The Ninth

Federal Forecasters

Conference - 1997

Papers and Proceedings

Edited by

Debra E. Gerald

National Center for Education Statistics

Office of

U.S. Department of Education

Educational Research and Improvement

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Superintendent of Documents, Mail Stop SSOP, Washington, DC 20402-9328

ISBN 0-16 -049530-X
U.S. Department of Education

Richard W. Riley

Secretary

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Pascal D. Forgione, Jr.

Commissioner

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Suggested Citation

U.S. Department of Education. National Center for Education Statistics. The Ninth Federal Forecasted

Conference -1997, NCES 98–1 34, Debra E. Gerald, Editor. Washington, DC: 1998.

Content Contact:

Debra E. Gerald

(202) 219-1581
Federal Forecasters Conference Committee

Stuart Bernstein

Bureau of Health Professions

U. S. Department of Health and Human Services

Paul Campbell

Bureau of the Census

U.S. Department of Commerce

Debra E. Gerald

National Center for Education Statistics

U.S. Department of Education

Howard N Fullerton, Jr.

Bureau of Labor Statistics

U.S. Department of Labor

Karen S. Hamrick

Economic Research Service

U.S. Department of Agriculture

Stephen A. MacDonald

Economic Research Service

U.S. Department of Agriculture

Jeffrey Osmint

U.S. Geological Survey

John H. Phelps

Health Care Financing Administration

Department of Health and Human Services

Norman C. Saunders

Bureau of Labor Statistics

U.S. Department of Labor

Clifford Woodruff

Bureau of Economic Analysis

U.S. Department of Commerce

Peg Young

U.S. Department of Veterans Affairs

...

111

Federal Forecasters Conference Committee

(Left to right) Peg Young, Department of Veterans Affairs, Stuart Bernstein, Bureau of Health Professions, Howard

N. Fuiierton, Jr., Bureau of Labor Statistics, Norman C. Saunders, Bureau of Labor Statistics, Paui Campbell,

Bureau of the Census, Jeffrey Osmint, U.S. Geological Survey, Karen S. Hamrick Economic Research Service,

and Debra E. Gerald, National Center for Education Statistics. (Not pictured): Stephen A. MacDonald, Economic

Research Service, John H. Phelps, Health Care Financing Administration,

Economic Analysis.

and Clifford Woodruff, Bureau of

v

Norman C. Saunders and welcoming remarks from Katharine G. Abraham, Commissioner of Labor Statistics

from the Bureau of Labor Statistics. Katherine K. Wallman, Chief Statistician of the United States Office of

Management and Budget, delivered the keynote address. The address was followed by a panel discussion with

comments from Katharine G. Abraham, J. Steven Landefeld, Director of the Bureau of Economic Analysis, and

Alan R. Tupek, Deputy Director, Division of Science Resources Studies, National Science Foundation. Paul

Campbell of the Bureau of the Census and Jeffrey Osmint of U.S. Geological Survey presented awards for 1996

Best Conference Paper and a Special Award for Presentation. Debra E. Gerald of the National Center for

Education Statistics and Karen S. Hamrick of the Economic Research Service presented awards from the 1997

Federal Forecasters Forecasting Contest.

In the afternoon, two concurrent sessions were held featuring 26 papers presented by forecasters from the Federal

Government, private sector, and academia. A variety of papers were presented dealing with topics related to

agriculture, the economy, education, health, labor and issues regarding community policies, forecast evaluation,

futures research, and global forecasting. These papers are included in these proceedings. Another product of the

FFC-97 is the Federal Forecasters Directory 1997.

vii
.
announcement and call for papers and provided conference materials. Karen S. Harnrick of ERS organized the

afternoon concurrent sessions and conducted the Federal Forecasters Forecasting Contest. Howard N Fullerton,

Jr., of the Bureau of Labor Statistics secured conference facilities and handled logistics. Peg Young of the

Department of Veterans Affairs provided conference materials.

Also, recognition goes to Stuart Bernstein of the Bureau of Health Professions, Paul Campbell of the Bureau of

the Census, John H. Phelps of the Health Care Financing Administration, and Clifford Woodruff of the Bureau

of Economic Analysis for their support of the Federal Forecasters Conference.

A special appreciation goes to Peg Young and the International Institute of Forecasters for their support of this

year’s conference.

A deep appreciation goes to Thomas F. Hady, retired from the Economic Research Service, for reviewing the

papers presented at the Eighth Federal Forecasters Conference and selecting awards for the 1996 Best Conference

Paper and a Special Award for Presentation.

In addition, many thanks go to Linda D. Felton and Patricia A. Saunders of the Economic Research Service for

assembling the conference materials into packets and staffing the registration desk.

Last, special thanks go to all presenters, discussants, and attendees whose participation made FFC-97 a very

successful conference.

ix

Energy Information Administration

HONORABLE MENTION

Ken Beckman, U.S. Geological Survey

Patrick Walker, Administrative Office of the United States Courts

Thomas D. Snyder, National Center for Education Statistics

Joel Greene, Economic Research Service

Peggy Podolak, U.S. Department of Energy

John Golmant, Administrative Office of the United States Courts

W. Vance Grant, National Library of Education

Betty W. Su, Bureau of Labor Statistics

David Torgerson, Economic Research Service

CONTEST ANSWERS

U.S. Civilian Unemployment Rate 4.9%

Treasury Bond Ask Yield 6.64%

Cash Price of Cotton $0.7090

Average Temperature 72.5°

Lead team in the American League East Winning Percentage 0.625

“Beyond

Bureau of Economic Analysis

HONORABLE MENTION

Ten Years: The Need to Look Further”

R.M. Monaco

University of Maryland

John H. Phelps

Health Care Financing Administration

“California’s Growing and Changing Population:

Results from the Census Bureau’s 1996 State

Population Projections”

Larry Sink

Bureau of the Census

SPECIAL A WARD FOR PRESENTA TION

“World Agriculture to 2005”

Stephen A. MacDonald (Chair)

Jaime A. Castaneda

Mark V. Simone

Carolyn L. Whitton

Economic Research Service

. . . xiii

CONCURRENT SESSIONS I

THE ECONOMIC OUTLOOK

The Economic Outlook (Abstract),

Ed Gamber, Congressional Budget Office . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...11

U.S. Economic Outlook for 1998 and 1999,

Paul A. Sundell, Economic Research Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

The Long-Term Economic Outlook: Is This the EraofDiminishing Resources?

R. M. Monaco, INFORUM, University ofMarykmd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...17

INDUSTRY MODELING AT THE BUREAU OF LABOR STATISTICS

A Model of Detailed Industry Labor Demand,

James C. Franklin, BureauofLabor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...25

AModel ofDetailed Personal Consumption Expenditures,

Janet E. Pfleeger, BureauofLabor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...35

A Commodity-Specific Model for Projecting Import Demand,

Betty W. Su, Bureau of Labor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...45

A Model of New Nonresidential Equipment Investment,

Jay M. Berman, Bureau of Labor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...53

GLOBAL FORECASTING AND FORESIGHT

Global Forecasting and Foresight (Abstract),

Kenneth W. Hunter, World Future Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...61

COMMUNITY POLICY MODELS

The Impact of Medicare Cavitation Payment Reform: A Simulation Analysis,

Timothy D. McBride et al., University of Missouri -St. Louis . . . . . . . . . . . . . . . . . . . . . . . ...65

The Community Policy Analysis System (ComPAS),

Thomas G. Johnson and James K. Scott, Rural Policy Research Institute,

University of Missouri - Columbia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...85

Projections of Regional Economic and Demographic Impacts of Federal Policies,

Glenn L. Nelson, Rural Policy Research Institute, University of Missouri - Columbia . . . . . . . ● . . 91

xv
CONCURRENT SESSIONS II

FORECASTING CROP PRICES UNDER NEW FARM LEGISLATION

Forecasting Crop Prices Under New Farm Legislation,

Peter A. Riley, Economic Research Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...129

Forecasting Annual Farm Prices of U.S. Wheat in a New Policy Era,

Linwood A. Hoffman, James N. Barnes, and Paul C. Westcott,

Economic Research Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...133

An Annual Model for Forecasting Corn Prices,

Paul C. Westcott, Economic Research Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...147

Forecasting World Cotton Prices,

Stephen MacDonald, Economic Research Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

FORECAST EVALUATION

An Evaluation of the Census Bureau’s 1995 to 2025 State Population

Projections-One Y-Later

Paul R. Campbell, U.S. Bureau of the Census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...161

Evaluating the 1995 BLS Labor Force Projections,

Howard N Fullerton, Jr., Bureau of Labor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

Industry Employment Projections, 1995: An Evaluation,

Arthur Andreassen, Bureau of Labor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...195

Evaluating the 1995 Occupational Employment Projections,

Carolyn M. Veneri. Bureau of Labor Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...205

xvi
Consumer Health Accounts: What Can They Add to Medicare Policy Analysis?

R. M. Monaco, INFORUM, University of Maryland, and

J.H. Phelps, Health Financing Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...295

xvii

The Ninth

Federal Forecasters Conference

Forecasting in an Era of Diminishing Resources

Photos by Department of Labor Staff Photographer

J. Steven Landefeld

Director

Bureau of Economic Analysis, U.S. Department of Commerce

Alan R Tupek

Deputy Director

Division of Science Resources Studies

National Science Foundation

This session examined the federal forecasters’ role in a shrinking federal sector. The critical scrutiny of the

government’s role in society has affected federal forecasting. Budgetary realities, widespread skepticism

regarding efficacy of social engineering, and spending priorities have cut resources available to many forecasting

agencies--often drastically. The outlook for forecasting resources at the individual and institutional level is very

uncertain.

The keynote speaker and panelists looked at the appropriate role of the public sector in an information economy;

how forecasters can maintain timely, reliable forecasting with shrinking resources for themselves and fellow

agencies; and how forecasters can contribute to answering these questions for policymakers and the public.
The Ninth Federal Forecasters

Conference was held on

September 11,1997 at the Bureau

of Labor Statistics. These photos

highlight the morning session.

Katherine K. Wallman, Chief Statistician,

Office of Management and Budget delivers the

keynote address.

Katherine K. Wallman makes a point about collaboration

among federal agencies.

Katharine G. Abraham and J. Steven Landefeld address

questions from the audience.

John H. Phelps, Health Care Financing Administration,

poses a question to the panelists.

4
Katherine G. Abraham, Commissioner of Labor Statistics,

Bureau of Labor Statistics (right), J. Steven Landefeld,

Director, Bureau of Economic Analysis (below left), and

Alan R. Tupek, Deputy Director of Division of Science

Resources Studies, National Science Foundation (below

right), lead off the panel discussion on getting the job done

with fewer resources in the wake of downsizing.

Following the morning session, Debra E. Gerald,

National-Center for Education Statistics (NCES)

presents an award to one of the 1997 Federal

Forecasters Forecasting Contest winners, Thomas

D. Snyder, NCES.

5

Concurrent Sessions I

7

THE ECONOMIC OUTLOOK

Chair: Ed Gamber

Congressional Budget Office

The Economic Outlook (Abstract),

Ed Gamber, Congressional Budget Office

U.S. Economic Outlook for 1998 and 1999,

Paul A. Sundell, Economic Research Service, U.S. Department of Agriculture

The Long-Term Economic Outlook: Is This the Era of Diminishing Resources?

R. M. Monaco, INFORUM, Department of Economics, University of Maryland

Discussant: Herman O. Stekler

Department of Economics, The George Washington University

to rise? Over the longer term, what are the prospects for economic growth over the next 5 to 10 years? This panel

discussion will present varying viewpoints on these and related questions about the economic outlook for the United

States. Ed Gamber will discuss the short-term outlook (the current and next quarter). Paul Sundell will discuss the

two-year outlook and Ralph Monaco will discuss the 5- to 10-year outlook. Herman Stekler will critique the

forecasts.

Panelists: Ed Gamber

Congressional Budget Office

Paul Sundell

Economic Research Service, U.S. Department of Agriculture

R. M. Monaco

INFORUM, Department of Economics, University of Maryland

Discussant: Herman O. Stekler

Department of Economics, The George Washington University

11

inflation, and only modest additional tightening of

monetary policy in the spring of 1998. Only a modest

tightening of monetary policy is expected in 1998 given

the outlook of only a small increase in inflation coupled

with continued moderate gains in productivity, and

moderating growth in the final demand for goods and

services. Economic growth will be held down by

expected much slower inventory growth in 1998.

Real GDP growth is expected to slow to 2.3 percent in

1999. Slower growth relative to 1998 will be caused by

tighter resource markets, coupled with the lagged effects

of higher interest rates in 1998, a slowing in the

extremely rapid pace of business equipment investment,

a modest increase in the rate of consumer saving out of

personal disposable income, and slightly higher

inflation. Productivity growth is expected to remain

moderate by historical standards and average slightly

over one percent rate in 1999. Productivity will continue

to gets a boost from strong competitive pressures and the

continuation of strong business investment in the post1993 period.

Tight Labor and Product Markets to Constrain

Growth and To Put Mild Upward Pressure on

Inflation

Thus far, through the third quarter of 1997, there is no

significant evidence of accelerating price pressures in

terms of the broad price indices. The favorable inflation

performance in 1997 has been aided by broad-based

favorable price developments, in the following areas:

employee benefit costs, falling energy and import prices,

and a continued moderate level of worker uncertainty.

A moderate level of worker uncertainty has occurred

despite a low overall unemployment rate, high labor

force participation, and a relatively long average work

week in the private sector. In 1998 and 1999, price

movements in employee fringe benefits, energy, and

imports are not expected to be nearly as favorable.

Likewise, the continuation of very low rates of

unemployment coupled with continued moderate gains in

worker productivity and strong corporate balance sheets

rate stood at 4.9 percent. Historically, the unemployment

rate has not been below 5.0 percent for a prolonged

period since 1973. In 1997, labor force participation

rates have reached an all time high. The average private

nonfarm workweek and overtime hours in manufacturing

have remained high by historical standards since 1993.

Most Federal Reserve Districts are reporting growing

shortages of skilled labor. Historically, such signs of

prolonged labor market tightness have normally been

associated with accelerating inflation.

Capital goods markets are moderately tight by historical

standards. Overall capacity utilization in August stood at

83.9 percent while capacity utilization in manufacturing

stood at 83.1 percent. Over time, prolonged capacity

utilization in the manufacturing sector above 82 percent

generally has been associated with periods of rising

inflation. Tight factor markets typically slow economic

growth by generating slower deliveries of goods and

services and by raising inflation. Higher inflation slows

economic growth by raising economic uncertainty

through increasing uncertainty concerning inflation and

therefore expected real returns to labor, capital, and

financial investment.

Favorable price movements have occurred in 1997 in

terms of very mild increases in employee fringe benefit

costs, sharply lower energy prices, and a strong dollar.

These favorable relative price movements have helped

keep inflation very low. Employer fringe benefits costs

increased by only 1.4 percent on a seasonally adjusted

annualized basis in the first half of 1997. Crude energy

producer prices, led by sharply falling crude petroleum

prices, have fallen 15 percent through the third quarter of

1997 relative to the fourth quarter of 1996. In 1996, the

Federal Reserve Board real trade weighted dollar index

rose 6 percent and thus far in 1997 has risen 11 percent.

The strong value of the dollar is the primary factor in the

overall fall in import prices of over 3 percent in the first

half of 1997. The fall in import prices is also

constraining the ability of U.S. manufacturers that face

significant foreign competition to raise prices.
Worker Concerns Over Job Security Should Lessen

and put Upward Pressure on Wages In 1998 and

1999

Although the unemployment rate has fallen below 5.0

percent in recent months, other measures of labor market

tightness involving job prospects and job search time fail

to indicate as much job tightness as suggested by the

unemployment rate and average hours worked data. The

perceived continued difficulties of unemployed workers

in finding new employment has been a factor in

moderating wage increases despite a low unemployment

rate and along average workweek. The employment cost

survey indicated wages and salaries increased 3.4 percent

in 1996 and at a 3.6 percent annualized rate in the first

half of 1997.

The continued relatively long duration of average time

spent unemployed and the continued relatively high

levels of job layoffs, given the low level of the

unemployment rate, have lowered worker job security.

Unemployment data indicates that the duration of

unemployment for the unemployed remains relatively

high and that job losses remain the dominant source of

unemployment. Since the beginning of the current

expansion in the spring of 1991, the duration of

unemployment for those who are unemployed has

actually increased. Normally, the duration of

unemployment for the unemployed falls in an economic

expansion. Further evidence of continued worker

uncertainty is that roughly 45 percent of those

unemployed are unemployed because of losing their

previous job.

Worker uncertainty and its inhibiting impact on wage

gains should decline in 1998 and 1999. A slower pace of

corporate restructuring, the continuation of tight labor

market conditions, as well as moderate gains in labor

productivity, strong corporate balance sheets, and

moderate increases incorporate profits in 1998 and 1999

point toward a modest to moderate acceleration in the

rate of wage gains.

capital available per worker. Gradually improving

worker skills that have allowed workers to better utilize

improvements in capital and technology are also a factor

in recent productivity gains. Increased domestic and

foreign competition have also boosted productivity in

recent years and should continue to boost productivity in

1998 and 1999.

Demand Side Factors Point To Slower Growth in

1998 and 1999 As Well

Although the recovery is currently in its seventh year, the

economy has failed to generate the sectoral imbalances

that turn a mature but slowing economic recovery into a

recession. Inflation has remained low, thus reducing

economic uncertainty and promoting relatively low real

long term interest rates. Consumers are not currently

overspending relative to their income, confidence levels,

or balance sheets. Corporate balance sheets have

improved substantially in recent years. Improved

corporate balance sheets are allowing firms to raise more

and less expensive capital. The banking system remains

liquid, highly profitable, and desires to expand lending,

especially in the business loan area.

Despite the lack of major sectoral imbalances, growth in

aggregate demand should slow in 1998 and 1999.

Business investment both in terms of fixed capital and

inventory should slow in 1998 and 1999. Strong

business investment since 1993 has reduced the capacity

utilization rate in manufacturing by 1.5 percent since

early 1995. Lower capacity utilization rates have

resulted in a smaller gap between the actual and desired

capital stock, which should slow the pace of business

fixed investment Very lean inventories relative to sales

entering 1997 have encouraged business firms to increase

inventories by over $70 billion per quarter in the first half

of 1997. As inventories move closer to desired levels

relative to current sales levels and growth in final

demand slows in 1998 and 1999, growth in inventories

should slow significantly.

14
g pg

include slightly lower consumer confidence, slower gains

in household financial wealth (resulting primarily from

much slower gains in equity prices), continued

tightening of consumer lending standards by commercial

banks, higher interest rates, and reduced pent-up demand

for consumer durables.

Real government spending is expected to continue its

trend of very slow growth in 1998 and 1999. Combined

real federal and state and local spending grew 0.5

percent in 1996 and 0.7 percent in the first half of 1997.

Real federal government spending is expected to decline

at an annual rate of 1.1 percent in 1998 and 1999. State

and local spending is expected to rise at slightly above 2

percent rate in real terms in 1998 and 1999.

Federal Reserve policy is expected to raise the target for

the federal funds rate to 6 percent by late spring of 1998.

Federal Reserve pronouncements have showed

continued concern over tight labor market conditions.

As the pace of inflation accelerates slightly and economic

growth remains strong in the second half of 1997, the

forecast assumes the Federal Reserve reacts quickly to

raise the federal funds rate. Inflation as measured by the

GDP deflator is expected to average 2.6 percent in the

second half of 1998 and 2.7 percent in 1999.

The increase in the federal funds rate and slightly higher

inflation is expected to push the average ten year

Treasury bond rate to 6.7 percent by the second half of

1998 and 1999. Higher short and Iong-term interest

rates can be expected to raise required returns on equity

instruments, thus further slowing the demand for funds

by business fins. Given the substantial lags between a

tightening of monetary policy and real economic growth,

the impacts of higher long-term interest rates are

expected to be felt more in 1999 than 1998.

ppggg

developed countries and slower U.S. inventory

accumulation on net exports will be offset by continued

relatively strong U.S. growth and the lagged effects of

the large rise in the real value of the dollar since late

1995. The value of the dollar is expected to slowly

decline over the latter half of 1998 and 1999 as real

growth and expected asset returns gradually increase in

other developed countries. Persistent large U.S. trade

deficits will put additional downward

pressure on the dollar by reducing the willingness of

foreigners to hold additional dollar denominated assets.

15

macroeconomic performance has been quite good. The

unemployment rate is at generation-low levels and this

has been achieved with surprisingly little inflation.

Interest rates remain relatively low. But judging by

the performance of previous expansions, our recent

performance has been about average to below-average.

Table 1 contains the evidence, which shows average

annual growth rates for selected economic indicators

between peak years in US business cycles. (Business

cycle peak dates were taken from the National Bureau

of Economic Research. Data in Table 1 were

calculated using annual data, not quarterly or monthly

data, and so provide approximations to the exact NBER

peak dates. In addition, one short cycle -- peaking in

July 1980 -- was lumped into a longer cycle with a

peak in 1973 and the next peak in 198 1.)

Looking Backward

Table 1 shows that real GDP growth has been the

lowest of any peak-to-peak period in the postwar years.

However, the real rate of appreciation in stock prices

has been very good in the last 6 years, and the average

inflation and interest rates have been the lowest since

the 1960-69 period.

The figures in Table 1 show some other

remarkable features. First, the table contains some

warnings for those who may have come to rely on

stock-market price increases to power their retirement

incomes or supplement their labor earnings. From the

peak in 1969 to the peak in 1981, nominal stock prices

grew about 2.2 percent a year, about 4 or 5 percentage

points below the average inflation rate for the same

period. This is all the more sobering because businesscycle effects are mostly filtered out by calculating

increases using only the NBER peak years.

The peak-to-peak figures in Table 1 also show

some interesting features of population and labor force

dynamics. The effect of the Baby Boom entering the

working age population is shown clearly in the 1969-73

period compared to previous and subsequent

expansions. It’s also interesting to note that for the

1948-96 period as a whole. while labor force

17

whole, there was no net change in the employment rate.

In other words, labor market developments have had

little -- on average -- to do with long-term economic

growth. This suggests that one key variable to forecast

the potential labor contribution to output is the growth

in the working -age population, as opposed to how the

population participates in the labor force or how

successfully it is employed. (Note this is definitely not

true within a cycle, nor for any cycle in particular. But

it is true of the long sub-periods.)

Ten-Year Outlook: More of the Same

The outlook for the next 10 years is for average

economic performance to be similar to the last 7 years.

As suggested above, one of the keys to forecasting

growth over the longer term is a good forecast of the

growth in the working -age population. The second

important key is growth in output per hour. While

there is little debate about how fast the working-age

population is likely to grow over the next decade, there

is some disagreement among forecasters about how fast

productivity will grow.

Some of that disagreement is likely due to the

way that longer-term forecasters look at productivity.

Most forecasters take a macro view -- productivity

behavior is forecast for the nonfarm sector as a whole.

Others look at productivity in various industries and

then attempt to aggregate these into an overall

productivity number.

The sectoral approach to productivity forecasting

leads to a lower forecasted rate of growth in overall

productivity than does the macro approach. Table 2

shows why. Employment shares have grown most in

non-medical services -- including jobs like lawyers,

consultants, private education, movies and amusements

. . and medical services. As the second panel of Table

2 shows, these sectors have had negative measured

productivity growth from 1973 to 1994. At the same

time, those sectors with relatively rapid growth in

productivity account for shrinking shares of

employment. Even with relatively generous

assumptions about how fast measured productivity will
in the future, rather than a productivity rebound, as

some are projecting.

The productivity forecast is clouded by many

measurement issues, some of which have been brought

to the fore by the recent investigation into whether the

Consumer Price Index overstates inflation. If

consumer price increases have been overstated, then

“real” purchases in these sectors have been understated,

which implies that “real” production has been

understated. If we have accurately counted the number

of hours worked in the sector, then the understatement

of output implies an understatement of productivity.

The problem of measuring output is especially

difficult in the services sectors, which account for a

large portion of employment. For many of these

sectors, there is virtually no data available on the

“quantity” of services provided. For sectors like the

medical services sector, while you can easily count the

number of doctor or hospital visits and thus obtain a

quantity index, it is apparent to even casual observers

that a lot of quality change has taken place. Quality

change obviously needs to be accounted for if we are

to measure productivity well. Some estimates suggest

that productivity growth in the medical services sector

may be understated by several percentage points a

year!

These thoughts put us in a Catch-22. We may

believe that true productivity growth for the next 10

years will be close to or even higher than the 1.4

percent annual rate predicted by many analysts.

However, based on the figures we have, it appears 1.4

percent will be hard to achieve. The forecast contained

in the Inforum column of Table 1 is a forecast based on

numbers that we have, even though we believe that

they substantially understate the actual rate of

productivity growth. At the moment, we simply don’t

have enough information to do otherwise.

More Than Ten Years After

According to the standard economic growth model, this

will lead to increasingly lower rates of capital

formation, increasingly higher interest rates, and

increasingly lower labor productivity growth.

The projected deficit problem is so severe that

reasonable economic simulation models cannot

meaningfully calculate the economic outlook -- the

models break -- unless taxes are raised, benefits are

reduced, or other federal outlays are reduced. The tax

increases projected to lead to sensible model results are

large. Pay-as-you go financing -- raising taxes to

match spending increases -- leads to a doubling of

payroll tax rates to keep the federal budget balanced

(Monaco and Phelps, mimeo 1997).

The expectation of rising federal deficits and

their actual onset will probably bring on a host of

“structural” changes in the economy. When we think

about what these adjustments might be, we round up

the two usual suspects: higher taxes and reductions in

benefits (including raising the retirement age). In

closing, however, here is some speculation about other

ways the economy will adjust:

Surprisingly large increases in labor force

participation among the 65.

Encouragement of immigration.

Encouragement of fertility.

Changing the entitlement nature of Medicare and

Social Security. At some point, government

payments will be linked to “need” rather than

age.

Analysts will adjust too. Over the next several

years, we will likely redefine service price and output

measures. This will reduce the severity of the

measured productivity slowdown after 1973, and

provides a “truer” picture of real economic well-being.

18
. . ., . .“ - - .- . -.

. .

Table 1

Economic Performance and Forecasts, 1948-2006 .

History Forecasts

Infomm CBO SPF Blue Chi

1948-96 194&53 1953-57 1957-60 1960-69 1969-73 1973-81 1981-90 1990-96 1997-06

Average annual growth

!brtdng Age Population 1.4 0.8 1.2 1.4 1.5 2.3 1.8 1.2 1.0 1.0

+ Participation rate 0.3 0.0 0.3 -0.1 0.1 0.3 0.6 0.5 0.1 0.3 : =Labor force 1.7 0.8 1.5 1.3 1.6 2.6 2.4 1.6 1.0 1.3

+ Employment rate 0.0 0.2 -0.4 -0.4 0.2 -0.4 -0.4 0.2 0.0 -0.1

=Employment 1.6 1.0 1.1 0.9 1.9 2.2 2.1 1.9 1.1 1.2

Employment (nonfarm business) 1.7 1.7 0.8 0.3 2.2 1.9 2.1 2.0 1.3 1.2

+Average weekly hours -0.3 -0.2 -0.3 -0.1 -0.4 -0.6 -0.7 0.0 0.1 -0.1

=Total hours 1.4 1.4 0.6 0.2 1.8 1.3 1.4 2.0 1.3 1.0

+output per hour 2.0 3.4 1.9 2.5 3.0 3.0 0.9 1.1 1.0 0.8 1.4

=Total output 3.4 4.7 2.5 2.7 4.8 4.3 2.3 3.1 2.3 1.8

Ieal GDP 3.2 4.6 2.5 2.8 4.5 3.6 2.3 2.9 2.0 1.8 2.4 2.4 2.4

nflation and Ptices

Consumer Price Index 3.9 2.1 1.2 1.7 2.4 4.8 9.0 4.0 3.0 3.2 3.0 2.9 3.

GOP Impticit Price Deflator 3.8 2.2 2.4 1.6 2.4 5.0 7.8 3.9 2.7 3.1 2.6 2.

s& P500 7.8 9.3 14.6 7.7 6.2 2.3 2.2 10.7 11.6 9.0

Average of variables during business cycJes

nterest rates, in percentages

3-month Treasury bill 5.1 1.4 2.1 2.9 4.0 5.7 8.2 8.5 4.9 5.3 4.6 4,7 5.

10-year Treaswy bond 6.4 2.4 3.0 3.9 4.7 6.6 9.0 10.3 7.1 6.4 5.8 6.4 6.

Wilian unemployment rate, % 5.7 4.0 4.3 5.5 4.8 5.0 6.7 7.1 6.3 5.4 5.7 5.

kmrces: Inforum, May 1997 Forecast, FRB of Philadelphia, Survey CM Pm6sskwa/ FOmcastem (SPF), First Quarter 1997

Congressional Budget Office, The Economic ad B@@~,L4$date September 1997, 8/ue Ch@ Ecormrnk h?dcetors, March and September 1997
Transportation

Utilities

Trade

Finance, insurance, real estate

Services, nonmedical

Medical services

Civilian Government

3.4

2.3

20.7

4.9

11.0

3.1

14.8

3.4

1.8

22.0

5.9

20.9

7.5

15.8

1.4

2.3

1.2

0.3

-1.8

-2.0

1.3

3.4

1.3

0.9

0.6

0.3

1

Source: Inforum data files. Employment shares will not add to 100 because civilian

jobs includes domestic servants, which are not included in any listed sector.

opulation Concepts

Total

Working Age (BLS definition)

Aged 20-64

Aged 65+

Ratio People aged 20-64 to 65+

Total Population

Voting Age (BLS definition)

‘opulation, 20-64

Table 3

Population Projections 1996-2050

1996 2006 2015 2030 2040 2050[

millions of people I

265.7 287.6 304.8 327.2 336.2 342.7

207.6 228.6 245.5 266.2 274.6 280.3

155.4 172.0 181.0 180.9 185.8 189.7

33.9 36.1 44.0 64.3 67.8 69.3

4.6 4.8 4.1 2.8 2.7 2.7

annual growth from previously displayed year, percent

0.8 0.6 0.5 0.3 0.2

1.0 0.8 0.5 0.3 0.2

1.0 0.6 0.0 0.3 0.2

Source: Inforum population forecast, November 1996

20
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I I I

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, I

1 I I I I

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21

INDUSTRY MODELING AT THE BUREAU OF LABOR STATISTICS

Chair: Norman C. Saunders

Bureau of Labor Statistics, U.S. Department of Labor

A Model of Detailed Industry Labor Demand,

James C. Franklin, Bureau of Labor Statistics, U.S. Department of Labor

A Model of Detailed Personal Consumption Expenditures,

Janet E. Pfleeger, Bureau of Labor Statistics, U.S. Department of Labor

A Commodity-Specific Model for Projecting Import Demand,

Betty W. Su, Bureau of Labor Statistics, U.S. Department of Labor

A Model of New Nonresidential Equipment Investment,

Jay M. Berman, Bureau of Labor Statistics, U.S. Department of Labor

23

pg g pjpyy

industry and occupation. These projections are

developed to facilitate understanding of current and

future labor market conditions and are disseminated for

use in career guidance and public policy planning that

is related to employment issues. A system of several

component models is used by OEP to develop these

projections. This paper presents the industry level

labor model. The labor model and its sub-components

are defined and the integration of the labor model with

the larger OEP projections system is described.

The labor model

The labor model is actually a group of equations and

identities which are solved independently. The main

component of the labor model is the equation that

estimates the demand for wage and salary hours. It is

derived from a constant elasticity of substitution (CES)

production fiction. The remaining equations and

identities are necessitated by the availability of data

and the relationship of the labor model to the other

components of the OEP projections system. The labor

equation which estimates demand for wage and salary

hours is based on a theoretical economic structure

while the other estimated equations are time and other

variable extrapolations.

The CES derived labor equation

The demand for wage and salary hours for each

industry is estimated using the first order conditions of

a CES production function modified to include a time

variable. The time variable captures disembodied

technical change or shifts in the production function

that do not affect the labor and capital ratio. These

shifts of the production indicate increased efficiencies

in the use of the capital and labor inputs.

The basic form of the production function is:

Equation 1

Y= f(t,L,K)

t time

The model assumes perfect competition and profit

maximization so that:

both factors are indispensable in the production of

output – ~(O,K)=~(O,L)=O

both marginal products are nomegative —

dfm=x), aflx=xl

and the marginal products are equal to the real factor

prices — ~f/t3L = w/p, i3f/i3K = r/p

where ‘w,r,p’ are the nominal prices for labor, capital

and output.

It is also assumed the rate of growth of disembodied

technical change is proportionate and constant.

The functional form of the labor demand model is:

Equation 2

Y = Aemt[~ L-P+ (1 - ~)K-fl]-fi

where

A

6

P

Y

L

K

m

t

is a scale parameter, A > O;

is a distribution parameter, O <5<1

is a substitution parameter, ~ 2-1

is real output

is labor, measured as annual wage and salary

hours in millions

is the capital stock

is disembodied technical change growth rate

is time, measured as the year

The marginal product of labor can be written:

25
;=(A%%’)2(;)*

taking logs:

0

1

MO-ML) =J - - -----— _g*

$+1 ; p+l lr@) p+l

then solving for labor:

hi(L) 1lrI(A) 0

—— lW

-p+l -&+l@)— — – ‘ ‘p+l p+l p

results in the final basic form of the equation. The

estimated form of the equation is:

Equation 3

( )

lnL=tzO +alt+azln Y+a~ln y

P

Other equations and identities

Equation 3 requires estimated data for real output,

nominal wage, and output price by industry for a

solution. The output level is supplied by the inputoutput system as an exogenous variable. The industry

price and wage data are estimated using projected

variables from the macro-economic model which

produces the aggregate projections in the OEP

projection system.

Given an industry’s output, wage and output price,

equation 3 will solve for the required wage and salary

hours. The end product of OEP’S projection system,

however, includes the employment level by industry for

wage and salary and self-employed and unpaid family.

The solution for equation 3 must be converted to an

employment level for wage and salary using an

extrapolated estimate for each industry’s average

 hours. The number of self-employed and

following equations are used to estimate the industry

average hourly wage.

Equation 4

TotAHW = aO + a1ECIFJ5 +a2ur

Equation 5

AH~ = aO +alTotAHW: aO= O

where

TotAHW is the total average hourly wage

EC.lWS is the employment compensation index

for wage and salary

AHWj is the average hourly wage for industry i

ur is the aggregate unemployment rate

ai are constants/coefficients

Industryprice index estimation

The price index for each industry is estimated with the

following equation:

Equation 6

ln(p,) = aO +al in(P)

where:

pi is the price chain

industry

weighted index for each

P is the GDP chain weighted price index

ai are constantskodiicients

Industry average weekly hours estimation

Average weekly hours for both wage and salary and

self-employed and unpaid family are estimated as a

function of the year and the aggregate unemployment

rate.

26
Industry selfemployed and unpaid family estimation

The number of self-employed and unpaid family for

each industry was derived by using an estimate of the

ratio of the self-employed and unpaid family to total

employment to derive the level of total employment for

each industry from the level of wage and salary, and

then subtracting the wage and salary from the total

employment. The logit transformation of the selfemployed and unpaid family workers ratio to total

employment was estimated as a function of the year

and the aggregate unemployment rate using the

following equation.

Equation 8

(R) In SR

— = aO +alt +a3ur 1-s

where

SR is the ratio (self-employed and unpaid family

workers/total employment)

t is time, measured as the year

ur is the aggregate unemployment rate

&li are constants/coefficien@

Employment, hours and average weekly hours identity

Employment, measured in thousands, for wage and

salary, and self-employed and unpaid family, is related

to the annual hours measured in millions and the

average weekly hours by the following identity.

Equation 9

where

Ei employment level in thousands

Li annual hours, measured in millions

A WHi average weekly hours

3.

4.

5.

6.

7.

8.

y 

The industry levels of wage and salary jobs in

thousands are then derived from the estimation of

hours and average weekly hours.

The ratio of self-employed and unpaid family

workers to total employment is extrapolated.

The extrapolated ratio is then used to derive the

level of self-employed and unpaid family workers

from the number of wage and salary jobs.

Self-employed and unpaid family average weekly

hours are estimated.

The hours for self-employed and unpaid family

workers are then derived from their estimated

average weekly hours and the estimated number of

self-employed and unpaid family workers.

Finally, wage and salary, and self-employed and

unpaid family worker employment and hours are

combined to calculate a total level of employment

and hours for each industry.

Data sources

The output measures follow the definitions and

conventions used by the Bureau of Economic Analysis

(BEA) in its input-output tables, published every five

years. These industry output measures are based on

producer’s value and include both primary and

secondary products and services. The main data

sources for compiling the output time series for

manufacturing industries are the Census and Annual

Survey of Manufactures. Data sources for

nonmanufacturing industries are more varied. They

include the Semite Annual Survey, National Income

and Product Accounts (NIPA) data on new

construction and personal consumption expenditures,

IRS data on business receipts, and many other sources.

The constant dollar industry output estimates for the

most recent years are based on BLS employment data

and trend projections of productivity. The output series

are benchmarked to the BEA input-output tables for

1987 which was adjusted by BLS to reflect the 1987

27
change are made by the BEA from other data series.

The employment data come from the BLS current

employment statistics (the establishment data series for

wage and salary jobs and average weekly hours), the

current population survey (for self-employed and

unpaid family worker jobs, agricultural employment

except for agricultural services, and private household

employment), ES202 Employment and Wages data

collected for the unemployment insurance program

(agricultural services and total wages paid), and some

unpublished data sources within the Bureau. Average

hourly wages were calculated using the ES202 wage

data and the annual hours estimate developed from the

current employment statistics data for each industry,

except for the government sector.

All data series are developed on an annual basis. The

beginning and ending years differ between the data

series. The industry output and price series begins in

1972; the employment series varies by industry, the

earliest year being 1958; the wage data series begins in

1975. The industry output and price data end in 1996,

although for most industries the 1995 and 1996 data

points are extrapolations. The employment data also

ends in 1996. The wage data ends in 1994. The

regression estimates, limited by common years, are

based on data from 1975 through 1994.

Regression and result ` s

All regression estimates for the equations of the labor

model are estimated using ordinary least squares.

With the exception of the agricultural and public

complete for the agricultural sectors. 

employment in the agricultural sectors is covered by

the unemployment insurance program.

The sectoring plan which OEP uses to define the

industries consists of 185 sectors, 8 of which are

special accounting industries that have no associated

employment. Sectors 170 through 179 are government

industries and sectors 1 through 3 are agricultural

industries. That leaves 164 industries for which

employment is estimated using the fidly expressed

labor equation. The discussion of the regression results

will focus on these 164 industries. The regression

statistics for all industries are listed in table 1.

All of the industries which are estimated using the full

labor equation have very high r-squares and

significance levels as given by the F-test. However, the

three explanatory variables (year, real output real

wage) are significant in only 28 out of 164 industries

(see figure 1). This is only seventeen percent of the

industries. The year variable is included to capture

possible shifts in the production function that underlie

the regression equation and is not strictly an economic

variable. If it is ignored and only the economic

variables (real output and real wage) are considered,

then only 52 of the 164 industries, or 32 percent, have

both variables significant.

Conclusion

These results suggest a strong multicollinearity

problem. Given the nature of the data set however,

this is not unexpected. These are strongly correlated

lFigure 1 Number of industries Percent of industries

correct Significant Correct Sign correct Significant @ Correct sign

Sign @ .05 and significant Sign .05 and signifcant

@ .05 @ .05

Year NA 94 NA NA 57% NA

output 150 117 114 91% 71% 70%

Real wage 129 74 69 79% 45% 42%

Output and real wage 118 52 47 72~0 32% 29%

Year, output and real wage NA 28 23 NA 17% 14yo

28
First, the labor model is a formal model based on

strong economic principals. Second, t h e

multicollinearity does not invalidate the significance of

the regression equations. It does make analysis of the

explanatory power of the individual variables

problematic. Since the purpose of the labor model is

projections work, the explanatory power of the

variables are of lessor importance than the significance

of the whole equation. And finally, the labor model in

practice seems to perform well enough. Its principal

purpose is to estimate an initial level of projected employment by industry. The OEP projections process

is an iterative one with several points of subjective

review. Consequently, the labor model is not expected

to produce a final and publishable projection without

review and adjustment. As an estimator of the initial

industry level employment projections and an adjunct

to subjective analysis it has proved useful.

29





size and demographic composition of the labor force; 2)

the growth of the aggregate economy; 3) final demand

or gross domestic product (GDP) subdivided by

consuming sector and product; 4) inter-industry

relationships (input-output); 5) indust~ output and

employment; and 6) occupational employment.

The Personal Consumption Expenditures (PCE) Model

falls under step 3. Within step 3, each component of

GDP is projected—PCE, business investment,

government spending and foreign trade. This paper

presents a dynamic model for PCE that projects

consumer spending for 80 product groups. 1 It was

originally estimated by Houthakker-Taylo# in the mid1960’s and is based on the theory that current consumer

purchases depend not only on current income and

relative prices, but on a stock variable representing

either the adjustment of a pre-existing inventory of the

product in question to a desired or equilibrium level, or

habit formation from past consumption.

The Functional Form

The standard approach to demand analysis involves

estimation of the following demand equation:

(1) qit =fi(Xt,J2it,zll, z2t,...,zn*,Uit)

where:

qit: per capita consumption of the ith commodity

in year t

f.: function whose mathematical form is specified

l;ter

x,: per capita real disposable income

Pit: deflated price of the ith commodity

Z,zz: It 2t’”””’ nt any other explanatory variables, such

as the price of one or more substitute or complimentary

goods of the ith commodity, lagged values of xt or pit,

or a time trend.

Uit: disturbance term representing both the effect

of variables that are not explicitly introduced into the

equation and errors in measurement of qit.

where:

state variable

:: state coefficient

c: short-run derivative of consumption with

respect to income (the marginal propensity to consume)

d: short-run derivative of consumption with

respect to relative price

To define the state variable in (2), the change in either

of the two types of stocks (inventory or habit formation)

is assumed to be new purchases less the depreciation of

existing stock, where the depreciation rate is assumed

constant. This stock depreciation equation is expressed

as:

(3) S, =q, -es,

where:

s: the rate of change in the stock

and either

e= a constant rate of stock depreciation for

goods

or

e=a constant rate at which habit-formation

wears off

s can now be eliminated by combining (2) and (3):

(4) st=qt–(elb) *(q, -a–cxl–dpf)

Differentiating (2) with respect to time, substituting (4)

for ~ , and combining the different variables yields:

(5) q,= ae+(b–e)q~+cex, +dep, +ci +dpt

(5) is a first-order difference equation that deals only

with the variables q, x and p, all of which are ‘observed’

variables.

Before estimating the model, qt must be eliminated

from the right hand side of(5). For computational

reasons, it is also desirable to eliminate the current year

35
where zx~xt-xt- 1 and zpt=pt-pt- 1

Combining the cwfficients of (6) yields the:

Equation to be Estimated:

(7) q,= A.+ A,q, -,+ A2AX + A~X, - ,+ AApt +

Asp, -1

where:

% per capita PCE of the item in question in year t

(millions of chain weighted 1992 dollars)

q~.1 : lagged value of q

Xt: total per capita PCE in year t (millions of chain

weighted 1992 dollars) (a proxy for per capita personal

disposable income)

Axt=xt-x~- 1: change in per capita PCE

X,.1 : lagged value of X

Pt: relative price in year t of the good in question

(1992=100), calculated as the implicit deflator for that

good divided by the implicit deflator for total PCE

APt=Pt-Pt-l : change in relative price of the good in

question

pt-l=laggd relative price

Estimation Procedure

The PCE model was estimated using the following

Histon”cal Data Sources:

% National Income and Product Accounts,

Bureau of Economic Analysis, Commerce Department

(the conversion to per capita is made using the Total

Population figures from the macro econometric

model-this number includes Armed Forces overseas

and is in millions)

Xt: National Income and Product Accounts,

Bureau of Economic Analysis, Commerce Department

Pt: National Income and Product Accounts,

estimate the historical data so that the residuals could be

examined. The residuals were analyzed by looking for:

positive or negative groupings of error terms; outliers;

tails at the end of the historical series that would

indicate problems in using the equation to project; and

similar patterns among the residuals of different

equations. See Table 2 for the equation coefficients and

related statistics.

Given the problem of serial correlation in time series

data, tests were performed to determine whether the

error terms were correlated. Ordinarily, the Durbin

Watson d Statistic would be used to test for positive or

negative serial correlation. However, when an equation

includes a lagged dependent variable as one of the

independent variables, the d statistic cannot be used (it

is biased toward 2, which would suggest no serial

correlation). Instead, testing for serial correlation is

done with a normally distributed statistic, Durbin’s h

Statistic.3 Of the 80 PCE equations, seven (equations

33,34,40,47,56,75 and 77) were corrected for serial

correlation using an iterative procedure in which

estimates for rho (p, where u~put-l+et) were derived at

each iteration. The rho-transformed variables were then

used for the next iteration. Table 2 includes the

coefficients and regression statistics for all equations,

including those corrected for serial correlation. Note

that the summary statistics are based on the rhotransformed variables.

Projecting with the PCE Model

Once the equation specifications were finalized based

on the evaluation described in the preceding section, the

projections were made using the following Projected

Data Sources for Independent Variables:

%1: The dependent variable generated for each

year’s projection is used as the lagged dependent

variable in the following year.

Xt: Generated by the OEP macro econometric

model

Bureau of Economic Analysis, Commerce Department

36
1 See Table 1 fore the list of the 80 product groups.

2 H.S. Houthakker and Lester D. Taylor. Consumer Demand in the United States, 1929-1970, Analyses and

Projections, Cambridge: Harvard University Press; 1966.

3 h = (l–5DW)~-

where:

DW= Durbin Watson d Statistic

n= number of observations

S2= s squared, the estimated variance of the estimated coefficient of the lagged dependent variable

4 The technique fits a trend model across time such that the most recent data are weighted more heavily than data in

the early part of the series. The weight of an observation is given by an exponential function of the number of

periods that the observation extends into the past relative to the current period.

37
10 Books and maps

11 Wheel goods, durable toys, and sports equipment

12 Food for off-premise consumption (excluding alcohol)

13

14

15

16

17

18

19

20

21

22

23

24

Iampshades)glass products; and plastic products]

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

Purchased meals and beverages

Food furnished to employees

Food produced and consumed on farms

Alcoholic beverages (in off-premise)

Shoes

Clothing and luggage

Military issue clothing

Gasoline and oil

Other fuels

Tobacco products

Toilet articles and preparations

Semidurable house furnishings [misc. textile products(bmshes, brooms,

Cleaning and miscellaneous household supplies & paper products

Stationery and writing supplies

Drug preparations and sundries

Magazines, newspapers, and sheet music

Nondurable toys and sporting goods

Flowers, seeds, and potted plants

Expenditures abroad by U.S. residents

Personal remittances to nonresidents

Space rent from owner-occupied nonfarm dwellings

Rent ikom tenant-occupied nonfarrn dwellings

Rental value of farm dwellings

Other housing (hotels and other lodging places

Electricity

Gas

Telephone and telegraph

Water and sanitary services

Domestic services

Other household operation

Automobile repair

Bridge, tunnel, ferry and road tolls

Automobile insurance less claims paid

Intracity mass transit

Taxicabs

Railway transportation

Xntercity bus

38
60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

Brokerage charges and investment counseling

Bank service charges

Services furnished without payment by financial intermedities

Expense of handling life insurance

Legal services

Funeral and burial expenses

Other personal business services

Radio and television repair

Motion picture admissions

Legitimate theater admissions

Admissions to sports events

Clubs and fraternal organizations

Commercial participant amusements

Pari-mutuel net receipts

Other recreation services

Higher education

Elementary and secondary education

Other private education and research

Religious and welfare activities

Foreign travel by U.S. residents

Expenditures in the U.S. by foreigners

39

. .
Table 2. PCE Model Coefficients (with corresponding t-statistics)

F

change in change in lagged

lagged per capita lagged per relative relative

Eqn intercept dependent PCE capita PCE price price R-squared F-value

1 Motor Vehicles -69.165068 0.665869 0.22608 0.016067 0 0

-3.508 9.935 12.994 4.189 0.9828 609.84

2 Tires tubes accessories -10.1904849 0.8402261 0.01032823 0.001858 0 0

&otherparts -1 339 7.411 4.49 1.511 0.9936 1644.058

3 Furniture, including -4.10952~266 0.7242035 0.01877184 0.002763 0 0

matresses and bedsprings -1.301 4.985 5.409 1.932 0.9822 587.63

4 Kitchen &otherhousehold -2.433643351 0.9064753 0.0110716 0.000608 0 0

appliances -1.286 9.978 5.779 1.113 0.9833 626.177

5 China, glassware, 5.35669551 0.8649853 0.00467677 0.00068 -34.5153 -5.10335

tableware and utensils 0.886 7.093 3.067 1.167 -2.388 -1.098 0.9869 451.087

6 Video & audio products, 114.5666581 1.2630805 0.00903983 -0.008044 -19.1449 -8.52689

computers&musical instruments 2.44 55.898 2.52 -2.855 -1.492 -2.39 0.9985 3945.743

7 Otherdurable -10.43959892 0.9150171 0.01928001 0.001539 0 0

housefurnishings -1.65 9.422 7.084 1.102 0.9942 1837.946

8 Jewelry&watches 59.67019616 0.7487659 0.01240473 0.000963 -64.4086 -44.8662

2.793 8.029 4.134 1.031 -3.937 -3.207 0.993 856.148

9 Opthalmic products & -3.957224542 0.8886221 0.00580455 0.000514 0 0

orthopedic appliances -1.808 10.49 3.574 1.508 0.974 398.916

10 Books&maps 3.64031853 0.7524215 0.00207436 0.000724 -63.4757 2.114598

0.512 6.432 0.951 0.948 -2.97 0.156 0.9453 103.763

11 Wheelgoods, sports &photoequip- -14.32139871 0.8760107 0.0190725 0.001844 0 0

merit, boats& pleasure aircraft -1.5 6.388 4.781 1.119 0.9833 628.543

12 Food purchased foroffpremise 195.0733028 0.8373804 0.04950332 0.001842 0 0

consumption 1.877 9.566 3.081 0.948 0.8998 95.81

13 Purchased meals &beverages 49.72053746 0.679777 0.05094726 0.014875 0 0

1.852 4.918 5.674 2.38 0.9937 1690.703

14 Food furnishedto employees 7.331023391 0,8743301 -0.0021527 -0.000201 0 0

1.69 10.415 -1.568 -1.48 0.8768 75.884

15 Food produced and consumed 4.501447118 0.7805485 -0.0009165 -0.000242 0 0

on farms 2.558 13.617 -1.991 -2.364 0.9865 778.637

16 Alcohol beverages purchased for 8.432669861 0.9907576 0.00760356 -0.000469 0 0

off-premise consumption 1.89 18.442 1.714 -0.573 0.9839 650.865

17 Shoes -2.030890189 0.8849396 0.01005989 0.001007 0 0

-0.884 11.197 5.042 1.781 0.9878 865.404
. . . . . . . . . . . . . . . . .

Table 2. PCE Model Coefficients (with corresponding t-statistics)

change in change in lagged

lagged per capita lagged per relative relative

Eqn intercept dependent PCE capita PCE price price R-squared F-value

18 Clothing & luggage -27.56525987 0.9386914 0.04724968 ~ 0.004923 0 0

-2.051 14.989 5.513 1.418 0.996 2625.051

19 Military clothing 0.592165344 0.766812 -3.72E-06 -3.03E-05 o 0

2.052 7.229 -1 .80E-02 -1.716 0.7185 27.221

20 Gasoline & oil 21.99757699 1.0105687 0.02503072 -0.001281 -48.3427 -10.9281

2.453 24.572 4.703 -1.605 -3.971 -2.053 0.99 592.411

21 Fuel oil & coal 44.19687801 0.7975012 0.0180928 -0.001852 -15.1241 -10.1809

1.795 8.164 4.134 -1.736 -1.537 -2.494 0.9862 430.323

22 Tobacco products 124.7230478 0.7151042 0.00347347 -0.001267 -183.04 -55.0203

1.689 4.209 0.657 -1.809 -3.497 -1.361 0.9714 203.585

23 Toilet articles & -0.876664776 0.7359864 0.00859361 0.002586 0 0

preparations -0.421 9.135 4.937 3.02 0.9951 2182.647

24 Semi-durable house -0.429708958 0.9210679 0.00800665 0.000434 0 0

furnishings -0.197 10.507 4.957 1.078 0.9789 495.244

25 Cleaning & polishing preparations, 14.76823488 0.8051723 0.00832595 0.001165 0 0

& mist hh supplies and paper 2.563 10.232 1.995 1.695 0.9545 223.512

26 Stationery & writing supplies 20.02803626 0.68983 0.00296569 0.000785 -38.8391 -16.6268

2.093 5.285 2.912 2.226 -4.225 -2.175 0.9881 497.803

27 Drug preps & sundries -5.061206039 0.8486983 0.0020434 0.003289 0 0

-0.637 12.381 0.584 1.944 0.9963 2844.121

28 Magazines & newspapers & sheet 20.10697233 0.9212841 0.00643461 0.000827 -169.213 -26.8674

music 0.642 10.136 1.276 0.406 -4.231 -0.401 0.9012 54.758

29 Nondurable toys & sport supplies -9.58111687 1.1140654 0.0073733 -0.000442 -28.3447 4.750178

-0.723 14.76 5.128 -0.456 -1.711 1.008 0.9979 2854.924

30 Flowers seeds & potted plants -3.265688273 0.836491 0.00347996 0.000673 0 0

-1.58 8.543 2.934 1.731 0.9846 679.903

31 Expenditures abroad 7.728292135 0.8939302 0.00060746 -0.000396 -20.7369 -0.71437

0.895 9.867 0.212 -0.977 -2.842 -0.074 0.9445 102.168

32 Less: remittances in kind -0.233272949 0.8075157 -0.0002 -3.22E-05 o 0

-0.604 7.707 -0.555 -1.015 “ 0.7477 31.613

33 Owner occupied nonfarm dwellings -18.4312 0.7342 0.0131 0.0311 0 0

space rent -0.5917 7.5267 1.1884 2.6888 0.9931 1487.023

34 Tenant occupied nonfarm dwellings 51.9219 0.567 0.0156 0.0136 0 0

rent 0.9581 1.6799 2.7413 1.3846 0.9455 179.3034

..—
Table 2. PCE Model Coefficients (with corresponding t-statistics)

change in change in lagged

lagged per capita lagged per relative relative

Eqn intercept dependent PCE capita PCE price price R-squared F-value

35 Rental value of farm housing -3.075058442 1.0440527 0.00049198 0.00017 -10.2296 -1.59999

-0.468 12.491 0.751 0.534 -2.437 -1.071 0.9964 1649.732

36 Other housing 30.16236256 0.6437707 0.00779096 0.003505 -76.7756 -57.7017

‘J. 164 5.373 4.99 2.805 -2.872 -2.911 0.9859 418.66

37 Electricity 4.02178004 0.918343 0.00714205 0.001453 0 0

0.636 11.514 1.797 0.788 0.9946 1962.522

38 Natural gas 35.17793689 0.7846202 0.00364142 -3.21E-05 1.825955 -9.50096

3.844 11.252 1.15 -0.096 0.155 -2.248 0.8948 51.052

39 Telephone&telegraph -23.98676262 1.0601487 0.00448537 0.000627 -46.8722 7.490352

-1.236 25.88 1.797 0.503 -3.108 1.318 0.9987 4529.599

40 Water&sanitary services 1.7738 0.3899 0.0017 0.0045 0 0

0.3565 1.7149 1.1803 2.7912 0.9375 155.0737

41 Domestic services 50.39709854 0.7482592 0.00500697 -0.000919 -14.9465 -25.3079

2.822 9.118 3.296 -1.989 -1.118 -3.546 0.9941 1012.106

42 Other household operation 26.32904745 0.727681 0.0125075 0.002797 -84.477 -38.0714

1.617 4.264 3.129 2.206 -2.332 -1.036 0.9673 177.694

43 Auto repair, rental & other 79.4767373 0.6769278 0.0445545 0.012833 -69.6816 -171.095

1.633 4.668 6.797 2.356 -0.443 -1.954 0.9891 543.718

44 Bridge & road tolls 1.142103506 0.9047858 0.00058075 -3.02E-05 o 0

2.595 15.214 1.64 -0.92 0.9134 112.459

45 Net auto insurance premiums 4.550558132 1.0269625 0.00337303 -0.000533 0 0

2.3 18.566 1.924 -1.557 0.9842 665.94

46 Mass transit systems 6.904058981 0.8830308 0.0003858 -9.12E-05 -22.3492 -3.29916

1.478 15.232 0.592 -0.632 -6.449 -1.196 0.9923 776.911

47 Taxicab 13.7742 0.729 0.0022 -0.0003 -8.4975 -7.1903

0.603 2.6735 2.1305 -0.5412 -0.6712 -0.379 0.6724 11.9043

48 Intercity railways 1.307523547 0.7925395 3.9156E-05 -0.000149 -6.2654 1.736944

1.23 9.954 0.11 -1.682 -1.97 1.645 0.9732 217.823

49 Intercity buses 1.086383969 1.0209821 0 0 -15.3683 -1.70175

0.594 12.382 -2.674 -1.23 0.9493 199.769

50 Airline 19.16364036 1.0443154 0.00900127 -0.00076 -36.9361 -11.1673

2.187 12.467 4.337 -0.919 -4.453 -2.2 0.9928 825.399

51 Other transportation services -0.506542312 0.848938 0.00123325 0.000333 -9.0193 -3.22966

-0.566 7.713 3.632 1.956 -3.527 -2.401 0.9896 573.399
Table2. PCEModel Coefficients (with corresponding t-statistics)

change in change in lagged

lagged per capita lagged per relative relative

Eqn intercept dependent PCE capita PCE price price R-squared F-value

52 Physicians -14.03442139 0.791911 0.00967307 0.008981 0 0

-1.157 8.954 1.028 2.39 0.9913 1211.272

53 Dentists 12.56296075 0.7799664 0.00784128 0.00301 -141.009 -27.3329

1.233 5.719 2.879 1.38 -2.433 -1.005 0.9884 512.969

54 Other professional medical 65.3093726 0.9988075 0.01860577 0.010421 -240.088 -222.532

2.089 25.127 2.925 2.384 -2.462 -2.337 0.9948 1141.314

55 Hospitals & Nursing homes 20.78759 0.937729 0.011082 0.018024 -266.813 -223.72

0.738 21.962 1.029 2.594 -0.935 -2.699 0.9986 175.2681

Cleaning, storage, & repair of clothing

56 & shoes 1.8264 0.9424 0.0065 -0.0001 0 0

0.1204 9.3531 4.3173 -0.1621 0.9515 202.5202

Mist personal, clothing & jewelry

57 services -9.1012O8133 0.672162 0.00748425 0.001535 0 0

-2.352 4.766 4.102 2.273 0.9758 430.094

58 Barbershops, beauty, &health 40.52502961 0.8093954 0.00933814 0.000537 -71.7242 -35.4994

2.527 10.785 3.657 1.204 -1.932 -2.065 0.8987 53.226

59 Health insurance 5.891828625 0.7769452 0.00558431 0.002815 -27.5207 -15.2093

2.41 8.908 2.457 2.603 -4.606 -2.883 0.9926 805.386

60 Brokerage charges & 3.837342246 0.8265951 0.00788671 0.002869 -14.0227 -23.9712

investment counseling 0.328 6.62 1.187 2.334 -0.937 -1.728 0.9608 147.132

61 Bank service charges 15.85973924 1.0996065 0.00082411 -0.00069 -19.1032 -14.937

1.923 10.975 0.335 -0.752 -0.775 -2.654 0.9908 647.796

62 Services furnished without payment 20.46466813 1.0182991 0.02146155 -0.00202 0 0

by financial intermediaries 1.338 11.76 1.875 -0.528 0.9882 889.929

63 Expenseof handling Iife insurance -0.997416788 0.7535678 -0.0092282 0.00419 0 0

-0.121 7.363 -1.228 2.29 0.9682 324.399

64 Legal services 23.61133701 0.6251781 0.0059356 0.002434 -92.4282 3.309106

2.818 4.615 1.518 1.464 -1.807 0.172 0.9621 152.413

65 Funeral&burial expenses 19.77258229 0.6902426 0.00265773 -0.00024 -~,9;9285 -3.44018

2.39 6.258 3.362 -2.554 -4.623 -0.883 0.9691 187.926

66 Other personal business 21.45633174 0.8883785 0.00467213 0.001124 -65.1701 -33.0244

3.075 13.368 6.052 2.22 -3.138 -2.878 0.9945 1075.511

67 Repairofaudio& videoeqpt -1.143635698 0.9228922 0.00141543 0.000102 -11.0975 0.477366

-0.225 9.649 2.29 0.707 -2.072 0.29 0.9509 116.172

..-. . ..—
Table 2. PCE Model Coefficients (with corresponding t-statistics)

change in change in lagged

lagged per capita lagged per relative relative

Eqn intercept dependent PCE capita PCE price price R-squared F-value

68 Motion picture admissions 14.99069691 0.6289972 -0.0014543 -0.000455 0 0

3.076 6.384 -1.283 -2.438 0.9299 141.442

69 Live entertainment excl. sports 0.183892514 0.9616302 0.00154097 0.000286 -18.8872 -3.13738

u.123 13.38 2.035 1.468 -2.333 -1.294 0.9856 410.635

70 Spectator sports 5.944692177 0.8583291 0.00042772 -2.64E-05 -3.29878 -3.13147

1.741 9.973 0.545 -0.26 -0.312 -1.112 0.9458 104.611

71 Clubs & fraternal organizations 15.42291685 0.8992262 -6.47E-07 0.000315 -42.9759 -15.0852

1.641 11.159 -1 .00E-03 1.931 -3.314 -1.75 0.9894 559.889

72 Commercial participant amusements 5.361446897 1.0147802 0.00264823 0.000272 -2.44101 -7.39964

0.237 13.3 1.316 0.355 -0.038 -0.313 0.9939 970.405

73 Pari-mutuel net receipts 2.428194668 0.9235094 0.00057839 -0.000184 -12.5072 1.118777

1.406 24.486 1.375 -5.218 -3.954 0.667 0.9721 209.286

74 Other recreation 33.16959623 0.9502094 0.01271414 0.001205 -88.6246 -33.3085

2.208 21.527 4.503 1.001 -1.898 -3.009 0.9987 4663.279

75 Higher education 21.5723 0.7922 0.0024 0.0023 114.6761 -19.3651

1.1651 2.5916 1.2555 E0.8837 2.1638 -0.7786 0.9508 111.9822

76 Private lower education 12.04526626 0.9041854 0.00171505 0.000143 -41.5907 -7.49022

3.265 12.284 1.349 0.495 -2.583 -1.471 0.9606 146.26

77 Other education & research -0.1457 0.7466 0.0069 0.0021 -18.7034 -12.9302

-0.008 2.3246 3.753 0.8555 -0.5707 -0.5145 0.972 201.4424

78 Religious & welfare -13.07434988 0.977623 0.02280565 0.001827 0 0

-1.257 18.309 4.02 0.968 0.9956 2434.807

79 Foreign travel by U.S. residents 86.07765334 0.3663459 0.01365169 0.007324 -105.422 -98.2379

3.385 2.229 2.383 3.655 -3.814 -3.857 0.9828 342.098

80 Less: expenditures in U.S. by 308.5286131 0.5986434 0.00493658 -0.003654 -196.72 -355.309

foreigners 2.499 4.292 0.65 -2.44 -0.899 -2.272 0.9883 505.219
A COMMODITY-SPECIFIC MODEL FOR PROJECTING IMPORT DEMAND

Betty W. Su

Bureau of Labor statistics, Washington, DC 20212

Introduction

The purpose of developing an import model is to refine

the Bureau of Labor Statistics (.BLS) projections

method in the area of imports. In previous projections,

imports had been treated in a manner analogous to

exports.l The drawback with this, however, is that the

commodity distribution of imports is not directly

influenced by the sectoral composition of demand. To

remedy this, we have developed a set of cornrnodityspeci.fic import fimctions. This import model is being

used for the first time in the Bureau’s projections

System. z

The study described here is a model which estimates

import flows for 107 commodities.3 This model

operates as one of several sub-models within the BLS

employment projections model system. The most

important element of this model is its commodity

detail. As will be apparent, import demands vary

considerably by commodity.

A Commodity-Specific Import Model

Import demand basically depends on the consumer’s

income, the price of imports, and the price of the other

goods; in this case, the price of domestic goods,

because individuals allocate their income among

consumable commodities in an effort to achieve

maximum satisfaction. This suggests that for an

economy we may write import demand as:

M = f(Y, p., pY)

where

M: Import demand

Y : Domestic income

Pm. “ Price levels of imports

Py : Price levels of domestic goods

In addition to income and price, the two key variables,

the Learner and Stem model suggested that other

possible explanatory variables should also be

considered. A complex demand phenomenon requires

more variables than the usual two. Five other variables

are described in the Learner and Stem model:4

1. Lagged variable: It is particularly important in

measuring the influence of past changes in the

independent variables on the current behavior of

imports.

2. The capacity-utilitition variable: An increase in

domestic demand may not be met immediately by price

increases. Rather, domestic producers may ration the

available supply by delaying deliveries. The consumer

may look to foreign sources of supply to avoid the

delay in delivery and pay two prices for the goods he

desires during the waiting period. Capacity utilization

is a proxy to reflect the length of queues at home and

abroad.

3. The dollar’s exchange rate: It significantly afhct the

prices of foreign goods transacted from other

currencies to the U.S. dollars.

4. Dummy variables for unusual periods: It allows for

the effects on imports of unusual occurrences such as a

strike, war, or natural disaster. Such variables would

assume a value of one for the duration of the unusual

period and zero otherwise.

5. Credit variable: It indicates the availability and

terms at which credit is provided for the financing of

imports. Such a variable is important in linking the

current and capital accounts of balance of payments.

The variables discussed above are in general the most

important ones, especially for the manufacturing

industries, although the list is by no means allinclusive. Many other explanatory variables will

suggest themselves in particular situations.

For empirical, data availability, and accommodating

reaso~ a cmnrnodity-specific import model is formed

using a log-linear least squares regression:

(1) logW = * + al logyit + a2 logM M + a310gR +

dogpk + D

where

M: The volume (value) of

commodity, 1992 dollars.

imports for that

45
Y:

R

P:

D:

and

t:

“. 1.

Total supply (domestic output plus imports) of

that commodity, 1992 dollars.

Trade-weighted U.S. dollar’s exchange rate,

1990=100.

Relative price irnpor@ 1992=100, defined as

the ratio of import price deflator for that

commodity to comparable domestic producer

price deflator for that commodity.

Dummy variable for the eff’ upon imports

of unusual occurrences such as a strike, war,

natural disaster, or oil price shock. One for

the duration of the unusual period and zero

otherwise.

time period

ith commodity

It is important to note that in this model, total supply,

defined as domestic output plus imports, is chosen as a

proxy to reflect the economy-wide demand as well as to

test the sensitivity of import demand.

Estimation Procedure, Data Sources, and

IU@wssion Results

The import model estimates the import trend by

commodity. The data used to implement this import

model are primarily developed by BLS, Office of

Employment Projections (OEP) commodity time-series

data base. The import and domestic output data are

estimated by OEP at 3-digit SIC level. The tradeweighted U.S. dollar’s exchange rates are obtained

from the OEP’s macro econometric model data base.

The domestic producer price index and import price

index are also developed iiom OEP’S data base and

BLS estimates.

The regression equation is mtimakd with ordinary

least squares. Several specifications of equation (1) are

edmated foreachcornmodity. l%isismzesmryto

ensure the estimation technique yields a reasonable

coefficient in terms of the sign and statistical

significance. The expected sign for the coefficient of

total supply (al) and the coefficient of lagged

dependent variable (az) should be positive, and the

coefficient of exchange rate (as) should also hold a

positive sign. On the other ban* the price coefficient

(@) shodd have a negative sign. u the variable

coefficients show a wrong si~ the variable are

dropped fkom the estimated equation. For this reaso~

eight specifications of equation(1) are used as:

(1.1) 10*= a+ allogyit + a2WM it-l + @@k+

aJogPti + D

(1.2) 10*= aJogYh + aJogM N + a310gRt+ ~ogpit

+D

(1.3) loghk= m + allogyk + a210gM it-l + a310@? + D

(1.4) loghlit= allogYit + aJogM it.1 + aJogRt + D

(1.5) 10~k= ~ + allogyfi + azloghl it-l + do@% + D

(1.6) loghlti= allogYit + aJogM it-l + do@% + D

(L7) logh&= m + allogyit + aJogM it-l + D

(1.8) logh&= allogyit + azlogM it-l + D

To choose among the 8 equations for each commodity,

the equations are estimated over the 1977-93 period.

of the 107 commodities, 2 commodities-wood

containers and miscellaneous wood products

(timmtity 14) and Office and miscehus fbrniture

and fixtures (Commodity 17)-have data available only

in the period of 1978-1993 and 1983-1993

respedvely; 4 commodities-oil and gas field seMces

(Commodity 7), construction (Commodity 9),

partitions and fixtures (CommO@ 16), and metal

coating, engraving, and allied seMces (Commodity

34)-show no import values in the input-output

accounts.

Table 1 shows the results that the explanatory variable

coefficients have the %orrect” sign. Historical

simulation is also tested to examine how closely each

simulated variable tracks its corresponding data series.

Other statistics such as R-sq~ F-values, and tstatistics are also shown in table 1.

The Projections of Commodity Trends and

Aggregate Trends

To solve the projections, the variables of exchange rate

can be produced by the OEP’S macro econometric

model, but the outputs and prices by commodity must

be extrapolated fkom the estimated historical series.

For this set of projections, due to time constraints, the

projected total supply for the import model is not

iteratively derived. In@@ the historical total supply

series is extrapolated and used as a proxy.

As mentioned earlier, this model deals with 107 goods.

However, the OEP projections system contains 185

commodities or industries. These include 107 goo@

46
72 services, and 6 special industries. Outside of

agriculture, mining, and manufacturing, import entries

only exist in 21 seMces and 2 special industries. (See

Appendix.) Among the 21 seMces, almost all of their

time-series data on imports are available only from

1986 to 1993. In order to project sewices of imports,

an alternative estimating method is used.

Import demand is initially projected by the macro

econometric model, which generates values for 8 major

end-use categories of imports-7 for goods and 1 for

sexvices. The single cohmm of seMces control is

allocated among commodities based on the estimates

for a most recent historical year distribution. Two

special industries-noncomparable importss and scrap,

used and secondhand goods, are treated as dummy

commodities, and are projected based on their

historical trends of the commodity as a share of total

imports.

To make sure the goods estimates from the model are

consistent with the macro controls, the 107 estimates

plus the 2 special ones are then disaggregate into 7

columns based on the bridge table developed by OEP.

Footnotes

Obviously, summation dMerences are expected fkom

these two estimates. The Werences are then carefidly

examined ant generally, scaling adjustments are

introduced in order to reproduce the macro model

control values. The macro model controls may

themselves be modified in

developed detail at the

projection.

Conclusion

response to the more finely

commodity level of the

Since the import model is being developed and used for

the first time in the projection system extensive work

is needed to improve the model. This includes in

particular the generation of proxy variables. For

example, to avoid the technical simulation probleu

should the lagged total supply, rather than the current

value, be used as an explanatory variable in the model?

To operate this import model, the price equation is

essential, and a price model is also needed as a vehicle

to fit the import model. It is hoped that further work

with the model will truly improve the projections in the

area of imports.

‘ For expo@ the projection process involves allocating tha projected 3 See Table 1 fw the M of 107 commodities.

category control W generated hxn the macro ccimometric model to 4kmWr,W VV/miE.a ndS~RObCrtM. Ouantitative htenmtional

commdtica baacdon pattcmgesthtcd fbrareccnt hiatofical year Economics (130stoQ M/k Allyn and IhcoQ Inc. 1970).

bridge table. 3 h-~~le~(l)-~”~--c

2 Thk ayatcm waa developed by the CMke of Employment Rojcctiq producti~ e.g., coffee beam and ~, of (2) the item is prchssd

Bumu of Labor Statistics Ike projcctiona cover the fiture size snd and used outside the U. S., such as consular f- and

CoquWkion of tha labor fbrcc, aggregate

Commlmicatioa

aconomic growtl& detailed -,w(3)bkkti~e,m&wti~=dtiealmtes ofhdustrial pductimand imhshhl aadoocupational

CqkymuW

Appendix: A List of Import-Related Services and Special Industries

1 Railroad transportation

2 Water transportation

3 Air transportation

4 Electric utilities

5 Gas utilities

6 Wholesale trade

7 Depository institutions

8 Insurance carriers

9 Advertising

10 Personnel supply seMces

11 Computer and data processing seMces

12 Miscellaneous business seMces

13 Miscellaneous repair seMces

14 Motion pictures

15 Producers, orchestras, and entertainers

16 Legal seMces

17 Educational seMces

18 Engineering and architectural seMces

19 Research and testing seMces

20 Management and public relations

21 Accounting, auditing, and other seMces

22 NoncomParable imports

23 Scrap, used and secondhand goods

47
Table 1 Import Estimating Equations

Model logMh = ~ + al logYh + a2 loglklkl +a3 Io* +% 10#it

Total Exchange Relative

Intercept supply Imports Rate Price

t t-1 t t

commodity R-squared F-vah

aO al a2 a3 a4

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Agricuhra.1 production

Agricultural services

Forestry, fim hunting, and trapping

Metal mining

Coalmining

crude Petrolq natural gas, and gas liquids

-9.0528

(-1.1572)

-5,6967

(-0.5895)

-6,2719

(-2.3581)

0.7296

(1.0691)

1,2825

( 2.2181)

0.9521

( 2.6033)

0.6211

( 2.2713)

(:E2)

0.5782

( 0.5294)

0.9391

( 9.3318)

0.1813

(0.6312)

0.4489

(2.3792)

0.3604

(1.7374)

1.0862

(5.4114)

0.6834

(2.4926)

.

0.1612

(0.6992)

0.948

0.527

0.926

0.542

0.811

0.726

.

0.007

0.495

0.657

0.708

0.965

0.609

0.991

.

0.973

0.974

0.727

0.820

0.605

0.294

0.127

72.67:

4.455

49.712

3.54$

27.86S

17.251

.

0.048

3.919

8.285

6.676

83.698

4.289

312.830

146.717

151.656

7.992

18.232

4.604

1.807

1.017

-1.1784

(-0.7009)

0.3720

(2.0725)

0.3010

(1.3159)

4.5157

(-1.3948)

4.3411

(4.0572)

-3.4050

(-0.3320)

Oil and gas field services* .

Nonmetallic miIMl&3, except fhds

~on*

m&m8

%VIdS aud @Ul@ XUih

MiUworlq plyvo@ and structural members

Wood containers and misc. wood products

Wood buildings and mobile homes

Household fiuniture

Partitions and Mums*

Of#iceandmisc.llhmiture and fixtwes

Glass andglassproducts

Hydraulic cement

Stone, Ckijf, and misc. mineral products

concrete, gypsuQ and plaster products

Blast fhmacesandbasic steel products

Iron and steel foundries

. .

0.6242

( 3.6852)

0.1398

(0.5992)

. . .

-3.3471

(-0.3365)

0.3076

( 0.3151)

0.4063

( 2.0656)

0.6628

( 2.7133)

1.1650

(2.5649)

2.6119

( 2.0702)

1.1567

( 2.5740)

0.5981

(2.5888)

0.3252

(1.3331)

0.1343

(0.6267)

0.5370

(2.%57)

0.6846

(4.0242)

0.7504

(5.4403)

0.4266

(0.5848)

0.3212

(2.1079)

0.0613

(0.2077)

0.5640

(3.0612)

0.2855

(0.3607)

0.1349

(0.2316)

.

2.3693

(0.4613)

-0.6799

(-1.2782)

-2.2485

(-2.8753)

-5.1375

(-2.0353)

-1.3834

(-1.4400)

.

-3.8482

(-0.5482)

. .

-9.6217

(4.3477)

-13.3584

(-2.5849)

1.5587

( 4.6627)

1.1195

( 2.3493)

1.0689

( 2.0383)

0.6789

( 1.5338)

0.2065

( 0.3726)

0.3392

( 3.5518)

0.1403

0.0876

(0.5104)

0.8768

(12.8754)

0.7570

(4.3747)

0.8485

(6.3858)

0.6662

(3.3930)

0.2319

(1.3684)

0.7534

0.3462

(2.1 157)

0.7029

(3.3986)

0.3280

(0.5816)

0.2990

(0.9741)

0.0654

(0.0980)

0.7245

(2.9408)

-2.0098

(-2.1509)

-6.86%

(-1.2759)

-0.16%

(-0.2738)

48
Table 1 Import Estimating Equations-Continued

Model logMk = ~ + al logYk + az logltll~.l +a3 Io* @ logPk

commodity ~

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

Primary nonfimous smelting and reftig

All other primary metals

Nonkrmus rolling and drawing

Nonferrous foundries

Metal cans and shipping containers

Cutlexy, hand tools, and hardware

Plumbing and nonelectric heating equipment

Fabricated structural metal products

Screw machine products, bolts, rivets, etc.

Metal forgings and stampings

Metal coating, engraving, and allied services*

Ordnance and ammunition

Miscellaneous fabricated metal products

Engines and turbines

Farm and garden machinery and equipment

Construction and related machinery

Metalworking machinery and equipment

Special industry machirmy

General industrial machinery and equipment

Computer and office equipment

Reiligeration and service industry machinery

Industrial machinery, nec

Total Exchange Relative

Intercept supply Imports Rate Price

t t-1 t t

aO al S2 a3 a4

2.2853

(0.8086)

14.5343

(1.0266)

-7.7613

(- 0.6403)

-3.9256

(-0.7919)

-0.9746

(-O. 1894)

.

-4.6761

(-2.6601)

-15.0183

(-2.2485)

-11.9449

(-1.7248)

7.5676

(1.5010)

-6.8838

(-1.7729)

-9.5336

(-3.4320)

-5.7185

(-0.4564)

-6.8881

(4.7845)

-27,4561

(4.8575)

-27.8624

(-3.0330)

( 1.1847)

0.4646

( 1.8874)

0.1187

( 1.2640)

0.8943

( 1.3661)

0.6249

( 0.9625)

0.1399

( 1.5176)

0.5157

( 1.4958)

1.1452

(0.8289)

0.0728

( 0.3014)

1.1088

( 2.5354)

0.0441

( 0.0970)

.

0.5197

( 2.1725)

1.6480

( 2.7798)

0.9500

(2.1167)

0.0408

( 0.1729)

0.0527

( 0.1404)

0.6218

( 2.2121)

1.0477

( 4.0387)

0.8954

( 1.5444)

1.3111

( 4.6571)

2.5939

( 4.6833)

3.2080

( 3.6866)

(3.6956)

0.1862

(0.8308)

0.7863

(4.9509)

0.3083

(1.1983)

0.6697

(4.3500)

0.7378

(4.1 139)

0.9555

(12.5068)

0.4377

(1.8300)

0.6433

(3.3491)

0.6117

(3.0441)

0.9375

(9.2229)

.

0.8484

(7.7856)

0.5576

(5.5819)

1.0052

(6.7601)

0.4387

(1.8162)

0.9082

(4.1227)

0.7389

(8.2347)

0.4772

(4.5002)

0.7436

(9.3778)

0.2165

(1.3141)

0.5553

(5.5309)

0.4470

(3.4299)

0.2134

(0.5678)

0.3989

(1.9461)

0.2465

(0.3598)

0.3158

(0.6994)

0.0561

(0.1435)

0.2136

(0.6700)

.

0.2181

(0.7673)

0.3195

(1.5828)

0.5255

(1.1870)

0.3393

(0.6815)

0.5672

(2.3654)

0.7386

(4.3969)

0.0998

(0.0850)

0.03%

(0.1817)

0.9291

(2.7869)

4.1948

(-2.3309)

-0.8910

(-0.7827)

-1.4183

(-1.7350)

-0.8132

(-1.1857)

.

-0.7514

(-1.5101)

-0.2953

(-0.3678)

-0.4108

(4.3174)

-0.1352

(-0.2%7)

bquared F-valw

0.281

0.560

0.921

0.903

0.521

0.950

0.317

0.437

0.907

0.893

0.969

0.830

0.858

0.595

0.784

0.851

0.901

0.945

0.998

0.958

0.933

2.542

8.912

31.88:

24.871

7.623

57.512

1.857

3.362

26.978

33.47a

.

125.491

19.559

24.163

5.877

10.867

22.88a

36.565

47.007

1651.783

91.044

55.680

49
Table 1 Import Estimating Equations-Continued

Model 1-=~+ al logYh+ ~ log& +a3 1- +q IogPk

commodity

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

Ekctric distribution equipment

Electrical industrial apparatus

Household appliances

Ekctric lighting and wiring equipment

Household audio and video equipment

communications equipment

Electronic canponents and mcewories

Miscellaneous electrical equipment

Motor vehicks and equipment

Aerospace

Ship and boat building and mpaking

Railroad equipment

Miscellaneous tnmqmtation equipment

Seamh and navigation equipment

Meamring and controlling devices

Medical equipmen~ instruments, and supplies

Ophthalmic goods

~O@Xll@iC equipment and sqqdks

Watches, clocks, and parts

Jewelry, SihkXW& and Phlted ware

Toys and spwting goods

Manufactured products, nec

Meat products

Total Exchange Relative

Intercept supply Imports Rate Price

t t-1 t t

aO al a2 a3 d

-10.8493

(-2.0432)

-12.9278

(-1.4091)

-4.5989

(-5.0848)

-8.9115

(-2.6053)

-5.0043

(-1.9908)

-15.3566

(-3.8324)

1.3331

(0.2948)

4.%27

(-1.5993)

-11.4412

(-1.8801)

23.8178

(1.9989)

-17.0319

(-3.0686)

-6.9177

(-1.9585)

-3.5574

(-1.1998)

0.1463

( 0.3174)

0.1909

( 0.5070)

1.0676

( 1.6803)

1.8406

( 3.2718)

1.0164

( 5.0819)

1.3557

( 3.3605)

0.8949

( 5.7875)

0.5630

( 1.7244)

0.3419

( 2.4934)

1.6244

( 4.7193)

0.1465

( 1.0299)

0.2573

(1.3501)

0.2160

( 0.4917)

1 .3n3

( 1.8871)

1.4547

( 4.3689)

0.5699

( 2.1495)

0.7246

( 3.9766)

1.95%

( 3.1522)

0.6490

( 2.4099)

0.5229

( 1.1977)

1.2691

( 6.1905)

0.3066

( 0.5364)

0.1238

0.7970

(3.5975)

0.8991

(4.2866)

0.7264

(3.99%)

0.4847

(3.4233)

0.3150

(2.1930)

0.3061

(1.6960)

0.3222

(2.9511)

0.6524

(4.3410)

0.9276

(7.6726)

0.4453

(4.7387)

0.7795

(3.4388)

0.5535

(3.2887)

0.6764

(2.7254)

0.4415

(1.6097)

0.4011

(3.6610)

0.7371

(4.9998)

0.5776

(5.6330)

0.4153

(2.4916)

0.9509

(10.3815)

0.5395

(2.0955)

0.4486

(5.2754)

1.0124

(10.5947)

0.7905

0.0176

(0.0247)

0.0208

(0.0596)

0.5338

(2.3079)

0.1116

(0.0915)

0.1983

(1.1948)

0.1358

(0.4859)

0.0388

(0.4589)

0.4934

(2.6634)

0.0708

(0.5783)

0.3172

(1.2013)

0.1223

(0.3516)

0.0912

(0.2536)

0.3019

(0.6940)

0.1508

(0.9824)

0.2694

(3.4%1)

0.4859

(2.3885)

0.4353

(1.5954)

0.0082

(0.0244)

0.1925

(0.8208)

0.1462

(0.7091)

0,2087

-0.2555

(-0.3150)

-0.3905

(-0.37%)

-0.7747

(-4.6320)

-0.8376

(-2.1803)

-0.2987

(4).7855)

-0.8263

(-1.0881)

-0.0237

(-00529)

4.1423

(-2.2683)

-0.8982

(4.8591)

3.2196

(4).4783)

-1.0711

(-2.3463)

-0.8380

(-0.8498)

-0.1471

R-squared F-vah.u

0.555

0.860

0.928

0.967

0.986

0.%7

0.994

0.977

0.954

0.972

0.402

0.371

0.562

0.864

0.991

0.995

0.980

0.964

0.918

0.759

0.994

0.962

0.850

5.40!

