DESTINY

 

      PLANNING AND FORECASTING PROGRAM

 

                 DESCRIPTION OF CAPABILITIES

 

 

 

                                  May 1, 1995

 

                         Reformatted December 9, 2005

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                          Joseph George Caldwell, PhD

                              503 Chastine Drive

                           Spartanburg, SC 29301 USA

             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

 

I. Introduction 3

II. What DESTINY Does 5

III.  How to Use the DESTINY System 6

IV.  Special Features of the DESTINY System 8

V.  Examples 9

 


I. Introduction

 

 

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.


II. What DESTINY Does

 

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.

 

 

III.  How to Use the DESTINY System

 

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.

 

 

IV.  Special Features of the DESTINY System

 

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.

 

 

V.  Examples

 

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):   12:22:57

 

 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 Maricopa County (where Phoenix is located) for 1990 is 1,893,992, compared to a 1990 Census value of 2,122,101 -- an error of -10.7%.  DESTINY projects the 1990 Indian population of Pima County (where Tucson is located) as 21,069, compared to a 1990 Census value of 20,330 -- an error of 3.6%.

 


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):   12:53:32

 

 PARAMETER FILE NAME: AZ803C.DAT     

 GENERAL POPULATION DESCRIPTION:

 ARIZONA RESIDENT POPULATION BY COUNTY AND RACE (W/I/O)                         

 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.

 YUMA         108068.

 

 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.

 YUMA          76044.       4582.      27442.

 


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):   12:59:49

 

 PARAMETER FILE NAME: AZ80HC.DAT      

 GENERAL POPULATION DESCRIPTION:

 ARIZONA RESIDENT POPULATION BY HISPANIC STATUS AND REGION                      

 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.

 YUMA         111200.

 

 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.

 YUMA          36480.      74720.

 


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 United States, 1981.  Data are for 1978.  Source table figures adjusted from Civilian Non-institutional Population base to Resident Population base.

 

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:

 ARIZONA RESIDENT POPULATION BY COUNTY AND RACE (W/I/O)                         

 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 United States, 1981.  Data are for 1980.  Source table figures adjusted from Civilian Non-institutional Population base to Resident Population base.  National data used as proxy for state data.

 

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):   13:25:44

 

 PARAMETER FILE NAME: AZ803CE.DAT    

 GENERAL POPULATION DESCRIPTION:

 ARIZONA RESIDENT POPULATION BY COUNTY AND RACE (W/I/O)                         

 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.

 YUMA         108068.

 

 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.

 YUMA          27238.

 

 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.

 YUMA          23393.

 

 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.

 YUMA          23393.

 

 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.

 YUMA           1170.

 

 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.

 YUMA       20118045.

 


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 Arizona resident population.

 

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 Arizona Correctional Statistics, 1980 (admission numbers for 1980 were divided by the 1980 resident population for each age-by-sex category)

 

The per-inmate operating cost in Arizona is approximately $16,000 per year (source: Arizona Department of Corrections Information Office).

 

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):   13:29:55

 

 PARAMETER FILE NAME: AZ803CP.DAT    

 GENERAL POPULATION DESCRIPTION:

 ARIZONA RESIDENT POPULATION BY COUNTY AND RACE (W/I/O)                         

 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 United States, 1980.  Rates are derived from age and facility distributional data from 1974 and 1976.

 

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):   13:32:50

 

 PARAMETER FILE NAME: AZ803CH.DAT    

 GENERAL POPULATION DESCRIPTION:

 ARIZONA RESIDENT POPULATION BY COUNTY AND RACE (W/I/O)                         

 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):   13:36:48

 

 PARAMETER FILE NAME: AZ803CS.DAT    

 GENERAL POPULATION DESCRIPTION:

 ARIZONA RESIDENT POPULATION BY COUNTY AND RACE (W/I/O)                         

 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.

 YUMA          91888.

 

 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.

 YUMA          10114.

 

 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.

 YUMA            506.

 

 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.

 YUMA           1012.

 

 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.

 YUMA           8100.

 

 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.

 YUMA         379681.

 

 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.

 YUMA           6075.

 

 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.

 YUMA           3544.

 

 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.

 YUMA           5062.

 

 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.

 YUMA           5062.

 

 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.

 YUMA           1012.

 

 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.

 YUMA         402462.

 

 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.

 YUMA           5062.

 

 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.

 YUMA          15187.

 

 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.

 YUMA         402462.

 

 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.

 YUMA           5062.

 

 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 Philippines.

 

He conducted needs assessment and client satisfaction surveys under the project, “Measuring the Effectiveness of Social Services,” for the State of West Virginia.  He developed the sampling plans for a number of major nationwide surveys, such as the Survey of Institutionalized Persons, the Elementary and Secondary School Civil Rights survey, the National Center for Health Services Research (NCHSR) Survey of Hospital Costs and Utilization, the Profession Services Review Organization (PSRO) Data Base Development Study, the US Department of Housing and Urban Development (HUD) Housing Market Practices Study, and the Vocational Rehabilitation Follow-up Study.

 

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 America Survive?

 

Dr. Caldwell received his BS degree in mathematics from Carnegie-Mellon University and his PhD degree in mathematical statistics from the University of North Carolina at Chapel Hill.  He previously served as Director of Management Systems for the Bank of Botswana; as Manager of Research and Development and Principal Scientist at the US Army Electronic Proving Ground’s Electromagnetic Environmental Test Facility; and as Adjunct Professor of Statistics at the University of Arizona in Tucson, Arizona.