERS
BY REGION
APACHE 997.
COCHISE 1020.
COCONINO 1126.
GILA 445.
GRAHAM 293.
GREENLEE 119.
MARICOPA 19601.
MOHAVE 836.
NAVAJO 1070.
PIMA 6836.
PINAL 1159.
STA.CRUZ 256.
YAVAPAI 952.
DISTRIBUTIONAL
ANALYSIS OF SALARIES
TOTAL SALARIES
= 683216526.
DISTRIBUTION OF
SALARIES
BY AGE
0‑4 41667598.
5‑9 277127660.
10‑14 222212066.
15‑19 142209203.
20‑24 0.
25‑29 0.
30‑34 0.
35‑39 0.
40‑44 0.
45‑49 0.
50‑54 0.
55‑59 0.
60‑64 0.
65‑69 0.
70‑74 0.
75+ 0.
DISTRIBUTION OF
SALARIES
BY REGION
APACHE 17142535.
COCHISE 17538363.
COCONINO 19369276.
GILA 7650993.
GRAHAM 5044023.
GREENLEE 2044383.
MARICOPA 337133416.
MOHAVE 14370726.
NAVAJO 18399076.
PIMA 117583189.
PINAL 19938284.
STA.CRUZ 4396677.
YAVAPAI 16374272.
Example 8. Criminal Justice,
Projection of Prison Admissions and Operating Cost
This example illustrates the use of DESTINY to predict
the prison inmate population in 1990, from the base year of 1980. In 1979, the admissions rate and average
times to parole eligibility were as follows.
The admissions rate is calculated as a proportion of the total
Admission Rate
Age Male
Female
15-19 .0020 .00020
20-24 .0042 .00019
25-29 .0029 .00024
30-34 .0021 .00016
35-39 .0013 .00010
40-44 .00082 .00006
45-49 .00070 .00006
50-54 .00060 .00003
55-59 .00026 .00001
60+ .00007 0
Average Time to Serve to Parole Eligibility
Male: 35.8 months
Female: 27.7 months
Source: Derived from
The per-inmate operating cost in
In the printout, the acronym ADMIS'NS denotes
"Admissions," SENTYR denotes "sentence-years," CELL denotes
"cell," and OP COST denotes the cost of care for inmates (exclusive
of capital costs).
In an actual policy analysis, a number of runs similar
to the example would be required. For
example, to determine the effect of sentencing only violent criminals to
prison, and at different sentence lengths from current practice, a run would be
required which included not just one but several inmate ("target")
populations, each representing a different level of violence and past behavior.
Listing 8. PROJ
Run for Example 8 (Criminal Justice, Projection of Prison Admissions and
Operating Cost)
DESTINY
PLANNING AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME: PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS):
PARAMETER FILE
NAME: AZ803CP.DAT
GENERAL
POPULATION DESCRIPTION:
BASE YEAR: 1980
TARGET/SERVICE
POPULATION DESCRIPTION:
PRISON
ADMISSIONS; PRISON CELLS; OBLIGATED OPERATING COST
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3735219.
DISTRIBUTION OF
POPULATN
BY SEX
MALE 1849940.
FEMALE 1885279.
DISTRIBUTIONAL
ANALYSIS OF ADMIS'NS
TOTAL ADMIS'NS
= 2262.
DISTRIBUTION OF
ADMIS'NS
BY SEX
MALE 2118.
FEMALE 145.
DISTRIBUTIONAL
ANALYSIS OF SENTYR
TOTAL
SENTYR = 6686.
DISTRIBUTION OF
SENTYR
BY SEX
MALE 6353.
FEMALE 333.
DISTRIBUTIONAL
ANALYSIS OF INCARC'N
TOTAL INCARC'N
= 6686.
DISTRIBUTION OF
INCARC'N
BY SEX
MALE 6353.
FEMALE 333.
DISTRIBUTIONAL
ANALYSIS OF CELL
TOTAL CELL =
6686.
DISTRIBUTION OF
CELL
BY SEX
MALE 6353.