18.38

51.94

81.47t

272.001

118.9X

487.934

168.485

61.75#

141.202

4.699

2.56(I

3.536

25.414

287.60d

508.218

150.288

108.667

45.027

9.453

452.086

75.385

17.049

50
Table 1 Import Estimating Equations-Continued

Model logMk = ~ + al logYk + az logMk.l +as 10g& +% l@it

commodity

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

Dairy prOdUCtS

Preserved tits and vegetables

Grain mill products and fats and oils

Bakery products

Sugar and confectionery products

Beverages

Miscellaneous food and kindred products

Tobacco products

Weaving, finim y- and thread milk

Knitting mills

Carpets and rugs

Miscellaneous textile goods

Apparel

Miscellaneous fabricated textile products

hip, paper, and paperboard III&

Paperboard containers and boxes

Converted paper products except containers

Newspapers

Periodicals

Books

Misceilaueous publishing

Commercial pinting and business fTotal Exchange Relative

Intercept supply Imports Rate Price

t t-1 t t

aO al d a3 a4

4.5460

(-0.5574)

-9.1740

(-1.4554)

-11.6847

(-0.4166)

-11.5713

(-1.6349)

-1.9838

(41.8974)

1.2300

(0.2189)

-19.1748

(-1.6621)

-2.6714

(4).2191)

45472

(-1.3948)

-16.6047

(-5.5881)

61.9525

(0.8302)

9.1072

(0.2648)

-39.8313

(-3.2656)

-1.4988

(-0.2866)

0.1285

(0.2675)

-10.4999

(-2.9846)

( 0.6194)

0.7%3

( 0.9043)

1.2665

( 2.7194)

0.0219

( 0.0918)

1.1452

( 0.4064)

1.6290

(2.4151)

0.3792

( 3.3738)

0.3115

( 1.1563)

0.2087

( 0.3754)

1.9686

( 2.7358)

0.7553

( 1.0650)

0.6591

( 1.9822)

0.5539

( 2.3052)

1.6921

( 5.3871)

0.5570

( 1.5393)

0.6955

( 2.9328)

0.8868

( 0.4171)

0.1874

( 0.6560)

4.7893

( 2.6207)

0.9598

( 1.3880)

0.2615

( 1.3370)

1.4904

( 3.0460)

(4.1350)

0.3655

(1.2110)

0.3476

(2.6246)

0.9534

(4.5897)

0.9520

(7.3062)

0.0090

(0.0335)

0.2151

(1.1990)

0.7397

(5.6705)

0.6575

(2.0947)

0.7260

(5.9931)

0.9607

(8.5759)

0.6739

(6.9830)

0.4770

(2.0938)

0.5980

(7.8705)

0.8727

(7.7536)

0.4751

(2.6844)

0.6521

(3.1772)

0.8814

(9.2582)

0.2397

(1.0752)

0.2698

(0.9608)

0.7326

(5.6987)

0.9470

(5.9536)

0.3498

(1.6887)

(1.9326)

0.3813

(0.9228)

0.8459

(3.6597)

0.0269

(0.0904)

0.0419

(0.0685)

0.6001

(1.9813)

0.5859

(3.6716)

0.1885

(1.8660)

0.4285

(0.6667)

0.1955

(0.2154)

0.1035

(0.3968)

0.0475

(0.2770)

0.3230

(3.1624)

0.6768

(4.2712)

0.1501

(1.2311)

0.2580

(0.2965)

0.2095

(1.0342)

0.8413

(1.1263)

0.2783

(1.6018)

(4M492)

-0.3745

(-0.9406)

-0.6821

(-1.6346)

4.1971

(-0.4626)

4).3081

(-0.2383)

-1.0833

(4).5614)

-0.3992

(-1.3231)

-15.1106

(-0.9383)

-0.7702

(-2.3692)

-3.7912

(-1.1155)

-0.4369

(4).7882)

-0.8787

(-1.0345)

4.8114

(-1.7091)

-0.4336

(-1.7587)

-0.2784

(4).9845)

R-squared l?-vahu

0.782

0.962

0.859

0.901

0.424

0.651

0.940

0.355

0.919

0.%2

0.940

0.644

0.992

0.994

0.937

0.902

0.970

0.867

0.604

0.937

0.717

0.%2

9.86:

70.00(

26.3(X

36.36:

2.942

8.069

62.19!

2.202

31.164

70.319

62.959

5.419

476.580

427.575

44.539

25.442

%.628

17.851

6.0%

44.883

35.446

102.577

51
m.,

Table 1 Import Estimating Equations-Continued

Conunodity

91

92

93

94

95

%

97

98

99

100

101

102

103

104

105

106

107

Greeting cards

Bankbooks and bookbinding

Service industries for the printing trade

Industrial ChCllliCdS

Plastics materials and synthetics

Soap, cleaners, and toilet goods

Paints and allied products

i@iCUkllld chemicals

Miscellaneous chemical products

Petroleum retlning

Miscellaneous petroleum and coal products

Tires and inner tubes

Rubber products, pklStiC hose and fwtwear

Miscellaneous plastics products, nec

Footwear, except rubber and phlStiC

Luggage, handbags, and leather products, nec

● No-V&ES.

nec=notelsewkeclassified.

Total Exchange Relative

Intercept supply Imports Rate Price

t t-1 t t

aO al d a3 a4

-5.9901

(-2.4183)

-5.3930

(-0.8834)

-21.6951

(4.3087)

-5.0303

(-1.6473)

4.3556

(-2.6452)

-5.4589

(4I.6103)

26.4461

(1.1951)

-8.3497

(-3.3633)

-4.6981

(-0.6588)

-0.7984

(-0.1001)

-7.0911

(-3.7556)

-7.3359

(-1.8463)

-11.5455

1.2408

( 2.7664)

0.8713

( 1.3436)

2.6650

( 5.4191)

0.4952

( 2.2076)

0.6468

( 1.02%)

1.1219

( 3.8078)

1.0324

( 1.5036)

0.9940

( 1.%34)

0.6687

( 1.3105)

1.0802

( 3.7806)

0.3660

( 1.2227)

0.4588

( 0.6865)

0.8300

( 2.4772)

0.7227

( 3.7840)

1.0073

( 4.7715)

1.5880

(4.7381)

1.2226

@!EL

0.4886

(2.5708)

0.7263

(4.1561)

0.3303

(2.2517)

0.8357

(10.4551)

0.9764

(7.9186)

0.1345

(0.6278)

0.4847

(2.7271)

0.6176

(4.5867)

0.5897

(3.1436)

0.6091

(5.3724)

0.8311

(4.4903)

0.5573

(2.4221)

0.7545

(5.8616)

0.1267

(0.5478)

0.3704

(3.6583)

0.6239

(6.8610)

0.8745

(14.0027~

0.6049

(2.0682)

0.1663

(0.3144)

0.1869

(1.0002)

0.6278

(1.8224)

0.0084

(0.0825)

0.2799

(0.8610)

0.8637

(2.1094)

0.4426

(1.6628)

0.1110

(0.6572)

0.6339

(3.2953)

0.6027

(1.4294)

0.3591

(1.4628)

0.3335

(1.5176)

0.0637

(0.3079)

0.6000

(3.3405)

-0.4455

(-1.4010)

-0.7143

(-1.1 145)

-2.1135

(-1.3952)

-0.6399

(-1.7682)

-2.4360

(-2.8015)

-6.9582

(-1.6547)

-1.2306

(-2.5403)

-1.5003

(-1.0405)

-0.0883

(4.1594)

-0.9947

(-1.7645)

-0.2837

k!w!u

kpred F-value

0.973

0.901

0.871

0.924

0.952

0.981

0.973

0.970

0.791

0.968

0.877

0.631

0.939

0.642

0.993

0.994

0.964

142.406

25.045

26.956

48.583

59.378

205.662

100.186

%.608

10.413

120.532

21.348

6.846

42.347

12.549

377.167

464.276

73.105

There are 56 of the 103 estimating equations with R-squared over .900, and 11 equations have R-squared under .500.

t4atisti~ at the .10 probability level with 16 degrees of- the cxitical t value is 1.75.

F distributi~ at the .001 probability level with4 and 11 degrees of fhdorn, F value is 10.35.

52
A MODEL OF NEW NONRESIDENTIAL EQUIPMENT INVESTMENT

Jay M. Berman

Bureau of Labor Statistics, Washington, D.C. 20212

Overview

The overall steps taken to estimate new nonresidential

business investment, by type were:

(1)

(2)

(3)

A model was derived to estimate total capital

stocks on hand, by industry. The model was used

to estimate the demand for capital for 53 industries.

See Appendix A.

Along with developed annual rates of efficiency

loss, the implied new investment, by industry was

determined.

Developed investment flows tables to translate the

new investment, by industry estimates to new

investment, by type of asset. The investment flows

table estimated the investment demand for 22 types

of nonresidential new equipment. See Appendix B.

L Capital Stocks, by industry Model

CES production function

The demand for capital was estimated using the first

order conditions of a CES (constant elasticity of

substitution) production fhnction modified to include a

time variable. The time variable is meant to capture

disembodied technical change or shifls in the

production arising from long term increased

efficiencies in the use of inputs.

The basic form

The basic linearly homogeneous production fimction is:

Equation 1:

where:

Y output

L labor

K capital

t time

Model assumptions

Y = J(t, L,K)

The model assumes perfect competition and profit

maximization so that:

both factors are indispensable in the production of

output,

j(O,K) =j(O,L) = O

both marginal products are nonnegative,

MP~ = t3fl~L => O, MP~= @%K => O

and the marginal products are equal to the real factor

prices,

ZY7~L = wfp, 8j78K = rlp

where w, r, and p are the nominal price of labor,

capital, and output.

The functional form

The fictional form of the capital demand model is:

Equation 2:

Y = Aemt [&Z-p

where

Y output

K capital

L labor

+(l-@K-~]-”fl

A the efficiency parameter: indicates the

state of technology, A>O

6 the distribution parameter: indicates

the relative factor shares in the

product, 0<6<1

P the substitution parameter:

determines the value of the (constant)

elasticity of substitution, -1 <&O

m the rate of growth of disembodied

technical change

t time (measured as the year)

The derivation of the capital demand model

Estimating the CES and using the conditions of profit

maximization, the marginal product of capital can be

written:

where

A,– (1 3)-

AP

and

g=-m”

Assuming perfect competition and profit maximization,

the marginal product can be set to equal the real rental

cost of capital:

( )

Y

l+p

Ae@ ~ =~

P

where

K producers capital stocks

Y industry output

53
c rental cost of capital

P output price .

Solving for capital productivity

(1)

taking the logs Xx

in(Y) - in(K) = -*ln(A’) -~t+~ln(g~

p+l p+l p

then solving for capital

. .

in(K) =* ln(A’) + fi t + in(Y)- Aln(~) p+l p

results in the final basic form of the equation.

The estimated form of the demand for capital equation,

derived from the first order conditions under the

assumptions of perfect competition is: / \

Equation 3: [)

In(K) =aO +a1t+a21nY+a31n :

P

where:

K capital stocks

c rental cost of capital

Y lagged industry output

P output price

t time in years

%3 coefficients

Note on variables:

Industry output. Because industry output is determined

iteratively from the input-output system and the

projections for final demand, it was necessary to relax

the model assumption of instantaneous capital stock

adjustment (this year’s capital stocks are based on this

year’s output). Therefore, the adjustment to capital

stocks was assumed to be a finction of last year’s

output.

Rental cost of capital. Definition - estimated current

dollar rent on one dollar’s worth of constant (1987)

dollar stock. The commercial prime rate, derived from

the macro model, was used as a proxy to extrapolate the

historical cost of capital series.

Industry prices. The forecast of industry prices was

derived by extrapolating the industry price index as a

fi.mction of the GDP price index, which is solved for by

the macro model.

Capital stocks, rental cost of capital, and gross new

investment. Historical data was derived from the

BLS/Office of Productivity and Technology’s capital

input data that is used as a part of their of productivity

measurements.

II. New Investmen& by Industry

After the demand for capital stocks was estimated, new

investment by industry was derived by subtracting the

current year’s demand for capital from

level from the previous year adjusted

efficiency.

Equation 4: It = K~ -&t.l

where:

I gross new investment by

the demand

for loss of

industry

6 annual rate of efficiency loss

The historical annual rate of efllciency loss by industry

was derived by rearranging the investment function.

K~ -It

Equation 5: 8

 = —

K~.l

A linear extrapolation of the historical annual rate of

efficiency loss was then used in the projected period.

III. Investment Flows Tables and National Income

and Product Accounts

Investment flows tables, which were developed for the

years 1977 through 1994, translates new investment or

final demand by 53 industries to 22 types of

nonresidential new equipment (NIPAs). Examining the

table’s rows illustrates the dominate assets that each

industry demands. Examining the table’s columns

illustrates the dominate industries that demand each

asset type.

Because the sale of scrap is omitted from the new

investment by industry series, there remains a small

difference between that series and the NIPAs. To

account for this, a scalar series was added to the table

and used to derive annual adjusted coefficient tables,

whose asset totals equaled the NIPA controls.

The scalar coefficients were computed by taking the

difference between the investment by type total,

implied by the gross new investment by industry series,

and the NIPA totals for each asset type and then

dividing that by the sum of the differences between the

gross investment series and the NIPAs for all assets.

(Z1 XNjl ~Ii-Nj

SCdcXj = .-1

where:

1 gross new investment by industry

N new nonessential equipment by type

of asset

i

industry (1-53)

j

asset types (l-22)

54
The projected gross investment by industry series was

then transposed against the 1994 investment

coefficients table to derive the initial projected NIPA

controls.

IV. Problems’

(1) The historical data obtained from the Office of

Productivity &d Technology is in 1987 constant, fixed

weighted, dollars. Furthermore, the data will not be

converted to 1992 constant, chain weighted, dollars

until the end of 1997. Therefore, instead of rebasing

the entire historical data base, the final new investment

by industry estimates were rebased before being

translated, via the flows table, to new investment by

type of asset. The rebasing ratio formula is as follows:

2006 C, 1992$=

( )

1992: K,1992 $ *2006. ~ 1987$

1992: C,1987$ “ ‘

(2) Due to time constraints, the lagged projected

industry outputs for the capital stocks model was

not iteratively derived. Instead, the historical

industry output series in 1987 dollars was

extrapolated and used as a proxy.

55
Appendix A: Industry Codes (1987 SIC codes in parentheses)

1 -.

2 . .

3 . .

4 . .

5 -.

6 -.

7 . .

8 .-

9 . .

10 --

11 --

12 --

13 --

14 --

15 --

16 --

17 --

18 --

19 --

20 --

21 --

22 --

23 --

24 --

25 --

26 --

27 --

Agricultural services, forestry, and fisheries

Metal mining (10)

Coal mining (1 1,12)

Oil and gas extraction (13)

Nonmetallic minerals, except fhels (14)

Construction (15,16,17)

Lumber and wood products (24)

Furniture and fixtures (25)

Stone, clay, and glass products (32)

Primary Metal Industries (33)

Fabricated metal products (34)

Industrial machinery and equipment (35)

Electronic and other electric equipment (36)

Transportation equipment, including motor

vehicles (37)

Instruments and related products (38)

Miscellaneous manufacturing industries (39)

Food and kindred products (20)

Tobacco manufactures (21)

Textile mill products (22)

Apparel and other textile products (23)

Paper and allied products (26)

Printing and publishing (27)

Chemicals and allied products (28)

Petroleum and coal products (29)

Rubber and miscellaneous products (30)

Leather and leather products (31)

Railroad transportation (40)

28 --

29 --

30 --

31 --

32 --

33 --

34 --

35 --

36 --

37 --

38 --

39 --

40 --

41 --

42 --

43 --

44 --

45 --

46 --

47 --

48 --

49 --

50 --

51 --

52 --

Local and interurban passenger transit (41)

Trucking and warehousing (42)

Water transportation (44)

Transportation by air (45)

Pipelines, except natural gas (46)

Transportation services (47)

Communications (48)

Utilities (49)

Wholes trade (50,5 1)

Retail trade (52 - 59)

Deposito~ institutions (60)

Non depository institutions (61, 67)

Security and commodity brokers (62)

Insurance carriers, agents, brokers, and service

Real estate (65,66)

Hotels and other lodging places (70)

Personal services (72)

Business services (73)

Auto repair, services, and parking (75)

Miscellaneous repair services (76)

Motion pictures (78)

Amusement and recreation services (79)

Health services (80)

Legal services (81)

Educational services (82)

53 -- Social services, museums, etc. (83,84,86,87,89)

Appendix B: Final Demand Sectors

1 . . 13 -- Electrical transmission, distribution, and

2 . .

3 -.

4 --

5 . .

6 .-

7 .-

8 -

9 .-

10--

11 --

12--

Furniture and fixtures

Fabricated metal products

Engines and turbines

Tractors

Agricultural machinery, except tractors

Construction machinery, except tractors

Mining and oilfield machinery

Metalworking machinery

Special industry machinery, n.e.c

General industrial, including materials handling,

OffIce, computing, and accounting machinery

Service industry machinery

14--

15 --

16--

17--

18--

19--

20--

21 --

22--

industrial apparatus

Communication equipment

Electrical equipment, n.e.c.

Trucks, buses, and truck trailers

Autos

Aircrall

Ships and boats

Railroad equipment

Instruments; photocopy and related

equipment

Other nonresidential equipment

56
Investment model regression results for capital stocks.

Output is lagged one year.

Industry

1 Farms

2 Metal Mining

3 Coal mining

4 Oil and Gas Extraction

5 Nonmetallic Minerals, except fuels

6 Construction

7 Lumber and wood products

8 Furniture and fixtures

9 Stone, day, and glass products

10 Primary metal industries

11 Fabricated Metal Products

12 Industrial machine~ and equipment

13 Electronic and other electrical equip

14 Transportation equipment, inc. motor

15 Instruments and related products

16 Misc. manufacturing industries

17 Food and kindered products

V14 18 Tobacco manufactures

19 Textile mill products

20 Apparel and other textite products

21 Paper and allied prducts

22 Printing and publishing

23 Chemicals and allied products

24 Petroleum and coal products

25 Rubber and misc. plastics products

26 Leather and leather products

27 Railroad transportation

28 Local and interurban passenger tran

29 Trucking and warehousing

30 Water transportation

31 Transportation by air

32 Pipelines, exe. natural gas

33 Transportation services

34 Communications

35 Utilities

36 Whotesala trade

37 Retail trade

38 Depository institutions

39 Non depository institutions

40 Security and commodity brokers

Intercept

-99.465

-50.347

-75.691

-115.123

-63.269

2.328

-35.375

-99.244

-37.442

+8.070

-90.964

-93.344

-265.223

-77.185

-218.173

-57.326

-88.940

-83.317

-20.987

-102.014

-115.206

-37.950

-114.765

-137.163

-113.104

-4.345

10.506

22.409

-82.506

-13.270

-79.466

120.796

-102.245

-80.777

-129.979

-198.700

-250.346

-428.910

-127.820

-119.303

Year

0.054

0.032

0.046

0.063

0.040

-0.003

0.022

0.059

0.020

0.028

0.052

0.054

0.148

0.042

0.118

0.034

0.064

0.050

0.019

0.055

0.066

0.021

0.066

0.084

0.063

0.004

0.000

-0.003

0.048

0.012

0.042

-0.071

0.058

0.042

0.071

0.114

0.147

0.237

0.066

0.058

0.268

-0.531

-0.679

0.108

-0.693

1.119

0.054

-0.906

0.682

0.297

-0.116

-0.191

-1.485

0.421

-0.603

-0.213

-2.128

-0.802

-0.556

0.067

-0.460

0.463

-0.430

-1.664

-0.144

0.406

-0.034

-0.851

-0.160

-0.034

0.613

2.931

-0.308

0.726

0.037

-1.218

-2.196

-2.518

0.796

1.083

Cap cost

0.299

-0.563

-0.185

0.147

-0.067

-0.522

-0.077

0.069

-0.033

-0.105

0.059

-0.013

0.567

0.069

0.226

-0.027

0.105

-0.110

-0.134

-1.189

0.298

-0.494

0.121

0.167

-0.140

0.126

-0.372

-0.293

0.004

-0.301

-0.271

-1.343

0.178

0.007

0.315

-0.753

1.001

-1.237

-0.183

0.055

T intercep

-16.263

-9.766

-2.54

-15.346

-13.66

0.413

-3.199

-8.616

-5.839

-11.189

-20.708

-3.838

-7.859

-16.343

-6.006

-12.332

-7.281

-13.255

-4.388

-5.162

-12.651

-2.768

-24.537

-9.922

-17.423

-0.501

1.145

4.974

-2.895

-2.898

-5.794

6.143

-5.347

-3.458

-24.79

-2.278

-7.032

-4.45

-2.148

-1.532

T year

13.065

11.439

2.528

16.36

13.294

-0.699

3.079

8.48

5.083

12.393

18.171

3.876

7.677

14.349

5.79

11.985

7.359

21.152

6.2

4.465

11.829

2.518

24.011

9.767

16.926

1.054

0.042

-1.6

2.837

4.418

5.547

-6.321

5.576

3.238

22.901

2.248

6.669

4.367

2.088

1.45

T indout

1.19

-3.329

-1.038

0.497

-3.644

3.652

0.175

-3.995

3.233

2.984

-0.893

-0.671

-3.789

3.936

-1.633

-1.756

-5.33

-2.155

-3.761

0.133

-2.549

1.689

-6.162

-5.559

-1.814

2.18

-0.116

-3.383

-0.369

-0.256

4.866

8.643

-1.899

3.385

0.39

-1.179

-3.365

-2.59

2.627

4.487

T kdivp

3.167

-7.332

-1.416

1.473

-0.823

-4.792

-0.627

0.598

-0.521

-2.612

0.655

-0.108

2.76

1.49

1.244

-0.356

1.589

-1.007

-1.483

4.429

4.136

-5.07

2.543

2.078

-2.601

1.204

-4.42

-1.393

0.04

-2.902

-3.095

-8.247

2.595

0.087

4.403

-1.517

2.892

-3.051

-0.461

0.107

R2

0.976

0.880

0.723

0.939

0.922

0.708

0.803

0.933

0.894

0.945

0.980

0.939

0.947

0.986

0.975

0.947

0.938

0.941

0.594

0.833

0.984

0.983

0.992

0.915

0.994

0.556

0.744

0.338

0.936

0.504

0.969

0.807

0.925

0.991

0.990

0.953

0.941

0.966

0.944

0.839

F

“436.237

78.350

27.785

164.639

125.963

25.801

43.598

148.579

89.971

183.879

512.726

164.731

191.373

738.702

421.605

191.762

162.226

169.869

15.597

53.299

634.848

596.887

1399.503

115.217

1710.162

13.362

30.996

5.444

156.644

10.846

334.457

44.647

131.533

1221.363

1011.381

217.685

170.683

301.465

178.907

55.618

ndustry123456789

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40
Investment model regression results for capital stocks.

Output is lagged ona year.

41 Insurance carriers, agents, brokers

42 Real estate

43 Hotels

44 Personal servioes

45 Business services

46 Auto repair, sendces, and paddng

47 Misc. repair services

48 Motion pictures

49 Amusement and recreation services

50 Heatth sewices

51 Legal services

52 Educational sendces

53 Social services, museums, mem. org

-349.013

-287.113

-73.809

-87.069

-276.246

-185.185

-132.637

-179.053

23.908

-338.024

110.388

-302.697

-513.017

0.208

0.165

0.034

0.041

0.146

0.095

0.069

0.091

-0.015

0.187

-0.070

0.163

0.278

-4.601

-2.246

1.356

1.367

-0.226

0.623

0.378

0.636

1.488

-1.782

3.260

-1.356

-2.448

-1.342

0.605

-0.338

-0.178

0.019

0.757

0.144

0.223

-0.750

0.234

-0.254

1,615

1.426

-1.851

-7.402

-3.884

-4.872

-10.632

-10.368

-16.474

-12.022

1.017

-5.716

0.599

-5.924

-8.689

1.883

7.137

3.158

3.979

10.928

9.461

14.123

11.526

-1.15

5.633

-0.678

5.72

8.547

-1.709

-4.055

5.458

3.737

-2.039

2.951

1.778

5.002

4.83

-3.276

1.613

-2.363

-4.763

-3.231

2.214

-2.027

-0.705

0.059

4.579

1.276

1.23

-10.98

0.563

-0.365

7.142

4.108

0.797

0.957

0.988

0.968

0.986

0.985

0.969

0.986

0.956

0.971

0.699

0.750

0.936

41.918 “ 237.773

907.911

325.846

747.587

682.146

331.141

775.229

230.079

355.248

24.752

31.909

155.529

41

42

43

44

45

46

47

48

49

50

51

52

53
L

GLOBAL FORECASTING AND FORESIGHT

Chair: Kenneth W. Hunter

World Future Society

Global Forecasting and Foresight (Abstract),

Kenneth W. Hunter, World Future Society

59

Global Forecasting and Foresight

Chair: Kenneth W. Hunter

World Future Society

Professional fitures researchers are currently initiating an examination of the theories, methods and practices that

comprise the foundation of global fitures research, education, and policy advising. Forecasting and defining

policy and planning assumptions for several decades ahead is a critical component of fhtures research. In

addition, new initiatives for collaborative fbtures programs in major policy areas and for international and

domestic regions are driving the need to strengthen the core capacity for integrating knowledge and models,

baselines and assumptions, research agendas, and collaborative research support systems. This panel will discuss

several of the new programs and initiatives in the area of Mures research.

Panelists:

Kenneth W. Hunter

World Future Society’s 1999 Conf~ce “Frontiers of the 21st Century” and President, Collaborative Futures

International

Dennis Pirages

Director, The Harrison Rogram on the Future Global Agen@ University of Maryland, College Park

Paul J. Runci

Research Scientist, Battelle Pacific Northwest National Laboratory, Richland, Washington

Jerome C. Glenn

Executive Director, Millennium Reject, United Nations University

61
n
COMMUNITY POLICY MODELS

Chair: James K. Scott

Community Policy Analysis Center

University of Missouri - Columbia

The Impact of Medicare Cavitation Payment Reform: A Simulation Analysis,

Timothy D. McBride et al., University of Missouri - St. Louis

Community Policy Analysis System (ComPAS),

Thomas G. Johnson and James K. Scott, Rural Policy Institute,

University of Missouri - Columbia

Projections of Regional Economic and Demographic Impacts of Federal Policies,

Glenn L. Nelson

Rural Policy Research Institute, University of Missouri - Columbia

63

The Impact of Medicare Cavitation Payment Reform:

A Simulation Analysis

September 1997

Timothy D. McBride*

hdrew F. Coburn

Sam Cordes

Robert A. Crittenden

Charles W. Fluharty

J. Patrick Hart

Keith J. Mueller

Wayne W. Myers

Rura[ Policy Research Institute (R UPRI) Rural Health Panel

University of Missouri

Columbia, MO

*For ilimther information, contact the lead author at the following address: Timothy McBride, Department of

Economics, University of Missouri-St. Louis, 8001 Natural Bridge Rd., St. Louis, MO 63121-4499. Electronic mail

address: mcbride@umsl.edu

Paper presented at the Federal Forecasting Conference, Washington, D.C., September 11, 1997.

An ongoing Congressional Special Grant administered through the Cooperative State Research Education and

Extension Service, U.S. Department of Agriculture continues to provide core support for this panel’s activities,

including this report. The Robert Wood Johnson Foundation recently provided significant additional support for

this effort. However, none of the aforementioned organizations or agencies are responsible for the specific content

of this report.

65
On August 5, 1997, President Clinton signed

H.R. 2015, the Balanced Budget Act of 1997. This

legislation includes changes in the Medicare payment

methodology used to determine monthly, per member

payments to health plans participating in risk contracts,

typically entered into by Health Maintenance

Organizations (HMOS). Currently, the basis for

calculating current payment under “risk contracts” is an

average of previous expenditures in fee for service

payments received by Medicare beneficiaries in each

county. The county-specific approach to calculating

cavitation results in large variation in rates across

counties and large volatility in payment annually in

counties with small enrollment, contributing to lower

penetration in those counties and to the financial

instability of the Medicare program.

This paper uses simulations to project the

impact of the legislation on payments that will be

available for health plans in all counties. This paper

describes the problems with the current methodology and

provides details of the methodology used in these

simulations. Finally, the paper describes the final

legislative changes and outlines key findings from the

simulations.

I. WH+IS THE PROBLEM WITH THE

CURRENT AAPCC METHODOLOGY?

The increased use of managed care by private

insurance plans, and coterminous restraint in premium

charges, has intrigued public policy makers. As a result,

managed care is touted as a means of controlling

spending in the Medicaid and Medicare programs. While

enrollment in managed care by Medicare beneficiaries

has increased considerably in recent years, it remains

quite low in rural areas. In 1996, only 1.4 percent of

rural (nonmetropolitan) Medicare beneficiaries were

enrolled in HMOS, as compared to 14 percent in urban

(metropolitan) areas.

The relatively low adjusted average per capita

cost (AAPCC) rates paid in many rural counties as

compared to urban counties, and the volatility in these

rates from year to year, are often cited as the primary

reasons for lower rates of Medicare risk-plan enrollment

rates in rural areas of the country (Dowd, Feldman, and

Christianso~ 1996; Semato, Brown, and Bergeron,

1995). The current rate for “risk contracts” in

Medicareis set at 95 percent of the AAPCC rate in the

county where the beneficiary lives, with adjustments

made for a few selected enrollee characteristics. The

rate is based on historical expenditures in each county,

and therefore is lower in rural areas because it reflects

(a) historical inequities in Medicare reimbursement

rates, and (b) lower health care utilization in rural

areas. In addition the small size of rural counties

contributes to variation and volatility in rates across

counties and over time.

Y~ccIaks. Attention for years

has focused on the substantial variation in AAPCC rates

across counties in the US. As shown in Figure 1, rates

varied from an extreme low of $221 in two rural counties

in Nebraska (Banner and Arthur) to an extreme high of

$767 in Richmond county, New York. Although the

overall average rate was $467 in 1997, the lowest rates

were found in rural counties and the average AAPCC rate

in rural counties not adjacent to urban counties was only

$374, as compared to $395 in rural counties adjacent to

urban counties, and $493 in urban counties in the US.

The lower rates in some counties, especially

rural counties, is largely attributable to the methods used

to construct AAPCC rates. The rate is based on actual

historical expenditures in each county, and therefore is

lower in rural areas because it reflects (a) lower medical

care prices, especially historical inequities in Medicare

reimbursement rates, and (b) lower health care

utilization in rural areas.

First, it is well known that the prices of some

goods and services are lower in rural areas, such as rent

and housing prices. In addition, medical care prices are

likely to be lower especially because of tie historical

structure of reimbursement in the Medicare system

which built in inequities against rural areas. Since these

reimbursement rates are currently used to reimburse

providers under the Medicare fee-for-semice syste~ and

AAPCC rates are based on historical Medicare FFS

spending, the AAPCC rates will reflect these lower

prices.

Figure 1.

Variation in Medicare AAPCC Rate,

by county location, 1997

Significant variation in AAPCC rates across “’place”

E3El S403

A

$767

+

%-. R.** ?“k, &.”.C.+ 1“- ,Uu?nl, 11..hb ?Wl
Second, AAPCC rates are lower, especially in

rural areas, due to a historically lower use of medical

services in rural areas. There may be many reasons for

these differences, but this difference is likely attributable

in large part to the historical access problems in rural

areas, especially. a lower physiciadpopulation ratio and

low availability of other medical care services.

The small size of many rural counties

exacerbates these two problems. In particular, since

AAPCC rates are based on a five-year historical average

of utilization in the county, the increased service use of

only a few beneficiaries could have a huge impact on

AAPCC rates. To cite an extreme example, Loving

county in Texas has only 19 Medicare enrollees. Thus an

increase in consumption of Medicare services of only

$10,000 by only one beneficiary (easily the bill for an

inpatient hospital episode) would increase rates in the

county by over $100 per month once that medical bill

was averaged over 19 beneficiaries and five years.

It is largely believed that basing reimbursement

to Medicare HMOS on historical prices and utilization is

inequitable to rural areas for two main reasons. To the

extent that price differences measure true differences in

the cost of living in rural as opposed to urban areas, then

some difference in reimbursement rates is justified.

However, to the extent that the structure of Medicare’s

FFS reimbursement system exaggerated these

differences, these price differentials are less justified.

Second, differences in AAPCC rates that reflect lack of

access to services “lock in” historical access problems.

p q

Variation

Definition. The difference between Medicare

AAPCC and cavitation rates across counties.

Statistical definition. Variation in cavitation

rates across time can be most simply described

by a single number: the ratio of (a) the standard

deviation of cavitation rates in the U.S. to (b) the

mean cavitation rate in the US. This is a

standard measure of “variation” used by

statisticians.

Variation in current rates. In 1997, the

average variation in AAPCC rates across the US

was 21.4 percent. This means that the average

difference (higher or lower) between a county’s

AAPCC rate and the national average of AAPCC

rates was roughly 21 percent.

Illustrative Example. In 1997, AAPCC rates

varied from an extreme of $767 in Richmond

(NY) to $221 in two counties in Nebraska

(Banner and Arthur). Since the average

Table 1 shows that the variation in rates across

the U.S. has been increasing over time, with variation

measured by the average difference between the county

rates and the average AAPCC rate in the U.S. (see box).

From 1990 to 1997, the variation in AAPCC rates

increased from 17.7 percent to 19.4 percent, indicating

that the problems with the AAPCC methodology are

getting worse over time.

m WCC rti. An issue that has

not received as much attention from researchers and

policymakers is the volatility in AAPCC rates over time.

But in a 1986 survey of Medicare HMOS, 86 percent of

respondents contended that AAPCC payment levels

within counties were unpredictable (Brown et al. 1993).

Moreover, the year to year volatility in AAPCC

interfered with the ability of the HMO to anticipate

revenue flows and plan effectively. Because health plans

serving counties with volatile rates face greater

uncertainty regarding payment rates for fbture years, they

may be less willing to enter the market with a Medicare

risk-contract product (PPRC, 1996).

Figure 1.

Volatility in AAPCC Rates, 1990-97

AAPCC rata arc wlntile

1*OU

?s% -

T

-

71%

8s% -

49%

n% - - $7%

2s% -

on - -

***?

.2s% - .

— .s0%

.22% -26%

-50% - .43% 4*U

s-u.al140, *.4 .md U“*. ,1,,9,

J

Over the 1990-97 period, the volatility in

AAPCC rates was considerable. The year-to-year

changes in rates were often dramatic. For example, in

1997 one county experienced a 37 percent increase in

their AAPCC rate, while another county experienced a

dramatic 40 percent drop in their rate (Figure 2). Over

the 1990-97 period, the steepest increase in rates was an

85 percent increase observed in 1993 and a 43 percent

drop witnessed in 1991.

While these extreme cases are illustrative of the

dramatic volatility that can occur under the AAPCC

methodology, of coume most counties do not experience

volatility to the extent illustrated here. Using a measure

of volatility across counties (see box), the average

volatility experienced in the 1990-97 amounted to 3.4

67
percen~ indicating that in tie 1990-97 period the average

change in rates (positive or negative) over and above the

growth in national average Medicare spending was 3.4

percent annually. Table 2showsthat thelocal volatility

in AAPCC rates was greater in rural adjacent counties

(3.3 percent) and rural nonadjacent counties (4. 1

percent) than it was in urban counties (2.6 percent).

These results support the intuition that AAPCC rates

will be more volatile in rural areas because of the

smaller populations in these areas, because a few high

cost procedures or patients could have a large influence

on the AAPCC rate in any given year.

Figure 3 also presents counties differentiated

by other important characteristics that might be likely to

vary with volatility at the local level. Given that

volatility is likely to be higher in counties with a

smaller number of Medicare beneficiaries, it is not

surprising that volatility is higher in counties with less

than 500 Medicare eligibles (volatility of 7.3 percent) as

compared to larger counties, especially counties with

more than 100,000 Medicare beneficiaries (2. 1

percent). Volatility also seems to be related to the risk

plan penetration rate (enrollees in Medicare risk plans

as a ratio to total Medicare beneficiaries) in the county,

since volatility is higher in counties with no Medicare

risk enrollment (4.4 percent) than it is in counties with

Figure 3.

Volatility in AAPCC rates

by size and location of county, 1990-97

Volatility ia rates is highert in areas with low population

Aueraw VOMNly, 1,s6-s7

0% 2% 4% 6% s%

A8 COIMIW#

By

7.1%

Number Soo.me

Of t .000-e ,esm

Mrdkare t o.000-ee.eee

redpkets 100,000 of mote

[..”. M.Uw&. ?..I.4 .4 M“il.s ,1..7,

considerable risk enrollment (2.4 percent) (Table 2).

Finally, the results also indicate that volatility is highest

in counties with the lowest AAPCC rates in 1997. The

main source of volatility in a county’s AAPCC rate is

fluctuations in service use patterns because AAPCC are

based on the five years of recent historical expenditures

in the county for recipients not enrolled in health

maintenance organizations. Thus, the AAPCC rate is a

function of utilization and prices, both of which have

been lower in rural counties, the latter a function of

recent Medicare reimbursement policies. Consequently,

the problem of volatility may be exaggerated in rural

Volatility

IkftitiOn. The year to year fluctuation in cavitation rates across counties.

Statistical definition. Volatility of cavitation rates over time is defined as the absolute value of (a) the

annual growth in the cavitation rates for a year less (b) the growth rate in per capita national average

Medicare spending across the U.S. (see McBride, Penrod, Mueller, 1997, for a full description of this

measure). This measure is based on the reasoning that an HMO should reasonably expect rates at the local

level to keep pace with national average per capita spending on Medicare and that any increase above or

below that is “local volatility. ” Since cavitation rates may not grow as fast as the growth in Medicare

spending per capita in many counties -- arm a Medicare risk plan may be as concerned about uncertainty on

either the positive or negative side -- what is presented here is the average of the absolute value of growth.

Recent volatility. Between 1990 and 1997, volatility is AAPCC rates across the US averaged 3.4 percent,

indicating that the average change (positive or negative) in AAPCC rates, over and above the change in

national average per capita Medicare spending was 3.4 percent (Table 2).

Illustrative Example. To illustrate this method, suppose a county has a cavitation rate of $300 in 1997

and the cavitation rate rises to $330 in 1998, an increase of 10 percent. Suppose also that the growth in per

capita national average Medicare spending was 5 percent in 1997. Based on these data, the total growth in

cavitation rates in this county would be 10 percent, 5 percent due to national Medicare per capita spending

growth, and 5 percent due to “volatility” at the local level.

Counties with volatility. The county that experienced the most volatility in rates over the 1990-97 period

was Loving county (TX), where the AAPCC rates over the 1990-97 period were: $293,$254,$434,$378,

$443,$501,$881$527.

68
areas because fluctuations in service use patterns tend to

be larger for areas with small Medicare populations

(McBride, Penrod, Mueller, 1997; PPRC, 1997).

Impkalims of Vaxialhruld Volatlllfy . .

While it is quite clear that the methods used to

compute local area AAPCC rates lead to substantial

variation and volatility, this is not by itself indicative of

the effects of these problems on health systems across the

U.S. Instead, the major impetus for reform of the

AAPCC rate-setting methodology stems from the

conclusion that variable and volatile AAPCC rates have

contributed to several problems:

. Managed care penetration is low in

counties with lowest WCC rates. It is

logical that Medicare risk plans would be

more reluctant to offer plans in areas where

AAPCC rates are low and volatile. The

evidence bears out a strong relationship.

For example, while enrollment in Medicare

risk plans exceeded 14 percent in urban

areas with rates above $400 in 1996,

enrollment was only 2 percent in urban

areas with AAPCC rates below $300 and

0.5 percent in rural areas (Figure 4).

Overall, rural enrollment in Medicare risk

plans was only 1.4 percent in 1996.

However, enrollment rates only reached 4.1

percent in rural areas with AAPCC rates

exceeding $500, indicating that even higher

AAPCC do not guarantee significantly

higher risk plan enrollment. Nevertheless

these data do suggest that lower and more

volatile AAPCC rates have inhibited the

growth of managed care in rural areas, and

inhibited the pace of health market reform

in those areas.

Figure 4.

Medicare Risk Plan Enrollment

and AAPCC Rate, 1996

Low rates contm”bufc 10 \ow risk plm pCtICtrllIiOtl

1s%

● ☎

. T 14.a%

L

n

ORural

■ Urban

3.8%

1.4

2.9%

0.s 1.0

s“,!.. ,-., ,.*m.r.,,a,—,numl, M..* h-,

● Inequity in benefits offered across

counties. Under HCFA regulations, risk plans

are required to submit cost reports that estimate

the cost of providing benefits to Medicare

recipients in managed care. If the payments

received by the plan exceeds these costs, then

the plan has two options: offer more benefits to

recipients or return the difference to HCFA. To

date, all plans have chosen the first option of

offering more benefits to recipients. For

example, 62 percent of risk plans in the US

offer prescription drug coverage, 97 percent

offer routine physicals, 74 percent offer

hearing exams, and 38 percent offer dental

benefits to cite just a few examples (PPRC,

1997, Figure 2-14). In addition, 65 percent

provide all their extra benefits without charging

an additional premium to recipients and many

$

plans reduce or eliminate out of pocket

copayments and deductibles.

The evidence shows that plans located in

areas that receive much lower AAPCC

rates are much less likely to offer enhanced

benefits (PPRC, 1996; PPRC, 1997). Thus,

the large variation in rates across counties

contributes to inequities in benefits

available, especially for rural residents.

Most rural Medicare recipients are not

enrolled in managed care and those who

are enrolled will not have extra benefits

available to them. This inequity is

considered to be unfair by many Medicare

recipients aware of the problem.

. &APCC policies contribute to Medicare

financial problems. Originally Medicare

managed care was devised as a strategy for

reducing and stabilizing the growth in

Medicare expenditures. However, most

analysts have concluded that the Medicare

risk program has increased Medicare

spending. This largely results from two

phenomena. First, as described above,

when the costs of plans are well below the

payment received from HCFA, the

difference is not returned to HCFA, thus

resulting in additional aggregate

expenditures. Secon& a series of studies

provide evidence to support the proposition

that Medicare risk plan participants are

healthier than Medicare fee-for-service

recipients (described as “favorably

selected”), but plans are still compensated

according to average Medicare FFS

spending through the AAPCC formula
(PPRC, 1997). Thus favorable selection

contributes to increases in Medicare

spending.

. Policies contribute to inadequate

Medicare reimbursement rates in rural

areas. Since Medicare risk plans have

actually led to an increase in Medicare

spending, risk plans have actually

contributed to the fmcial problems facing

Medicare. Over the years, the major

response by Congress to financial problems

in Medicare has been to reduce the growth

in reimbursement rates for providers,

especially hospitals and physicians. Thus

the problems with the AAPCC

methodology described above have

contributed to continued restraint on the

growth of reimbursement rates for rural

providers and have impeded efforts to

reduce inequity in rates.

II. DESCRIPTION OF PROVISIONS TO

REFORM AAPCC METHODOLOGY

On August 5, 1997, President Clinton signed

H.R. 2015, the Balanced Budget Act of 1997. Table 3

presents an outline of the change considered here. As

indicated in the table, the adjusted cavitation rate in a

given year would be equal to the greatm ofl

(I) a blended cavitation rate,l determined as

(ii)

(iii)

the weighted average of the area-specific

adjusted cavitation rate (ASACR) and the

input-price-adjusted national adjusted

cavitation rate (IPANACR) phased in over

six years until rates are based on a

50%/50% average of the ASACR and

IPANACR rates,

a floor, initially set at $367 in 1998 and

indexed for Medicare per capita spending

growth thereafter, and

a hold harmless rate, set at 102 percent of

the area’s adjusted cavitation rate in the

previous year.

lNote the terminology “blended cavitation rate” is

the term used to describe this method in the literature (PPRC,

1996; PPRC and ProPAC, 1995), but not typically in any of

the proposed legislation.

While this formula seems complicate~ essentially it sets

the new cavitation rate at the blended rate, although the

cavitation rate is not allowed to fall below the payment

“floor” or a “hold harmless” rate.

The provision for blending the cavitation rates

is designed to achieve the objective of equalizing

cavitation rates across counties, since this method uses

the area-specific cavitation rate (ASACR) and averages

it with the input-price-adjusted national adjusted

cavitation rate (IPANACR). The ASACR is based on the

previous WCC rates in the county. The IPANACR is

essentially based on the average of AAPCC rates across

the US (called the USPCC, the United States per capita

cost), adjusted to reflect variations in local prices across

counties. Adjusting rates for local prices is done in the

realization that the prices facing managed care

organizations vary across areas. The price index for

computing the IPANACR is specified in the legislation

and constructed using price indices computed by HCFA

to set reimbursement rates for hospitals and physicians.

To calculate the blended cavitation rate, each

piece of legislation uses an “area-specific percentage”

(ASP) to weight the ASACR and a “national percentage”

@P) to weight the IPANACR. Since essentially &kPCC

rates under current law are based on 100 percent of the

area-specific rate, these percentages are phased in over

time, until the applicable percentages are reached in

2002. The ASP is set at 50 percent in the year 2003 and

phased in over the 1998-2003 period, set at 90, 82,74,

66,58 and 50 percent in those six years.

The floor is also used to equalize cavitation rates

across counties and is used to reflect the realization that

historical prices and utilization may be so low that it

would not be possible for any managed care organization

to operate in a local area if the cavitation rate fell too

low. Thus, the floor is designed to guarantee that each

county has a cavitation rate that would make MCOS

viable in that area. The hold harmless provision is

desi~ed as a protection against fluctuations in cavitation

rates, guarantee that the growth in cavitation rates is at

least 2 percent in I!hture years.

In each year, the ASACR and IPANACR will be

increased by the national average per capita growth

percentage (NAPCGP), basically the average national

increase in per capita Medicare spending, less some

specified percentages in the period 1998 through 2001.

In 1998, 0.8 percentage points are subtracted from the

growth rate, while 0.5 percentage points are subtracted

from the growth rate in the period 199 through 2001.

This provision is important because it guarantees that

rates in the fiture will increase a steady and predictable

70
rate, thus removing the year-to-year volatility that has

been witnessed in previous years.

A “budget neutrality adjustment” (BNA) is used

in all pieces of legislation to insure that the aggregate

payments made under the proposed policies shall be

equal to the aggregate payments that would have been

made if the rates had been calculated with the areaspecific percentage (ASP) set equal to 100 percent. In

other words, total expenditures under these provisions are

not allowed to exceed what would have been paid if

every county was paid their area-specific rate and no

county was affected by provisions such as the floor and

blended rates. If the budget neutrality provision is

triggere~ it is used only to adjust blended cavitation

rates. For example, if expenditures under the proposed

legislation exceed expenditures that would have been

made otherwise by 5 percent, then blended rates would

be reduced by 5 percent.

The legislation reduces local area cavitation

rates (the ASACR) for the amount of spending made in

the county for the graduate medical education (GME)

pro-. This program pays hospitals for expenditures

incurred for education of medical residents. Since these

expenditures are included in the payments to hospitals

under the fee-for-service portion of the Medicare

progr~ these payments will implicitly be included in

the calculation of A4PCC payments. This is considered

to be a problem since the AAPCC payments are made not

to hospitals, but to managed care organizations. Thus,

the payments designed to help defray education

expenditures may never reach the hospital. In H.R. 2015,

GME expenditures are carved out from the cavitation

rates over a five year period with 20,40, 60, 80, and 100

percent of GME spending carved out in the years 1998

through 2002, respectively.

From this summary, several provisions are

important in the eventual setting of cavitation rates across

counties. Key factors in the determination of the new

Medicare cavitation rates are the percentages used in the

blended rates, the methods used to set the floor, the

carving out of GME payments, and the use of

standardized predictable growth rates in the setting of

fbture cavitation rates.

HI. SIMULATION METHODS AND DATA

The Rural Policy Research Institute (RUPRI)

Health Panel has constructed a comprehensive data set

and simulation model that is used to simulate the effects

of the policy changes described here. This file, called the

RUPRI Medicare Cavitation County Data File, was

constructed by merging data from several sources, but

primarily from obtained from the Health Care Financing

Administration (HCFA). The file contains over 1,300

variables that generally fall into the following categories:

o

0

0

0

0

0

0

0

Historical AAPCC rates, 1990-97

Medicare enrollment in county, 1996

Number of enrollees in Medicare HMOS,

1996

including details on enrollment by

each plan enrolling in the county

Number of Medicare HMO plans with

Medicare emollees enrolled in the plan by

county (includes distinction for whether

plan includes county in its Geographic

Service Area )

Characteristics of Medicare risk plans

offered in the county. For each plan data

includes:

Name and location of plan

Dates of HCFA contract

Type of plan (risk, demo, or cost)

Type of HMO (staff, group or IPA)

Profitinon-profit status of plan

Benefits offered by plan (e.g.,

preventive, dental, eye, ear, drugs)

Premium charged by plan

County share of Graduate Medical

Education (GME) and Disproportionate

Share (DSH) spending

An “input price adjustment” for the county,

based on the formula described below

County descriptive variables (Beale codes,

population, population in poverty)

In general, most of this data was originally obtained from

the Health Care Financing Administration, from files

available on the Internet. However, additional data was

obtained directly from other HCFA offices, specifically

the Office of the Actuary and the Office of Managed

Care. Finally, additional data was obtained from the

1990 Census, the Economic Research Service and the

Federal Register.

The data from each of these sources was merged

at the county-level using the county FIPS code and the

county name (since FIPS code was not available for the

HCFA data). There is not a one-to-one correspondence

between the FIPS county code and the code assigned by

HCFA. However, mismatches were corrected in all

cases, except for Alaska counties. Due to

incompatibilities between the HCFA and FIPS

classifications, all Alaska counties were dropped from

the analysis. Data from U.S. territories (Guam, Puerto

Rico, Virgin Islands) was also dropped fkom the analysis.

71
The file containing “input price adjustments”

(IPA) was computed at the local level by the RUPIU

Health Panel by inputting data on Medicare Prospective

Payment System (PPS) rates and Resource Based

Relative Value System (RBRVS) rates reported at the

county level in the Federal Register (Federal Register,

1996). The IPA was computed according to the formula

specified in legislation dealing with this issue. In

particular, the legislation specifies, in part: “Medicare

services shall be divided into 2 types of services: part A

services and part B sewices . . . . The proportions.. for part

A services shall be the ratio (expressed as a percentage)

of the national average annual per capita rate of

payment forpart A for 1997 to the total national average

annual per capita rate ofpayment for parts A and B for

1997, andforpart B services shall be 100 percent minus

the Part A ratio . . . . For part A services, 70 percent of

payments attributable to such services shall be adjusted

by the index used under section 1886(d)(3)(E) to a@st

payment rates for relative hospital wage levels for

hospitals located in the payment area involved; for part

B services, 66 percent of payments attributable to such

services shall be adjusted by the index of the geographic

area factors under section 1848(e) used to adjust

payment rates for pilysicians’ services furnished in the

payment area, and of the remaining 34 percent of the

amount ofsuch payments, 40percent shall be a~usted by

the index for relative hospital wage levels]. ” (Source:

1997 Balanced Budget Act, HR 2015).

As noted above, H.R. 2015 specifies the use of

a “budget neutrality adjustment” (BNA) to insure that the

aggregate of the payments under shall be no greater than

the aggregate payments that would have been made if the

previous AAPCC-based methodology were continued.

The budget neutrality adjustment (BNA) was computed

by aggregating expenditures for risk enrollees across

counties using the multiple of (a) cavitation rates that

would exist in absence of the legislation (i.e., 100 percent

of the area-specific cavitation rates) and the (b)

enrollment in Medicare risk plans. Then the BNA was

computed as equal to:

BNA = [Spending using area-specific rates]/

[Spending using new legislated rates]

As specified in the legislation, this BNA is applied to the

cavitation rates if the BNA is less than one in the

definition defined above. I reality, it has been found that

the BNA would not be triggered under any of the

legislation proposed to date because the legislation has

the effect of lowering rates, relative to the area-specific

rates, in counties with high HMO enrollment.

Assumptions are made about several key factors

in the determination of the new Medicare cavitation rates

under proposed policies. These include the determination

of the BNA, the national average per capita growth

percentage (NAPCGP), and the determination of the

input-price-adjusted national adjusted cavitation rate

(IPANACR). In particular, the IPANACR would be used

directly in the formula for a Medicare area, both in the

blended cavitation rate and often in the “floor,” set at

some percentage of the IPANACR. Thus, important

assumptions had to be made about:

(a) growth in the “national average per capita

growth percentage” and “area-specific

cavitation rates.” Recent Congressional

Budget Office (CBO) estimates of the

growth in Medicare spending were used for

this purpose (CBO, 1997a), and

(b) growth in the Consumer Price Index (CPI).

CBO estimates (CBO, 1997b) were also

used to compute projected increases in the

CPI, to use in computing the value of

cavitation rates in “constant dollars.”

Some assumptions have to be made in the simulations

due to the uncertainty about the future. Actual Medicare

risk plan enrollment in December of 1996 is used to

determine aggregate spending under policy provisions,

especially when computing the budget neutrality

adjustment (BNA). In reality, actual enrollment in the

future will be used for this purpose. Actual enrollment in

risk plans in the future is likely to be much higher than

enrollment in 1996. However, the growth in enrollment

will not affect the estimates here unless the growth in

enrollment is radically different across counties.

IV. SIMULATION RESULTS

Tables 4 through 8 summarize the detail of the

simulation results. Table 4 presents descriptive statistics

summarizing the effects of the proposals on various

measures including the mean or average cavitation rates

and the lowest or minimum cavitation rate found in any

county. In addition, the tables present measures of the

average variation in rates across counties and the average

volatility in rates across time since, as indicated above,

most analysts have concluded that the variation and

volatility in AAPCC rates are the biggest problems with

the current methodology for setting rates. Finally the

table presents the percentage of counties that would have

cavitation rates set by the provision setting a “floor” on

cavitation rates and the percentage of Medicare eligibles

residing in counties that would receive the floor.

72
Under the new legislation, average cavitation

rates would increase over time, as would be expected

since Medicare spending is rising over time. However,

the growth in average cavitation rates masks important

changes in the legislation. The key change in the new

legislation occurs because of the reduction in variation

and volatility and in comparisons of growth rates across

counties.

_ in Qitation Rats

I$ycum!ntAAPaRate

ih24%

1%

lm7AAPcciMo tncOlSwy

Figure 5 shows that growth rates in cavitation

rates will be much higher in areas with currently low

AAPCC rates than they will be in areas with currently

high AAPCC rates. For instance, the growth rate in

cavitation rates in counties with AAPCC rates currently

below $300 will be about 65 percent in the 1998-2004

period (38 percent when adjusted for the rate of price

inflation, see Table 6), while cavitation rates will grow

only 15 percent in counties with rates exceeding $500 per

month in 1997 (and experience a negative growth rate of

3 percent when adjusted for price inflation). This vast

difference in growth rates obviously reflects the

significant features in the proposals designed to

“equalize” rates, including the blending of local and

national rates, the floor, the averaging of area-specific

rates, and the came out of GME spending.

Since one goal of reforming the Medicare

cavitation payment policy is to encourage growth in

Medicare managed care plans, areas with currently low

managed care penetration rates will need to experience

dramatic growth in cavitation rates to encourage plans to

offer plans to enrollees. Figure 6 shows that the

proposed policy changes will lead to faster growth in

cavitation rates in the areas with the lowest managed care

penetration, with growth rates in the areas with less than

no HMO penetration expecting growth exceeding 35

percent, but only 27 percent growth in areas with risk

plan penetration exceeding 10 percent. It remains to be

seen whether this growth rate differential is large enough

to encourage plans now aggressively offering plans in

some areas to pursue managed care growth in areas with

low penetration.

m~. The tables

present the simulation results separately for urban

(metropolitan) and rural (nonrnetropolitan) counties.

Since counties with low rates today are most likely to be

rural counties, it is not surprising that the average growth

in cavitation rates will be highest in rural counties,

especially rural nonadjacent counties. For example, the

growth in cavitation rates in rural nonadjacent areas is

projected to be more than 50 percent higher than the

growth in urban areas (Table 6). However, it is worth

noting that difference in growth rates displayed in Figure

7 is not nearly as large as the difference in rates displayed

in Figure 6, reflecting the fact that some urban counties

have low AAPCC rates, and some rural counties have

higher AAPCC rates.

73
ct on v . All of the

proposals significantly reduce volatility and variation in

cavitation rates. In particular, thevariation in rates is

equal to roughly 21 percent in 1997, indicating that the

average difference between a county’s AAPCC rate and

the national average rate is21 percent (higher or lower).

The new legislation will reduce this variation

significantly (Figure 8). By the year 2004, the variation

is reduced to about 14 percent. Table 7 illustrates the

reduced variation in another way, showing the percentage

of counties distributed by the ratio of a coun~’

cavitation rate to the national average rate.

Fifye 8.

Inqact on Viuiation in Cavitation Rates

19% 19%

17%

1

16%

l-u

15%

19371 SS81Q39zooo2x)lmQm m4

Year

By design, the new legislation reduces

significantly the year-to-year volatility in AAPCC rates.

This is mostly achieved by basing the growth in

cavitation rates on the growth in national average per

capita Medicare spending (slightly lowered by specified

amounts). The results indicate that volatility, which was

3.4 percent in the 1990-97 period, would be reduced to

only 1.3 percent under most proposals by the year 2003

(Figure 9). This measure of volatility indicates the

Figlle 9.

hrqlact on volatility in ckpitation Rates

Vdwiliv in Rata is Rc&dSig@YOItly

25%

1.3% 1.3% 1 .3% 1.3%

al 1 .0% L0.3%

Year

Su= adl+k-jlbm4haR(2uwo) t*lw

average change in a county’s cavitation rate would

exceed (or be less than) the growth in national average

per capita Medicare spending by only 1.3 by the year

2003. By the year 2004, after all the provisions are

phased in, volatility in rates almost completely

disappears, because the fiture increases in rates would be

based on growth in national rates, and because the effects

of the transitions built into current policies would have

been completed by this time.

. A “floor” on the cavitation

rate is important in the short run and is the most

important provision for counties with the lowest rate in

1997. In some counties, the blended rates are so low that

they will fall below a “floor” set initially at $367, and

adjusted for growth in Medicare spending thereafter

(Figure 10). Initially in 1998, 29.9 percent of all

counties, and 44 percent of rural nonadjacent counties,

will be raised to the “floor” rate of $367 per member per

month (Table 8). Over time, however, fewer counties

would have their rate set at the floor because of the

phase-in of the blending provisions. By 2003, only 12.9

percent of counties (but 23.3 percent of rural nonadjacent

counties), will have their rates at the floor.

Figm 10.

Mnimrn Gpitation Rate

RIUc3 tire signl$candy itt urau wiilt Iowst mtcs

$5001 S460

5464

S429

S409

2s400

$392

$367

S376

2

%C

@o

j; ~

%

u $221

S200 .-:

1997199e lm2000mlzf)cQm 2U04

Year

However, there are several reasons why the

floor, and the dollar amount of the floor, is not as

significant as it may seem at first glance. First, the

percentage of counties that will have cavitation rates set

by the floor provision declines significantly over time.

This can be seen in Figure 11, which shows that only 13

percent of counties will reside at the floor in 2004

(Figure 11). This is because of the transition to more

aggressive blending (50/50) over time, which has the

effect of liftig most counties above the floor. Second,

the percentage of beneficiaries living in counties at the

floor is even lower than the percentage of counties at the

floor. For example, less than 3 percent of beneficiaries

would reside in counties with a rate set by the floor in

74
2004. This occurs because counties at the floor tend to

have much smaller populations than other counties.

Figuell.

W&h Pnwidon’is Most IrmOrtant?

Parcm @cowttta with mrc dactminal by Maul, jlou M hold Imnnks \w&iwl

100%

+ -4 +

1996 1999 Zow Zool Zou? ZO03 200 4

Year

The hold harmless” provision initially protects

many counties from low growth in cavitation rates as a

result of other provisions, guaranteeing that a county’s

rate will not fall below 102 percent of the previous year’s

rate. Initially in 1998, 14.7 percent of counties, and 36.2

percent of central urban counties, will be guaranteed a

growth rate of 2 percent as a result of this provision

(Table 8). These counties would have experienced lower

growth rates without the provision primarily as a result of

the GME carveout provision and the phase-in of blended

rates. Over time, however, the percentage of counties

protected by the hold harmless provision falls to 1.9

percent of all counties and 8.6 of central urban counties

by the year 2004. This occurs primarily because by 2004

the effects of blending and the GME carveout are fully

phased in and Medicare per capita spending is growing

faster. As shown, however, a significant proportion of

counties will also have their rates set by the “hold

harmless” provision in 1998 and 1999 (because of slower

Medicare growth in these years). But over time the hold

harmless provision also becomes less important with

many of these counties having their rates set by the

blending provisions.

11~. The final legislation

subtracts (“carves out”) from the local area-specific rate

an amount representative of the amount of spending in

the county that would go towards the Graduate Medical

Education (GME) program. The provision to “carve out”

GME finding could further the equity goals of policy

reform since it leads to a significant lessening of the

variation in rates across counties. In addition, this

provision could improve health care delivery if the funds

are sent to providers that use the funding. However, the

ultimate effects of this provision on rural health delivery

depend on the distribution of the carveout funds, totaling

roughly $14.8 billion over the 1998-2004 period. If

funds are not distributed to undeserved areas, and a

disproportionate amount of the funds are directed to

central urban areas, then this provision will have less of

an impact on equity and on meeting the needs of rural

and underserved persons.

Caution in viewing the results of the GME

carveout is also warranted because the carveout might

help lead to a triggering of the budget neutrality

provisions. An example might better illustrate this issue.

Consider a hypothetical county with a 1997 AAPCC rate

of $700 where 10 percent of payments made to recipients

are destined for the GME program. Suppose this

county’s area-specific rate would increase by 4 percent

to $728 in 1998 after applying the growth rate in national

average payments. To compute the new cavitation rates

in 1998 under the new legislation, for example, this areaspecific rate would be reduced by roughly 2 percent (20

percent of the GME payments), reducing it to roughly

$713. When this area-specific rate is blended with the

lower price-adjusted national rate (assume equal to

!$500), the blended rate would be $692, below the hold

harmless rate of$714 (102 percent of the 1997 AAPCC

rate). Thus, this hypothetical county would receive a

cavitation rate of$714 in 1998.

There are two important issues raised by

examples like this. First, even though the legislation is

designed to caxve out GME payments, the full effects of

the carve out are not achieved because only a portion of

these payments are carved out in some counties.

Second, this provision has important

implications for the budget neutrality of these proposals.

The amount of dollars carved out and transferred for use

in the GME program is equal to the amount carved out of

the area-specific cavitation rate ($728*.02=$ 14.56).

However, since this amount is greater than the amount

typically carved out of a county’s rate, this provision ends

up negatively affecting the budget neutrality of the

proposal. For example, in the example cited above, the

blended rate would also have been $714 without the

GME carve out (because of the hold harmless provision).

Thus the GME carveout does not lead to any decline in

rates, yet about $15 per member, per month, enrolled in

managed care is transferred to GME funds.

It is important to realize that if this provision

triggers the budget neutrality adjustment (BNA), then the

net effect of this will be to reduce blended rates in those

counties with their rates set by the blended rates (the

majority of counties). In effect, the increased

expenditures triggered by the GME payments are

subsidized by lower payments to counties receiving

blended cavitation rates. For example, if this provision

75
leads to expenchtires that exceed expenditures that would

have been made otherwise by 2 percent, then blended

rates (and not the floor and hold harmless rates) will be

reduced across the board by 2 percent.

Despite this discussion, the budget neutrality

problems created by the GME carve out are not large,

primarily because GME finds are carved out over a fiveyear period.

VII. IMPLICATIONS

This paper presents detailed findings about the

effects of the Balanced Budget Act on Medicare

cavitation rates in the 1997-2004 period. However, the

ultimate impact of these policy changes is not clear from

these results. In particular, there are important questions

about the impact of these proposed changes on the rural

health system. In particular:

. If cavitation rates are increased, will

enrollment increase? In other words, if

the cavitation rates are increased in rural

areas and other areas with low AAPCC

rates, will this increase in rates lead to

more penetration of managed care plans?

And will it encourage more recipients to

enroll in managed care plans?

In the counties with the lowest AAPCC

rates in 1997, the increases in cavitation

rates will be significant, but it is not clear

whether this will be enough to spur

significant growth in managed care.

Further research on this question will

provide some evidence. However, if

enrollment is not significantly spurred by

this legislation, then the importance of the

legislation is significantly reduced.

. How much of increase will actually flow

to rural counties? In particular, payments

to Medicare risk plans go directly to

managed care organizations (MCOS). It is

not clear how much of these funds will

flow back to the counties where the

recipients live. MCOS will keep some of

the funds for administrative costs md

profits. In addition, if the MCO is an

urban-based plan, then those administrative

costs will remain in the urban area even if

the recipient lives in a rural area.

. How much of increase will flow to rural

providers? Since some of the cavitation

rate will be kept by MCOS to cover their

costs, this will reduce the finds that might

flow to rural providers. In addition, it is

well known that MCOS achieve their cost

savings primarily through reduced

hospitalizations and by making contracts

with providers for discounted or reduced

reimbursement rates. All this might lead to

the conclusion that rural providers

(especially hospitals and physicians) may

actually see a drop in revenue after

cavitation rates are increased, especially if

it spurs some recipients to switch from

Medicare FFS to an MCO plan. It is

difficult to predict the outcome, and it will

differ by county. Also, some providers --

such as primary care providers -- will likely

be impacted positively by increased

managed care enrollment, while other rural

providers -- especially specialist physicians

and hospitals -- may be impacted

negatively.

. Ultimately, the goal of Medicare reform

policy should be improving the delivery

of health care to recipients, if possible

within budgetary constraints. If

legislation to increase cavitation rates spurs

increases in enrollment in MCOS, the

ultimate impact on the health care received

by recipients will depend of course on the

quality of care provided by the MCO.

There is considerable controversy about the

impact of MCOS on the quality of care.

However, early evidence suggested that

managed care did not significantly reduce

the quality of care and may have increased

it. However, more recent evidence

focusing more directly on populations at

risk (e.g., the elderly, seriously ill,

disabled) suggests that the health care

received by these population groups may

be adversely affected by enrollment in

managed care plans. Measuring this effect

is very difticult, however, and much further

research is being conducted on that.

To assess the impacts of the new methods for computing

Medicare cavitation rates on the issues raised here,

further research is needed and this research is being

pursued now in further extension of this work.

76.
REFERENCES

Brown, R. S., D.G. Clemen~ J.W. Hill, S.M. Retchin, and

J.W. Bergeron, “Do Health Maintenance

Organizations Work for Medicare”, Health

Care Financing Review (Fall, 1993): 7-23.

CBO (Congressional Budget Office). 1997a. “CBO

January ~ 1997 Ba seline: Medicare and

Medicaid.” Washington: CBO, January 16.

CBO (Congressional Budget Office). 1997b. lhe

touhkllsd . “ Yean

98-2.QQZ. Washington: CBO, January.

Dowd, B., R. Feldman, and J. Christianson. 1996.

Competitive Pn”cing for Medicare. Washington,

D. C.: The AEI Press.

Federal Register. 1996. “Rules and Regulations”

EcdmalXe@x 61: 170 (August 30), pp.

46256-46266.

GAO (General Accounting Office). 1997a. “HCFA

Could Promptly Reduce Excess Payments by

Improving Accuracy of County Payment

Rates,” GAOfl-HEHS 97-78, February 25.

GAO (General Accounting Office). 1997b. “Medicare

HMOS: HCFA Can Promptly Eliminate

Hundreds of Millions in Excess Payments,”

GAO-HEHS 97-16, April.

GAO (General Accounting Office). 1997c. “Medicare

HMO Enrollment: Area Differences Affected

by Factors Other Than Payment Rates.” GAOHEHS 97-37, May.

McBride, Timothy D., Joan Penrod, Keith Mueller.

1997. “Volatility in Medicare AAPCC Rates:

1990- 1997,” 16(5,

September/October): 172-180.

PPRC (Physician Payment Review Commission). 1996.

~.Washington:

GPO.

PPRC (Physician Payment Review Commission). 1997.

~-.Washington:

GPO.

PPRC and ProPAC (Prospective Payment Review

Commission). 1995. MnLRqmt to Cmgmss

ge.d_Cm. Washington:

GPO, October.

77

Porell, F. W., and C.P. Tompkins, “Medicare Risk

Contracting: Identifying Factors Associated

with Market Exit,” Inquiry (Summer, 1993):

157-169.

Rural Policy Research Institute (RUPRI) Rural Health

Expert Panel. 1997a. “The Rural Implications of

Medicare AAPCC Cavitation Payment

Changes: An Analysis of the Balanced Budget

Act of 1997,” Rural Policy Research Institute

Rural Policy Brief PB97-2, August.

Rural Policy Research Institute (RUPRI) Rural Health

Expert Panel. 1997b. “The Rural Implications

of Medicare AAPCC Cavitation Payment

Changes: Assessment and Simulation Findings

Regarding Proposals Before the House/Senate

Conference Committee On Budget

Reconciliation,” Rural Policy Research Institute

Working Paper P97-9, July.

Rural Policy Research Institute (RUPRI) Rural Health

Expert Panel. 1997c. “The Rural Implications of

Medicare AAPCC Cavitation Payment

Changes: Background Assessment and

Simulation Results of Key Legislative

Proposals,” Rural Policy Research Institute

Working Paper P97-8, May.

Rural Policy Research Institute (RUPRI) Rural Health

Expert Panel. 1997d. “Achieving Equity in

Rural Medicare Cavitation Payment: Reforms

of the AAPCC Methodology,” RUPRI Rural

Policy Brief 1:1, February.

Rural Policy Research Institute, Rural Health Expert

Panel. 1996. “Anticipated Impacts of the Rural

Health Improvement Act,” Rural Policy

Research Institute Working Paper P96-5, July.

Serrate, C., R.S. Brown, and J. Bergeron. 1995. “Why

Do So Few HMOS Offer Medicare Risk Plans in

Rural Areas?,” Health Care Financing Review

(Fall): 85-97.
4

00

Tal)lc 1.

Monthly Adjusted Average Per Capita Cost (AAPCC) rates paid to Medicare risk plans, 1990-97

..-

1990 1991 1992 1993 1994 1995 1996 1997

All counties (N=3,1 13)

Mean !$247 $251 $266 $302 $315 $333 $372 $395

Minimum $126 $126 $139 $168 $171 !$177 $207 $221

Maximum $477 $481 $508 $599 $653 $679 $881 $767

Variation: Standard deviation/mean 17.7’%0 18.0’%0 17.8% 18,4% 18.5% 18,6% 18.9% 19.4!40

Percentage change in mean Na 1.8% 5.9% 13,4940 4.3% 5.6% 11.9% 6.l%

Largest decrease in AAPCC rate Na -42.7Y0 -29.9% -22, l% -21.3% -25.6% -12.1’%0 -40,2%

Largest increase in AAPCC rate Na 46.9’XO 71 .2?40 84.7% 26.6% 40.l% 75.9% 37.0%

Urban counties (N=836)

Mean $277 $280 $296 $338 $356 $376 $414 $439

Ratio of mean to national average 1.12 I l l 1,11 1,12 1.13 1.13 1.11 1.11

Minimum $149 $150 $161 $191 $210 $212 $231 $256

Maximum $477 $481 $508 $599 $653 $679 $759 .$767

Variation: Standard deviatiordmean 17’4’XO 17.5’XO 1703% 17.7% 17.9% 18.1% 18.1’%0 18.l%

Percentage change in mean Na 1,3% 5.7% 14.2% 5.2% 5.6% 10.0’%0 6.1%

Largest decrease in AAPCC rate Na -8.9% -15.8% -1.7Y0 -10.5’%0 -5.1% 1.3% -3.7%

Largest increase in AAPCC rate Na 25.4% 18,5% 32.8% 26.6% 17.7% 25.6% 24.2%

Rural adjacent counties (N=1,001)

Mcm $239 $244 $259 $294 $308 $326 $365 $390

Ratio of mean to national average 0.97 0.97 0.97 0.97 0.98 0.98 0.98 0.99

Minimum $157 $157 $166 $187 $196 $205 $216 !$231

Maximum !$392 $408 $444 $513 $487 $527 $674 $693

Variation: Standard deviationlmean 14.6% 15.070 14.7’%0 15.3% 15.0% 15.1’%0 16.2% 16.9%

Percentage change in mean Na 2.2’%0 6.4% 13.4% 4.5’%0 5.9% 1200’% 7.l%

Largest decrease in AAPCC rate Na -13.OYO -8.8’XO -2.3Y0 -11.7’XO -25.6Y0 -4.3Y0 -9.8’%0

Largest increase in AAPCC rate Na 20.5% 27.6’% 84.7’XO 20.7’XO 21 .0’%0 33.9% 33.2’%0

Rural non adjacent counties (N=1,276)

Mean $234 $239 $252 $284 $294 $309 $351 !$369

Ratio of mean to national average 0.95 0,95 0.95 0.94 0.93 0.93 0.94 0.94

Minimum $126 $126 $139 $168 $171 $177 $207 $221

Maximum $406 $418 $464 $485 $528 $540 $881 $647

Variation: Standard deviationlmean 16,1% 16.8% 16.8% 17.0’%0 16.6% 16.5?40 18.3% 18.7V0

Percentage change in mean Na 1.8’%0 5.5’% 12.8% 3.4% 5.4% 13.4% 5.2%

Largest decrease in &lPCC rate Na -42.7?40 -29.9’70 -22.1% -21.3Y0 -16.7% -12.lYO -40.2V0

Largest increase in AAPCC rate Na 46.9% 71.2% 50.4% 18,2% 40.l% 75.9% 37.O’XO

SOIJRCE: Rural Policy Research Institute (RUPRI) Health Panel AAPCC file (see text).
Table 2.

Volatility in Medicare MPCC Rates, 1990-97

Average annual Average of absolute values of annual

percent change percent change in:

in nominal AAPCC Nominal Medicare risk plan Number of

rittc AAPCC rate Local voia[~ Penetration rate counties

All counties

-. —. .

7.04?40 -

7.61% 3A2% 10.94% ‘ 3,113”

Ily location:

Urban 6.92% 7.21?40 2.59% 1 3.99% 836

Rural adjacent 7.38% 7,76940 3.30% 2.06% 1,001

Rural nonadjacent 6.85?40 7.75’XO 4.05% 0.57%’0 1,276

By Number of Medicare eligibles in county,

December 1996

Less than 500 6. 17’%0 9.32% 7.26% 0.39% 113

500-999 6.27’XO 7.64?40 4.79’?40 0.78V0 205

I ,000-4,999 7,18% 7.70% 3.52’?40 0086% 1,463

5,000-9,999 7.31’XO 7.64% 3.02% 1.84V0 626

I 0,000-99,999 6.90% 7.17’%0 2.59% 8.27% 641

I 00,000 or mOre 6.57?40 6,66% 2.10% 21.35’70 65

By Risk Plan penetration rate, Dccembcr 1996

None 6.88V0 8.02% 4.36% 0.00Y0 528

o- I Yo 7.18% 7.64% 3.349”0 0.13$’0 1,936

1 ‘XO-5Y0 6.87% 7.42% 3.25% 2.85% 260

5?40- 1 Ovo 6.91!40 7.24% 2.60% 7.52% 143

10-20% 6.90% 7.21?40 2.80’?XO 14.079’0 138

20% or more 6.12% 6.47’XO 2.43% 32.53% 108

By AAPCC rate in 1997:

Less than $300 5.13% 6.43% 3.88’3fo 0.63%

$300-$399

271

6.53% 7.19% 3.38% 3.51% 1,506

$400-s499 7.85’XO 8.18?40 3.3W0 9. 13’%0

S500-S599

1,049

8.54% 8.84?40 3.44’?40 18.27% 248

$600 or more 8.67% 8.75% 3.34% 20.88V0 39

SOURCE: Rural Policy research Institute (RUPR1) Rural Health Panel AAPCC file (see text).

NOTE: 1. Local volatility= nominal AAPCC growth less average growth in CP1 (3.42 percent) less growth in the inflation-adjusted U.S.

per capita cost (USPCC, 3.5 percent).

—
Table 3.

Provisions for setting Medicare risk plan cavitation rates: H.R. 2015

r

Provision I Description of movision

Mending h 2002 I

50% Area-specific

W% National (adjusted for price differences acnws counties)

Phased in over a six-year period, 199&zO03

Floor

I

$367 in 1998

(adjusted for Medicare per capita spending growth thereafter)

Hold harmless I 102’%0 of previous year’s rate

Graduate Medical

I

Fully cawed out over a five year period, 1998-2002

Education (GME) Carve-out

Growth rate

I

Growth in per capita fee-for-service Medicare spending

used to project lCSS 0,8% in 1998 and 0.5% in 1999-2002, 0.0% thereafter

future rates ●

WRCE: Rural Policy Research Institute (RUPRI) Health Panel.
Table 4.

Impact of Cavitation Payment Reform on Medicare Cavitation Rates

Metra/Nonmetro

Central Other Rural Rural

Total Urban Urban Adjacent Nonadjacent

AAPCC rate, 1997

Cavitation rate, 1998

Cavitation rate, 1999

Cavitation rate, 2000

Cavitation rate, 2001

Cavitation rate, 2002

Cavitation rate, 2003

Cavitation rate, 2004

Average

Minimum

‘ Variation in percent (a)

Average

Minimum

Variation in percent (a)

Average

Minimum

Variation in percent (a)

Average

Minimum

Variation in percent (a)

Average

Minimum

Variation in percent (a)

Average

Minimum

Variation in percent (a)

Average

Minimum

Variation in percent (a)

Average

Minimum

Variation in percent (a)

Average Annual Volatility in rates (b)

1998

1999

2000

2001

2002

2003

$467

$221

21 .4%

$484

$367

19.4%

$494

$378

19.2%

$510

$392

18.3%

$529

S409

17.2%

$551

$429

16.0%

$585

$454

14.6%

$619

2.5%

1 .0%

1.Zyo

1.370

1.3~o

1 .3%

S542

$349

16.0°k

$555

$370

15.5%

$567

$385

15.5%

$582

$408

14.9%

$599

$434

14.0%

$620

$465

13.0?40

$653

S502

11.5%

$689

$534

10.7%

1.8?’0

1.29’0

1.670

1.7%

1.8?40

1.5%

$435

$256

15.7%

$451

$367

13.9%

$462

$378

~3.5Yo

$479

$392

12.6%

$499

S409

11.570

$523

$429

10.3’3!0

$560

$454

9.2%

$594

$480

9.09’0

1.9%

0.9%

1.1%

1.19’0

1.1%

1.370

$395

S231

16.3%

$416

S367

12.7%

$425

$378

12.4%

$441

S392

11 .6°A

$459

$409

lo.Tyo

$481

$429

9.8?40

$514

$454

8.8’%

$545

$480

8.5%

3.6V0

0.8?40

0.9?40

0.9~o

1 .O%

1.l%

$374

$221

17.1%

$402

S367

11.7%

$412

$378

11.4%

$427

S392

10.6%

$446

$409

9.7%

$468

$429

8.9V0

$499

$454

8.1?40

S530

$480

7.9%

5.70Xo

0.770

0.9%

0.9%

0.9~o

1.09’0

2004 0.3’% 0.6?40 0.1% 0.1’% 0.1?0

SOURCE: Rural Policy Research Institute (RUPRI) Rural Health Panel. Auaust 1997.

NOTES: (a) Variation is defined as the average difference between county rites and the national average rate

(computed as the ratio fo the standard deviation to the mean); (b) volatility is the average of the absolute value of

annual percentage changes in cavitation rates, over and above change attributable to growth in national average

per capita Medicare expenditures (RUPRI, 1997).
‘,

Table 5.

Projected Medicare cavitation rates in illustrative counties

Number of Medicare Actual

Metro/Nonmetro Medicare penetration /V4Pcc Projected capitatiin rates

County State Status eligibles rate rate, 1997 1998 2000 2002 2004

Arthur NE RNA 84 2.0% $221 $367 $392 $429 $480

Webster NE RNA

San Juan c o RNA

Billings ND RNA

Clay GA RNA

Walla Walla WA RA

Stevens MN RNA

Los Alamos NM Ou

York ME RA

Mecklenberg NC Cu

Polk 1A Ou

Marquette Ml RNA

Jefferson KY Ou

Montgomery OH Ou

Dallas TX Cu

San Diego CA Cu

Jefferson AL Ou

Worcester MA Ou

Fulton GA Cu

Lake CA RA

Cook IL Cu

Assumption LA RA

Los Angeles CA Cu

Philadelphia PA Cu

1,116

62

90

626

8,638

1,912

2,058

27,655

66,514

46,723

110,157

113,063

94,411

201,343

328,387

113,608

113,740

82,308

13,429

703,319

3,100

903,758

257,428

214,151

0.1%

1.6%

0.0%

0.2%

13.5%

0.1%

4.9%

0.1%

0.1%

0.0’%

0.0%

6.5%

5.1%

9.9%

46.9%

11.9%

31.1%

1 .6%

5.970

12.5%

11.570

34.570

24.8%

T.syo

$236

$241

$257

$307

$325

$340

$365

$366

$381

$402

$.465

$465

$467

$514

$517

$525

$527

$536

$550

$559

$563

$623

$704

$713

$367

$.367

$367

$367

$367

$367

$389

$386

$399

$416

$476

$477

$475

$524

$532

$536

$538

$547

$561

$570

$574

$635

$718

$727

$392

$392

$392

$392

$392

$392

$433

$422

$432

$443

$498

$499

$493

$546

$562

$558

$559

$569

$583

$594

$597

$661

$747

$757

$429

$429

$429

$429

$454

$444

$497

$478

$484

$489

$539

$541

$533

$577

$615

$580

$588

$592

$614

$617

S622

$693

$778

$787

$480

$480

$480

$480

$530

$512

$575

$553

$557

$557

$612

$606

$650

$698

$638

$672

$657

$688

$685

$647

$778

$809

New York NY Cu $819

Dade FL Cu 303,108 37.2?J’o $748 $763 .$794 $826 $859

SOURCE: Rural Policy Research Institute (RUPRI) Rural Health Panel, August 1997.

NOTE: Metro/Nonemtro status: CU=Central Urban, OU=Other Urban, RA=Rural adjacent, RNA= Rural Non-adjacent.

Table 6.