FEMALE 333.
DISTRIBUTIONAL
ANALYSIS OF OP COST
TOTAL OP
COST =
106971223.
DISTRIBUTION OF
OP COST
BY SEX
MALE 101648203.
FEMALE 5323019.
Example 9. Health
Care, Projection of the Need for Short-term and Long-term Beds
This example shows a ten-year projection (by year) of
bed needs for short-term care (less than 30 days) and for long-term care (30
days or more). This projection assumes
the bed rates in the table presented below.
The bed rates are expressed in terms of the ages of the bed users for
short-stay hospital beds, and in terms of the ages of the residents for the
long-term facility beds. The rates are
relative to the resident population.
Long-term Care
Facilities
Short-stay Nursing Home Mentally
Age Hospitals
Male Female Psychiatric Handicapped
0-4 .00142
.00015 .00015 .00046 .00092
5-14 .00056
" " " "
15-19
.00175 " " " "
20-24
" .0012 .0012
.00024 .00095
25-34
.00242 " " " "
35-44
.00255 " " " "
45-64
.00428 " " " "
65-74
.01114 .011 .012
.00020 .00024
75+
" .063 .097 " "
Source: Derived from Tables 2,
29, 30, 77, 178, 179, 182, 183, 184, 185, 188, 530, and 597, Statistical
Abstract of the
In the printout, ST BEDS refers to beds in short-stay
hospitals, and the acronyms NH BEDS, PSY BEDS, and NH BEDS refer to beds in
long-term care facilities (nursing homes, psychiatric facilities, and
facilities for the mentally handicapped, respectively).
This example illustrates the application of DESTINY to
address four different target populations (i.e., beds in the four types of
facilities) simultaneously.
Listing 9. PROJ
Run for Example 9 (Health Care, Projection of the Need for Short-Term and
Long-Term Beds)
DESTINY PLANNING
AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS):
PARAMETER FILE
NAME: AZ803CH.DAT
GENERAL
POPULATION DESCRIPTION:
BASE YEAR: 1980
TARGET/SERVICE
POPULATION DESCRIPTION:
NEED FOR SHORT‑TERM
& LONG‑TERM BEDS (LONG‑TERM: NURSING, PSYCH, MENTAL HAND.)
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 2
DISTRIBUTIONAL ANALYSIS
OF POPULATN
TOTAL POPULATN
= 2718215.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 8883.
NH BEDS 13025.
PSY BEDS 837.
MH BEDS 2337.
YEAR: 1981
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 2819125.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 9219.
NH BEDS 13656.
PSY BEDS 867.
MH BEDS 2423.
YEAR: 1982
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 2920035.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 9555.
NH BEDS 14287.
PSY BEDS 897.
MH BEDS 2510.
YEAR: 1983
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3020944.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 9891.
NH BEDS 14918.
PSY BEDS 927.
MH BEDS 2596.
YEAR: 1984
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3121854.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 10227.
NH BEDS 15549.
PSY BEDS 957.
MH BEDS 2682.
YEAR: 1985
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3222764.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 10563.
NH BEDS 16180.
PSY BEDS 986.
MH BEDS 2768.
YEAR: 1986
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3325255.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 10889.
NH BEDS 16852.
PSY BEDS 1018.
MH BEDS 2857.
YEAR: 1987
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3427746.
TOTALS OF TARGET
POPULATION(S)
ST BEDS 11214.
NH BEDS 17523.
PSY BEDS 1050.
MH BEDS 2945.
YEAR: 1988
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3530237.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 11539.
NH BEDS 18195.
PSY BEDS 1082.
MH BEDS 3034.
YEAR: 1989
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3632728.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 11864.
NH BEDS 18867.
PSY BEDS 1114.
MH BEDS 3122.
YEAR: 1990
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3735219.
TOTALS OF
TARGET POPULATION(S)
ST BEDS 12189.
NH BEDS 19538.
PSY BEDS 1146.
MH BEDS 3211.
Example 10. Social
Services: Forecasting the Counselors and
Budget to Provide Certain Services to the Elderly Population
This example illustrates the use of the DESTINY
program to project service levels, personnel, and budget levels required to
provide social services to the elderly.
The data used in this example are hypothetical. This example assumes that approximately five
percent of the elderly population requires social services (specifically, 3% of
those aged 65-69, 5% of those aged 70-74,and 7% of those aged 75+). The following tables indicate the average
numbers of units of several types of service, resources, and cost required to
provide social services to this served population.
Average
Number of Service Units Per Case
Service No.
of Units
S1. Counseling 2 hours
S2. Chore Services 16 dollars
S3. Homemaker Services 750 dollars
S4. Substitute Care 12 dollars
S5. Day Care 7 dollars
S6. Transportation 10 dollars
S7. Other 10 dollars
Average
Number of Resource Units Per Service Unit
(Service types as defined in the preceding table)
No. of
Resource Units per
Service
Unit (by Service Type)
Resource S1 S2
S3 S4 S5
S6 S7
R1. Counselor 1 0 0 0 0 0 0
R2. Dollar (Purchase) 0
1 1 1 1 .5 .5
R3. Dollar (Payment) 0
0 0 0 0 .5 .5
Average
Cost Per Resource Unit
(Resource types as defined in the preceding table)
Cost Per
Resource Unit
(by Resource
Type)
Cost Category R1
R2 R3
C1. Direct Service 15 0
0
C2. Purchased Service 0
1 0
C3. Payment 0 0 1
In the social services example presented here, we show
a three-year-ahead projection, as opposed to the ten-year-ahead projections
that were illustrated in the other examples.
The printout shows the services, resources, and budget
required to provide services to the served population. In a real application, the service ratios
(3%, 5%, and 7%) would be estimated from client caseload data, and the unit of
service, unit of resource, and unit cost data would be estimated from program
administrative records.
The DESTINY program could be used to estimate the
changes in the budget that would occur if different service levels or different
service costs were adopted. Used in this
way, DESTINY is an ideal tool in the evaluation of alternative strategies for
rationing social services.
The example illustrates several of the different types
of tables and crosstabulations that can be
constructed by the DESTINY program. The
total number of possible crosstabs is very large, and
typically only a few of them would be selected by the user for printout. Up to nine different tables or crosstabulations may be constructed for each of the many
variables forecast by the program (general population, target population (the
elderly), service population (those receiving social services), seven social
services, three resources, and three cost categories). These include tables or crosstabs
by age, sex, race, age by sex, age by race, sex by race, age by sex by race,
region, and race by region. In addition,
nine different service-system-related distributions or crosstabs
may be specified:
o
distribution of services by type
o
distribution of resources by type
o
distribution of costs by type
o
distribution of services by served population
o
distribution of resources by served population
o
distribution of cost by served population
o
distribution of resources by services
o
distribution of costs by services
o
distribution of costs by resources
In this example, we present only a small fraction of
the possible output tables.
Data such as these may be used to back up legislative
budget request, to prepare Comprehensive Annual Services Program plans, or to
estimate staffing levels. Additional runs
could reveal the budgetary impact of changes in service levels or costs.
Listing 10. PROJ
Run for Example 10 (Social Services, Projection of Counselors and Budget Needed
to Provide Social Services to the Elderly Population)
DESTINY
PLANNING AND FORECASTING COMPUTER PROGRAM PACKAGE, VERSION 1.0
PROGRAM NAME:
PROJ
DATE OF RUN
(DD/MM/YYYY): 5/ 6/1995
TIME OF RUN
(HH:MM:SS):
PARAMETER FILE
NAME: AZ803CS.DAT
GENERAL
POPULATION DESCRIPTION:
BASE YEAR: 1980
TARGET/SERVICE
POPULATION DESCRIPTION:
SOCIAL SERVICES
FOR THE ELDERLY; 8 SERVICES, 3 RESOURCES, 3 COST CATEGORIES
NO OF FIVE‑YEAR
PERIODS TO PROJECT = 1
YEAR: 1983
DISTRIBUTIONAL
ANALYSIS OF POPULATN
TOTAL POPULATN
= 3020944.