Impact of Capitatiin Payment Reform on

Average growth in Medicare cavitation rates, 1997 to 2004

Average growth rate, 1997 to 2004

Nominal Adjusted

dollars for inflation

Average growth rate, all counties aB.Byo 12.1%

By Metro/Nonmetro

Central Urban 24.4% 4.6%

Other Utian 31 .0% 10.2%

Rural Adjacent 31.9$’0 10.9%

Rural Nonadjacent 36.8% 15.0%

By AAPCC Rate in 1997

Less than $300 64.5% 38.2%

$300-$399 37.5%

S400-S499

15.7%

24.1% 4.370

$500 or more 1 5.4% -3.0’?40

By Risk Plan Penetration Rate, 1996

None 35.7% 14.1%

Less than 1% 34.2% 12.8%

1 %4.99% 31.170 10.2’%

5%-9.99% 26.9% 6.7%

1 O% or more 27.0% 6.8%

SOURCE: Rural Policy Research Institute (RUPRI) Rural Health Panel, August 1997.

82
Table 7.

Distribution of U.S. counties:

by projected cavitation rate as a percentage of national average cavitation rate, 1997-2004

Percent of counties

Local cavitation rate as a , Actual

percent of national rate 1997 1998 1999 2000 2001 2002 2003 2004

Less than 70% 18.3% o.o% 0.0% 0.0% 0.0% o.o’% 0.0% 0.0%

I 70Y0-799!0 24.9% 42.5% 41.270 36.6% 32.19’. 26.09’o 20.5’% 19.070

I 80?40-69’% , 23.8% 26.5V0 28.OVO 32.37. 35.6% 40.2yo 44.1% 43.970

90%-99% 1 7.4% 16.6% 16.5% 17.770 19.570 21.6’% 24.2% 25.5?4.

1 00%-109?40 8.270 8.0% 8.0% 7.8V0 7.770 8.170 7.7% 8.596

11 0%-119’70 4.69’o 3.8’% 3.7?40 3.4% 3.2’% 2.6% 2.3% 2.3V0

120’Yo-1 29% 2.9’% 2.5% 2.5% 2.29fo 1 .99!0 1.470 1 .2% 0.8%

TOTAL 100.09’0 100.0% 100.0% 100.0% 100.0% 100.0% 100.09’0 100.0%

SOURCE: Rural Policy Research Institute (RUPRI) Rural Health Panel, August 1997.

Table 8.

Impact of Cavitation Payment Reform on

Percent of counties with rates determined by the “floor,” “hold harmless,” or “blending” provisions

Metro/Nonmetro

Central Other Rural Rural

Total Urban Urban Adjacent Nonadjacent

Percent of counties at the “floor”

1998 29.9% 0.0?40 12.7?’o 28.57.

1999 29.5’% 0.0’% 11 .4% 28.4V0

2000 26.2’% 0.0% 7.6% 25.5%

2001 22.6V0 0.0% 5.0% 20.9%

2002 17.2% 0.0!40 2.1?40 15.2%

2003 12.9’-% 0.070 1.2% 9.7%

2004 12.9?/o 0.0% 1.2% 9.7yo

Percent of counties at “hold harmless” rate:

1998 ~4.7~o 36.2% 15.3?40 14.4~o

1999 22.3% 48.3’% 24.4% 22.2?40

2000 13.6% 34.570 13.5V0 13.6%

2001 9.8% 27.0’%. 9.4% 9.7~o

2002 8.6’?40 23.0’% 6.8% 8.4%

2003 4.3% 13.2’% 4.l% 4.2%

2004 1 .9’MO 8.6% 1.8% 2.070

Percent of counties receiving “blended rate”

1998 55.4’% 63.8% 72.0% 57.1%

1999 48.2% 51.7% 64.2V0 49.4%

2000 60.2% 65.5’%0 78.9% 60.9%

2001 67.6% 73.OVO 85.6% 69.4%

2002 74.2% 77.0’?40 91.1% 76.4?40

2003 82.8°& 86.8’?40 94.7’%0 86.1%

2004 65.2% 91.4% 97.0% 88.370

SOURCE: Rural Policy Research Institute (RUPRI) Rural Health Panel, August 1997.

44.0?40

43.7!40

39.9~o

36.1’70

28.99’0

23.3~o

23.3’?40

11.8%

17,7?40

lt).7yo

7.8’?40

7.870

3.4’%

().9yo

44.2’%

38.6%

49.470

56.170

63.3’XO

73.370

75.8%

83
m

,..

$.

P-
THE COMMUNITY POLICY ANALYSIS SYSTEM (COMPAS)

A PROPOSED NATIONAL NETWORK

OF ECONOMETRIC COMMUNITY IMPACT MODELS

Thomas G. Johnson

James K. Scott

: Rural Policy Research Institute

University of Missouri-Columbia

Devolution of authority and responsibility from the

Federal Government to state and local governments is,

and will continue to be, one of the most dominant

public policy issue for communities for the next

decade. Block grants, deregulation, welfme reform,

health care reform, education reform, agricultural

policy reform, various state waivers, and other terms

fill the national policy dialogue and all are symptomatic

of devolution.

To communities, especially rural communities,

devolution spells the end of many of the safety nets that

protected local governments, school districts and other

public entities from some economic and social

hardships. At the same time devolution enhances

opportunities for local leadership and increases the

returns to aggressive and innovative public decision

making. In this environrnen~ the value of economic and

social information, accurate projections and analyses of

policy alternatives is particularly great. This in turn is

creating an opportunity for those involved in the

decision support sciences.

The Community Policy Analysis System (COMPAS)

initiative is a response to this opportunity. It addresses

the information needs of policy makers at the Federal,

state and local levels. At the Federal level, there is a

growing need for a better understanding of the local

consequences of federal policy, especially policy that

devolves responsibility to local governments.

Similarly, state governments require information on the

consequences of their policies on local governments as

both state and local responsibilities change.

The need, under these emerging circumstances, for

better decision support at the local level is obvious.

The diversity of conditions in rural communities means

that generic, or aggregated decision support tools

probably conceal more than they reveal. Broad

generalizations about policy impacts are usually

uninformative at best, misleading at worst. It is clear,

for example, that to conclude that trade liberalization

will lead to overall increases in income and

employment is an important aggregate projection but it

tells us little about the changes that will be experienced

by individual communities or what their optimum

responses to these changes might be.

In response to these policy trends, a group of regional

economists and rural social scientists have identified a

set of modeling tools which can be used to provide

policy decision support for state and local government

ofllcials, including input-output modeling, cost.benefit

analysis, and industrial targeting. In addition, the group

has developed a plan to build a collaborative

community policy analysis network that will eventually

extend to selected rural communities in twenty-five

states. With initial support from the Rural Policy

Research Institute (RUPRI), the four regional rural

development centers and a variety of other sources, the

group has also outlined the structure of econometric

community models for each state that will compare the

economic, demographic, and fiscal impacts of a variety

of economic or policy scenarios. The models are

intended to be used in conjunction with other decision

tools to provide maximal flexibility and a capacity for

rapid response to queries by local and state policy

decision makers. This paper will focus on the

specification and development of the COMPAS

econometric community models. It will describe the

conceptual fkamework of the proposed models, report

on applications of the models in two states, and briefly

discuss plans for fbture development and support of the

COMPAS network. The plan takes into account the

realities of secon~ data availability at the

community level and it attempts to build on cment

conceptual foundations from the social sciences and

regional science. It is evolutionary in that it will be

designed to be flexible and continually improved upon;

and it addresses the institutional and constitutional

85
differences among states and communities.

The COMPAS model discussed below is based

primarily on the authors’ experiences with the Virginia

Impact Project (VIP) model, and Missouri’s Show Me

Community Impact Model which have evolved over the

last decade. However, these models, are themselves

just a recent chapter in a long tradition of community

modeling by rural development researchers (see

Halstead, Leistri@ and Johnson for a history of just

some of these models). The novel aspect of this project

is the attempt to create models for communities

throughout the nation.

KEY PRINCIPLES

There are many considerations involved in modeling a

community for policy analysis. The following

assumptions are based on conceptual logic and/or

empirical studies of communities. Each are reflected in

the proposed COMPAS framework.

1. While economic and social relationships know

no geopolitical boundaries, policy provisions,

public services, taxing authority, and data, do.

Therefore, county, municipal, and public

service boundaries should be at the basis of

any policy model.

2. Communities within states share common

constitutional limitations and responsibilities,

and have developed comparable institutions.

3. Communities with similar economic bases

have similar economic structures. Because of

the importance of climatic, geographic, social

and political influences, economic bases are

frequently quite homogeneous across

geographic regions.

4. Communities of similar size and with similar

geographic relationships to nearby larger and

smaller communities, petiorm similar central

place roles and are likely to exhibit similar

responses to economic (and policy) stimuli.

5. The fi.mdamental engine for economic growth,

decline, and change at the local level is

employment. Community impacts are effected

through the labor market which allocates jobs

between the currently unemployed, residents

of nearby communities (incommuters), current

residents who work outside the community

(outcommuters), and new entrants to the local

labor market.

6. Changes in employment, unemployment,

commuting, labor force, population, school

enrollment and income, lead to changes in

housing needs, property tax base, public

service demands, and transfers to households

and local governments.

These principles guided the estimation and

development of the Virginia Impact Projection (VIP)

model and the Show Me Model for Missouri

communities. Both models are systems of

econometrically estimated equations for rural towns,

counties and cities in the respective states, using both

cross-sectional and time series data. Experience with

the estimation of these models indicates that with

careful selection of variables and fi.mctional form,

stable coefficients can be estimated for communities

with a wide variety of sizes and economic bases. Basic

institutional differences cannot be captured with a

single set of parameter estimates, however.

Furthermore, attempts to apply the model to other states

have underscored the importance of differences in the

structure of public service provision. Therefore, only

states with very similar local government structures will

be candidate for grouping together.

MODEL STRUCTURE

While many different model structures could generate

comparable policy analyses, the COMPAS models will

share a basic structure, The COMPAS models will

based on the assumptions above as well as others about

the way in which rural and small city economies work,

about the way in which local governments make

decisions, and about the conditions under which local

public services are provided. In the following pages,

the first and most simple of the COMPAS models will

be described.

Labor Market Equations

The labor market concept plays a central role in the

COMPAS models. The models are built on the

assumption that economic growth is caused largely by

exogenous increases in employment. This is not to say

that employment at the community level is not

responsive to local conditions but rather, that these

responses will be dealt with as direct changes or shocks

to be introduced to the models. In this simple model,

86
In- and out-commuters are separated here, rather than

combined into net commuters, because they exhibit

different in preferences for public services, spatial

amenities, occupational characteristics of households,

and because sub-markets for different labor skills

persist Labor force and incommuters are positive

components of supply and outcommuting is a negative

component. Unemployment is a residual negative

component of supply. Eliminating wages from the

component supply curves by substituting the inverse

demand curve, as amended, derives the expressions.

This introduces employment (demand) to the supply

components. More formally, the model is developed as

follows:

(1) %=)5.

equates demand and supply (local employment and

employed labor force from all locations). The demand

ctuve is

(2) x, - f(w).

(where w is the wage rate) which when inverted

becomes

(3) w ‘g&J.

Decomposing labor supply into its components gives

( 4 1 &=& -x”-&+&

where, X,, is labor demand (local emplcyment), X, is

labor supply, made up of its components, & (resident

labor force), X, (outcornmuters), X, (incommuters), and

y(unemployed), w is the wage rate, and the 2s are

supply shifters for the various components of supply.

Given the discussion and the conceptual model above,

equations 4 through 7 can be expressed as follows in

equations 8 through 11.’

(8) Unemployed = Labor Force + Inccmunuters -

Employment - Outcommuters

All three components of labor supply will be primarily

determined by employment in the location in question.

In addition, they will depend on relative housing

conditions, costs of living, quality of public services,

tax levels, the mix of jobs, and similar variables in the

location of employment, versus alternative locations. A

very important variable in the supply components is

area of the data unit. Smaller units will include fewer

resident laborers, and define more as outcommunters

and incommuters because the cross the borders of the

unit. Larger units will incorporate more destinations

and residences of workers and, therefore, define more

workers as being locally employed, and thus fewer

wtconunuters an d incommuters. In addition,

commuting will depend on the distance between place

of residence and place of work.

(9) Labor force = @!rnployrnenf housing

conditions, cost of living, public services,

taxes, industry mix, area).

(10) Outcommuting = f@mploymmt, external

87
employment, external labor force, housing

conditions, cost of living, public services,

taxes, industry mix, area, distance to jobs).

(11) Incommuting = f(employment, externa l

employment, external labor force, housing

conditions, cost of living, public services,

taxes, industry mix, area, distance to

residence).

Population is hypothesized to be a fi.mction of labor

force and variables that affect the labor force

participation rate and the dependency ratio.

(12) Population = f(labor force, participation rate,

dependency rate),

Where the dependency rate is the ratio of the nonworking population to the working population.

Fiscal Impact Equations

Changes in the tax base and changes in the need for

expenditures usually accompany changes in

employment. New employers, employees and

population require expenditures for services and

investments in infi-astructure. The demands for public

services by residents depend on such factors as income,

wealth, unemployment, age, and education. As growth

changes these characteristics, the demand per resident

will rise or fall. Furthermore, as a community grows

the average cost of producing public services often

decreases, until all economies of size are captured, and

then increases, when inefficiencies creep in to the

process. Together, the changing demand and efficiency

determinants mean that each economic change will

have a unique effect on needed expenditures.

It is assumed that local governments consider the

demands of their constituents, and provide the desired

level of services at the lowest possible cost. When tax

bases and the demand for expenditures are known, local

governments are assumed to adjust tax rate to balance

their budget.

Following Hirsch (1970 and 1977); Beaton; Stinson;

and Stinson and Lubov; unit cost of public services are

hypothesized to be a fiction of the level, and quality

of services, important local characteristics (input factors

and demand factors), input prices, and the rate of

population growth. Furthermore, theory suggests that

public services may be subject to increasing, and/or

decreasing returns to size. Based on these theoretical

relationships local government service expenditures per

capita are hypothesized to be determined as follows:

(13) Expenditures = f(quality, quantity, input

conditions, demand conditions).

For each type of expenditures (public works, police

protection, administration, parks and recreation,

welfare, education, fwe protection, etc.) the

independent variables are defined differently. For

education enrollment is the quantity variable, teachers

per thousand students is a quality variable, federal aid

and change in enrollment are input conditions, and

income, real property, and employment are demand

conditions. For police protection, population is the

quantity variable, solved crimes is the quality variable,

percent population in towns, incommuters, and miles to

the nearest metropolitan area are input conditions, and

income and personal property are demand conditions.

Many non-local revenues (from state and federal

agencies) are at least partially formula driven. Even

when this is not the case, certain local characteristics

may indicate the expected level of these revenues. In

addition, non-local revenues are frequently an inverse

Ilmction of the locality’s ability to pay and a direct

fimction of its degree of political influence. Ability to

pay is usually related to per capita income, personal

property per capita, and real property per capita.

Non-local aid = f(expenditures, income,

personal property, real property).

important source of local revenues is sales tax

(14)

Another

revenues. The level of retail sales is primarily a

fimction of income. This relationship is expected to

change with the size of the locality since larger

localities are usually higher order service centers. The

number of incommuters is also hypothesized to

influence sales because they increase the daytime

population of the community. Sales tax revenues are

hypothesized, therefore, to be:

(15) Sales tax Revenues = f(income, employment,

incommuters).

Other local revenues, other than property taxes, include

licenses, fees, fines, forfeitures, and special

assessments. These revenues are hypothesized to be

related to the level of commercial activity (retail

activity) in the community and the income level. Thus:

88
(16) Other Tax Revenues = f(Sales tax revenues,

income).

Real property includes both residential and business

property and, therefore, will be influenced by the level

of personal income as well as the size of the economic

base. Both personal and real property are hypothesized

to be positively related to the number of outcommuters

since these families represent a source of wealth that is

not supported by the local economic base.

(17) Real Property = f(income, employment,

outcommuters),

(18) Personal Property = f(income, outcommuters).

There are a number of ways to close this type of mode.

In the case of the VIP model it is assumed that local

government expenditures are determined first, and real

and personal property tax rates are set to cover those

expenditures not met by non-local aid and sales tax

revenues and other tax revenues. This implicitly

assumes that budgets are balanced each year. An

alternate assumption (the one used in the Show Me) is

that the tax rate remains constant and that economic

changes lead to fiscal deficits or surpluses.

THE MODELS APPLIED

To date, the VIP and Show Me models have been

developed for forty to fifty communities. Similar

models have been developed and applied in the several

communities in Iowa (Swenson, 1996), Idaho (Fox and

Cooke, 1996) and Wisconsin (Deller and Shields,

1996). Local advisory committees are usually

appointed to review the baseline projections, help form

the scenarios, review the model’s projection, and to

help interpret the results. The models have been used

for a variety of purposes including analyses of

annexations, jurisdictional mergers, new industries,

existing industries, industry closures, university

research parks, shopping centers, residential

developments, location of industrial sites and, and

general development strategies. They have also been

used for goal planning for several communities. Goal

planning with the models is achieved by estimating the

conditions necessary to bring about a desired set of

terminal conditions.

The models have generally been popular with local and

state governments. Policy makers are generally

somewhat skeptical until they come to appreciate the

information generated and become more confident in

the projections. Repeat users of the model’s

projections especially like the comparability of the

results from case to case, and across communities.

DISCUSSION

The devolution of policy decisions from central to local

control will bring communities many new opportunities

and many significant new challenges. Especially those

that are small or othenvise disadvantaged may now

need the capacity to assess the fiture impacts of a

variety of expected or proposed changes. The

Community Policy Analysis System is one approach

for rural development researchers to assist in

developing that capacity.

COMPAS models now exist in at least five states.

Preliminary plans are now in place to extend that to

seven, fifteen, and ultimately twenty-five states over

the next three years. In the next six months, researchers

involved in this initiative will review, refine and test the

conceptual framework of the COMPAS models and

speci~ data and research standards that will make

results from these models comparable and compatible.

If resources are available, these researchers will form a

network designed to provide analysis of the

community impacts of local, state and federal policy

alternatives.

89
REFERENCES

Beaton W. Patrick. The Determinants of Police Protection Expenditures. National Tax Journal 24 (1974): 335-349.

Deller, Steve and Martin Shields. The Wisconsin Extension Fiscal Impact Model: Preliminary Results. Presented at

the Community Economic Modeling Conference. June, 1996. Madison, Wisconsin.

Fox, Linnette and Stephen Cooke. Data for the Idaho Fiscal Impact Model. Presented at the Community Economic

Modeling Conference. June, 1996. Madison, Wisconsin.

Halstead, John M., F. Larry Leistritz, and Thomas G. Johnson. 1991. The Role of Fiscal Impact Models in Impact

Assessments. Impact Assessment Bulletin 9 (Fall): 43-54.

Hirsch, Werner Z. Economics of State and Local Government. McGraw-Hill, New York, 1970.

Hirsch, Werner Z. Output and Costs of Local Government Services, Paper for the National Conference on

Non-Metropolitan Community Services Research, Ohio State University, Columbus, Ohio, January 21, 1977,

Johnson Thomas G. A Description of the VIP Model, Unpublished manuscript, Department of Agricultural

Economics, Virginia Tech, Blacksburg, Virginia, April 1991.

Johnson, Thomas G. Representative Community Analysis, paper presented in a symposium entitled, “Rural Impacts

of Public Policies: Improved Analytic Frameworks,” at the annual meetings of the American Agricultural

Economics Association, Orlando, Florida, August 2, 1993.

Stinson, Thomas F. The Dynamics of the A@stment Period in Rapid Growth Communities, Prepared for

Presentation at the WAEA Annual Meetings, Bozeman, Montana, July 24, 1978.

Stinson, Thomas F. and Andrea Lubov. Segmented Regression, Threshold Eflects, and Police likpenditures in

Small Cities. American Journal of Amicultural Economics 64 (November) 1982:738-746.

Swenson, David. Spatial/Institutional Interrelationships. Presented at the Community Economic Modeling

Conference. June, 1996. Madison, Wisconsin.

‘ If ones estimate of employment is defined as jobs, rather than the number of persons employed, then it will include second jobs.

In this case, employment as defined here equals jobs less second jobs. Alternatively, one must augment the supply of labor by the number of

individuals holding second and third jobs.

90
I

PROJECTIONS OF REGIONAL ECONOMIC AND DEMOGRAPHIC IMPACTS

OF FEDERAL POLICIES

Glenn L. Nelson. Chair. Regional Analysis Work Group

,Rural Policy Research Institute, University of Missouri, Columbia, Missouri 65211

Improved information on the regional impacts of

f~ral policies would address an important need of

decision makers, including citizens who make their

political choices seriously. Regional variations in the

demographic and economic environment lead to

regional variations in the impacts of federal policies.

For example. the percentage of the population that is

elder~y and that is poor varies by region; many

programs use age and income as factors in

determining eligibility and payments. Looking at

another important example, regional economies va~

in the importance of new investment and of

international competition; the change in the real

interest rate due to changes in the federal budget

deficit wili have Merent impacts in tierent regions.

Little is known about these regional variations because

fw analysts are studying them.

The objective of the work described in this paper

is to provide estimates of the regional demographic

and economic impacts of current and proposed federal

policies. The primary audience consists of decision

makers and their stai%s at the f~ral level. An

important secondary audience consists of state and

local decision makers and of citizens who seek

information on f~ral policies in order to influence

policy in an informed manner. Another important

audience consists of researchers who seek better

methods of sub-national analyses and improved data.

The remainder of this paper will address the

research design for this project, the explicit inclusion

of the f-ml budget deficit, mwiel solution

procedures, Medicare and Medicaid data, a

preliminary baseline solution, and analyses of

alternative policies.

Research Design

This research produces information on subnational regions as traditionally defined and on county

groupings representing the rural-urban continuum.

The four subnational regions used in the analysis are

shown in Figure 1. They follow the boundaries of four

Rural Development Centers who are clientele for this

work. A larger number of smaller sub-national

regions would be desirable, but the use of only four

regions is not a major problem at this stage of the

Figure 1. RUPRI Sub-Natioml Regions

North

Central

West

Northeast

u

south

research.

The counties within each sub-national region are

partitioned into four categories representing the ruralurban continuum. Nonrnetropolitan counties not

adjacent to a metropolitan area makeup the most rural

category. Nonrnetropolitan counties adjacent to a

metropolitan area make up a rural category which is

less remote from a large urban concentration than

other nonrnetropolitan counties, The central cities of

the 32 largest metropolitan areas are represented by 62

densely populated counties and are the most urban

category. The remaining metrqmlitan counties make

up a less densely settled urban category.

These categories are proving to be workable, but

they have signifhnt problems. I would prefer a

definition that treats rural and urban as partitions on a

symmetrical continuum rather than the current out-

&t@ urban-centered definition of metropolitan and

nonmetropolitan. I am currently exploring the

suggestion of John Adams to use relative density

within the nation and state, to which I would add a

third factor of relative &nsity within a sub-national

region such as those in Figure 1.

Several large counties of mixed rural-urban

character lessen the distinctions between the

categories. For purposes of analyses, we should

partition six large counties in southern California and

the four large counties containing Duluth, Phoeniz

Reno, and Tucson into their rurai and urban parts. We

91
could then treat each part in exactly the same way as

we treat other counties in the analysis.

The combination of four sub-national regions and

four categories on the rural-urban spectrum yields

sixteen county groupings. These are the geographic

units of analysis. The counties within each grouping

do not forma contiguous set which makes this

fimnework different horn most other regional studies.

Table 1 presents a few descriptive statistics on the

county groupings.

This work emphasizes insights on the spatial

impacts of federal policies. The Rural Policy Research

Institute (RUPRI) utilizes the work of other credible

sources on national impacts in order to conserve scarce

resources and to avoid conflicts peripheral to RUPRI’S

primary mission. We use the demographic projections

of the U. S. Bureau of the Census and the economic

and budget pro~ons of the Congressional Budget

Offke (CBO) for assumed national totals (Day;

Congressional Budget Offke).

RUPRI purchases the seMce of building and

maintaining the sixteen models for the county

groupings from Regional Economic Models. Inc.

(REMI) We adopted this approach because we prefer

to have RUPRI staff focused on policy analysis and

because an established outside vendor could deliver

operational models with short notice and on time. The

REMI modeling framework is described in Treyz and

is widely recognized as one of the best systems for

quantitative regional analyses. The models include

integrated demographic and economic components.

They are a hybrid of input-output, econometric and

selected computable equilibrium characteristics. They

estimate a series of annual solutions rather than utilize

benchmarks. The sixteen models solve interactively

with labor moving to county groupings with higher

expected returns to labor and with capital flowing to

county groupings with high expected returns to

investment. The aggregate solution for the U.S. is the

sum of the county groupings, that is, the solution is

“bottom up” rather than “top down”.

RUPRI has chosen to purchase REMI models

employing the standard 14 industrial sectors at the

single-digit SIC code level. We would prefer 53 sector

models-the next step up in the REMI options-+xcept

that, with current resources, we prefer to constrain the

fimds &voted to model pchases in order to devote

them to activities directly focused on policy analyses.

The major advantage of the 53 sector option to us

would bean explicit medical seMces industry.

Because much of the health sector is subsumed within

the government sector, however, the 53 sector option

is not as big of improvement as casual obsemrs might

expect. With both the 14 and 53 sector models the

analyst must carefidly specify, external to the model,

the sectors being affixted by health policy changes.

Incorporating the Federal Budget DefW

Changes in federal policies typically include two

complementary f-. The degree to which both are

visible varies from case to case. One fiwet is the

change in a specific program of interest, such as

Medicare or Medicaid The other facet is the change

Table 1. Selected Descriptive Statistics on the RUPRI

County Groupings, percent of U.S.

Potndation ( 1994}

N.E. SOUth N.C. West Total

Nonmetro 2.7 8.3 6.3

Not Adjacent 0,9 3.4 2.9

Adjacent 1.7 5.0 3.3

3.2

1.9

1.4

20.5

9.1

11.4

Metro 20.2 23.4 17.3

Not Cen City 13.5 17.6 10.6

Central City 6.7 5.8 6.7

18,6

10.0

8.6

79.5

51.6

27.9

Total 22.9 31,7 23.6 21,8 100.0

Area (sauare miles]

N,E, SOllth N.C. west Total

Nonrnetro 3,3 17.5 17.5

Not Adjacent 1.5 8,6 11.8

Adjacent 1.8 8.9 5,7

42.5

35,3

7.2

80.9

57.3

23.6

Metro 2.3 6.1 3.7

NotCen City 2.2 5.8 3.5

Central City 0.1 0.3 0.2

7.0

6.4

0.6

19.1

18.0

1.2

Total 5.6 23.6 21.2 49.5 100.0

counties and Countv Equivalents

N.E. SOUth N.C. West Total

Nonmetro 4.7 30.5 26.6

Not Adjacent 2.0 14,5 16.7

Adjacent 2.7 16.0 9.9

11.9

8.9

3.1

73.8

42.1

31.7

Metro 4.9 11.4 7.1

Not Cen City 4.3 10.8 6,6

Central City 0.6 0.6 0.5

2.9

2.6

0.3

26.2

24.2

2.0

Total 9,6 41.9 33.7 14.9 100.0

92
in the federal fiscal situation and programmatic mix

which accompanies the specific program at the center

of the discussion. For example, a cut in projected

Medicare expenditures is associated with a

combination of a lower f~ral budget deficit, lower

f~ral taxes, and increased spending on other f~ral

programs.

Good policy analysis should take into account

both facets. Ignoring one facet reduces the Scientilc

rigor of the analysis and leads to erroneous estimates.

In addition, citizens and decision makers dMer in the

relative importance they place on the facets. An

analyst who ignores, for example, the positive effects

of deficit reduction while fining on the negative

effects of cuts in projected Medicare spending is

appropriately viewed as adopting a partisan stance. In

RUPRI we seek a long term, constructive, non-partisan

engagement with decision makers.

Many local and regional policy analyses have

violated this principle. A lack of attention to the

elT&ts of changes in the f-ral budget deficit has

been a particular. important problem in many cases.

One of the important strengths of the RUPRI

analysis is that it accounts for changes in the feral

budget deficit. This is especially imptant in policy

analyses of proposals to lower projected federal budget

deficits by cutting projected entitlement spending on

the baby boom population when its members become

eligible for programs targeted on the elderly.

The particular manner in which RUPRI

incorporates the federal budget deficit is applicable to

many other modeling situations. The REMI model

does not have an explicit f~ral fiscal component.

The effects of federal fiscal actions consistent with

existing policies (the “baseline” solution) are

incorporated as follows. (The table references in the

remainder of this paragraph refer to Council of

Economic Advisers. These tables illustrate the

identity being discussed and provide historical &ta. )

First. CBO estimates of national GDP and of the

federal budget deficit are adopted as RUPRI

assumptions. Second we in RUPRI estimate national

gross saving by major component for each year in the

projection period. These components include the

f~ral budget deficit. federal consumption of fixed

capital. state and local government saving. personal

saving. and gross business saving (Table B-30).

Third we estimate the components of national

gross investment: gross private domestic investment,

gross government investment. net exports. and net

foreign investment other than net exports (Tables B-22

and B-3 1). Fourth. having now estimated the

investment and net export portions of GDP, we

estimate how the remaining portion is di~ided between

consumption and government. taking into account the

previously estimated gross government investment

(Tables B-1 and B-18). In conclusion, the estimates of

consumption, investment, government, and net exports

are the variables that reflect RUPIU assumptions with

regard to the f~ral budget deficit-as well as

numerous other matters.

The analysis of a proposed policy alternative

proceeds analogously to the above pr*e. The

change in the f~ral budget tilcit as well as the

programmatic change are introduced explicitly in the

formulation of macroeconomic assumptions. In

general, this explicit consideration of the change in the

f~ral budget deficit leads to the conclusion that the

policy change aikcts every component of GDP, and

often total GDP as well.

Consistent National and Sub-National Solutions

The sixteen models for the county groupings must

be solved in a manner that preserves the rigor of the

structure within the models driving the solution (that

is, the rigor of a “bottom up” approach) and that also

incorporates the assumptions with respect to national

GDP by major component. This is accomplished by

solving the system of sixteen models iteratively,

making changes in selected assumptions in the models

in each iteration, until the summations from the

sixteen models match the assumed national totals.

In slightly more detail, the solution procedure

flows as follows. First, assumptions are made in the

models with respect to demographic variables such as

birth rates, death rates, and immigration and with

respect to economic variables such as labor force

participation. productivity, exports. imports, and

govermnent spending that we believe will lead to

national demographic and economic outcomes

consistent with our assumptions. Sem@ we solve the

models as an interactive system and compute sums

over the sixteen models to derive national totals.

Third we compare the output of the models with our

assumptions. If the output is consistent with our

assumptions. we have a satisfiwtoIY solution which we

proceed to analyze. If the output di.Hers significantly

from our assumptions with regard to national totals,

we return to step one noted above,

Medicare and Medicaid Data

RUPRI scientists are analyzing Medicare and

Medicaid policies because of their importance to the

nation, their rapid growth since inception, and their

rapid projected growth in the future--especially when

the members of the post-war baby generation become

93
eligible. The analysis of policy alternatives has been

slowed by the poor quality of the data.

RUPRI has contributed significantly to a marked

improvement in readily accessible county-level

Medicare &ta. When RUPRI regional scientists

began work on Medicare in 1994, they found that the

county-level data from the Consolidated Federal Funds

Report, originally estimated by the Health Care

Financing Administration (HCFA) and disseminated

by the U.S. Bureau of the Census, were poorly

documented and inaccurate. These Medicare

estimates were also used by the Bureau of Economic

Analysis (BEA) in estimating “medical transfer

payments” in the Regional Economic Information

System (REIS). Working with the Oflke of the

_ in HCFA RUPRI developed a superior

county-level &ta base and disseminated it in 1995

(Nelson and Braschler). BEA began utilizing these

higher quality Medicare data in REIS with the release

of its county-level CD-ROM in September 1997

(Bureau of Economic Analysis). This recent release

also marks the first occasion in which BEA is

disseminating an explicit Medicare series, in contrast

to the previous practice of issuing an aggregate total

containing Medicare and other medical transfers. The

Census Bureau has not changed its sources and

methods as of early September 1997.

The available county-level Medicaid data are

needlessly inaccurate. As of September 1997 the best

source of county-level Medicaid payments is the BEA

CD-ROM (Bureau of Economic Analysis). BEA

obtains these data through direct contacts with

individual states. BEA has actual county-level data on

34 states of which 26 are uptodate (Bureau of

Economic Analysis, Table F). For those states not

providing data, BEA estimates county payments using

the distribution of AFDC payments, which is likely

inaccurate because the majority of Medicaid payments

go to the elderly population rather than the AFDC

ppulation.

HCFA manages the Medicaid Statistical

Information System (MSIS) project which currently

includes 34 states, The recent balanced-budget

legislation contains a provision requiring all states to

participate in the MSIS project, so this itiormation

source will be increasingly complete as time passes,

The MSIS records include a county identifier and

could be used to produce county-level data. HCFA has

declined to produce these data in its responses to

RUPRI requests.

The needed next step to improve our county

Medicaid &ta base is for HCFA to compile countylevel Medicaid payment data for those states in the

MSIS project and to provide the data to BEA. BEA

could then incorporate these data in its REIS CDROM which is distributed widely at low cost. If this

were done, the BEA would lack actual county-level

Medicaid for only seven states--and this gap would

decline in the future as the MSIS project expands, So

long as the gap in coverage exists, a better county

estimation procedure should be developed for those

states not supplying county data to either HCFA or

BEA.

As is often the case, improvements in the

information base underlying the analysis of policies

have been an important component of good policy

analysis.

Baseline Solution

Elements of the initial baseline solution from the

current models and methods are displayed in Tables

2-6, (A baseline from an earlier, simpler mdel

without explicit attention to the federal budget deficit

was published in 1995; see Braschler, Nelson, and

Van der Sluis.) This baseline was estimated using the

CBO current policy projections as of January, 1997

(CBO). These initial projections are of interest

primarily because they help us to understand and

critique the models.

The insights of informcx$ concerned people must

be solicited and then incorporated into the estimates

before the baseline becomes an insightfid tool for

policy purposes. This has not yet occurred with this

baseline. The baseline will change significantly in

subsequent revisions in order to reflect the consensus

of experts as well as to incorporate updated CBO

projections. Readers should keep these points in mind

as they review the results in Tables 24, I solicit and

welcome f~ck from readers who have comments

on the results.

I will not attempt a summary of the projections in

this paper, The following are three, related f~tures

that surprise me and warrant further attention. The

relative competitive position of central cities is

projected to improve markedly, especially in the

Northeast and North Central regions. The projected

changes in manufacturing jobs are often markedly

different from recent trends. The ratios of variables

such as jobs to population and income to gross product

change in ways not consistent with recent trends.

We in RUPRI find it intellectually and practically

satis$ing that the critique of these projections must

include experts on trends in urban areas, and

especially in central cities, if we are to gain insights on

likely trends in rural areas. Regional economic and

demographic changes are typically the result of

relative shifts in causaI variables in multiple regions

94
Table 2. Projected Population Assuming Current Policies Continue, 1987-2005

Area

United States

Rural-Urban County Groupings of the U.S.

Nonmetro Not Adjacent to Metro

Nonmetro Adjacent to Metro

Metro Other Than Central City

Central City of Large Metro

Sub-National Regions of the U.S.

Northeast

South

North Central

West

County Groupings by Rurality and Region

Nonmetro Not Adjacent to Metro

Northeast

South

North Central

West

Nonmetro Adjacent to Metro

Northeast

South

North Central

West

Metro Other Than Central City

Northeast

South

North Central

West

Central City of Large Metro

Northeast

south

North Central

West

Population (thou. )

1987

242,321

22,866

28,127

121,939

69,389

58,006

75,520

59,026

49,769

2,336

8,564

7,576

4,390

4,343

12,298

8,412

3,074

33,493

40,659

25,830

21,956

17,834

14,000

17,208

20,348

1996

265,408

23,623

30,065

138,134

73,586

60,184

84,912

61,909

58,403

2,358

8,772

7,621

4,872

4,620

13,006

8,727

3,712

35,626

47,427

28,113

26,968

17,580

15,707

17,448

22,851

2005

286,454

23,709

30,994

152,331

79,420

63,716

93,001

63,792

65,945

2,332

8,784

7,459

5,134

4,890

13,276

8,655

4,173

38,178

53,272

29,497

31,385

18,317

17,669

18,182

25,252

Pomktion as a % of U.S.

1987

100.00

9.44

11.61

50.32

28.64

23.94

31.17

24.36

20.54

0.96

3.53

3.13

1.81

1.79

5.08

3.47

1.27

13.82

16.78

10.66

9.06

7.36

5.78

7.10

8.40

1996

100.00

8.90

11.33

52.05

27.73

22.68

31.99

23.33

22.00

0.89

3.31

2.87

1.84

1.74

4.90

3.29

1.40

13.42

17.87

10.59

10.16

6.62

5.92

6.57

8.61

2005

100.00

8.28

10.82

53.18

27.73

22.24

32.47

22.27

23.02

0.81

3.07

2.60

1.79

1.71

4.63

3.02

1.46

13.33

18.60

10.30

10.96

6.39

6.17

6.35

8.82

Pop. Change (OA)

1996/1 987 2005/1996

9.53

3.31

6.89

13.28

6.05

3.75

12.44

4.88

17.35

0.94

2.43

0.59

10.98

6.38

5.76

3.74

20.75

6.37

16.65

8.84

22.83

-1.42

12.19

1.39

12.30

7.93

0.36

3.09

10.28

7.93

5.87

9.53

3.04

12.91

-1.10

0.14

-2.13

5.38

5.84

2.08

-0.83

12.42

7.16

12.32

4.92

16.38

4.19

12.49

4.21

10.51

—
Table 3. Projected Gross Product Assuming Current Policies Continue, 1992 dollars,

Gross Product (bil. )

1987-2005

Gross Product as a % of U.S.

Area

United States

Rural-Urban County Groupings of the U.S.

Nonmetro Not Adjacent to Metro

Nonmetro Adjacent to Metro

Metro Other Than Central City

Central City of Large Metro

Sub+atiinal Regions of the U.S.

Northeast

South

North Central

west

Co@ Groupings by Rurality and Region

Nonmetro Not Adjacent to Metro

Northeast

South

North Central

west

Nonmetro Adjacent to Metro

Northeast

South

North Central

west

Metro Other Than Central City

Northeast

South

North Central

west

Central City of Large Metro

Northeast

South

North Central

west

1987

5,677.4

402.5

482.1

2,698.5

2,094.2

1,450.2

1,679.1

1,333.4

1,214.7

40.3

146.3

130.9

85.0

78.1

208.7

139.7

55.6

790.7

896.0

549.4

462.5

541.1

428.1

513.4

611.6

1996

6,908.5

511.9

611.1

3,405.8

2,379.6

1,647.2

2,120.4

1,628.5

1,512.4

48.9

181.8

167.3

113.9

94.2

259.8

182.9

74.2

935.7

1,152.8

694.7

622.6

568.3

525.9

583.6

701.8

2005

8,320.3

594.3

714.9

4,117.3

2,893.8

1,975.6

2,523.1

1,939.6

1,882.0

57.9

209.1

194.0

133.3

114.2

296.0

215.0

89.7

1,126.4

1,378.1

833.8

779.0

677.1

639.9

696.8

880.0

1987

100.00

7.09

8.49

47.53

36.89

25.54

29.58

23.49

21.40

0.71

2.58

2.31

1.50

1.38

3.68

2.46

0.98

13.93

15.78

9.68

8.15

9.53

7.54

9.04

10.77

1996

100.00

7.41

8.85

49.30

34.44

23.84

30.69

23.57

21.89

0.71

2.63

2.42

1.65

1.36

3.76

2.65

1.07

13.54

16.69

10.06

9.01

8.23

7.61

8.45

10.16

2005

100.00

7.14

8.59

49.48

34.78

23.74

30.32

23.31

22.62

0.70

2.51

2.33

1.60

1.37

3.56

2.58

1.08

13.54

16.56

10.02

9.36

8.14

7.69

8.37

10.58

Gr. Prod. Change (%)

1996/1 987 2005/1 996

21.68

27.18

26.76

26.21

13.63

13.58

26.28

22.13

24.51

21.34

24.27

27.81

34.00

20.61

24.48

30.92

33.45

18.34

28.66

26.45

34,62

5.03

22.85

13.67

14.75

20.44

16.10

16.99

20.89

21.61

19.94

18.99

19.10

24.44

18.40

15.02

15.96

17.03

21.23

13.93

17.55

20.89

20.38

19.54

20.02

25.12

19.14

21.68

19.40

25.39
Table 4. Projected Jobs Assuming Current Policies Continue, 1987-2005

Area

United States

Rural-Urban County Groupings of the U.S

Nonmetro Not Adjacent to Metro

Nonmetro Adjacent to Metro

Metro Other Than Central City

Central City of Large Metro

Sub-National Regions of the U.S.

Northeast

South

North Central

West

County Groupings by Rurality and Region

Nonmetro Not Adjacent to Metro 3

Northeast

south

North Central

West

Nonmetro Adjacent to Metro

Northeast

South

North Central

West

Metro Other Than Central City

Northeast

South

North Central

west

Central City of Large Metro

Northeast

South

North Central

West

Jobs (thou. )

1987

129,995

10,803

12,547

63,617

43,028

32,020

39,221

31,535

27,220

1,064

3,807

3,794

2,138

2,000

5,305

3,846

1,396

18,154

21,237

13,201

11,025

10,802

8,872

10,694

12,660

1996

148,892

12,472

14,426

75,382

46,612

33,779

46,701

36,146

32,266

1,187

4,285

4,313

2,687

2,210

6,019

4,437

1,760

19,711

26,030

15,707

13,934

10,671

10,368

11,688

13,886

2005

166,342

13,229

15,401

84,874

52,838

37,781

51,431

39,461

37,670

1,291

4,439

4,520

2,979

2,480

6,246

4,680

1,995

22,046

29,051

17,385

16,391

11,963

11,695

12,875

16,305

Jobs as a % of U.S.

1987

100.00

8.31

9.65

48.94

33.10

24.63

30.17

24.26

20.94

0.82

2.93

2.92

1.64

1.54

4.08

2.96

1.07

13.97

16.34

10.16

8.48

8.31

6.82

8.23

9.74

1996

100.00

8.38

9.69

50.63

31.31

22.69

31.37

24.28

21.67

0.80

2.88

2.90

1.80

1.48

4.04

2.98

1.18

13.24

17.48

10.55

9.36

7.17

6.96

7.85

9.33

2005

100.00

7.95

9.26

51.02

31.76

22.71

30.92

23.72

22.65

0.78

2.67

2.72

1.79

1.49

3.75

2.81

1.20

13.25

17.46

10.45

9.85

7.19

7.03

7.74

9.80

Jobs Change (%)

1996/1 987 2005/1 996

14.54

15.45

14.98

18.49

8.33

5.49

19.07

14.62

18.54

11.56

12.56

13.68

25.68

10.50

13.46

15.37

26.07

8.58

22.57

18.98

26.39

-1.21

16.86

9.29

9.68

11.72

6.07

6.76

12.59

13.36

11.85

10.13

9.17

16.75

8.76

3.59

4.80

10.87

12.22

3.77

5.48

13.35

11.85

11.61

10.68

17.63

12. ~1

12.80

10.16

17.42

. . . ..-
Table 5. Projected Total Personal income Assuming Current Policies Continue, 1992 dollars, 1987-2005

Area

United States

Rural-Urban County Groupings of the U.S.

Nonmetro Not Adjacent to Metro

Nonmetro Adjacent to Metro

Metro Other Than Central City

Central City of Large Metro

Sub-National Regions of the U.S.

Northeast

South

North Central

West

County Groupings by Rurality and Region

Nonmetro Not Adjacent to Metro

Northeast

South

North Central

west

Nonmetro Adjacent to Metro

Northeast

South

North Central

West

Metro Other Than Central City

Northeast

South

North Central

West

Central City of Large Metro

Northeast

South

North Central

West

Total Personal Income [bil. )

1987 1996 2005

4,700.1

329.7

423.0

2,394.4

1,553.1

1,286.7

1,289.6

1,111.9

1,011.9

35.1

112.3

116.2

66.1

74.0

168.9

131.7

48.2

756.7

715.1

488.0

434.7

421.0

293.3

376.0

462.8

5,711.4

395.6

510.3

2,988.4

1,817.1

1,496.6

1,639.2

1,323.1

1,252.5

41.0

134.7

134.3

85.6

86.3

207.3

153.7

62.9

888.0

931.5

598.7

570,2

481.3

365.7

436.4

533.8

6,764,7

441.7

579.9

3,576.2

2,166.9

1,754.7

1,931,2

1,517.3

1,561.4

46.6

148.4

145.7

100.9

100.9

231.3

170.9

76.7

1,046.2

1,112,6

698.0

719.4

560.9

438.9

502.7

664.3

Personal Income as a % of U.S.

1987

100.00

7.01

9.00

50.94

33.04

27.38

27.44

23.66

21.53

0.75

2.39

2.47

1.41

1.58

3.59

2.80

1.03

16.10

15.21

10.38

9.25

8.96

6.24

8.00

9.85

1996

100.00

6.93

8.93

52.32

31.82

26.20

28.70

23.17

21.93

0.72

2.36

2.35

1.50

1.51

3.63

2.69

1.10

15.55

16.31

10.48

9.98

8.43

6.40

7.64

9.35

2005

100.00

6.53

8.57

52.87

32.03

25.94

28.55

22.43

23.08

0,69

2.19

2.15

1.49

1.49

3.42

2.53

1.13

15.47

16.45

10.32

10.63

8.29

6.49

7.43

9.82

Inoome Change (%)

1996/1987 2005/1996

21.52

19.98

20.64

24.81

17.00

16,31

27.11

19.00

23.78

16.80

20.01

15.52

29.48

16.57

22.73

16.70

30.35

17.35

30.27

22.69

31.16

14.32

24.65

16.08

15.35

18.44

11,66

13.64

19,67

19.25

17.25

17.81

14.68

24.66

13.75

10.15

8.54

17.91

16.92

11.58

11.17

21.99

17.81

19.44

16.60

26.16

16.56

20.02

15.19

24.45
Table 6. Projected Manufacturing Jobs Assuming Current Policies Continue, 1987-2005

Area

United States

Rural-Urban County Groupings of the U.S.

Nonmetro Not Adjacent to Metro

Nonmetro Adjacent to Metro

Metro Other Than Central City

Central City of Large Metro

SuHlatiial Regions of the U.S.

Northeast

South

North Central

west

County Groupings by Rurality and Region

w Nonmetro NcX Adjacent to Metro

w

Northeast

North Central

west

Nonmetro Adjacent to Metro

Northeast

North Central

west

Metro Other Than Central City

Wheast

North Central

west

Central City of Large Metro

Noftheast

south

North Central

west

Manufacturing Jobs (thou. )

1987

19,575

1,615

2,438

9,800

5,722

32,020

39,221

31,535

27,220

1,064

3,807

3,794

2,138

2,CO0

5,305

3,846

1,396

18,154

21,237

13,201

11,025

10,802

8,872

10,694

12,660

1996

18,841

1,787

2,625

9,516

4,914

33,779

46,701

36,146

32,266

1,187

4,285

4,313

2,687

2,210

6,019

4,437

1,760

19,711

26,030

15,707

13,934

10,671

10,368

11,688

13,886

2005

17,966

1,741

2,511

8,954

4,759

37,781

51,431

39,461

37,670

1,291

4,439

4,520

2,979

2,480

6,246

4,680

1,995

22,046

29,051

17,385

16,391

11,963

11,695

12,875

16,305

Mftg Jobs as a % of U.S.

1987 1996 2005

100.00

8.25

12.45

50.06

29.23

163.58

200.36

161.10

139.05

5.44

19.45

19.38

10.92

10.22

27.10

19.65

7.13

92.74

108.49

67.44

56.32

55.18

45.32

54.63

64.67

100.00

9.48

13.93

50.51

26.08

179.28

247.87

191.85

171.25

6.30

22.74

22.89

14.26

11.73

31.95

23.55

9.34

104.62

138.16

83.37

73.96

56.64

55.03

62.03

73.70

100.00

9.69

13.98

49.84

26.49

210.29

286.27

219.64

209.67

7.19

24.71

25.16

16.58

13.80

34.77

26.05

11.10

122.71

161.70

96.77

91.23

66.59

65.10

71.66

90.75

Mftg Jobs Change (“A)

1996/1 987 2005/1 996

10.65

7.67

-2.90

-14.12

5.49

19.07

14.62

18.54

11.56

12.56

13.68

25.68

10.50

13.46

15.37

26.07

8.58

22.57

18.98

26.39

-1.21

16.86

9.29

9.68

-4.64

-2.57

-4.34

-5.91

-3.15

11.85

10.13

9.17

16.75

8.76

3.59

4.80

10.87

A2.22

3.77

5.48

13.35

11.85

11.61

10.68

17.63

12.11

12.80

10.16

17.42

—
and thus cannot be analyzed correctly in isolation.

Rural and urban are intimately related. Our analytic

frameworks and our mental world images should

reflect this.

Analyses of Alternative Policies

We in RUPRI have not yet applied the current

version of the models to the analysis of policy

alternatives. We are preparing to do a simulation of

Medicare cuts used to lower the federal budget deficit

or federal taxes. I am currently studying the degree to

which rural beneficiaries of Medicare purchase the

services of urban health providers. This information

will bean important input into the appropriate spatial

allocation of the direct impacts of Medicare cuts.

At this point we know, as shown in Table 7, that

Medicare payments tend to equal a larger proportion

of personal income in rural than urban areas;

Medicare payments in Table 7 are allocated to the

residence of the beneficiaries in contrast to the

location of the provider, These results suggest that

cuts in Medicare projected spending will have greater

adverse effixts in rural than urban areas. However,

the forces associated with the fti outcome are

sufficiently comple~ as outlined in this paper, that the

results of the analysis are not a foregone conclusion.

Decision makers need this information. We should

provide it to them.

Table 7. Medicare Payments Relative to Personal

Income, 1993, percent

SubNational Region of the U.S.

N.E. South N.C. West Total

Nonmetro

Not Adjacent

A$jacent

Metro

Not Cen City

Central City

Total

3,25 4,05 3,29 2,94 3.53

3,18 4.04 3.44 2.45 3.42

3.29 4,05 3,16 3,74 3,61

2.66 2.64 2.34 2.22 2.48

2,47 2.77 2.27 2.09 2.45

2.98 2.32 2,43 2,35 2,54

2,71 2.95 2.54 2.30 2.65

References

Adams. John S. “Classi@ing Settled Areas of the

United States: Conceptual Issues and Proposals

for New Approaches.” m Metromditan and

Nonmetrotwlitan Areas: New Amroaches to

Gewzraphic Definition. Working Paper No. 12,

Population Division, U.S. Bureau of the Census,

Washington, D, C., September 1995. pp. 9-83.

Braschler, Curtis. Glem Nelson, and Evert Van der

Sluis. 1995 RUPRI Rural Baseline. Report P95-1,

Rural Policy Research Institute, University of

Missouri. April 21, 1995.

Bureau of Economic Analysis, U.S. Department of

Commerce. “Regional Economic Information

System, 1969-95.” CD-ROM available from

Regional Economic Information System. BEA.

Washington, DC. September 1997.

Congressional Budget ~lce. The Economic and

Budget outlook: Fiscal wars 1998-2007. U.S.

Government Printing CMce, Washington. DC.

January 1997.

Council of Economic Advisers. Economic Reuort of

the President. U.S. Government Printing OfHce,

Washin@on, DC, February 1997.

Day, Jennifer Cheesernan. Population Projections of

the United States bv ke, Sex. Race. and Hisrxmic

Orkin: 1995 to 2050. U.S. Bureau of the Census,

Current Population Reports, P25-1 130, U.S.

Government Printing Oflke. Washington, DC.

1996,

Nelson, Glenn, and Curtis Braschler. Nonmetrouolitan

and Metropolitan Medicare Enrollments and

Pavments. Data Report P95-7, Rural Policy

Research Institute, University of Missouri,

September 13, 1995.

Treyz, George 1, Retional Economic Modelimz: A

Systematic Awreach to Economic Forecasting

and Policv Analvsis. Boston: IUuwer Academic

Publishers, 1993.

Sources: Medicare payments horn OfHce of the

Actuary, HCFA with fiuther manipulations by

RUPRI; personal income horn BEA.

100
TOPICS IN FORECASTING

Chair: Karen S. Hamrick

Economic Research Service, U.S. Department of Agriculture

Forecasting Farm Nonreal Estate Loan Rates: The Basis Approach,

Ted Covey, Economic Research Service

U.S. Department of Agriculture

Revising the Producer Prices Paid by Farmers Forecasting System,

David Torgerson and John Jinkins, Economic Research Service

U.S. Department of Agriculture

A Revised Phase Plane Model of the Business Cycle,

Foster Morrison and Nancy L. Morrison

Turtle Hollow Associates, Inc.

The Educational Requirements of Jobs: A New way of Looking at Training Needs

(Abstract),

Darrel Patrick Wash, Bureau of Labor Statistics

U.S. Department of Labor

101

FORECASTING FARM NONREAL ESTATE LOAN RATES:

THE BASIS APPROACH

Ted Covey

Economic Research Service

U.S. Department of Agriculture

“Basis” is the difference between a commodity’s cash

price for immediate local delivery (spot price) and its

nearby fhtures price:

(1) BASIS = SPOT PRICE - FUTURES PRICE

There exist as many bases as there are local cash

markets for that commodity. Given that Iitures-cash

price relationships change daily, a commodity’s basis

has both a regional and temporal aspect, differing with

respect to time and location. The t%tures price is that

commodity’s nearby fitures price which will expire at

least one month following the month of occurrence of

the local spot price.

Farmers will use their average basis for a particular

period to make a forecast of their future basis, called the

expected basis. For example, if the average local basis

for the past three years (1994, 1995, and 1996) for the

first week in August was 103 cents per bushel, then the

expected basis for the first week of August 1997 for that

local cash market is 103 cents per bushel. Methods of

calculating the average and therefore expected basis

vary; and there is no empirically demonstrated superior

approach.

On the day of the forecast, the farmer uses that day’s

settle fitures price FP for the delivery month

immediately following the anticipated future marketing

month and adds his expected basis E(B) to calculate a

predicted spot price E(SP):

(2) E(SP) = FP + E(B)

For example, suppose on May 1, 1997, a farmer wishes

to forecast his local spot price for corn for the first week

November 1997, a possible marketing date. If his past

average basis for the first week in November is -7 cents

per bushel and the May 1, 1997 corn settle futures price

for December 1997 delivery is 269 cents per bushel,

then his forecasted spot price for the fwst week in

November 1997 is:

262 cents/bu. = 269 cents/bu. -7 cents/ bu.

This approach has been advocated and extensively used

to forecast agricultural product prices (Peck; Hauser et

al.; Irwin et al.; Liu et al; Howard; McDonald and Hein;

Kamara; Fama and French; French).

This paper will evaluate whether this approach may be

usefid in forecasting the price of debt in agricultural

credit markets.

Method

Using the basis forecast approach developed in

commodity markets as an analogy, the equation for

forecasting interest rates on farm loans is:

(3) E(LR) = FY + E(B)

or the expected farm loan rate E(LR) is equal to the

yield calculated from the nearby interest rate futures

price FY plus the expected nearby basis E(B).

Data for spot prices will consist of quarterly average

effective interest rates on new nonreal estate farm loans

(all loans) made by commercial agricultural banks.

Agricultural banks are those that have a proportion of

farm loans (both real plus nonreal estate) to total loans

that is greater than the unweighed average of all

commercial banks (usually around 16Yo) from 1977-

1994. These rates are taken born a quarterly survey

conducted by the Federal Reserve on the first full week

of the second month of each quarter.

The nearby futures price consists of the expected yield

calculated from the settle price for the U.S. T-bill

futures contract (International Monetary Market of the

Chicago Mercantile Exchange) for delivery in the

month following the month of the Fed’s farm loan rate

survey. This settle futures price and yield is based on

the average settle futures price for the 5 days of the first

full week of the second month of each quarter,

concurrent with the Fed’s survey. For a simple example

of the method of calculating the expected futures yield

FY see Chance pp. 302-304.

A basis series for each quarter is calculated by

subtracting the interest rate on nonreal estate loans for a

particular quarter from the expected futures yield

103
.-.

calculated for that quarter. The historical basis series is

then used to calculate an average and therefore expected

basis E(B) series.

Two methods of calculating the average basis will be

used; each generating a different expected basis and

therefore a different expected loan rate. The first

approach will calculate the expected basis as an average

of the four bases for the four quarters previous to the

forecasted quarter. For example, the average of the

bases of the four quarters for 1990 is used as the

expected basis for the first quarter of 1991.

The second approach uses last year’s actual or realized

basis for the same quarter as the expected basis for that

quarter this year. For example, the basis observed in the

first quarter of 1990 is the expected basis for the first

quarter of 1991.

These two approaches are based on commonly used

methods of calculating an expected basis in agricultural

commodity markets.

As an example of calculating an expected loan rate

E(LR) for the next quarter, assume it is the third week

in October 1994 and the frost approach is used to

calculate an expected basis E(B). If in the fourth

quarter of 1994 the average of the last 4 quarters’ (i.e.

all 4 quarters of 1994) bases is 2.85’%0 (= E(B)) and the

settle fhture price on that day for March 1995 T-bills

gives a yield of 5.75% (= FY), then that day’ forecast

for the 1995 first quarter loan rate E(LR) is:

8.6%= 5.75% i- 2.85%

The two different approaches to calculating an expected

basis allowing testing two different fbtures or basis

forecasting models. The forecasts generated by these

two basis models (BAVG and BLAG) will be contrasted

to forecasts issued by: 1) NAIVE: a naive model where

next quarter’s loan rate is the same as this quarter’s; 2)

TREND: a “trend is your friend” model where next

quarter’s loan rate is equal to this quarter’s loan rate

plus the change between this quarter and last quarter;

and 3) COMP: a composite model with it’s forecasts

generated as an unweighed average of the forecasts of

the four other models.

whether futures can contribute to price discovery in

farm loan markets.

A total of 68 out-of-sample forecasts will be issued by

each of the 5 forecast models starting with the first

quarter of 1978 through the fourth quarter of 1994.

Forecast accuracy will be evaluated by standard

statistical methods: root mean squared error (rinse),

mean absolute error (mae), mean absolute percentage

error (mape), mean forecast error (mfe), and the range

of the forecast error of each model (range).

Results

Table 1 shows the rankings of the 5 different models

based on the different statistical results calculated using

the 68 out-of-sample forecasts generated by each model

for the period 1978:1-1994:4.

The BAVG model (the model which used a moving

average of the past four quarterly bases as the expected

basis) issued the “best” forecasts based on three of the

five forecast criteria: range, rinse, and mape. The

TREND model placed fmt in mfe, and the composite

model placed first on the basis of mae.

With all 5 forecast criteria are considered together

(averaged and assigned equal weights), COMP issues

the best forecasts, while the best-performing fi.dures

model BAVG still outperforms the best performing

nonfbtures model NAIVE.

Conclusions

The superior forecast performance of the model relying

on fitures information BAVG over the best forecast

model relying solely on information in cash prices

NAIVE suggest that fbtures plays a price discovery role

in farm loan markets.

Future research should consider the role of interest rate

expectations in affecting farmer financial decisionmaking, the costs to fmers of forecast error, and how

forecasts relying on fhtures might reduce those costs by

generating better-informed credit decisions.

The NAIVE and TREND models rely solely on

information contained in cash prices. The two basis

models rely on the relationship between the cash and

futures market. Comparing the forecast errors between

the cash only (NAIVE and TREND) vs. cash and

fhtures (BLAG and BAVG) models allows a test of

104
References Tablel. Rankings of Forecast Models

Chance, Don M. An Introduction to Options & Futures.

2nd ed. The Dryden Press. Harcourt Brace Jovanovich

Publishers; New York; 1992.

Fama, Eugene F. and K. R. French. Commodity

Futures Prices: some evidence on forecast power,

premiums, and’the theory of storage. Journal of

Business 60(1987):55-73.

French, K. R. Detecting Spot Price Forecasts in Futures

Prices. Journal of Business 60(1987):S39-S54.

Hauser R. J., et al. Basis Expectations and Soybean

Hedging Effectiveness. North Central Journal of

Agricultural Economics. 12( 1990): 125-36.

Howard, C. T. “Are T-bill Futures Good Forecasters of

Interest Rates? The Journal of Futures Markets

1(1982):305-15.

Irwin, S. et ali. The Forecast Performance of Livestock

Futures Prices: a comparison of USDA expert

predictions. The Journal of Futures Markets.

14(1994):861-75.

Kamara, A. Forecasting Accuracy and the

Development of a Financial Market: the T-bill fhtures

market. The Journal of Futures Markets

10(1 990):397-405.

Lui, Shi-Miin; et al. Forecasting the Nearby Basis of

Live Cattle. The Journal of Futures Markets

14(1994):259-74.

McDonald, Scott and S. Hein. Futures Rates and

Forward Rates on Prediction of Near-term T-bill Rates.

The Journal of Futures Markets 9(1989):249-62.

Models

NAIVE TREND BAVG BLAG COMP

Criteria:

range 2 5 1 4 3

rrnse 3 5 1 4 2

mfe 4 1 5 3 2

mae 2 4 3 5 1

mape 3 4 1 5 2

overall 3 4 2 5 1

The “overall” criteria is based on the average of the

other 5 criteria, each weighted equally. The numbers

1-5 ranks each model on its forecast error relative to the

other 5 models under a particular forecast error criteria.

A” 1” indicates the smallest forecast error, a “5”

indicates the largest forecast error.

Peck, Anne. Hedging and Income Stability.

American Journal of Agricultural Economics

57(1975):410-19.

105
m

$.

.. .
REVISING THE PRODUCER PRICES PAID BY FARMERS FORECASTING SYSTEM

David Torgerson and John Jinkins, Economic Research Service/USDA

Summary

The prices paid by farmers ten-year-ahead projection

equations had last been estimated in the early 1990s.

The Economic Research Service (ERS) revised this

system using expert judgment and regression. We

describe how the revision was done, focusing on the

fertilizer and fuel price equations. The revised

fertilizer price and he] price equations are superior, in

forecast accuracy, to the associated ARIMA

(autoregressive integrated moving average) and the old

equation. For fuel prices, the superiority of the new

regression equation to the optimal ARIMA evaporates

at the end of the out-of-sample forecasting period.

Introduction

The aggregate prices paid by farmers index and its

subindices are forecast by the ERS of USDA as part of

the ten-year-ahead President’s Budget baseline

forecasting process. The index is constructed like the

Bureau of Labor Statistics (BLS) producer price index

(for the items used in farm production.) In particular,

the prices paid by farmers subindices are used in

forecasting farm production expenses. The National

Agricultural Statistical Service (NASS) of USDA, the

agency which produces the historical values for these

indices, reformulated most of the subindices when

changing the benchmark year to 1992 from 1977. For

example, some of the prices which had been taken

from the NASS survey of farmers for Agricultural

Prices were instead taken from BLS’S PPI price series.

As a result of the rebenchmarking and the change in

estimation procedure, the statistical properties of the

prices paid by farmers (PPF) subindices changed

drastically. For this reason, ERS reestimated the prices

paid forecasting equations of the 16 sub-components of

the index of prices paid by farmers. We describe the

equation revision, focusing on the prices paid for

fertilizer (PPFERT) and the prices paid for fuel

(PPFUEL) forecast equations.

The criteria for the new forecasting equations were:

(1) the exogenous variables had to be available from

the USDA baseline process, the forecasting activity to

which the new equations were to be added.

(2) the equations should pass the phone call (PC) test

for reasonable structure. As forecasts are inevitably in

error, it is desirable to easily explain those errors in

terms of changes in variables apparently tied to the

relevant farmers prices paid index when the phone calls

come. The economics consistent with the equations

had to be simple and transparent.

(3) as a Government agency forecast simplicity and

transparency of methodology were important.

(4) the forecasting equations should produce forecasts

superior to those obtained from a simple ARIMA

model.

(5) the equations had to be operational for the summer

1997 baseline and documented in the June 1997 issue

of the Agricultural Inputs and Finance Situation and

Outlook Report.

To meet these objectives John Jinkins of the Rural

Economy Division (RED) of ERS created a committee

including representatives from NASS and the parts of

ERS involved in the budget baseline process.

Committee members Jinkins and Torgerson estimated

the new equations. At each committee meeting three to

five equations were reviewed and critiqued. As a result

of committee discussion, some equations were again reestimated.

To fulfill criteria 1 to 3, and recognizing that the

revised price index data were limited to annual series

from 1974 to 1995, it was decided that all the new

equations were to be linear, log-linear or in percentage

change. C.W.J. Granger and Paul Newbold in

Forecasting Economic Time Series (GN) criticized the

use of linear regressions for forecasting because of the

problem of spurious correlation. A regression equation

could show a good in-sample fit as measured by R*,

and perform miserably in out-of-sample forecasting.

107
The example GN presented was aregression of one

random walk on another random walk resulting in good

in-sample R2 but a poor forecasting petiormance outof-sample. We guard against this problem by the PC

criteria and the use of out-of-sample testing. Equations

which contain variables related by economic theory

appear to be somewhat resistant to the spurious

correlation problem. Table 1 presents the functional

form of all the resulting forecasting equations.

To illustrate the forecast reestimation we review the

development of two of the more important forecasting

equations. As data revisions had only been back to

1974, we backcasted using the percent change in the

old indices back to 1970. We then estimated the

equations over 1970-1990 to reserve the 1991 to 1996

observations for out-of-sample testing. (For the fuel

price equation, the 1988 to 1996 period was reserved

for out-of-sample testing to mitigate the problems

related to the volatile oil market of 1990 and 1991.

The panel thought it unlikely that the next twenty years

would have three oil supply shocks.) The old set of

equations, estimated in the early 1990s, was the second

benchmark. Those equations, given the large data

revisions for many of the price subindices, had to be

revised to maintain forecast credibility.

There are three types of prices paid by farmers

equations: (1) prices determined by farm sector supply

and demand (2) prices determined by demand from the

farm sector and supply from the general economy and

(3) prices determined by the general economy. The old

feed price paid equation, is close to an accounting

identity, given the abundance of commodity price data

forecasted in the budget baseline. With the major feed

ingredient prices as explanatory variables, the equation

error term reflects markup changes and other random

shocks which would not be easily improved upon. For

the feed price equation it was only necessary to reestimated the old equation with new data as the

forecasts based on actual grain prices were excellent.

Other prices, such as the fertilizer prices paid index

(PPFERT) reflect an interplay of the farm sector and

overall economy. Fertilizer prices are demand driven

by farm commodity prices, acreage decisions, and other

specifically agricultural variables. The supply side is

strongly influenced by the price of energy, as natural

gas is the largest variable cost in producing ammoniabased fertilizers. We examine this equation in detail.

There is a widespread recognition of macro-prices,

prices which agriculture is subject to but are

determined by economy-wide forces. Using less than

3 percent of total liquid fiels, U.S. farming has small

influence on liquid fbel prices. The fiel prices paid by

fmers index (PPFUEL) is the macro-price examined

in detail.

Modus Operandi

We present the results of our search for “good”

forecasting equations for fertilizer and fuel prices in

table 2 and figure 1, and table 3 and figure 2,

respectively. Each forecast equation development

started with estimation of an autoregressive moving

average (ARMA) model of the specific prices paid

subindex. The dependent variable is the level of the

variable in question. The independent variables can be

thought of as lags of the variable reflected in the

moving average (MA) terms; the correlations of the

dependent variable with itself across time periods are

reflected in the autoregressive (AR) terms. A

maximum likelihood estimation is done to compute

these estimates. For a sample this small (21

observations), only fmt and second order MA and AR

terms were estimated. Any standard econometrics

package such as SAS or EVIEWS can be used to

estimate AR and MA coefficients and select the

optimal orders for the AR and MA, concomitantly.

The problem with the above procedure is that

economic variables seldom pass the statistical tests for

stationarity as required for the optimality of the ARMA

estimator. The first assumption for stationarity to hold

is that the mean of the variable, say the fertilizer price

index, is constant. The second stationarity assumption

is that the covariance between fertilizer prices in

various periods depends only on the differences

between the time periods. Most of the indices of prices

paid by farmers are not stationary as measured by

standard statistical tests.

The standard operating procedure is to difference a

variable until the transformed (difference) variable

passes the standard stationarity test--the augmented

Dickey-Fuller test (DFT). Seldom do economic

variables need to be difference more then twice to

attain stationarity, Why do an ARMA as the DFT on

undifferenced data are very likely to reject stationarity

and an ARMA model which incorrectly assumed

stationarity would generally do quite poorly in

forecasting, with rapidly increasing forecast exror as the

forecast period lengthened? The DFT for stationarity

is not a very powerful test so that it will “often” reject

a series which is actually stationary, Further, the

ARMA estimation process will be an independent test

for some kinds of nonstationarity, Finally, running an

108
ARMA is easier than running a regression and often

gives some insight into the process which generated the

data. We refer to the estimation of ARMA on levels of

a variable explained as naive ARMA. All the orders of

integration were determined by DFTs.

The next step - was to estimate an ARMA on the

integrated series. For both fuel and fertilizer prices,

differencing once made the series stationary. The

difference between the current fertilizer price paid and

the previous price paid is the appropriate variable for

ARMA estimation. We will refer to this as the ARIMA

model of fertilizer prices--as the once difference

fertilizer price is consistent with stationarity. To get an

actual forecast, one simply adds the ARIMA estimated

difference to the series value in the previous period.

To give the old forecasting equation a fair chance, we

estimated the reduced form with the same variables and

functional form as the old forecasting equation using

new data. We label this oldhew in tables 2 and 3

below.

The new specification of the forecasting equations was

developed by committee. The small amounts of data,

the desire to be understandable, and time constraints

prevented any use of sophisticated techniques. The

shortage of data was the binding constraint. (The

cutting room floor models which were rejected for very

bad Durbin-Watson statistics and bad forecasting

performances are not included here.)

We should point out that none of these regression

models was the explicit result of taking structural form

models of supply and demand and solving them

explicitly for a specific reduced form. [n using the

term structural models we simply mean regression

equations with variables which are apparently related

to the prices paid index.

And of course, the out-of-sample model forecast

performance is important in sorting the wheat from the

chaff. A major contribution of the time series literature

is emphasizing out-of-sample forecasting as a way to

validate economic models. Many would go even

further and say that if a model does not forecast well

out-of-sample it is not a valid reflection of reality.

Fertilizer Prices Paid Forecast Equation

PPFERT is the variable forecasted. Table 2 reports the

standard statistics for each competing forecasting

equation. Greater detail is available in tables 2a

through 2h, available from the authors on request.

Figure 1 graphically depicts the competing forecast

equations. A result of the DFTs is that the regression

equations were not balanced in the sense of all

variables in the regression equation having the same

order of integration. In particular, the crude oil price

(RAC) in the new regression equation

(PPFERTFNEW) is integrated of degree 2. That is, the

WC has to be difference and that result difference

again to be consistent with stationarity, as tested by the

DFT. The standard t-tests implicitly assume both the

dependent and independent variables are integrated of

the same order. In this case they are not, since

PPFERT is integrated of degree one. So one can get a

spurious correlation problem by including variables not

related to the forecasted variable in the forecasting

equation even though the t-test says they are related.

That is why GN note that having variables of different

orders within a regression equation makes for potential

problems for use in out-of-sample forecasting.

The use of out-of-sample forecasting should mitigate

the potential problem of an equation with a good insample fit producing a bad forecast.

The ARMA (PPFERTFARMA) summary statistics

verifi the results of DFT for PPFERT. Indeed not only

is the price index not stationary but the estimated AR

coefficient is slightly above one, indicating unstable

behavior, likely that of a random walk. Note that the

mseout is larger for the old forecast equation estimated

with the new data (labeled oldhew in Table 2 with

equation name PPFERTFOLD in Figure 1) than for

PPFERTFARMA. The old/old regression had a non

significant constant while big8acres (the sum of the

acreage of the eight highest acreage planted crops) is

close to significant. The old equation with new data

had the situations reversed. The instability inherent in

such drastic changes in which variables are apparently

significant greatly detracts from the confidence one

places in either equation and may well reflect a

spurious conflation problem. The evidence against the

oldhew formulation is overwhelming when the mseout

is sharply in favor of the naive ARMA and the basic

coefficients are unstable. Further, the lagged

dependent variable (PPFERT(- 1 )) tends to bias the DW

upward as well as make for more potential out-ofsample forecast error. Getting rid of lagged dependent

variables was a major operational goal of updating the

forecasting equations. The old fertilizer price equation

with new data performed very poorly in terms of R

2 if

PPFERT(- 1 ) (the actual fertilizer index lagged one

period) was omitted, revealing that an equation

restructuring necessary.

109
On several grounds then, the competition is between

the new regression equation (PPFERTFNEW) and the

first difference ARIMA formulation

(PPFERTARIMA). The big8acre and the constant

were insignificant in an equation with the average of

the current market year and prior market year price of

com averaged (avg(compr& compr(- 1 )). (This gives a

flavor of forecasting with expected values since

fertilizer is purchased prior to the marketing year corn

price being determined. It is not fi.dly consitent with

using expectations in a more rigorous sense since the

expected com price is not determined endogenously in

the estimated model.) And the crude oil price (RAC)

was significant with those variables while the log of the

real crude oil price term was not. So the RAC and

average com price, over the current and prior

marketing year, were the first two variables selected for

the new forecasting equation.

In the committee discussions of the above preliminary

version of the new forecasting equation, it was pointed

out that there had been an improvement in fertilizer

input quality over time. Further, it was noted at the

time most fertilizer was purchased the average com

price for the current marketing year was not known.

We dealt with the fertilizer quality improvement by

adding a simple time trend which proved superior in

terms of out-of-sample forecasting to using any of the

standard inflation variables. This change also made the

R* comparable to the old equation’s R*. The final form

of the new regression equation then had the current

crude oil price, the average of the expected com price

and the previous marketing year com price, and a time

trend. Since com prices are forecast in the baseline, the

forecasted com price was used in the actual fertilizer

price forecasts. The superior mseout of the new

structural equation is tabulated in Table 2 and shown

graphically as PPFERTFNEW in Figure 1. Either

representation dramatically demonstrates the striking

forecasting superiority of the new equation relative to

the other candidates.

Since the mseout of an equation difference is not

comparable with a level equation, it is necessary to

look at the actual out-of-sample forecast to determine

which fommlation is superior in forecasting. Figure 1

graphically shows the clear superiority of the new

structural equation over even the best ARIMA. The

moral of this story is that a good structural equation

beats an ARIMA. (But a bad structural equation can be

worse than a naive ARMA as the above comparison of

PPFERTFARMA and PPFERTFOLD seen in Figure 1

clearly demonstrate.)

Farm Fuel Prices Paid Index Forecast

PPFUEL is the variable to be forecasted. Table 3

summarizes the standard statistics for alternative

forecasting equations. More detail is in tables 3a

through 3h, available from the authors on request.

Figure 2 graphically depicts the comparative forecasts.

As above, all variables in the structural equations do

not have the same order of integration. Again, the

crude oil price (RAC) and price paid by farmers for

fuel (PPFUEL) are integrated of degree 2 and 1

respectively. The lack of order consistency of

dependent and independent variables could mean poor

out-of-sample forecasting for both the old and new

regression equations.

The ARMA (PPFUELFARMA) supports the results of

DFT for the fuel price index. The fuel price index is

not stationary and the estimated AR coefficient is

above one, indicating explosively unstable behavior,

likely a random walk. Note that the mean-squared

error in the out of forecast period (mseout) for the

ARMA is larger than that statistic for the old forecast

equation estimated with the new data (labeled old/new).

The old/old regression had decent t-statistics for all

coefilcients. The old forecast structure for fuel prices

may be superior in forecasting to the naive ARMA in

sharp contrast to the fertilizer price situation. Figure 2

indicates both the old equation with new data

(PPFUELFOLD) and the ARMA are quite bad

forecasting models relative to the ARIMA or the new

regression model. Further, the farmers prices paid fuel

index lagged one year (PPFUEL1(- 1 )) tends to bias the

Durbin-Watson upward. Yet even so the measured

Durbin-Watson is smaller than the R2. This strongly

suggests out-of-sample forecasting problems. As in the

case of the fertilizer price forecast equation, finding a

replacement for the lagged dependent variable was a

major goal. (Some analysts would have appropriately

used the Durbin h test in the presence of lagged

dependent variables to test for first order

autocorrelation. We chose not to since we were getting

rid of lagged endogenous variables anyway.

Forecasting out ten years with lagged dependent

variables is problematic at best and we did not expect

to have any final equation with lagged endogenous

variables. )

In summary, ARMA (PPFUELFAIUVL4) is bad and the

oldhew equation (PPFUELFOLD) is only marginally

better in a forecasting sense. This reflects the typical

situation in attempting to forecast a number of years

out using with ARMAs on undifferenced data and

regression equations with lagged dependent variables.

110

F--
The new equation reported in Table 3 and depicted in

Figure 2 as PPFUELFNEW is not the first new

equation. Having PPFUEL as a function of RAC and

PPI lagged one period for 1970 to 1990 had R2 go up

marginally compared to the old equation’s R2. But the

lagged endogenous variable is no longer needed and

the Durbin-Watson has improved. Further, without the

lagged endogenous variable, the Durbin-Watson is a

valid test for serial correlation. Still the DurbinWatson was smaller than the R2

, making forecasting

problematic as this equation fits the typical spurious

correlation pattern detailed in GN. To mitigate this and

the problem of the unstable oil market during 1990 and

1991 we shortened the estimation period to 1970-1987,

and reserved a larger out-of-sample period (1988 to

1996) to test the reestimated equation.

The in-sample problems remain. The Durbin-Watson

is smaller than the R2

. But the coefficients of RAC and

PP1(- 1 ) continue to be significant and the mseout is

noticeably lower than the ARIMA.

In some sense, the structural fuel price forecasting

equation is more satisfactory than analogous fuel price

equation. Again, the crude oil price (RAC) replaces the

natural log of the real crude oil price. The PPI lagged

one year (PPI(- 1 )) is superior to a time trend in

projecting the tendency of fuel prices to rise over time.

The use of the PPI makes it far easier to interpret the

fuel price equation than a time trend does, as prior

inflation is a partial explanation of higher fuel prices

today.

The ARIMA model of the first difference of the fuel

price is an improvement over the naive ARMA. Again,

the gain is not as dramatic as in the case of fertilizer

prices. Neither the AR nor MA terms are statistically

significant. This is not crucial, as t-statistics are not as

definitive in an ARIMA context as they are in the

regression framework.

The new regression equation and the first difference

ARIMA formulation are again the apparent

competitors. Since the mseout of an equation

difference is not comparable with a level equation one

needs to look at the actual forecasts to see whether the

new equation or the ARIMA is superior in forecasting.

Figure 2 reflects a better out-of-sample forecasting

record for the improved structural fuel price equation

(PPFUELFNEW) relative to the ARIMA

(PPFUELFARIMA). Note the forecast superiority is

lost at the end of the forecast period, 1994-1996.

Nevertheless, because of simplicity, interpretability,

and overall forecast superiority the new structural

equation is the best choice, despite the marginal

forecast victory. Using criteria 1 to 3 the margin of

victory of the new fiel price structural model over the

ARIMA is larger than in the fertilizer price situation.

Implications

With some effort, it is possible to beat ARIMA in

forecasting. When human capital is lost it sometimes

is possible to partly recover by taking a fresh look at

the data and in a fairly economical fashion use expert

judgment in the formulation of the equations instead of

the forecasting process directly. The involvement of

the producers of the prices paid indices and as well as

the users of the prices paid index forecasts made the

equation revision process feasible.

111

m--
Table I--Forecast farm prices paid indices equation structure

An increase in

this item will

have this effect

For this prices paid index: ERS will consider the outlook for these items when making the 1998 forecast: on the forecast:

(mya = marketing year average 1/)

feed ratio of 1998 com mya price to 1997 com mya price increase

ratio of 1997 com mya price to 1996 com mya price increase

ratio of 1997 soy meal mya price to 1996 soy meal mya price increase

ratio of 1998 all hay mya price to 1997 all hay mya price increase

livestock & poultry

seeds

fertilizer

agricultural chemicals

fbels

supplies & repairs

autos & trucks

farm machinery

building material

farm services

rent

interest

1998 feeder steer price, 750-800 pounds, Oklahoma City increase

1997 feeder steer price, 750-800 pounds, Oklahoma City increase

1998 milk price increase

1997 com mya price decrease

sum of 1997 acres planted to corn, wheat, and soybeans increase

1998 inflation as measured by the producer price index increase

1997 com yield decrease

1998 crude oil price increase

average of 1998 and 1997 com mya prices increase

trend over time measured as a constant annual increase increase

average of 1998 and 1997 com mya prices increase

1997 fertilizer producer price index increase

trend over time measured as a constant annual increase increase

average of 1998 and 1997 crude oil prices decrease

1998 crude oil price increase

1997 inflation as measured by the producer price index increase

1998 inflation as measured by the producer price index increase

1997 inflation as measured by consumer price index increase

1998 inflation as measured by the producer price index increase

1998 inflation as measured by the producer price index increase

1998 inflation as measured by consumer price index increase

1998 land prices increase

sum of 1998 values of production of corn, soybeans, & wheat increase

1998 Moody’s AAA bond rate

1998 prime rate

increase

increase

taxes 1998 inflation as measured by consumer price index increase

wage rates 1998 average hourly earnings in nonagricultural industries increase

trend over time measured as a constant annual increase increase

1/ com marketing year begins September 1, soy meal marketing year begins October 1, hay marketing year begins May 1

112
Table 2 Fertilizer Price Index-Forecast Comparison

constant PPFERT(- 1 ) Ln RAc Big 8 Avg(compr& Time AR(1) MA(1) Durbm R

2 Mseot

(real WC) acres Compr(-1) uend Watson

Intcgtation order

ARMA

t-smts

ARIMA-fkst differcnse

t-stats

Old/old

C=

awmw

t-stats

New

t-stata

1 2

-1.20 O.a 0.11

-0.38 6.S0 1.60

99.76 0.73 17.80

1.71 8.tw 2.43

2

nom 0.61

3.10

1 0 NA

1.01 4).65

2s.14 3.60

nonstationary

43.40 0.97

-1.66 20.26

inverted roots

4.4 4.97

0.50

1.70

4.18

43.98

19.75 2.12

11.03 8,10

2.23 0.84 144.85

1.92 0.21 6?.06

NA 0.92 NA

1.69 0.89 S25.79

1 .s3 0.93 20.26

Table 3 Fuel Price Index-Forecast Comparison

PPFUEL(- 1 ) In RAc PP1(-1) AR-tmn MA-term Durbin R2 Mseout

(real RAc) Watson

Integration order 1 2 2 1

ARMA

t-stats

ARIMA-first difference

t-stats

1.04

25.34

tUXIStXION~

0.36

0.39

inverted roots

0.36

0.59

3.11

0.33

0.41

4.33

1.87 0.93 3.77

1.87 0.16 2.36

Okkld J .?$ 0.61 0.05 NA 0.89 NA

t- 3.03 4.67 1.84

C31dhww 27.92 0.88 10.27 0.83 0.93 7.~~

HnLs 3.21 13.18 2.67

New -9.78 1.3i 0.65 0.92 0.99 1.46

t-stats 4.38 3.10 8.10

113

. ..
‘MO

130

120

110

100

90

00

.

.

.

.

1 I I 1 1 1 I 1 1 t I

wu?’7uwM4

Rm?lwww

/

1W6 1907 1968 lW 1s0 ml wm 19m 19e4 1s6 19e6

Figure 1 NEW FERTILIZER PRICE REGRESSION OUT FORECASTS ARIMA

140

120

100

m

60

E=- 1

. . , , , n 1

. .

*#uiEuiiAM

+Iu=LemD

#uLEmEw

++ m%Emww4

1987 1968 19e9 1s0 lsl 19W mm 1s4 1s 19e6

—— ———— —--— ..— — . — — —

Figure 2 NEW FUEL PRICE REGRESSION OU-l JKM?.ECASTS ARIMA

114

F-
A REVISED PHASE PLANE MODEL OF THE BUSINESS CYCLE

Foster Morrison & Nancy L. Morrison

Turtle HO11OW Associates, Inc .

PO BOX 3639

Gaithersburg, MD 20885-3639

Phone: 301-762-5652

Fax: 301-762-2044

email : 71054. 1061@compuserve .com

1. MACROECONOMIC FORECASTING

The overwhelming majority of macroeconomic data series are produced by

federa l agencies, mostly ones i n

USDOC (U.S . Department of Commerce)

and in the Department of Labor. Al 1

macroeconomic forecasters, whether in

government, academe, or the private

sector, a r e highly dependent upo n

these data series.

ln 1995 USDOC began a process of privati.zing production of the indices of

leading, coincident, and lagging indicators. Changes resulting from

this action did provide additional

verification of the robustness of a

model based on these indices, but

they also raised serious questions

about the difficulties individuals

and small organizations will have in

accessing this data economically and

in a timely manner.

2. WHAT ARE BUSINESS CYCLES?

An awareness of cycles other than the

daily alternation Of night and day

and the annua l progression of the

seasons has existed throughout history . The biblical story of Joseph ,

which straddles Genesis and Exodus,

is one example [Genesis 4 1 , 42, 47;

Exodus 1$ 13; Anderson, 1966].

By interpreting the dreams of Pharaoh

to mean that 7 years of abundant harvests would be followed by 7 years of

famine conditions, Joseph warned the

Egyptians to store enough grain from

the good years to cover the shortfall

in the bad ones. Biblical scholars

plac e the time frame at abou t 1635

BCE, but there are no supporting ma -

terials i.n any known Egyptian

sources. Even so, the story demonstrates an early awareness of climatic cycles ‘and how these can affect

the economy and politics.

Joseph ros e to high positions in th e

Egyptian government, but his political career came to an abrupt end with

the reign of a new Pharaoh. Eventually the Hebrews departed from Egypt,

bear ing the bones o f Joseph with

them. This shows how changing political and social conditions can alte r

the situation of federal forecasters.

Recent changes have not been so drastic, but there are numerous chal -

lenges for forecasters, whether in

the government or not, who us e fed -

eral data and services.

Collection of detailed macroeconomi c

data did no t begin in the United

States unti l afte r World War 11 .

This, however, had not prevented th e

launching of economics as an academic

discipline, beginning with the wor k

of Adam Smith (1723-1790) [Samuel son

& Temin, 19761.

In the depths of the Great Depression, Edward R. Dewey, then Chief

Economic Analyst at USDOC$ was assigned the task of discovering what

had caused this economic catastrophe.

With this began his decades long

study of cycles, which produced a

115

b-
large number of publications [Dewey,

1970; Dewey & Mandino, 1971].

Dewey collected and analyzed a huge

amount of data , some of it going a s

far back as the Middle Ages. What he

lacked, however, were some of th e

necessary tools developed in more recent decades. Computers had become

common by 1960, but they were ex -

tremely expensive and available onl y

to privat e and public organization s

with large budgets; this began t o

changed in 1980.

Nonlinear feedbacks, a possible caus e

of cycles, were little known excep t

to a few specialists. Jay Forreste r

[1961] founded a specialt y calle d

system dynamics, which used nonlinear

ODE S (ordinar y differentia l equations) and mainframe computers t o

model industria l and economic Sys -

terns.

However, chaos and rounding errors,

and especially their mutual interactions , make numerical solutions

of nonlinear ODES too unstable to be

useful for forecasting. Whether such

models are really any better qualitatively for policy analysis than “back

of the envelope” calculations is debatable. System dynamics is in a

stall [Wils, 1988].

Time series methods are quite suitable for analyzing and forecasting

what might be called cycles. The y

offer a wide variety of algorithms

for spectral analysis, correlation

analysis, linear filtering, and linear prediction. Various software

packages allow one to use all these

techniques with little concern as to

what they assume and what they imply.

Fortunately, there is a very simple

dynamical interpretation of time series analysis. It is a nonhomogeneous linear ODE with constant

coefficients where the “right-hand

side” is “noise” rather than a

smooth, mathematical function

[Jordan, 1972]. To be valid, the

corresponding homogeneous ODE must

have only damped solutions; the real

components of its (possibly complex)

eigenvalues must be negative. The

concept extends readily to systems of

equations and to difference equations

[Morrison, 1991a].

The dynamical interpretation of a

time series model of the business

cycle is an aggregate of all markets

decaying toward equilibrium. As an

economic theory it asserts that the

characteristic damping time of this

system is significantly longer than

the sampling interval [Morrison,

1991b] . This is certainly more plausible than static equilibrium and has

many practical ramifications for

forecasting and decision making.

3. CONSTRUCTING THE BUSINESS CYCLE

MODEL

To construct a business cycle model

one needs data. GDP (gross domestic

product) data have been available on

a quarterly basis since 1947, but the

most recent revision by USDOC goes

back only to 1959. Some estimates of

earlier annual values can be found;

the HANDBOOK OF CYCLICAL INDICATORS

[19841 lists peaks and troughs of the

business cycle back to December 1854.

A far better model can be constructed, however, using the indices

of leading and coincident indicators

[Morrison & Morrison, 1997]. The se

not only provide a phase plane plot,

the y al so provide numbers on a

monthly basis. Some economists do

use the index of lagging indicators

in creating forecasts, but we have

not incorporated it into our business

cycle model.

Due to growth and inflation, a trend

must be subtracted from the data,

whether it is the GDP or any of the 3

indices. Time series methods implicitly assume the data has a zero mean

116
value . Before detrending, the data

must first be converted to logarithms

to achieve stationarity. It is percent deviations from the trends that

are fairly consistent, not absolute

deviations.

For the business cycle and other economic data we have developed the ramp

filter as a trend model. It does not

change the trend as new data points

are added, a problem with polynomial

or other multiple regressions. The

extrapolation of this trend model is

always stable, which is never true of

polynomials of degree 2 or higher.

And unlike a moving average, the ramp

filter trend is not displaced down

(up) when the data values are increasing (decreasing) [Morrison &

Morrison, 1997].

Since the indices can be forecast

with a fair degree of reliability for

2 months ahead, the business cycle

model is up to date. Delays in collection and analysis cause the indices to be released about 2 months

after the fact. GDP data are not

only delayed, but provided only on a

quarterly basis.

The phase plane model of the business

cycle, with a l-year forecast, has

been published in our newsletter

CRITICAL FACTORS since August 1992,

when it replaced 3-year forecasts of

the 3 indices. A 60-point ramp filter is used for both the trend model

and in the forecasts. The ramp filter formulas are given in our earlier

paper [Morrison & Morrison, 1997], so

anybody can duplicate the historical

model .

4. DIMINISHING FEDERAL RESOURCES

During the past two decades or so,

users of federal services and data

have been faced with growing user

fees and, in some cases, 10SS of access when programs are curtailed or

canceled. The private sector has

done much the same (recall the road

maps that once were free at any gas

station) and for the same reason: to

save money.

Privatization has been used in attempts to save tax dollars. This may

involve contracting out a specific

activity or attempting to make an entire agency self-supporting. Users

may be disrupted by more than higher

fees or loss of the benefit, if competitors do not face similar problems.

In the case of the business cycle

model , the anticipated disruptions

had been the regular revisions of the

indices, especially the ones involving movement ‘of the base year forward

in time. A positive aspect of this

had been that it demonstrated robustness in the model [Morrison & Morrison, 1997].

In 1995 USDOC announced a plan to

turn over the production of the indices to a private organization.

There was no contract money for supporting the indices in the future and

the successful bidder would incur

significant expenses in assuming the

responsibility. No mechanism was included in the contract to guarantee

future creation and distribution of

the indices or to exercise quality

control .

Fortunately, a number of highly qualified organizations stepped forward

to meet the challenge and they offered bids. The winner was The Conference Board, Inc. (TCB), a well regarded, New York city-based, not-forprofit organization.

From the viewpoint of index users,

the privatization process seemed to

offer more risks than rewards. TCB

might decide to stop producing the

indices or it might raise the cost of

access . Changes might make the indices better, or it might make them

worse . These indices are not just

117
weighted averages and constructing

them is an arcane craft.

If production of the indices ceased,

it would be feasible to recreate

them, since current federal agency

plans are to continue production of

all the component series. This, however, would entail considerable effort and might create more delays.

There is always the possibility of

developing new indices, just as there

is of making the business cycle model

more elaborate, using the index of

lagging indicators or other data

sources.

5. CHANGES DUE TO PRIVATIZATION

For the data for the month of December 1995, USDOC and The Conference

Board collaborated to produce the indices. Then for about a year TCB

continued to produce the indices using the established USDOC algorithm.

But starting with the values for December 1996, the algorithm was

changed along with the base year

(advanced from 1987 to 1992).

Two components were removed from the

leading index because they seem not

to work as well anymore: 1) changes

in sensitive materials prices and 2)

changes in unfilled orders for

durable goods. The yield curve, the

difference between the interest rate

on 10-year Treasury notes and the

federal funds rate, has been added,

so that the number of components is

10 rather than 11.

Some economists have been recommending this new statistic and TCB

moved quickly to implement the change

[Estrella & Mishkin, 19961. An attractive feature of this data series

is that it is easy to collect and unambiguous, like stock market indices.

The replaced series required an extensive data collection effort (of

many different numbers) and making a

lot of assumptions and extrapolations . The USDOC had been

rather hesitant in making changes, so

this first improvement is due to

privatization.

But is the new leading index really

better? According to TCB itself, it

is a marginal improvement, not a

breakthrough. Our business cycle

model changed significantly more than

it did when USDOC last made a major

revision of the indices. (The radial

coordinates did drop roughly in half,

but the phase angles shifted only by

a few degrees.)

Compare the phase plane plot in Figure 1 with the one from our previous

paper [Morrison & Morrison, 1997].

The roughly &lliptical curve has rotated about 30 degrees counterclockwise , but the major quadrant crossings were virtually the same. The

new and revised indices extend back

only to 1959 (not 1947), so the revised model itself does not start until December 1963 (the ramp filter

trend model requires 60 points from

the past). Among the older cycles

that can be compared, the most noticeable change is that the dive into

the 3rd quadrant during the recession

of 1974-75 was not as pronounced.

When USDOC similarly moved the base

year for GDP from 1987 to 1992, it

also started the new data series with

1959. One reason for cutting series

short is that changing economic conditions make it very difficult to

compare data from one year with that

from another year decades earlier or

later . Some indicators that worked

well before no longer do. The product mix in GDP has changed significantly. During its final major revision of the indices before privatization, USDOC had used different

weighting for two time periods in one

of the indices.

As a result of the curtailed time

coverage, the business cycle models

from the two data series can be

118

F-
cross-checked only for the period

1963-1996. That is 34 years, but it

is barely more than the 15-25 years

of the Kuznets cycle and much less

than the 50-60 years of the Kondratieff wave. . This is too little data to

determine the statistics of such

cycles and thereby verify their existence, let alone construct a theory

for their cause. However, it is more

than enough to analyze and forecast

the typical short-term business

cycles of 5-10 years.

new indices is given in Figure 1,

along with a forecast for more than

one year. Numerical values for the

phase plane coordinates for the forecast and the past 2 years of observed

values are provided in Table 3.

During 1995 the business cycle moved

in an orderly way through the 2nd

quadrant, apparently heading for the

recession-prone 3rd quadrant. However, in February 1996 the model collapsed into the origin and has been

bouncing around there ever since.

Shift Numb er

+1 3

0 13

-1 2

- 2 0

- 3 3

- 4 1

.

I Total 22

Table 1. MaJor quadrant crossings. stllf+s

in months from old model to new model.

TO provide a capsule comprison of

the new model with the old one, Table

1 lists the number of major quadrant

crossings and the months by which

they were shifted. Major crossings

exclude those cases where the model

stalled near a quadrant boundary and

jumped back and forth over it several

times . Of the 22 counted, more than

half (13) did not shift at all.

There are, of course, no possible

comparisons for the period 1953-1962

or for 1997.

Shifts in the phase angles for the

beginnings and endings of recessions

are compared in Table 2. The official dates are designated by the National Bureau of Economic Research

(NBER), a private, not-for-profit

organization.

The most recent phase plane plot for

the business cycle model based on the

The dynamical interpretation of time

series forecasting tells us that only

a strong move in the indices will

signal a resumption of activity in

the business’ cycle. Forecasting implicitly sets the “noise” input to

its zero expected value, so predictions always have a general tendency

to spiral into the origin. The forecasting model is a homogeneous linear

ODE with constant coefficients, always having eigenvalues with negative

real parts. The variance estimate of

the forecast, however, asymptotically

approaches the RMS of the “noise, ”

indicating the growing uncertainty of

phase information for future values.

Some analysts, buoyed by the soaring

stock market, have declared that the

business cycle finally has been

tamed. Some credit improved information technology and just-in-time

delivery with eliminating inventory

buildups.

Having seen the apparent successes of

Keynesian economists and Federal Reserve micromanagers fall apart during

previous decades, we remain skeptical

of any claims of permanently taming

the business cycle. After all, what

magic al even t occurred in February

1996 to make all this new technology

suddenly work to perfection? The reports of Mark Twaints death were

greatly exaggerated once; those of

the business cycle, many times.

119
I Time Frame Begin Z(old) Z(new) End Z(old) Z(new)

1963-72 12/69 210 214 11 /70 238 245

1972-76 11/73 95 111 03/75 219 231

1976-84 01 /80 204 212 07 /80 228 246

1976-84 07/81 264 273 11 /82 307 305

1983-96 07 /90 221 233 03/91 241 258

Table 2. Phase angles (Z) in degrees at official (according to National

Bureau of Economic Research) beginnings and ends of recessions. Note

that the 1976-1984 cycie had an official “doubie dip” recession .

90°

BUSINESS CYCLE (1990 - 1997)

FORECAST MADE ON 9/3/97 +3 1

EXPANSION

I

96/uL

180 °

1

- 3

-J%F ‘2 ‘

3

0 °

f

B

l)’

- 2

t$

92/0 1

RECESSION

%

I

A FORECAST

O ACTUAL VALUE

91 /01

w2!00

● JANUARY VALUE

TCB 92

Figure 1. The current cycle with September forecast. The business cycie modei is a phase

plane piot of detrended ieading and coincident indicators, as X- and Y-coordinates ,

respective y. Normai cycies foiiow a counterclockwise roughiy eiiipticai path wit h

occasional stalls and reversais. Time is indicated aiong the cycie path . Expansions

occur in the first quadrant (between 0° and 90°) and contractions in the third quadrant

(between 180° and 2700). Other angies (second and fourth quadrants) denote transition

periods. An “official” (Nationai Bureau of Economic Research) beginning of a recession is

indicated by a iabel ‘tB’t and an end by ~’E.”

120
Date P(L) P(C) R Z Quad

OBSERVED

95 /01

95 /02

95 /03

95 /04

95 /05

95 /06

95 /07

95 /08

95 /09

95 /10

95/11

95/12

96 /01

96 /02

96 /03

96 /04

96 /05

96 /06

96/07

96 /08

96 /09

96 /10

96/11

96/12

97 /01

97 /02

97 /03

97 /04

97 /05

97 /06

97 /07

-0.023 2.693 2.693 90.5 I I

-0.471 2.387 2.433 101.2 11

4.900 2.080 2.266 113.4 11

-1.020 1.696 1.979 121.0 11

-1.224 1.320 1.800 132.8 1 1

-1.137 1.300 1.727 131.2 II

-0.961 0.924 1.333 136.1 1 1

-0.688 0.998 1.212 124.6 1 1

-0.604 0.806 1.007 126.8 1 1

-0.778 0.546 0.951 145.0 11

-0.742 0.480 0.883 147.1 1 1

-0.428 0.504 0.661 130.3 11

-1.041 -0.048 1.042 182.6 111

-0.142 0.375 0.401 110.7 11

0.047 0.100 0.111 65.1 I

0.217 0.173 0.278 38.5 I

0.462 0.318 0.561 34.6 I

0.593 0.363 0.696 31.5 I

0.516 0.239 0.569 24.8 I

0.545 0.207 0.583 20.8 I

0.572 0.173 0.597 16.8 I

0.505 -0.105 0.516 348.2 IV

0.539 0.051 0.541 5.4 I

0.581 0.039 0.583 3.8 I

0.796 0.029 0.797 2.1 I

1.079 0.259 1.109 13.5 I

1.152 0.149 1.161 7.4 I

0.944 0.203 0.966 12.1 I

1.019 0.009 1.019 0.5 I

0.999 0.071 1.002 4.1 I

1.161 -0.033 1.162 358.4 IV

FORECAST

97 /08

97 /09

97 /10

97/11

97 /12

98/01

98 /02

98 /03

98 /04

98 /05

98 /06

98/07

98 /08

98 /09

98/10

98/11

98/12

1.129 -0.097 1.133 355.1 lV

1.098 -0.097 1.102 354.9 lV

1.151 -O.lOO 1.155 355.1 I v

1.097 -0.028 1.098 358.5 IV

1.024 -0.041 1.025 357.7 I V

0.961 4.054 0.963 356.8 IV

0.808 0.014 0.808 1.0 I

0.682 -0.063 0.685 354.7 IV

0.644 -0.136 0.658 348.1 IV

0.523 -0.126 0.538 346.4 IV

0.407 -0.189 0.48 335.1 IV

0.305 -0.084 0.316 344.5 IV

0.204 -0.142 0.248 325.2 IV

0.107 -0.039 0.114 340.0 Iv

0.014 -0.020 0.024 305.0 IV

-0.082 -0.079 0.114 224.0 111

-0.095 -0.059 0.112 211.9 111

Table 3. Recent and forecast values for the

business cycle state variabieso P(L) is th e

x-coordinate and P(C) is the y-coordinate ,

which are the percent deviations from th e

trend of the indices of leading and coincident

indicators. R is the radiai coordinate and Z

the phase angle in degrees; quad is the quadrant of Z.

6. CONCLUSIONS

This very simple phase plane model of

the business cycle has once again

demonstrated its robustness and

usefulness . It has survived the privatization and revision of the indices used to construct it and it

could be maintained as long as the

basic data continue to be collected

and published in a timely fashion.

We have doubts that collection of the

basic data could be privatized.

Legal, logistic, and political problems might arise and we have no expertise in even anticipating what

they might be. The cost of collecting all macroeconomic data is an

insignificant fraction of the federal

budget and it seems to us to be necessary for managing that budget and

conducting fiscal policy.

These data and the business cycle

model can be invaluable to businesses

and investors, but too few are inclined to use them. What do people

use? Intuition, technical analysis,

cycle theories that have more in common with numerology than mathematics,

and even astrology. Those without

scientific training are more inclined

to accept dubious claims of certainty

than to try to cope with the reality

of forecasting errors that grow re

-

lentlessly with time.

191
7 ● REFERENCES

Anderson, B. W., 1957: UNDERSTANDING THE OLD

TESTAMENT, 2nd Edition, Prentice-Hail, inc.,

Engiewood Ciiffs, NJ.

Dewey, E. R., 1970: CYCLES: SELECTED WRiTiNGS,

Foundation for the Study of Cycies, Pitts -

burgh, PA.

Dewey, E.R. with O. Mandino, 1971: CYCLES: THE

Mysterious FCRCES THAT TR i GGER EVENTS,

Hawthorn Books, inc., New York.

Estrei la, A. & F.S. Mishkin, 1996: “Predictin g

U-S. Recessions: Financiai Variables as Leading indicators,!! FEDERAL RESERVE BANK OF KW

YORK RESEARCH PAPER No. %09, New York (in

press: REViEW W ECONOMiCS & Statistics) .

Forrester, J.W., 1961 : industrial DYNAMICS,

The MiT Press, Cambridge, MA.

HANDBCK)K OF CYCLiCAL iNDiCATG/S: A SUPPLEMENT

TO THE BUSiNESS CONDITIONS DIGEST, 1984: US

Dept. of Conmwce, Bur. of Economic Anaiysis,

Washington, DC.

Jordan, S.K., 1972: “Seif-mnsistent Statistical Modeis for the Gravity Anomaiy, Verticai

Defiecl’ions, & Undulations of the Geoid,” J.

GEOPHYS. RES., vol. 77, No. 20, pp. 3660-3670 .

Morrison, F., 1991a: lliE ART OF MCOELiNG DYNAMIC SYSTEMS: FORECASTING FOR CHAOS, RANDOMNESS, & Determinism, Wiiey-i nterscience, kw

York.

Morrison, F. 1991b: “Linear Fiitering: What ,

Why, & HOW,” THE JOURNAL OF BUSINESS FORECASTING, Voi. 10, No. 1

FOB 159, Station C ,

for singie issue].

Morrison, F. & N.L.

Piane Modei of the

FEDERAL F~ECASTERS

(Spring), pp 13-18 [JBF,

Flushing, NY 11367, $15

Morrison, 1997: “A Phase

Business Cycie,’l IHE 8TH

CONFERENCE - 1996 & THE

7TH FEDERAL FORECASTERS CONFERENCE - 1994,

COMBiNED PAPERS & PROCEEDINGS, NCES 97-341,

Debra E. Geraid, Editor, Washington, DC.

4

Samueison, P.A. with P. Temin, 1976: ECO -

NOMICS, 10th edition, McGraw-Hi ii Book Co.,

New York.

Wiis, w., 1988: OVERViEW OF SYSTEM DYNAMiCS

THE WCRLD OVER, Croon DeVries, 55 Parkweg,

3603 AB Maarssen, Netherlands.

122
The Educational Requirements of Jobs: A

Looking At Training Needs

New Way of

Darrel Patrick Wash, Bureau of Labor Statistics, U.S. Department of Labor

This study presents a new way of classi~ing occupations by training requirements. This more detailed approach,

which now covers several different categories of employer-provided training as well as the more traditional

academic training categories, k presented, along with a discussion of how it can be combined with previously

developed estdates of occupational net replacements to formulate a much clearer picture of fiture education and

training demand.

123

Concurrent Sessions II

125

FORECASTING CROP PRICES UNDER NEW FARM LEGISLATION

Chair: Peter A. Riley

Economic Research Service, U.S. Department of Agriculture

Forecasting Crop Prices Under New Farm Legislation,

Peter Riley, Economic Research Service

U.S. Department of Agriculture

Forecasting Annual Farm Prices of U.S. Wheat in a New Policy Er~

Linwood A. Hoffman, James N. Barnes, and Paul C. Westcott,

Economic Research Service

U.S. Department of Agriculture

An Annual Model for Forecasting Corn prices,

Paul C. Westcott, Economic Research Service

U.S. Department of Agriculture

Forecasting World Cotton Prices,

Stephen MacDonald, Economic Research Service

U.S. Department of Agriculture

127

Forecasting Crop Prices Under New Farm Legislation

by

Peter A. Riley

Economic Research Sewice

U.S. Department of Agriculture

The tradition of strong government involvement in agriculture is starting to break down around

the world. This tendency has been particularly evident in the United States over the last few years

and especially since 1996, when the most recently enacted f- legislation sharply reduced

government intervention and gave more weight to market forces. This has prompted a

reevaluation of forecasts that have been heavily shaped by policy variables. This paper will set the

stage for presentations on price forecasting for 3 major U. S. field crops.

USDA regularly forecasts a variety of annual supply and demand components for the major field

crops, as well as f- prices. These are published monthly and are widely used by f-ers,

processors, and other market participants, as well as USDA policy makers. The price forecasts

are particularly important as critical input for other calculations and forecasts, such as farm

income and food prices. 1

I will focus on three main points as an introduction to the specific forecasting models. The first

concerns the relevance and importance of accurate forecasting of agricultural prices for the farm

sector and the economy as a whole. Second, forecasting in the agricultural sector involves some

special challenges not necessarily encountered in other sectors. Third, there is a brief overview of

the broad changes in agricultural policies that have occurred in the last few years that have

prompted a new look at our forecasting tools.

Importance of Agricultural Price Forecasts

The monetary amounts involved in crop prices may seem small at first glance. However, the huge

quantities produced add up to very large sums. For example, U.S. com production has averaged

more than 8.9 billion bushels a year between 1994 and 1996. Thus just a one-cent change in the

farm price of com represents a change of $89 million for the economy, with fhrmers capturing

that much more or less revenue while end users pay a corresponding amount more or reap the

savings.

The actual magnitude of changes in farm prices is typically much larger (for reasons that will be

discussed later). Using the same years as above (1 994-96), the annual variation in season average

farm price of com has averaged nearly 59 cents per bushel, translating into huge sums at the

* USDA does not forecast the U.S. farm price of cotton because this is prohibited by law.

Today’s cotton presentation will examine a world cotton price.

129
national level--an average approaching $5.3 billion. Ironically, these last 3 years have been one of

the most volatile periods in the history in the com market. For the other 2 crops discussed today,

wheat and cotton, both the absolute quantity of production and the variability are smaller.

Although the role of crop prices may seem obscure to some people outside of agriculture, they do

have a very large impact on the general economy by several measures. The multiplier effects of

changes in prices of corn, wheat, and cotton will be felt at various levels and have implications for

inflation. Spending by farmers in rural communities and expenditures on capital goods such as

tractors and other f- equipment and land expenditures, whether through purchases or leases,

are prime examples.

Another dimension of the general importance of these commodity prices is their use as raw

materials for other products. Among the cases reviewed today, wheat is probably the most

appreciated by the general public when they consume bread, pasta, bagels, and other baked

goods. Cotton is also easily recognized in its clothing and textile applications. Perhaps com is

the least obvious with most of its use as a feed for livestock and poultry and as an input for many

food and industrial products. These include com sweeteners used in the major soft drinks and a

tremendous variety of processed foods.

Finally, the farm price has a important role in shaping our export competitiveness. The United

States has a consistently strong positive trade balance in agriculture. Although the U.S. is the

world’s leading exporter of wheat, corn, and cotton, the marketplace for each commodity is

highly competitive and our market shares are far from assured.

Special Challenges for Agricultural Price Forecasts

Agriculture has some special features that have an important bearing on forecasting under any

policy regime. Foremost is the large variability in supply and prices, largely stemming from

weather impacts. As mentioned earlier, there is considerable price volatility fi-om year to year for

most field crops, and similarly on a monthly, weekly, or even daily basis.

Weather plays an important role in determining the size of annually produced crops, and therefore

is the major culprit underlying large production variability. Most field crop production is

critically dependent on rainfall, with only small portions of the crops produced under irrigation.

Droughts, high temperatures, excessive moisture, and other weather factors often harm yields and

reduce output. Sometimes, conditions are excellent, pushing production beyond expectations.

Swings in production also reflect changes in plantings so the total area devoted to a crop can vary

from year to year. The driving force behind acreage changes are primarily economic as fmers

anticipate net returns from competing crops. Weather can also play a role, generally in a negative

sense, whez for example, it is too wet to plant a particular crop on time.

Demand for these crops is generally much steadier than supply, but demand changes can also add

to the variability in prices. Domestic demand for wheat for food use is ftirly inelastic, but a

portion of the crop is fed to livestock and this portion is more variable. Domestic use of cotton

has been relatively steady in recent years. Although cotton competes with manmade fibers,

130
substitution in the short run is quite limited. Domestic com use displays strong annual variability,

in large part because of the changes in supply. Some com demand can be characterized as

inelastic, including industrial uses such as starch and sweeteners and food use in breakfast cereals.

However, the largest category of com is used as a livestock feed and this portion fluctuates

greatly, reflecting changes in animal inventories, livestock cycles, competition with other

feedstuffs, and large swings in the supply and price of corn.

International developments play an important role in agricultural markets and tend to increase

price variability. Each of the crops discussed today is highly dependent on exports. Exports of

com have accounted for around 20-25 percent of total disappearance in recent years, cotton

exports more than 40 percent, and wheat around 50 percent. These sectors are thus vulnerable to

the export fluctuations which are common for most field crops. Many inter-related factors

account for this, such as changes in import demand, the availability of exports from competing

suppliers, policy factors such as export subsidies and import tariffs, and tastes and preferences.

Probably, everyone vaguely remembers the sporadic grain import binges of the Soviet Union

during the 1970’s and 1980’s that added instability to world markets. This is the extreme case of

variability in agricultural trade, but unexpected spurts or sharp drops in imports and exports by

other countries on a smaller scale are not that uncommon, with corresponding effects on fiirm

commodity prices.

Changes in the Policy Environment

There is a ve~ strong tradition of heavy government intervention in U.S. agriculture that started

in the early years of the 1930’s Depression. At that time, the Government began to provide price

supports to farmers and started programs to idle acres to avoid overproduction. Over the years,

these basic elements were continued, with many variations and often complex formulas that were

little understood by many people outside the farm economy.

During the mid- 1980’s, policy changes began to take effect to reduce the role of government and

allow more market influence. This stemmed from a f- crisis partly triggered by a declining

export market share as competitors undercut high U. S. prices. Thus, policies shifted somewhat,

and U.S. price supports were reduced to enhance competitiveness. Another important step at the

time was to start reducing large stocks of grain and other crops that had accumulated when the

government offered an assured home for virtually all the major fields crops. By the early 1990’s,

like many other sectors of the economy, agriculture had reduced inventories. This reflected both

budgetary pressures in the public sector reducing publicly held stocks and developments in the

private sector adopting a “just in time” deliveries approach.

While still heavily shaped by government decisions, these and other changes initiated in 1985 fm

legislation and continued by legislation in 1990 gradually were introducing more market forces to

shape production decisions. Then in 1996, a more radical Farm Bill was enacted, signaling a more

abrupt break with past tradition. Often called “Freedom to Farm,” the new law essentially

allowed f-ers to make decisions completely free from government influence.

131
,“. .

,..

, .

Nearly all government f- programs were eliminated, such as the acreage “setaside” or modern

land idling program used when supplies were very large. Another important change was the end

of the “base acres” concept under which f-ers devoted a certain amount of land to a particular

crop in order to qualifj for farm program benefits. This tended to keep acreage at fairly

predictable levels depending on various program parameters. Now, farmers have complete

flexibility to plant what they wmt based entirely on market signals.

New Forecasting Approaches Contribute to USDA Market Analysis

The presentations to follow will look at forecasting models for 3 agricultural commodities and

how analysts are dealing with the changing policy setting. Many of the broad similarities have

been mentioned, and now some of the distinct traits of each market will be discussed. The com

and wheat papers focus on U. S. policy change, and the cotton paper deals with the interaction of

policy variables in a global setting. The forecasting approaches can accurately be characterized as

constantly evolving, as analysts incorporate more years of data and react to shifts in policies.

The context in which these forecasting models are used at USDA is important. First, these are

applied tools that are used to help develop price forecasts that, except for cotton, are regularly

published. USDA publishes a longer-term projection annually, while the forecast for the nearby

year is published monthly. The latter price forecast is released at 8:30 AM on the day of the

reports, along with various supply and demand elements. Thus, much of the analysis is

conducted during the night, incorporating new information made available to USDA economists

only shortly before being released to the public. The quick turnaround of this process requires

highly practical tools. Second, the price is just one component of a broader analysis of supply,

use, and stocks. These price models may be embedded in spreadsheets to provide a simultaneous

determination of prices to changes in supply or demand forecasts. USDA presents this

information in commodity “balance sheets” to provide a consistent analysis of each market.

132
FORECASTING ANNUAL

Linwood A. Hoffman, James

Introduction

FARM PRICES OF U.S. WHEAT IN A NEW POLICY ERA

N. Barnes and Paul C. Westcott, U.S. Department of Agriculture, ERS

Information regarding wheat prices is critical to market

p~rticipants who are making decisions about managing

price risk. Market information is also important to

policymakers who have to assess the impacts of domestic

or international events upon wheat farm prices.

Concern about U.S. wheat farm prices rose significantly

during the 1995/96 crop year, as world crop shortfalls

caused USDA’s price projection for the crop year to rise

from a range of $3.25-$3.45 per bushel in May 1995 to

$4.20-$4.50 per bushel in November 1995. Two years

later, as world production recovered, producers’ wheat

prices are expected to fall. USDA’s price projection for

1997/98 dropped from a range of $3.60-$4.20 in May

1997 to $3.05-$3.65 in August 1997.

Price information has become even more important due,

in pati, to changes in U.S. agricultural policy. Passage of

The Federal Agriculture Improvement and Reform Act of

1996 (1996 Act) continues the sector’s trend toward

market orientation. The Farmer-Owned Reserve (FOR)

is suspended and wheat loan rates are capped at the 1995

level of $2.58 per bushel, well below current and

expected future market prices. Such a situation suggests

little, if any, government stockholding, which may

contribute to increased price sensitivity.

The 1996 Act also eliminated government price

assurances. Under the 1996 Act, annual production

flexibility contract payments remain fixed regardless of

market prices, in contrast to deficiency payments which

varied inversely to market prices. Consequently,

producers face greater risk of income volatility because

of market price variation.

Previous analyses have studied relationships between

prices and ending stocks for com (Baker and Menzie;

Van Meir; Westcott, Hull, and Green), wheat (Westcott,

Hull, and Green), and rice (Hoffman, Livezey, and

Westcott; and Lin, Novick, and Livezey) as a price

forecasting tool. Whether such a relationship can

continue to provide short and long term price forecasts in

the new policy environment remains to be seen.

The purpose of this article is to present a model designed

to forecast the U.S. season average price of wheat at the

farm level.’ The U.S. Department of Agriculture

analyzes agricultural commodity markets on a monthly

basis and publishes annual current year market

information, including price projections. Because of

changes in policy, price forecasting equations need to be

re-evaluated.

Background: Factors Affecting the

U.S. Farm Price of Wheat

Some of the most important variables to be considered in

forecasting the price of wheat include supply and demand

factors and domestic agricultural policy (Appendix

Tables 1, 2, and 3). Prices are determined by the

interaction of the supply and demand functions which are

influenced by government policies. The supply and

demand components are briefly discussed because they

affect the stocks and use variables which are included in

the price equation.2 Agricultural policies may also affect

the factors of supply and demand. Many of these effects

are captured in the stocks or use variables and those that

are not will be accounted for separately in the price

model.

Wheat Supply

The elements of supply are beginning stocks, imports,

and production. Wheat is the principal food grain in the

United States and throughout much of the world. The

United States is the third largest producer of wheat in the

world, averaging 61.6 million metric tons in 1994-96, or

11 percent of world production. U.S. wheat’s farm value

of production totaled $9.8 billion in 1996, the fourth

largest of all field crops or 11.4 percent of total U.S. crop

value.

Be?innin Q Stocks--Last year’s carryover becomes the

current year’s beginning stocks. Large or small carryover

levels usually have the most impact on price. Large

stocks can provide a cushion in a short crop year or a low

carryover may exacerbate a low production situation.

1 This price model is one of many price forecasting tools used

by the USDA. Other price forecasts used by USDA are based on futures

market prices and other econometric models. Anal ysts’ expeti opinions

also enter into the forecasting process.

2 Stocks are equal to ending carryover inventories and use

refers to total use of a commodity, both total domestic and export use.

133
h?u2Qm--wheat imPorts ‘ere 

an insililnificant factor for

U.S. supply for many years. Imports were fairly low in

volume and less than 1 percent of supply between 1960

and 1989. However, wheat imports became an issue in

the 1993/94 marketing year, as they reached 109 million

bushels, including products, or 4 percent of supply.

Imports have since declined to about 3 percent of supply,

but the U.S. remains an attractive market for Canadian

wheat.

Production--U.S. wheat production, the major component

of suppl y, is determined jointly by the area harvested for

grain and yield per acre. Until the 1996 Act, acreage

planted and harvested was affected by farm program

requirements and participation rates. The relationship

between area planted and harvested varies substantially

by region although it is fairly stable at the national level.

Producers in cattle feeding areas typically graze out some

of their wheat fields, rather than harvesting them for

grain.

Prior to 1992, sharp declines or increases in planted area

were usually the result of changes in government

programs requiring acres to be idled. In an effort to

control production, support farm income, and limit

government costs, various acreage limitation programs

were employed, such as the acreage reduction program,

paid land diversion, 50/92, 0/92, and 0/85.s These supply

management programs were eliminated in the 1996 Act.

Thus, market prices rather than farm programs now have

a greater influence on acreage planted to wheat.

Average U.S. wheat yields have risen from around 30

bushels per acre in the mid- 1970’s to an average of 38

bushels per acre in the 1990’s. Wheat yield growth has

slowed in the last 15 years. Many factors affect U.S.

yields, including climatic conditions, weather, farm

management practices, variety, and soil type.

Wheat Demand

Components of wheat demand are food use, feed and

residual, seed, exports, and carryover stocks. Domestic

use is a growing component of total U.S. wheat

disappearance because of increased food use. Domestic

use claims about 50 percent of total disappearance, up

from an average 40 percent during 1975-84.

3

If supplies were estimated to be in excess by the U.S.

Department of Agriculture, acreage reduction programs (ARPs) were

required and paid land diversion programs (PLDs) were permitted.

Wheat producers had the option of under-planting their maximum

payment acres and receiving deficiency payments on a portion of the

under-planted acres (0,50/85-92).

Food--Food use has been the largest and most stable

component of domestic use, characterized by a steady

growth rate. Wheat is unique because it is the only cereal

grain with sufficient gluten to produce bread without

requiring mixing with another grain. The domestic

demand for wheat food use is relatively unaffected by

changes in wheat prices and disposable income and is

closely tied to population, tastes. and preferences.

Feed and Residual--Feed and residual use is more

variable than food use and is related to corn/wheat prices

and wheat crop quality. Wheat feed use is particularly

prominent at wheat harvest time when wheat prices are

low and new crop corn and sorghum have not been

harvested. Feed and residual use totaled about 19 percent

of total disappearance in the 1986 and 1990 crop years,

years of lower wheat prices, compared to about 6 percent

during 1988 and 1995, years of higher wheat prices. The

residual component includes negligible quantities of

wheat used for alcoholic beverages and estimation error

from other categories.

&!2!l&-ExPorts are imPortant to the U“s” wheat market!

as U.S. exports account for about half of total

disappearance. Wheat exports accounted for 11.7 percent

of the total value of U.S. agricultural exports or $7.0

billion in fiscal 1996. The United States is the world’s

largest exporter of wheat with a world market share of

about 33 percent.

Food Aid under P. L.480, guaranteed export credit, and

special export programs have been very helpful to U.S.

wheat exports. The Export Enhancement Program (EEP)4

was important to U.S. wheat exports between 1986 and

1994 as over half of the U.S. exports in some of these

years received EEP subsidies (Fig. 1). EEP has not been

used for U.S. wheat exports since July 1995. It is unclear

whether EEP will be used in the future, but the 1996 Act

authorizes its use at reduced levels.

Carrvover Stocks--Carryover stocks reached levels

greater than 1 billion bushels between 1981 and 1987,

with ending stocks representing an average of 60 percent

of one year’s use. However, as policies steered the sector

4 The Export Enhancement Program was initiated in May

1985 under the Commodity Credit Corporation (CCC) Charter Act and

later formally authorized by the Food Security Act of 1985 and extended

by the Food Agriculture Conservation and Trade Act of 1990 and the

1996 Act. The major objective of this program is to help U.S. exporters

compete against unfair trade practices used by other countries. Export

bonuses were used to make U.S. agricultural commodities competitive

in world markets. Exporters received generic certificates prior to

November 1991 and cash bonuses thereafter.

134

.
toward greater market orientation, ending stocks declined

and a more balanced supply and use situation arose in

199 I -96 with an average stocks-to-use ratio of 21

percent.

Agricultural Policies

Domestic agricultural policies may also affect the factors

of supply and! demand. Many of these effects are

captured in the stocks or use variables. For example,

Government price support programs have affected levels

of carryover stocks over time. Between the 1973 and

1996 farm bills, the loan program,5 farmer-owned

reserve, food security reserve and production controls

have been used to support prices. Also, various export

programs may enhance consumption of wheat by

subsidizing the price of wheat. How these programs

affected the price and stocks-to-use relationship is

important for modeling wheat prices. Situations where

policies altered the market price and stocks-to-use

relationship must be specifically accounted for in the

price equation. Consequently, a review of agricultural

policies is necessary to determine when adjustments to

the price and stocks-to-use relationship occurred.

The Agriculture and Consumer Protection Act of 1973

changed the existing income programs by replacing the

wheat certificate program with the target price concept

(Hat-wood and Young). Carryover stocks consisted only

of free stocks in 1974-76 and the stocks-to-use ratio

ranged from 26 to 65 percent for those years. Because

groins and oilseeds generally had favorable prices during

1974-76 there was an effort to make farm programs more

market oriented. The target price accompanied with

deficiency payments was designed to support income

without affecting market price. However, strong prices in

1974-76 Icd to increased production and larger stocks

(Appendix Tables I and 2).

The Food and Agriculture Act of 1977 established the

farmer-owned groin reserve (FOR), which was in

response to the growing importance of exports and the

potential for greater global demand and price instability.

5 Price suppon for wheat producers is provided through

nonrecourse loans at the announced price support loan rate. A

participating farmer can pledge his crop as collateral to the Commodity

Credit C’orporatwn (CCC) and then recewe a 9-n~onth loan pledged at a

predetermined mte per bushel. If the market price is above the loan mte

plus Interest. the producer usually repays the loan with interest,

However. it’ the market price M below the loan rate plus interest, the

producer may forfeit the wheat at the end of the loan term to the

Commodity Credit Corporation in full satwt’action of the loan.

In return for loans and annual storage payments, farmers

agreed not to market their grain for an extended period (3

to 5 years), unless the average farm price reached a

specified level called the release price. The farmerowned reserve allowed the producer to maintain

ownership of the grain in contrast to a situation where a

producer would forfeit stocks to the government at low

prices under the regular loan program with no opportunity

to realize a gain if prices rose.

Entry into the wheat FOR, the FOR loan rate, and the

regular wheat loan rate appeared to heavily support

annual farm prices during the late seventies to mideighties (Figs. 2. 3, and 4). Prices approached the loan

rate in 1977. the year when the Farmer-Owned Reserve

was introduced. Minimum loan rates were written into

The Agriculture and Food Act of 1981. The regular loan

rate for wheat was $3 a bushel in 1980 and reached $3.65

in 1983. Defaults to the CCC began to rise and CCC

stock levels surged. Loan rates were reduced to $3.30 in

1984 and 1985. During the mid- 1980s, market prices

were pressured downward when accumulated CCC stocks

were released upon the market. In retrospect. loan rates

in the early 1980’s were set so high that they supported

prices above market clearing levels.

During 1980-82, the FOR was implemented as a price

enhancement tool by offering producers reserve loans at

rates above the regular loan rate. This situation raised

questions about the FOR’s goals, price stability or price

enhancement. The FOR loan rate was set at $4.00 per

bushel in 1982/83, $0.45 above the regular loan rate.

Harvested acres were the second highest ever in 1982/83

and this contributed to a rise in ending stocks to 1.52

billion bushels of which over I billion bushels were in the

FOR (Fig. 3).

The Food Security Wheat Reserve was created in the

1980/8 1 marketing year to provide a government-held

reserve of up to 4 million metric tons of wheat for

emergency food needs in developing countries. This

reserve was also part of the Government’s response to

criticism for the Russian grain embargo. In general this

reserve has not been a factor in the wheat market during

the 1990’s, although some wheat was released during the

1995/96 marketing year. The authority for the Food

Security Wheat Reserve was repealed with the 1996 Act

and a new Food Security Commodity Reserve was

established that includes wheat, corn, grain sorghum, and

rice,

Because of large stock buildups, the Food Security Act

(FSA) of 1985 was designed to increase U.S.

competitiveness in world markets and to support farm

la<
income (Hoffman, Schwartz, and Chomo). The FSA

moved agriculture toward a more market-oriented farm

policy that would enable farmers to respond to economic

and market signals. The legislation lowered loan rates

and provided discretionary authority for their adjustment,

modified the FOR to prevent large buildups in stocks,

reversed upward trends in target prices, generally froze

program yields, and authorized EEP and initiated the

Targeted Export Assistance Program (TEAP) to promote

agricultural exports in response to subsidized

competition. The Conservation Reserve Program was

implemented with a goal of retiring 40-45 million acres

of highly erodible cropland from production for a period

of 1O-15 years.G

In 1986 stocks had been equal to 83 percent of total use

but declined to 16 percent in 1995 (Fig. 4). Generic

certificates helped reduce the level of government and

FOR stocks.

The Food, Agriculture, Conservation, and Trade Act of

1990 as well as the subsequent Omnibus Budget

Reconciliation Act of 1990 (OBRA), followed the ground

work laid by the FSA of 1985. The main goals of the

FACT Act of 1990 were to further reduce spending, to

help maintain farm income growth through expanding

exports, and to enhance the environment. Major

mechanisms used to accomplish reduced budget

expenditures and improved agricultural competitiveness

were reduced payment acres (as authorized by the

Omnibus Budget Reconciliation Act) and planting

flexibility. The Conservation Reserve Program of the

1985 FSA was altered to cover lands adversely affecting

water quality and wetlands, and a new Water Quality

Protection Program was added.

6 A program where producers sign contracts to convert

environmentally sensitive cropland to approved conservation uses for a

10 to 15 year period, in exchange for rental payments and payments to

share costs of establishing conservation practices. The CRP program is

available to participants and non-participants in the annual farm

programs. The producer submits a bid for a 10 or 15 year contract

stating the annual payment they would accept to convert this land to a

conserving use. If the bid is accepted, USDA pays an annual rent to keep

this land in a conservation use. There were about 9 million acres of

wheat base acres voluntarily enrolled in the CRP in 1997. Obviously,

such a program reduces wheat’s production potential.

7 Negotiable certificates, which do not specify a certain

commodity, issued by USDA in lieu of cash payments to commodity

program patiicipants and sellers of agricultural products. The cetiificates

can be used to acquire stocks held as collateral on Government loans or

owned by the Commodity Credit Corporation. Farmers have received

generic certificates as payment for participation in numerous Government

programs. Grain merchants and commodity groups also have been

issued certificates through the Export Enhancement Program and the

Targeted Export Assistance Program.

The Food, Agriculture, Conservation, and Trade (FACT)

Act of 1990 continued keeping commodity loan rates low

and reducing the role of the Farmer-Owned Reserve,

thereby phasing out government-owned stocks as a

stabilizing device. Wheat FOR activity declined under the

1990 Farm Bill and ceased during the 1993/94 marketing

year. Annual acreage reduction programs helped

maintain stabilization. The stocks-to-use and price

relationship seems to have changed for the years of 1990

through 1994. Some of the factors that may have caused

this change include a change in EEP program

administration where subsidies were switched from

generic certificates to cash; passage of trade agreements,

the Canada-U.S. Free Trade Agreement and the North

American Free Trade Agreement, allowing for increased

trade between Canada and the U. S.; and a general policy

change that minimizes government stocks.

The 1996 Act continues the trends of the previous two

major farm acts toward greater market orientation,

thereby gradually reducing the Government’s commodity

program influence in the agricultural sector (Young and

Westcott). Annual production flexibility contract

payments replace the deficiency payment income support

mechanism. Price support programs are continued but

loan rates are kept at minimal levels, the FOR is

suspended, annual supply control programs are

eliminated, and planting decisions are decoupled from

program parameters.

The 1996 Act continues the marketing loan provisions for

wheat but since the wheat loan rate is capped at the 1995

level of $2.58 per bushel, significant activity under these

provisions is unlikely. Marketing loan provisions for

wheat began with the 1993 crop year. This program has

had little effect on wheat prices because prices have

generally been above the loan rate8.

Analytical Framework

This section illustrates how stocks are related to supply

and demand within a general equilibrium model and

develops the statistical model.

A general equilibrium model is illustrated which reflects

competitive behavior (Labys; Westcott). The model

features supply, demand, stocks, and a market-clearing

identity.

8

In 1993, some loan deficiency payments (LDPs) were

made to wheat farmers. Most payments were for soft red winter wheat

located in certain Texas counties. Total wheat LDPs paid to farmers

were less than $1 million.

136
s = f, (p, z. flp)

D = fl(p, y. Z)

] = fl(p, Z. flp, D9094)’

S-D-1=0

where endogenous variables

demand. 1 = ending stocks,

Exogenous variables are flp =

are: S = supply, D =

and p = market prices.

FOR and loan program, y

= disposable income, D9094 = a period of an apparent

shift in the pricing relationships, and z = other exogenous

variables.

With the system in equilibrium, prices can be determined

from the inverse of the stocks function. At that price, the

supply and demand levels give an ending stocks estimate

which is consistent with the equilibrium price, through

the price-ending stocks relationship.

In the inverse stocks function-price determination

equation, prices are negatively related to stocks. Ending

stocks of an annual storable commodity, such as wheat,

reflect the relationship between supply and use (Labys).

If total use rises relative to supply, farm prices tend to rise

as ending stocks decline. On the other hand, if supply

rises relative to total use, prices tend to decline as ending

stocks accumulate.

p

= f7-’(l/D, flp, D9094)

The stocks variable is transformed to reflect stocks

relative to total consumption (Westcott). Therefore, the

stocks variable (I) is expressed as a percent of total use

(l/D). This has particular importance over time as

demand for carryover stocks may increase because of

growth in the size of the wheat sector, measured here by

total demand.

Prices are expected to be positively related to the loan

rate, especially in those years that loan rates were set high

relative to market prices and the loan program and

farmer-owned reserve isolated stocks from the

marketplace. Price support and stabilization measures

tend to increase the price received by producers usually

through government purchases.

9 Additional determinants of stock demand include differences

between current and expected futures prices and interest rates.

Entry into the wheat FOR, FOR loan rates, and regular

loan rates tended to limit price reductions especially

during 1979-85 (Fig. 1, Appendix Table 2, and Fig. 2).

Many grain price models have been estimated with the

dependent variable of price minus loan rate. This

relationship was used in past unpublished wheat price

forecasting equations, by Baker and Menzie’s annual com

price model, and by Van Meir’s analysis of com prices

and stocks, Such a dependent variable is no longer valid

in today’s market as market prices are well above support

prices.

Although there was a return to market orientation during

1986-96, some of the different relationships found from

1990 through 1994 could be due to a number of factors

(Fig. 4). First, EEP program administration may account

for some of this change because program subsidies

switched from generic certificates to cash in November

1991. Second, passage of trade agreements, CFTA and

NAFTA, allows for increased trade between Canada and

the U.S. Third, general policy level changes minimize

government stocks. Additional research is required to

explain these relationships.

Model Specification

The price-carryout stocks relationship, equation (1),

specifies annual producer price as a function of the

stocks-to-use ratio, loan rates for the period 1979 through

1985, and a dummy variable to capture a shift in the

pricing relationships during 1990 through 1994. Based

on the relationships observed in figure 4, it appears that

a logarithmic functional form would best fit the data

between 1975 through 1996. A double log function is

specified and estimated with ordinary least squares (OLS)

regression to explain the all-wheat price with 22

observations.

(1) Log(P) = a + b Log(I/D) + c Log(FLP)*(D7985)

+ d (D9094)

Where:

P = Weighted season average farm price for all wheat.’”

a = intercept term. It is hypothesized that this coefficient

10 This price is computed by the USDA’s National

Agricultural Statistics Service. A monthly survey is conducted to

determine the price producers receive. These prices are weighted by the

monthly percent of marketing for the total marketing year. In the

process each of the five wheat classes are taken into account to arrive

at an all wheat price.

137
,:

.,

is positive. If the logarithm of the stocks-to-use ratio is

zero, price is expected to be a positive number.

Table 1: Ordinary Least Squares Estimates for the Wheat Price Equat]on,

1975-96

b = Estimated coefficient for the stocks-to-use variable.

It is hypothesized that this coefficient is negative. As the

stocks-to-use ratio declines, reduced stocks cause

increased upward pressure on the farm price. The

demand for carryout stocks is less at higher prices.

1 = Ending stocks, i.e. total carryover inventories.

(2) Log(P) = 2,6225 -0,40263 Log(l/D) + 0.21941 Log(FLP) * D7985

(19,73) (-l 1.08) (7.116)

-0.2217 (D9094).

(-5.522)

R2=0.883

Standard error of the estimate = 0.066807

l.)urbin-Watson statistic = 2,2679

Degrees of freedom = 18

Note: Autocorrelation adjustments were not necessary.

D = Total domestic and export disappearance.

Price Forecasts

c = Estimated coefficient for the FOR and loan program

variable, representing years, 1979 through 1985, when the

FOR and loan programs kept market prices artificially

high. The sign of this coefficient is expected to be

positive.

Log(FLP)*(D7985) = An intercept shifter for the years

1979 through 1985, a time when prices were heavily

supported by the FOR and loan programs. FLP = Regular

loan rate and D7985 = 1 in 1979 through 1985 and zero

for other years.

d = Estimated coefficient for a dummy variable that

represents an apparent shifl in the pricing relationship for

the years of 1990 through 1994.

D9094 = A dummy variable equal to 1 for 1990 through

1994 and zero for other years. This variable is an

intercept shifter, in contrast to a slope shifter.

Data

Data for the estimation of equation (1) are found in

Wheat: Situation and Outlook Yearbook. Data are

shown in Appendix Tables 1, 2, and 3.

Results

The estimated price equation is shown in Table 1. The

coefficients for the intercept and loan rates are positive

and the coefficient for the stocks-to-use variable is

negative, all as hypothesized. The coefficient for the

1990-94 dummy variable was negative. The estimated

price equation has significant t-statistics and 88 percent of

the variation (log of annual wheat prices) is explained by

the equation. The t-statistics are shown in parentheses

below each estimated coefficient. All estimated

coefficients are significant at the 1-percent level, Price

forecasts based on equation (2) and a range of

corresponding stocks-to-use ratios are shown in Figure 5,

The annual 1997/98 price forecast for all wheat at the

producer level is $3.54 per bushel, based on results found

in equation (3).

(3) p= e,2.6225-o4o263*Log(l/D)+o.2l94l*Log,FLP)*D7985-o22,7*D9o)4)

This price forecast falls within the upper end of the price

projection range of $3.05 to $3.65 per bushel released in

the World Agricultural Supply and Demand Estimates

(WASDE) report, August 12, 1997. Based on the August

1997 WASDE report, the projected 1997/98 stocks-to-use

ratio was 29.3 percent. Inserting this ratio into equation

(3) yields a price projection of $3.54 per bushel. With the

standard error of the estimate equal to 0.0668 there is a

two-thirds chance that the price will fall within a range of

$3.31 to $3.78 per bushel.

Price forecasts and ranges corresponding to different

stocks-to-use ratios are shown in Table 2.

Table 2: Season Average Price Forecasts for All Wheat,

Assuming Different Stocks-to-use Ratios

Stocks-to-use Price Price Range

Ratio ProjectIon + I Standard error of

Estimate

Percent

5.0

7.5

10.0

12.5

15.0

17.5

20.0

22.5

25.0

27.5

30.0

32.5

------ Dollarx per Bushei -------

7.20

6.12

5.45

4,98

4.63

4.35

4.12

3.93

3,77

3.63

3.50

3,39

6.74--7.70

5.72--6.54

5.10--5.83

4.66--5.32

4.32--4.95

4.07--4.65

3.86--4.41

3.68--4.20

3,52-4.02

3,39--3.88

3,27--3,74

3.17--3.62

138
Model Performance

The performance of the wheat price equation was deemed

satisfactory (Fig 6). i i Although it captured only 6 of the

8 turning points in the period 1975-96, the mean absolute

error for the period was $0.150 per bushel or a mean

absolute percentage error of 4.8 percent. The mean

absolute error ranged from $0.006/bushel in 1990 to

$0.329htshel in’ 1977. In comparison, the mean absolute

percentage error for corn price forecasts during the period

1975-96 for a similar model was $0.12 per bushel and the

mean absolute percentage error was 5 percent (Westcott

1 997).

Conclusions

The wheat price forecasting model presented a stocks-touse ratio to explain the annual farm price of wheat. A

double log price equation was estimated which related the

stocks-to-use ratio of wheat to the annual producer price.

[n-sample performance of this model was deemed

satisfactory with 88 percent of the price variation

explained. Although the wheat price equation had strong

statistical properties, further efforts are needed to explain

the relationships during the period of 1990 through 1994.

This time period may have been affected partly affected

by interactions with the global wheat marketplace and by

the U.S. EEP program, factors not explicitly represented

by the model.

This price model should be used with care. The model

may omit other factors that can influence price.

However, it is argued that the effects of these variables

are largely captured in the stocks and use variables. The

main variables included in this model. stocks and use,

may be related to each other in ways that suggest use of

estimation techniques more sophisticated than regression

analysis. Nevertheless, this model provides a strong

analytical tool in the arena of price forecasting. It is

simple and easy to use and has reasonable forecasting

accuracy.

Suggestions for Further Research

Several additional approaches seem warranted with the

stocks-to-use model.

. The relationship between nominal wheat prices and

inflation should be examined.

● The relationship between free stocks and prices should

be examined, thereby removing the stocks that were

isolated from the marketplace by Government programs.

Also, what relationship exits between Government

stocks/total stocks and prices?

. The effects of EEP, imports, and other global market

interactions should be explored. What effects has EEP

had on the U.S. producer price for wheat? How have

these effects affected the level of imports? Also, have

imports affected the U.S. producer price for wheat?

. The different effects of food, feed, and export demand

should be explored, to represent valuations of wheat

quality factors implicit in different uses.

Lastly, is a stocks-to-use model, simultaneous set of

equations model, or simulation model adequate to

forecast prices in the new policy era? Each model type

relies upon past observations influenced by past policies

and events. In the past 22 years the wheat sector has been

free of government and FOR stocks for only 3 years,

1974-76. Are there other approaches that would be more

appropriate?

References

Baker, Allen and Keith Menzie. “Drought Effects on

Corn Price Forecasts,” Feed Situation and Outlook

-. Fds-307? August 1988! PP.~5-~8”

Gardner, Bruce L. “The Political Economy of U.S.

Export Subsidies for Wheat”, National Bureau of

Economic Research, Working Paper No. 4747, May

1994.

Gardner, Bruce L. and Richard Gray. “The Impact of

Canadian and U.S. Farm Policies on Grain Production

and Trade.” University of Maryland and University of

Saskatchewan, respectively. Paper prepared for a

workshop on the Canada/U. S. Joint Commission on

Grains, New Orleans, Louisiana. February 22-23, 1995.

Harwood, Joy L. and Edwin Young. “Part 1: Wheat,” a

chapter in Food Grains: Backmound for 1990 Farm

Le~islation. C. Edwin Young, Editor, Ag. Info. Bull. No.

602. August 1990.

Hoffman, Linwood A., Sara Schwartz, and Grace V.

Chomo. Wheat: Backg o d or 1995 Farm Legislation. run f

Ag. Econ. Rpt. No. 712. April 1995.

llPert”ormance was based on in-sample statistics. Insufficient

observations preclude out of sample statistics.

139

w’
Hoffman, Linwood A., Janet Livezey, and Paul Westcott.

“Relationships Between Annual Farm Prices and Ending

Stocks of Rough Rice”. Rice Situation and Outlook

Report. U.S. Department of Agriculture, Economic

Research Service, RS-60, April 1991.

Labys, Walter C. Dvnamic Commod ity Models:

Spedification. Estimation. and Simulation. Lexington

Books, D.C. Heath and Company, Lexington,

Massachusetts. 1973. pp. 109-166.

Lin, William, Andrew Novick, and Janet Livezey.

“Projecting the Market Price for Rice in 1988/89: The

Stocks-to-Price Relationship,” Rice Situation and

Outlook. RS-53. October 1988, pp.8- 11.

U.S. Department of Agriculture. heat: situation and

Outlook Yearbook. Economic Research Service, WHS1997. March 1997.

U.S. Department of Agriculture. World Azricultural

~t)lv and Demand Estimateq. Approved by the World

Agricultural Outlook Board. WASDE-329, August 12,

1997.

Van Meir, Lawrence W. “Relationships Among Ending

Stocks, Prices, and Loan Rates for Corn,’’FeedOutlook

and Situation. Fds-290, August 1983, pp. 9-13.

Westcott, Paul C. “An Annual Model for Forecasting

Corn Prices,” a paper presented to the 1997 Federal

Forecasters Conference, Washington, D. C., September

11, 1997.

Westcott, Paul C., David B. Hull, and Robert C. Green.

“Relationships between Quarterly Corn Prices and

Stocks,” &ricultural Economics Researc h. Vol. 37, No.

1, Winter 1985, pp. 1-7.

Westcott, Paul C., David B. Hull, and Robert C. Green.

“Relationships Between Quarterly Wheat Prices and

Stocks,” Wheat Outlook and Situation. WS-268, June

1984, pp. 9-13.

Young, C. Edwin and Paul C. Westcott. The 1996 U.S.

Farm Act Increases Market Orientation. Ag. Info. Bull.

No. 726. August 1996.

140
Appendix Table I: U.S. Wheat Supply and Disappearance by Marketing Year. ] 974/75- 1997-98 1/

supply Disappearance Ending Stocks May 31

Year Domestic Use Total

bqynning Beginning lmpcms Total Exports Disap- Government Privately Total

Junel Stocks- Prodcutlon 2/ Food Seed Feed 31 Total 2/ pearance owned owned 4/

, Milhon bushels

I 974175

1 975/76

1976/77

1977178

1978/79

1979/80

1980/8 1

1981 /82

1982/83

1983/84

1984/85

1985/86

1986/87

1987/88

1988/89

1989/90

I 990/9 I

1991 /92

1992/93

I 993194

I 994/95

1995/96

1996/97

340.1

435.0

66s.6

1,113.2

1,177.8

924.1

902.0

989.1

I , I 59.4

1,515,1

1,398.6

1,425.2

},905<0

1.820.9

1.260.8

701.6

536.5

868.1

475,0

530,7

568.5

506.6

376.0

1,781.9

2,126.9

2,148.8

2,045.5

3.4

2.4

2.7

1.9

2,125.4

2,564.3

2,817.1

3,160.6

545.0

588.5

588.0

586.5

92.0

100.0

92.0

80.0

34.9

37.3

74.4

192.5

671.9

725.8

754.4

859,0

1.018.5

1,172.9

949.5

1,123.8

1,690.4

1,898.7

1,703.9

1.982.8

NA

NA

NA

43.3

51. I

187.8

199.7

190.3

192.0

188.0

377.6

601.7

830.1

283.0

190.5

116.6

162.7

152.0

I 50.0

150.3

142.1

118.2

93.0

93.0

435.0

665.6

1.113.2

1,129.5

435.0

665.6

1,113.2

1.177.8

1,775.5

2.134.1

2,380.9

2.785.4

I .9

2.1

2.5

2.8

2,955.2

3,060.3

3,285.4

3,777.3

592.4

596.1

610.5

602.4

87.0

101.0

113.0

110.0

I 57.5

85.9

59.0

134.8

836.9

783.0

782.5

847.2

1,194.2

1.375.3

1,513.8

1,770.7

2,031.1

2.158.3

2,296.3

2,617.9

873.0

714.2

789.4

969.1

924. I

902.0

989.1

1,159.4

2,765.0

2,419.8

2.594.8

2,424. I

7.6

3.8

9.4

16.3

3,932.0

3,938.8

4.002.8

3,865.6

616.4

642.6

651.O

674.3

97,0

100.0

98.0

93.0

194.8

371.2

407, !

284.2

908.2

1,113.8

1,156,1

1,051.5

1,508.7

! ,426.4

1,421.4

909. I

2,416.9

2,504.2

2.577.6

1.960.7

1.323.1

1.210.6

1,047.6

1,303.3

1.515.1

1,398.6

1.425.2

1.905.0

2,090.6

2.107.7

1,812.2

2,036.6

2,729.8

21.3

16.1

22.7

22.5

36.4

4,016.8

3.944.7

3,095.7

2,760.7

3.302.6

712.2

720.7

725.8

748.9

789.8

84.0

85.0

103.0

I 04.3

92.9

401.2

290.2

150.5

139.1

482.4

1,197.4

1,096.0

979.2

992.3

1,365.1

998.5

1,587,9

1,414.9

1,232.0

1,069.5

2,195.9

2,683.8

2.394.1

2,224.3

2,434.5

990.8

977.8

511.1

419.9

705.4

1,820.9

1,260.8

701.6

536.5

868.1

1,980. I

2,466.8

2,396.4

2,321.0

2,182.6

40.7

70.0

108.8

91.9

67.9

2,889.0

3,011.8

3,035.9

2.981.4

2,757. I

789.5

834.8

871.7

853.0

883.0

97.7

99. I

96.3

89.2

104.1

244.5

193.6

271.7

344.4

151.9

1,131.6

1,127.6

1,239.7

1,286.6

1,140.0

1,282.3

1,353.6

1,227.8

1,188.3

1,241.1

2,413.9

2,481.2

2,467.4

2,474.8

2,381.1

323.0

380.7

418.2

364.5

257.8

475.0

530.7

568.5

506.6

376.0

2,281.8

2,530.5

90.0

95.0

2,747.8

3,069.7

892.0

900.0

103.O

I 00.0

310.0

275.0

1,305.0

1,275.0

1,001.0

1,100.0

2,306.0

2,375.0

351.2

601.7

444.2

1997/98 5/ 444. 2 694,7

NA=Not available

l/Totals might not add because of rounding.

2/imports and exports include flour and other products expressed in wheat equivalent.

3/Residual; approximates feed use and includes negligible quantities used for distilled spirits.

4/lnciudes outstanding and reserve loans.

5/projected as of August 12, 1997.

Source: Wheat: Situation and Outlook Yearbook. U.S. Department of Agriculture. Economic Research Service. WHS- 1997. March 1997.

141
Appendix table 2: Wheat: Carryover Stocks, Farm Prices, and Support Prices 1974/75-1997/98

Crop Carryover Stocks

year Price Loan Target Direct

ccc FOR 1/ Free Total 21 received rate price payment

1974/75

1975/76

1976/77

1977/78

1978/79

1979/80

1980/81 *

1981/82*

1982/83*

1983184*

1984185*

1985186*

1986187*

1987188*

1988189*

1989/90 *

1990/91 *

1991/92*

1992193*

1993194*

1994195*

1995196*

1996197*

------------------- Million bushels -------------------------- --------------------- $/bushel -----------------

---

---

---

48

51

188

200

190

192

188

378

602

830

283

190

117

163

152

150

150

142

118

95

1997198*7I 93

---

---

---

342

393

260

360

562

1,061

611

5/ 654

51 433

5/ 463

467

287

144

14

50

28

6

0

0

0

0

435

666

1,113

788

481

454

429

407

262

600

393

870

528

511

225

275

691

273

353

412

365

258

349

602

435

666

1,113

1,178

924

902

989

1,159

1,515

1,399

1,425

1,905

1,821

1,261

702

536

868

475

531

568

507

376

695

4.09

3.56

2.73

2.33

2.97

3.80

3.99

3.69

3.45

3.51

3.39

3.08

2.42

2.57

3.72

3.72

2.61

3.00

3.24

3.26

3.45

4.55

4.35

3.35

1.37

1.37

2.25

2.25

2.35

2.50

3.00

3.20

3.55

3.65

3.30

3.30

2.40

2.28

2.21

2.06

1.95

2.04

2.21

2.45

2.58

2.58

2.58

2.58

2.05

2.05

2.29

2.90

3.40

3.40

3/3.63

3.81

4.05

4.30

4.38

4.38

4.38

4.38

4.23

4.10

4.00

4.00

4.00

4.00

4.00

4.00

N.A.

N.A.

---

---

---

0.65

0.52

---

---

4/ 0.15

0.50

0.65

1.00

1.08

1.98

1.81

0.69

0.32

1.28

6/1.35

0.81

1.03

0.61

0

0.87

0.63

--- = Not applicable.

N.A.= Notavailable.

* = Includes food security resrve. l/Farmer-ov.medreserve. 2/Totalsmightnotaddbecauseof rounding

3/Growers who planted in excess of their normal crop acreage were eligible for a target price of $3.08 a bushels.

4/Deficiencypaymentrate, 1981/82to1995/96;productiontlexibility contract payment rate, thereafter.

5/ Includes special producer storage loan program. 6/Winter wheat option 1.25. 7/Projected as of August 12, 1997.

Source: Wheat: Situation and Outlook Yearbook. U.S. Department of Agriculture. Economic Research Service.

WHS- 1997. March 1997.

142
Appendix Table 3: U.S. Wheat Exports by Selected Programs

Total confessional,

CCCexport credit,

Export and EEP exports divided

Fiscal Section Food for Aid Total enhancement Total U.S. by total exports

Year P. L.480 416 Progress 1/ confessional CCC export credit program wheat exports 2/

I

-------------------------------- 1,000 Metric Tons ----------------------------------------- Percent

1 978/79 3,234

1979/80 2.785

1980/8 1 2,537

0 --

0 --

0 --

7

44

4

0

123

0

74

513

1

292

806

28

0

0

NA

NA

NA

3.241

2,829

2.541

2,684

1,945

3.261

0

0

0

31,340

36,066

42.246

19

13

14

1981/82 2,978

1982/83 3,340

1983/84 3.442

1984/85 4.392

1985/86 4.685

0 --

0 --

0 --

0 --

76 --

2.978

3,463

3.442

4.466

5,274

3,725

8,597

11,406

8,221

7,740

0

0

0

0

4,916

44,607

36,701

41,699

28,524

24,626

15

33

36

44

59

1986/87 3.927

1987/88 3,321

1988/89 3+()~()

1 989/90 2.985

1990/91 3,067

406 --

1.186 --

137 --

0 52

0 92

4,334

4,799

3.963

3,065

3.159

8,125

9,273

8,897

7,759

8.339

12,214

26,679

17,906

12,806

15,150

28,204

40,523

37,660

28,064

26.792

67

80

68

70

78

1991/92 2,286

1992/93 3/ 2,043

1993/94 3/ 2.801

1994/95 3/ 1.491

0 130

890 1,067

0 726

0 457

2,416

4,001

3.527

1.948

12,334

8,538

5,874

4,202

21,111

21.806

18,157

18,073

34322

36081

31145

32088

76

79

75

68

l/U. S. Agency for International development Commodity Import Program. 2/Shares of wheat exports take into consideration the overlap

between sales under the EEP and export credit guarantee programs. 3/Prelin~inary. --= Not applicable. NA = Not available.

!%urces: P. L.480 shipment data are developed by USDA. ERS as of 2/19/97; export credit guarantee and EEP data are from

USDA. FAS. Export Credits Divisions: export data are from USDA. ERS, Foregin Agricultural Trade of the United States.

143



AN ANNUAL MODEL FOR FORECASTING CORN PRICES

Paul C. Westcott, Economic Research Service, USDA

The U.S. com crop plays a major role in the

agricultural sector. As a source of income to farmers,

com is the largest crop in terms of cash receipts. Over

the last 5 years, com cash receipts have averaged more

than $16 billion; accounting for about 17 percent of

total crop cash receipts. Corn also has an important

role in linkages within the agricultural sector among

various crops and between crops and livestock. Corn

competes with other crops for land in farmers’

production decisions, particularly soybeans. Corn is

also the largest feed grain used by the livestock sector.

Further, the U.S. is the largest exporter of corn,

accounting for over 70 percent of global com trade thus

fhr in the 1990s. Consequently, events which affect the

corn sector and com prices are carefully watched by

many subsectors within agriculture.

New agricultural legislation enacted in 1996

fundamentally changed the nature of farm commodity

programs in the United States, furthering trends

towards market orientation in the sector. In particular,

changes in the income support program shifted much of

the risk of price volatility from the Government to

producers (see Young and Westcott). As a result,

market information affecting com prices is particularly

important under the 1996 Farm Act as farmers seek to

make informed farm management decisions to manage

risk and other market participants work within a more

market-oriented agricultural sector.

To provide market information regarding the

agricultural sector, each month the U.S. Department of

Agriculture (USDA) analyzes major agricultural

commodity markets and publishes annual supply,

demand, and price projections for the current year.

Additionally, once a year USDA publishes longer-term,

10-year projections for the agricultural sector that

include commodity supply, demand, and prices.

This paper examines some of the factors that affect

farm-level com prices. An annual fiarnework is

employed to develop a com price model, designed to

be used in USDA’s projection activities in conjunction

with ongoing commodity market analysis of supply and

demand factors. The relationship estimated builds on

2 types of factors that influence prices--market supply

and demand conditions, and Government price support

programs.

Market forces, as measured by supply and demand,

influence prices. Year-ending stocks of an annually

produced commodity, such as corn, summarize the

effects of both supply and demand factors during the

year, and are a usetil indicator of price movements for

the commodity. Annual prices for grains tend to have

a strong negative correlation with their ending stocks.

High stocks typically result in lower prices, while low

stocks put upward pressure on prices.

Government programs have also been important in

influencing farm-level prices for grains. Some

programs have influenced prices indirectly by placing

restrictions on the use of land for agricultural

production, for example. Historically, the nonrecourse

commodity loan program has directly affected prices

by providing support to farm-level prices and affecting

market equilibrium in some periods. The key policy

variable used in the price modeling effort in this paper

is the price support loan rate. However, the role of the

loan rate in influencing prices has differed historically

as the nature of the commodity loan program has

changed under different farm legislation.

Previous Research

Many com price models have employed the stocks-touse ratio to represent market conditions in explaining

movements in com prices. The stocks-to-use ratio is

defined as stocks of the commodity at the end of a

particular time period divided by use of the commodity

during that time period. As such, market conditions of

supply and demand are summarized in this measure.

Van Meir, and Baker and Menzie used stocks-to-use

ratios in annual frameworks analyzing com prices,

while Westcott, Hull, and Green used such an approach

in a quarterly model for com prices. Numerous other

unpublished annual com price models using stocks-touse ratios have been used internally within USDA in its

forecasting activities. In each model, the stocks-to-use

variable is negatively related to com prices and

provides a downward sloping nonlinear cuwe of prices

plotted against ending stocks-to-use,

To represent the effects of Governmental price support

programs on prices, many grain price models have been

estimated with the dependent variable of price minus

loan rate. The Baker and Menzie annual com price

147
model and part of the Van Meir analysis of corn prices

and stocks used this approach, as did most of the

unpublished USDA models. The U.S. price support

program affected grain prices, particularly in the late1970s through the mid- 1980s. During this period, the

support program’s loan rate for corn was generally high

enough to influence market prices. However, changes

in the price support program since 1986 have resulted

in less interference of that program with price

determination.

J%ice Sumort and Commodity Storage Promams for

Q2!ll

The commodity price support program for corn allows

producers to receive a loan from the Government at a

designated loan rate per unit of production by pledging

some of their corn production as loan collateral.

Following harvest of the corn crop, a farmer who has

enrolled in the corn program may obtain a loan for

some portion of the new crop. For each bushel put

under loan and pledged as loan collateral, the farmer

receives a per-bushel amount equal to that year’s loan

rate. Under the loan program, the producer must keep

the crop designated as loan collateral in approved

storage to preserve the crop’s quality. The producer

may repay the loan at any time during the length of the

loan, usually 9 months. However, at the end of the

9-month loan period, the farmer may choose instead to

default on the loan rather than repaying it, keeping the

loan money and forfeiting ownership of the loan

collateral (the corn) to the Government. Defaulting on

the loan would make economic sense for the producer

if the market prices were below the loan rate, because

the producer would effectively have received the loan

rate for the crop rather than the lower market price.

Historically, loan rates were set high relative to market

prices in the late 1970s through the mid- 1980s (see

figure 1). Loan program defaults resulted in the

acquisition of com by the Government, and

Government stocks of com reached over 1.1 billion

bushels in 1982, or 15 percent of annual use. Also, a

multi-year Farmer-Owned Reserve program was begun

in the late- 1970s, which provided storage subsidies to

farmers to store grain under loan for 3 to 5 years.

Additional price support was provided under FarrnerOwned Reserve program in some years. The long

duration of this storage program combined with high

release prices needed for grain to exit the reserve

effectively isolated a large amount of grain from the

marketplace. By 1982, com held in the Farmer-Owned

Figure 1. Corn price and loan rate

Dollars per bushel

3.50

3.00

2.50

2.00

1.50

1.00

0.50

1

,.’

.’

975 1980 1985 1990 1995

Crop year

148

m-
Reserve rose to almost 1.9 billion bushels, about 26

percent of annual use.

Changes in the price support program since 1986 have

resulted in less interference of that program with price

determination., Farm legislation enacted in 1985

significantly changed the loan program and the effect

of price supports on market prices, starting in 1986.

Prices supports for grains were sharply reduced. The

loan rate for corn was lowered from $2.55 per bushel

for 1985 to $1.92 per bushel in 1986. Additionally, no

further com was permitted to enter the Farmer-Owned

Reserve during 1986-1990. Also, com in the reserve

was more accessible to the marketplace as a new policy

instrument introduced under the 1985 farm law, generic

certificates, allowed early access to grain in the reserve

before its contract expiration. Essentially, the loan

program continued to provide producers a source of

short term liquidity, but it no longer supported com

prices.

Policy changes since 1990 have continued to keep the

price supporting aspects of the loan program at a

minimum. Since 1986, the com loan rate has ranged

from $1.57 per bushel to $1.92 per bushel, well below

market prices for com in most years. Implementation

of marketing loans for com starting in 1993, which

allow repayment of loans at less than the original loan

rate, fhrther reduced the loan program’s potential effect

on market prices. As a consequence, since 1986, price

determination for com has largely occurred in the

marketplace based on supply and demand conditions

without the influence of the Government price support

program.

The Model

The general framework used here relating prices to

ending stocks derives from an equilibrium model. In

its simplest form, without the Government price

support program, supply, demand, and stocks are each

a fi.mction of price, with the market-clearing,

equilibrium condition of determining the price at which

supply equals demand plus stocks (equations 1-4).

(1) S= f(p) (Supply fhnction)

(2) D=g(p) (Demand function)

(3) K=h(p) (Stocks Iimction)

(4) S- D-K=O (Equilibrium condition)

S is supply, D is demand, K is ending stocks, and p is

market price. Supply is positively related to price

while demand and stocks are negatively related to

price.

In equilibrium, prices can be determined from the

inverse of the supply, demand, or stocks fhnction.

Taking the inverse of the stocks fimction provides a

price determination equation, with prices negatively

related to stocks.

(5) p = h-l(K) (Inverse stocks function;

price equation)

Introducing the Government price support loan

program adds to the stocks fimction by incorporating

the commodity loan rate to the function, as represented

in equation 3a.

(3a) K = h (p; LR) (Stocks fi.mction with

Government loan

program)

K’ is the revised stocks fimction and LR represents the

loan rate. The Government loan program provides an

additional feature to stockholding behavior that

depends on the loan rate incentive to use the loan

program,

With this alternative stocks function, the inverse stocks

function gives the following price determination

equation.

(5a) p = h-i(K; LR)

Prices would be expected to be negatively related to

stocks. Prices would be expected to be positively

related to the loan rate, particularly in those years that

loan rates were set high relative to market clearing

price levels and the Farmer-Owned Reserve isolated

stocks from the marketplace, thereby resulting in

interference of the loan program with price

determination.

Mode 1 ImplementatloQ.

The fictional form used to estimate equation 5a for

annual com prices is logarithmic. Semi-log and

exponential Iimctional forms can alternatively be used

and provide similar estimation results to those

presented here.

(6) Ln (p)= a + b Ln (K’AJ)

+ c Ln (LR) * Dum7985

149
U represents annual com utilization, Dum7985

represents a dummy variable equal to 1 in 1979-1985

and equal to O in other years, and a, b, and c are

parameters to be estimated.

In equation 6, stocks (K’) are measured relative to an

indicator of the “scale of activity” in the com sector,

represented by the realized level of demand, actual

utilization (U). This adjustment is needed because of

growth in the com sector over the last 20 years, so a

particular level of stocks today represents a smaller

portion of total use (or realized industry demand) than

the same level of stocks in 1975. The result is a stocksto-use variable commonly used in price models,

providing a measure of relative market tightness for the

commodity. The expected sign of the stocks-to-use

coefficient (b) is negative.

The interaction term of the loan rate (LR) times the

dummy variable (Dum7985) represents the effects of

the loan program on com prices from the late-l 970s

through the mid- 1980s. The years chosen for the

interaction term were when the commodity loan

program, in conjunction with the structure of the

Farmer-Owned Reserve program, resulted in the loan

rate interfering with the sector reaching its market

clearing price level. Loan rates were relatively high in

those years and the multi-year Farmer-Owned Reserve

program, with high release prices, isolated those

reserve stocks from the market. The price supporting

aspects of the loan program in those years imply that

the expected sign for the coefficient (c) for the loan rate

interaction term is positive.

The specification of the interaction term represents an

intercept shift related to the loan rate rather than a slope

shift related to the stocks-to-use variable. An

alternative specification that included a slope shift

adjustment for 1979-1985 produced a result that was

not statistically significant.

Farm-level prices used to estimate the model are season

average prices collected by the U.S. Department of

Agriculture’s National Agricultural Statistics Service

and re-published in the Economic Research Service’s

Feed Situation and Outlook Yearbook (March 1997).

Stocks, utilization, and loan rate data also are from the

Feed Situation and Outlook Yearbook.

Model Resul@

The model was estimated using ordinary least squares

regression, with annual data from 1975 through 1996.

The estimated logarithmic regression equation is

(7) Ln (p)= 1.539-0.2426 Ln (K’/U)

(19.2) (9.1)

+ 0.2896 Ln (LR) * Dum7985

(7.3)

R*= 0.845 D.W. = 1.854

with t-statistics shown in parentheses under each

coefficient.

Over 84 percent of the variation in annual com prices

is explained by estimated equation 7. Each coefllcient

has the expected sign, with a negative sign for the

stocks-to-use variable and a positive sign for the loan

rate shift variable. Each coefllcient is significant at the

1 percent level.

A graph of the regression equation results is shown in

figure 2, adjusting from logarithms to levels of each

variable. Corn prices are plotted against ending stocksto-use ratios. The circles in figure 2 represent the

historical observations for the 1975-1996 estimation

period. The lower price curve applies for all years

except 1979-1985 and represents the equation that

would currently be used for forecasting com prices.

The higher price curve represents the years 1979-1985,

which incorporates the average price supporting effect

of high loan rates in those years. The average

difference between the 2 price curves for the

1979-1985 period is about 60 cents a bushel.

Model Evaluatio~

Figure 3 shows a graph of the predicted values derived

from estimated equation 7 along with the actual com

prices. In general, the price model tracks actual com

prices well. Most differences between the model

estimate and the actual com price are less than 15 cents

a bushel. The largest difference is in 1988, the year of

a major drought in the Corn Belt region of the United

States.

150
Figure 2. Corn price equation

Dollars per bushel

4.00

3.50

3.00

2.50

2 00

1.50

\

\

\

● ✼

Loan rat e effect, 1979-1985

a-

●

●

●

● 0

●

--

Ln (Corn price)= 1.539-0.2426 Ln (K’/U) . 