DISTRIBUTION OF
POPULATN
BY SEX
MALE 1490114.
FEMALE 1530830.
DISTRIBUTION OF
POPULATN
BY REGION
APACHE 56418.
COCHISE 86046.
COCONINO 78946.
GILA 37068.
GRAHAM 23057.
GREENLEE 10853.
MARICOPA 1564786.
MOHAVE 62054.
NAVAJO 70367.
PIMA 545662.
PINAL 90021.
STA.CRUZ 20673.
YAVAPAI 73847.
DISTRIBUTIONAL
ANALYSIS OF ELDERLY
TOTAL
ELDERLY = 344542.
DISTRIBUTION OF
ELDERLY
BY SEX
MALE 151885.
FEMALE 192658.
DISTRIBUTION OF
ELDERLY
BY REGION
APACHE 3592.
COCHISE 10119.
COCONINO 7908.
GILA 4198.
GRAHAM 2481.
GREENLEE 1232.
MARICOPA 186166.
MOHAVE 7886.
NAVAJO 5903.
PIMA 63245.
PINAL 9106.
STA.CRUZ 2345.
YAVAPAI 9390.
DISTRIBUTIONAL
ANALYSIS OF ELDR(SV)
TOTAL ELDR(SV)
= 17254.
DISTRIBUTION OF
ELDR(SV)
BY SEX
MALE 7505.
FEMALE 9748.
DISTRIBUTION OF
ELDR(SV)
BY REGION
APACHE 181.
COCHISE 507.
COCONINO 396.
GILA 210.
GRAHAM 124.
GREENLEE 62.
MARICOPA 9322.
MOHAVE 395.
NAVAJO 296.
PIMA 3167.
PINAL 456.
STA.CRUZ 117.
YAVAPAI 470.
DISTRIBUTIONAL
ANALYSIS OF COUNSLNG
TOTAL COUNSLNG
= 34507.
DISTRIBUTION OF
COUNSLNG
BY SEX
MALE 15010.
FEMALE 19497.
DISTRIBUTION OF
COUNSLNG
BY REGION
APACHE 361.
COCHISE 1013.
COCONINO 793.
GILA 421.
GRAHAM 248.
GREENLEE 123.
MARICOPA 18645.
MOHAVE 790.
NAVAJO 592.
PIMA 6334.
PINAL 911.
STA.CRUZ 235.
YAVAPAI 941.
DISTRIBUTIONAL
ANALYSIS OF CHORE SV
TOTAL CHORE SV
= 276057.
DISTRIBUTION OF
CHORE SV
BY SEX
MALE 120083.
FEMALE 155973.
DISTRIBUTION OF
CHORE SV
BY REGION
APACHE 2890.
COCHISE 8106.
COCONINO 6343.
GILA 3366.
GRAHAM 1988.
GREENLEE 987.
MARICOPA 149160.
MOHAVE 6322.
NAVAJO 4740.
PIMA 50668.
PINAL 7292.
STA.CRUZ 1878.
YAVAPAI 7527.
DISTRIBUTIONAL
ANALYSIS OF HOMEMAKR
TOTAL HOMEMAKR
= 12940160.
DISTRIBUTION OF
HOMEMAKR
BY SEX
MALE 5628914.
FEMALE 7311246.
DISTRIBUTION OF
HOMEMAKR
BY REGION
APACHE 135452.
COCHISE 379987.
COCONINO 297316.
GILA 157762.
GRAHAM 93181.
GREENLEE 46267.
MARICOPA 6991865.
MOHAVE 296350.
NAVAJO 222174.
PIMA 2375079.
PINAL 341809.
STA.CRUZ 88026.
YAVAPAI 352815.
DISTRIBUTIONAL
ANALYSIS OF SUBSCARE
TOTAL SUBSCARE
= 207043.