–

20

Stocks

40

to use ratio

Figure 3. Corn prices--Actua

Dollars per bushel

3.50

3.00

2.50

2.00

1.50

60

(percent)

80

and model estimate

1.00

1975 1980 1985 1990 1995

Crop year

151
.,

Table 1 --Model performsnce measures. se 1 ected De riods

Mean Mean absolute

Time Deriod absolute error ~ercenQge error

Cents per bushel Percent

1975-1996 12.1 5.1

1990-1996 9.7 4.6

Table 1 shows mean absolute errors and mean absolute

percentage errors for the full estimation period,

1975 -1!396, and for a selected subsample of recent

years covering 1990-1996. For the full sample, the

mean absolute error is about 12 cents a bushel, with a

mean absolute percentage error of about 5 percent.

Importantly, for price forecasting applications, model

performance is somewhat better in recent years (the

1990s), with a mean absolute error under 10 cents a

bushel and a mean absolute percentage error of 4.6

percent. These statistical performance measures

indicate good performance for the corn price model.

Cone lusion~

The corn price model presented in this paper uses a

stocks-to-use ratio formulation. The model also

addresses issues regarding the historical influence of

Government commodity loan and storage programs on

com price determination. Loan programs are shown to

have had an effect on corn prices in the late-l 970s

through mid- 1980s. However, with farm program

changes of 1985 farm legislation, lower loan rates and

other features of commodity loan and storage programs

have not had as much influence on prices over the last

decade. Price determination now occurs in the

marketplace, based on supply and demand factors. The

stocks-to-use ratio used in the model captures these

market effects.

The statistical performance measures as well as the

graph of actual prices and model estimates indicate

good performance for the corn price model. This is

particularly the case given the large range of corn

prices over the sample period used to estimate the

model (1975- 1996) as well as the changing nature of

the influence of Government programs on com price

determination.

The relatively simple structure of the estimated reduced

form model for com prices and the model’s minimal

data requirements lend itself to easy use in com price

forecasting applications in conjunction with market

analysis of supply and demand conditions. In

particular, the model is used within USDA as part of

the Department’s short-term market analysis and longterm projections activities.

Baker, Allen and Keith Menzie. “Drought Effects on

Corn Price Forecasts,” Feed Situation and Outlook

Report, FdS-307, USDA-ERS, August 1988, pp.

25-28.

Lin, William, Peter Riley, and Sam Evans. Feed

Grains: Background for 1995 Farm Legislation.

AER-7 14, USDA-ERS, April 1995.

U.S. Department of Agriculture. Economic Research

Service. Feed Situation and Outlook Yearbook.

FDS-1 997, March 1997.

U.S. Department of Agriculture. Economic Research

Service. Storage Subsidy Programs. Staff Report

AGES-9075, December 1990.

Van Meir, Lawrence W. “Relationships Among

Ending Stocks, Prices, and Loan Rates for Corn,” Feed

Outlook and Situation Report, FdS-290, USDA-ERS,

August 1983, pp. 9-13.

Westcott, Paul C., David B. Hull, and Robert C. Green.

“Relationships between Quarterly Corn Prices and

Stocks,” Agricultural Economics Research, Vol. 37,

No. 1, Winter 1985, pp. 1-7.

Young, C. Edwin and Paul C. Westcott. The 1996

U.S. Farm Act Increases Market Orientation,

Agricultural Information Bulletin 726, August 1996.

152
FORECASTING WORLD COTTON PRICES

Stephen MacDonald, Economic Research Service, USDA

This paper presents a forecasting model used by the

Economic Research Service (ERS) of USDA for its

contribution to the unpublished forecasts of the

Department’s , interagency Cotton Estimates

Committee. USDA does not publish any cotton price

forecasts, and its unpublished price forecasts are not

directly derived from this or any other single model.

Since 1929, Congress has forbidden USDA from

publishing forecasts of cotton prices (see Townsend for

a discussion of the circumstances surrounding this

legislation). However, commodity price forecasting by

USDA is not solely geared towards publication, and the

Department’s Interagency Cotton Estimates Committee

calculates unpublished estimates of world and domestic

cotton prices each month. This paper details a single

equation forecasting model for world cotton prices used

to assist ERS in its contribution to the USDA price

forecasts. Several aspects of the model’s specification

are discussed in the context of developments in U.S.

and foreign cotton markets over the last 25 years. The

model is based on the relationship between price,

consumption, and stocks, and also attempts to account

for the wide variety of policies in the United States and

overseas.

World Prices

There are a wide variety of prices available for any

given major commodity, each associated with a specific

location and function. Determining which price is

“the” world price is occasionally difficult, and even

widely accepted choices involve trade-offs between

varying degrees of specificity and generality. At

USDA, world prices for wheat, corn, and soybeans are

generally accepted to be described by the Agricultural

Marketing Services (AMS) prices at U.S. Gulf Ports

(USDA). The drawbacks to this choice include the loss

in generality stemming from fixing the price to a

specific quality from a single origin. At times, world

com markets may be influenced by different factors

than those heavily weighing on number 3 com or U.S.

corn.

An average per-unit value--like an import unit value or

average price received by farmers--may generalize with

respect to origin or quality, but has the disadvantage of

potentially varying as the shares of origins or qualities

vary.

For cotton, an average of price quotes for a specific

quality of cotton ( middling 1-3/32”) grown in various

regions, but quoted for delivery in Northern Europe,

has become accepted as a measure of the world price.

This average is published daily by Cotlook Limited,

and is known as the A-index. Cotlook Limited has

registered the Cotlook A-index as a trademark, and

each issue of Cotton Out/ook magazine details how the

index is derived. U.S. legislation has led to the

adoption of an index identical to the A-index to help

trigger policy decisions for the U.S. cotton marketing

loan program (MacDonald). Thus, forecasts of the Aindex are usefi.d to both public and private sector

policy-makers.

The Model

Assuming production of cotton is fixed before the

beginning of the marketing year, then the following

equilibrium model of the cotton market can be

specified:

supply, S.Q

Demand, c= f(P)

Stocks, I=g(P)

S-C-1=0

where: Q is amount of crop production for that given

year (plantings depend of previous year’s price, and the

size of the harvest is determined before the beginning

of the marketing year, although it is not necessarily all

immediately available at that time) and P is price.

Prices can be determined from the inverse of either the

consumption or stocks finction. Taking the inverse of

the consumption function yields a price determination

equation with prices directly related to consumption,

p= f’(c )

Following Westcott, this consumption variable is

measured relative to a “scale of availability” in the

cotton sector, represented by the realized end of year

stocks ( I ). Textile production is a capital-intensive

process; and is mo~t

operates continuously.

suggested a depIetion

profitable when machinery

A rate of consumption that

of the current year’s cotton

153
supply before the availability of the following year’s

harvest would suggest a period during which textile

producers would have no income to meet their fixed

costs. Uncertainty regarding the size, timing, and

transportation of the following year’s crop suggests

consumption should never be large enough to

completely deplete the year’s supply by, or even

shortly after, the end of the year. Thus, we are left with

a relationship stating prices are directly proportional to

the ratio of consumption and ending stocks, and the

implication that over a long time period this

proportionality can shift as the risk of beginning the

next marketing year with low supplies varies,

p= f-](C/I, Z)

where z is the set of exogenous factors that can shift

the relationship between current year price and

use/stocks. Figure 1 illustrates how trends in inflationadjusted cotton prices and global use/stocks have

varied since 1971. The growing divergence between

these two variables suggests the relationship has not

been constant.

In this model the exogenous shift variables include:

dummies representing different U.S. agricultural policy

regimes, dummies for a few years of specific economic

or policy shocks, variables capturing the impact of U.S.

payments to cotton exporters (under a program that has

made payments to both domestic users and exporters of

cotton), and variables capturing consumers’

expectations of changes in the cost of consuming

cotton. A real U.S. exchange rate is also included as a

separate variable since much of the world’s

consumption occurs in economies where neither costs

nor returns are calculated in U.S. dollars. This could be

specified by adjusting the A-index (which is reported

in terms of U.S. currency) by the exchange rate.

However, exchange rate changes are not exactly the

same as changes in product prices (Goldstein and

Khan), so the exchange rate was used as an explanatory

variable, and the price remained in U.S. currency.

Given the somewhat ad-hoc nature of the exogenous

shift variables introduced into the model, hypothesis

testing of parameter values is not reliable, However,

the model is still suitable for forecasting purposes since

the large number of explanatory variables ( 11 ) suggests

the danger is greater that the model includes irrelevant

variables than is the danger that it omits relevant

variables. Omitting relevant variables introduces bias

and inconsistency into the parameter estimates for the

included variables, in most cases (Pindyck and

Rubinfeld). Irrelevant variables reduce efilciency but

do not introduce bias or inconsistency, and, when the

concern is with forecasting rather than hypothesis

testing, the cost of mistakenly excluding a relevant

variable is greater than the cost of including one that is

irrelevant. Therefore, some variables for which theory

suggests inclusion, but test statistics suggest omission,

remain in the model.

Data

The dependent variable ( P ) was an unweighed

marketing year average of the daily A-index in U.S.

currency, adjusted by the U.S. GDP deflator. This is

the deflator used for all the real prices in USDA’s

baseline forecasts. The stocks variable ( I ) is world

ending stocks according to USDA’s official database,

minus China’s ending stocks. Similarly, the

consumption variable ( C ) is world consumption minus

China’s consumption, plus an additional factor. The

additional factor added to consumption is net imports

by China. These data are published in USDA’s Cotton

and Wool Situation and Outlook Yearbook. These

adjustments largely reflect the uncertainty regarding the

actual amount of cotton produced, consumed, and

stored in China. One source of this uncertainty is the

lack of a clear relationship between China’s apparent

domestic cotton supplies and its trade volume.

Regardless of whether or not China’s consumption and

stocks have been correctly estimated, China’s

government prevents the rest of the world from

accessing China’s stocks at will, and prevents

consumers in China from accessing world stocks at

will. In a given year, China’s most important effect on

world prices comes from its trade, which either adds to

cotton consumption when China is a net importer, or

effectively reduces world consumption when China is

a net exporter. These adjustments are similar to those

followed by the International Cotton Advisory

Committee (ICAC) in its world price forecasting

model. One difference is that the ICAC includes

China’s trade as a separate variable rather than adding

it to consumption or stocks.

The model was estimated over 1971-95, since 1971 is

initial year of the macroeconomic database developed

for ERS’S long-range baseline forecasting for the

President’s Budget (USDA). A semi-logarithmic

ilmctional form is used since the non-linear relationship

captured turning points slightly better.

The estimated equation is (t-statistics in parentheses):

154
Ln(P) = 7.70-0.47 Dum8695 -0.22 Dum9195

(23.8) (5.1) (3.0)

-0.29 Dum8485 + 0.54 Dum73 + 0.15 Dum7476

(3.9) (6.8) (2.8)

-0.74 Step2 -0.44 Step2d + 2.87 Infl -0.45 Prodd

(0.5) (1.0) (1.97) (2.6)

-0.004 Xr + 0.~8 C/l

(1.97) (3.9)

R

2

= .98 D.W .= 2.54

Figure 2 illustrates the performance of the

model.

estimated

Dum8695 is a variable with the value of 1 during 1986-

1995 and zero otherwise. Dum9 195 is a variable with

the value 1 during 1991-1995 and zero otherwise.

These dummies correspond to shifts in U.S. agricultural

policy which significantly changed the relationship

between prices and stockholding for all commodities in

the United States, the world’s largest cotton

stockholder throughout much of the period analyzed.

The United States has held as much as 35 percent of the

world’s non-Chinese stocks before 1986, but has

remained below 20 percent every year since 1988.

Recall the divergence in trends in prices and the world

use/stock ratio illustrated in Figure 1. While some of

this divergence represents improvements in global

communication, transportation, and trade that reduce

the risk of not holding stocks, a large part of the

divergence represents progressive changes in U.S.

government efforts to keep U.S. cotton stocks off the

market.

Dum8485 is a variable with the value 1 in 1984-1985

and O otherwise. It represents the combined effect of

anticipation of the 1985 U.S. farm legislation and a

shift in China’s trade policy. The 1985 U.S. farm

legislation lowered the high loan rates of the 1981

legislation and opened U.S. stocks to world markets for

a variety of commodities. World prices fell in

marketing year 1984 as the legislation took shape,

partly in anticipation of lower prices in the subsequent

year, and continued falling as lower priced production

and pent-up stocks from the United States subsequently

became available. These years also mark the initiation

of large exports by Chin% only a few years after China

had culminated 19 years of continuous net imports by

becoming the world’s largest importer. Between 1980

and 1985, China went from the world’s largest importer

to the world’s largest exporter. China has generally

been a net importer since then. Thus, it is not

altogether clear if Dum8485 is capturing just the effect

of U.S. policy, Chinese policy, or both.

Dum73 and Dum7476 are variables with the value 1 in,

respectively, 1973 and 1974-76. The first oil shock

and the USSR’s “great grain robbery” (Morgan) of the

early 1970’s introduced significant volatility into

commodity prices. Efforts to capture the volatility in

expectations associated with this price volatility

through other variables such as inflation and exchange

rates were not completely successful. Simple

specification testing (Durbin-Watson statistics)

indicated that a model that included the years 1971-76

with two associated dummies was superior to one that

excluded these years. In general, a modeler must use

judgement to distinguish between outliers that are so

extreme that they suggest measurement error or sim i Iar

flaws and events that contain valuable information

about the process being modeled (Pindyck and

Rubinfeld).

U.S. MarketinP Loan Program

Step2 is expenditure on exported cotton in a given year

by the U.S. government under Step 2 of the cotton

marketing loan program, divided by the value of all

U.S. cotton exports that year. Export values are from

USDA’s Outlook for U.S. Agricultural Exports and

data on the spending for Step 2 are Ilom USDA’s Farm

Service Agency. Direct expenditures to support exports

are thereby converted into percent equivalents. Both

the export and expenditure data are on a fiscal year, but

the difference between fiscal and marketing year is

only 2 months, and the two months are those typically

with the lowest export activity.

Step 2 of the marketing loan program was introduced

in the 1990 U.S. farm legislation (MacDonald), so the

variable is zero before 1991. Note that this program is

also available to domestic consumers of cotton, and

was therefore unaffected by the Uruguay Round

Agreement. However, the United States unilaterally

modified the program in 1996 to shift the expenditures

even further in favor of domestic cotton consumers.

This suggests that the relationship between

expenditures under Step 2 and prices will be different

in the future than what has been estimated here.

The expected sign of this variable is negative, as is the

expected sign of the other variable associated with the

Step 2 program, Step2d. Step2d is the first difference

of the Step2 variable. Since the expenditures on

155
exported cotton under the Step 2 program acted like an

export subsidy by a large exporter when they occurred

during 1991-1995, they would be expected to lower the

world price of the commodity (Tweeten). The Step2d

variable is intended to capture the additional effect of

changes in the expenditures. The Step 2 program is not

in effect continuously, and if market participants

assumed the previous year’s expenditures were a guide

to the current year, Step2d is the adjustment in their

expectations.

Step 2 payments to exporters were equivalent to 2-3

percent of the value of global raw cotton trade in 1992

and 1993, and the relationship between U.S. and world

prices appears to have shified since the step 2 program

began. During 1972-90, the average premium

Memphis 1-3/32” cotton received in Northern Europe

to the A-index was 3.4 percent (excluding the policy

transition period just before the 1985 farm legislation

took effect). During 1990-96 the average premium was

6.8 percent. The shift from 3.4 percent to 6.8 percent

may stem from the introduction of a price wedge

between U.S. and world cotton prices through the Step

2 program. Some of this price wedge would derive

from higher U.S. prices, but some would derive from

lower world prices (Tweeten). The t-statistics for these

parameters do not support the hypothesis that either is

significantly different from O. But, as noted earlier,

the specification of this model does not lend itself to

hypothesis testing. Both variables remain in the model

due to their ability to improve short-run forecasts when

recent years of data are dropped from the sample, and

out of sample forecasts derived for those years.

One difficulty in determining the impact of the Step 2

on world prices is that Step 2 payments are correlated

with the appearance of inexpensive Central Asian

cotton on world markets. The ICAC, rather than

including Step 2 data in their model, incorporates the

bartered share of Central Asian exports (ICAC). While

theory suggests that payments like Step 2 would put a

price wedge between U.S. and world prices, the

widening gap between the price of U.S. cotton and the

A-index may also reflect changes in the A-index. Low

production costs and falling domestic demand within

the Former Soviet Union (FSU) meant increased

quantities of Central Asian cotton were available at

extraordinarily low cost in the early 1990’s. Significant

quantities of FSU cotton were held by former

consumers which had acquired cotton under traditional

barter arrangements, and marketing outside of the FSU

was novel for Central Asian exporters. Thus much of

the cotton left Central Asia under barter arrangements,

resulting in its availability on world markets for low

prices when sold for convertible currencies. Illustrative

of the shift in costs to non-FSU markets is the shift in

Central Asian cotton’s rank among exporters: from a

typical position of the third least expensive cotton in

the world during the late 1980’s, to consistently the

least expensive during the early 1990’s. More recently

it has ranked closer to second least expensive.

Prices were particularly low during the 1992 marketing

year when, in addition to the influx of Central Asian

cotton, China was a net exporter for the only time

between 1988-96, and India was exporting afler an

unusual decline in its cotton consumption. Much of the

marketing year 1993/94 Step 2 export expenditures

were based on commitments made during the 1992/93.

Expectations of exogenous changes in the future cost of

procuring cotton are assumed to be encompassed by

current year inflation and by an average change in

production over the last 2 years.

Infl is the annual percent change in the U.S. GDP

deflator. It has been assumed that consumers and

stockholders believe the current year’s inflation rate is

the best guide to future inflation, which influences

their willingness to accept a given price at a given

use/stocks ratio. If inflation is higher, they will accept

a higher price, since it reduces the likelihood they will

be able to consummate postponed transactions later at

a lower price. The estimated parameter has the

expected positive sign.

Prodd is the average of the percent difference between

the previous year’s world production (excluding China)

and each of the preceding 2 years. The preceding year

is used because current year’s actual production is

generally not known until late in the year. While it is

a safe assumption that the size of the crop is largely

determined before the year begins, the poor

communication and transportation infrastructure of the

developing countries that account for much of the

world’s cotton production mean that early season

estimates of production are unreliable.

Also, since developing countries heavily intervene in

their economies, the price signals their producers

receive are often at variance from those suggested by

world prices. The best guide to the nature of the price

signals many cotton producers receive, and to their

ability to respond to them, is the resulting change in

production. Since much of government economic

policy, and the shifts in weather and insect pressures

156

w“
that influence yields, are exogenous from world price

signals, it is appropriate to incorporate the past

performance information as an instrument representing

these complex factors in a consumer’s calculation of an

appropriate price. As Prodd rises (declines) it is

indicative of a rising (falling) exogenous trend in

cotton availability, and price should fall (rise). Thus,

the expected sign of the estimated parameter is

negative. ‘l’he’ estimated parameter’s sign is in

accordance with this expectation.

Finally, Xr is the International Monetary Fund’s tradeweighted, real exchange rate index for the United

States. Weighting exchange rates by some other

measure than the value of U.S. merchandise trade

might seem appropriate, but since a significant portion

of cotton imports are for producing textiles whose

ultimate consumption occurs in developing countries,

the IMF’s weights seemed generalizable.

As the index rises, the strength of the dollar increases,

and the cost of cotton in other currencies rises. Thus,

as the exchange rate index rises, foreign consumers are

less willing to pay a given price in dollars, and the

expected sign of the exchange rate variable parameter

estimate is negative. The estimate’s sign is in

accordance with this expectation.

Conclusions

Forecasting a price open to as many changing

influences as the A-index is difficult. Even after years

of global economic liberalization, government

intervention in world cotton markets is significant. The

second largest consumer of cotton in the world, India,

continues to regulate its exports through quotas, and the

second largest exporter, Uzbekistan, seems impervious

to changes in world prices.

This model has been built by step-wise regression over

several years. The variables associated with the Step 2

program are the only variables in the model where tstatistics show a pronounced lack of significance. They

remain in the model nonetheless since theory suggests

a they will affect prices, and because out of sample

testing with truncated data sets gives far more accurate

estimates for 1994 and 1995. The change in the Step 2

program since 1996 also means that even correctly

modeling the impact of the program on past prices will

be insufficient for forecasting prices in the future.

The mean absolute percent error of the model is 4.4

percent over 1971-1995, and the error for its first out of

sample estimate (1996) is 4.7 percent. While this is

promising, the new U.S. Step 2 program, and the

possible specification errors concerning some of the

other variables, suggest that further stepwise revisions

in this forecasting model will be needed.

References

Goldstein, Morris, and Moshin S. Khan. “Income

and Price Effects in Foreign Trade.” In Handbook of

International Economics, Vol. II. R. W. Jones and

P.B. Kenan (cd.). Elsevier Science Publishers.

Amsterdam, Netherlands. 1985.

International Cotton Advisory Committee. Review of

the World Cotton Situation. Various issues.

MacDonald, Stephen. “Cotton Marketing Loan

Program Shifts Away from Imports.” Agricultural

Outlook. July 1997.

Morgan, Dan. Merchants of Grain. Penguin Books.

Harmondswotth, England. 1980.

Pindyck, Robert S., and Daniel L. Rubinfeld.

Econometric Models & Economic Forecasts.

McGraw-Hill. New York. 1981.

U.S. Department of Agriculture. Economic Research

Service. Cotton and Wool Situation and Outlook

Yearbook. Various issues.

U.S. Department of Agriculture. Economic Research

Service. International Agricultural Baseline

Projections to 2005. Agricultural Economic Reports

Number 750. Washington, DC. 1997.

Townsend, Terry P. “This Was a High-Powered

Instrument That Sent Its Projectile To the Vitals of

the Industry--why USDA Does Not Forecast Cotton

Prices.” In 1989 Proceedings of the Cotton Beltwide

Conferences. National Cotton Council. 1989.

Tweeten, Luther. Agricultural Trade: Principles and

Policies. Westview Press. Boulder, CO. 1992.

Valderrama, Carlos. “ Modeling International Cotton

Prices.” In 1993 Proceedings of the Cotton Beltwide

Conferences. National Cotton Council. 1993.

Westcott, Paul C. “An Annual Model for Forecasting

Corn Prices.” Paper presented to Ninth Federal

Forecasters Conference. Washington, DC.

September 1997.

157

w..
. .

. .

. .

\-

Figure 1--World Price and Use/Stocks

4500

4000

3500

s 3000

a

z!U& 2500

2000

1500

1000

1971 1974 1977 1980 1983 1986 1989 1992 1995

~ Use/Stocks -MI- World Price

Figure 2--World Cotton Price

4500

4000

3500

+

= 3000

a

d

uJ z500

u

2000

1500

1000 -—.

-— -— 1971 1974 1977 1980 1983 1986 1989 1992 1995

+ Actual price -E~rnated Price

158
FORECAST EVALUATION

Chair: Norman C. Saunders

Bureau of Labor Statistics, U.S. Department of Labor

An Evaluation of the Census Bureau’s 1995 to 2025 State Population

Projections-One Year Later

Paul R. Campbell, Bureau of the Census

U.S. Department of Commerce

Evaluating the 1995 BLS Labor Force Projections,

Howard N Fullerton, Jr., Bureau of Labor Statistics

U.S. Department of Labor

Industry Employment Projections, 1995: An Evaluation,

Arthur Andreassen, Bureau of Labor Statistics

U.S. Department of Labor

Evaluating the 1995 Occupational Employment Projections,

Carolyn M. Veneri, Bureau of Labor Statistics

U.S. Department of Labor

159

“An Evaluation of the Census Bureau’s 1995 to 2025 State Population Projections --

One Year Later”

Paul R. Campbell, U.S. Bureau of the Census and the Federal Forecasters Conference

Abstract. This paper evaluates the Census Bureau’s 1996

state population projection results using recent population

estimates. ] Besides reporting on the accuracy of the

projections, the paper evaluates the components used in

the projections. The results and discussions are useful in

identifying which components of change - births, deaths,

interstate migration, and international migration - need to

be fullher refined in order to improve future state

population projections produced by the Census Bureau.

Introduction. One step toward improving a frequently

used population projection model is to examine the

accuracy of the results. This study examines the results

of the Census Bureau’s recent state projections for July 1,

1996 using the latest available state estimates for the

same date. The detailed methodology and results for the

state projections for the 1995-2025 period are available in

Population Paper Listing 47 ( PPL-47, see Campbell,

1996a). Besides state population projection totals this

paper also examines the components of population

change: births, deaths, state-to-state migration, and net

international migration. The evaluation of projections

against post-census estimates is an important quality

control tool for both the producers and users of the

projections. This evaluation considers the reliability of

the projections, identifies changes in population trends,

and addresses’ issues related to the efficiency of the

current projection model.

The Census Bureau’s state population projections have

recently been evaluated by the producers of the

projections, see Campbell ( 1996b), Wetrogan and

Campbell (1990), and Sink (1989 and 1990), as well as

other researchers, Smith and Sincich (1992), interested in

accuracy and bias. While demographers at the Census

Bureau are frequently evaluating their elaborate

projection model to improve the quality of the results,

others are concerned with identifying competing models

that may yield just as reliable results.

Some researchers have classified and compared various

sources of state population projection totals, including

those produced by the Census Bureau. Smith and

Sincich (1992) have identified four general categories:

(1) trend projections, where historical trends in states

total population are extrapolated using mathematical

formulas or statistical techniques; (2) ratio projections,

where state population is expressed as a proportion of the

nation and the historical trends in proportions are

extrapolated and applied to independent projections of the

national population; (3) cohort-component projections,

where births, deaths, and migration are projected

separately for each age-sex cohort in the states

population; and (4) structural-causal projections, where

relating population change to economic and/or other

variables are used to project state population. In some

instances these categories overlap.

Smith and Sincich (1992) have evaluated various state

population projections for the 1960 to 1990 period --

several trend and ratio extrapolation techniques, an

ARIMA time series model, the Census Bureau’s cohortcomponent model, and structural models developed by

the Bureau of Economic Analysis (which relates

migration to projections in employment) and the National

Planning Association (economic based model). They

used the Mean Absolute Percentage Error (MAPE),

which is the average error when the direction of error

(i.e., positive or negative) are ignored, as well as several

other techniques that measure forecast error. They found

no support for the argument that complex and/or

sophisticated projection techniques produce more

accurate or less biased forecasts than simple, naive

techniques. Nevertheless, less complex projection

models usually do not produce as much detailed

demographic information.

This paper f~st describes the evaluation techniques used

in this study and gives a brief overview of the Census

Bureau’s state population estimates and projections

methodology used to obtain 1996 figures. Second, the

evaluation undertaken compares 1996 state population

projection totals with corresponding state population

estimates. Next, 1995-96 state population projection

components of change are compared with corresponding

state estimates. Finally, the conclusions section

summarizes the implications of the results for the

improvement of future state population projections.

‘This paper was presented earlier at the Population Association of

America Meeting, Washington, DC, March 1997.

161
Methods. The Census Bureau’s population projections

are not forecasts (or predictions) of future populations.

However, the evaluation of population projections is

often referred to as the measurement of forecast error.

Smith and Sincich ( 1992) note that forecast error refers to

the percentage difference between a population projection

and the ‘true’ population enumerated or estimated for the

same year. To evaluate forecast error in this study, the

mean absolute percentage error (MAPE) was derived,

where:

MAPE=( 100/n) *2[lprojection -estirnatel/estimate]

MAPEs were developed for the United States (the states

and the District of Columbia), where n equaled 51, and

for each census region or division, where n equaled the

number of states in each region or division (see

Armstrong, 1978, and Smith and Sincich, 1992, for

several alternative forecast error measurements).

Additionally, the net and percent population differences

at the state level are used to compare the projections and

estimates. The results show the size and direction of

growth in the state populations and their components of

change.

There can be some problems with aggregation over states

to get MAPEs (or other statistics) if states differ in the

predictability of their population. For instance, percent

differences and MAPEs (1) cannot be calculated when

either populations or components equal zero, or (2) are

not very meaningful when percentages are greater than

100 percent.

State Estimates for 1996. State population estimates

used to measure forecast error in the state population

projections were derived from the Census Bureau’s

annual county estimates. These estimates were obtained

from a demographic procedure called the “component

change” method (U.S. Bureau of the Census, 1996a, and

U.S. Department of Commerce, 1996). The “component

change” method update’s population estimates using

administrative records data for counties. The county

population estimates for 1996 were derived from the

continuous process of updating the 1990 enumerated

census population distributions. State estimates were

obtained by summing up the appropriate county estimates.

For a detailed discussion on the production of state and

county estimates, see Byerly and Deardorff (1995).

It is important to note that the 1995 (one year-out) state

projection totals are more consistent with preliminary

state estimate totals, since the 1995 projections are

summed prorata to the preliminary 1995 state estimates

by age and sex ( released in January 1996, see U.S.

Bureau of the Census, 1996b). In other words, the 1995

total state population estimates (released during January

1996, U.S. Bureau of the Census, 1996a) are a second

round of state population estimates. The state estimates

are not ‘truth’, since subnational estimates are likely to be

updated again as corrections or revisions to administrative

records data become available during the post-censal

period. The ‘true’ state population estimates may not be

completed until the next census has been taken and an

intercensal evaluation is completed. What this implies is

that for 1995 an evaluation is available for preliminary

1995 state population estimate totals using revised 1995

state population estimate totals, see Table 1.

Until the state population estimates for the 1990s are

accepted as ‘truth’, the 1996 state estimates (released in

December 1996) may not bean ideal evaluation standard.

The 1996 estimates are likely to be updated when the

1997 subnational estimates are released, as well as after

the 2000 census. However, it appears that current state

estimates are sufficient for an early comparison to

provide useful information. The resulting MAPEs for the

preliminary 1995 state estimates and the updated 1995

state estimates in Table 1 suggest that error in the 1995

estimates are fairly low. The 1995 MAPEs are much

lower than the one year-out results in previous

evaluations (Campbell, 1996b).

State Projections for 1996. The Census Bureau’s state

population projection model is a complex demographic

model that projects the geographic growth of populations

by accounting for annual aging, fertility, mortality,

internal migration, and international migration of state

populations. State population projections prepared for

July 1, 1995 to 2025 use the cohort-component method.

Each component of population change -- births, deaths,

internal migration (domestic or state-to-state migration

flows), and international migration (immigration and

emigration) -- requires separate projection assumptions

for each birth cohort by single year of age, sex, race, and

Hispanic origin. The race and Hispanic origin groups

projected were non-Hispanic White; non-Hispanic Black;

non-Hispanic American Indian, Eskimo, and Aleut; nonHispanic Asian and Pacific Islander; Hispanic White,

Hispanic Black, Hispanic American Indian, Eskimo, and

Aleut; and Hispanic Asian and Pacific Islander. The

detailed components used in the state population

162
projections are derived from vital statistics, administrative

records, 1990 census data, state population estimates

(U.S. Bureau of the Census, 1996c), and the middle series

of the national population projections (in report P25-

1130, see Day, 1996).

The cohort-component method is based on the traditional

demographic accounting system:

\

P, =P()+B

where:

P,

P,,

B

D

DIM

DOM

IIM

IOM

-D+ DIM- DOM+IIM-IOM

population at the end of the period

population at the beginning of the

period

births during the period

deaths during the period

domestic in-migration during the

period

domestic out-migration during the

period (Both DIM and DOM are

aggregations of the state-to-state

migration flows)

international in-migration during the

period

international out-migration during

the period

To produce population projections with this model,

separate data sets were created for each component.

Detailed assumptions and procedures by which these data

were generated by single year of age, sex, race, and

Hispanic origin are described in report PPL-47

(Campbell, 1996a). Overall the assumptions concerning

the future levels of fertility, mortality, and international

migration are consistent with the assumptions developed

for the national population projections (Day, 1996).

Once the data for each component were developed, the

cohort-component method was applied producing the

detailed demographic projections. For each projection

year the base population for each state was disaggregate

into race and Hispanic origin categories (the eight groups

previously identified), by sex, and single year of age (O to

85+). Components of change are individually applied to

each group to project the next year’s population. Survival

rates were used to survive each age-sex-race/ethnic2

group forward one year. The internal redistribution of the

‘Hispanic origin is referred to as an ethnic group. Hispanic origin

may be any race group.

population was accomplished by applying the appropriate

state-to-state migration rates to the survived population in

each state. The projected out-migrations were subtracted

from the state of origin and added to the state of

destination (as in-migrants). Next, the appropriate

number of immigrants from abroad were added to each

group, while emigrants were subtracted. The populations

under one year of age were created by applying the

appropriate age-race/ethnic-specific birth rates to females

of childbearing age. The number of births by sex and

race/ethnicity were survived forward and exposed to the

appropriate migration rate to yield the population under

one year of age. The results for each age group were

adjusted to be consistent with the national population

projections by single years of age, sex, and

race/ethnicity. 3 The entire process was then repeated for

each year of the projections.

Although, two sets of state population projections were

prepared, the only component specified differently in

each projection model was the domestic migration

component. The dynamic possibilities of change in stateto-state migration makes it the most difficult component

to forecast. Migration trends projected in Report PPL-47

are based on matched Internal Revenue Service (IRS) tax

return data sets containing 19 annual observations (from

1975-76 to 1993-94) on each of the 2,550 state-to-state

migration flows.4 The two projection series provide users

with different scenarios based on past domestic migration

trends. Both sets of state projections are summed and

adjusted by age, sex, race and Hispanic origin to agree

with the national population projection middle series. A

brief description of each series follows: (1) Series A uses

a time series model. The first five years of projections

use the time series projections exclusively. The next ten

years of projections are interpolated from the time series

projections toward the mean of the series, while the final

15 years use the series mean exclusively. (2) Series B is

an economic model. Changes in state-to-state migration

rates are derived from the Bureau of Economic Analysis

projected changes in employment in the origin and the

destination states.

‘The state projections for one year-out are a special case because

they were controlled first to the 1995 national population projections

by age, sex, race, and Hispanic origin; and second to the preliminary

1995 state estimates, which were only mailable by age and sex.

devaluation of the migration models was performed by

withholding the recent data md using the models to predict the

withheld data (i.e., 1975-76 to 1992-93 data wem used to predict

1993-94). For a discussion of the evaluation of previous migration

models see Sink (1990),
Findings -- Table 1 presents a comparison of the MAPEs

for the Census Bureau’s 1996 state population projections

(both Series A and B). These projections represent a lead

time of two years-out from the base year of 1994.

Examining the MAPEs for the United States, its regions,

and its divisions suggests that most results in Series B

were slightly more accurate than those in Series A. As

noted earlier, the 1995 MAPEs for the state population

totals, in table 1, should be viewed as estimate error

between the first round (or preliminary) state estimates

and the second round of the state estimates.

In Series A, the maximum MAPEs for the 1996 division

projections was 0.8 percent compared to 0.7 percent for

Series B. Most division projections were within 0.4

percent of the estimates, see Figure 1. For both Series A

and B, the results for the West are much worse than for

the rest of the country.

Table 2 presents a comparison of the net and percent

differences between the projected and estimated total

populations for the United States, regions, divisions, and

states. In essence, it shows which states have the most

accurate projection results. In Series A for 1996

projections, states ranged from -2.0 to 1.6 percent

difference between the projections and estimates, while

Series B had a slightly narrower range of percent

differences from -1.9 to 1.3 percent. Outliers in Series A

showing the greatest percent differences between

projections and estimates were Arizona (-2.0 percent),

Idaho (1.0 percent), Alaska (1.2 percent), Hawaii (1.4

percent), and Wyoming (1.6 percent). Three of the same

outliers (Arizona 1.9 percent, Hawaii 1.1 percent, and

Wyoming 1.3 percent) were found in Series B, see

Figures 2 and 3. Except for outliers, states’ percent

differences in 1996 were fairly accurate, ranging from

less than plus or minus 1.0 percent.

Components of Change for 1996. The components of

change account for the growth or decline in state

population. The comparison of the state projection

components of change in report PPL-47 with

corresponding state estimate components of change in

press release CB96-224 (see U.S. Department of

Commerce, 1996) is useful to identify the accuracy of the

birth, death, net internal migration, and net international

migration components.

The states’ fertility and mortality rates were projected

following trends (i.e., rate of change) in corresponding

national rates. However, the states’ annual fertility and

mortality components (annual vital events, i.e., total

births and total deaths) were not controlled or summed to

corresponding national projection components.

International migration rates for states were assumed to

be constant and consistently follow national trends over

the 30-year projection period. In the present evaluation,

the states estimated internal migration component

includes federal citizens movement5. The residual

component calculated for state estimates was excluded.b

The residual component is the net difference between the

sum of the states’ components of change and the national

controls.7

One should be cautious in comparing the state estimates

and projections components of change, since this involves

several inconsistence. There are five reasons for being

cautious in comparing the net and percent differences on

the components of population change. First, the state

estimates and projections use different data sets and

methodologies. State projections use a cohort component

model and many static assumptions, while state estimates

use a county “component change” model. Second, when

the state estimates components are close to zero and very

different from state projections components; the resulting

percent differences can be extremely large (more than 100

percent). The percent difference also cannot be

calculated when the state component equals zero. Third,

the state estimates and projections models diverge in their

demographic accounting of the movement of the domestic

and international migration components. When the

components of change are summed (i.e., births - deaths,

+/- domestic migration, +/- international migration), the

results equal the net population change for the states

estimates for 1995-96. The components of change for the

state projections are not controlled back to the national

totals; therefore, the sum of the components does not

equal to the net population change in the state projections

for the periods studied. Fourth, residual changes

introduced through state and national controls can cause

problems for this evaluation in the state projections, since

some net differences (or residual differences) are

introduced when the projections are summed and adjusted

“’Federal citizen movement component is the net movement of

federally associated civilians and military personnel to each state

from outside the country,” see U.S. Bureau of the Census (1996a).

The exclusion of the residual component from the state estimates

and projections components imply that the sum of the components

shown in table 3 will not equal the net population change.

7“R~idual is the eff~t of national controls on subnational

estimates. It is the difference between the implementation of the

national estimates model and the county/state estimates models.”

U.S. Bureau of the Census, 1996a.

164
to the age and sex distribution of the national population

projections. Finally, the error in one component may

compound errors in other components. For example,

projecting too many migrants increases the population,

which may raise the number of births, if they are a more

youthful population. This analysis identifies which

components of change account for the most errors in the

projections.

Findings -- Es’tirnated components of population change

for states shown in Table 3 identify the gains or declines

of state population through births, deaths, internal

migration, and international migration for the period July

1, 1995 to July 1, 1996. Among the four regions, the

West and South, followed by the Midwest, had the fastest

growth.

Fertility and mortality levels are expected to be static or

slow to change in comparison to changes in migration

trends. Consequently, projected birth and death

components are likely to be more accurate than projected

internal migration or international migration components.

Although natural increase (births minus deaths) has

played a major role in this growth, domestic migration in

the South and international migration in the West

accounted for the regional differences. Areas having the

greatest population losses through domestic migration

were the Middle Atlantic states in the Northeast and the

Pacific states in the West.

Table 4 shows the results of calculating 1996 components

of change MAPEs for Series A projections in report

PPL-47 using the most recent state population estimates.

The birth component is the most accurate component for

most regions and divisions, followed by the death

component. While both migration components are major

sources of error in the state projections, domestic

migration is consistently the least accurate component

across all regions and divisions. Additionally, the

MAPEs for the domestic migration components are the

highest (greater than 100 percent) in the West, Midwest,

and South regions, see Figure 4.

Table 5 shows the net and percent differences between

the projected and estimated components of state

population change for 1996. The results show the general

accuracy of each component of change. Clearly, the birth

component is the most accurate, followed by the death

component. The direction of the error varied with about

half the states having too many birth and the other half

having too few births, while deaths were most often too

high for most states. The fertility and mortality

methodology in the current projections need to be further

reviewed to identify the reasons for (1) predicting too

many deaths for most states and (2) a number of outliers

on both components with a difference of 10 percent or

more.

As expected, the major sources of errors in the state

projections were the state-to-state migration component,

followed by the international migration component.

Frequently, both projected migration components are too

low, however, in these projections, the domestic

migration was too high for most states, while immigration

was too low. Contrary to these findings, California, with

the largest share of the nation’s domestic and international

movement, was too low on the domestic migration

component (projected in excess of 207,874 out-migrants

in 1996) and too high on immigration (a surplus of

44,907 immigrants in 1996).

Conclusions. The MAPEs calculated using state

population totals imply that the projections are fairly

accurate (less than 0.5 percent error) for all regions but

the West. Furthermore, the current two years-out

projection results are within the range of forecast error

found in previous state projection results one year-out

(see Table 6).

The high net and percent differences for several states in

the West point to the difficulty of the migration models in

predicting turning points or the reversal in migration

streams. Clearly, states like Alaska, Arizon% Hawaii, and

California are outliers (with the least accurate

projections) in the current and previous sets of

projections.

This was the second time an economics model was used

to predict domestic migration flows. In this instance, the

economics model (Series B), which uses inputs from the

Bureau of Economic Analysis, produced slightly more

accurate projections than the time series model (Series

A). The economic model, seems useful, but also failed to

predict reversal or turn-around in migration trends.

Future refinements of the current economic model maybe

hampered by the fact that current subnational economic

projections may not be updated.

Past evaluations -- In essence, the evaluation process

began with the examination of the internal migration

component long before each set of state projections was

produced. The internal migration component, the most

difficult component to predict, often suffers the greatest

loss of accuracy over the projection horizon. Past

165

b-
evaluations of our internal migration models indicated

that the mean predicts more accurately than our time

series model for projections ten or more years out, to

which the necessary adjustments were made.g

Similar to previous projections those in report PPL-47 do

not adequately predict turning points in the domestic

migration flows. It appears that these changes are linked

regionally, which may require more complex nmdeling to

predict trends. For example, as California began to have

losses through domestic migration, other states in the

region began to show rapid growth. International

immigration also peaked during 1992-93 then began to

decline. For an evaluation of forecast errors in previous

state projections in Current Population ReportP25-1111,

see Campbell, 1996b and 1994.

Implications for Future Projections -- This evaluation did

not attempt to examine the methodological errors

introduced by other differences in the projections and

estimates. Several methodological problems that are

likely to contribute to projection inaccuracies are as

follows: (1) dated domestic and international

migration rates based on the retrospective data from the

1990 census; (2) projections based on inadequate or

incomplete baseline race and Hispanic origin

characteristics; and (3) problems caused by controlling

the projections to the national estimates and projections,

and less detailed state estimates. The state-to-state

migration models used in the projections makes no

attempt to forecast turning points in migration. The

recurring problem of failing to produce accurate

projections for states in the West suggests that perhaps

more attention should be directed to the potential for

changes in those states with more rapid growth than in the

past. Perhaps, a sub-state projection model would better

capture and extrapolate emerging migration trends than

the current state-to-state projection model.

Future work on state population projections at the Census

Bureau will explore the possibility of producing sub-state

level projections. For instance, metropolitan -

nonmetropolitan projections may identify counter substate migration trends that could improve our ability to

project state populations accurately.

Additionally, the current state projections were

constrained by pressures from users for more

demographic characteristics based on less detailed data

and fewer staff resources. The future evaluation and

tracking of the Census Bureau’s state population

projections on detailed demographic characteristics (age,

sex, race, and Hispanic origin) should further identify the

essential refinements necessary to produce more accurate

state population projections.

“Smith and Sincich (1990) evaluating several simple forecasting

techniques for state projections found that 10 years of base data are

adequate for accurate projections, however more lengthy base period

data are needed for longrange forecasts for the most rapidly growing

states.

166
References

Armstrong, J. Scott, 1978, Lon~-Ran~e Forecasting:

From Crvstal Ball to Comrwter, John Wiley & Sons, New

York.

Byerly, Edwin R., and Kevin Deardorff, 1995, National

and State Po~ulation Estimates: 1990 to 1994, Us.

Bureau of the Census, Current Population Reports, P25-

1127, GPO, Washington, D.C.

Campbell, Paul R., 1997, Pormlation Projections: States.

1995-2025, Current Population Reports, P25-113 1, U.S.

Bureau of the Census, Government Printing Office,

Washington, DC.

Campbell, Paul R., 1996a, “Population Projections for

States, by Age, Sex, Race, and Hispanic Origin: 1995 to

2025,” PPL-47, U.S. Bureau of the Census, Population

Division, October. For data files see Census Bureau,

Population Division, PE-45 (a tape or diskettes) or go to

the INTERNET at http://www.census. gov.

Campbell, Paul R., 1996b, “How accurate were the

Census Bureau’s state population projections for the early

1990’s?”, Federal Forecasters Conference in Washington,

DC.

Campbell, Paul R., 1994, Population Projections for

States. by A~e. Sex, Race, and HisDanic Origin: 1993 to

2020, Current Population Reports, P25-111 1, U.S.

Government Printing Office, Washington, DC.

Campbell, Paul R, 1991, “Evaluating the Age Distribution

in State Populations,” Federal Forecasters

Conference/1991, Papers and Proceeding of the

Conference, Washington, DC.

Day, Jennifer Cheeseman, 1996, Population Projections

of the United States by A~e, Sex, Race, and Hispanic

Origin: 1995 to 2050, U.S. Bureau of the Census,

Current Population Reports, Series P25- 1130,

Government Printing Office, Washington, DC.

Sink, Larry D., 1989, “The Efficacy of Economic and

Demographic Models in Predicting Interstate Migration

Rates,” Paper presented at the Western Regional Science

Association Meeting, April.

Smith, Stanley K., and Terry Sincich, 1990, “The

Relationship between the Length of the Base Period and

Population Forecast Errors,” Journal of American

Statistical Association, vol. 85, no. 410, June.

Smith, Stanley K., and Terry Sincich, 1992, Evaluating

the Forecast Accuracy and Bias of Alternative Population

Projections for States, International Journal of

Forecasting, vol. 8, pp. 495-508, North-Holland.

Wetrogan, Signe I., 1990, Projections of the Population

of States by AEe, Sex, and Race: 1989 to 2010, Current

Population Reports, P-25, No. 1053, GPO, Washington,

DC.

Wetrogan, Signe I., 1988, Projections of the Population

of States, by AEe, Sex, and Race: 1988 to 2010, Current

Population Reports, P-25, No. 1017, GPO, Washington,

DC.

Wetrogan, Signe I., and Paul R. Campbell, 1990,

“Evaluation of State Population Projections,” Paper

Presented at the Population Association of America,

Toronto, Canada, May 3-5.

U.S. Bureau of the Census, 1996a, “Estimates of

Population and Demographic Components of Change for

States: Annual Time Series, 1990-96,” ST-96-1, data file

issued by the Population Distribution Branch.

U.S. Bureau of the Census, 1996b, PE-38, “Population of

States by Single Years of Age and Sex: 1990 to 1995,”

Population Division.

U.S. Bureau of the Census, 1996c, PE-47, “Estimates of

the Population of States by Age, Sex, Race, and Hispanic

Origin: 1990 to 1994,” on 3 diskettes, Population

Division.

U.S. Department of Commerce, 1996, News, “ Population

Growth Remains Fastest in Western and Southern States,

Census Bureau Reports,” U.S. Bureau of the Census,

Press Release CB96-224 issued 12/30/96.

Sink, Larry D., 1990, “Evaluating Migration Projections,”

Paper presented at Federal-State Cooperative Programs

for Population Projections Conference held in May.

167
0 ● 8

0 ● 6

04●

02●

o

Fig.1 MAPEs for State Projections

Series A and B: 1996

----

----

+

.-

. .

.-

---- . .

---- --

--- ---

---- ---

t +——

Northeast Midwest South West

Regions

■ Series A ❑ Series B
Fig. 2 Highest and Lowest Percent

Difference Series A 1996

Wyoming

Hawaii

Alaska

Idaho

South Dakota

New York

Oregon

California

Texas

Arizona

1 1

-2 - 1 0 1 2

Percent

169
m

Fig. 3 Highest and Lowest Percent

Difference Series B 1996

Sou<

Wyoming

Hawaii

Idaho

h Dakota

Kansas

Washington

Oregon

California

Texas

Arizona

-2 - 1● 5 -1 -05 0 0 5 1 1●5

Percent ●

170
L

Fig. 4 MAPEs for Components of

Change for Regions, Series A: 1996

160-

140-

120 ;

100:

8 0 :

6 0 :

40-

20 ;

o

---

. .

---

--

. . .

--

. .

-.. . .-

---- . .

-.. . .-

---- .-

. .

.-

-.

+

. .

-.

. .

. .

. .

. .

--

—

. . . --

.-. --

-. . .

. . . .-

--- .-

--- --

. . .

+

--

-.

. .

-.

. .

--

.-

—

.-

--

-.

--

. .

--

+

Northeast Midwest South West

Regions

Births Deaths

❑ Domestic migrants International migrants

--

--

--

--

171
Table 1. Mean Absolute Percentage Error for State Estimates 1995 and State

Projections 19$6

Regions and 1995 -----------------1996 Projections -------------”

divisions Estimates Series A Series B

United States 0.18 0.40 0.33

Nol’the8st 0.11 0.24 0.17

New England 0.10 0.22 0.12

Middle Atlantic 0.15 0.26 0.26

Midwest 0.10 0.26 0.23

East North Central 0.15 0.19 0.19

West North Central 0.07 0.31 0.25

South 0.12 0.30 0.25

South Atlantic 0.09 0.29 0.22

East South Central 0,12 0.31 0.30

West South Central 0.16 0.29 0.27

West 0.37 0.77 0.62

Mountain 0.42 0.83 0.68

Pacific 0.29 0.68 0.53

172

1995 Estimates: Mean absolute percentage error (MAPE’s) based on an initial set of state population

estimates released January 1996 and a revised set of state population estimates released December

1996. Data from PE-45 diskettes and press release CB96-224.

1996 Projections: Results for 2 years-out from the 1994 base year population. MAPEs are based on

State population projections from report PPL-47 and State population esimates in press re!ease

CB96-224.
Table 2. Population Estimates, Net and Percent Difference between Projected and Estimated Population,

by Series and Geography 1996

Regions, Divisions,

and States

United Statea

Northeaat

New Engfand ‘

Middle Atlantic

Midwest

East Nofih Central

West Notth Central

South

South Atlantic

East South Central

West South Central

West

Mountain

Pacific

New England

Maine

New Hampshire

Vermont

Massachusetts

Rhoda Island

Connecticut

Middle Atlantic

New York

New Jersey

Pennsylvania

East North Centrat:

Ohio

Indiana

Illinois

Mich@an

Wsconsin

West North Central:

Mkmeeota

Iowa

Missouri

North Dakota

South Dakota

Nebraska

Kansas

South Atlantic

Delaware

Ma@and

District of Columbia

Vkginia

West Virginia

North Carolina

South Carolina

Georgia

Florida

East South Central:

Kentucky

Tennessee

Atabama

Mississippi

West South Central:

Arkansas

Louisiana

Oklahoma

Texas

Mountairx

Montana

Idaho

Wyoming

Colorado

New Mexico

Arfzona

Utah

Nevada

Pacific

Washington

Oregon

California

Alaska

Population

265,283,783

51,580,085

13,351,266

36,228,819

62,08&428

43,613,999

18,468,429

93,0s7,801

47,615,690

16,192,576

29,269,535

58,523,469

16,117,631

42,405,638

1,243,316

1,162,481

588,654

6,062,352

880,225

3,274,236

18,184,774

7,987,933

12,056,112

11,172,782

5,840,528

11,846,544

9,584,350

5,159,795

4,657,758

2,851,792

5,358,692

643,539

732,405

1,652,093

2,572,150

724,842

5,071,804

543,213

6,675,451

1,825,754

7,322,870

3,698,746

7,353,225

14,399,985

3,683,723

5,319,654

4,273,084

2,716,115

2,508,793

4,350,579

3,300,902

19,128,261

679,372

1,189,251

481,400

3,822,676

1,713,407

4,428,066

2,000,494

1,603,163

5,532,939

3,203,735

31,676,234

607,007

1,163,723

----------- Series A ----------------

Net

dtiarence

(30,254)

38,954

22,131

14,823

95,524

59,285

36,239

(12,775)

43,438

53,398

(109,611)

(149,957)

(33,174)

(1 16,783)

2,044

3,013

3,165

8,667

1,160

4,042

(43,840)

9,647

48,716

16,233

15,244

31,772

(12,970)

7,006

(673)

2,588

11,192

2,144

6,836

(587)

14,959

3,345

23,561

2,134

25,282

5,182

(4,824)

13,404

(10,045)

(14,601)

5,121

21,305

21,106

5,864

4,916

8,696

(3,919)

(119,504)

7,666

12,382

7,607

14,768

6,018

(68,305)

3,363

887

(1 1,486)

(8,429)

(120,298)

7,132

16,308

Percent

dflerenca

-0.01

0.07

0.17

0.04

0.15

0.14

0.20

-0.01

0.09

0.33

-0.37

-0.26

-0.21

-0.28

0.16

0.26

0.54

0.14

0.12

0.12

-0.24

0.12

0.40

0.16

0.26

0.27

-0.14

0.14

-0.02

0.08

0.21

0.33

0.93

-0.04

0.58

0.46

0.46

0.39

0.38

0.28

-0.07

0.36

-0.14

-0.10

0.13

0.40

0.49

0.22

0.20

0.20

-0.12

-0.62

0.87

1.04

1.84

0.39

0.47

-1.88

0.17

0.06

-0.21

-0.26

-0.36

1.17

1.38

------—--- Series B -----------------

Net

dflerence

(30,351 )

U,920

18,406

26,512

99,713

71,731

27,982

(4,292)

42,742

51,165

(98,1 99)

(170,692)

(40,660)

(130,032)

106

1,380

1,077

12,495

(859)

4,209

(36,773)

11,990

51,295

22,353

16,069

35,292

(7,873)

5,890

(2,61 3)

525

11,626

637

4,914

(2,136)

15,027

1,373

22,604

1,797

21,713

3,113

(2,900)

12,343

(7,643)

(9,658)

3,870

22,400

18,401

6,494

2,671

12,328

(4,704)

(108,494)

4,850

9,480

6,325

11,713

7,035

(83,525)

3,189

273

(16,675)

(9,758)

(120,188)

3,531

13,058

Percent

difference

-0.01

0.06

0.14

0.07

0.16

0.16

0.15

-0.00

0.09

0.32

-0.34

-0.29

-0.25

-0.31

0.01

0.12

0.18

0.21

-0.06

0.13

-0.20

0.15

0.43

0.20

0.28

0.30

-0.08

0.11

-0.06

0.02

0.22

0.10

0.67

-0.13

0.56

0.19

0.45

0.33

0.33

0.17

-0.04

0.33

-0.10

-0.07

0.10

0.42

0.43

0.24

0.11

0.28

-0.14

-0.57

0.55

0.80

1.31

0.31

0.41

-1.89

0.16

0.02

-0.30

-0.30

-0.36

0.58

1.10

Notes: Negative vafuea are shown in pamtheaia. Net diffemnos obtained as projected populations minus estimated populations. Figures may not

sum to totals due to rounding. Percent differenoa for each year equala (projection - estimate/estimate) ● 100.

Sources See taxt for details.

173
.,

.,”.

,.,,

,“

Table 3. Estimated Component of Population Change, for States: July 1,1995 to July 1,1995

Regions, Divisions,

and States

United States

Northeast

New England

Middle Atlantic

Midwest

East North Centrai

West North Centrai

south

South Atlantic

Eaat South Central

west south central

weat

Mountain

Paafic

New England:

Maine

New Hampshire

Vermont

Massachusetts

Rhode island

Connecticut

Middle Atlantic:

New York

New Jersey

Pennsylvania

East MCentral:

Ohio

Indii

Illinois

Michigan

Wk3c0nain

West North Central:

Minnesota

iowa

Missouri

North Dakota

South Dakota

Nebraska

Kansas

South Atlantic

Deiaware

Marytand

District of Columbia

Virginia

West Virginia

North Carolina

South Carofha

Georgia

Florida

East south cerrtrd:

Kentucky

Temwaeae

Alabama

Miippi

Weat South Central:

Louisiana

Oklahoma

Texas

Mountain

Montana

IdwColorado

New Mexico

Arizona

Utah

Nevada

Paafic:

Washhgton

Oregon

California

Alaska

HawtM

Births

3,879,771

697,466

168,492

528,974

877,611

624,684

252,927

1,349,597

654,419

226,169

469,009

955,087

254,558

700,539

13,983

14,993

6,828

75,029

12,427

45,252

284,068

113,808

151,100

153,909

83,216

185,882

134@6

87,671

63,493

36,985

72,919

8,508

10,487

23,319

37,218

10,246

71,934

7,889

91,115

21,238

101,293

50,649

111,707

188,348

52,080

73,094

60,017

40,978

35,141

65,118

45,374

323,376

11,175

18,114

6#91

54,541

26,944

72,615

39,771

25,107

77,456

42,918

551,744

10,181

18&10

Deaths

2,330,870

498,972

122,005

376,987

575,484

400,929

174,555

635,673

440,302

157,779

237,592

420,741

117,518

303J?23

11,884

9,408

5,025

56,400

9,841

29,447

172,051

76,801

128,315

106,710

53,923

109,874

84,414

46,008

38,197

26,461

54,919

6,139

7,091

15,519

24,229

6,354

42,675

6,042

52,423

20,462

65,152

33,617

58,889

154,888

37,304

51,458

42$?58

28,759

26,770

39,788

32,737

138,319

7,794

6,598

3,773

25,192

12,708

35,672

10,970

12,613

41,255

26,559

223,976

2,826

6,805

migrants

(32239:]

(298,177)

[gA&l;

20;844

361,888

244,254

70,712

(%%

188,772

(209,784)

2,304

7,723

1,547

(15,325)

(5,967)

(20,497)

(216,831)

(39,346)

(40,000)

(14,802)

10,707

(58,353)

(5,750)

13,070

11,640

(1 ,998)

16,867

(91 1)

(1 ,075)

3,309

(7,188)

2,845

(1 1,679)

(17,205)

2,436

(584)

77,947

12,416

76,828

101,450

10,439

48,188

7$16

4,887

15,626

(15,917)

10,176

57,017

5$05

11,039

(640)

38,049

4,692

72,465

9,885

50,097

33,100

(22,%

(4,150)

(13,185)

International

migrants

855,848

205,272

31,705

173,587

64,866

65,828

19,088

247,708

147,735

7,883

92,088

317,774

40,333

277,441

468

935

537

19,069

2,023

6,673

118,496

40,649

14,422

7,426

3,795

38,741

12,881

2,983

6,227

2,505

4,410

655

600

1,810

2,661

1,141

15,457

3,835

19,591

468

7,025

2,454

14,434

83,330

1,884

3,033

2,138

848

1,037

3,115

3,573

84,383

333

2,438

320

9,410

4,719

13,204

3,492

6,417

16,319

7,116

246,376

1,078

6,550

Federal ciiZSll

movement

(10,400)

p]

(297)

$56]

(426)

$s774]

{573)

(1 ,356)

(3,387)

(714)

(2,673)

(35)

(5)

(4:)

(26)

(75)

(170)

(88)

(39)

(68)

(9)

(232)

(16)

(5)

(16)

(1)

(103)

(66)

(28)

(74)

(138)

(33)

(320)

(45)

(1,218)

(1)

(790)

(287)

(519)

(832)

(193)

(125)

(129)

(128)

(34)

(150)

(228)

(944)

(31)

(29)

(26)

(227)

(116)

(176)

~]

(450)

(11)

(1 ,871)

(153)

(388)

Residual

f,2!3~

{861)

~,&3]

‘ (88)

1,889

292

176

1,401

2,049

2,529

(480)

(72)

(1)

(9)

(1 ,054)

(92)

(408)

700

(1 ,36~)

(1 ,007)

(206)

201

(505)

(16)

(2)

(312)

183

(14)

12

35

10

(44)

(25)

253

716

(161)

212

131

1,188

(1 ,978)

(40)

(%)

124

32

107

(126)

1,388

133

173

38

535

29

818

62

741

49

28

“1 32-

113

Note Negative veha are shown in pamhaie.

Sources: ‘See text for details. Data rewted in the U.S. Bureau of the Census, 1996, “Estimates of Population and Demw@ic COmpments of change

for States: Annual Time series, 1990--98,’ ST-96-1, Population Dwiaion.

174

m-
Table 4. Mean Absolute Percentage Error for State Projections Components of

Change: 1996

Domestic International

Regions, Divisions, Births Deaths migrants migrants

and States

United States 4.4 7.0 136.1 23.90

Northeast ‘ 7.5 8.3 58.7 30.38

New England 9.3 7.7 72.3 42.82

Middle Atlantic 3.8 9.6 31.7 5.50

Midwest 1.3 6.2 155.7 23.52

East North Central 1.8 6.1 138.4 18.14

West North Central 0.9 6.3 168.1 27.37

South 3.9 7.4 145.3 15.72

South Atlantic 5.9 8.2 224.0 16.64

East South Central 1.2 5.9 77.0 11.01

West South Central 2.4 7.0 36.5 18.39

West 5.7 6.1 159.7 30.46

Mountain 5.9 3.5 223.2 42.87

Pacific 5.4 10.3 58.0 10.62

Sources: See text for details. Mean absolute percentage error based on state projections Series A from

report PPL-47 and state estimates from PE-45 data files.

175
Table 5. Net and Percent Difference between Projected and Estimated Components of Population Change: 1995 and 1996

Regions, Dividons,

and states

United states

Northeaat

New En@and

Middle AtiSldiC

Midwoat

East North Central

Wesf Nom Central

south

south Atlantic

East SMh Centrat

West South Central

weat

Padfic

New En@mct

Maine

New Hamoahira

Vermont

Rhoda ldand

Mf&%&%~d

New York

New Jemay

Penn@vanta

E#d NOtth Centrat:

Indiana

lltinda

Michigan

W~in

West NotUI Centrak

Minnesota

iowa

Mlaaouri

North Dakota

South Dakota

Nebraska

South Atlantic

Dafawara

Mafyiand

Diatdcf of Columbia

Virginia

Waat Vifginia

Nofth Carolha

SouttI Camfina

Georgia

Florida

East South Central:

Kentucky

Tennessee

Alabama

weat south Cantrat:

Louisiana

Okfahoma

Texaa

Mountalm

Montana

Idaho

Wyoming

Colorado

New Mexioo

Arizona

Utah

Nevada

Pacific:

Washington

Oqorl

Caliimia

Alaska

Hawaii

Siftha Deaths

Net

68,686

35,551

15,893

22,958

5,105

5,123

(18)

(f~)

(2:612)

(3,754)

23,855

(12,767)

42,432

1,639

791

764

10,471

1,544

684

14,432

1,475

7,051

(i%)

(470)

5,934

422

(2;;)

1,247

67

(3)

(445)

(676)

136

2,052

2,496

2,111

(3,A%)

3,131

(4,344)

(1,267)

(569)

(2,009)

(245)

211

(1,023)

2,951

(215)

(5’467)

(50)

(1s435)

285

(1 ,423)

(4,039)

(3,615)

(3,424)

(1,009)

(1,934)

43,335

1,444

Notes Negative vatuea are in pamthaaia.

1.77

5.57

9.43

4.34

0.58

0.82

-0.01

-0.37

0.22

-1.15

-0.80

3.11

-5.02

6.06

f 1.74

5.28

11.19

13.86

12.42

1.51

5.47

:::

1 .(M

-2.77

-0.25

4.42

0.62

-0.34

0.03

1.71

0.79

-0.03

-1.91

-1.82

1.33

2.85

31.66

2.32

0.77

-3.01

8.18

-3.89

-0.67

-1.09

-2.75

-0.41

0.51

-2.91

4.53

-0.47

-1.69

90.4$

-7.92

4.53

-2.61

3.69

-5.64

-9.09

-13.64

-1.30

-4.51

7.67

5.26

7.32

Net

169,586

50,055

10,933

39,135

35,782

24,976

10,806

47,533

20,651

6,684

18,218

36W

678

35,325

846

286

295

5,612

1,033

2,875

22280

5,161

11,674

5,773

2,598

8,099

5,631

2,875

2,168

2,355

2,977

289

447

1,239

1,331

451

1,894

2,073

3,622

1,714

2,488

706

1,761

5,942

2,505

1,317

2,761

2,061

1,573

3,103

1,994

11,542

244

334

(6)

935

(%)

(l,2&)

2,380

1,021

30,153

(54)

1,825

7.28

10.03

8.S6

10.38

&22

6.23

6.19

5.69

4.89

5.49

7.67

8.80

0.75

11.65

7.12

2.83

5.87

9.95

10.58

9.76

12.95

6.76

9.10

5.41

4.82

7.37

6.67

6.25

5.66

8.27

5.42

4.71

6.30

7.36

5.49

7.10

4.44

34.31

6.91

8.38

3.62

2.10

2.99

3.84

6.72

2.56

6.58

7.70

5.90

7.80

6.03

8.34

3.13

3.89

-0.16

3.71

4.90

-1.33

2.64

-8.45

5.77

3.56

13.46

-2.08

26.82

Net

10,402

15,580

(19,035)

34,885

(6,540)

(35,255)

28,715

97,545

56,844

28,502

12,502

(35,566)

30,773

(187,339)

(3,276)

1,025

2,401

(14,778)

(264)

(4,141 )

(2,686)

10,304

27,077

(7,731)

7,919

(4,875)

(31,667)

1,099

715

2,058

3,499

1,836

7,044

2,032

11,531

3,058

10,161

1,199

17,766

1,650

1,683

2,127

3,143

15,827

870

10,120

13,041

4,471

5,875

(;%)

13,421

6,866

17,230

6,384

19,977

14,877

3,076

13,579

6,760

6,049

(406)

(207,874)

5,078

9,616

-100.02

-4.7s

62.61

-11.70

18.55

83.57

142.03

25.02

23.64

40.64

19.07

3e5.lo

48.27

88.19

-144.47

13.28

155.20

96.15

4.41

20.13

1.24

-26.13

-67.63

51.98

74.02

8.32

549.20

8.41

6.15

-102.95

20.87

-187.92

-638.82

62.61

-157.40

109.62

-84.68

-6.95

1458.62

-316.24

2.18

17.54

4.13

15.70

8.49

21.06

183.96

94.30

37.68

-6.22

-78.35

23.93

132.74

156.49

-955.69

55.77

325.25

4.26

138.19

17.55

18.53

-1.22

73.77

-116.01

-72.32

International migrants

Net

(33,074)

14,784

12,259

2,525

&7213;

(1:442)

(65,134)

(24,582)

(44,472)

28,002

(15,287)

41,289

208

(%

6,225

1,618

2,291

(?%)

(1,337)

708

(3,7R)

(2,745)

1,469

25

(E)

(227)

(397)

(739)

638

(338)

3,879

503

408

(1)

(372)

(430)

(4,005)

(24,206)

349

266

240

45

(1A)

(575)

(43,726)

(1,;:)

(164)

(4,448)

(3,736)

(2,867)

(665)

(2,401 )

(2,827)

(287)

44,907

(75)

(429)

Percent

-3.67

7.20

38.67

1.45

-5.74

-6.51

-7.56

-27.51

-16.63

11.42

48.29

8.18

-37.90

14.88

44.44

17.33

-45.62

43.13

79.98

26.42

4.27

-2.95

-9.27

9.53

0.92

-9.68

-21.31

49.25

0.40

6.63

-20.59

-34.68

-66.17

-40.63

22.30

-28.62

25.10

13.12

2.06

-0.21

-5.30

-17.52

-27.75

-29.05

16.72

8.77

11.23

5.31

0.10

-5.52

-16.09

-51.83

40.54

-45.98

-51.25

-47.27

-79.17

-21.71

-19.62

-37.42

-17.32

-4.03

18.23

-6.96

-6.55

Net diffemnoa equate pmjactbn - estimate. Percent difference for eaoh year equals (pmjadion - estimate)/ estimate) ● 100.

● Doma!Mo migrants indudea net movement of Federal Citizens from abroad to the States, so the natjona totai does not equal to zero.

SOIJ- series A wrnponenta of change from PPL47 and PE-45, see text for detaile.

176
Table 6. Mean Absolute Percentage Error for State Projections from the Most Recent

Census Bureau Publications

Two year-out results

Regions ‘ Report PPL-47

.------- .--.. --.. ----.-..,

Series A Series B

t

United States 0.4 0.3

Northeast 0.2 0.2

Midwest 0.3 0.2

South 0.3 0.3

West 0.8 0.6

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..- One year-out results . . . . ------------------------

Report P25-111 1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Series A Series B Series C

0.3 0.5 0.3

0.3 0.6 0.3

0.2 0.2 0.2

0.2 0.3 0.2

0.5 0.9 0.5

Report P25-1 053

---------------------------------------- Report

Series A Series B Series C P25-1017

0.4 0.7 0.4 0.5

0.3 0.5 0.4 0.2

0.2 0.3 0.3 0.2

0.3 0.7 0.3 0.4

0.6 1.1 0.7 1.1

Report PPL-47: Mean absolute percentage error (MAPEs) are based on State projections for 1996 two years-out frorr

the 1994 starting points or base year. - “ ‘

Report P25-111 1: MAPEs are based on State projections for 1993 one year-out from the 1992 starting points.

Report P25-1 053: MAPEs are based on State projections for 1990 one year-out from the 1989 starting points.

Report P25-101 7: MAPEs are based on State projections for 1989 one year-out from the 1988 starting points.

Sources: Various Census Bureau publications, see text for details and references.

177

. .
:, . . . m
EVALUATING THE 1995 BLS LABOR FORCE PROJECTIONS

Howard N Fullerton, Jr., Bureau of Labor Statistics

BLS, OffIce of Employment Projections, Washington, DC 20212-0001

KEYWORDS: Mean absolute percent error, index of

dissimilarity, growth rate errors

1. Introduction

The Bureau of Labor Statistic (BLS) has made labor

force projections since the late 1950s. They have

generally been for a 10 to 20 time span. These

projections by age and sex, since the late 1970s, by

race, and since the late 1980s, by Hispanic origin.

Beginning in 1968, the Bureau of Labor Statistics has

not considered the projection process complete until it

assesses the accuracy of its projections (Swerdloff

1969). Such evaluations help the developers of the

projections to better understand the causes of projection

errors and provide users with information on the

accuracy of specific components of the projections.

This article examines the errors in the labor force

projections to 1995 and the sources of the errors. The

analysis compares projected and actual (most recent

Current Population Survey estimate) levels of the labor

force and the rates of labor force participation of

specific age groups for men and women, and for whites

and blacks and others. Where appropriate, the accuracy

of the six 1995 labor force projections are compared

with evaluations of BLS projections of the 1985 and

1990 labor force (Fullerton 1988 and 1992). Each of

the six labor force projections to 1995 are identified by

the year in which they were published.

One of the challenges in evaluating projections is

that the actual data are not strictly comparable to that

projected. For example, the projections to 1985 were

different from the actual 1985 numbers because of

changes in how undocumented workers were estimated.

Generally, some changes in the Current Population

Survey are introduced after each census. The redesign

after the 1990 census, implemented in 1994, was

particularly extensive (Polivka and Miller 1994). Some

changes affected the number of persons counted in the

labor force, by adjusting for the census undercount.

Other changes affected the proportion of the population

for some demographic groups counted in the labor

force. It is estimated that a slightly greater proportion of

women are and a higher proportion of older persons are

now placed in the labor force. It is not possible to

quantifi the effect of these improvements in the survey,

so it is not possible to know how much they affect

projection accuracy.

2. Evaluation of the aggregate 1995 projections

Each of the six projections to 1995 had three

alternatives: high, moderate, and low. This analysis,

for the most part, focuses on the middle or “moderate”

growth projection in each series (Fullerton and

Tschetter 1983, and Fullerton 1980, 1985, 1987, 1989,

and 199 1). (See table 1.) The following tabulation

shows the projections to 1995 (in millions) and the

numerical and the percent error made in each year the

projections were developed.

Projection for

1995 made in:

1980

1983

1985

1987

1989

1991

1995

Labor force Error

Number Percent

(millions)

127.5 -4.8 -3.6

131.4 -0.9 7

129.2 -3.1 -;:4

131.6 -0.7 -.5

133.2 0.9 .7

134.1 1.8 1.3

132.3

The overall error was greatest in 1980 and 1985; the

pattern of low but increasing error exhibited since 1987

is due to over projecting labor force participation

slightly for most groups. In the past, BLS projected the

male labor force too high and the female labor force too

low. As table 1 indicates, in every year except 1991,

men’s labor force was projected too low. Previous

evaluations indicated that the error for women’s labor

force was greater than that for men and that women’s

labor force was projected too low. In contrast to

previous evaluations, this analysis shows only the 1980

and 1985 projections had women’s labor force too low

and only the 1991 labor force projection for women

was worse than that for men. The two years with the

largest errors were years in which the labor force for

both men and women were too low.

Because whites make up about 85 percent of the

labor force, the numerical emors for this group should

be larger than for blacks and others; this was true for

every projection except that made in 1983. However,

because sampling variability, the relative error for

blacks and others should be greater than their share of

the labor force; this is also true.

179

.
Projections made for a longer time span should be

less accurate that those made a shorter span. We adjust

for different time spans by using annual growth rates.

The following tabulation displays the growth rates for

the total civilian labor force historically with the

projected annual rate and the actual annual rate of

change. All three rates are measured over the same

number of years. The historic rate is calculated over the

same number of years before the date of the projection

as 1995 is after the date of the projection:

Projection for Historical Projected Actual Error

1995 made in: rate rate rate

(in percent)

1980 2.40 1.23 1.46 -.23

1983 2.42 1.36 1.42 -.05

1985 2.19 1.18 1.40 -.22

1987 1.95 1.24 1.30 -.06

1989 1.63 1.30 1.20 .10

1991 1.57 1.45 1.18 .27

The first two columns indicate that the Bureau

expected labor force growth to slow, especially in the

earlier projections. For example, in 1980, the labor

force growth was expected to drop from the historical

rate of growth, 2.4 percent a year, to 1.2 percent and in

1985 to drop from 2.2 percent yearly to 1.2 percent. In

fact, the labor force did slow dramatically, though not

by as much as BLS anticipated. Between 1989 and 1995

and between 1991 and 1995 however, the labor force

growth slowed even more than BLS anticipated.

3. Population projections

The two components of BLS labor force projections

are 1) age-race-sex specific labor force participation

rates, made by BLS, and 2) age-race-sex specific

population projections prepared by the Bureau of the

Census (U. S. Bureau of the Census 1977, 1982, 1984,

1989). Analysis indicates that population increase

underlies most of the labor force increase. (See, for

example, Fullerton 1993). The past two evaluations of

the labor force projections indicate that a major source

of error has been not accounting for undocumented

immigration in the population projections. Once the

Census Bureau began incorporating an estimate of

undocumented immigration into their population

projections, the labor force projection error dropped

significantly (Fullerton 1988, 1992). For this

evaluation, there is an additional complication, the

Current Population Survey estimates are adjusted for

the 1990 census undercount which none of the

population projections anticipated.

The following tabulation shows 1995 projections

for the civilian, noninstitutional population aged 16 and

over for men and women (in millions) and the errors

associated with the total population projections:

Projection of

1995

population

made in:

1980

1983

1985

1987

1989

1991

1995

Total

186

194

194

196

196

198

199

Men Women Error of

Total

@ercent)

(i% millions)

88 98 -6.3

92 102 -2.4

92 102 -2.4

93 102 -1.4

93 102 -1.4

95 103 -.4

95 103

The source of population projection error for the

ages of interest, 16 to 64, and for the time-span of these

projections is net immigration. For an analysis of the

effect of different assumptions embodied in the

population projections on various age groups in the

population in different time periods, see Long (1991).

The errors in the population projection declined as

the projection period gets shorter. For the projections to

1995, the errors attributed to the population projections

are uniformly lower than in earlier evaluations. Until

1989, the Bureau of the Census did not incorporate

estimates of undocumented immigrants into the middle

population projection series because such persons were

not included in current population estimates. Once this

was done, errors in the labor force projections

attributed to errors in population projections dropped.

The following tabulation shows total labor force errors

attributable to participation and population errors (in

millions):

Error attributed to:

Projection for Total labor Participation Population

1995 made in: force error

(in millions)

1980 -4.8 11.7 -16.4

1983 9 10.0 -10.9

1985 -L 6.9 -10.0

1987 -.7 8.2 -8.9

1989 .9 10.0 -9.1

1991 1.8 8.9 -7.1

The most remarkable aspect of this tabulation is that

in each projection the participation rate and the

population errors offset each other. (See tables 2 and 3).

There is no intrinsic reason why this should be. In fact

for some earlier projections, the errors did not offset.

The errors in the participation rate were derived by

multiplying the 1995 annual average civilian

noninstitutional population by the projected

participation rates. Any difference between the these

180
numbers and the 1995 annual average civilian labor

force is due to labor force participation rate error.

Comparing the error of the published projection with

the errors attributable to participation rate projections

yields the errors due to population projections. Not

only do the errors in the population projection drop as

the time horizon shortens, they shrink as a source of

labor force projection error, becoming smaller than

participation rate error by 1989.

Population projection errors were fairly evenly

divided between men and women. If we expect more

undocumented men than women, this is surprising. We

would expect a greater error for men. It is also

interesting to find that the number of those 60 to 69

were underprojected. Half the population error for

white men was due to ages 55 and over for the 1980

projection. For the 1991 projection the errors for white

men 60 and over exceeded the total error for white

men. (There were small offsetting errors at the younger

ages.) By race as a whole, the errors for whites were 80

percent of total error in the 1980 projection. This

increased to 85 percent by the 1991 projection. Errors

by race were in proportion to their population size.

4. Labor force participation rate error

Labor force participation rate error did not decrease

as the projection period decreased. (See table 2.) Errors

by race were roughly proportional to their share of the

labor force. If anything, blacks and others error was

slightly lower than their proportion of the labor force,

especially for the earlier projections.

Projection errors by sex were not equally divided.

Men accounted for 54 percent of the labor force, but

from 38 to 45 percent of the error. Black and other

men were accurately projected in the earlier projections

to 1995. This may be attributed to chance. Because the

labor force participation rates of men have not been

changing as rapidly as that for women, it is easier to

project their activity. Projections of the white women’s

labor force participation rates consisted of half the error

in the 6 projections. Similarly, the projection for black

and other women accounted for 10 percent of the error,

almost twice their proportion of the labor force.

Women’s labor force was more dynamic and harder to

project.

This analysis proceeded by. multiplying the 1995

population estimate by the projected labor force

participation rates for the six labor force projections

and compare the resulting labor force with the actual

level; another approach would be to compare the

projected labor force participation rates with the

1995Current Population Survey estimates. (See table

4.)

5. Measures of errors in labor force participation

The later labor force projections were made for

more age groups and more race or Hispanic origin

groups than the earlier ones. For this analysis, 13 age

groups were reviewed for men and women, for whites

and blacks and others. If projections were made for

additional groups, the totals for those groups are shown

in table 1. The analysis of labor force participation rates

was conducted on sets of 52 detailed participation rates.

The evaluation of the projections to 1990 only

reviewed 20 sets of labor force participation rates.

Much of the work of bettering the labor force

projections has come by providing more detail by age

and race or Hispanic ongin, not by increasing the

sophistication of the projection.

The median error in labor force participation for

each of the 52 errors per projection period ranged from

0.3 percentage points for the 1983 projection to 2.0 in

the 1987 projection. (See chart 1.) However, none of

the medians were significantly different from zero. (A

median error of zero indicates that half the errors were

above and half were below zero.) The range of median

errors was much greater than for the projections

analyzed for 1990. This reflects the use of smaller age

groups and accounting explicitly for race.

Projection for Median of Mean

1995 made in: error absoiute

deviation

1980 1.22 5.6

1983 0.33 4.7

1985 1.00 3.8

1987 2.05 3.6

1989 1.81 2.8

1991 1.67 2.0

Despite the greater median of the errors, the spread

of the errors, the mean absolute deviation or MAD, was

less. The greatest over projection for the 1995 labor

force was 16.7 percentage points (for white women 18

and 19 years of age, made in 1980). The lowest under

projection, 10.6 percentage points (white men 65 to 69,

made in 1985), was less than half the comparable error

made in 1990. Generally speaking, the more aggregated

the groupings, the smaller error we would expect. This

suggests that there may have been a modest

improvement in the projections over those made for

1990.

Another summary of the error ofien given for a

wide variety of projections and forecasts is the mean

absolute percentage error or MAPE. This measure

attaches more significance to errors in the smaller

groups.

181
Projection for

1995 made in:

1980

1983

1985

1987

1989

1991

Mean absolute percentage

error

Level Participation

rates

11.6 11.7

11.2 12.7

10.2 14.3

9.4 11.7

6.4 6.4

4.2 4.0

The MAPE’s for the level or overall projections

show a satis~ing decrease through time. The errors due

to the population projection display the same patter.

These measures indicate the importance of the

population projection to the overall labor force

projection error and that as time passe~ the projection

of the smaller groups improved. This also confirms the

impression of lower spread of errors that the analysis

using the median and the box-plot presents.

The MAPE’s for the labor force participation rates

show errors rising through the 1985 projection and then

declining, with the MAPE for participation rates less

than the MAPE for the overall projection. This pattern

gives weight to the emors in the groups with lower

participation, younger and older segments of the

population. The analysis of the labor force errors due to

participation based on comparing the labor force

derived by combining the projected labor force

participation rates with the actual population gives

different information on the errors. The overall

projection was fairly good because those groups with

high attachment to the labor force were accurately

projected. The groups with low attachment to the labor

force (with low participation rates) were less accurately

projected.

6. Errors in participation by age, sex, and race

As the discussion above shows, we know that the

errors in labor force participation were greatest for

young and older persons. For the 1980 projections,

errors tended to be higher for young women than young

men, while for the remaining projections, errors were

generally greater for young men. Errors for young

white men tended to be greater than for young black

men, but white rates were over projected and black

rates under projected in the early years. After 1983,

rates for both groups of men were over projected. For

young women, white rates were generally less accurate

than young black women. Rates for both groups of

young were likely to be too high. Over projection of

labor force participation in the 1980 labor force

projection extended into ages 25 to 29 for both groups

of women.

The projected labor force participation for older

people provided another source of error. Generally, the

rates were projected too low. In part this error is due to

the change in the CPS, which now counts more older

persons in the labor force. However, for white men, this

under projection of participation extended down to ages

50 to 54. The more recent labor force projections have

had significantly lower errors for older people. The

following tabulation shows the best and worst

projection for each projection:

Projection for Greatest Lowest

1995 made in: over pro- under projection jection

1980 16.7 -10.0

1983 12.6 -10.4

1985 9.3 -10.6

1987 7.8 -8.4

1989 6.5 -4.2

1991 4.8 -5.1

When the error in the participation rate is 5.1

percentage points and the participation rate for the

group was 59 percent in 1995, the error is almost 9

percent. One may take the position that the percent

error and not the percentage point emor is a better

measure of the accuracy. The 1980 labor force

projection’s greatest percent error was 34 percent, for

white men ages 16 and 17. This was lower than the

greatest percent error for other years. Generally, the

1980 projection was not the year with the lowest error.

The projection for 1985 had the greatest percent error,

60 percent, for black men ages 70 and over. The groups

that had the highest percent error had low labor force

participation rates. So, they also have high percent

errors. This set of projections had most of their errors in

either the youngest or oldest members of the labor

force.

Compared with the labor force projections to 1990,

the relative errors are larger, the greatest relative error

was 32 percent, made in 1973. The increase in relative

errors may reflect the greater variability because of the

smaller groups being projected. The error was for black

women aged 65 to 69, such a small population group

was not evaluated last time.

7. Composition errors

For some users of the projections, the key question

is not “what is the level,” or “how fast,” but what

‘proportion of the labor force is comprised of a

particular group. This may be measured by the index of

dissimilarity (White, 1986), which measures how much

the projected distribution would have to change to be

the actual

182
1980 1983 1985 1987 1989 1991

3.8 3.0 2.8 2.1 1.6 1.1

This measure indicates a steady improvement in

projecting the labor force composition, by age, sex, and race.

The greatest error (1980) is considerably lower than the

greatest enor in projections made to 1990. The least error is

also smaller than the least error made in the projections to

1990. For those who rely of labor force projections to

indicate the likely future composition of the labor force, these

numbers offer reassurance. Increasing the number of groups

evaluated may have reduced the size of this error. By looking

at the projections made to both 1990 and 1995, it is possible

to see if the improvement is due to more groups. For the

projections made in 1980, 1983, and 1985, the errors are

greater for the 1995 projections than the 1990 projections.

8. Alternative projections

For each projection, two alternative projections

were made. Did the range from low-to-high alternatives

span the actual? And, was the high or low alternative

close to the 1995 actual? For evidence, we turn to chart

2, which shows the high and low alternatives for each

of the six labor force projections to 1995. The actual is

“covered” by the alternatives. The alternatives did

function as confidence or credible intervals. Generally,

the high projection was closer to the actual than the low

projection. However, for the more recent projections,

the low was closer.

The gap between high and low should narrow for

the more recent projections. This happened, but the

interval for the 1983 projection was wider than for the

1980 labor force projection. This reflects a decision

made in 1983 that reflected the evaluation of the

projections to 1980. The high alternative projection,

beginning in 1987, has reflected higher net

immigration. This implies higher labor force

participation rates as well as higher population

numbers. This is one reason the high alternative labor

force projection increased between 1987 and 1989.

Summary

Overall Comparison. Ten measures of projection

accuracy were made of the six labor force projections

for 1995. Which projection was best? In considering

this, there are several ways a projection can be best. For

example, if the errors are offset, the projected level of

the labor force would be very near the actual level, yet

the participation rates and the projected population

would be incomectly projected. However, if the main

use of the projected labor force was the level or growth

of the labor force, the details would not matter. The

following tabulation lists the number of times a

projection of the 195 labor force was calculated to be

best or worst:

Projection Best Worst

1980 6

1983 2

1985 1 2

1987 1 1

1989 1

1991 5 1

The tests described earlier help users evaluate the

projections in terms of their own needs: for an accurate

level of the total labor force, for accurate labor force

participation rate projections, or for accurate

projections of composition of the labor force. Different

tests of the accuracy of the participation rate projections

allow the user to focus on overall accuracy or accuracy

of specific groups.

Earlier evaluations. Because the projections were

evaluated at a greater level of detail than in the past,

comparison with earlier projections is difficult.

Evaluations of the accuracy of the level and of the

growth rate are at the same level as in previous

evaluations. Evaluations of the components, could look

worse without being worse. An obvious questions is:

Did the more detailed projections yield more accurate

projections?

Error level

(in millions):

Best

Worst

Error in

growth

rate

(percent):

Best

Worst

Mean

absolute

percent

emor:

Best

worst

Index of

dissimilarity:

Best

worst

Projection

to 1995

Error

0.9

-4.8

-.05

.27

4.0

14.3

1.1

3.8

Year

1989

1980

1983

1991

1991

1985

1991

1980

Projection

to 1990

Error

.2

-14.2

.02

-.68

6.8

10.8

2.6

7.6

Year

1980

1970

1983

1973

1985

1973

1985

1973

The results are fascinating. In terms of the error in

millions, for the projections made to 1990, those made

in 1980 had the lowest error, but the 1980 projections

were the worst (and longest) to 1995. The least error of

the projections to 1995 was greater than the least error

183

m-
,. ,

.’

to 1990, but the worst error to 1995 was almost a third

the worst error to 1990. If the error is measured by the

annual growth rate, once again, the best (least error) for

the 1995 projection was greater than the best projection

to 1990, but the worst 1995 error was significantly

better than the worst 1990 error. The 1991 projection to

1995 was the worst, even though it was the shortest.

Focusing on the best projections to 1990 and 1995,

leads one to say that the 1995 projections were not as

good as the 1990 ones. Looking at the worst errors

(MINIMAX) leads to the opposite conclusion.

Looking at the two remaining measures, which do

reflect the greater detail evaluated, one gets a mixed

picture. The best 1995 projection was better than the

best projection made to 1990, but the worst to 1995 had

a greater error than the worst 1990 projection. For the

three projections that were evaluated earlier to 1990,

the MAPE’s for 1995 are higher than for 1990. The

additional 5 years has resulted in lower accuracy. The

highest MAPE for the 1995 projections is the same as

the highest for the 1990 projections, but the two most

recent projections have lower MAPE’s than any of the

projections to 1990. This suggests that the errors for the

groups with lower participation rates were improved in

the 1995 projections.

For those interested in the composition of the labor

force, the index of dissimilarity indicates that the best

projection to 1995 had an error less than half the best

projection to 1990 and that the worst projection to 1995

had half the error of the worst projection to 1990.

The projection for 1990 made in 1983 had a greatest

relative error of 17 percent, for 1995, the greatest

relative error for the projection to 1995 made in 1983

was 53 percent. The greatest MAD was less than the

greatest for 1990 and the least was just less than the

least for 1990. The median MAD for 1995 (3.7) was

less than the median MAD for 1990 (4.05). Even

though the groups being analyzed in 1995 are smaller

and thus more variable than the groups for 1990, the

spread of errors is smaller. The greater variability

resulted in the extreme errors being greater than in

1990.

BLS labor force projections to 1995 were

marginally better than the projections to 1990 because

the Bureau of the Census is projecting the population

more accurately, because BLS is not projecting as far

forward as in the past, and because the labor force itself

is not growing as rapidly. However, the most stable

population groups, white, non-Hispanics, are expected

to be a smaller portion of the future labor force. Thus,

fiture labor force projections may not be as accurate.

As the baby boom ages, projecting their labor force

activity at the older ages should also be more difficult.

References

Fullerton Jr., Howard N (1980), “The 1995 labor force:

a first look,” Monthly Labor Review, December, pp. 11-

21.

Fullerton, Howard N, and Tschetter, John (1983), “The

1995 labor force: a second look,’ , Monthly Labor

Review, November; pp. 3-10.

(1985), “The 1995 labor force: BLS’S latest

projections,” Monthly Labor Review, November pp. 17-

25

(1987), “Labor force projections: 1986 to 2000”,

Monthly Labor Review, September, pp. 19-39

(1988), “An evaluation of labor force projections

to 1985,” Monthly Labor Review, November, pp. 7-17.

(1989), “New labor force projections, spanning

1988 to 2000,” Monthly Labor Review, November, pp.

3-12

(1991), “Labor force projections: the baby boom

moves on,” Monthly Labor Review, November, pp. 31-

44.

(1992), “Evaluation of labor force projections to

1990,” Monthly Labor Review, August, pp. 3-14.

Long, John (1991), “Relative effects of fertility,

mortality, and immigration of projections of age

structure,” in Wolfgang Lutz, cd., Future Demographic

Trends in Europe and North America (Academic Press,

New York), pp. 503-22.

Polivka, Anne E., and Miller, Stephen M. (1994), “The

CPS after the redesign: refocusing the economic lens.”

Paper presented at the (Labor Statistics Measurement

Issues Conference, Conference on Research in Income

and Wealth, Washington, DC, December, available

from the Bureau of Labor Statistics).

Swerdloff, Sol (1969) “How good were manpower

projections for the 1960’ s,” Monthly Labor Review,

November, pp. 17-22.

U. S. Bureau of the Census (1 977), Current Population

Reports, “Projections of the Population of the United

States: 1977 to 2050,” P-25, No. 704;

U. S. Bureau of the Census (1982), Current Population

Reports, “Projections of the Population of the United

States: 1982 to 2050,” P-25, No. 922;

U. S. Bureau of the Census (1 984), Current Population

Reports, “Projections of the Population of the United

States: 1983 to 2080,” P-25, No. 952;

U. S. Bureau of the Census (1 989), Current Population

Reports, “Projections of the Population of the United

States by Age, Sex, and Race: 1987 to 2080,” P-25, No.

1018.

White, Michael J., “Segregation and Diversity

Measures in Population Distribution,” Population

Index, Summer 1986, pp. 198-221.

184

Table 1. The 1995 labor force, and labor force participation rates, actual and as projected

in 1980,1983,1985,1987,1989, and 1991-

Labor force group

Women

16and17years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

lack

*n and other

lisQanic

:ontinued

Labor force (in thousands) Partki@on rate (in

As published in - I Actual Aspublishedin - I Actual

1980 1983 1885 1987 1989 1991 1995 1980 1983

9,510

245

300

1,207

1,373

1,554

1,435

1,170

853

584

379

244

112

54

—

9,781

175

268

1,089

1,367

1,568

1,523

1,361

941

659

423

244

122

41

9,694

167

271

1,010

1,304

1,562

1,513

1,312

1,044

694

449

261

75

32

14,796

–l–l– 1

9,991

234

356

1,147

1,288

1,510

1,536

1,313

1,018

711

487

261

74

56

9,921

231

361

1,106

1,268

1,506

1,532

1,318

1,004

695

480

243

117

60

10,OO6I

223

344

1,136

1,297

1,492

1,489

1,341

1,008

742

477

258

122

77

10,140 63.5

237 38.fl

374 49.6

1,179 75.4

1,369 85.3

1,502 86.7

1,545 83.7

1,320 77.5

997 70.6

731 60.3

485 50.4

249 36.5

82 16.2

70 4.4

61.7

28.4

45.0

69.8

80.1

82.()

82.5

83.9

70.5

65.7

51.6

34.6

18.8

2.8

—

1885 1987 1988 1891 1895

61.1 60.5 60.1 59.7

64.5

76.5

81.5

81.9

80.9

78.0

69.1

54.7

36.8

11.6

67.6 65.2 65.4

73.1 72.0 71.6

77.0 76.9 74.3

81.2 81.1 77.6

79.6 79.9 80.5

75.9 74.8 74.4

67.5 65.9 69.7

55.7 54.9 54.(I

34.6 32.2 33.7

10.5 16.5 17.a

58.9

30.1

50.2

62.7

70.7

72.2

74.7

76.2

73.5

65.5

59.1

34.3

12.3

2.2 3.5 4.2 5.4 5.4

65.3 65.6 65.9 65.3 63.7 — 65.8 65.3 66.1 65.8

— — 11,787 11,939 11,900 12,267 – – — 66.7 68.7 68.5 65.8

Iote: Dash indiites data not available J

186
Table 2.

Labor force group

Total ,

hen, 16 and older

Mmen, 16 and older

Yhiie

Men

16and17 years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

Women

16 and 17 years

18and19years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

IIack and other

Men

16and17years

18and19years

20 to 24 yean

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

characteristics of the 1995 labor force, actual and as projected using the

participation rates projected in 1980,1983,1985,1987’, 1989 and 1991

with the actual 1995 population and associated errors

Labor force (in thousands)

Using rates published in - Actual

1980 I 1983 I 1985 I 1987 I 1989 I 1991 1995

143,975 142,311 139,175 140,526 142,276 141,228 132,305

75,833 75,197 74,293 74,759 75,566 75,367 71,361

68,143 67,114 64,882 65,768 66,709 65,860 60,944

122,129 121,203 117,980 119,061 120,807 119,956 111,950

65,340 65,089 63,908 64,082 64,855 64,703 61,146

2,327 2,306 2,270 2,213 2,303 2,303 1,429

1,801 1,684 1,409 1,461 1,472 1,430 1,998

5,788 5,759 5,415 5,265 5,200 5,107 6,096

6,872 6,740 6,910 6,856 6,810 6,733 7,224

8,387 8,387 8,476 8,414 8,468 8,441 8,445

8,699 8,718 8,764 8,754 8,754 8,745 8,587

8,005 8,123 8,005 7,972 8,022 8,005 7,827

7,037 7,022 6,956 6,941 6,970 6,926 6,740

5,251 5,370 5,268 5,302 5,314 5,268 4,991

4,090 4,063 4,077 4,063 4,067 4,104 3,589

3,237 3,204 3,216 3,163 3,274 3,306 2,220

2,148 1,991 1,814 2,007 2,155 2,179 1,074

1,698 1,721 1,327 1,611 2,045 2,156 ,926

56,789 56,114 54,072 54,979 55,952 55,253 50,804

1,716 1,696 1,651 1,688 1,716 1,702 1,320

1,742 1,466 1,251 1,460 1,449 1,352 1,798

5,806 5,319 4,633 4,998 5,041 4,719 5,170

6,814 6,589 6,131 6,100 6,116 5,867 5,890

7,993 7,394 7,305 7,144 7,180 6,947 6,766

7,174 7,450 7,477 7,367 7,275 7,036 7,024

7,037 6,944 6,876 6,893 6,901 6,741 6,674

6,029 6,253 5,939 6,059 6,133 6,126 5,856

4,125 4,213 4,367 4,573 4,650 4,715 4,218

2,999 3,270 3,232 3,241 3,372 3,450 2,908

2,244 2,329 2,302 2,406 2,594 2,657 1,714

1,542 1,468 1,491 1,551 1,639 1,788 837

1,569 1,721 1,417 1,498 1,885 2,154 629

21,846 21,109 21,195 21,466 21,469 21,272 20,354

10,493 10,108 10,385 10,677 10,711 10,664 10,215

570 564 573 574 579 579 239

177 176 198 237 244 233 370

816 781 911 997 936 920 1,243

1,163 1,122 1,259 1,282 1,241 1,277 1,428

1,571 1,499 1,501 1,562 1,580 1,583 1,573

1,666 1,543 1,536 1,573 1,585 1,587 1,497

1,348 1,336 1,317 1,338 1,342 1,319 1,277

1,052 1,018 1,020 1,018 1,002 1,011 ,932

840 802 832 830 844 832 759

537 542 525 526 528 524 427

369 363 361 401 404 389 268

228 230 213 192 238 231 124

155 131 138 148 187 180 77

Errors due to participation rate projections’

11,671 10,007

4,472 3,836

7,199 6,171

10,179 9,253

4,193 3,943

898 877

-196 -314

-308 -337

-353 -484

-58 -58

112 131

178 296

297 282

261 380

501 474

1,017 984

1,073 917

772 795

5,986 5,310

396 376

-56 -332

636 149

925 700

1,227 628

151 426

363 270

173 397

-93 -4

91 362

530 615

705 631

939 1,092

1,492 754

278 -107

331 325

-193 -194

-427 -462

-265 -306

-2 -73

169 46

71 59

120 86

82 44

110 115

101 95

104 106

78 54

1985

6,871

2,933

3,939

6,031

2,762

841

-588

-681

-314

32

176

178

216

278

488

996

740

401

3,268

331

-547

-537

242

538

454

202

83

149

323

588

654

787

841

170

334

-172

-332

-170

-71

39

40

88

73

98

93

90

61

1987

8,222

3,398

4,824

7,111

2,936

844

-537

-831

-368

-31

167

145

201

312

474

943

932

685

4,175

368

-338

-172

211

377

344

219

203

356

333

692

714

869

1,111

462

335

-133

-246

-147

-11

76

60

86

71

100

133

68

70

9,972 8,924

4,206 4,007

5,766 4,917

8,857 8,006

3,709 3,557

874 874

-525 -568

-896 -989

-414 -492

23 -4

167 158

195 178

231 187

323 278

478 515

1,054 1,086

1,081 1,105

1,119 1,230

5,148 4,449

396 382

-349 -447

-129 -451

226 -22

413 181

252 13

228 67

278 270

432 497

464 541

880 943

802 951

1,256 1,525

1,114 917

496 450

340 340

-126 -137

-306 -323

-188 -152

7 11

88 90

65 41

70 79

86 73

101 97

136 121

114 107

110 102

187
., ’.. .-

,:

.<’

Table 2.

Labor force group

Women

16 and 17 years

18and19years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

Hack

Asianand other

Ikpanic

1

Difference from actual

m . .

uhamcwwsofthe 19951aborforce# actual endasprajededusingthe

Wtki@on rates -in18M, 1883,.1985, 1s07, 1989, and 1881,

withtheactual 1895popuwonand MsoclaM ermrs-cwtinued

Using rate$ publii in -

1980

11,353

499

289

932

1,46(I

1,775

1,793

1,449

1,051

788

495

366

244

213

1983

11,001

485

211

846

1,352

1,666

1,696

1,428

1,13EI

787

539

374

231

247

1885

10,810

480

201

855

1,249

1,592

1,686

1,418

1,097

870

567

397

246

152

15,180

1987

10,789

476

245

987

1,309

1,521

1,593

1,406

1,080

847

554

404

231

138

15,249

II 5,537

12,426

1988 1991

10,758 10,607

472 469

241 230

1000 93a

1,263 1,267

1,498 1,490

1,591 1,537

1,404 1,344

1,084 1,092

835 830

541 572

398 392

215 225

217 223

15,319 15,180

5,495 5,563

12,798 12,761

Actual

1995

10,140

237

374

1,179

1,369

1,502

1,545

1,32(.I

#997

731

485

249

82

70

14,817

5,539

12,267

-m

Tm

1,213

262

-85

-247

91

272

248

129

54

57

10

117

162

142

861

248

-163

-333

-18

164

151

108

141

56

54

125

149

176

participation rate projecbs’

19851 19871 19881 199 1

670 649 618

243 239 236

-173 -129 -133

-324 -192 -179

-120 -60 -107

89 19 -4

141 48 46

98 86 84

100 82 87

140 116 104

82 69 56

148 155 149

164 149 133

82 67 146

363 432 502

-1 -43

159 531

468

232

-144

-241

-103

-13

-8

24

95

99

87

143

143

153

363

24

494

1995 values

188
Table 3. Difference between the pro@cted and actual labor

force, and between the ori ‘inal labor force

and one using the actual 1 1 L 5 opulation, by

characteristic, 1980,1983,198,1987,1989, and 1991 f

Numbers in thousands]

Labor force group

Total

Men, 16 and older

N/omen, 16 and older

tVhite

Men

16 and 17 years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 yeals

60 to 64 years

65 to 69 years

70 years and older

Women

16 and 17 years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

Difference between the pro-acted and the Errors due to population projections’

actual 1885 labor force bas on projections

made in% - -

1980 1983 1985 1887 1989 1991 1980 1883 1985 1987 1989 1991

-4,762 -917 -3,136 -706 911 1,781 - -8,928 -9,061 -7,143

16,433 10,924 10,007

-3,750 -1,391 -2,079 -969 -141 788 -8,222 -5,227 -5,011 -4,367 -4,346 -3,218

-1,013 474 -1,058 263 1,052 993 -8,212 -5,697 -4,996 -4,562 -4,714 -3,924

-2,658 I 443 -1,864 I -264 1,350

-2,275 -389

313 209

-25 3

-569 -464

-671 -227

-561 -118

-400 181

-77 122

-55 312

206 148

24 3

-29 -161

-257 -233

-174 -164

-383 832

343 86

253 114

569 537

529 325

-141 384

353 481

-5 35a

-650 -407

-462 -142

-488 -346

-255 -272

-218 -151

-211 -141

-1,252 -675 80

-55 22 33

-94 -99 -115

-323 -336 -366

-150 -208 -198

-55 94 76

48 133 187

53 107 124

180 146 157

172 159 164

16 -19 105

-347 -118 38

-427 -264 -46

-270 -292 -79

-612 411

-119 100

-130 41

136 211

246 176

400 391

415 444

5 158

-210 -23

-194 -191

-383 -262

-254 -193

-274 -239

-250 -202

1,270

89

58

229

206

299

451

242

75

-29

-54

-106

-84

-106

1,933

807

4

-111

-223

27

231

247

122

119

220

150

65

11

-55

- -8,810

12,837

-6,469 -4,332

-585 -668

172 317

-261 -127

-319 257

-503 -6C

-512 50

-255 -174

-352 30

-54 -231

-477 -471

-1,046 -1,145

-1,331 -1,15C

-946 -959

1,126 -6,368 -4,478

6 -53 -29C

-42 309 446

99 -67 388

120 -395 -374

140 -1,368 -244

310 203 61

252 -368 88

170 -823 -804

76 -369 -137

19 -579 -708

39 -785 -887

24 -923 -782

-87 -1.151 -1,233

-7,894

-4,014

-896

495

358

164

-86

-129

-125

-36

-105

-472

-1,343

-1,167

-671

-3,880

-450

417

673

5

-139

-38

-197

-293

-343

-707

-842

-92fl

-1,038

-7,375

-3,611

-822

43a

495

160

125

-34

-3a

-55

-152

-493

-1,061

-1,197

-977

-3,764

-26E!

379

383

-34

13

101

-61

-226

-546

-595

-885

-953

-1,071

-7,507

-3,62S

-841

411

530

216

53

20

-71

-73

-159

-373

-1,016

-1,127

-1,198

-3,878

-307

407

358

-20

-115

2(XI

15

-202

-461

-518

-986

-886

-1.362

-6,073

-2,750

-870

458

766

518

235

89

-56

-67

-57

-365

-1,021

-1,094

-1,285

-3,323

-376

404

550

143

-41

298

185

-1oo

-421

-523

-904

-927

-1,612

189
Table 3. Difference between the Droiected and actual labor

force, and between the br- “inal labor force

and one using the actual 1 5 ‘$8

F’

ulation, by

characteristic, 1880,1983,198,1887,1889, and

1991-continued

Numbers in thousands]

Labor force group Difference between pro’

actual 1885 labor force bas ‘J

tied al

In proj

En Irs due to population projections’

1880 1989 1991 1980 1983 1985 1887 1989 1891

IIack and other

Men

16 and 17 years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

-2,104

-1,475

-80

-1oo

-273

-254

-155

-25(I

-164

-54

-87

-15

-5

-28

-9

-1,360

-1,002

-87

-118

-345

-163

-57

-12

-33

3

-78

-13

-22

-56

-20

-1,272

-827

-68

-69

-226

-163

-55

-29

-28

43

-96

-15

-39

-53

-28

-442

-294

3

15

-9$

-12C

-50

-15

-19

35

-54

94

-23

-2a

-32

-439

-221

11

-1

-136

-11(I

-49

-17

-39

51

-51

9a

36

-2

-5

-152

-19

3

-5

-58

-53

5

-11

-11

48

-50

84

30

-6

6

-3,596

-1,753

-411

93

154

11

-153

-419

-235

-174

-168

-125

-106

-132

-87

-2,115

-895

-412

76

117

143

17

-5a

-92

-83

-121

-128

-117

-162

-74

-2,113 -1,554 -1,554 -1,070

-997 -756 -717 -468

-402 -332 -329 -337

103 148 119 132

106 147 171 265

6 26 77 98

17 -39 -56 -5

-68 -91 -105 -101

-68 -80 -104 -53

-45 -51 -19 -31

-169 -125 -136 -123

-113 -5 -3 -13

-132 -156 -1oo -91

-142 -96 -116 -113

-89 -103 -115 -97

-1,220 -1,116

-310 -313

57 70

243 155

15 55

-98 -30

-173 -173

-67 -106

-197 -53

-128 -176

-116 -118

-130 -136

-109 -171

-206 -120

-798

-242

111

160

-21

-11

-57

-93

-62

-136

-67

-143

-157

-82

-837

-241

120

106

5

8

-59

-86

-80

-140

-61

-155

-98

-157

-601

-246

114

198

30

2

-48

-3

-84

-88

-95

-134

-103

-146

Women

16 and 17 years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 y~rs

70 years and older

-630 -359 -44[

8 -62 -7C

-74 -106 -103

28 -90 -16S

4 -2 -65

52 66 6C

-110 -22 -32

-150 41 -8

-144 -56 47

-147 -72 -37

-106 -62 -36

-5 -5 12

30 40 -7

-16 -29 -38

-149

-3

-la

-32

-81

8

-9

-7

21

-20

2

12

-a

-14

-219

-6

-13

-73

-101

4

-13

-2

7

-36

-5

-6

35

-lo

-134

-14

-30

-43

-72

-lo

-56

21

11

11

-8

9

40

7

-1,843

-254

11

275

-87

-221

-35a

-279

-19a

-204

-116

-122

-132

-159

lack

sian and other

NA

NA

NA

NA

-21

NA

241

-685

303

-744

285

-439

NA

NA

Iispanic NA I NA I NA I -480 -328 -367 NA NA I NA I -639|-859 | -86 1

190
‘able 4. Difference between the 1995 labor force and the

projections made in

1980,1983,1985,1987, 1989,

and 19

Labor force< group

Totab

km, 16 and older

fomen, 16 and older

ilhne

16 and 17 years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

55 to 69 years

70 years and older

women

16and17 years

18 and 19 years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

Percentage point difference Absolute relative error

1980 I 1983 I 1985 [ 1987 I 1989 I 1991 1980 I 1983 I 1985 I 1987 I 1989 1991

2.0

1.8

2.3

1.7

2.(I

15.3

10.9

3.9

0.3

0.4

1.6

2.6

0.6

1.8

0.6

0.5

-5.9

-1.0

1.7

15.8

16.7

15.5

13.5

2.4

6.6

1.7

-8.3

-9.6

-10.0

-4.9

-4.7

-1.6

1.2

1.1

1.4

1 .(I

1.3

11.2

10.5

2.2

0.3

0.6

3.0

2.4

2.7

1.2

-0.2

-3.5

-5.6

-2.0

1.0

5.9

9.9

12.6

6.8

5.4

5.5

4.7

-6.8

-4.0

-8.1

-6.5

-3.4

-1.4

0.0

0.3

0.0

-0.3

0.1

1.6

5.7

4.4

1.3

1.1

1,6

1.5

0.9

1.5

0.1

-8.0

-10,6

-3.4

-0.6

-1.8

0.3

6.7

5.8

5.7

4,7

0.5

-4.2

-4.8

-8.7

-6.0

-6.0

-2.3

0.6 1.5 1,2 3.0 1,8 0.0 ().9 2.2 1.8

0.3 1.3 1.3 2.4 1.5 0.4 0.4 1.8 1.8

0.9 1.7 1.2 3.8 2.3 0,1 1.5 2.8 2.0

0.4 1.4 1.2 2.6 1,5 0.4 0.6 2.1 1.8

0.2 1.2 1.2 2.6 1.7 0.1 0.2 1.5 1.5

3.4 3.8 2.3 32.0 23.4 3.3 7.1 7.9 4.8

3,6 2.7 1.4 15.6 15.1 8.2 5.2 3.9 2.0

3.7 3.1 2.1 4.6 2.6 5.2 4.3 3.6 2.5

0.6 1.2 0.9 0.4 0.4 1,4 0.7 1.3 1.0

1.0 1.0 0.9 0.4 0.6 1.1 1.0 1.0 0.9

1.2 1,8 1,6 1.7 3.2 1.7 1.3 1,9 1.7

1.3 1.7 1,1 2.8 2.6 1.6 1.4 1.8 1.2

1.5 1.7 0.9 0.7 3.0 1.0 1.7 1.9 1.0

1.2 1.3 2.1 2.0 1.3 1.7 1.3 1.5 2.4

-1.2 1.5 2.3 0.7 0.3 0.1 1.6 1.9 2.9

-3.1 0.7 1.3 0.9 6.5 14.8 5.7 1.3 2.4

-7.0 -1.5 -O.1 21.6 20.5 38.7 25.6 5.5 0.4

-3.7 -1.0 -0.7 8.1 17.4 29.1 31.9 8.8 6.2

0.7 1.7 1.2 3.0 1.8 0.9 1.3 3.0 2.1

5.7 5.3 1.8 33.9 12.7 3.8 12.2 11.4 3.9

5.4 6.0 1.5 25.8 15.3 0.4 8.3 9.3 2.3

6.3 6.5 3.3 21.4 17.4 9.3 8.7 9.0 4.6

4.0 4.4 1.8 17.8 9.0 7.7 5.3 5.8 2.4

4.5 3.5 0.9 3.2 7.2 7.6 6.o 4.7 1.2

4,9 5.0 3.1 8.7 7.2 6.2 6,5 6.6 4.1

2.1 3.1 3.0 2.1 6.0 0.6 2.7 3.9 3.8

-0.7 0.6 1.7 10.6 8.7 5.4 0.9 0.8 2.2

-4.6 -1.9 -0.3 13.4 5.6 6.7 6.4 2.6 0.4

-6.4 -2.2 -0.8 16,7 13.5 14.5 10.7 3.7 1.4

-4.7 -2.8 0.4 12.8 17.0 15.7 12.3 7.3 1.1

-5.3 -2.0 0.3 25.9 18.7 33.1 29,2 10.9 1.8

-1.8 -1.0 -0.8 30.0 26.4 42.4 32.7 18.1 14.4

191
.,

f,. ‘ . .

,.

MM(! 4. Difference between the 1885 labor force and the

pfo~iS made h

1880,1883,1885,1887, 1888,

and 1881-continued

Labor force group

 lack and other

Men

16 and 17 years

18and19years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 years

70 years and older

women

16 and 17 years

18and19years

20 to 24 years

25 to 29 years

30 to 34 years

35 to 39 years

40 to 44 years

45 to 49 years

50 to 54 years

55 to 59 years

60 to 64 years

65 to 69 yetlrS

70 years and alder

Madan

Mean absolute percent error

Percentage point difference

1980

2,7

0.6

-5.1

-3.0

-3.5

1,2

6.4

6.6

6.3

5.7

4.0

-0.5

-0.1

-7.5

-0.9

4.6

8.7

-0.6

12,7

14.6

14.5

9.0

1.3

-2.9

-5.2

-8.7

2.2

3.9

-1.0

1.2

1983

1.4

-0.1

-5.2

-5.1

-6,0

-2.8

-0.6

5,8

3.3

1,7

4.7

-1.6

0,3

-10.4

-2.3

2.8

-1.7

-5.2

7,1

9.4

9.8

7,8

7.7

-3.0

0.2

-7.5

0.3

6.5

-2.6

0.3

1885

1.6

1.0

-2.2

2,7

2.3

-2.7

-1.0

4.5

3.5

4.8

2.1

-2.0

-3.1

-9,5

-3.2

2.2

-3.1

-4.7

1.8

5.8

9.3

7.2

4.7

4.5

3,6

-4.4

2.5

-0.7

-3.2

1.0

1987

1.4

1.1

3.3

7.8

3.7

0.7

1.1

5.9

3.3

4.6

2.3

5.2

-7.6

-8.4

-4,1

1.6

2.8

2.3

4.9

2.4

4.8

6.5

3.4

2.4

2.0

-3.4

0.3

-1.8

-loa

2.0

1889

1.5

1.8

4.3

4.2

1.2

1.7

1,8

6.2

1.9

6.1

2.5

5.7

2.1

-3.7

-1,1

1.2

2.3

3.0

2.5

1.3

4.7

6.4

3.7

1.3

0.4

-4.2

-2.1

4.2

-1.2

1,8

I Absolute relative error

1991

1,2

1.7

2.8

3.2

3.4

1.9

1.9

4,6

2,7

4.8

1.9

3.1

0.6

-4.6

0.1

0.8

0,8

-0.3

2.7

0.9

2,1

2.9

4.3

0.9

4,2

-5.1

-006

4.7

0.0

1.7

1

1880

4.2

0.8

17,1

5.8

4.7

1.4

7.3

7.7

7.3

6.9

5,0

0.8

0.1

28.9

9.4

7.9

28.8

1.3

20.2

20.6

20.1

12,1

1.7

4.0

7.9

14.7

6.4

32.2

18.3

11.6

7

1983

2.2

0.2

17.4

9.9

8.1

3.2

0.7

6.8

3.8

2.0

5.9

2.4

0.7

40,0

24,9

4.8

5.7

10.4

11.3

13.3

13.6

10.5

10,1

4.1

0.3

12,6

0.8

53.4

48.2

11.2

* 1985

2.5

1.4

7.4

5.2

3.1

3.1

1.1

5.3

4.0

5.8

2.6

3.0

6.4

36.5

34.7

3.8

10.4

9.4

2.8

8.2

12.9

9.7

6.1

6.1

5.5

7.4

7.2

5.3

59.6

10.2

1987

2.2

1.5

11.0

15.0

4,9

0.8

1.2

6.9

3.8

5.6

2.9

7.8

15.8

32.3

44.9

2.8

9.2

4.5

7.8

3.4

6.6

8.7

4.4

3,2

3.1

5.7

0.8

14.3

33.9

9.4

1989

2.4

2,5

14.3

8.1

1.6

1.9

2.0

7.3

2.2

7,4

3.1

8.6

4.4

14.2

12.1

2.1

7,6

5.9

3.9

1.8

6.5

8.6

4.8

1.7

0.6

7.1

6.2

34.7

21.7

6.4

1881

1,9

2,4

9.3

6.1

4.5

2.2

2.2

5.4

3.1

5.8

2.4

4.6

1.3

17.7

1.0

1.4

2.6

0.7

4.3

1.3

2.9

3.9

5.6

1.2

6.4

8.6

1.8

38.7

0.7

4.2

192
Chati 1. Errors in the participation rate projections to 1995

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1980 1983 1985 1987 1989 1991

- . . . . . . . . — -. . .. —...- ._,_----- .— --- -------- -— -— . ..-- .— --- ..—

----- ..— — __ — ——

Chart 2. Range projections to 1995

Millions

of labor force

145

140

135

130

125

120

115

-u----# Hgh

, 0 Middle

Low

—

—

1980 1983 1985 1987 1989 1991

—.. . .

193

Industry employment projections,1995: an evaluation

Arthur Andreassen

Office of Employment Projections

Bureau of Labor Statistics

. U.S. Department of Labor

The Bureau of Labor Statistics’ 1984 to 1995 -

employment projections underestimated the

growth of ~age and salary employment by 6.5

million employees. Manufacturing employment

was over-projected by 2.2 million while

employment in the service and government

industries was under-projected by 6.8 million.

Those major industrial sectors which were

projected to have healthy growth rates did so and

the sectors projected to have moderate or

negative rates actually did grow slowly or

declined. Projections of industry employment

however showed a much greater deviation from

what actually occurred than did the sector

employment projections..

Major Industry Sectors

Considering the condition of the economy in

1984 and the economic turmoil of the prior

eleven years viz. a viz. inflation, recessions and

unemployment, the picture projected for 1995

was uncannily accurate. Fortunately, over the

projected period the economy was not impacted

by outside shocks approaching those of the

preceding eleven years so trends in industry

employment were mainly responses to domestic

economic forces. Along with those few shocks

that did actually occur were some errors within

the macro assumptions that netted to an underprojection of jobs. These errors were included an

under projection of the labor force of 3 million

persons, an unemployment rate projected to be

6% as opposed to the actual 5.670, productivity

projected to grow 20% versus an actual 13%

growth and, finally an under projection of total

jobs of 8 million. The combination of these

sometimes offsetting errors resulted in the Gross

Domestic Product (GDP) growth rate to be over

project at 38% as opposed to the actual31 %.

The following discussion focuses on wage and

salary employment as opposed to total

employment, total includes wage and salary plus

unpaid family and self employed workers.

Further, employment in this study refers to a jobs

concept as opposed to a person conceDt which

exceeds the percent of the total labor force

employed by the amount of multiple job holding

by some workers. It is for this reason that the

actual employment is a larger percent of the labor

force than that projected.

As can be seen, (table 1.), the projected 1995

distribution of employment by sector closely

matches the actual distribution. The projected

shares of only two sectors, agriculture and

wholesale trade, were wrong in direction as

compared with 1984. Agriculture employment

grew faster than projected because the

agricultural services component of this industry

showed more vigor than expected. Agriculture

still held in 1995 the 1.7% share it had in 1984

rather than dropping to a projected 1.290.

Agriculture is the only sector whose employment

was projected to decline but which actually

increased. Wholesale trade at a 5.7!Z0 share in

1984 was projected to rise to a 5.990 share but

actually dropped to 5.3910. More actual growth in

the service sectors and less growth in the goods

sectors than projected accounted for this

discrepancy since service output requires less

trade to distribute than does goods output.

Wholesale trade is the also only sector whose

employment growth was incorrectly projected

be faster than the total employment growth

(18. 1% as compared to 15.9% for total) but

turned out to be slower; the actual rates are

to

13.6%J for wholesale trade and 22.7% for total.

Not one sector whose employment was projected

to grow slower than total actually grew faster.

195
Table 1. Projected and actual wage and salaried employment by major industry group: 1984-1995

(Numbers in thousands)

1- Parcent w ~ Pafeant Shuaof totat

w

$f——Y~ growth

1664 -1995 amor armr 1984 -1995

1984 Laval %ahare Levat %ahare ~ - 1995 16$5 ~ -

Total, alt hUlUaWM1 86,843 112,267 100.0 118.833 100.0 15.9 22.7 -6,566 -5.5 100.0 100.0

me,w,and ~ 1,668 1,401 1.2 1,076 1.7 -16.0 18.4 -575 -28.1 -1.7 1.4

601 .5 418 .4 -3.1 -32.6 183 43.8 -.1 -3

4,726 5,225 4.7 5,407 4.5 10.6 14.4 -182 -3.4 3.2 3.1

Manufectudng 19,389 20,683 18.4 18,405 15.5 6.8 -5.0 2,278 12.4 8.5 -4.4

1 1,476 12,986 11.6 10,598 8.9 13.2 -7.7 2,390 22.6 9.8 -4.0

7,894 7,697 6.9 7,808 6.6 -2.5 -1.1 -112 -1.4 -1.3 -.4

r~,comm@ca60ns, and utiliws 5X42 6,031 5.4 6,280 53 15.3 20.0 -249 -4.0 5.2 4.8

whOk@atnBda 5*568 6,578 5.9 6,324 5.3 18.1 13.6 254 4.0 6.5 3.4

Rataittrada, hdudaaeaUng andddnkhgptaWS 16,512 19,549 17.4 20,840 17.5 10.4 262 -lal -6.2 19.7 19.7

Rnanca, Insunmw, and fad estate 5*663 6,740 6.0 6,949 5.8 18.0 22.3 -203 -3.0 6.9 5.8

21817 28,468 25.4 33,042 27.8 32.3 53.6 -4,574 -13.8 45.1 52.4

Eusblaaa &ld pm&adm# aawfcaa, exeept nladkd 0,011 11,728 10.4 13,479 11.3 46.4 68.2 -1,751 -13.0 24.1 24.9

Other swvteea 13,506 16,740 14.9 19,564 16.5 23.9 4.9 -2,824 -14.4 21.0 27.5

15,947 16,921 15.1 19,192 16.2 6.5 20.4 -2201 “11.5 6.8 14.8

‘ Employment data for wage and salaried employment are from the WS current Employment Statistics (payroll) WWY, whi~h counts jobs. Agriculture and private

household data are from the Current Population Survey (household survey), which counts workers.

“.1.. !.,,
Manufacturing employment was projected to

drop from a 20% share to 18.4% but this fell

short of the actual decline to 15 .5?10. Employment

in this sector was projected to rise from 1984’s

level of 19.3 million but instead fell to 18.4

million. These projections were made in 1984 as

the economy was emerging from the deepest of

two recessions. It was assumed that

manufacturing would recover from its recession

loses as exports and domestic demand for

durable goods increased. This did not occur.

Durable goods employment was the only sector

projected to be greater in 1995 than in 1984 but

which turned out to be lower. The nondurable

goods employment level had too great a

projected decline while mining’s was too little.

Construction’s employment grew as was

projected, but the actual increase was slightly

greater. Both the business service sector and the

other service sector increased their shares of

employment by growing faster than total, as

projected, but both did so at a greater rate.

Together the employment in the business service

sector and the other service sector increased their

share of total employment from 22.2% in 1984 to

27.8% in 1995 and except for a slight increase of

0.4% by retail trade these two were the only

sectors whose share did not just hold steady or

have a decline. The 1984 to 1995 percent increase

in employment of the business service sector was

three times that of the total while that of the other

services sector was twice. Government

employment fell as a share as projected but the

actual drop was much less than projected.

Ultimately it is the satisfaction of GDP that

determines industry output and thus its

employment. Output is produced either to

directly satisfy demand or to be used as an input

by other industries whose output is sold to

demand. Changes over time to the structure of

demand and the production process affect the

distribution of industry employment. As one

moves down the chain from total demand to

demand by major sector to demand by industry

the econometric relationships become less stable

with more deviation introduced to the projected

results. Projections of gross domestic product by

major demand sector is the next step in the

process of deriving industry outputs (see Table

2.).

197
Table 2.Demand Components, 1984 and 1995, projected and actual:

Annual Average Percent Change

(Constant Dollars)

Gross National Gross Domestic

Product Product

1984 to 1984 to

Projected 1995 Actual 1995

Total

Personal Consumption Expenditures

Durable Goods

Non-durable Goods

Services

Gross Private Domestic Investment

Producers’ Durable Equipment

Nonresidential Structures

Residential Structures

Change in Business Inventories

(1977=100)

2.9

2.8

2.8

1.9

3.4

2.8

3.8

2.0

2.1

0.3

(1992=100)

2.5

2.7

3.7

1.9

2.9

1.8

4.7

-1.2

1.4

-4.0

Net Exports

Exports 5.6 8.1

Imports -4.0 -6.3

Government 2.5 2.0

Federal Government 2.8 0.2

National defense 3.4 -0.7

Non-defense 1.1 2.5

State and local Government 2.3 3.3

198
Changes have been made in the definition and

calculation of real demand since the year 1984

such that a comparison of the projected with the

actual dollar values of 1995 GDP is difficult.

Therefore only relative values will be discussed.

A total GDP that was projected to grow at a

2.9% aierage annual rate only grew 2.5%. This

slower growth occurred because the actual higher

employment level and lower unemployment rate

were offset by a lower growth in productivity.

Although few years display a complete lack of

destabilizing forces, 1995 could be considered

very stabile. Even though it was the fourth year

after the last recession the upturn was still not

showing signs of major imbalances and a low

unemployment rate was accompanied by low

inflation and interest rates.

Within total GDP personal consumption

expenditures (PCE) is the hugest and most

stabile sector and its projection was right on.

This fortuitous event resulted from some

offsetting within PCE. Among the three

components of PCE was a correct projection

non-durable consumption and an under

projection of durable consumption balanced by

an over projection of service consumption. The

growth rate of gross private domestic investment,

the most variable of demand sectors, was over

projected due to a too low projection of

equipment swamped by a too high projection of

construction and inventories. Producer durable

equipment demand in 1995 has benefited from

the purchases of computer and communications

equipment purchases by business which has

downsized and sought to increase productivity.

Non-residential construction has still not

recovered from the tax encouraged over building

of the middle eighties; residential construction no

longer benefits from the demand it had when the

baby boomers were entering the home buying

age. Change in business inventories is just that,

the increase or decrease in the inventory stock

over a year. At end 1995 business’ were in the

process of working off an excess of inventories

and this created the large negative shown.

helped exports despite the tepid growth by the

U.S. main trading partners. Import growth

reflected the continuing healthy domestic

economy as well as imports of unexpectedly

cheap oil. Although exports grew faster than

imports the U.S. still had a foreign trade deficit

because exports started from a lower base than

imports. Projected government demand was only

slightly higher than actual due to within sector

offsets, i.e., defense purchases were much lower

while non-defense and State and local purchases

were higher than projected. The unexpected

ending of the Cold War reversed the defense

buildup which had started in the late seventies.

Projected cut backs in non-defense spending did

not occur. State and local governments continued

to increase spending on health and education.

The impact of all this on sector employment

results in some surprises. For instance, although

projected durable purchases by persons and

businesses were too low projected durable

manufacturing employment was too high.

Defense purchases are heavily weighted towards

durable goods so an over projection here

counterbalanced the durable demand under

projection. Downsizing by manufacturing

companies has resulted in the contracting out of

many operations that were previously done in

house, this outsourcing moves the enumeration of

employees performing service functions from the

manufacturing sector to the service sector. In

addition, although healthy growth in foreign

trade was projected, the actual growth was

greater still and an larger part than expected of

durable demand was satisfied by imports.

Inventory change, which primarily consists of

manufactured goods, was over projected. The

projected employment for the construction sector

turned out to be close to actual because an overprotected demand was offset almost exactly by

an over-projected productivity increase. Final

demand is one outlet for industry output, the

other is purchases by other industries to be used

as inputs. Since little of the output of the

agriculture and metal mining industries, is sold as

final demand errors in the projected demand of

Trade, both the export and the import industries which use them ‘m inputs will affect

components, grew even more vigorously than the their projected employment. Mining and a

smart growth projected. The value of the dollar number of manufacturing industries’ employment

reached a trade weighted low in 1995 which were over-projected as a result of the overprotection of durable goods output. Further,

199
although the personal consumption of services

was over projected the employment in the service

sector was still under projected because of the

unexpected heavy use of contracted services as

inputs on the part of manufacturing industries.

When one moves from major sector employment

to industry employment the errors in assumptions

and in GDP have a more serious impact (table 3).

Right off ones sees evidence of what will is a

recurrent theme of these projections, agricultural

services, as with almost all services, was under

projected. Mining employment was over

projected partly because it was expected that oil

prices would continue to increase to double the

$25 per barrel price of 1984 whereas it actually

was at $20 in 1995. Higher international oil

prices would have made more domestic

production profitable while also decreasing

imports causing more domestic employment.

Coal mining employment was also expected to be

helped as cheap domestic coal would be

substituted for the more expensive oil and more

coal would be exported. Metal mining sells most

of its output as inputs to durable manufacturing

industries whose output fell.

Of the 122 industries in this study manufacturing

represents 75, a 61% share of the industries while

its employment share is only 15?40. Within this

industry scheme not one manufacturing industry

had as many as one million jobs. Durable goods

encompass 44 industries with employment in 30

of these actually less in 1995 than in 1984 while

only 10 were projected to do so. Low

employment in those durable goods industries

that are defense related such as ordnance,

aerospace, ship building and communications

equipment reflect the slow down in defense

purchases., while an unforeseen surge in imports

in the computer industry caused employment to

come in at half the projected level. On the other

hand, actual employment in nine durable goods

industries exceeded the projected level. In one of

these, the motor vehicle industry, an increase of

U.S. production by foreign companies replaced

substituted for both imports and the drop in

defense demand. Non-durable manufacturing

comprise 31 industries and, although the total

sector employment projection was close, the

industries themselves show a mixed bag of

results. Most of the industries have a small

amount of employment and for the large ones the

projections were close. The processed food

industries did much better than expected. Drugs

actually grew at a healthier rate than projected

caused the faster than expected growth in the

heath service and government health industries.

The transportation industries were slightly under

projected with the utilities industries over

projected, the latter reflecting deregulation.

Trade was slightly under projected in the retail

area.

When we get to the service industries is hard to

chose the brightest star since what could be

viewed as very optimistic projections were easily

surpassed in reality. The business service

industry, admittedly a catch all, increased by 5.4

million or almost 50% over the 1984 level. This

industry has been driven in part by temporary

help suppliers and reflects the downsizing and

cost cutting that has been going on in the rest of

the economy. Many firms are replacing

permanent employees with temporary ones in

order to save on benefit costs and to have more

flexibility with their production levels. Further,

this industry is where employees will be

classified if they supply such services as

accounting, marketing, personnel and computer

consulting, etc. on a contract basis to firms that

previously performed these services in house.

Finally, state and local governments added 2.8

million employees, twice what was projected as

education and heath output surpassed what was

projected. In general, the industries which are the

largest and most important in the economy were

projected to grow the fastest and this actually

occurred. The smaller industries, especially in

durable manufacturing did not do as well as

projected but these were not projected to have

large growth anyway.

An important result of evaluating projections is

to highlight the changes, especially the

unexpected ones, in the economic structure that

have transpired since the projections were made

and reflect what these changes mean and how

they can be ameliorated.
Table 3. Projected and actual wage and salaried employment by industry: 1984-1995

(Numbers in thousands)

I Industry descrtptton

Total, all industries

Agricultural production

Agricultural services

Forestry, fish@g, hunting, and trapping

Metal mining

Coat mining

Crude petroleum, naturat gas, and gas tiquids

Nonmetallic minerals, except fuels

Construction, including oil and gas svcs

Logging

Sawmills and ptaning mitls

Wood products and mobile homes

Household furniture

Misc. furniture and fixtures

Gtass and glass products

Cement and concrete

Stone, day, and misc. mineraf products

Bfast furnaces and basic steet products

Foundries, forging, and refining

Metaf cans and shipping containers

Cutfery, handtools, and hardware

Plumbing and nonelectric heating equipment

Fabricated structural metal products

Screw machine products, bofts, rivets, etc

Ordnance and ammunition

Miscellaneous fabricated metal products

Engines and turbines

Farm and garden machhwy

Construction and retated machinery

Metafwwtdng machinery and equipment

Special indust~ machinery

I 1995 PWWnt change

1984 Level % share

96,843 112,267

1,126 1,021

501 330

41 50

55 44

196 185

261 263

109 109

4,726 5,225

88 78

202 180

428 435

296 321

191 242

163 167

223 242

176 212

261

770 785

59 52

148 162

65 60

430 514

96 108

76 111

342 394

113 124

108 136

257

327 367

158 197

100.0

.9

.3.

.0

.0

.2

.2

.1

4.7

.1

.2

.4

.3

.2

.1

.2

.2

.2

.7

.0

.1

.1

.5

.1

.1

.4

.1

.1

.3

.3

.2

n

Level % share ~ -

118,833 100.0

1,064 .9

872 ,7

4 0 .0

51 .0

107 .1

155 .1

105 .1

5,407 4.5

82 .1

186 .2

490 .4

279 .2

221 .2

152 .1

.2

167 .1

239 .2

721 .6

41 .0

131 .1

60 .1

428 .4

99 .1

51 .0

379 .3

87 .1

104 .1

217 .2

340 .3

167 .1

15.9 22.;

-9.3 -5.!

 34.2 73.{

22.0 .2.4

-20.3 -7.:

-5.4 45.4

.8 *O.!

.5 .3.4

10.6 14.4

-10.9 -6.7

-6.1 -8.:

1.7 14.:

8.6 -5.[

27.0 15.[

2.5 -6.7

8.7 -.4

20.5 -4.(

-21.9 -28.4

1.9 -6.4

-11.3 -29.1

9.7 -11.C

-8.0 -7.7

19.6 -A

12.1 2.6

46.8 -32.8

15.4 10.8

9.7 -22.8

26.4 -3.4

29.8 -15.8

12.3 3.9

24.6 5.6

.

T

Numedc Percent

error error

1995

-6,566

-43

-542

10

-7

78

108

4

-182

-4

4

-55

42

21

15

20

45

22

64

11

31

0

86

9

60

16

37

32

117

27

30

1995

-5.5

-4.0

-62.2

25.0

-14.1

73.2

69.5

4.1

-3.4

-4.4

2.3

-11.2

15.1

9.6

9.8

9.1

26.6

9.1

8.9

26.2

23.3

-.3

 20. 1

9.3

118.5

4.1

42.2

30.9

54.1

8.0

18.0

1

Share of total

job growth

1984

1OO.(I

-.7

-1.1

.1

-.1

-.1

.0

.0

3.2

-.1

-. 1

.0

.2

.3

.0

.1

.2

-.5

.1

.0

.1

.0

.5

.1

.2

.3

.1

.2

.5

.3

.3

-1995

Actual

100.0

-.3

1.7

.0

.0

-.4

-.5

.0

3.1

.0

-.1

.3

-.1

.1

.0

.0

.0

-.4

-.2

-.1

-.1

.0

.0

.0

-.1

.2

-.1

.0

-.2

.1

.0

—
General industrial machinery and equipmmt

Computer and office equipment

Refrigeration and service industry machinery

Industrial machmery nec

Electric distribution equipment

Electrical industrial apparatus

Househotd appliances

Electric tighting and wiring equipment

Househofd audio and video equipment

Communication and scientific equipment

Electronic components and accessories

Miscellaneous electrical equipment

Motor vehicles and equipment

Aerospace

Ship and boat buitdhg and repairing

Railroad equipment

Mjscetlaneous transportation equipment

Medical equipment, instruments, & suppties

Photographic equipment and sup~les

Watches, clocks and parts

Jewefry, silverware, and pfated ware

Manufactured products, nec

Meat products

Dairy products

Grain mill products, fats and oils

Bakery products

Sugar and confectionery products

Beverages

Mkceflaneoua foods and kindred products TObacco products

Weaving, finishing, yam and thread mills Knitting milts carpets and rugs Miscellaneous textite goods

Apparef Misceflaneous fabricated textile products Paperboard containers and boxes Pufp, paper, and paperboard Newspapers Periodiis, except newspapers Printing Industrial chemicals

Ptastics materials and synthetics Drugs

Soap, cteaners, and toilet goods

252

515

171

317

111

201

146

202

90

986

657

170

862

729

192

35

65

208

124

14

55

327

355

163

166

218

102

214

390

64

432

208

53

53

1,000

185

197

477

440

277

659

305

178

206

145

325

756

194

322

231

241

150

223

85

1,164

846

186

826

866

220

36

59

234

135

14

78

328

126

124

181

85

191

415

56

354

168

43

46

808

174

183

480

508

313

785

305

162

243

160

.3

.7

.2

.3

.2

.2

.1

.2

.1

1.0

.8

.2

.7

.8

.2

.0

.1

.2

.1

.0

.1

.3

.3

.1

.1

.2

.1

.2

.4

.0

.3

.1

.0

.0

.7

.2

.2

.4

.5

.3

.7

.3

.1

.2

.1

253

340

200

336

81

160

123

182

93

737

582

154

933

541

163

38

70

262

878

51

343

465

150

159

212

99

179

421

39

350

191

65

51

705

211

215

473

453

341

762

272

158

260

152

..2#.3 .3

.2

,.3

.1

.1

.1

.2

.1

.8

.5

.1

.fG

..

.1

.C

.1

.2

,1

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Now

Paints and atlied products

Agricultural chemicals

Miscellaneous chemical product6

Petroleum and coal product manuf*@lg

Tires and inner tubes

Rubber products, ptastic hose and footwear

Miscellaneous plastics products, nec

Footwear and other leather products

Railroad transportation

Local and interurban passenger transit

Trucking and warehousing

Water transportation

Air transportation

Pipelines, except natural gas

Miscellaneous transportation services

Communications

Electric utilities

Gas utilities

Water and sanitation

Wholesale trade

Retail trade, exe. eating and drintdng places

Eating and drinking places

Banking and brokerages I nsurance

Real estate and royalties Ledging places and residential care

Beauty and barber shops

Personal and repair services, nec

Advertising

Misc. business, professional, social svcs A utomotive services

Motion pictures and video tape rental Amusement and recreation services, nec

Doctors, nursing homes, and misc. health Hospitals Edurational, job training, child care, etc Private households u .S. Postal Service Federal government enterprises, nec G eneral government Local government passenger transit s tate and local govt enterprises, nec

62

61

92

189

95

184

534

190

376

270

1,317

190

488

19

253

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645

223

110

5,568

11,131

5,381

2,852

1,765

1,067

1,532

341

706

183

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682

276

859

3,115

3,004

1,755

1,238

703

197

14,325

186

536

57

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101

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86

132

707

140

283

267

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206

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430

1,160

227

11,501

864

243

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4,796

3,253

1,964

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677

140

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83

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766

17

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1,358

585

189

218

6,324

13,617

7,223

3,316

2,243

1,390

2,259

395

881

241

13,238

1,025

586

1,474

5,454

3,816

2,712

963

843

194

17,389

212

554

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.1

.1

EVALUATING THE 1995 OCCUPATIONAL EMPLOYMENT PROJECTIONS

Carolyn M. Veneri

Bureau of Labor Statistics, Washington, D.C. 20212

The Bureau’s occupational employment projections

captured most general occupational trends over the

1984-95 period. Some of the most glaring inaccuracies

in the pro~ctions for detailed occupations reflect the

conservative nature of projected growth rates that was

identified in previous evaluations. Although the impact

of inaccurate industry employment projections on the

occupational employment projections was significant,

the projections of the changes in the utilization of

occupations by industry resulted in the biggest source

of projection error as in past evaluations.

Major Occupational Groups

The direction of employment change was projected

correctly for all nine of the major occupational groups.

The absolute projection error was less than 10 percent

for eight out of the nine groups, and 11.3 percent for

professional specialty occupations, the major

occupational group with the largest absolute error. (See

table 1.)

Projected employment was lower than actual

in six major groups: executive, administrative, and

managerial occupations; professional specialty

occupations; marketing and sales occupations;

administrative support occupations, including clerical:

services occupations; and operators, fabricators, and

laborers. Employment was overestimated for precision

production, craft and repair occupations, and

technicians and related support occupations. This latter

group was projected the most accurately with projected

employment less than 1 percent more than actual

employment. The decline in employment was slightly

overestimated for agriculture, forestry, fishing and

related occupations.

Not only was the direction of employment

change anticipated correctly for all the major groups,

but the projected distribution of employment growth

among the groups was relatively accurate. i For

example, the professional specialty occupational group

had the largest absolute numerical error, nearly 2

million, but the share of total employment growth was

under-projected by only 3.4 percent. Thus, the

projection for total employment—low by about 7.3

million— had an impact on projection accuracy.

The largest error in the projected share of

employment growth was for precision production, craft,

and repair occupations. This group’s share of total

employment growth was over-projected by about 7

percent, in line with employment being overprotected by 886,000. For each major group,

however, the same pattern of projection of

employment growth and projection of share of

employment growth does not apply. In the case of

operators, fabricators, and laborers, for example,

employment was slightly under-projected but the

share of employment growth was over-projected.

The projected share of total employment growth

was almost exact for marketing and sales workers,

although the level of employment was underprojected. (See table 1.)

The fastest growing occupational groups

had the largest absolute projection errors.

Technicians and related support was projected to

be the fastest growing group, but was outpaced by

four other groups. The two actual leaders,

professional specialty and executive,

administrative and managerial occupations, were

projected to grow faster than average, but not as

fast as they really did grow.

Administrative support workers, including

clerical, made up the largest group of workers in

1984 and was also projected to be, even though it

did not grow more slowly than average, as

projected. The projection error for this group was

8.3 percent. The group’s projected slow growth

was based on the anticipated effect of the rapid

spread of computerized office equipment. As a

result, many clerical occupations were correctly

projected to grow slowly or decline. However,

employment increased more rapidly than projected

in several large clerical occupations such as bill

and account collectors, adjustment clerks, and

teachers aides and educational assistants.

Projection errors were also relatively large

for both the service occupations and marketing and

sales occupations. Each of these groups grew

faster than projected and together they accounted

for a combined employment growth of about 8.1

million jobs rather than the expected 5.6 million.

More than 60 percent of the projection error of 1.1

million for the marketing and sales occupations can

be attributed to an under-projection of two

occupations, cashiers and

These occupations accounted

of almost 700,000 workers.

retail salespersons.

for an underestimate

205
Projection errors for agriculture, forestry,

fishing, and related occupations, and operators,

fabricators, and laborers were relatively small. Part of

this accuracy can be attributed to growth among

occupations less effected by technological change such

as transportation and material moving occupations and

nursery workers and animal caretakers, except farm.

Conversely, precision production, craft, and repair

occupations were over-projected by almost a million

workers. Since employment of these workers is

concentrated in the construction and manufacturing

industries, the projected increase in employment was

tied primarily to the projections for those industries.

Professional specialty occupations grew by

about 4.7 million over the 1984-1995 period, almost 2

million more than projected. Significant errors in the

projections for detailed occupations with sizable

employment had a tremendous impact on the overall

projection error for this group. For example, an underprojection for five ,professional specialty occupations–

computer systems analysts, engineers, and scientists;

college and university faculty, adult and vocational

education teachers, registered nurses, and social

workers-contributed significantly to the underprojection of professional workers. Under-projection

of these occupations accounted for about 38 percent of

underestimated employment for this major group. The

fact that the projection error for professional specialty

occupations was only 11.3 percent, though, can be

attributed to some offsetting of this under-projection

caused by an over-projection of engineers.2

Detailed occupations

The evaluation of the 1995 projections covered 348

detailed occupations. Table 2 presents data on the 207

occupations for which 1984 employment was greater

than 50,000, ranked by absolute projection error.3 The

absolute percent errors for all 348 averaged about 24

percent. More than three-fiflhs of the occupations,

however, had below-average errors.

The Bureau can only evaluate the projections

for occupations that had comparable definitions in

surveys used to compile employment data in the base

year and the target year of the projections.

Consequently, in past evaluations, relatively few

occupations could be evaluated because of

classification system changes. However, many more

occupations maintained comparability between 1984

and 1995 than in past evaluation periods, because the

OES survey occupational classification remained very

stable over that period. As a result, the number of

occupations included in this evaluation is much larger

than in past evaluations. 4

2

Projection error is inversely related to

employment size, as past projection evaluations

have indicated. In 1984, fewer than 100,000

workers were employed in 211 of the 348

occupations included in the evaluation. These 211

had an average projection error of about 29

percent, whereas 32 occupations with more than

500,000 workers in 1984 had an average error of

about 12.2 percent.

The direction of employment change was

projected correctly for more than 70 percent of the

occupations included in the evaluation.

Employment growth was forecast for the majority

of the occupations. Of the 231 occupations for

which employment actually grew from 1984 to

1995, an increase was projected for all but 16.

However, of the 117 occupations for which

employment declined, only 37 were projected to

decline over the period.

The errors among the occupations

included in the evaluation ranged widely. (See

table 2.) For example, the difference between

projected and actual employment was

underestimated by about 42 percent for physicians

assistants, but overestimated by about 190 percent

for roustabouts. Since roustabouts are primarily

concentrated in the oil and gas industry, this

projection reveals how the effects of incorrect

industry projections can impact the projections for

individual occupations.

The last two columns in table 2 present

the projected and actual share of the overall

employment increase for each of the 207

occupations for which 1984 employment was

greater than 50,000. Although there are some

notable exceptions, the projected shares for the

detailed occupations, like the major groups, are

relatively accurate.

Sources of error

Errors in the projections for the detailed

occupations included in the evaluation can

ultimately be traced back to errors in assumptions

or judgment, resulting in incorrectly projected

changes in staffing patterns, industry projections,

or a combination of both. In order to identifi the

sources of error, two simulated matrices were

created. The first of these was generated by

multiplying the projected 1995 staffing patterns of

industries by actual 1995 industry employment.

The second was produced by multiplying the actual

1995 staffing patterns by projected employment by

industry. The first simulation reveals the outcome

if the Bureau projected perfect industry

06
employment totals and the second if it had projected

perfect occupational staffing patterns. Table 2 presents

projection errors from the two simulated matrices

created to analyze these effects.

In viewing the projection levels and errors for

the detailed occupations, it is possible to identify the

effects analytical judgments had on the individual

projections. Analyses of underlying trends or impacts

of technological change were used in developing both

the indust~ projections and the projected staffing

patterns of industries. As mentioned in Arthur

Andreassen’s contribution to this article, industry

employment projections were impacted by

unanticipated economic changes.s Employment in

manufacturing, for example, did not increase as

projected, but declined because of a number of factors,

including a reduction in defense spending, rapid growth

of imports, and a trend towards outsourcing all types of

services. In contrast, employment in services grew at a

much faster rate than predicted.

The effect of industry errors on the

occupational employment may be clearly seen in an

example taken from table 2. Earlier, it was mentioned

that one of the largest projection errors occurred for

roustabouts, around 190 percent. Examining the

projection errors from both simulations, shown in table

2, one can see clearly that the error is attributable more

to incorrect industry projections for the related oil and

gas industries than to the projected staffing pattern: the

error in the simulation using actual staffing patterns and

projected industry totals is 115 percent while it is only

38 percent with actual industry totals and projected

staffing patterns.

Job clusters. In investigating sources of projection

error, it is helpful to examine groups of related

occupations, or job clusters. A number of such clusters

have been selected for closer examination in this

section because they show large projection errors or

highlight specific sources of error. For example, the

under-projection of the education industry affected the

projections for education-related occupations. This

conclusion is supported by a review of the two

simulated matrices for the projection errors for college

and university faculty and teachers’ aides and

educational assistants. Between 1984 and 1995,

employment of college and university faculty was

projected to decline about 14 percent, though

employment actually increased around 12 percent. The

projected decline was based primarily on the U.S.

Department of Education’s National Center for

Education Statistics’ projected drop in college

enrollments reflecting the shrinking population of 18-

to 24- year olds. Enrollment rates, however, increased

during the 1980s as colleges enrolled greater

numbers of older individuals and enrollment rates

of students of traditional college age rose more

rapidly than expected.

Due to similar misconceptions about

enrollments in higher education, employment of

adult and vocational education teachers also was

significantly under-projected. However, the

absolute percent error for adult and vocational

education teachers is actually larger in the

simulation using the projected staffing patterns and

actual 1995 industry totals than in the simulation

using actual staffing patterns and projected

industry employment. (See table 3.) This indicates

that the error in projecting this group of teachers

was in fact more the result of the underlying

assumptions or judgments that went into

determining the utilization of the workers in the

education industry, rather than the projection for

the education industry. Although moderately

rising demand for adult education was anticipated,

the declining population of 18-22 year olds was

expected to lead to declining demand for

vocational education and training. However, the

growing number of both entry-level and

experienced workers in need of vocational training

or retraining in order to update job skills and keep

up with rapidly changing technology, had more of

an impact on demand for these teachers than was

anticipated.

Likewise, errors in projections for select

computer-related occupations were a significant

contributing factor in the underestimate of

employment for professional specialty

occupations.s A closer examination of the

simulated matrices for certain others reveals the

cause of error to be largely a result of incorrect

assumptions behind the projections of the

utilization of these workers by industry. (See table

4.) In the case of computer programmers, very

significant increases were projected across all

industries as improvements in both computer

hardware and software made computer technology

more versatile, cheaper, and easier to use. But it

was precisely these improvements that led to more

moderate growth as computer users, other

computer professionals, and automation were able

to take over many of the tasks previously

performed by only programmers. Similarly,

moderate increases were expected for computer

operators and operators of peripheral EDP

equipment across all industries as computer usage

rose throughout the economy. However,

expanding technologies not only reduced both the

207

R-
size and cost of computer equipment, but also

automated the tasks previously performed by numerous

operators.

In contrast to programmers, significant

decreases were projected across all industries for data

entry keyers, composing, as a result of anticipated

technological change. Although the trend was correctly

anticipated, it appears that technology had more of an

even greater impact on data entry keyers, composing

than was projected. Virtually the entire error for this

projection can be attributed to incorrect staffing

patterns.

Overall industry employment for health

services was underestimated significantly for hospitals,

as well as doctors, nursing homes, and miscellaneous

health services. Absolute errors for health-related

occupations ranged from 53 percent for emergency

medical technicians to 1 percent for pharmacists.

Although the industry projections had a profound

impact on estimated employment for these occupations,

much of the error in the individual health-related

occupations also can be attributed to errors in stafllng

patterns. (See table 5.) For example, the 1984

projection of a growing demand for physicians’

assistants wits realized as the health care industry began

to focus more and more on cost containment. The

Bureau projected the employment of physicians’

assistants to increase moderately in hospitals and

significantly in outpatient care facilities. However, the

projection was too conservative. Similarly, the impact

of a growing demand for rehabilitation and long term

care services was underestimated, as employment of

occupational therapists, projected to grow much faster

than average, 36 percent, grew by 159 percent. In the

case of licensed practical nurses, the projection was

relatively accurate, off only 4 percent, resulting from

both the under-projected industry totals for health

services and incorrect stafllng patterns that offset each

other. Along with changes in patient care requirements,

the trend toward reliance on nursing personnel with

higher levels of clinical skill was expected to slow

growth among licensed practical nurses. However,

rapid industry growth in health services and an aging

population spurred more demand than originally

expected in nursing homes and residential care

facilities.

The BLS evaluation of industry projections

also revealed that employment in manufacturing was

projected to increase over the 1984-95 period rather

than decline. As a result, the majority of the declining

occupations that were projected to increase are in

manufacturing-including engineers, precision

production workers, machine operators and tenders,

and hand workers and fabricators. For example,

employment of workers concentrated in industries

that manufacture computer and office equipment,

electrical industrial apparatus, electronic

components and accessories, and aerospace, such

as those listed in table 6, was projected to grow

slightly when in fact it fell from 1984 to 1995. As

indicated in the table, the projected industry totals

were the major contributing factor to the error for

these occupations.

Implications for future analyses

The most recent occupational projections

to be evaluated were those ending in the year 1990.

Looking at the major occupational groups, we can

readily see that the projecticms for 1995 appear

more accurate overall than do the 1990 projections.

No major group was off by more than in the past

and the largest absolute error for a major group

was only 11.3 percent compared to 22 percent in

1990 for the group-marketing and sales

occupations. It is important to remember,

however, that projections on such an aggregate

scale are by their nature uncertain. Because

individual occupations, and not major groups, are

analyzed, errors are compounded as these

occupations are combined. The detailed

occupations that comprise each group can be large

enough that any error in their individual projection

can affect the outcome for the overall major group.

In developing the detailed 1984-95

occupational projections, analysts reviewed all

available data from both the OES survey and

Current Population Survey (CPS) and projected

changes in the occupation-industry cells

accordingly, bringing knowledge gained through

experience and studies in preparing the

Occupational Outlook Handbook. As a result,

numerous changes were made to the occupational

coefficients-changes affecting the proportion of

an occupation within each industry-as analytical

judgments were translated into numerical

estimates. The bulletin Employment Projections

for 1995: Data and Methodk, indicates that, in

order to maintain consistency among the

judgments of the analysts, guidelines for increasing

or decreasing coefficients were implemented to

develop the initial projected coefllcients for all

occupations across all industries: small change-1

to 4 percent; moderate change-5 to 9 percent;

significant change— 10 to 20 percent; and very

significant change-20 percent or more.7 The

evaluation of the 1984-95 projections has provided

analysts the fwst chance to look back at this work.

208
Prior to the current evaluation, every BLS

evaluation of its occupational projections revealed the

projections to be conservative. As the analysis of both

the individual projections for detailed occupations and

Flandbook percent-change ranges indicate, the 1984-95

projections are also on the conservative side. The

majority of the occupational projections were clustered

around average growth, when, in actuality, more appear

to have grown much faster than the average or to have

declined. The inherent conservatism contributed to

overall errors in staffhg patterns as well, and part of

the reason appears to have been that analysts were too

conservative in projecting the matrix coefficients. This

trend towards conservatism is recognized by the Bureau

and since 1984, guidelines for projecting changes in the

occupational coefficients have been revised. For

example, when developing the most recent round of

projections horn 1994-2005, the Bureau set forth the

following guidelines for interpreting the projected

range of changes in the coefficients: small change—1 O

percent; moderate—20 percent; significant-35

percent; and very significant-50 percent or more.

Expanding the range for identi~ing small, moderate,

and large changes should clearly have an impact on the

conservatism inherent in the methodology.

With the help of the simulated matrices

prepared for the current evaluation, however, it has

been possible to pinpoint the major source of error for

each detailed occupation. Although the impact of good

industry projections on developing good occupational

projections cannot be underestimated, the chief

weakness appears to be in projecting the staffing

patterns. In the simulated matrix for which the

projected staffkg patterns were applied to actual 1995

industry totals, the sum of the absolute errors, weighted

by 1995 employment, was 7.7 percent. By contrast, in

the matrix for which actual 1995 staffing patterns were

applied to projected 1995 industry totals, the sum of the

absolute errors weighted by 1995 employment dropped

to 5.6 percent.

In addition to the influence of conservative

coefllcient change factors on the projections, the

impacts of features such as technological change and

trends that were not filly realized contributed

significant y to errors in staffing patterns. Incorrect

analytical judgments relating to the rate and impact of

technological change and to trends such as outsourcing

and the growth of temporary help agencies played a

large role in this regard. The analysis behind the

projection for typists is a case in point. For example,

the use of computers and word processing equipment

was expected to have a negative impact on employment

of typists and word processors across all industries.

But because it was assumed that a very large number of

establishments were already using such equipment,

and because future technological advances in

computing technology were not fully realized at

the time, the declining trend in employment of

typists and word-processors was not expected to

accelerate. The analysis wound up

underestimating the impact of changing

technology, as actual employment in 1995 fell

short of projected employment by more than 290

thousand workers.

Similar to unforeseeable events such as

the reduction in defense spending resulting from

the breakup of the Soviet Union, changes brought

about by technology are becoming harder to

predict. For example, the impact of Internet

Technology is nowhere near being fully realized

today, just as the expansion of computer

technology into both the home and workplace and

the impact of the personal computer, was

recognized but not fully accounted for in past

projection cycles.

Technical note

Projections framework. The 1984-95 projections

of occupational employment were developed

within the framework of an industry-occupation

matrix containing 378 industries and over 500

occupations. Data used to develop the 1984” matrix

and projected 1995 matrix came from a variety of

sources. For industries covered by the

Occupational Employment Statistics (OES) survey,

the most current survey data were used to develop

the occupational distribution or staffing patterns

for estimating 1984 wage and salary employment.

Employment by occupation in each industry were

derived by multiplying the occupational

distribution of employment by 1984 wage and

salary worker employment for each industry,

which were obtained from the BLS Current

Employment Statistics (CES) survey. Both the

CES and OES surveys are surveys of business

establishments, covering only wage and salary

workers. The 1984 CPS data were used to develop

the occupational distribution patterns for workers

in agriculture and private households, as well as to

develop economy wide estimates of self-employed

and unpaid family workers by occupation.

Occupational distribution patterns for the Federal

Government were developed from data compiled

by the OffIce of Personnel Management. National

Center For Education Statistics (NCES) data on

teachers were used for these workers as were data

from other independent sources for select

occupations.

209

P“
The most recent OES survey data available to

develop the 1984 matrix came from the following

survey years: mining, construction, finance, insurance,

real estate, and services, other than hospitals and

education, 199 1; trade, transportation, communications,

public utilities, and State and local governments, 1982;

manufacturing industries and hospitals, 1983. The OES

survey occupational classification system was revised

significantly in 1983 to make it compatible with the

newly released Standard Occupational Classification

(soC). As a result, only the 1983 survey of

manufacturing and hospitals conformed to the 1983

classification so the 1981 and 1982 data were forced

into the new 1983 configuration.9 However, some

occupations were split into more than one occupation

or had not been previously identified separately. One

of the difllculties in evaluating the 1984-95 set of

projections results fi-om the 1984 matrix being

constructed with OES survey data collected prior to

1983. The Bureau has developed a national industryoccupation matrix time series covering the 1983-95

period. The series is as consistent as possible with the

occupational classification used in the 1994 matrix

which was the most current matrix available at the time

of development. 10 The actual 1995 employment data

used for purposes of this evaluation were taken from

that time series. The 1986 matrix, the first matrix in

which all of the OES survey data came from surveys

conducted afier 1983, was used to develop the 1984

data. For this reason, the original 1984-95 published

matrix data was not comparable to the actual 1984 and

1995 data published in the historical time series.

In order to reconcile the projected matrix with

the historical time series for the purpose of the

evaluation, simulated 1984-95 projections were created.

To develop these projections, each employment cell

coefllcient (percent of industry employment accounted

for by the occupation) in the 1984 matrix in the

historical time series was multiplied by the 1984-95

percent change in that coefficient (change factor) in the

original 1984 and 1995 matrices, and the resulting

distribution of occupational employment by industry

was then benchmarked to the projected industry

employment in the original 1995 industry projections.

In the resulting simulated projections for most

occupations, the projected 1984-95 percent changes

remained the same or were very close to the original

projections, but the data were defined consistently with

the historical time series. Employment of selfemployed and unpaid fmily workers was taken

directly from the original published 1984-1995

projections, because the CPS data in 1984 were

comparable to the CPS data in 1995, with some minor

exceptions. Occupations in the historical matrix that

did not appear in the original 1984-95 projections

matrix were aggregated into the appropriate

residuals; for example, loan interviewers were

aggregated into the “all other clerical occupations”

residual.

Once the data were prepared, the next task

was to select the occupations for evaluation.

Occupations were selected only if they met certain

criteria. To begin with, all residual occupations

were dropped fkom the evaluation, because

occupations for which there were only aggregated

data were not necessarily comparable, as indicated

in the preceding paragraph. Of the remaining

detailed occupations, only those for which the

definition remained comparable over the time

period were included. Occupations also were

dropped if examination of the historical time series

indicated inconsistencies with logical expectations

for the year to year total employment trend with

logical expectations. Because occupations covered

in the OES surveys changed over time as

improvements in the quality of the survey were

made, problems occurred in the development of a

comparable time series. In order to make the

historical time series as consistent as possible with

the 1994 matrix, a number of steps were taken to

achieve uniformity over time. 11 For example,

occupations appearing in an earlier matrix that

were collapsed in the 1994 matrix were also

collapsed in the time series. Finally, seven

occupations were eliminated because the base year

numbers from the original 1984 published matrix

were so different from those in the historical 1984

matrix that they did not appear comparable.

Consequently, additional occupations were

eliminated because the difference in employment

between the original 1984 published matrix and the

1984 historical matrix was too large based on

specific criteria. 12

Other data errors. The discussion thus far has

focused on errors in individual projections which

can be traced back to incorrectly anticipated

changes in staffing patterns or incorrect industry

projections. Also, comparability problems

stemming from inconsistencies in the classification

system over time were highlighted. However, it is

important to bear in mind that other data problems

exist and that differences in actual and projected

employment levels are not necessarily due to

projection errors. Consequently, real employment

trends in an occupation may not necessarily be

measured by a comparable survey 10 years apart.

And although survey data are generally considered

210
to be fact. sampling and response errors certainly had

an impact on the data in both the initial and terminal

years of the projection period evaluated.

Footnotes

1 In discussing ,the accuracy of the projected distribution of

employment growth among major groups, it is worth addressing a

claim made by John H. Bishop in a recent article evaluating the

accuracy of BLS projections that “The BLS systematically underprojects the growth of skilled jobs and over-projects the growth of

unskilled jobs.” (See John H. Bishop, “IS the Market for College

Graduates Headed for a Bust? Demand and Supply Responses to

Rising College Wage Premiums” New England Economic Review.

May/June 1996. pp. 115-134. quote from p. 123.) In his comparison

of projected and actual growth for the major occupational groups

from 1984 to 1995. Bishop concluded that BLS projected shares of

employment growth for professional. technical. and managerial jobs.

and operative. laborer. and service jobs were ““far off the mark.”’ If

we examine theprojected and actual shares of employment growth

presented in table 1. however. this does not appear to be the case.

Using Current Population Survey (CPS) based data as the ‘“actual”

data to compare with the projections rather OES survey-based data

which is what is used by BLS in developing the projections. Bishop

concluded that from 1984 to 1995. professional, technical. and

managerial jobs accounted for 58.3 percent of employment growth

and operative. laborer. and semice jobs accounted for 15.9 percent.

However. OES survey-based data paints a different picture. The

problem is that the two data sources are not comparable when one

estimates employment growth shares for the major occupational

groups. BLS industry-occupation matrix data (see table 1) indicate

that professional. technical. and managerial jobs accounted for only

38.5 percent of employment growth from 1984 to 1995. which is

much closer to the projected 35.5 percent. Likewise. operative.

laborer. and service jobs accounted for 26.3 percent which is only

slightly lower than the projected 27.8 percent. (John H. Bishop and

Shani Carter. ““How accurate are recent BLS occupational

projections?”” ,Wonfhly Labor Revie\t!. October 1991. pp. 37-43; and

John H. Bishop and Shani Carter. ““The Worsening Shonage of

College-Graduate Workers.”” Educational Evaluation and Policy

Anaosis. Fail 1991. pp.221 -46.) 2

 More details on assumptions leading to the over-projection of

engineers are presented in the Occupational Outlook Quarter;’

(Bureau of Labor Statistics. Fall 1997).

3

 Detail on all 348 occupations included in the evaluation is

available from the OftIce of Employment Projections. Data

comparing employment. percent changes. and employment growth

categories for all 348 occupations included in the evaluation appear

in the Fall 1997 Occupational Outlook Quarter~’. 4 Only 132 occupations were covered in the evaluation of the 1990

projections. (See Neal H. Rosenthal. “-Evaluating the 1990

Projections of Occupational Employment.”” .Vonthly Labor Review.

August 1992. pp. 32-48.)

5 See page 00.

6 The detailed professional occupation with the largest underestimate was computer systems analysts. engineers. and scientists.

So as not to lose a group with significant employment change in the

evaluation. the three occupations were combined in order to

accommodate the change in the occupational classification when

computer engineers and the residual. all other computer scientists.

were added to the OES survey in 1989. Though it is not

classification pure—that is to say. a change occurred with the

addition of computer engineers in 1989— the combined group is

assumed to account for the same group of workers for which

projections were developed in 1984. The OES Survey

definition of what computer engineers do is: “Analyze data

processing requirements to plan EDP system to provide system

capabilities required for projected work loads. Plan layout and

installation of new systems or modifications of existing system.

May set up and control analog or hybrid computer systems to

solve scientific or engineering problems” (Occupafionaf

Employment Statistics Dictionary of Occupations). Because the

title “computer engineer” is ofien interpreted to denote

engineers who design computer hardware. it is likely that some

workers in the group were being collected as part of all other

engineers or as, electrical and electronics engineers. However,

the historical time series does not reveal any tremendous shift

in employment when the title was added. indicating computer

engineers were already distributed between electrical and

electronic engineers and other computer professionals. CPS

data reveal a similar trend and there is no separate category for

computer engineers.

Some shitl in employment away from engineers

probably accounts for a portion of the growth in this

occupation. Nonetheless. even with the addition of the title

“computer engineer.’” the phenomenal growth of computerrelated occupations was underestimated, in large part due to the

unanticipated rapid advancement of computing technology,

particularly the expansion of personal computers. Over the

1984-95 period. employment of computer systems analysts and

scientists was projected to increase 64 percent, making it one

of the ten projected fastest growing occupations. However,

employment actually increased 181 percent over this period.

7 See Employment Projections for 1995: Data and Methods,

Bulletin 2253 (Bureau of Labor Statistics. April 1986).

S Prior to 1990. NCES data were used for teachers, preschool

and teachers. kindergarten and elementary school. However, in

the 1990. 1992. and 1994 matrices. OES survey data for these

occupations were used. but the OES classification was different

than that used by NCES—teachers. elementary and teachers.

preschool and kinderganen. Similar difllculties were

encountered with higher education teachers. For these reasons.

employment data for the elementary, preschool, and

kindergarten teaching occupations were rolled up in the

historical time series. but dropped from this evaluation.

“ A comprehensive methodological statement outlining the data

sources and procedures is published in Employment

Projections for 1995: Data and Methods.

‘“ For more information on the methodology used to develop

the projections. see the appendix to the series of articles under

“Employment Outlook: 1994-2005.”” Monthly Labor Review,

November 1995. pp.85-87; and the BLS Handbook of Methods,

Bulletin 2490 (Bureau of Labor Statistics. April 1997)..

1 [ For a more comprehensive methodological statement , 7he

National lndust~-Occupation Employment Matrix 1983-1995

Time Series technical note is available from OffIce of

Employment Projections.

12 Occupations were dropped if employment for 1984 differed

by more than 40,000. but less than 100.000 from the original

1984 level to the actual 1984 level in the historical matrix and

the percent difference in 1984 employment was more than 50

percent: and if employment for 1984 differed by more than

100.000 from the original 1984 level to the actual 1984 level in

the historical matrix and the percent difference in 1984

employment was more than 30 percent.