DISTRIBUTION OF
SUBSCARE
BY SEX
MALE 90063.
FEMALE 116980.
DISTRIBUTION OF
SUBSCARE
BY REGION
APACHE 2167.
COCHISE 6080.
COCONINO 4757.
GILA 2524.
GRAHAM 1491.
GREENLEE 740.
MARICOPA 111870.
MOHAVE 4742.
NAVAJO 3555.
PIMA 38001.
PINAL 5469.
STA.CRUZ 1408.
YAVAPAI 5645.
DISTRIBUTIONAL
ANALYSIS OF DAY CARE
TOTAL DAY CARE
= 120775.
DISTRIBUTION OF
DAY CARE
BY SEX
MALE 52537.
FEMALE 68238.
DISTRIBUTION OF
DAY CARE
BY REGION
APACHE 1264.
COCHISE 3547.
COCONINO 2775.
GILA 1472.
GRAHAM 870.
GREENLEE 432.
MARICOPA 65257.
MOHAVE 2766.
NAVAJO 2074.
PIMA 22167.
PINAL 3190.
STA.CRUZ 822.
YAVAPAI 3293.
DISTRIBUTIONAL
ANALYSIS OF TRANSPRT
TOTAL TRANSPRT
= 172535.
DISTRIBUTION OF
TRANSPRT
BY SEX
MALE 75052.
FEMALE 97483.
DISTRIBUTION OF
TRANSPRT
BY REGION
APACHE 1806.
COCHISE 5066.
COCONINO 3964.
GILA 2103.
GRAHAM 1242.
GREENLEE 617.
MARICOPA 93225.
MOHAVE 3951.
NAVAJO 2962.
PIMA 31668.
PINAL 4557.
STA.CRUZ 1174.
YAVAPAI 4704.
DISTRIBUTIONAL
ANALYSIS OF OTHER
TOTAL
OTHER = 172535.
DISTRIBUTION OF
OTHER
BY SEX
MALE 75052.
FEMALE 97483.
DISTRIBUTION OF
OTHER
BY REGION
APACHE 1806.
COCHISE 5066.
COCONINO 3964.
GILA 2103.
GRAHAM 1242.
GREENLEE 617.
MARICOPA 93225.
MOHAVE 3951.
NAVAJO 2962.
PIMA 31668.
PINAL 4557.
STA.CRUZ 1174.
YAVAPAI 4704.
TOTALS OF
SERVICE(S)
COUNSLNG 34507.
CHORE SV 276057.
HOMEMAKR 12940160.
SUBSCARE 207043.
DAY CARE 120775.
TRANSPRT 172535.
OTHER 172535.
DISTRIBUTION OF
SERVICE(S)
BY SERVED
POPULATION(S)
COUNSLNG CHORE SV HOMEMAKR
SUBSCARE DAY CARE TRANSPRT
OTHER
ELDR(SV) 34507.
276057. 12940160. 207043.
120775. 172535. 172535.
TOTAL 34507. 276057.
12940160. 207043. 120775.
172535. 172535.
DISTRIBUTIONAL
ANALYSIS OF COUNSELR
TOTAL COUNSELR
= 34507.
DISTRIBUTION OF
COUNSELR
BY SEX
MALE 15010.
FEMALE 19497.
DISTRIBUTION OF
COUNSELR
BY REGION
APACHE 361.
COCHISE 1013.
COCONINO 793.
GILA 421.
GRAHAM 248.
GREENLEE 123.
MARICOPA 18645.
MOHAVE 790.
NAVAJO 592.
PIMA 6334.
PINAL 911.
STA.CRUZ 235.
YAVAPAI 941.
DISTRIBUTIONAL
ANALYSIS OF PURCHSVC
TOTAL PURCHSVC
= 13716570.
DISTRIBUTION OF
PURCHSVC
BY SEX
MALE 5966649.
FEMALE 7749921.
DISTRIBUTION OF
PURCHSVC
BY REGION
APACHE 143580.
COCHISE 402786.
COCONINO 315155.
GILA 167228.