211
Table 1. Employment by major occupational group, 1984 actual and 1995 projected and actual

(Numbers in thousands)

Industrv-occupation matrix

Occupation

-, –__ —r—.--— ---------

Employment Percent Change

Share of total job growth

Projected 1995 Actual 1995 1984-95 1984-95

Numerical

Actual 1984 Level Percent Level Percent Projected Actual error Percent error (projected- Projected Actual

actual 1995)

Total, all occupations 106,729 122,758 100.0 130,009 100.0 15.0 21.8 -7,250 -5.6 100.0 100.0

lxecutive, administrative, and managerial 9,95 I I I ,933 9.7 13,244 10.2 19.9 33.1 -1,311 -9.9 12.4 14.1

wcupations

%fessional specialty occupations 13,020 15,701 12.8 17,696 13.6 20.6 35.9 -1,995 -11.3 16.7 20. I

technicians and related support occupations 3,535 4,564 3.7 4,54 I 3.5 29.1 28.5 23 0.5 6.4 4.3

ularketing and sales workers 10,978 13,322 10.9 14,389 11.1 21.3 31.1 -1,068 -7.4 14.6 14.7

administrative support occupations, including

:Ierical

19,670 21,731 17.7 23,710 18.2 10.5 20.5 -1,979 -8.3 12.9 17.4

krvice occupations 16,244 19,510 15.9 20,889 16. I 20.1 28.6 -1,379 -6.6 20.4 20.0

agriculture, forest~, fishing, and related

wupations

3,798 3,641 3.0 3,779 2.9 -4.1 -0.5 -138 -3.6 -1.0 -0.1

%cision production, craft, and repair

wwpations

13,469 15,110 12.3 14,224 10.9 12.2 5.6 886 6.2 10.2 3.2

)perators, fabricators, and laborers 16,064 17,246 14.0 17,536 13.5 7.4 9.2 -290 -1.7 7.4 6.3

.,.

.:, . -:1-.


Table 2. Total employment by occupation, 1984,1995, and projected 1995

[Numbers in thousands]

I occupation

SmplOy interviewers, private or public employment

service

Cost estimators

Central off ice and PBX installers and repal rers

Insulat ion workers

Chemises

Personnel clerks, except payroll and c imekeeplng

Helpers, construct ion c rades

Librarians, professional

Chemical equipment control lers, cperators and

tenders

Police detectives and investigators

Plastic molding machine operators and tenders,

setters and setup operators

Registered nurses

Maintenance repairers, general utility

Physicians

Painters and paperhangers, construct ion and

ma int ensnce

Refuse CO1 lectors

Welfare eligibility workers and interviewers

Concrete and terrazzo finishers

Mobile heavy equipment mechanics

New accounts clerks, banking

Oata ● nt ry keyera, except composing

Institutional cleaning supervisors

Taxi drivers and chauffeurs

Sewing machine operators, garment

Payrol 1 and timekeeping clerks

Artists and comercial artista

Combination machine tool setters, setup operators,

operators, and tenders

Cooks, restaurant

Respiratory therapist

I

Actual 1984

63

143

77

53

84

115

466

128

77

55

142

1,326

999

407

368

106

82

100

102

87

369

108

84

673

192

184

95

485

53

Total employmen

Projected 1995 I

L*vQ1

89

171

86

60

89

135

484

139

79

60

185

1,758

1,173

600

384

125

94

117

119

99

370

141

95

568

182

240

122

630

64

Porcont

shara

0.07

0.14

0.07

0.05

0.07

0.11

0.39

0.11

0.06

0.05

0.15

1.43

0.96

0 49

0.31

0.10

0.08

0.10

0.10

0.08

0.3C

0.12

O.oe

o.4f

0.1!

0.2C

O.lc

0.51

0.0:

t

Actual 1995

Laws 1

83

184

80

65

96

125

325

152

73

66

169

1,937

1,294

548

424

114

105

130

107

111

414

128

107

511

163

272

109

714

73

Porcont

sharm

0.06

0.14

0.06

0.05

0.07

0.10

0.40

0.12

0.06

0.05

0.13

1.4s

1.OC

0.42

0.33

0.0$

O.of

0.10

O.o8

0.09

0.32

0.10

0.08

o.39

0.13

0.21

0.08

0.55

0.06

Percent change

1984-95

projected

39.5 

19.2

11.4

12. 9

5.6

16.5

3.8

8.5

3.1

10.1

29. 0

32.66

17.4

23.2

4.3

17.9

15. 0

17. 0

16. 6

14.4

0.4

30.9

12.9

-15.7

-5.6

30.2

28.1

29. 9<

20.6

Actual

30.1

28.5

3.8

21.9

14.0

8.2

12.5

18.2

-5.1

20.8

18.9

46.1

29.5

12.6

15.2

7.0

28.1

30.4

5.6

27.7

12.3

18.2

26.7

-24.1

-15.2

47.3

14.8

47.0

36.6

error, 1995 percent

pro je cted error,

actual ) 1995

6

-13

6

-5

-7

10

-41

-13

6

-6

15

-179

-121

52

-40

12

-11

-13

11

-12

-44

14

-12

57

19

-31

13

-83

-9

7.2

7.3

7.3

7.4

7.4

7.7

7.8

8.2

8.6

8.9

9.0

9.2

9.4

9.4

9.5

10.2

10.2

10. 3

10.4

10.4

10.7

10.8

10.8

11.1

11.4

11. 6

11. 6

11.7

11. 7

Absolute I Share of total job growth

percent err Actual

industry

totals to

pro j e ctad

staffing

pattern

23.0

7.3

9.1

4.7

2.6

7.9

6.1

9.6

1.4

0.1

6.7

3.7

5.5

22.3

2.3

46.4

2.2

2.0

8.1

17.7

4.6

28.8

6.0

2.7

16.7

2.7

3.0

4.2

0.8

m, 1995 1984-

Actual

staffing

pattern to

 ro e ctod

industry

totals Pro j e cted

13.3

0.3

17.8

3.4

11 3

0.”1

0.4

16.0

9.6

7.5

2.7

12.3

7.4

9.5

5.8

22.8

8.3

8.3

3.6

9.0

7.9

12,8

18.0

9.2

3.7

5.7

13.7

9.4

12.2

0.16

0.17

0.05

0.04

0.03

0.12

0.11

0.07

0.01

0.03

0,26

2.70

1.08

0.70

0.10

0.12

0.08

0.11

0.11

0.08

0.01

0.21

0.07

.0 .66

.0.07

0.35

0.17

0.90

0.07

95

Actual

O 08

0.18

0.01

0.05

0.05

0,04

0.25

0 10

-0.02

0.05

0. 12

2.63

1.27

0.26

0.24

0,03

0.10

0 .13

0.02

0.10

0.2C

O 08

O.l0

-0.70

-0.13

0.37

0.06

0.98

0.08
Table 2. Total employment by occupation, 1984,1995, and projected 1995

[Numbers in thousands]

Occupation

Machine feeders and off bearers

Lsvyers

Fire fighters

Machinists

Clinical lab technologists and technicians

Aircraft pilots and f 1 ight engineers

Hairdressers, hairstylists, and cosmetologists

Food preparation workers

Home appliance and power tool repairers

Child care workers, private household

Cutting and slicing machine setters, operators and

t endera

Bus drivers, school

Writers and editors, including technical writers

Library assistants and bookmobile drivers

I

Insurance ssles workers

Wholesale and retai 1 buyers, except farm products

Cashiers

Insurance adjusters, examiners, and invest igators

Science and mathematics technicians

Autanotive body and related repairers

Bricklayers and stone masons

Cleaners and servants, private household

Personnel, training, and lakmr relations specialists

Underwriters

Woodworking machine operators and tenders, setters

and setup operators

Inspectors and compliance off icers, exe@’ut

construct ion

Machine assemblers

Hotel desk clerks

Draft ers

Plumbers, pipef itters, and steamf itters

hotual 1*M

270

527

214

386

231

66

525

B79

75

401

82

311

194

97

425

1B9

2,016

109

233

202

154

532

221

9C

72

12C

51

97

326

384

Total ~loymant

ProjO&d 1995 Actual

Pucant

Laval Sbaro L8val

296 0.24

722 0.59

247 0.20

424 0.35

245 0.20

82 0.07

687 0.56

1,078 0.88

86 0.07

347 0.28

B1 0.07

364 0.30

246 0.20

107 0.09

474 0.39

214 0.17

2,627 2.14

138 0.11

268 0.22

243 0.20

170 0.14

409 0.33

262 0.21

112 0.09

77 0.06

133 o.~1

61 0.05

115 0.09

367 0.30

442 0.36

265

645

220

378

279

93

611

1,230

7-i

308

94

42C

284

123

415

187

3,08C

162

233

21C

14E

484

31C

9i

6:

15[

52

131

31:

37f

Porcsot

1995 1904

ParCant

Sbara Pro j ● Ctad

0.20

0.50

0.17

0.29

0.21

0.07

0.47

0.95

0.06

0.24

0.07

0.32

0.22

0.09

0.32

0.14

2.37

0.12

0.18

0.16

0.11

0.37

0.24

0.07

0.05

0.12

0.04

0.11

0.24

0.29

6.6

37.1

15.3

9.7

6.0

24.0

30.8

22.6

15.2

-13.5

-0.7

17.2

26.9

9.7

11.4

13.2

30.3

26.7

15.3

19.9

10.6

-23.1

18.5

24.1

6.7

10, $

19. e

18.3

12.7

14. s

)5

Ilu9arical

uror, 1995

(proj aotod

Actual 8etual)

-4.7 31

22.6 76

2.9 27

-2.1 46

20.6 -34

41.2 -11

16.4 75

39.9 -152

2.3 10

-23.2 39

14.4 -12

35.0 -55

46.5 -38

26.6 -16

-2.5 59

-1.2 27

52.8 -453

48.9 -24

0.1 35

3.9 32

-4.2 23

-9.1 -75

40.2 -48

7.4 15

-7.8 10

32.2 -26

2.9 9

41.6 -23

-3.5 53

-1.7 64

Ab801

parcant eI

Actual

iadustry

Absolute totals to

psrcant projactod

arror, staffin g

1995 pattorrl

11.8

11.8

12.1

12.1

12.1

12.2

12.3

12.4

12.6

12.7

13.2

13.2

13.4

13.4

14.2

14.6

14.7

14.9

15.2

15.4

15.4

15.4

15.5

15.6

15.7

16.1

16.4

16.5

16.8

16.9

11.7

9.E

27.7

0.4

2.3

15.5

13.5

O.c

16.2

6.4

10.9

14.6

6.9

2.8

23.5

18.2

8.e

12.1

25.3

24.4

20. i

20.1

15. :

19.4

~5.:

11.2

2.4

8.f

3.4

21. i

Luta Sbar. of total job growth

x, 1995 1984-95

Actual

staffing

● ttora to

>ro j ● ctad

imduatry

totals Proj ● ctod Actual

0.8 0.11

3.8 1.22

12.1 0.20

11.9 0.23

12.7 0.09

24.6 0.10

0.2 1.01

11.5 1.24

0.0 0.07

5.9 -0.34

1.9 0.00

23.8 0.33

1.0 0.33

15.4 0.06

6.1 0.30

1.6 0.16

7.1 3.81

3.7 0.18

9.3 0.22

5.9 0.25

3.3 0.10

5.9 -0.77

0.7 0.26

1.3 0.14

0.8 0.03

5.1 0.08

21.7 0.06

8.6 0.11

10.9 0.26

3.6 0.36

-0.06

0.51

0.03

-0.04

0.21

0.12

0.37

1.51

0.01

-0.40

0.05

0.47

0.39

0.11

-0.05

-0.01

4,57

0.23

0.00

0.03

-0.03

-0.21

0.38

0.03

-0.02

0.17

0.01

0.17

-0.05

-0.03
Table 2. Total employment by occupation, 1984,1995, and projected 1995

Numbers in thoussnds ]

Oeeupatioa

Farm managers

Gardeners and groundskeepers, except farm

Physical therapists

Designers

BBrokerage clerks

Automot ive mechanics

Dental hygienists

Bus and truck mechanics and diesel engine

specialists

Water and liquid waste treatment plant and systems

operators

Inspectors, testers, and graders, precision

Driver/sales workere

Cooks, short order and fast food

Order fillers, wholesale and retail sales

Roofers

Counselors

Office machine and cash register servicers

Laundry and drycleaning machine operators and

enders. except pressing

Millwrights

Customer service representatives, utilities

IIndustrial truck end tractor operators

Upholsterers

Hosts and hostesses, restaurant/lounge/cof f ee ahop

Reservation and transportation ticket agents and

travel clerks

File clerke

Recreation workers

Pychologists

Electronics repairers, commercial and industrial

equipment

Bartenders

statistical clerks

Actual 1984

164

597

59

198

51

706

83

256

71

701

253

543

188

137

116

53

130

87

104

426

68

158

106

223

154

92

77

366

6S

Total employmen

Pro j roject

Level

180

639

86

252

60

859

107

309

79

791

276

637

182

156

135

70

145

94

122

301

77

203

115

231

180

110

91

466

60

1995

Percent

shere

0.15

0.52

0.07

0.21

0.05

0.70

0.09

0.25

0.06

0.64

0.23

0.52

0.15

0.13

0.11

0.06

O.l2

O.o8

O.l0

0.31

0.06

0.17

0.09

0.19

0.15

0.09

0.07

0.38

0.05

Actual 1995

Percent

Level s hare

154

545

104

307

73

728

130

262

97

668

339

785

225

131

168

58

182

78

152

477

64

255

145

292

220

139

76

385

77

0.12

0.42

0.08

0.24

0.06

0.56

0.10

0.20

0.07

0.51

0.26

0.60

0.17

0.10

0.13

0.04

0.14

0.06

0.12

0.37

0.05

0.20

0.11

0.22

0.10

0.11

0.06

0.3C

0.06

Psrcamt cha~o I I I Absolute I Share of total job growth ,.

1984-95

Mumarical

e rror, 1995

{projactad

pro j e cted Actual SCtuel)

9.8 -6.1 26

7.0 -0.8 94

45.3 76.6 -19

27.7 55.5 -55

18.0 43.7 -13

21.6 3.1 131

28.7 56.9 -23

20.4 2.0 47

11.6 36.6 -18

13.0 -4.6 123

9.2 33.9 -63

17.3 44.6 -148

-3.1 19.6 -43

13.5 -4.6 25

16.0 44.0 -34

31.8 9.9 12

11.5 39.2 36

7.0 -10.9 16

16.7 45.9 -31

-10.6 11.0 -96

11.9 -6.8 13

28.5 61.4 -52

9.0 37.0 -30

3.4 30.6 -61

16.8 47.7 -48

19.0 51.5 -29

18.4 -2,3 16

27.4 5.1 82

-12.6 11.4 -17

porcant err

Actual

industry

Ahsoluta totda to

p.rcmt projoctad

error, staffing

199s pmttorm

17.0

17.2

17.8

17.9

17.9

17.9

18.0

10.1

18.4

18.4

18.5

18.9

19.0

19.0

19.9

19.9

19.9

20.0

20.0

20.1

20.1

20,3

20.5

20.8

20.9

21.0

21 1

21.2

21.5

17.0

36.2

6.6

4.9

11.2

28.9

6.5

28. 9

5.2

5.2

15.6

9.3

18.1

24.7

1.s

18.8

5.0

15.1

31.5

18.4

16.8

12.2

4.8

16.0

4.1

6.5

15.3

35.7

16.0

or, 1995 1984-

Actusl

Staffimg

pttern to

projected

industry

totals Pro je cted

0.8

14.1

12.2

4.3

6.4

7.9

12.2

7.4

13.8

9.0

6.9

9.4

0.8

3.4

17.3

4.5

14.9

3.6

16.5

2.7

5.6

10.7

22.2

6.8

15.2

11.6

4.4

10.9

9.2

0.10

0.26

0.17

0.34

0.06

0.95

0.15

0.33

0.05

0.57

0.15

0.58

-0.04

0.12

0.12

0.11

0.09

0.04

0.11

-0.28

0.05

0.28

0.06

0.05

0.16

0.11

0.09

0.62

-0.05

.95

Actual

-0.04

-0.22

(1 Iq

0.47

0.10

0.09

0.20

0.02

0.11

-0.14

0.37

1.04

0.16

.0.03

0.22

0.02

0.22

-0.04

0.21

0.22

-0.02

0.42

0.17

0.29

0.32

0.20

-0.01

0 08

0.03


TabJe 2. Total employment by occupation,

[Mumbers in thousands]

I

1984,1995, and projected 1995

mtal ~l~t Berceot Ohsnga *oluta Shsrm of total job ~outb

Projscted 1995 Aotual 1995 1904- w pecoeot ● rro=, 199 5 19s4-95

XOtual Actual

i@aaetry •ta~f iog

oc~tion Uumerical Abeoluto totsze to pattero t o

● rror, 1995 poroont prsjscted projeoted

Puceot Puoent (pro j ● ctsd ● rror, Stdfiog hdllstry

Actual L9C4 Level Shera LeWel share Projected AOtud ● ctual ) 199s pettsrn totals Pro j Scted Actual

Adjustment clerks 130 158 0.13 384 0.30 21.9 196.0 -226 50.8 59.5 0.4 0.18 1.09

Precision instrument repairers 57 65 0.05 39 0.03 13.4 -31.3 26 65.0 21.4 22.4 0.05 -0.08

Sewice station attendants 289 287 0.23 167 0.13 -0.8 -42.1 120 71.4 85.6 16.3 -0.01 -0.52

Electrical snd electronics engineers 399 607 0.49 3s3 0.27 52.2 -11.4 253 71.8 41.4 20.2 1.30 -0.20

Roustabouts 78 79 0.06 27 0.02 1.1 -65.2 52 190.1 37.6 114.6 0.01 -0.22
Table3. Sources ofprojection error foreducation-related occupations, 1995, andprojected andactuai share

oftotaljobgrowth, 1984-95.

I Absolute Absolute percent Absolute percent Share of Share of

percent error (actual error (actual total job total job

error industry staffing growth growth

totals/projected pattern/projected

staffing industry totals)

pattern) Projected Actual

College and 22.8 4.9 18.8 -0.65 0.38

university, faculty I

Adult and vocational

education teachers

40.1 26.4 15.1 0.30 1.22

Teacher aides and 27.6 7.6 20.8 0.66 1.60

educational

assistants

Table4. Sources ofprojection error for computer-related occupations, 1995, and projected and actual share

oftotaljobgrowth, 1984-95.

Absolute Absolute percent Absolute percent Share of Share of

Computer

programmers

Computer operators ,

except periphera l

equipmen t

Peripheral ED P

equipment operator s

Data entry keyers ,

except composin g

Data entry keyers ,

composinq

percent error error (actual error (actual total job total job

industry staffing growth growth

totals/projected pattern/projected

staffing industry totals)

pattern) Projecteci Actual

40. 0 46. 2 3 . 3 2.1 3 0.5 1

31. 0 35. 3 1 . 6 0.6 8 0.11

100. 7 108. 4 4 . 2 0.1 2 -0.0 5

10.7 4.6 7.9 0.01 0.20

79.8 79.9 5.3 0.06 -0.03

221
. ..”

,.

.,.

. “

.,. ,

Table 5. Sources of projection error for health-related occupations, 1995, and projected and actual share of

total job growth, 1984-95.

Absolute Absolute percent Absolute percent Share of Share of

percent error (actual error (actual total jo b total jo b

erro r industr y staffin g growth growth

totals/projecte d pattern/projecte d

staffin g industry totals)

pattern ) Projected Actual

Physician s

assistant s

Occupational

therapists

Occupational

therapy

assistants an d

aide s

Emergenc y

medica l

technician s

Licensed

practica l

nurses

47.4 39.9 13.0 0.05 0.15

41.9 33.8 13.6 0.01 0.03

53. 1 39. 0 23. 1 0.0 2 0.3 3

4 . 0 10. 0 12.6 0.64 0.56

Table6. Sourcesof projection errorfor production workers, 1995,and projected and actualshareof totaljob

growth, 1984-95.

Absolute Absolute percent Absolute percent Share of Share of

Aircraf t

assemblers,

precisio n

Electrical an d

electroni c

equipment

assemblers,

precisio n

Electromechanical

equipment

assemblers,

precisio n

Electrical an d

electroni c

assemblers

percent error (actual error (actual total job total job

error industry staffing growth growth

totalslprojected pattern/projected

staffing pattern) industry totals)

Projected Actual

27.1 16.0 52.1 0.0 2 -0.0 1

38. 9 6 . 8 48. 7 0.1 9 -0.1 2

55.1 0 . 7 42.3 0.0 9 -0.0 5

37. 6 4 . 6 43. 0 0.2 7 -0.1 6

222
EARLY WARNING AND THE NEED FOR INFORMATION SHARING

Chair: Kenneth W. Hunter

World Future Society

Early Warning and Futures Research: An Argument for Collaboration and Integration

Among Professional Disciplines,

Kemeth W. Hunter, World Future Society

223
.’

.,
Early Warning and Futures Research

An Argument for Collaboration and Integration Among Professional Disciplines 1

b

Kemeth W. Hunter, CPA

September 10, 1997

For contact:

Tel: 3015964743

Fax: 3015960946

E-mail: khunter100Qaol.com

Mail: 9738 Basket Ring Road, Columbia, MD 21045

Positions:

Chair, World Future Societ y’s 1999 Conference on “Frontiers of the 21st Century”

Senior Associate, The Harrison Program on the Future Global Agenda, University of Maryland,

College Park

President, Collaborative Futures International

225
Early Warning Professional Services and Accountability for Actions and Inaction

Early warning is a professional responsibility and service provided by many

disciplines in most institutions to inform direction-setting policy making and

implementation, including course corrections and emergent y responses. Policy

makers and their advisors are accountable for their inaction as well as actions. In

the current era of large scale systemic transformations missed opportunities can be

very costly to individual institutions and civil society in the long run, thus making

accountability for inaction especially important today.

Early warning, like futures research, is not a traditional discipline itself, but is a

critical responsibility of most professionals. However, the traditional professions do

not use these terms in describing their functions and services. Therefore, we have

to look to what they do and what they call it to assess the nature of early warning

and futures research methods and practices today. I will begin that task in this

paper.

In my judgment, early warning and futures research are based on implicit general

mental models of the relationships among values, change drivers and shapers, and

institutions. Change drivers include demographic and technologic forces. Change

shapers include ecological systems constrains and patterns of change in institutions,

such as globalization and restructuring into more modular and networked

relationships. These forces impact all institutions and create a wide range of

direction-setting policy choices. Values shape the criteria used explicitly and

implicitly in making those choices. Inaction is an implicit choice not to change

directions by the institution.

Each professional discipline has evolved its own process and language for doing its

work. However, all professionals do share responsibilities. All professionals advise

their individual and organizational clients on critical choices. They base their

advice on their understanding of changing conditions and relative capabilities of the

client and their assessment of the likely consequences and risks of alternative

actions and inaction. That is what being a professional is all about. As real world

situations get more complex -- the choices involve larger scale systems -- we need to

engage a wider range of disciplines collaboratively. Since the languages and

underlying paradigms are different, policy makers confront a difficult to impossible

task of information integration and evaluation of choices.

In my opinion, we in the professions should take the initiative to collaborate and

integrate our knowledge and services in support of direction-setting policy making

for large scale systems. Let me illustrate. In my view, the following ten large scale

human systems are undergoing transformational changes thus driving complex

policy choices for everyone:

226
1. Life relationships: gender, generational, family, rational-creative, spiritual

and work.

2. Civil society and communities; neighborhood cohesion; tolerance and

cultural passions and identities; confronting enduring ethnic, religious, racial

and nationalistic conflicts.

3. Human capacity development: leaning, knowledge building, and wellness

4. Human services for vulnerable and troubled people and peoples to lessen

human suffering and build connections to communities.

5. Eco-econo-socio systems: climate change, water, energy, ecosecurity,

sustainability.

6. Habitats and mobility: megacities, communities, corridors, gateways,

regional systems, and logistics.

7. Big collaborative science.

8. Big emerging markets: China, India, Indonesia, Brazil and Russia.

9. Global standards and best practices; accountability

transparency and reporting.

10. Dealing with wrong-doing: early warning, early

police, justice.

and governance;

intervention, military,

In my opinion we do share this agenda of major changes and share a basic mental

model as professionals. These provide a starting point for collaboration and

integration of our professional knowledge and skills to meet our early warning and

futures research responsibilities more effectively.

Early warning of what? .

First, however, we need to wrestle with the question “early warning of what?”

Policy makers and the public want warning of potential events, changes in

relationships, and changes in systems capabilities with possible significant

consequences. These of course are interrelated, but the way we think and talk about

each is a little different.

Events involve both substantive action and timing. Significant events can be

breakthroughs on one end of a continuum and crises on the other. In fact what may

be a crisis for some institutions and indiviciuals are likely to be breakthroughs or at

least opportunities for others. Actions on tobacco and greenhouse gases are

illustrations. By events we usually mean the action that gives public recognition to

a major change and drives the need for attention. However, breakthroughs and

crises do not happen spontaneously. Collapses of political regimes usually result

from long-term disintegration and corruption at the core. Scientific “discoveries”

result from years of hard thinking and work to make them happen. Effective

monitoring of actions or inaction of key people and institutions can provide early

signals of the possibility of a major event at some time. However, anticipating the

exact timing of the “capping” event is very difficult, even when they appear to be

getting near. Nevertheless, informing policy makers and the public of the nature,

likelihood and timing of a major event is a critical early warning service.

227
Understanding changing relationships is also fundamental to all policy choices. The

end of the Cold War, the diffusion of information technology, climate change, and

changing gender and generational relationships all make it certain that future

conditions will be significantly different. We all do make assumptions about what

those changing relationships and future conditions are likely to

professional judgments about them into our policy advice.

be and put our

and routine

Political regimes,

companies, churches,

Large scale systems and organizations involve well established

relationships and thus relatively rigid structures and processes.

militaries, social security systems, tax systems, industries, large

etc., are all such systems. Significantly changing such systems means changing their

design, rules, practices and processes and getting everyone involved to adapt --

changing the culture as well as the structure and functions. These are complex

undertakings as we experience with Russia and China and in many traditional

industries. Some institutions disintegrate, usually from inaction, incompetence and

corruption at the core; they eventually implode. At the same time new ventures are

continuously created and some “take off” in a rapid process of integration of

knowledge, vision, resources, and opportunities. There are thresholds involved

here which when passed result in transformation, takeoff or collapse of the system.

Early warning is most effective when we understand the system dynamics and we

make informed assessments of the thresholds and monitor movement of systems

toward them.

Understanding the likelihood and nature of critical events and changes in

relationships and systems requires knowledge and skills from many disciplines.

Integrative fields such as ecological security, sustainable development, economic

security, human capacity development, human services and humanitarian

assistance, bioengineering, etc., all illustrate progress in shaping multiple

disciplinary approaches. These enhance our early warning capabilities. We need to

do much more such collaborative work.

Early Warning Methods and Practices

I find it most useful to group early warning and futures research professional

approaches, methods and practices into five sectors of work: (1) security and politics;

(2) business and finance; (3) human rights, development and assistance; (4) ecology;

and (5) integration (see attached chart “Defining and Assessing Institutions, Their

Important Relationships and Integrative

sketches of the approaches of the groups

hope to learn and be corrected by people

Capac~ties”). Following thumbnail

(these are based on my understanding; I

more knowledgeable about each):

1. Security and politics.

Military and political intelligence

information on changing military

professionals provide continuous

and diplomatic conditions, assessment

228
Defining and Assessing Institutions, Their Kenneth W. Hunter

Important Relationships and Integrative Capacities Rims Shaffer

July 8, 1997

1. Boundaries of the institutions and its environment as part of systems: geopolitical, socioeconomic

sectors, interest groups and networks, ecological.

2. Relationships: common boundaries, stakeholders, sister communities, competitors, customers,

collaborators, systemic connections and integration. Effective socio-economic institutions are built upon effective relationships.

Effective relationships are built upon understanding, trust, communications, competence and shared visions of possible and

preferable future conditions, opportunities and challenges.

3. Understanding and evaluating five communities of concern and capabilities of all institutions and the dynamic tensions

inherent in their systems and relationships:

Dominance, Power,

State and Community

Security: Protecting

Political and Civil Order:

Sovereignty, peacemaking,

peacekeeping, and policing;

containment and resolution of

conflicts.

Protection

of Systems:

EcOlogiti

Political, ~

Environmental

Protection: Protecting

the Ecological Order:

Constraints on the use of

natural resources; conservation;

preservation; biological

diversity.

Control, Exploitation

Consensus, Sharing,

Nurturing, Preserving

Economic Competitiveness

and Justice: Protecting

the Economic Order:

Rugged individualism; economic

security; property rights and

contracts; rules by law.

Human Rights,

Responsibilities and

Development: Protecting

Individual Rights and

Cultural Order: Individual

freedoms and responsibilities,

dignity and respecc human

development; ethics; cultural

diversity, human expression.

4. Integrationinvolves balancing competing rights and competing responsibilities; setting and changing directions; fostering

inclusion, collaboration and dialogue; and continuous exploration and assessment of ideas, paradoxes, conditions. Integrative

capacity draws upon the full range of disciplines and thinking approaches, human expression, philosophy, theology, integrative

dimensions of science, knowledge building, and continuing search for wisdom. Integration requires comprehensively and

simultaneously engaging a wide range of institutions and the use of our full tool kit of relationship, trust, network and institutk

building approaches and methods. It also demands respecq patience, persistence, and comfort with ambiguity, uncertainty,

constant change, use of intuition, and continuous search for shared interests. It involves recognizing where possibilities for

integration exist and acting to create collaboration for new initiatives; framing open questions and ideas and avoiding either/or

situations; inclusion of nontraditional approaches and people who would not usually be included or would not think to include

themselves; recognizing that each collaborator will add a fuller understanding and enrich the array of approaches to solving

problems; and continuously dealing with the tensions created by the needs for completion of tasks, broad inclusion, and dynamic

of interactions among people and groups.

229

m--
.,

. .

. .

information on potential threats and risks of military and diplomatic

conflicts, analytic information on alternative strategies and their likely

consequences. It uses massive information collection (by both human and

electronic observation and interception) and analysis capabilities and has used

a closed and compartmentalized organization and reporting structure. It has

evolved its own means and methods and traditionally has been reluctant to

share information, methods or models with others. Today the intelligence

community is actively seeking to comect with other communities to broaden

its scope and services.

Criminal intelligence provides information on alleged criminal activities for

community security, law enforcement, and criminal prosecution. It uses a

wide range of investigatory means and methods, involving field

investigations, monitoring, and scientific analysis of evidence. Organized

crime, drug trafficking and corruption are major systematic focuses. Early

warning has not been an explicit, priority function. However, recognition of

(1) the extent and negative impacts of corruption on civil society and

economic development and (2) the threats to financial systems of large scale

fraud, calls for the criminal justice community to work on such political and

business systems problems. Similarly, the prosecution for war crimes brings

criminal justice people directly into the horrific actions and hostile conditions

of failed states. However, as we develop more integrated approaches and

methods of dealing with criminal systems, the traditional separation of

military and police functions by the United States and other developed

countries is being breached. This illustrates the importance of values in

shaping collaborative institutions.

Country financial risk assessment and early warning of financial trouble are

functions of the international and United States financial institutions with

the International Monetary Fund and the US Export-Import Bank having

operational activities. These institutions have extensive financial exposure

in troubled countries and have had significant losses in several failed states.

They now are broadening the range of factors they formally consider in

assessing risk to include non-financial factors. They are extending their

collaboration accordingly.

2. Business and commercial finance.

Business intelligence and risk assessment include an expanding array of

commercial analytic services. An example is The Economist Intelligence

Unit, which integrates information from the various econometric services

with the journalistic coverage of the Economist and Financial Times news

service. These services cover not only economic conditions, but political,

social, and environmental conditions and trends. While they are relatively

short-term in their focus, they do discuss long-term structural changes of

major significance. The commercial financial community also uses many

230
rating and risk assessment practices, such as those of Dunn and Bradstreet.

be effective the rating services must be anticipatory and provide early

warning for investors.

Economic indicator functions movide information on trends in economic

To

conditions and serve to guide ~conomic research and policy analysis. These

services use statistical (survey and time series) methods. Their processes are

bpen; their information is disseminates and uses widely. They have evolved

well structured and disciplined approaches and methods that they

enthusiastically share in the belief that the indicators will be improved as

user provided feedback to guide enhancements. The models used are of

economic relationships and systems. Major restructuring of international

economic and political relationships, industries and organizations is driving

the demand for overhauling economic models and statistical series to reflect

new reality.

Corporate public issues management is a professional approach to identifying

and addressing emerging and emergency public policy issues by some

businesses. Anticipating potential public issues and being prepared to engage

in shaping policy responses is the objective. A network of professional

corporate public issues managers share experiences.

Corporate strategic planners tend to focus on the organization and its

operations over the next few years. However, the initial steps in their

processes do frequently involve external and longer-term inquiry. These

include identifying a range of demographic, technological, socio-economic,

and political changes; creating a set of forecasts of plausible future conditions

5 to 10 years ahead; and designing alternative business strategies for the

transition period. This type of analysis can identify needed policy changes to

guide organizations or sectors toward preferred future conditions and away

from adverse conditions. Professional associations of plamers share

methodological experiences. Real corporate strategies are highly confidential.

Market research is one of the most extensive and intensive early warning and

futures research business functions. Business choices about markets and

products and service design and delivery bring together information, ideas

and judgments from all disciplines and functions. Market research uses a

wide range of survey methods combined with demographic analyses. They

monitor customer opinions and satisfaction closely. New ideas are field

tested. They monitor competitors and their customers also. They assess

changing markets rigorously. Product, service and market policies are

reconsider regularly. They evaluate successes, failures, and missed

opportunities for lessons learned. They consider this intelligence and their

decisions highly confidential and do not share anything.

231
Financial auditing and reporting provide information to policy makers and

the public on financial conditions of organizations. Accountants’

responsibility for assessing enterprise’s capacity to continue as a “going

concerns” is a critical early warning function. The accounting profession has

standards for reporting and auditing. Most countries use certification and

licensing of individuals. With globe spanning, real-time financial markets

and systems and organizational networks, where does the boundary of the

entity being audited end for purposes of determining its financial condition

and “going concern” capabilities? What are the entit y’s long-term financial

risks for environmental and health damage? What is the “going concern”

status of an enterprise that is not changing? The auditing community (of

which I am a member) has hardly begun to open this Pandora’s box. To do so

will require broad collaboration and integration with other disciplines. I

believe that is achievable.

3. Human rights, development and services

Human rights monitoring has focused on calling attention to ongoing

violations of human rights and seeking actions to stop the practices. Through

networks of volunteer and professional observers using increasingly rigorous

documentation, communications, and advocacy methods human rights

violations have become better understood and reported. Early warning has

involved identification of minorities at risk of abuse and of patterns of

behavior by regimes that typically include human rights violations. Today

greater attention is being given to employing statistical methods, integrating

human rights monitoring with policy making, providing public information

and education, broadening monitoring capabilities, and expanding

preventive approaches. Models are of patterns of abuse and of dispute and

conflict processes. These activities are open and increasingly involve

integration with other policy areas, including the linkage to trade and

investment practices. In my opinion the human rights treaty regimes and the

supporting reporting requirements provide a potentially effective basis for

assessing country accountability for political, social and economic conditions

and their plans and progress in meeting basic human needs.

Emergency management and human services include anticipation of possible

disasters and creation of response capabilities. The United Nations High

Commissioner for Refugees, Red Cross, CARE, and other relief organizations

are the front line of services. Since they respond to all types of disasters, they

use a mix of early warning approaches drawing upon the services that forecast

natural disasters, the intelligence services warnings of military and political

conflicts, and their own vast network of people in the field. They strive to

integrate their activities with other international and national organizations

that have responsibilities and capabilities for addressing emergency

conditions. Their work is open and cooperative. The models used are of

disaster relief and recovery processes. Human made disasters in Central

232
Africa, Somalia, Bosnia, Cambodia, and more continue to drive the search for

more effective early warning and early intervention approaches and

methods. However, the international institutions are unable to intervene

unless requested by the sovereign government which is usually unwilling or

unable to do so early enough.

Community and regional human service providers wish to move toward

‘more integrated preventive and anticipatory approaches to meeting the needs

of vulnerable people. Human service professionals have traditionally used

diagnostic and remedial approaches and a wide range of specialists and

specialized services. The reason for the renewed interest in integrated and

early assistance is recognition that people and groups in vulnerable

conditions have a wide range of problems and require more integrated

strategies to work their way out of them. We need to devote more attention

to identifying the early signals of deteriorating conditions and strategies for

early support.

Social indicators, to parallel the economic indicators, have been evolving in

the United States and internationally. The Federal Forecasters network, the

Social Indicators network, counterparts in other regions, and the United

Nations Statistical Office could serve as focal points and facilitators for

continuing improvement of social statistical systems. However, there is no

consensus on key indicators of social conditions and the specific roles they

could play in policy making. There is no early warning capability. The

human rights treaty regimes provide a generally agreed upon set of socioeconomic factors that I believe should be the foundation for social indicators

work. However, there is little professional integration between the human

rights monitoring and the social statistics professional communities.

Disease control and prevention professionals provide information on disease

conditions and forecasts of their spread and likely health and health care

impacts. Health care providers and health researchers in the field provide

data through international reporting systems; then disease control groups

analyze it. They use survey research methods extensively. These activities

are open and involve extensive sharing of data, methods and models. The

scientific method guides this work. They operate a variety of notifications

and public educational processes. The extent of collaboration with others is

illustrated by the current work on the potential long-term health effects of

global climate change and from increased attention to the threats of microorganisms.

4. Ecology

Ecosystem monitoring provides information on ecological conditions and

forecasts potential crises, including storms and earthquakes. The models used

are of natural systems and the data is acquired by physical monitoring. More

233
m

-,

.,

recently attention is on global modeling and analysis of the interaction of

natural systems and human built and operated systems. The purpose is to

assess the potential consequences of long-term demographic and

technological changes and industrial development on the global ecosystem

and the consequences of climate change. These activities are open and

involve extensive collaboration and sharing of data, methods and models.

The scientific method guides this work. Weather forecasting and earthquake

monitoring are the most developed systems. The work on climate change

being coordinated by the World Meteorological Organization’s

Intergovernmental Panel on Climate Change illustrates the capacity to draw

upon the knowledge of scientists from around the world and to synthesize

their contributions into an integrated report for the public, peers and policy

people.

Local ecosystems are also being examined. Work over several decades on the

Chesapeake Bay and newer work on the Lake Tahoe ecosystem are

illustrative. In each case, early warnings of deteriorating water conditions

drove action to establish extensive regional collaborative monitoring and

evaluation processes. The President’s Council on Sustainable Development

serves to foster these initiatives and to support the sharing of expertise.

Eco-diplomacy by international institutions can serve as an early warning

capability on potential ecological crises and conflicts. This new initiative can

serve to draw together information from monitoring systems around the

world to document and report on changing ecological conditions. This

knowledge can be used with the many stakeholders involved to

collaboratively seek means of avoiding conflict and ecological crises and

design pathways to sustainability.

5. Integration for policy makers and the public

Futures research can serve as an integrative function. It does not do it now. I

am developing initiatives through the World Future Society’s Professional

Membership, The Harrison Program on the Future Global Agenda of the

University of Maryland, and Collaborative Futures International. The World

Future Society, with a general member of 30,000 and a professional

membership of 1,500, provides publishing and convening services. The

Harrison Program is the home of the Society’s professional membership

conducts education and research programs on major global issues and

structural changes. Collaborative Futures International is being designed

support the design and development of collaborative programs and to

to

support the establishment of centers for convening, learning and knowledge

sharing, and futures research. We are developing a proposal for a “Global

2030 Program” that will serve to integrate our best knowledge about the world

over the first three decades of the next century; ideas and collaborators are

being sought. A five-year calendar of events is being developed.

234
Technology forecasting and assessment have served as a major integrative

service. The former Congressional Office of Technology Assessments (which

the Congress abolished in the Fall of 1995) described its work as providing

decision makers “with objective analyses of the emerging, difficult, and often

highly technical issues of our time.” The act establishing OTA specified:

s “The basic function of the Office shall be to provide early indications of

the probable beneficial and adverse impacts of the applications of

technology and to develop other coordinate information which may

assist the Congress. In carrying out such functions, the Office shall: (1)

identify existing or probable impacts of technology or technological

programs; (2) where possible, ascertain cause-and-effect relationships;

(3) identify alternative technological methods of implementing specific

programs; (4) identify alternative programs for achieving requisite

goals; (5) make estimates of alternative methods and programs; (6)

present findings of completed analyses to the appropriate legislative

authorities; (7) identify areas where additional research or data

collection is required to provide adequate support for the assessment

and estimates described in paragraphs (1) through (5) of this subsection;

and (8) undertake such additional associated activities as the

appropriate authorities . . . may direct.”

This charter ‘clearly linked early warning with decision making and directed

OTA to perform the policy analysis functions needed to make the

comections. We still need such technology assessment services. Several

former leaders of OTA have created the Institute for Technology Assessment

to do that.

Formal and prominent policy plaming and evaluation functions in the

Federal government were abolished in 1981 and have not been reestablished

in any organized form. Existing Inspector General, General Accounting

Office, and staff offices are reactive for the most part and very narrow in their

approaches. While they could serve integrative and policy level early

warning services, they do not. one of these days something will happen to

drive the rebuilding of policy integration capabilities in the Congress or the

Executive branch. This should not be recreation of the old approach. We

should base it on today’s and tomorrow’s technology. Design and

development of “decision technology systems” should be one element.

Another should be a focus on policy implementation, including the

identification of possible implementation obstacles. By mapping the path of

implementation through the full array of institutions and processes involved

in carrying out a policy direction or change, it should be possible to identify

and assess the likely responses of stakeholders. This procedure can provide

early warning of the risk of delay, blockage, or redirection during

implementation. Policy evaluation professionals should monitor actual

implementation and focuses on systemic weaknesses and obstacles. The

models used in this type of work would be of bureaucratic and decision

235
making processes and organizational behavior and development. While

today we are in the middle of major restructuring of policies, programs and

organizational relationships, little attention is being given to the

implementability of the proposed changes - little early warning is being

provided.

I have outlined this range of approaches to make two points. First, there is a rich

array of capabilities for early warning and futures research support for policy

making. Second, we need collaborative approaches. For example, early signals for

assessing the risk of failure of a state or other system can come from any of these

sources. Signals coming from multiple sources can more quickly provide a picture

of a deteriorating situation. Furthermore, very early signals can come from

monitoring the extent to which a state or other institution is ~o~ responding to

long-term structural changes in its environment -- is falling behind by inaction.

Demographic, technological and ecological forces are very slow to impact

institutions until they pass some thresholds of tolerance. We can creating

integrating and boundary-spanning theories, methods and practices.

Common early warning and collaborative futures program functions and general

process

In all types of professional work common functions and processes have evolved.

Professional associations develop and test theories and share ideas and experiences,

and refine processes and practices. In my view, for early warning and futures

research the following eight-steps comprise a general process. (My colleague Dr.

Rims Shaffer and I have developed this model for use with organizations and

groups interested in adding futures research thinking and methods to their decision

making processes; a book is under way.) Following are sketches of the eight steps

illustrated in the chart.

1. Define and describe the target systems and institutions, their core processes,

and their important relationships visually and analytically. The entities for

which policy is being made include countries, regions, communities, business

sectors, corporations, nonprofit enterprises, religions, public services, human

rights and responsibilities, scientific sectors, etc. Five key features of systems

need to be addressed. (1) The major components of the organization and its

infrastructure. (2) The core capabilities, including knowledge, skills,

techniques, and talent. (3) Important relationships and the strength of the

comections -- boundary spamers, gateways. (4) Important flows of ideas,

people, information, financial resources, products and services, etc. (5)

Evolution -- important processes of change, pioneering, adaptation and

periodic transformation. From this information we can define the

boundaries of the system and institutions and the nature of its connections

with others.

236
Designing a Dynamic Eight-Step Process

.

for Shap\ng Direction-Setting Policies and Relationships

9

L Defining and

Describing Your

Institution/

Community and

Its Important

Relationships

●

Kenneth W. Hunter

Rims Shaffer

JUIY 8, 1997

6. Alternative Paths to the Fbture:

Evolution, Scenarios, Pattern!

enchmarks, Breakthroughs, Challenges

Policies and

Practices

8. Iterattve Mapping and Vlsuallzbq

Implementation; Reallzatlon

Monitoring and Evaluating; Early

Warning and Course Corrections;

and Continuous Systemic Evaluation

for Reconsideration

Social and Institution:

5. Assumptions

About the Future -

Plauslble Future

Relationships and

Conditions and

Frontiers of the

21st Century

------

Vlslons of What

Could Be

++++++’

Consideration of
. .

,,.

2. Understand the values, history, visions, culture and myths that shape the

criteria for making choices. Every institution and the people operating in it

make their day-to-day decisions and shape their actions and relationships on

the basis of their understanding of the values of the entity and their own.

These values and behavior evolve and comprise a culture. Responses to

earlier events and shared expectations help shape these values. In

organizations, we articulate them as myths and visions, missions and goals,

codes of conducts, guidelines and operational manuals, and in our

continuous dialogues about the enterprise. Understanding why an

institution works the way it does, why it makes the choices it does and thus

how it is likely to respond to early signals of change requires deep

understanding of its values.

3. Understand major change drivers and shapers and their impacts on current

and future conditions. Early warning and futures research involve extensive

and intensive monitoring, forecasting and impact assessment of at least four

groups of change drivers and shapers. (1) Demographic change drivers,

including population growth globally and locally, differential growth among

regions and groups, differential income and wealth among regions and

groups, aging and increases in longevity, and increases in mobility. (2)

. Technological change drivers, including the increasing array of digital

technologies, advances in the biological technologies, new and improved

materials, new nano technologies, and continuing struggles with nuclear

technologies. (3) Ecological change shapers, including climate change, usable

water limits, ecologically sustainable energy sources, maintenance of

biological diversity, and human relationships with microorganisms. (4)

Macro patterns in institutional change that shape individual entity’s

environment, including globalization processes, shifting from hierarchical to

more modular and networked structures, acceleration in the pace of change,

becoming real time all the time with global communications and mobility,

and renewed emphasis on ethics, opemess and rule by law. In addition, we

need to identify and assess change drivers that are specific to the institutions

we are working with.

4. Assess current conditions, capabilities and direction-setting policies and

practices. Brutally honest and rigorous assessments are hard to do before a

crisis occurs. Nevertheless, effective early warning and futures research are

dependent up it. Auditing, investigating, inspecting, surveillance, and many

other such functions provide information and evidence of conditions.

Process and policy evaluation provide information for assessing current

Amactices as well as ~olicies, since txactices mav be at odds with ~olicv. In

some cases

pioneering

These first

thoroughly

practice; lag in’ imple~enting cha~ges and in other; the; are

work well ahead of current policy.

four steps involve understanding the present situations as

and rigorously as we can. The next two steps jump to the future.

238
The last two steps focus on present direction-setting policy choices and their

implementation. -

5. Assumptions about the future -- visiom of what conditions could be

se~eral decades ahead. Futures research work underlying impact assessments

in Step 3 above also shapes assumptions about the next few decades.

Forecasting and statistical capabilities allow us to make fairly useful sets of

deinographic and infrastructure assumptions about the next three decades --

the next generation of people and the useful life of much infrastructure.

Using 2030 or so as the foresight period also shifts the nature of the policy

discussion to “the legacy for future generations” by actions of current policy

makers. One set of assumptions includes those that are highly probable -

providing a relatively surprise free vision of the future. In addition, we need

to define sets of potential crises and breakthroughs that would result in

significantly different futures. Each disciplinary group has its forecasters and

sets of assumptions and visions. Policy analysis and futures research can

integrate them for use in policy making.

6. Analysis and consideration of alternative paths to the future – systems

evolution, scenarios, patterns, benchmarks, breakthroughs, challenges and

threats, probing analogies, etc. Today scenarios are a popular practice for

describing and considering policy options and their consequences. Whether

developed as such stories or as more rigorous policy and implementation

analyses, “alternative paths to the future” are critical features of early warning

and futures research work or at least they should be. My preference is to use

our full professional policy analysis tool kit rather than relying on one or a

few tools. Modeling the system or institution, developing alternative paths,

conducting implementation analyses of alternatives, assessing costs and

benefits, etc., are all necessary components. Information and analytic

technologies make this work far more effective today. We should be giving

great attention to the integration of our approaches and methods into

“decision technology systems.” (I have been thinking about designing

decision technology systems for some time and now believe the latest

technology is making it really possible now. In addition, Dr. Shaffer and I are

developing an organizational behavior and development continuum

approach for creating sets of scenarios for more tailored to setting directionsetting policies.)

7. Reconsider important direction-setting policies and relationships and

criteria for choices. Today changing conditio~ are forcing reconsideration of

policies everywhere. Will ideology and political power overwhelm the

normal analytic and dialogue phases of policy reconsideration? In the 1970s

we gave much attention to the systematic reconsideration of public poliaes.

The driving proposal was for “sunset” provisions to be incorporated into all

legislation authorizing major programs. This Was to serve as an action

forcing mechanism for reconsideration. while the idea died and most form

{

I 239
of systemic policy work were discontinued in the 1980s, the need still exists

and the political institutions are in gridlock over their incapacity to do this

core function. Nevertheless, professionals and professional institutions need

to be more rigorous in their work and advising of policy making institutions.

This means doing all the functions described above continuously in order to

be able to synthesize knowledge for use by the public and policy makers in

their own ways in the chaotic policy arenas of the non-analytic real world.

We need to give special attention to reconsideration of the criteria for choices

-- the values underlying all of the evaluations, dialogue and debate. Values,

facts and opinions get hopelessly confused in the public discourse and it is the

continuing responsibility of professionals to clarify wherever possible during

the policy making process. Mixing lobbying and special interest advocacy

with professional analysis and advising undermines the credibility of the

latter and contributes to the public and political confusion, cynicism and

gridlock. It is up to the professions to address the conduct of their members.

8. Iterative mapping and visualizing implementation; realization monitoring

and evaluation; early warning and course corrections; and continuous

systemic evaluation for reconsideration. Choosing a new policy direction is

just one step in a continuous process. Implementation is the path to

realization. We need to provide visions and maps for guiding

implementation. We monitor and evaluate changing conditions, provide

early warning, and offer course corrections. Thus the iterative cycle of

professional work continues.

Evaluation of early warning and futures research performance

How should our professional early warning and futures research work be

evaluated? Using the “accuracy of our forecasts and warnings” as the key measure is

inappropriate and counterproductive. If we do our work effectively it should not

include “forecasts” but should present ranges of plausible assumptions about the

future. We should present “warnings” as likely consequences of inaction or specific

proposed actions. Furthermore, policy action and implementation will change the

future, making our earlier analyses outdated.

.

We should be evaluated on the effectiveness of our informing policy makers and

the public. It seems to me there are three dimensions of such evaluation. First is

the assessment of the policy makers with whom we work and who use the

knowledge we develop in collaboration with them. Second is the public judgment

of our service which to a large degree is filter through the media with whom we

deal directly as information users and as professional peers. This leads to the third

dimension that is the judgment of ourselves as professional peers.

Credibility of all professions is dependent upon their own capacity to establish and

adhere to performance and conduct standards. Early warning and futures research

240
are in their infancy in this regard. Establishing professional groups to continuously

examine theories, methods and practices in use and proposed is an important first

step. In my view we should strive to strengthen the early warning and futures

research component of each professional discipline while also developing

integrative theories and methods.

Closing 1

The purpose of this paper has been to pull together my own thoughts on early

warning and futures research in order to participate in dialogues on strengthening

our services to policy makers and the public. I look forward to engaging in these

dialogues at upcoming meetings being convened by the National Intelligence

Council, the Federal Forecasters Conference, The Harrison Program on the Future

Global Agenda, the World Future Society, and in other forums. We have not had

such a serious professional dialogue for several decades and I am pleased to

participate again.

Again, I encourage you to join in the professional meetings and collaborative

futures programs being developed. My coordinates for contact are on the cover.

241
E, .
FORECASTING PROGIUiM EXPENDITURES

Chair: Karen S. Hamrick

Economic Research Service, U.S. Department of Agriculture

Projections of Elementary and Secondary Public Education Expenditures by State,

William J. Hussar, National Center for Education Statistics

U.S. Department of Education

Medicaid Forecasting Practices,

Dan Williams, Baruch College

Consumer Health Accounts: What Can They Add to Medicare Policy Analysis?

R. M. Monaco, INFORUM, University of Maryland, and

J.H. Phelps, Health Care Financing Administration

243
.
PROJECTIONS OF ELEMENTARY AND SECONDARY PUBLIC EDUCATION

EXPENDITURES BY STATE

by

William J. Hussar

National Center for Education Statistics

I. Introduction

The National Center for Education Statistics (NCES)

has been producing projections of education finance

statistics at the national level for approximately

hventy-five years. For the fmt time, NCES is

investigating the development of state projections for

one of its finance statistics, public elementary and

secondary current expenditures. This paper presents a

methodology for producing a set of preliminary state

current expenditure forecasts. Two tests of these

projections are examined later in this paper.

The frost of this paper’s eight sections is this

introduction. The second section is a review of the

literature on examining cument elementary and

secondary school expenditure using time series data.

The third section presents the five combinations of

estimation technique and pooling techniques which

were used in this analysis, as well as the sources of the

data.

The fourth section examines a model for elementary

and secondary expenditures similar to one that Stephen

Barro developed in the early 1970’s. This model was

estimated using the five estimatiotipocding

techniques presented in the third part of this paper.

Ex-post mean absolute percentage errors (M.APES)

were calculated for each state using the five

estimation/pooling techniques. These MAPES are

examined, together with those fkom a naive model in

which cun-ent expenditures in each state were assumed

to increase throughout the forecast period at the

average annual growth rate of the previous three years.

The results concerning the estimation coefficients

largely followed expectations for each of the

estimation/pooling techniques. Using the ex-post

MAPEs as a measure of forecasting ability, one

estimation/pooling technique was found to be superior.

While the Barro model performed well, there was a

serious problem with using it to produce state

projections: there are no independently produced

projections of one of its key independent variables and

of the price deflator used for current expenditures.

Hence, an alternative model was produced for which

there are forecasts for all the variables. Both the

coefficients and the MAPEs of alternative model were

examined in the fifth section of the paper. The results

were very similar to those from the Barro model and

again the results for one estimation/pooling technique

were superior. Following Barre’s example, Alaska and

Hawaii had been excluded from the sample. They

were included in a final set of estimations.

Two sets of state projections were produced. The fmt

set was produced using the model developed in section

five. An alternative set is the one presented in this

paper, in which the state projections have been

adjusted to equal the national totals from the

Projections of Education Statistics to 2005. The

Projections of Education Statistics to 2005 was used in

the adjustments as the forecasts underlying the

projections presented in that edition were produced

about the same time as those used here.

In the seventh section, the results from two tests of the

state forecasts are examined. In the fmt test the

projections for 1993-94 are compared to the actual

values produced by National Center for Education

Statistics. In the second test, the projections for 1994-

95 and 1995-96 are compared to the National Center

for Education Statistics most recent Early Estimates.

Of these tests, the fmt is obviously the strongest. The

other test can only indicate if a state’s projections seem

grossly wrong.

In the final section, there is a summary and a

discussion of possible fhture work in this area.

II. Review

There has been a large body of work both theoretical

and empirical, on the demand for local public services

245

m’

.
m

.,

such as education. In most models, there are typically

four types of variables; 1) a measure of income; 2) a

measure of intergovernmental aid for education; 3) a

measure of the price of providing one more dollar of

education expenditures per pupil; and 4) one or more

measures of voter tastes for education. Largely

consistent results have been found for a great number

of empirical studies which have been conducted using

this framework. Most of the empirical work on

education expenditures has used cross-sectional data.

There has been a limited amount of work using data

which are simply time series data or are pooled time

series and cross-sectional data.

As noted in the introduction, the National Center for

Education Statistics (NCES) has been producing

projections of national education statistics for twentyfive years. It has been producing projections of

elernentiuy and secondary cuxrent expenditures using

an econometric model yearly since 1987.

The national NCES model has three independent

variables; one for each of the first three categories of

variables mentioned above; 1) personal disposable

income per capita in real dollars, 2) revenue receipts

from state sources per capita in real dollars, and 3) the

ratio of the enrollment to the population. (While the

ratio of enrollment to the population was not a direct

measure of the price of education, it was a measure

that had been successfidly used in other studies.)

In the early 1970’s Robert Barro developed a model

for the demand for education expenditures by state.

This model could have been used to produce state level

expenditure forecasts though it does not seem to ever

have been used for that purpose. It is that model that

forms the basis for the analysis presented in this paper.

Barro examined education expenditures using pooled

time-series cross-sectional data. He conducted his

analysis using data for the contiguous states for every

other school year from 1951-52 to 1967-68. Batro’s

model had as its dependent variable school spending

per pupil in real 1964-65 dollars. Barro used an

education price deflator that he developed for this

study.

The Barro model can be found in table 1. It had seven

independent variables. ‘The of these, income per

capita in real dollars, the sum of state and federal aid

for education in real dollars, and the ratio of the

enrollment to the population multiplied by the ratio of

an education price index and the CPI, corresponded to

each of the fmt three types of variables listed above.

The coefficients of those three variables were all

significant and followed expectations.

The Barro model had four additional variables. A

change in the enrollment was found to have a negative

impact on expenditures. Having a low population

density was found to have positive effect on education

expenditures. There were two variables which

measured the impact of being of a state being in the

South. These two variables had opposing effects on

current expenditures for the southern states: a dummy

variable for southern states had a negative coefficient

yet a variable which measured the different impact of

aid in the South had a positive coefficient.

III. Estimation/Pooling Techniques and Data

Sources

A. Estimation/Pooling Techniques

The two models presented in this paper were estimated

using five different combinations of estimation

techniques and the methods for pooling the datz 1) all

the states were pooled together and ordinary least

squares (OLS) was used to estimate the model; 2) the

states were pooled into nine regions, OLS was used to

estimate the model, and there were state dummy

variables; 3) the states were pooled into nine regions

and one of several generalized least squares techniques

was used to estimate the model; 4) the model was

estimated for each state using OLS; and 5) the model

was estimated for each state using ARl. (Also see

table 2.)

As noted above, there are several alternative

specifications for the generalized least squares

technique. Kmenta suggests one specification, the

cross-sectionally comelated an d time-wise

autoregressive procedure for a sample such as the

states of the United States.

The cross-sectionally comelated and time-wise

autoregressive procedure of the econometrics package

SHAZAM was used when the model was estimated

using the generalized least squares technique.

246

L?-”
The nine regions used in this analysis can be found in

table 3.

B. Data

A data base similar to that used by Barro was

constructed. Following Barre, it consisted of data for

each of thg forty-eight contiguous states and the

District of Columbia for each year from 1970-71 to

1992-93. Data for Alaska and Hawaii were also

collected but were not used in the initial estimations of

the models.

Most of the historical education data were from

NCES’S Common Core of Data. This included data for

current expenditures, revenue receipts from state

sources, revenue receipts from federal sources, and

enrolhnent as measured by average daily attendance.

The economic consulting fm DRVMcGraw-Hill

provided the historical data and the forecasts for

disposable income, state and local government tax

payments by state. DRI/McGraw-Hill also provided

the national consumer price index and the national

price deflator for personal consumption expenditures

which was used to place disposable income in constant

dollars.

The economic projections from DR1/McGraw-Hill

were produced in early 1995, shortly after the

production of the forecasts of the nationai economy

used in production of the Projections of Education

Statistics to 2005. As the projections were produced

close in time, the state productions will be treated as if

they had been produced with the Projections of

Education Statistics to 2005.

Forecasts for average daily attendance for each state

were required, both to be used as a component of an

independent variable and also to produce forecasts for

total current expenditures. Two sets of projections

were produced for each state. The fwst set of forecasts

for each state was calculated by multiplying each

state’s fall enrollment projections as presented in the

Projections of Education Statistics to 2005 by that

state’s average value of the ratio of average daily

attendance to the population for the previous ten years.

This method is similar to the method used to produce

the national projections for average daily attendance.

A second set of projections was produced for each

state by altering each state’s projection by the same

percent so that the sum of the state projections equaled

the national projections in the Projections of Education

Statistics to 2005.

A Barro type education price index was constructed by

using the National Education Association’s teacher

salary time series to measure personnel costs and the

consumer price index to measure other costs.

N . The Barro Model

This part of the paper presents the results horn thereestirnation of the Barro model. The results of the

estimations of the Barro model using each of the

pooling/estimation techniques are compared using expost MAPEs.

Table 4 contains specifications of the Barro model for

each of the five estimation/pooling techniques. Unlike

Barre’s original estimations, most of the variables were

in log form. The log form was used for all the

estimation presented in this paper because of results

from BOX-COX tests for fictional form that were

conducted when preparing the NCES national

projections.

The only time the specification was identical to that

used by Barro was when the data were pooled

nationally and ordinary least squares was used.

When the states were broken up by tegion or state,

there was no reason for either the SOUTH dummy

variable or the variable LTGRANTS to be present.

Further, in some of the regions, there was no variation

in the density variable. Hence, when estimating the

Barro model using data pooled either regionally or by

state, those three variables were excluded.

A. Results of the Estimations

Table 5 shows a summary of the estimations of the

Barro model using the five estimatiodpooling

techniques. (The results for each estimation are

available in a longer version of this paper available

horn the author.)

Ordinary Least Squares with Data Pooled

Nationally

247

IP-
These results are similar to those from the original

study by Barro presented in table 1. The coefficients

of all the variables except the change in enrollment

variable (GRADA) had the same signs as in the Bano

study and were significant.

Ordinary Least Squares, Data Pooled Regionally,

and Dummy Variables

The coefficient for the income, state aid and price

variables were significant for most of the regions. The

coefficient for the change in enrollment variable was

significant for only two regions.

Generalized Least Squares and Data Pooled

Regionally

The coefficient for the income, state aid and price

variables were significant for most of the regions. The

coefllcient for the change in enrollment variable was

significant for only three regions.

Ordinary Least Squares and Each State Examined

Individually

For most of the states, the coefficients of the income

and price variables were significant. For about half the

states, the coefllcient of the state aid variable was

significant. For less than half the states, the coefficient

for the change in enrollment variable was significant.

ARl and Each State Examined Individually

The results for ARl were very similar to those for

OLS. For most of the states, the coefficients of the

income and price variables were significant. For about

half the states, the coefllcient of the state aid was

significant. For less than half the states, the coei%cient

for the change in enrollment variable was significant.

B. Forecast Evaluation of the Barro Model

Mean absolute percentage emors (MAPEs) were

calculated for each technique for producing forecasts

to measure forecast accuracy. For the naive model,

MAPEs were computed for the case in which cument

expenditures per pupil in constant dollars were

assumed to increase throughout the forecast period at

the average annual growth rate of the last three years.

The MAPEs from each estimation/pooling technique

were compared to the MAPEs fkom the naive model.

To produce the state MAPEs, for each estimationpooling technique, a series of ex-post forecasts were

calculated. This was done by excluding fkom one to

five observations Ilom the end of the sample. Then,

forecasts were calculated using the parameter estimates

and the actual values for the independent variables.

This produced five one-year-out ex-post forecasts, four

two-year-out ex-post forecasts, three three-year-out expost forecasts, two four-year-out ex-post forecasts, and

one five-year-out ex-post forecast.

These ex-post forecasts were then compared to the

actual values. In this paper, MAPEs were calculated as

the primary measures of forecast accuracy. As up to

five years were excluded from the sample period to

produce the ex-post forecast five different ex-post

MAPEs were calculated. The one-year-out MAPE

examines the accuracy of the five one-year-out

forecasts, the two-year-out MAPE examines the

accuracy of the four two-year-out forecasts and so

forth.

The formula used to calculate the MAPE can be found

in table 6.

The MAPE is one of several measures for forecast

evaluation. Another standard measure is the root mean

square error (rinse). These will not be examined since

they were very similar to those for the MAPEs.

The Naive Model

First, we shall examine the MAPEs from the naive

model. The naive model produced relatively low

MAPEs for a large number of states (table 7). Fortyfive states had a fret-year-out MAPE of less than 5

percent and fifteen had a five-year-out MAPE of less

than 5 percent. There were some states, however, that

had quite large MAPEs, such as Louisiana.

Ordinary Least Squares and Data Pooled

Nationally

The MAPEs were generally higher than those Ilom the

naive model. When compared to the naive model,

only eight states had a lower one-year-out MAPE

using this estimation of the Barro model. (See table 8

for the number of states which have a MAPE less than

k-
the naive model for each estimatiordpooling

technique.) The MAPEs for this estimation technique

do not compare favorably to those calculated using the

naive model.

Ordinary Least Squares, Data Pooled Regionally,

and Dummy Variables

The results are mixed for this estimation/pooling

technique. Only twenty-three states had one-year-out

MAPEs less than those from the naive model.

However, at least thirty-three states had MAPEs less

than those of the naive model for each of the other

forecast horizons.

Generalized Least Squares and Data Pooled

Regionally

Unlike the case for the other estimation/pooling

techniques, the ex-post MAPEs were generally lower

than those from the naive model for the one-year-out

MAPEs. Thirty-four states had one-year-out MAPEs

which were smaller for this estimation of the Bamo

model than for the naive model. When this

estimation/pooling technique was use~ a majority of

states had two-year-out through four-year-out MAPEs

that were smaller than those from the naive model as

well.

The results for the five-year-out MAPEs when

generalized least squares was used are not directly

comparable to those for the other estimation/pooling

techniques. Generalized least squares could not be

used to estimate the Bmo model for the South Atlantic

region due to a lack of observations. Hence, there are

only 40 five-year-out MAPEs this estimation/pooling

technique rather than 49.

Ordinary Least Squares and Each State Examined

Individually

For twenty-one states, the one-year-out MAPEs for

this estimation of the Barro model were lower than

those of the naive model. However, for the two-yearout through the four-year-out time horizons, the

MAPEs were lower than those of the naive model for

at least thixty-one states.

ARl and Each State Examined Individually

For eighteen states, the one-year-out MAPEs for this

estimation were lower than those of the naive model.

However, for at least thirty-four states, the two-yearout through five-year-out MAPEs were lower than

those of the naive model.

c . Summary of the Results of the Barro

Model

Results of the estimations of the Barro model showed

that the estimated coefficients generally followed

expectations no matter which estimation pooling

technique was used. Also, the technique of breaking

the states up into regions and using generalbd least

squares, performed best as a method for producing

forecasts when using ex-post MAPEs were used as the

criteria.

v. The Forecasting Model

There are problems with using the Baxro model as

previously estimated as a forecasting tool as not all the

required data are available (table 9). First there are no

independently produced forecasts of the education

price index used to adjust current expenditures so no

cunent dollar projections could be produced. Secon&

there are no independently produced projections of the

independent variable state and federal revenue receipts

for education. To solve these difficulties, alternatives

for the education price index and state and federal

revenue receipts for education were found.

As a substitute for the education price index, the

national consumer price index was used. The

consumer price index has been used successfidly with

NCES’S national education expenditures.

State and local tax payments were used as a substitute

for state and f~eral revenue receipts for education.

This variable acts as a measure of the general financial

position of each state. It lacks any measure of the

actions of federal govermnen$ but this may not be a

major difficulty since federaI aid for education makes

up only a small portion of elementary and secondary

education revenue.

The national forecasts for the consumer price index

and the state forecasts of state and local government

tax payments were provided by the economic

forecasting firm, DRVMcGraw-Hill.

,.

249

KE-
m. ’

.-,

Hence, an alternative model, which will be called the

Forecasting model, was developed using these

substitutes. The alternative specifications for the

different estimation/pooling techniques can be found in

table 10.

The Forecasting model was estimated using the five

estimation/pooling techniques used to estimate the

Barro model. In presenting these results, there are two

differences born the presentation of the Barro model.

First only statistically significant variables were

included in the estimation of the Forecasting model

presented here. The second difference only concerns

the presentation of the case when each state was

examined separately. Rather than presenting the

results for both ordinary least squares and ARl for

each state, the results fi-om only one estimation are

presented for each state. The value of the DurbinWatson test were used to determine which results

should be presented. A summary of the dfierences

behveen the Forecasting model and the Barro model

can be found in table 11.

A. The Forecasting Model

Ordinary Least Squares and Data Pooled

Nationally

Two variables that had been included in the estimation

of the Barro model, the change in average daily

attendance and the dummy variable for southern states,

were excluded here because they were not statistically

significant (table 12).

Ordinary Least Squares, Data Pooled Regionally,

and Dummy Variables

The income variable was included in the estimations

of all nine regions. The state aid variable was included

in the estimations of seven of the regions and the price

variable was included in the estimations of five of the

regions. The change in enrollment variable was

included in the estimation of only two regions (table

12).

Generalized Least Squares and Data Pooled

Regionally

The income variable was included in the estimations of

all nine regions. ‘The state aid variable was included in

the estimations of eight of the regions and the price

variable was included in the estimations of seven of the

regions. The change in enrollment variable was

included in the estimation of only

12).

Ordinary Least Squares/ARl

Examined Individually

While estimations were produced

least squares and ARl, only

three regions (table

and Each State

using both ordinary

one estimation is

considered for each state. The Durbin-Watson statistics

for each state were examined to chose which equation

would be presented. For approximately two thirds of

the states, the results of the ARl estimation are

presented.

In thirty-two equations, the income variable was

included. In twenty-six equations, the aid variable was

included. In thirty-one equations, the price variable

was included. In twenty-two, the change in enrollment

variable was included (table 12).

B. Forecast Evaluation of the Forecasting

Model

As with the Barro model, ex-post MAPEs were

compared to those from a naive model in which

current expenditures per pupil in constant dollars was

assumed to increase at the average annual growth rate

of the last three years. These naive forecasts were

different forecasts than those used to evaluate the

alternative estimations of the Barro model because

diffkrent price indexes were used with the dependent

variable. Those MAPEs computed for the naive

forecasts estimated using the consumer price index

were higher than those estimated using the education

price index for most states.

Ordinary Least Squares and Data Pooled

Nationally

This specification does not do well when compared to

the naive model (table 13). Only eight states had oneyear-out MAPEs less than that from the naive model.

Ordinary Least Squares, Data Pooled Regionally,

and Dummy Variables

250
The one-year-out MAPEs for this estimation/pooling

technique were lower than those of the naive model for

only eighteen states (table 13). However, the twoyear-out MAPEs for this technique were lower for

thirty-two states. and the other types of MAPE were

generally lower for this technique.

Generalized hast Squares and Data Pooled

Regionally

As was the case with the Barro model, the MAPEs for

this estirnationlpooling technique (table 13) are

generally lower than those of the other

estimation/pooling techniques. Thirty-three states had

one-year-out MAPEs less than the naive model and

roughly forty states had two through four-year-out

MAPEs less than the naive model.

Ordinary Least Squares/ARl and Each State

Examined Individually

Only sixteen states had one-year-out MAPEs lower

than those of the naive model (table 13). For two

through five-year-out periods, for most states, this

estimatiodpooling technique did do better than the

naive model.

c . Summary of the Results of the Forecasting

Model.

As with the estimations of the Barro model, the

estimated coefficients of the Forecasting model

generally followed expectations no matter which

estimation pooling technique was used. Given the

similarity of results between these two models for all

the estimation/pooling techniques, the Forecasting

model was used to produce the state forecasts.

Secon~ the technique of breaking the states up into

regions and using generalized least squares, performed

best as a method for producing forecasts when using

ex-post MAPEs as the criteria. This superiority was

strongest for the one-year-out MAPEs but also held for

the two-year-out MAPEs, three-year-out MAPEs and

four-year-out MAPEs as well.

D. Forecasting Models for Alaska and Hawaii

This paper analyzes models using a database similar to

Barre’s which excluded two states, Alaska and Hawaii.

In order to produce projections for the entire country,

projections for those two states were needed. Two

options were considered: 1) estimate equations for

Alaska and Hawaii separately using either OLS or

AR1; and 2) include the two states in their respective

regions (the Pacific North West for Alaska and the

Pacific South West for Hawaii) and re-estimate the

model using the generalized least squares. In the end,

the generalized least squares method for each of the

states, including Alaska and Hawaii, was chosen to

produce the state forecasts.

VI. Forecasts by State

A set of forecasts for cument expenditures per pupil in

average daily attendance for the fifty states and the

District of Columbia were produced using the

generalixd least squares estimations. Those forecasts

were multiplied by the unadjusted state forecasts for

average daily attendance to produce the forecasts for

total cuxrent expenditures. Both sets of projections are

available in a data appendix which is available tlom

the author.

-.

A comparison of a forecast for the United States as a

whole produced by summing up the state forecasts was

compared with the national projections from both

Projections of Education Statistics to 2005 and

Projections of Education Statistics to 2007 (table 14).

The projections produced by summing up the state

projections were only slightly higher than the national

figures from Projections of Education Statistics to

2005. (While the forecasts were produced in 1964-65

dollars, they are presented in this paper using 1992-93

dollars.)

If state projections are ever presented in the

Projections of Education Statistics, the state

projections will be adjusted by the same proportion so

that the sum of the states projections equals the

national projection. That same method was used here

producing an adjusted set of state projections, equal to

those in Projections of Education Statistics to 2005.

Projections of Education Statistics to 2005 had similar

economic projections as used for the state f~asts in

this paper. As the projections were produced so close

in time, the state projections will be treated as if they

had been produced with the Projections of Education

Stat&tics to 2005. The adjusted current expenditure

projections were divided by the adjusted projections

251

E- .-
for average daily attendance to produce adjusted

projections for current expenditures per pupil in

average daily attendance.

Average annualized growth rates for current

expenditures for three different periods appear in table

15.

The period from 1980-81 to 1992-93 was a period in

which current expenditures in constant dollars grew in

all the states. The largest change was in Nevada which

had an average annualized growth rate of 6.7 percent.

The smallest was in IOWA with an average annualized

growth rate of 0.9 percent. In no region was an

average annualized growth rate less than 2.0 percent

and the nation as a whole had an average annualized

growth rate of 3.0 percent.

For the nation as a whole and for most states, the

average annualized growth rate for current

expenditures was lower for the most recent historical

period of 1989-90 to 1992-93, than for the entire

historical period of 1980-81 to 1992-93. Seven states

had negative average annualized growth rates over the

recent historical period.

Current expenditures were forecast to increase for each

state from 1992-93 to 1999-2000. The smallest

increases were projected for Iowa and North Dakota

with projected average annual growth rates of 1.0

percent and the greatest was projected for

Massachusetts with a projected average annual growth

rate of 4.6 percent. Regionally, the greatest increase

was projected for the South-Atlantic region, which also

had the greatest increase for the 1980-81 to 1992-93

period. The smallest increase was for the West-NorthCentral, which had the second lowest increase for the

historical period.

For many states and the nation as a whole, one f~or

pushing up the total current expenditures projections

was the increase in enrollment that was projected for

the forecast period. The average growth rate for the

historical period was only 0.4 percent. The projected

average annual growth rate for average daily

attendance was 1.7 percent. For each region, the

projected average annualized growth rate was greater

for the forecast period than for the historical period.

While four regions had negative average annualized

growth rates for the historical peri@ all regions had

increases projected for the forecast period.

For the entire historical peric@ current expenditures

per pupil had an average annualized growth rate of 2.6

percent (table 16) but there was considerable variation

among regions and states. The Mid-Atlantic region

had an average annualized growth rate of 3.6 percen~

while the Pacific-South-West had one of 1.3 percent

Maine had the highest average annualized growth rate

at 5.5 percen~ while Alaska had the lowest at -0.6

Percent”

The situation for the most recent historical peri@

tlom 1989-90 to 1992-93, was quite diffbrent than for

the entire historical period. From 1989-90 to 1992-93,

the average annualized growth rate was only 0.1

percent. The average annualized growth rate was

negative for three regions and for about half the states.

For the nation as a whole, an average annualized

growth rate of 1.5 percent was projected. The

projected average annualized growth rate differs

sharply by region. For five regions, an average

annualized growth rate of 2.0 percent or greater was

projected. For the Pacific-North-West an average

annualized growth rate of only 1.0 percent W*

projected and for the Pacific-South-West an average

annualized decrease of -0.3 percent was projected.

The main cause of the negative number for the PacificSouth-West was the -0.8 percent average annualized

growth rate projected for current expenditures per

pupil in California.

VII. A Further Examination of the Forecasts

Given the potentially controversial role that state

forecasts could have, care will be taken before any

state projections are presented in an edition of the

Projection of Education Statistics. This part of the

paper presents the results of two additional tests of

these projections.

A. Comparison with the 1993-94 Actual

Values

NCES has published actual values for 1993-94. These

values, in 1992-93 dollars, are compared with the

adjusted projection (table 17).

Overall, the national projection from the Projections of

252
Education Statistics to 2005 was slightly higher (0.6Yo)

that the actual value.

When we examine the regions, we see that for most

regions the projections were quite close to the actual

values. For six of the nine regions, the projection was

within one percent of the actual value. The greatest

difference was, for the East North Central with a

forecast 2.6 percent too large.

The results differed more widely by state. For about

half the states, the projection was within 2 percent.

However, there were seven states with projections off

by greater than 4.0 percent. The states with the

greatest differences were Ohio (8.8 percent) and North

Dakota (12. 1 percent).

B. Comparison with the Early Estimates

The 1994-95 and 1995-96 forecasts also were

compared to the 1996 NCES Early Estimates. Each

year NCES produces a series of early estimates for

current expenditures. This edition of the early

estimates, contains early estimates for 1994-95 and

1995-96. These numbers for the early estimates come

from a survey of state education agencies. In some

states, the state agencies may have a very good idea of

what will be spent. With other states, the estimates

may be significantly less accurate. Some states may

not respond to the survey so NCES estimates a figure.

On table 18, there is a comparison of the early

estimates with the projections for 1994-95 and 1995-

96, all in 1992-93 dollars. As the early estimates are

not final counts for each state, this comparison is not a

test of the accuracy of the projections. Rather, rather it

is a test to indicate if any states have any highly

improbable projections.

If the early estimates and projections for a state differ

sharply, this may mean one of several things. First it

could mean that this projections methodology had

difficulty forecasting current expenditures for the state.

Secon& it could mean that there was a problem with

the early estimate for that state. And third it could

mean that the early estimate has caught a change in the

trend of that state which was beyond the ability of the

forecasting. model to capture.

For the national total, the early estimates and the

projections produced similar numbers. For 1994-95,

the projections were 1.5 percent greater than the early

estimates and for 1995-96, they were 2.5 percent

greater. While these national numbers were fairly

close, there were significant differences at the state and

regional levels.

For both 1994-95 and 1995-96, the projections of six

of the nine regions were within 3 percent of the early

estimates. For both years, the greatest difference

between the early estimates and the projections was

with the East-North-Central region. The early

estimates showed virtually no change in that region

while the projections showed relatively rapid rises. (It

had the second highest increase of any of the regions.)

Both alternatives are possible so it will take more data

to see which, if either, is correct.

Greater differences between the early estimates and the

projections were found when individual states were

examined. For 1994-95, there were ten states for

which the difference was 2 percent or less, and for

1995-96, there were eleven. On the other hand for

1994-95 there were twenty-three states in which the

difference was 5.0 percent or greater, and twenty-six

states for 1995-96.

There were some states with very large differences.

For the District of Columbia for 1995-96, the

difference was 52.2 percent. This is caused by two

very different fbtures for expenditures in 1995-96.

The early estimates shows expenditures falling 33

percent from its 1993-94 level while the projections

show current expenditures increasing 1.6 percent.

Again both are plausible. It could be that there will be

states for which the early estimates provide a better

picture to the fidure and others for which it is the

projections.

VIII. Conclusions and Directions for Future

Research.

For the fnt time, NCES has produced a set of

projections for current expenditures by state. These

projections were produced using a model similar to

that developed by Barro and was also similar to the

model used to produce the national forecasts. Several

alternative methods of estimating this model were

examined. While each estimation/pooling method

gave estimation coefficients that were usually of the

253

me
expected sign and significant the generalhd least

squares technique with the states broken up by region,

proved to be the best technique for producing forecasts

using as criteria a series of ex-post MAPEs. This

technique was used to produce the forecasts of current

expenditures by state.

For most states, and for the nation as a whole, the

technique produced plausible projections of current

expenditures. For each year in the forecast period the

sum of the state projections was quite close to the

national projections presented in the Projections of

Education Statistics to 2005.

Fairly rapid growth for current expenditures was

projected for each state during the forecast period.

Each state had a projected average annualized growth

rates of at least one percent and some states had rates

greater than four percent.

Since enrollment is projected to grow during the

forecast period, current expenditures per pupil were

not projected to increase as rapidly, and were projected

to fall in a couple states including California.

Two simple tests of the projections were conducted. In

the fret, the projections for 1993-94 were compared to

the actual values for that year. In the second, the

projections for 1994-95 and 1995-96 were compared

to NCES Early Estimates. The second test was not a

particularly strong test. It was more usefid to pointing

out highly implausible projections, and perhaps early

estimates, which differ strongly from recent trends. As

actual values for other years become available,

forecasts for other years can be compared to other

years as well. More tests for these projections and the

early estimates will occur as more actual values are

produced. Of special interest will be areas such as the

East-North-Central in which the early estimates and

forecasts differed sharply.

Given the potentially controversial nature of state

expenditure projections, these preliminary projections

should be compared to at least one more set of actual

values for current expenditures, before consideration

should be given to presenting state current expenditure

projections in an edition of Projections of Education

Statistics.

Until then, other work may be done on this model for

forecasting current expenditures. One possible change

could be the introduction of variables to measure if a

state has had any court cases affecting the financing of

education, or other legislative or policy variables.

REFERENCES

Barre, Stephen M. “Cost-of-Education Differentials

Across the States:” National Center for Education

Statistics Working Paper Series. Washington D.C.:

National Center for Education Statistics, 1993.

Bamo, Stephen M. “An Econometric Study of Public

School Expenditure Variations Across States, 1951-

67.” Paper presented at the Annual Meeting of the

Econometric Society in Toronto, December 1972.

Washington D.C.: The Rand Corporation, 1972.

Bane, Stephen M. “Theoretical Models of School

District Expenditure Determination and the Impact

Grants-In-Aid.” Santa Monic~ Ca.: The Rand

Corporation, 1972.

Bradford, David, and Wallace Oates. “Suburban

Exploitation of Central Cities and Government

Structure.” In RedMribution l%rough Public Choice.

Edited by H. M. Hochman and G. E. Peterson. New

York: Columbia University Press, 1974.

Gerald, Debra E., and William J. Hussar. Projections

of Education Stattitics to 2005. U.S. Department of

Education, National Center for Education Statistics.

Washington, D.C.: Government Printing Otlice, 1994.

Gerald, Debra E., and William J. Hussar. Projections

of Education Statistics to 2006. U.S. Department of

Education, National Center for Education Statistics.

Washin@on, D.C.: Government Printing Office, 1996.

Gerald, Debra E., and William J. Hussar. Projections

of Education Stattitics to 2007. U.S. Department of

Education, National Center for Education Statistics.

Washin@on, D.C.: Government Printing Office, 1997.

Holden, K., D. A. Peel, and J. L. Thompson.

Economic Forecasting: An Introduction. Cambridge,

England: Cambridge University Press, 1990.

254
Xnman, Robert P. “The Fiscal Performance of Local

Governments: An Interpretative Review.” In Curren/

Issues in Urban Economics, edited by Peter

Mieszkowski, , and Mahlon Straszheim. Baltimore,

Md.: Johns Hopkins University Press, 1979.

Johnson, Frank H. Early Estimates: Public

Elementary and Skconduy Ei&cation Statistics:

School Year 1994-95. U.S. Department of Education,

National Center for Education Statistics. Washington,

D.C.: Government Printing Office, 1995.