GRAHAM 98772.
GREENLEE 49043.
MARICOPA 7411377.
MOHAVE 314131.
NAVAJO 235504.
PIMA 2517584.
PINAL 362317.
STA.CRUZ 93307.
YAVAPAI 373984.
DISTRIBUTIONAL
ANALYSIS OF PAYMENTS
TOTAL PAYMENTS
= 172535.
DISTRIBUTION OF
PAYMENTS
BY SEX
MALE 75052.
FEMALE 97483.
DISTRIBUTION OF
PAYMENTS
BY REGION
APACHE 1806.
COCHISE 5066.
COCONINO 3964.
GILA 2103.
GRAHAM 1242.
GREENLEE 617.
MARICOPA 93225.
MOHAVE 3951.
NAVAJO 2962.
PIMA 31668.
PINAL 4557.
STA.CRUZ 1174.
YAVAPAI 4704.
TOTALS OF
RESOURCE(S)
COUNSELR 34507.
PURCHSVC 13716570.
PAYMENTS 172535.
DISTRIBUTION OF
RESOURCE(S)
BY SERVED POPULATION(S)
COUNSELR PURCHSVC PAYMENTS
ELDR(SV) 34507.
13716570. 172535.
TOTAL 34507. 13716570.
172535.
DISTRIBUTION OF
RESOURCE(S)
BY
SERVICE(S)
COUNSELR PURCHSVC PAYMENTS
COUNSLNG 34507. 0. 0.
CHORE SV 0.
276057. 0.
HOMEMAKR 0.
12940160. 0.
SUBSCARE 0.
207043. 0.
DAY CARE 0.
120775. 0.
TRANSPRT 0. 86268. 86268.
OTHER 0. 86268. 86268.
TOTAL 34507. 13716570.
172535.
DISTRIBUTIONAL
ANALYSIS OF DIRCTSV$
TOTAL DIRCTSV$
= 517606.
DISTRIBUTION OF
DIRCTSV$
BY SEX
MALE 225157.
FEMALE 292450.
DISTRIBUTION OF
DIRCTSV$
BY REGION
APACHE 5418.
COCHISE 15199.
COCONINO 11893.
GILA 6310.
GRAHAM 3727.
GREENLEE 1851.
MARICOPA 279675.
MOHAVE 11854.
NAVAJO 8887.
PIMA 95003.
PINAL 13672.
STA.CRUZ 3521.
YAVAPAI 14113.
DISTRIBUTIONAL
ANALYSIS OF PURCHSV$
TOTAL PURCHSV$
= 13716570.
DISTRIBUTION OF
PURCHSV$
BY SEX
MALE 5966649.
FEMALE 7749921.
DISTRIBUTION OF
PURCHSV$
BY REGION
APACHE 143580.
COCHISE 402786.
COCONINO 315155.
GILA 167228.
GRAHAM 98772.
GREENLEE 49043.
MARICOPA 7411377.
MOHAVE 314131.
NAVAJO 235504.
PIMA 2517584.
PINAL 362317.
STA.CRUZ 93307.
YAVAPAI 373984.
DISTRIBUTIONAL
ANALYSIS OF PAYMENT$
TOTAL PAYMENT$
= 172535.
DISTRIBUTION OF
PAYMENT$
BY SEX
MALE 75052.
FEMALE 97483.
DISTRIBUTION OF
PAYMENT$
BY REGION
APACHE 1806.
COCHISE 5066.
COCONINO 3964.
GILA 2103.
GRAHAM 1242.
GREENLEE 617.
MARICOPA 93225.
MOHAVE 3951.
NAVAJO 2962.
PIMA 31668.
PINAL 4557.
STA.CRUZ 1174.
YAVAPAI 4704.
TOTALS OF COST
CATEGORY(IES)
DIRCTSV$ 517606.
PURCHSV$ 13716570.
PAYMENT$ 172535.
DISTRIBUTION OF
COST CATEGORY(IES)
BY SERVED
POPULATION(S)
DIRCTSV$ PURCHSV$ PAYMENT$
ELDR(SV) 517606.
13716570. 172535.
TOTAL 517606. 13716570.
172535.
DISTRIBUTION OF
COST CATEGORY(IES)
BY
SERVICE(S)
DIRCTSV$ PURCHSV$ PAYMENT$
COUNSLNG 517606. 0. 0.
CHORE SV 0.
276057. 0.
HOMEMAKR 0.
12940160. 0.
SUBSCARE 0.
207043. 0.
DAY CARE 0.
120775. 0.
TRANSPRT 0. 86268. 86268.
OTHER 0. 86268. 86268.
TOTAL 517606. 13716570.
172535.
DISTRIBUTION OF
COST CATEGORY(IES)
BY
RESOURCE(S)
DIRCTSV$ PURCHSV$ PAYMENT$
COUNSELR 517606. 0. 0.
PURCHSVC 0.
13716570. 0.
PAYMENTS 0. 0.
172535.
TOTAL 517606. 13716570.
172535.
Cost of Using DESTINY
The cost of making a forecast includes three
components -- the analyst time used in assembling the required data and in
setting up the run (data entry), the computer running cost, and the program use
charges (purchase or lease). For the
DESTINY runs presented here, the analyst time varied from a few minutes to
several hours. (These estimates assume
that census and vital-statistics publications are readily available; otherwise,
delays will be incurred in obtaining them.)
The several-hour run was the first one, in which the base-year
population data and other demographic parameters had to be collected. The health-care example required about an
hour to extract the bed-use rate by age from several Statistical Abstract
tables, none of which contained the information in the desired form. The other examples generally required only a
few minutes to assemble the required data.
DESTINY runs are very fast, so that the amount of
microcomputer time involved in making the projections is small. Apart from initial data entry, the major
component of computer time involves examining different model specifications
(changes in structural parameters or parameter values).
The major cost component of DESTINY application is the
cost of the analyst's time in assembling the required data and entering it into
the system. Compared to data-intensive
methodologies such as microsimulation, the cost of
analyst time would be low.
Joseph George Caldwell, PhD, developer of the DESTINY system, is a consultant
specializing in program planning, evaluation, and policy analysis. His consulting career has included much work
in studies, analysis, and system development in these disciplines, in a variety
of application areas.
As president of Vista Research Corporation, he directed the project to
develop the prototype MICROSIM microsimulation
forecasting program for the US Department of Health and Human Services, and the
Economic and Social Impact Analysis / Women in Development project for the
Government of the
He conducted needs assessment and client satisfaction surveys under the
project, “Measuring the Effectiveness of Social Services,” for the State of
He developed the sampling manuals for a number of government
statistical reporting systems, including the Social Services Reporting
Requirements (SSRR), the Office of Child Support Enforcement Reporting
Requirements, and the Utilization Review of Medicaid; conducted cost-benefit
studies of day care and alcoholism treatment centers; directed the Health Care
Financing Administration (HCFA) study of the cost impact of Skilled Nursing
Facility / Intermediate Care Facility (SNF/ICF) standards on nursing homes; and
developed improved matching/allocation formulas for distributing funds to
states under the Medicaid, Aid to Families with Dependent Children (AFDC), and
Vocational Rehabilitation programs. He
served as manager of monitoring and evaluation for the Egypt Local Development
II – Provincial project, the largest local-level infrastructure development
project in the world funded by the US Agency for International Development.
In the field of information technology, he developed the Personnel
Management Information System for the Government of Malawi civil service, and
the data processing and reporting component of the EdAssist
Education Management Information System used by the Government of Zambia
Ministry of Education (Academy for Educational Development / US Agency for
International Development / Zambia Ministry of Education).
He developed TIMES, the first commercially-available general-purpose
Box-Jenkins time series forecasting program, and the popular short course,
“Sample Survey Design and Analysis.”
He is the author of several books, including The Value-Added Tax: A New Tax System for the United States and Can
Dr. Caldwell received his BS degree in mathematics from