Johnson, Frank H. Early Estimates: Public

Elementary and Secon&ry Wucation Statistics:

School Year 1995-96. U.S. Department of Education,

National Center for Education Statistics. Washington,

D.C.: Government Printing Oflice, 1996.

Kmenw Jan. Elements of Econometrics, Second

Edition: New York, New York: Macmillan

Publishing Company, 1988.

255
Table l-Coefficients fkom the Barro paper

R-SQ CONSTANT PCI TGRANTENROLLPOP GRENROLL LD SOUTH TGIUINTS

0.850 218 0.20 1.15 -949 -372 34.4 -109 1.01

12.1 24.9 9.7 -11.5 -6.5 5.4 -7.1 4.1

Where:

The dependent variable is current expenditures per student in 1965 dollars using an education price index;

PCI is income per capita in 1965 dollars;

TGRANT is the sum of revenue receipts from state sources and federal sources per capita in 1965 dollars;

ENROLLPOP is the ratio of enrollment to the population multiplied by the ratio of the education price

index to the consumer price index;

GRENROLL is the change in enrollmen~

LD is a dummy variable = 1 for population density less than 30;

SOUTH is a dummy variable= 1 for soWhern states; and

TGRANTS is a the product of TGRANT and the SOUTH variable;

Table 2-Alternate Methods for Estimating the Barro Model

Method 1. Ordinary least squares and data pooled nationally

Method 2. Ordinary least squares, data pooled regionally, and dummy variables

Method 3. Generalized least squares and data pooled regionally

Method 4. Ordinary least squares with each state examined individually

Method 5. AR1 with each state examined individually

256
Table 3- List of states by region

Region 1

State 1

State 2

State 3

State 4

State 5

State 6

Region 2

State 1

State 2

State 3

Region 3

State 1

State 2

State 3

State 4

State 5

State 6

State 7

State 8

state 9

Region 4

State 1

State 2

State 3

State 4

Region 5

state 1

State 2

State 3

State 4

New England

Connecticut

Maine

Massachusetts

New Hamphire

Rhode Island

Vermont

Mid Atlantic

New Jersey

New York

Pennsylvania

South Atlantic

Delaware

District of

Columbia

Florida

Georgia

Maryland

North Carolina

South Carolina

Virginia

West Virginia

East South Central

Alabama

Kentucky

Mississippi

Tennessee

West South Central

Arkansas

Louisiana

Oklahoma

Texas

NENG

CT

ME

RI

VT

MATL

NJ

NY

PA

s A n

DE

Dc

FL

GA

NC

Sc

VA

ESC

AL

KY

MS

TN

Wsc

AR

LA

OK

Tx

Region 6

State 1

State 2

State 3

State 4

State 5

Region 7

State 1

State 2

State 3

State 4

State 5

State 6

State 7

Region 8

State 1

State 2

State 3

State 4

State 5

Region 9

State 1

State 2

State 3

State 4

State 5

State 6

East North Central

Illinois

Indiana

Michigan

Ohio

Wisconsin

West South Central

Iowa

Kansas

Minnesota

Missouri

Nebraska

North Dakota

South Dakota

Pacific North West

Idaho

Montana

Oregon

Washington

Wyoming

Pacific South West

Arizona

California

Colorado

Nevada

New Mexico

Utah

ENC

IL

IN

MI

OH

WI

WNc

IA

KS

MO

NE

ND

SD

PNW

ID

MT

OR

WA

Psw

Az

CA

co

UT

257
.“

Table 4-Alternate specifications of the Barro model

Equation 1. Ordinary least squares and data pooled nationally

LCUREXPE = PO + &LPCI + <GRANT + ~LADAPOPE

+ ~4GRADA + ~~LD + &SOUTH + ~7LTGRANTS + p

Equation 2. Ordinary least squares, data pooled regionally, and dummy variables

LCUREXPE = PO + ~lLPCI + <GRANT + ~LADAPOPE + ~4GRADA

+ ctlDUMIvlY1 + . . . ...+ a@JMMY~ + p

Equation 3. Generalized least squares and data pooled regionally

LCUREXPE = PO + (31LPCI + <GR.ANT + ~JLADAPOPE + ~4GRADA + p

Equation 4. Ordinary least squares with each state examined individually

LCUREXPE = PO + ($LPCI + <GRANT + &LADAPOPE + ~4GRADA + p

Equation 5. ARl with each state examined individually

LCUREXPE = PO + ~lLPCI + ~zLTGRANT + (33LADAPOPE + ~4GRADA + p

where:

LCUREXPE was current expenditures per student in ADA in 1%5 dollars using the education price index in log form;

LPCI was disposable personal income per capita in 1965 dollars using the national price deflator for personal

consumption expenditures in log form;

LTGRANT was the sum of revenue receipts from state sources and fderal sources per capita in 1965 dollars using the

national CPI in log form;

LADAPOPE was the ratio of average daily attendance to the population multiplied by ratio of the education price index

to the national consumer price index in log form;

GRADA was the change in average daily attendance;

LD was a dummy variable measuring states with a population density greater than 30;

SOUTH was a dummy variable for southern states;

LTGRANTS was the product of LTGRANT and the SOUTH variable; and

DUhWYi was a dummy variable equal to 1 for the ith state in alphabetical order in the region. ‘Ilme were dummy

variables for each state in the region except the last one alphabetically.

258

.
Table 5- Number of Regions/States in Which the Independent Variables Are Significant in the Estimations

of the Barro Model

OLS and Data P&led

Nationally

OLS, Data Poolid

Regionally And

Dummy Variables

GSL and Data Pooled

Regionally

OLS and Each State

Examined Individually

ARl and Each State

Examined Individually

LPCI LTGRANT LADAPOPE GRADA LD SOUTH LTGRANTS

1 1 1 0 1 1 1

8 7 5 2 .- . . .-

8 7 6 3 . .

40 24 38 15

40 26 41 13

. . . .

Table 6- Formula to calculate the ith-year-out MAPE

where

MAPEi is the i-year-out MAPE,

~ is the number of ex-post forecasts which are i years tim the last actual value,

Fij is the jth ex-post forecast which is i years from the last year in the sample @@

AU is the jth actual value which is i years tim the last year in the sample peri@

and

j = 1,2,...,r4.

259
Table 7-Mean absolute percentage errors (MAPEs) of the naive rnodell using a Barro type

education price index

NENG

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

MATL

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

sAn

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

ESC

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

Wsc

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

CT ME MA NH RI VT

3.8% 3.5% 3.3% 3.7% 4.7% 5.0%

9.9% 6.4% 7.9% 7.8% 6.7V0 9.3%

18.5% 14.3°A 16.OVO 13.6V0 11.OVO 16.l%

23.9% 16.5% 26.4% 19.8% 14. lVO 22.2%

19.5’% 20.lVO 33.3% 28.1’% 6.9% 36.5%

NJ NY PA

2.5% 2.7% 3.7%

5.6% 5.8% 8.4%

9.39io 10.3% 1 1.5%

16.l% 16.9% 9.4V0

26.5% 20.8% 7.9’%

DE DC FL GA MD NC

1.2% 8.9% 3.l% 3.5% 2.5% 3.6%

1.6% 19.4% 7.9% 6.4% 5.6% 7.9%

2.7V0 26.4% 10.4% 8.4% 7.8% 10.1%

4. lVO 27.3% 11.6% 10.8% 10.8% 8.0%

4.8% 14.4% 10.0% 8.8% 9.6% 3.7V0

AL KY MS TN

4.8% 3.9% 4.lVO 6.lVO

7.6% 6.2% 8.0% 8.2%

10.1% 6.8% 11 .2% 14.8%

9.7% 7.4% 9.9V0 20.2%

9.3V0 12.l% 16.5V0 13.2 %

AR LA OK TX

2.6% 5.2% 2.9% 1.3%

1.8% 10.9% 6.l% 2.3%

1.8% 15.5% 12.l% 3.6%

4.0% 23.5% 16.6% 3.4%

4.3% 25.6% 21.2% 4.9%

SC VA WV

1.9% 2.8% 4.6V0

3.9% 7.5% 5.7V0

4.2% 13.2% 1 1.7%

1.6’%0 22.9% 13.8%

O.OYO 27.3V0 9.8%

(Footnotes appear at the end of the table.)

260

m-
Table 7-Mean absolute percentage errors (MAPEs) of the naive model* using a Barro type

education price index

(Continued)

ENC

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

WNc

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

PNW2

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

PSW2

STATE

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

IL

2.7%

3.6%

5.4%

7.VXO

1 .9%

IA

2.5%

5.l%

3.8%

0.8%

1.4%

ID

2.3%

5.l%

8.4%

1 1.9%

14.3V0

M

1.7%

2.6%

4.8%

8.5%

15.7%

ml

2.7%

4.l%

5.7V0

6.4V0

1.2%

KS

3.lVO

4.l%

2.l%

3.3%

9.l%

MT

4.l%

6.9%

10.3%

13.9%

10.7%

CA

2.OVO

1.7%

2.l%

1.8%

2.4’%

MI

1.5V0

1 .7V0

2.l%

2.8%

2.7%

1.2%

1.6%

2.7V0

4.3%

2.7’%0

OR

2.4%

4.4%

6.2%

6.9%

4.2%

co

2.l%

3.0’%0

1.8%

2.7V0

0.9%

OH

4.2%

6.4%

8.4V0

10.4’%0

10.6%

MO

3.4%

6.8%

1 1.5%0

16.4%

13.2’%

WA

1.5%

3.7%

6.3%

10.5VO

13.6V0

Nv

2.6%

6.l%

6.6%

7.l%

4.6%

WI

1.9%

1.8%

3.2%

5.3%

2.2%

NE

3.7%

6.l%

7.8%

6.8%

1 1.5%

1.9%

2.l%

3.4%

5.9%

8.5%

6.8%

9.2%

10.3%

13.4%

14.l%

ND SD

4.oYio 2.0%

5.8% 2.3%

5.9% 2.9%

8.2% 5.4%

18.7’% 12.4%

UT

1 .9%

3.6%

7.0%

10.3%

11.7%

1 Forecasts were developed for the naive model by using the average annual growth rate of the last three years.

See the text for more details.

2 MAPEs are not presented for Alaska and Hawaii.

261
. .

,.,

,.

,.

Table 8-Number of states which have a mean absolute percentage error

(MAPE)less than that horn the naive modell using a Barro type

education price index for each estimation/pooling technique

using the Barro mode12

MAPEs using ordinary least squares and data pooled nationally

1 Year Out MAPE 8

2 Year Out MAPE 17

3 Year Out MAPE 21

4 Year Out MAPE 23

5 Year Out MAPE 22

MAPEs using ordinT

least squares, data pooled regionally,

and dummy variables

1 Year Out MAPE 23

2 Year Out MAPE 34

3 Year Out MAPE 36

4 Year Out MAPE 36

5 Year Out MAPE 33

MAPEs using generalized least squares and data pooled regionally

1 Year Out MAPE 34

2 Year Out MAPE 36

3 Year Out MAPE 38

4 Year Out MAPE 40

5 Year Out MAPE 274

MAPEs using ordinary least squares for each state examined individually

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

MAPEs using ARl

1 Year Out MAPE

2 Year Out MAPE

3 Year Out MAPE

4 Year Out MAPE

5 Year Out MAPE

21

31

34

36

34

for each state examined individually

18

34

36

35

34

1 Forecasts were developed for the naive model by using the average annual

growth rate of the last three years. Seethe text for more details.

2 See table 4 for a summary of the Barre. Only statistically significant

variables were included in these estimations.

3 Alaska and Hawaii were not included in the samples so MAPEs were not

calculated for those states.

4 Due to a lack of degrees of fkeedom, generalized least squares was

not used for the South Atlantic region when five years were omitted.

262
Table 9- Data Requirements for Forecasting the Barro Model by State

Are Forecasts Substitute

Available? If Needed

Personal Disposable Income

Population

National Price Deflator for Consumption

Expenditures

National Consumer Price Index

Average Daily Attendance

Sum of Revenue Receipts from State

and Federal Sources

Yes .-

Yes

Yes

Yes

Yes’

No

.-

.-

.-

.-

National Education Price Index No National Consumer Price Index

1 Average daily attendance forecasts computed using the state forecasts for fall enrollment.

263
. .

Table 10- Alternate specifications of the Forecasting model

Equation 1. Ordinary least squares and data pooled nationally

LCUREXPC = 130 + ~lLPCI + ~zLTPSL + ~~LADAPOP

+ ~dGRA’DA + &LD + ~bso~ + ~7LTPSLS + p

Equation 2. Ordinary least squares, data pooled regionally, and dummy variables

LCUREXPC = ($ + &LPCI + <PSL + ~ADAPOP + ~4GlU4DA

+ alDUMMY1 + . . . ...+ a@JMMY~ + w

Equation 3. Genemlized least Squares data pooled regionally

LCUREXPC = PO + ~lLPCI + ~zLTPSL + ~~LADAPOP + ~AGRADA + ~

Equation 4. Ordinary least squares with each state examined individually

LCUR.EXPC = PO + j31LPCI + ~zLTPSL + ~~LADAPOP + fIAGRADA + p

Equation 5. ARl with each state examined individually

LCUREXPC = PO + ~lLPCI + ~zLTPSL + ~~LADAPOP + ~4GRADA + p

Where:

LCUR.EXPC was current expenditures per student in ADA in 1965 dollars using the national consumer price index in

log form;

LPCI was disposable personal income per capita in 1965 dollars using the national price deflator for personal

consumption expenditures in log form;

LTPSL was the sum of state and local tax payments per capita in 1965 dollars using the national CPI in log form;

LADAPOP was the ratio of average daily attendance to the population;

GRADA was the change in average daily attendance;

LD was a dummy variable measuring states with a population density greater than 30;

SOUTH was a dummy variable for southern states;

LTPSLS was the product of LTPSL and the SOUTH variable; and

D~i was a dummy variable equal to 1 for the ith state in alphabetical order in the region. There were dummy

variables for each state in the region except the last one alphabetically.

264
Table 1 l-Comparison of the Barro model and the Forecasting model

Barro Model Forecasting Model

Differences in the Models

Price deflator Education price deflator’ National consumer price

for dependent , index

variable

State variable Sum of revenue receipts fkom Sum of state and local tax

state and federal sources per payments per capita in 1965

capita in 1965 dollars using using the national CPI in

the national CPI in log form log form

Price variable Ratio of the average daily Ratio of the average daily

attendance to the population attendance to the population

multiplied by ratio of the in log form

education price indexl to the

national consumer price index

in log form

Differences in the Estimations of the Models

Estimation

includes

statistically

insignificant

variables

Yes

Results for both Yes

Ordinary Least

Squares and ARl

presented for

case when states

examined

separately

N0

2

No

1 Definition of the education price index is presented in the text.

2 Statistically insignificant state dummy variables were included in

estimations.

265
.,

Table 12- Number of Regions/States in which the Independent Variables are Present in the Estimations of the

Forecasting Model

LPCI LTPSL

OLS and Data Pooled 1 1

Nationally

OLS, Data Pooled 9 7

Regionally And

Dummy Variables

GSL and Data Pooled 9 8

Regionally

OLS/ARl and Each State 32 26

Examined Individually

LADAPOP

1

5

7

31

GRADA LD SOUTH LTPSLS

o 1 0 1

2 . .

3 .- . .

22 -- --

266

P’
Table 13-Number of states which have a MAPE less than that from the naive modell for each

estimation/pooling technique using the consumer price index for each estimation/pooling

technique using the Forecasting mode12

MAPEs using ordinary least squares and data pooled nationally

1 Year Out MAPE 8

2 Year Out MAPE 20

3 Year Out MAPE 29

4 Year Out MAPE 30

5 Year Out MAPE 31

MAPEs using ordinary least squares, data pooled regionally, and dummy variables3

1 Year Out MAPE 18

2 Year Out MAPE 32

3 Year Out MAPE 35

4 Year Out MAPE 38

5 Year Out MAPE 38

MAPEs using generalized least squares and data pooled regionally- Alaska and Hawaii

not in the samples3

1 Year Out MAPE 33

2 Year Out MAPE 40

3 Year Out MAPE 41

4 Year Out MAPE 40

5 Year Out MAPE 264

MAPEs using generalized least squares and data pooled regionally- Alaska and Hawaii in the sampless

1 Year Out MAPE 33

2 Year Out MAPE 41

3 Year Out MAPE 40

4 Year Out MAPE 40

5 Year Out MAPE 284

MAPEs using ordinary least squares or AR1 with states examined individually

1 Year Out MAPE 16

2 Year Out MAPE 35

3 Year Out MAPE 37

4 Year Out MAPE 38

5 Year Out MAPE 38

1 Forecast were developed for the naive model by using the average annual growth rate of the last

three years. See the text for more details.

2 See table 10 for a summary of the Forecasting model. Only statistically significant variables were

included in these estimations.

3 Alaska and Hawaii were not included in the samples so MAPEs were not calculated for those states.

4 Due to a lack of degrees of ileedom, generalized least squares was not used for the

South Atlantic region when five years were omitted.

s Alaska and Hawaii were included in the samples yet those MAPEs were not included in this count.

267
Table 14-Current expenditures and current expenditures pa pupil m awmge daily attendance (both m constant 1992-93

dollars]): actual VtdM fw 198&81 tbmugh 1993-94; pmjwtioua ~Projk@ons #&#u@ion Stdxics to 2007

for 1994-95 to 1999-2000; projections fimn Projections of Education Statistia to 200S tir 1993-94 to 1999-2000;

and the sum of state Projectionsz fbr 1993-94 to 1999-2000.

Total current expenditures in billions Current expenditures per pupil

in constant 1992-93 dollars in constant 1992-93 dollars

Year Actual Values and Projections tlom Sum of the Actual Values and Pr@ections from Sum of the

ending Projections from Projections of state Projections km Projections of state

Projections of lliucation projections Projections of Education projections

ILkation StatiWics to 200$

Statistics to 2007

1982 $153.1

1983 157.3

1984

1985

1986

1987

1988

1989

1990

1991

1992

19933

19944

161.6

170.3

179.7

187.5

193.2

203.6

211.0

215.0

217.8

220.9 S220.8

225.6 227.0

1995 231.3 234.5

1996 237.6 242.1

1997 243.7 251.2

1998 250.4 260.6

1999 256.2 269.1

2000 263.4 276.6

Utcation Statistics to 2005

Statistics to 2007

$4,128

4293

4,445

4,678

4,919

5,087

5J15

5,463

5,581

5,595

5,591

s,im4 $5,X$6

$227.8 5,620 5,630 !$5,641

Projections

237.6 5,652 5,712 5,781

243.7 5,703 5,794 5,824

251.1 5,749 5,891 5,879

259.3 5,824 6,001 5,963

266.1 5,901 6,118 6,038

272.5 6,018 6222 6,119

1 Based on the Consumer Price Index for all urbau wnsumers, BurwJ of Labor Statistics, U.S. Department of Labor.

2 ‘I13e sum of state projections was not ad@stcd.

3 The value tim Projections of Education Statistiu @ 2005 is a projection.

4 The values &om Projections of Education Statistic to 2005 and the sum of state projections are projections.

268
Table 15-Average annualized percent change of current expenditures in constant dollars2 by

us

NENG

CT

RI

VT

MATL

NJ

NY

PA

SATL

DE

DC

FL

GA

NC

Sc

VA

ESC

AL

KY

MS

TN

Wsc

AR

LA

OK

Tx

state and region: 1980-81 to 1992-93; 1989-90 to 1992-93; and 1992-93 to 1999-2000

Average annualized percentage change

From 1980-81 to 1992-93 From 1989-90 to 1992-93

t

3.0% 1.7%

2.8% 0.2V0

3.9% -0.1’%0

5.2% 1.l%

1 .2% -0.4%0

4.7% 1.8%

3.1% 1 .9?40

4.5% 0.6’XO

3.OVO 1 .5’XO

4.3% 3.5%

2.7V0 1 .0%

2.5% 0.970

4.OVO

2.5’%0

2.7%

4.8%

5.5%

3.0%

3.070

4.l%

3.7%

2.3’XO

2.4%

1.l%

3.8%

2.6%

2.4%

3.5%

3.2%

0.8V0

1.8%

4.7%

1 .4%

1 .5’XO

-1 .9’%

1.5%

2.l%

1.8%

0.8’%0

1 .0%

0.7%

3.2%

1.6%

0.7%

6.3%

-1.1%

0.1%

2.0%

2.6%

0.6%

4.5%

1.8%

From 1992-93 to 1999-2000

3.2%

3.2%

2.3’%0

1 .4V0

4.6%

2.5%

1 .7%

1.7%

3.3%

2.8’%0

3.4%

3.6%

4.0%

3.l%

2.4%

4.8%

4.4%

3.4%

4.2’XO

2.8%

3.WO

2.8’XO

2.9’Mo

3.5%

1 .7%

2.4%

3.6’%0

3.8%

4.5%

4.l%

3.8%

3.6%

(Footnotes appear at the end of the table.)

269
-,, ,,.

,,

Table 15-Average annualized percent change of current expenditures] in constant dollars2 by

state and region: 1980-81 to 1992-93; 1989-90 to 1992-93; and 1992-93 to 1999-2000

(Continued)

Average annualized percentage change

From 1980-81 to 1992-93 From 1989-90 to 1992-93 From 1992-93 to 1999-2000

ENC

IL

IN

MI

OH

WI

WNc

IA

KS

MO

NE

SD

PNW

AK

ID

MT

OR

WA

Psw

AZ

CA

co

HI

Nv

UT

2.2%

2.0%

3.6%

0.9’XO

2.9%

3.3’XO

2.3%

0.9%

2.9%

2.4%

2.7V0

2.7%

1.6V0

2.8%

2.9’%0

1.8%

2.7%

1 .9’MO

2.5%

3.9%

1.7%

3.3%

3.8%

3.3%

2.2%

3.2%

6.7%

2.5%

3.0%

2.5%

2.9%

2.0%

1.9V0

2.2%

3.9%

1.6%

3.0%

2.3%

2.0%

0.2%

1.1%

-0.5Y0

3.3%

3.9’%0

1 .4%

4.5%

2.9%

3.4%

5.5%

-1 .4V0

1.l%

2.8%

0.l%

2.0%

6.4%

9.0%

2.7%

2.8%

3.3%

3.6%

3.0%

3.3’%0

3.3%

2.8%

1.9’%

1.0%

1.6%

2.4%

2.4%

1.5%

1.0%

2.5%

3.2%

1 .3%

3.6%

2.6%

3.5%

3.5%

2.0%

2.6%

3.5%

2.5%

2.7%

1 .7%

4.l%

2.4%

3.l%

1 State current expenditures projections were adjusted so that the sum of state projections equaled

the national projections from Projections of Education Statistics to 2005.

2 Based on the Consumer Price Index for all urban consumers, Bureau of Labor Statistics,

U.S. Department of Labor.

270
Table 16-Average annualized percent change of current expenditures per pupil in average daily

attendance inconstant dollars2by state and region: 1980-81to 1992-93; 1989 -90to

1992-93; and 1992-93 to 1999-2000

, From 1980-81

to 1992-93

u s

NENG

CT

ME

NH

RI

VT

MATL

NJ

NY

PA

SATL

DE

DC

FL

GA

MD

NC

Sc

VA

ESC

AL

KY

MS

TN

Wsc

AR

LA

OK

Tx

Average annualized percentage change

From 1989-90 From 1992-93

to 1992-93 to1999-2ooo

2.6% O.lvo

3.5’%0 - 1.6%

4.4’%0 -2.2Y0

5.5% 0.270

2.7% -1.8%

3.5% -1.8Y0

3.1?40 -0.4’%0

4.2% -1.5%

3.6?40 0.0%

4.8’%0 1 .7%

3.170 -0.5%0

3.3% -0.5V0

3.2%

2.0%

4.3%

2.5V0

4.3’MO

3.070

3.l%

4.1%

2.8%

3.8V0

-0.670

-0.6Y0

-1.8Y0

-1.8V0

-0.lYO

-0.6Y0

0.1’%0

0.3%

-1.3’%0

4.1’XO

2.5% 1.1%

1 .2% 0.2%

4.3’XO 5.7%

2.l% -0.9Y0

2.5?40 -1.0%

2.4% 0.7%

3.3’% 1.8%

0.7% 0.8%

1.6% 3.4%

2.9% 0.l%

1.5V0

2.0’%0

0.9’%

1 .2%

3.3%

1.6%

0.6%

1 .0%

1.8%

0.9%

2.0’%

2.3%

2.O’XO

1.0%

3.9%

2.lVO

2.2%

0.8’XO

2.0%

1 .3%

2.3%

3.3V0

2.0%

2.5%

1.2%

2.2’XO

2.lVO

2.5%

3.7%

3.9%

3.2%

1.9%

(Footnotes appear at the end of the table.)

271
❑

Table 16-Average annualized percent change of current expenditures per pupil in average daily

attendance inconstant dollars2by state andregion: 1980-81 to1992-93; 1989-90to

1992-93; and 1992-93 to 1999-2000

(Continued)

Average annualized percentage change

From 1980-81

to 1992-93

ENC

IL

IN

MI

OH

WI

WNc

IA

KS

MO

NE

ND

SD

PNW

AK

ID

MT

OR

WA

Psw

AZ

CA

co

HI

Nv

NM

UT

From 1989-90 From 1992-93

to 1992-93 to 1999-2000

3.0’% 1 .4%

2.4% 0.9%0

4.1%0 1.5%

2.2% 1 .4%

3.9’% 2.0%

3.lVO 1 .5%

2.lVO

1.5%

2.2%

2.OVO

2.6V0

2.6%

1 .7’%0

2.4’%

0.0%

1 .7’?0

0.7%

-0.2%0

-1 .2V0

-0.6V0

-0.9Y0

1.3%

1.8V0 1 .0?40

-0.6Y0 -2.6V0

1.6% 2.2%

1.7% 0.7%

1.8% 0.8%

2.5% 2.l%

1 .5% -2.4Y0

1 .3’%0

1.6%

1.3%

1.2’%

2.4%

3.3%

0.5%

0.5%

-0.6Y0

-0.3Y0

-1.OVO

-1.OYO

4.5V0

3.1’%

1.1’%0

0.8%

2.3%

2.8%

2.3%

2.0%

2.3%

1.8%

1.l%

0.7%

0.5%

1.1%

1.5%

0.9%

1 .5%

1.5%

1.0%

-1 .2%

2.0%

1.6%

1.4%

0.7%

1.8%

-0.3Y0

0.5%0

-0.8Y0

0.6%

-0.4%

0.2%

0.7’%0

2.1%

1 State current expenditures projections were adjusted so that the sum of state projections

equaled the national projections from Projections of Education Statistics to 200S.

2 Based on the Consumer Price Index for all urban consumers, Bureau of Labor Statistics,

U.S. Department of Labor.

272
Table 17-Current expenditures in billions of constant 1992-93 dollars2 by state and region; 1992-93 actual

values: 1993-94 actual values; 1993-94 projections; and percentage differences between 1993-94

actual values and projections

1992-93 Actual 1993-94 Actual 1993-94

values values Projections L

Billions of 1992-93 dollars

u s $220.9

NENG 12.8

CT 3.7

1.2

5.3

1.0

RI 0.9

VT 0.6

MATL 41.8

NJ 9.9

NY 20.9

PA 10.9

SATL 35.2

DE 0.6

DC 0.7

FL 9.7

GA 5.3

4.6

NC 4.9

Sc 2.7

VA 5.2

1.6

ESC 10.2

AL 2.6

KY 2.8

MS 1.6

TN 3.1

Wsc 22.5

AR 1.7

LA 3.2

OK 2.4

Tx 15.1

(Footnotes appear at the end of the table.)

$225.6

13.1

3.8

1.2

5.5

1.0

1.0

0.6

42.6

10.2

21.5

11.0

36.2

0.6

0.7

10.1

5.5

4.7

5.0

2.7

5.3

1.6

10.5

2.7

2.9

1.7

3.2

23.3

1.7

3.2

2.6

15.8

$227.0

13.0

3.8

1.2

5.5

0.9

0.9

0.6

42.8

9.8

21.6

11.3

36.4

0.6

0.7

10.1

5.5

4.6

5.1

2.7

5.4

1.7

10.5

2.7

2.8

1.6

3.3

22.9

1.7

3.3

2.5

15.4

Percentage differences between 1993-94

actual values and 1993-94 projections

Percent

0.6%

-0.7Y0

-1.7%

3.4’%0

0.7%

-6.5Y0

-4.4%

1.l%

0.4V0

-3.7Y0

0.7%

3.6%

0.5%

-2.6%

-1 .6’%0

-0.170

-0.1%

-0.4Y0

2.7%

0.6%

1 .2%

2.2%

-0.2%

-0.7Y0

-1.lVO

-2.2Y0

2.0%

-1.7Y0

0.3%

1.8%

-4.lVO

-2.3V0

273
Table 17-Current expenditures in billions of constant 1992-93 dollars2 by state and region; 1992-93 actual

values: 1993-94 actual values; 1993-94 projections; and percentage differences between 1993-94

actual values and projections

(Continued)

1992-93 Actual 1993-94 Actual 1993-94 Percentage differences between 1993-94

values values Projections actual values and 1993-94 projections

ENC

IL

IN

MI

OH

WI

WNc

I A

KS

MO

NE

SD

PNW

AK

ID

MT

OR

WA

Psw

M

CA

co

HI

Nv

UT

Billions of 1992-93 dollars

$38.4

9.9

4.8

9.5

9.2

5.0

15.0

2.5

2.2

4.1

3.7

1.4

0.5

0.6

10.6

1.0

0.8

0.8

2.8

4.7

0.5

34.5

2.8

24.2

2.9

0.9

1.0

1.2

1.4

$38.7

9.8

4.9

9.6

9.4

5.0

15.4

2.5

2.3

4.2

3.9

1.5

0.5

0.6

10.7

1.0

0.8

0.8

2.8

4.8

0.5

35.0

2.8

24.5

2.9

1.0

1.1

1.3

1.5

$39.8

9.9

4.8

9.8

10.2

5.0

15.4

2.4

2.2

4.3

4.0

1.4

0.5

0.6

10.9

1.0

0.8

0.8

2.9

4.8

0.6

35.3

2.9

24.7

3.0

0.9

1.1

1.3

1.4

Percent

2.6%

1.3%

-2.8Y0

2.8%

8.8%

-1.0?!

0.3V0

“3.7’YO

-2.5Y0

2.8%

2.9%

-6.5Y0

-2.OYO

12.lVO

1.8%

-0.9V0

0.7’%0

1 .3%

5.7%

0.4’?0

2.5%

0.8%

2.3%

0.8%

3.3%

-3.lYO

1 .9%

-1.5Y0

-3.7V0

* State current expenditures projections were adjusted so that the sum of the state projections equaled the

national projections from Projections of Education Statktics to 2005.

2 Based on the Consumer Price Index for all urban consumers, Bureau of Labor Statistics,

U.S. Department of Labor.

274
Table 18-Current expenditures in billions of constant 1992-93 dollars2 by state and region: 1994-95 early estimates;

1994-95 projections; percentage differences between 1994-95 early estimates and projections; 1995-96 early

estimates; 1995-96 projections; and percentage differences between 1995-96 early estimates and projections

u s

NENG

CT

RI

VT

MATL

NJ

PA

SATL

DE

DC

FL

GA

NC

Sc

VA

ESC

AL

KY

MS

TN

Wsc

AR

LA

O K

Tx

1994-95 1994-95

Early Projections

estimates

Billions of 1992-93 dollars

$231.1

13.2

3.7

1.2

5.6

1.1

1.0

0.6

43.9

10.4

22.1

11.4

37.2

0.7

0.6

10.4

5.5

4.6

5.7

2.7

5.3

1.7

11.4

2.9

3.3

1.8

3.4

24.2

1.4

3.2

2.2

17.4

$234.5

13.6

3.9

1,2

5.9

1.0

0.9

0.6

44.4

10.1

22.4

11.8

37.8

0.6

0.7

10.6

5.8

4.8

5.4

2.8

5.5

1.7

10.8

2.8

2.9

1.7

3.4

23.7

1.8

3.4

2.6

15.9

Percentage differences 1995-96 1995-96

between 1994-95 early Early Projections

estimates and 1994-95 estimates

projections

Percent Billions of 1992-93 dollars

1.5%

2.7%

4.3 %

5.3%

4.9 %

-8.9%

-3.5Y0

-0.9’YO

1.2’%

-2.3Y0

1.6%

3.59’0

1.6%

-6.3Y0

23.3%

1.lVO

4.2%

4.4%

-5.9%

3.7%

3.3%

0.8V0

-5.9??

-2.lYO

-12.4%

-8.4V0

-1.YYO

-1.8%

36.3%

6.6%

15.8%

-8.6’YO

$236.3

13.5

3.8

1.2

5.8

1.1

1.0

0.6

45.4

10.8

22.7

11.9

38.2

0.7

0.5

10.8

5.8

4.8

5.6

2.7

5.5

1.7

11.6

2.9

3.4

1.9

3.5

24.6

1.4

3.2

2.1

17.8

$242.1

14.0

4.0

1.3

6.2

1.0

1.0

0.6

45.7

10.4

23.0

12.2

39.3

0.7

0.7

11.0

6.0

5.0

5.6

2.9

5.7

1.7

11.1

2.9

3.0

1.7

3.5

24.6

1.9

3.6

2.7

16.4

Percentage differences

between 1995-96 early

esdmates and 1995-96

projections

Percent

2.5%

3.4%

4.7%

6.3%

6.0%

-8.7Y0

-5.7Y0

2.7V0

0.7%

-3.4%

1.5%

2.7%

2.9?!

-5.8Y0

52.2%

1.5%

4.5%

2.8%

-1.OVO

5.3%

4.5%

1 .7%

-4.7%

0.3%

-12.7Y0

-8.2%

0.9%

0.2%

40.7%

11.2%

24.0%

-7.8Y0

(Footnotes appear at the end of the table.)

275
Table 18-Current expenditures in billions of constant 1992-93 dollars2 by state and region: 1994-95 early estimates;

1994-95 projections; percentage differences between 1994-95 early estimates and projections; 1995-96 early

estimates; 1995-96 projections; and percentage differences between 1995-96 early estimates and projections

(Continued)

1994-95 1994-95

Early Projections

estimates

Billions of 1992-93 dollars

ENc $38.5 $41.6

I L 9.6 10.4

I N 5.1 5.0

MI 9.3 10.5

OH 9.3 10.5

WI 5.2 5.3

WNc

IA

KS

MO

NE

ND

SD

16.3

2.5

2.3

5.4

3.7

1.4

0.5

0.6

15.4

2.5

2.2

4.4

3.8

1.4

0.5

0.5

PNW 11.2 11.3

AK 1.1 1.0

ID 0.9 0.9

0.8 0.8

OR 2.7 3.1

WA 5.1 5.0

0.6 0.6

Psw 35.1 35.9

Az 2.9 3.0

CA 24.1 25.1

c o 3.1 3.1

HI 0.8 0.9

Nv 1.1 1.1

1.6 1.3

UT 1.5 1.5

Percentage differences 1995-96 1995-96

between 1994-95 early Early Projections

estimates and 1994-95 estimates

projections

Percent Billions of 1992-93 dollars

8.l% $38.9 $43.0

8.7% 9.6 10.9

-2.5Y0 5.2 5.1

12.7% 9.3 10.8

12.2% 9.5 10.8

1.5% 5.3 5.4

-5.4’70 16.3 15.9

-0.1% 2.5 2.5

4.3% 2.4 2.3

-17.5’90 5.3 4.6

4.1% 3.6 4.0

5.4% 1.4 1.5

-1.9?? 0.5 0.5

-9.OYO 0.6 0.6

0.49’0 11.3 11.6

-9.9?! 1.1 1.0

-6.3Y0 1.0 0.9

-2.6Y0 0.8 0.8

12.l% 2.8 3.2

-2.2Y0 5.1 5.2

2.3% 0.5 0.6

2.3% 36.4 36.9

003% 3.1 3.1

4.2% 24.7 25.7

-1.2% 3.1 3.1

22.8% 0.7 0.9

4.l% 1.2 1.2

-19.5Y0 1.9 1.3

-5.4Y0 1.6 1.5

Percentage differences

between 1995-96 early

estimates and 1995-96

projections

Percent

10.3%

13.lVO

-2.2Y0

15.1%

14.0%

2.5%

-2.3Y0

-0.3V0

-4.7Y0

-14.6%

12.3%

7.170

1.3%

“2.OYO

\

2.5’%

-8.5%

-5.6%

0.9%

13.5V0

O.lvo

7.8%

1.5%

-1.5Y0

3.79’0

2.39’o

27.5%

3.5%

-29.9%

-5.9??

1 State current expenditures projections were adjusted so that the sum of the state projections equaled the

national projections from Projections of Education Statistics to 2005.

2 Based on the Consumer Price Index for all urban consumers, Bureau of Labor Statistics, U.S. Department of Labor.

2’76
MEDICAID FORECASTING

PRACTICES’

Dan Williams

Baruch College

January 27, 1998

COMPARING MEDICAJD FORECASTS

Applied forecasting literature includes numerous

studies in which different approaches are compared

through simulated forecasting such as the M-competition, the M-2 Competition, and the M-3 Competition

(Makridakis, et. al., 1982; Makridakis et. al., 1989;

Hibon and Makridakis, 1997). Less frequently, studies

compare different actual forecasts of the same data series (&hley, 1988); however, because of the small

number of such multiple forecasts, there is limited opportunity to evaluate sources of variation.

State government forecasting provides an opportunity for studying a large number of forecasts of sirnikw

series to determine the effects of cMTerent variables on

forecasting practice. Often, many states forecast sirniIar series, such as tax revenue, nursing home bed need,

prison population, or educational enrollment. While

some characteristics of these series dMer from state to

state other characteristics may be similar. For exampie, the unit of analysis maybe similar between states -

each state might be interested in dollars of revenue, a

count of children at each age cohort, and so forth.

Also, the series in each state may experience the similar social and political pertu.rbances at about the same

time. The study of these such forecasts may provide

insight about variables that aflkct applied forecasting.

This paper examines forecasting activities among

Medicaid agencies in the f@ United States, Washing-ton, D. C., and five U.S. territories (American Samoa, Guam, Puerto Rico, Notiem Mariana Islands,

and Virgin Islands). Most frequently, studies of state or

local forecasting practice focus on revenue forecasting

(Rodgers and Joyce, 1996; Bretschneider and Schroeder, 1988; Bretschneider. et. al., 1989). There are

several reasons why comparison of state Medicaid

forecast practice may be better than comparison of state

revenue forecasting practices. First, there is no consistent reporting of state revenue estimates. States

make forecasts when it suits them and report them in a

reamer that is satisfactory to their governors or legislatures. Collection of data through national organizations such as the National Association of State Budget

Officers is not so rigorous as to assure that reported

1 ‘Ilk r~h is supported by PSC CUNY grant number 666$$6,

data are comparable. In contrast, Medicaid agencies

must report their expenditure estimates to the fderal

government using the ftierally specified HCFA-37

form once a quarter beginning roughly 30 months before the end of each fderal fiscal year.2

Second, determining the amcy of state revenue

forecasts relies on the validity of state reported dMerences between planned and actual expenditure. States

may be politically motivated to repoti these &ta in a

favorable manner. By con= Medicaid forecasts,

can be compared with accounting data as reported on a

federal report known as the HCFA-64. While these

data may not be bias fhx,3 biases are likely to be small

and similar from state to state.

MEDICAID FORECASTING BACKGROUND

Medicaid is a federal and state fimded health care

financing program. The federal government contributes 50% to 83’XO of the cost of the program in each

state, with states contributing the balance. Medicaid is

the largest human seMces program in state budgets,

accounting for 19.2’% of total state spending in 1995.

It is second only to education in share of state general

finds, and has the largest share of federal transfer

payments to states (National Association of State

Budget Ofllcers, 1996). Medicaid is an entitlement

program: once states establish rules about who is enrolled and what is covered they are barred from refusing to enroll eligible individuals or from refbsing to

pay for covered semices due to funding shortfalls.

Medicaid pays for health care through vendor payments to health care suppliers (called “providers” by

some states). Beneficiaries present their Medicaid

cards to enrolled suppliers who deliver semices and

submit claims to state Medicaid agencies. The state

Medicaid agencies pay for these sewices based on

“provider agreements,” which set payment conditions.

As a result, the Medicaid agency is usually the last to

find out about the smite. For these reasons, Medicaid

budgeting is highly dependent on forecasting.

Because the Medicaid program is fiuuled Witii both

state and fderal funds, there are two levels of government who use Medicaid forecasts for budgeting. Practices at these levels of government can be somewhat

2 The number of quartets from ffi reporting a fiscal y% to the end of

that year has varied k time to time.

‘ States may not anticipate rcwspective adjustments in the HCFA-37

although they appear in HCFA-64. Another disadvantage of comparing

state forecasting practice using HCFA-37 data is that statea maybe

more interested in their own budgets than in the reporting of their expectations to the fdcral govemmcnt. So, the HCFA-37 may not capture

the atate’s best fbrecast.

277
Werent. In a typical state government, a Medicaid

administering agency makes a forecast that is submitted to an executive budget office for review. Sometimes the executive budget office makes its own independent forecast which may be combined wi~ or substituted for, the Medicaid agency’s forecast (JLARC,

1997). This forecast is then submitted to the state legislature in the legislative budget process. The legislature may make another forecast or may choose to rely

on the executive forecast. There can be various mixed

practices, for example, a legislative agency may participate in selecting the executive forecast.

The fderal government uses the state forecasts in

a ~erent way. The states submit their estimates to

the Health Care Financing Administration (HCFA),

the federal agency responsible for administering the

Medicaid grants to states, each quarter using a federal

fo~ the HCFA-37 (formerly the HCFA-25). Forecasts reported on this form are combined to produce

mtional estimates. The fderal government adjusts

these estimates: (a) to account for new f*ral policy

making that the states could not have known about

when making their estimates; and (b) to correct for

perceived patterns of errors occurring in past forecasts

(llapnell, 1991). The fderal government uses these

corrected forecasts to estimate federal Medicaid outlays

for the next future fderal budget year. Estimates for

years beyond those reported in the HCFA-37 are made

by the HCFA OfEice of Actumy using algorithms developed by a contractor prior to 1980 (Trapnell, 1991).

In the fderal budgeting practice, HCFA budget estimates originating either from the states or the Office of

Actuary are subject to scrutiny by OMB and CBO.

In the late 1980s and early 1990s Medicaid agencies experienced several years of significant forecast

error. In 1991 HCFA was criticized for unprecedented

overages in the Medicaid budget (HHS NEWS, 1991;

Executive Oilke of the President and Department of

Health &Human SeMces, 1991). At that time, the

f~ral government concluded that state forecasting

was a significant source of forecasting efior @X+S

NEWS, 1991). Medicaid and related health care forecasting and cost estimation have been the center of

continued disagreement and concern throughout the

1990s (Ric~ 1991; Fkshei~ 1993; Doran, Roesenblatt,

and Yarnamoto, 1994; MU of Technology Assessmen~ 1994; Hola.han and Liska, December, 1996; Ratner, 1997; scanlo~ 1997; Holahan and Liska, 1997),

EMPIRICAL STUDIES OF MEDICAID PRACmcE

“Forecasting Techniques and Budgetary Issues of

State Medicaid Programs” (McKusick, 1980) examines

the forecasting practices of 10 state Medicaid programs. Data are gathered from site visits. McKusick

observes, “Although each state’s estimating techniques

are unique, there are patterns that are common to most

methodologies.” These common patterns include:

●

●

●

●

●

●

●

●

●

●

States attempt to estimate demand for sexvice, and

pay little attention to supply of service.

Most state forecasts are prepared on a cash budgeting basis although some forecast accruals and

convert to a cash basis.

In many circumstances, reimbursement rate increase decisions are known prior to budgeting and

can be used as an aid to expenditure forecasting.

Many states have poor quality data sources, but

they compensate through inventive use of forecasting techniques.

Forecasting is understaffkxi in many states, leaving

“many critical forecast issues. . . unanalyzed.”

Few states relate economic conditions to enrollment. Where they do, such analyses may bc primarily produced for other governmental fimctions.

State Medicaid budget estimates are “determined

by the political process,” with frequent reliance on

supplemental appropriations.

States forecast no more than a two years horizon.

States rely on trend analysis, rather than “looking

for the underlying diving forces in medical costs.”

Some state forecasts submitted to the HCFA are

consistent with their state budget estimates, while

others are “the best guess of the analyst.”

McKusick describes specific practices in each of

10 states (Caltiomia, Illinois, Maryland, Massachusetts, Michigan, Pennsylvania, Rhode Island, Texas,

Virginia and Wisconsin). Details are not summarized

here. The matters he addresses include: the participants in forecasting, the general forecasting approach

(e.g., California divides the forecast into cument services and poli~ modifications), number of periodic obse~ations available to the forecast model, level of data

(annual, monthly, etc.), sources of data, forecasting

techniques used, degree of data decomposition, frequency of forecasting, the state budget calendar, ability

to produce &ta reports for the fderal government,

relative size of the Medicaid program (to other Medicaid programs nationwide), breadth of Medicaid coverage, and components of Medicaid coverage. Some of

the techniques obsemd include: use of regression or

278
systems of regression models, analysis of “historical

trends,” use of a weighted average of inflation factor,

use of judgment, use of graphing techniques, use of

nursing home bed supply information, and use of negotiated rates (Texas).

Because of the interaction with the political process, McKusick expects that states are motivated to underestimate ex&nditures; however, he observes, “[We]

are puzzled that the aggregate of all state estimates

should have proven accurate in the past since many

have incentives to estimate low and none appear to

have incentives to estimate high.”

McKusick does not attempt to establish a relationship between these obsemations and relative forecasting accuracy.

“Better Management for Better Medicaid Estimates,” (Executive Office of the President 1991) reports that from 1980 to 1990 the overall average error

of state estimates is -0.30/o; however, the federal government is concerned because of error and expected

error for 1990 through 1992 as shown in Table 1.

I Table 1 I

Year Error Comment

1990 -9.2Y0 Actual

1991 -18.OYO Expected

1992 -16.OYO Exmxted

HCFA determined that in 1990, 19 states had errors greater than 10’XO and 4 (Alabama, Kansas, Arizona and Massachusetts) had errors greater than 20°/0.

Error rates for the largest states grew fkom below 5?40

in 1990 to an unweighed average of 17°/0 in 1991 as

follows: Texas - 27%, New York – 17Yq and California - 7Y0, for a gross total of $2.1 billion.

A HCFA/OMB task force visited nine large states

that account for approximately 50% of all Medicaid

expenditure in 1991 and 1992 (Alabama, California,

Flori~ Mar@@ Massachusetts, New York Ohio,

Pennsylvania, and Texas). They found: “Mis-estimates

in these States appear to be due primarily to changes in

Federal. . . policies. . . . Only about one-third of the

rnis-estimates were attributable to problems in the

States’ estimating processes. Economic trends appear

to play a lesser role.” Programmatic sources of cost

increase include health care inflation, court orders, and

use of provider taxes and refundable donations. Specific obsemations about state processes include:

Some States have well qualified personnel

and employ sophisticate estimating

models; others do not.

States that link Medicaid estimating to

their State budget prwesses appear to

produce more accurate estimates than

those that do not.

Many States do not take reporting to the

Federal Government. . . seriously, and

thus do not provide accurate, complete or

timely estimates.

Many States do not provide the Federal

Government with the assumptions used in

making estimates. No distinction is made

between baseline estimates and program

estimates.

Technical problems include differences in

fiscal years and State use of accrued versus cash budgeting. (Executive Office of

the President, 1991)

Some of these obsemations involve communication problems between the state and federal governments. Obsewations that appear to account for forecasting accuracy include (1) the assertion that forecaster qualification varies, and (2) the observation that

states who link state and fderal budgeting seem to

provide the fderal government better forecasts. Evidence is not presented.

Gordon R Trapnell examined Medicaid forecasting in the early 1990s and produced two reports

(’Ilapnell, 1991; Trapnell, 1994). The 1991 study reports empirical findings. It sexves two purposes, one is

to explain particular forecast errors occurring in 1991

and 1992 (as anticipated in 1991). The other is to provide some insight into the fdera.1 use of state forecasts.

While this discussion reveals some ftiliarity with

particular practices of some states, it does not show

comparison of actual practices and their forecasting

casting consequences among the states. Trapnell’s

data collection method is not revealed, it appears that

he relies primarily on idorrnation already in the hands

of HCFA. His observations regarding state practices

are as follows:4

. Variation in forecasting accuracy may relate to

composition of Medicaid beneficia~ enrollment.

● State legislators may choose to implement new

polices that are underestimat~ that i% where political pressure for policies is high in relation to

4 Trapnell ● ttributes some of these observations to McKusick*s study.

279
●

●

●

●

●

●

s

●

●

the determined costs.

States may defer spending at the end of a state fiscal year to ensure that state fiscal year estimates

are correct. This can happen because many states

budget on a cash basis rather than an accrual basis.

Data available for forecasting in some states may

be of poor quality or may not be reconciled with

actual expenditure experience.

Past fderal action may discourage states horn revealing their true estimates. In particular, in 1982

the f~eral government penalized states whose

actual expenditures exceeded their forecasts. As a

consequen~, states maybe motivated to overstate

their estimates.

There is wide variety in the sophistication of state

forecasting horn “trended forward total aggregate

spending by type of seMce” to “fully specified

econometric model. . . . refitted quarterly.”

Only a few states fully disaggregate data by the

type of seMce and type of beneficiary.

Locus of responsibility varies amonp the states,

with Medicaid agencies preparing some forecasts,

while budget officials preparing others.

There is “some correlation between how well officials understood the programs and the details incorporated in the cost estimates.”

State forecasts improve as the horizon between

forecast and the end of the fiscal year diminish.

States may not reconcile state and federal fiscal

year reporting (most state fiscal years are from

July to June, while the federal fiscal year is from

October to September).

Trapnell’s analysis of state variation is limited to

two paragraphs of his report in which he compares regional aggregate variation between the HCFA-37 and

HCFA-64 reports. He finds that states in Region 1

(states in each region are shown in Table 2) consistently underestimates its expenditures, states in Region

2 generally overestimate expenditures and states in Region 9 are usually very accurate. Trapnell’s report

makes no effort to account for these variations in accuracy. However, the most obvious characteristic of regions Trapnell mentions as having consistent patterns

of accuracy are that they are dornimted by a single

state. New York accounts for about 85°/0 of fderal expenditures in Region 2 and California accounts for a

similar amount in Region 9. In Region 1, Massachusetts accounts for about 60’XO of ftieral expenditures.

It is likely that the explanation for the various forecasting results in these three regions will be found at

these three states.

Table 2

Region 1 Region 2

Connecticut New Jersey

Maine New York

Massachusetts Puerto Rico

New Hampshire Virgin Islands

Rhode Island

Vermont

Region 3 Region 4

D.C. Alabama

Delaware Florida

Maryland Georgia

Pennsylvania Kentucky

Virginia Mississippi

West Virginia North Carolina

South Carolina

Tennessee

Region 5 Region 6

Illinois Arkansas

Indiana Louisiana

Michigan New Mexico

Minnesota Oklahoma

Ohio Texas

Wisconsin

“ Region 7 Region 8

Iowa Colorado

Kansas Montana

Missouri North Dakota

Nebraska South Dakota

Utah

Wyoming

Region 9 Region 10

American Samoa Alaska

Arizona Idaho

Caltiornia Oregon

Guam Washington

l-Iawaii

Northern Mariana

Nevada

Michele Insco of the Colorado state government

conducted a Medicaid budget survey and circulated reS Massachusetts has two Medicaid agenciq the smaller of which is

about 2?zf0 of the program. ~s analysis excludes the small= agency.

280
suits to participating states in May, 1992 (hsco, 1992).

This study consists of charts demonstrating factors that

might a.flkct forecast accuracy or expenditure values.

Some variables charted include: state populatio~ percent change in Medicaid expenditure fbm FY 91 to

FY 92, characteristics of the Medicaid program irtcluding various poli~ factors of current interest in

1992, basis of accounting, and beginning/ending dates

of state fiscal year. Some of the characteristics reported include the percent of poverty at which pregnant

women and certain children meet eligibility criteria,

experience with Boren Amendment lawsuits,G percentage of births in the state covered by Medicai~ coverage

of certain optional populations, etc. There is no accompanying written repmt to interpret these charts. By

implicatio~ these variables are thought to bear a relationship to expenditures and forecast accuracy.

The Human Services Finance Officers sponsored a

sumy of Medicaid budget estimation methods by

Deborah J. Lower (1993). Lower reports that her survey is an extension of the Insco sumy and is aimed at

“determining what techniques were being used in other

states to assist them in responding to legislative and

executive branch questions.” Lower suweyed the 50

US States and D. C., and reports a response rate of 84%

(43 states). Lower’s study fases on ident.@ing practices rather than determining sources of variation.

Lower does not attempt to evaluate the relationship

between these practices and forecast success. Practices

she finds are as follows.

General:

● States use their own forecasts as compared with

contracting out forecasting fimctionso

. Sta.fftime required to complete the HCFA-37

ranged ftom 0.1 to 15 FE and averaged at 1.6

FIE. (The phrasing of this question appears to

limit the this response to completion of the form,

and may exclude time required for forecasting.)

. The HCFA-37 may “be completed in differing categories than state budget forecasts.

. Technical background of sticompleting the

HCFA-37 or related forecasts includes actuarial

science, accounting, budge~ statistics, pro-

@policy analysis, economics, management

“The130rcnAmadmmt. M language in Title XIX of the Social Security

Act th8t allows states to psy institutional health cue providers f= dlcicnt dclivay of health cam. It is widely held that the amdmcnt was

@#xMIly pssstd to allow states to avoid paying excessive amounts to

hospii and nursing homes. However, courts have intqntod it to

prohibit st8tcs !lOm paying hospitals and nursing IaOrnes too little. The

Bofcn Amdmcntwas mpe8kdin1997.

●

●

●

●

●

●

●

analysis and demographics.

Budget office W ranged from 1 to 44 employees

and averaged at 7.8 FIE.

Primary responsibility for the Medicaid budget is

with budget or finance agencies in 17 states and

program or dzenti administrators or staff 25

states.

Twenty-six states report formal relationships between budget and finance staff and program staff.

Program staff have access to expenditure data in

35 states.

Some states report that program staff do not have

adequate technical skills for forecasting.

States report that Medicaid accounts for 4% to

52% of state budgets, averaging at 15.96Y0.

Forty states report the existence of written gubernato-rial guida&e in budget preparation.

Caseload Projections: ●

●

●

●

●

●

●

●

●

●

●

●

Thirty-seven states use data concerning eligibility (enrolled beneficiaries).

Thirty-one states evaluate population categories.

Twenty-seven states evaluate federal mandates

for impact on enrollment.

Twenty-three states use “program ~Ic information.”

Five states evaluate the impact of “retroactive

eligibility.’”

Twenty-eight states report that they forecast

based on cash data (expenditures on date of

claims payment).

Eight states report that they forecast based on

accrual (seMce date) &ta.

Four states report use of both cash and accrual

data.

Six states report lack of access to “extract

data.”

Lag time between seMce date and payment

date is variable between states and between

seMce categories.

The predominant periodicity of data is

monthly.

The average length of a forecasted data series

is 5 years.

Utilization

. Nineteen states report estimating utili=tion

directly horn enrolled beneficiaries.

● Twelve states report using an intermediate

‘ Rctroutive eligibility is the a warding of eligibility fw ● period that is

inthcpast.

281
determination of seMce recipients.

● Eleven states report use of both techniques.

● Eighteen states use seasonal adjustments for

some seMces.

Price Level

● States uses price indexes such as CPI or local

indexes.

● States evaluate the impact of lawsuits.

● Historical patterns may not be evaluated.

. Thirty-seven states evaluate price level change

by seMce typ.

● Twenty-one states evaluate price level change

by eligibility catego~.

. Eight states evaluate price level change by

other demographic categories.

Frequency of forecasts:

. Sixteen states report updating forecasts quarterly.

. Ten states report updating forecasts “as

needed.”

Software Usage includes:

. Spreadsheets (Lotus, Excel, Quattro Pro)

. Statistical software (SAS, SPSS)

● Forecast software (Forecast Pro)

. Mainframe forecasting programs (five states)

Program features:

. Filleen states report no HMO enrollment.

● Other states report enrollment from 553 to

384,377 (Lower does not provide bases for

percentage calculations).

● Twenty-two states report involvement in

Boren Amendment lawsuits.

● Ofthirty-two states reporting data, the percentage of births reimbursed through Medicaid ranged from 14% to 56% and averaged at

36.4%.

Data sources used by states to estimate impacts of

new policies include:

● Program information (41 states)

. Information from other states (40 states)

● Census data (36 states)

● Insumnce company consultation (14 states)

. Providers, actuaries, health or research data,

and historical patterns.

Difficulties states report include:

● Last minute program and policy changes,

● Accuracy of population growth estimates

● Accuracy of utilization estimates.

● Budgetary constraints (state restrictions vs.

federal mandates).

● Data validity.

● Technological advancements.

● Variation in lag between semice date and

payment date.

. Retroactive adjustments.

It is diflicult to compare these empirical studies

because of the small number of agencies studied and

differences in specific matters observed. Yet, some

topics persist. McKusick, Executive Oilice of the

President (EOP), Insco, and Lower look into whether

forecasts concern cash expenditures or intermediate

accruals, and each finds variation in state practices.

McKusic~ EOP, and Lower find variation in st.aH capacities. McKusick and Trapnell find a relation between Medicaid forecasting and the political ertvironment. McKusick, Trapnell, and Lower find variation

in techniques used, degree of data disaggregation, data

quality, and locus of forecasting responsibility. Also,

they find that many states attempt to account for policy

making that afkcts Medicaid expenditures. McKusick

and EOP find that some states treat federal forecast reporting differently than state purpose forecasting.

On most commonly discussed matters, findings are

consistent across the various studies. It is not possible

to detmninc change overtime. For example, McKusick’s data are too vague and his sample too small for

comparison with Lower.

NORMATIVE APPROACHES TO MEDICMD

FORECASTING

Three HCFA guidelines for forecasting are examined: (1) Charts from a presentation on forecasting, (2)

normative guidelines in “Better Management for Better

Medicaid Estimates” (1991), and (3) HCFA-37 reporting expectations.

HCFA (1990)8 recommends structuring the Medicaid forecast using of the formula:

E(Y+l)=Px UXC

or

E(y+l) = E(y) X(1+ AP) X (1+ AU) X (1 + AC)

where, E@+l) is the fhture year expenditure, P is the

projected price, U is the projected utilization,g C is the

8 ThCSC @i&lines arc demonstrated in ChlUtS and tablcq with limited

narrative discussion.

9 With Madieai~ utilization can have aevcral meanings. HCFA 0ff3’S

two dcfiiitions: Suvicc unit per enrolled beneficiary and claims ficqueney, HCFA rocommcnds the earlier deftition. This utilization is a

combination of two faetora: ratio of bcncficiarka using acrvicca

(sometimes called “recipients”) to bcncficiarica enrolled and ratio of

282
projected enrollment of beneficiaries,10 E~) is the current year expenditure, and A denotes year to year

change calculated as ((Year 2)- (Year 1))/(Year 1).

These formulae are sometimes called the PUC model

(Rice, Utilization, and Caseload),l ] Sometimes the

first version is called the direct method and the second

is calld the incremental method. They provide a

structure for calculating expenditures within each expenditure category. Although HCFA suggests that the

direct method is more accurate, some of their material

seems to recommend the use of the indirect method;

perhaps because it is thought that the it is less difficult

to find data supporting the indirect method. HCFA also

recommends adjusting forecasts to reflect cash flow

factors (lag between semice date and payment date),

decomposition of data into groups of homogeneous

sub-populations, calculation of marginal-to~ore ratios,]2 and the use of demographic data to help iden~

marginal and core groups.

Normative guidelines in “Better Management for

Better Medicaid Estimates” (1991) are primarily directed at HCFA. State oriented recommendations include: requiring states to provide baseline (no policy

change) estimates, listing of assumptions underlying

these baseline estimates, and requiring separate estimate of expected changes. It is not clear whether the

framers of these guidelines intend that states will make

better forecasts using these guidelines, or merely that

HCFA will have a better change to discovering poor

forecasts.

The HCFA-37 does not explicitly require states to

use any particular approach for forecasting. Nevertheless, the form calls for the state to report on various

forecast elements in a manner that is consistent with

the direct method PUC model aIong with separate reporting of base line and expected changes. To fultlll

HCFA’S reporting requirements, the state must either

aavicc units to recipients. Sometimes “utilization” is used to refer to

one or the other of these component concqxs.

10 HCFA recommends that caseload be measured in average monthly

emolled bcnct%iaries per year. 11s .

022wwM& owing to a rearrangement of the variableq this model is

callad the CUP model.

12 Marginal to core refm to an expectation that incremental element of

enrollment will have a diikent utikation pattern than the base (core)

enrollmti This view can involve acveml wrelated matters. Fncwl y enrolled benefkities may have a dHerent usage pattern. It may

take a while for them to establish relationship with mcdicd care provid-

~sotheiruaage may belower. Ontheother ha224theymaybemore

urgent to obtain services that have been postponed while they had no

health ~greaou~ so they may use more services. Secon4 it is

likely that there will be a lag between enrollment and payment fw services. A the f-govemrnent budgeta on a cash bask this lag appears

asareduced custinthefti yearofaemice.

structure its forecast to be consistent with the PUC

model or it must compute PUC model variables from

its actual forecast. Some states refer to this latter option as “backing into” the HCFA forecast. Overtime,

the HCFA-37 has also added schedules to capture data

on special issues of interest to the ftieral government.

For example, at sometimes HCFA-37 reporting has

included extensive reporting on the use of disproportionate share adjustments to hospitals.13 Sometimes

these schedules involve reporting anticipated efkcts of

recently passed federal legislation. The stmcture of

these schedules may or may not reflect normative

views concerning how HCFA thinks states should estimate these policy changes.

Trapnell (1991) discusses a variant of the PUC

model that includes the marginal-to-core ratio. This

model disaggregates Medicaid into 29 sexvice categories, which are fbrther decomposed into five components:i 4

E(y+l) = E(y) X(1 + P(y+l)) X (1 + U(y+l)) X

(I+ Mx C@+l))

where, E(y+l) is the fhture year expenditure, E@) is

the current year expenditure, P(y+l) is the projected

change in price, U(y+l ) is the projected change in

utilization, C(y+l) is the projected change in enrollment of beneficiaries, and M is the marginal-to-core

factor. Some of these components may require separate

calculation for each enrollment categoxy. Other components are essentially static, or are estimated from

sources outside of HCFA.

Txapnell implies that use of this or a similar model

would be the best method for states to forecast Medicaid expenditure. However, elsewhere he says that the

most important elements of good state forecasting are

use of a skilled and attentive staff working within a

comprehensive analytic ffamework. The PUC model

serves as the analytic fkamework.

Trapnell’s “Best Practices Guide for Preparation of

Medicaid Budget Estimates,” (1994) presumably reflects his findings in his 1991 study.ls Most of Trapnell’s advice is general with no special application to

Medicaid. For example, he advises agencies to avoid

expectations of accuracy that cannot be met ad to be

sure that outputs address client officials data needs. He

*3 Dkproportionate share hospital adjustmmts have been a source of

fi-iction between states and the federal govemmat since the early 1990s.

‘

4 Notation u slightly changed.

1’ The author has direct knowledge that Trapnell made site tilts to

oth=~beyondthose~intil~l~dy.

283
recommends evaluating forecasts through production

of numerous outputs that can be compared with actual$ comparing forecasts with earlier forecasts of the

same series, and making frequent forecast updates. He

recommends usual forms of pm-forecasting such as

disaggmgating data ~d adjusting for non-reaming

even~ missing daQ and lag time between accrual of

liabilities and cash transactions. He recommends optimizing the use of knowledge by such actions as establishing a relationship with progmm ~, distinguishing between matters that must be forecast and

those that can be know, and distinguishing between the

near fhture, about which forecasters may have special

knowledge, and the distant fhture, about which they

know considerably less.

Trapnell raises considerable concern over data

used in Medicaid forecasting. This concern reflects a

lack of fhith in Medicaid data and health care data in

general. Data concerns include completeness, reliability, validity and timeliness of data. He suggests several

methods of ver@ing data validity, by which he means

that the dala is what it purports to be. Data can be

validated by reconciling with accounting records, by

examining the process of its production when produced

by the Medicaid agency, and by examining the documentation of its production and meaning when produced by others.

Trapnell recommends the use of an analytic model

to assure that the fcrecast addresses all elements of expenditure. The model he recommends is a another

variant of the PUC model.

Trapnell lists nine special fwtures of Medicaid

that make forecasting Medicaid expenditures harder

than other health care expenditure forecasting these

are:

1.

2.

3.

4.

5.

6.

7.

Criteria for Medicaid eligibility are very complex.

Many beneficiaries are eligible and enrolled only

for short periods.

Some individuals become eligible for Medicaid in

part because they have high medical bills, so they

can be expected to continue to have high medical

bills.

Medicaid policy making leads to frequent changes

in payment levels.

Documentation of expenditures is incomplete.

There are inconsistent definitions of data in accounting and claims processing.

There are constant changes in the program resulting from federal and state legislatio~ new regulations, administrative initiativ~ and court decisions.

8.

9.

The political process leads to a random patterns of

intementions in payment rates and other program

fatures.

Medicaid agencies need to be able to explain their

forecasts to officials and interest groups.

Consequently, he recommends that the Medicaid

forecast should:

1.

2.

3.

4.

5.

6.

7.

●

●

●

●

Be easy to understand and test for reasonableness.

Be easy to adjust for changes.

Be easy to incorporate ad hoc adjustments.

Be easy and inexpensive to re-estimate using

newer data.

Be easy to change to accommodate program

changes.

Produce numerous outputs to compare with actual

experience.

Produce outputs that fulfill forecast users’ needs.

Trapnell also recommends that:

Medicaid forecasting methodology should be able

to detect fkequent unexpected shocks to the expenditure system,

Forecasting should be insulated horn political interferenm,

Staff should be assigned to forecasting as their iill

main work fiction, and

The loss finction that forecasters should minimize

is the consequences of forecast error. Trapnell assumes that there equally negative consequences for

positive and negative errors, thus, he recommends

a symmetrical loss fbnction.

Trapnell does not recommend any specific forecasting technique. He recommends against the use of

complex econometric models or “black box” techniques, excessive reliance on high technology, or reliance on any single technology. Positive recommendations include use of simple techniques, fitting techniques to the &ta and the mture of the problem, and

allocating resources for forecasting based on criteria of

importance and difficulty.

The National Association of State Budget Oflicers

(NASBO) issued a brief guideline on Medicaid estimation practices (National Association of State Budget

Oflkers, 1991). NASBO offers the general model:

“Simply pug a state’s expenditure on Medicaid is

the product of the number of people using its

seMces (caseload) and the cost of providing those

seMces (price). Several variables must be accurately forecast for the overall estimate to be correct: the economic environment in which the

Medicaid program operates, the eligible popula284
1.

2.

3.

4.

5.

6.

7.

8.

9.

t.io~ the types and prices of setices used, the participation rate amcng eligible participants, policy

initiatives, and the fderal match rate.

This guideline lists nine recommended practices:

Include the budget agency, the Medicaid agency,

and the legislative fiscal agency in the development of a Medicaid spending estimate. This is labeled a “gheral practice” which seines consensus

building.

Ensure that the economics of the spending estimate are consistent with those of the revenue estimate. This practice is of greater concern at turning points when revenue and spending assumptions may become disconnected.

Ensure that the population assumptions used in the

Medicaid estimate are consistent with overall state

demographic trends. Various state agencies

should communicate to assure that estimates of

related coverage gToups are adequately coordinated.

Maintain good data. Recommended data elements

include users, their attributes, and the semices they

use.

Establish. the price of seMces before the fiscal year

begins. The aim is to remove uncertainty. “In

general, states will find it easier to develop accurate spending estimates if the cost of semices is set

lxfore the fiscal year begins. States that reimburse

for actual costs incurred are at a disadvantage in

this respect.”

Account for caseload changes associated with outreach efiorts. This concern involves the interaction between various programs and the possibility

that outreach for one program will affect another

program.

Track federal and state legislation and regulations.

This practice concerns the ikquent changes in

policies that affkct Medicaid spending.

Know the federal match rate*G and the likelihood

of it changing. This concern involves the distribution of costs between fderal and state governments. Forecasting, in this sense, is oriented towards the costs to the state.

Monitor the estimate. Even good estimates can be

wrong. Medicaid agencies should produce

1’ “Match rate,” is one of several terms used to refer to the propotiion of

Medicaid mats paid by the fbl government. Other terms ase FMAP

rate, FFP, ftil share, matck etc. This rate is published in the Federal Register in Janumy or Febmary each year with an effkctive &ta of

the following October 1. Federal Funding Information fbr States (FFIS),

an 05shoot of the NASBO, monitors matching rates and forecasts

changes prior to their oflkial publication in the Federal Register.

monthly or quarterly reports that compares Medicaid spending with the original estimates. Deviations should be explained, so that forecasting can

improve over time.

Recommendations 2 and 3 may improve accuracy

if the facilitate communication between forecasters

who have different perspectives on the state economy.

Recommendation number 4 is similar to one of Trapnell’s chief concerns, but Trapnell provides more explicit advice concerning its implementation. Where

states are able to comply with recommendation number

5, they eliminate the need to forecast what they cart

know. Recommendation number 6 appears to be idiosyncratic to particular concerns of the early 1990s;

however, it also seems to reflect Trapneil’s more general principle to maximize the use of knowledge.

HCFA, Trapnell and NASBO all show considerable

concern over the impacts of policy making as discussed

in recommendation 7. Recommendation number 8 involves monitoring, not forecasting. It also reflects

Trapnell’s principle to maximize use of knowledge.

The Congressional Budget Office uses of a model

similar to HCFA’S (Muse, 1993). Muse’s concern is

not to describe the forecasting of the ongoing program,

but the method of estimating costs of program changes.

The particular changes he is concerned about involve

preventive child health. He recommends the use of the

following model:]’

AT=ACXAPXAU+AA-O

where, A means “cha.qe in,” T is total payments, C is

population, P is price, U is utilization, A is administrative costs or savings, and O is offsets. Muse’s notation is confhing. I.fone assumes that AC, AP, and AU

are ratios] 8 as discussed by HCFA or Trapnell, then

this formula needs the modification of including the

base reimbursement in the right hand and the deletion

of the change symbol on the left hand side of the formula; let BI = the base expenditure level for medical

care and Bz = the base expenditure level for administration:

T=B1 X (l+AC) x (I+AP) X (l+AU) + B2 + AA -0

or

AT=BIXACXAP XAU+AA-(O+BI)

If one assumes that AC, AP, and AU are numbers

17 Notation is modified,

1’ AA would still be a fblly dimensional number.

285
. .

in their original dimensions, rather than ratios, then a

much more complex formula would be required:

AT = AC XPXU+APX CXU+AUXCXP+

AC XAPXAU+AA-O

where, C, P, and U are the population, price, and utilization levels ajler the policy change and AC, AP, and

AU are the changes that led to these new levels.

Muse’s multiplication of changes by changes omits the

eflkct of changes on the base program, which is likely

to be the main efkct of poliq changes except in the

rare circumstance that new beneficiaries getting new

setices and no old beneficiaries or old services are involved.

Muse’s formulation adds two important considerations, impact on administration and offsets. These

concerns are more pertinent with program changes

where administration may not already be pro%ided for

and offsets may have a direct budgetary effect. Presumably, when forecasting the ongoing program, administration is already included in the budget and can

be estimated directly. This principle is reflected in the

HCFA-37 form, which has a separate section for administrative expenses. Offsets resulting from the base

program are reflected in the base and do not require

separate estimation.

Muse also provides considerable discussion of data

quality. This discussion fbcuses on three data sources,

the Current Population Survey, the “Statistical Report

of Medical Care: Eligibles, Recipients, Payments, and

Sexvices,” (also known as the HCFA-2082 report), and

the Medicaid Statistical Information System (MSIS).

Muse discusses the uses and wcxdcnesses of these data

sources. This discussion is not spccitlcally normative;

however, it exhibits the same sort of concern raised by

Trapnell and NASBO, the forecaster/estimator must

attend to data quality.

Of special concern for estimators of new policy

impacts is an estimate of participation level for newly

eligible individuals. For the base program, participation level is not a signifkant issue except where forces,

such as outreac~ might be changing this level. For

newly eligible individuals, the estimator needs to anticipate the degree to which this new population will

seek to obtain seMces. Muse says that this question is

not easily resolved.

Muse raises several objections to including estimates of ofkts in projecting new program costs. Two

major reasons for this objection are (1) uncertainty -

evidence may be weak, elements of cost maybe omitted

from calculation of offsets, etc.; and (2) lack of impact - the beneficiary of the offket may be someone

other than the entity who must make the cash outlay to

obtain the offset. In estimating and forecasting for

budgets, one must remain aware of the cash outlay

consequences of forecasted events.

Muse also observes that estimators should engage

in reality checking; that is, comparing calculated results with the views that people might expect in the

real world.

The Joint Legislative Audit and Review Commission of the Virginia General Assembly (JLARC) has

reviewed Virginia’s Medicaid forecasting in 1992 and

1997.19 In reports of these studies, JLARC recommends six criteria for forecast models and five criteria

for a forecasting process (1991; 1997). These criteria

are shown in Tables 3 and 4.

Table 3: Criteria for Forecast Models

1. Model assumptions are clearly understood by participant and periodically reviewed.

2. Variables used in models’ equations are tilcient,

accurately measured, and the best information available at the time.

3. Equations are mathematically sound and tested to

ensure mathematical precision.

4. Diil’erent regional conditions are taken into account

stdTlciently.

5. Forecast errors are analyzed on an ongoing basis.

6. Forecast models are reviewed and dmented well,

including any judgmental or policy adjustments

I Table 4: Criteria for Evaluating Forecasting Process I

1. The degree of uncertainty associated with forecasts

should be understood by process participants.

2. The agen~ making forecasts should have the data

and pe~nnel required to generate a good estimate.

3. Regular reports on actual expenditures and their

variance from forecasts should be developed and available to agency staff and interested external participants, as appropriate.

4. The process should maintain the flexibility to respond to dramatic changes in recipient utilization and

program expenditures by revising the forecasts.

5. The process should include a mechanism requiring

some level of expanded review of the forecasts.

(Expanded review means review by people not involved in initial forecasting, such as an external panel.

19(1) ‘he author was the budget director at the agency that administers

Medieaid in Virginia in 1991. (2) Other states have conducted similar

studi=, however, there is no @ematic method for finding such reports.

286
Excepting criterion 4 in Table 4, these criteria

might be applied to any forecasting problem. Most appear to be a subset of more extensive criteria used in

evaluating revenue forecasting (Joint Legislative Audit

and Review Cogun.ission, 1991).

Many of these normative guidelines support the

goal of forecastkg accuracy. For example, JLARC

recommends that formulae be valid and NASBO,

among many others, rec.ornmends the use of good data.

Yet, many other recommendations concern other matters. For example, Trapnell recommends against unrealistic expectations of forecast results; similarly JLARC

recommends that forecast participants should understand the degree of forecast uncertainty.

RELATION TO REVENUE FORECASTING

Revenue forecasting literature suggests a tendency

for an asymmetrical forecasting loss fimction, favoring

a cushion between total revenue and total expenditures

(Rodgers and Joyce, 1996). The rationale is that the

penalty for overestimated revenue is greater than the

penalty for underestimated revenue. Similar reasoning

would have states overestimate expenditures in major

expenditure categories such as Medicaid. Surpluses

are less damaging and, in many states, can be reprogrammed at the end of the fiscal year to offset deficits

elsewhere.

However, cushioning budgets through overestinlation of Medicaid expenditures differs from underestimation of revenue in one important respect. It changes

the locus of control over the cushion. Unappropriated

revenue - which is the status of unexpected revenue

resulting from greater receipts than budgeted - is, generally, controlled by the central administrative agencies

or the legislature. over-appropriated finds, resultant

from appropriating fimds to Medicaid agencies based

on overestimated forecasts, are controlled by line agencies. As there is a mtural distrust between central administrative agencies and line agencies, it is unlikely

that states would intentionally allow line agencies to

control surplus fimds,

Nevertheless, the line agencies, who submit the

HCFA-37, maybe motivated to seek surplus funding as

a cushion against their own forecast error. There would

be no advantage for these line agencies to make separate lower estimates for HCFA.

Trapnell argues that agencies might find differing

but equally negative consequences for overestimating

and underestimating expenditures. McKusick proposes

that there is lower penalty for underestimation. On the

other han~ Muse’s rationale for not counting offsets in

estimating program changes suggests a higher penalty

for underestimation.

The conflict concerning presence and direction of

bias can be e@ained by several factors. Firs~ McKusick’s study reported in 1980 reflects a relatively small

Medicaid program. With this small program, political

decision making may be more impotit than financial

risk, as is also suggested in some of Trapnell’s discussion. In their effort to maximize their distribution of

benefits, elective officials may consider a small risk of

over expenditure to be less important than their ability

to distribute benefits to more people. Still, neither

McKusick or Trapnell found empirical evidence of actual underestimation.

SOME HYPOTHESES

These studies provide little explanation of relative

forecasting accuracy. However, they are a source for

many hypotheses about Medicaid forecasting. In general these hypotheses are found by extrapolating the

objective of normative guidelines or the reasons for inquiry in empirical studies. To a large degree, where

there is expkmatoxy discussion, most of the views agree

with each other. In a few cases, as with the matter of

a-etriud loss fimction, there is disagreement.

Where there is disagreement, the cited sources may not

all support the form of the hypothesis ex--ressed here.

Following are hypotheses that can be extracted from

this body of literature:

1. States’ loss fi.mctions will be asymmetrical with a

preference for overestimation (Trapnell, McKusick, Muse). McKusick proposes a preference for

underestimation. Trapnell offers conflicting

views, (1) he argues that the political environment

equally punishes over- and underestimation, (2) he

points out that past f~eral behavior may create a

bias for overestimation, and (3) he proposes that

underestimation bias arises from frequent selection

of policy initiatives that are underestimated.

2. States manipulate fiscal year end results to improve forecasting results within state budgeting.

By implication states whose fiscal year ends coincide with federal fiscal year ends will appear to be

more accurate (’EOP, Trapnell, Insco).

3. Forecast models result in more accurate forecasts

when they:

a) Account for delivery of medical care on service date with lagged tradormation to cash

payment date (McKusick, Insco, Lower, Trap287
,. .,,

. “ .,,,

,.

b)

c)

d)

e)

f)

@

h)

i)

j)

nell, Muse).

Decompose data into homogeneous sewice and

enrollment categories (McKusiclq Trapnell,

Lower).

Reflect the PUC model or an extended version

of the PUC model (HCF~ Trapnell, Muse,

NASBO).

Relate enrollment to economic conditions

(McKusick).

Relate seMce utilization with seMce supply

(McKusick).

Decompose series into baseline and policy

events (McKusic~ Trapnell, Lower, EOP,

HCFA, NASBO, Muse, JLARC).

Decompose utilization into recipients per

beneficiary and units of service per recipient

(Lwer).

Relate price estimates to price indexes (Lower,

Trapncll).

Account for fderal matching rates (NASBO).

Account for regional variation (JLARC).

4. Forecasting accuracy is affkcted by:

a)

b)

c)

d)

e)

f)

g)

h)

i)

J)

k)

Stm

i) Skill (EOP, Lower, JLARC).

ii) Quantity (McKusick, Lower, JLARC).

iii) Dedication to the forecasting fiction, as

opposed to part-time forecasting (Trapnell).

Forecaster understanding of

i) Forecast model assumptions (JLARC).

ii) The Medicaid program (Trapnell).

Forecaster access to program staff (Trapnell).

Data:

i) Quality (McKusick, Trapnell, Lower,

NASBO, Muse, JLARC),

ii) Sources (Lower, Trapnell, Muse).

iii) Periodicity (monthly, etc.) (McKusick,

Lower).

iv) Series length (Lower).

Setting rates prior to forecasting (McKusick,

Trapnell, NASBO).

Composition of the Medicaid program (McKusick, Trapnell, Insco, Lower),

Update frequency (Trapnell, Lower).

Length of forecast horizon (Trapnell).

Decomposition of forecast into near fbture and

distant future (’Ikapnell).

Whether seasonality is c.xarnined (Lower).

Use of software:

i) Spreadsheets (Low)er).

ii) Statistical software (LOver).

1)

m)

n)

o)

iii) Forecast sofhvare (Lower).

Use of pre-forecast data editing (Trapnell).

Insulation of forecasting from politics (Trapnell).

Allocation of forecasting resources to problems (components) according to difficulty and

importance (’Ilapnell),

Use and quality of forecast evaluation (Trapnell, NASBO, JLARC).

5. Intra-governrnental forecasting factors that affkct

forecasting accuracy include:

a)

b)

c)

d)

Locus of primary forecasting responsibility -

Medicaid agency or other state ageng (McKusick, Lower).

Cooperation between forecasting bodies

(Lower, NASBO, JLARC).

Whether there is coordination between Medicaid forecasting and other state forecasting

(NASBO).

Use of “expanded review” (JLARC).

6, Accuracy is improved when forecasting techniques

are:

a) Simple (Trapnell).

b) Not “black box” (Trapnell).

c) Fit to the nature of the problem (Tkapnell).

d) Fit to the quality of the data (Trapnell).

e) Easy to use (Trapnell).

f) Capable of detecting the effects of policy

shocks (Trapnell).

7. Forecasting accuracy is not associated with the use

of any particular forecasting techniques (Trapnell).

However, McKusick suggests the opposite, implying that more sophisticated techniques may result

in greater accuracy.

8. When multi-stage forecasts are used, accuraq’ of

later stage forecasts depend on the accuracy of

earlier stage forecasts (Trapnell).

9. L,arge policy events a.tlect forecasting accuracy. In

particular, forecasting accuracy is tiected by:

a) Boren Amendment Lawsuits (Insco, Lower).

b) Federal policies concerning pregnant women

and children (Insco, Lower).

c) State initiatives involving Disproportionate

Share Hospitals (Trapnell, EOP).

10. Perceived forecasting importance affects accuracy

and bias. In particular:

a) The relative size of the Medicaid program to

other state programs affects accuracy. As the

program increases in relative size, accuracy

becomes more valued (McKusick, Lower).

McKusick does not offer this view, instead it

288
is implied in his obsemation of low concern

for accuracy in 1980, when Medicaid progmms were comparatively small components

of state budgets.

b) The centrality of forecast preparation to state

budgeting ‘@lcKusick, EOP). EOP obsewed

that states who prepare their fderal budget

forecast in connection with their state budget

forecast appear to submit more accurate federal forecasts. However, it is the current

author’s observation that this relationship may

be complex. If state budget forecasting is biased for political reasons, independent forecasts may be more accurate.

c) The relative share of expenditures paid by

state funds, as compared with federal funds,

affects forecast accuracy (NASBO). Where

states pay a higher share - that is, have a

lower match rate – they will be seek greater

forecasting accuracy.

fialysis of this hypothesis is based on data from

the HCFA-37/HCFA-25 budget requests and the

HCFA-64 accounting records for the years 1982

through 1995. Over this period, the longest horizon

consistently available is 24 months before the end (12

months before the beginning) of the fiscal year. Data

are divided into two groups, one for the 50 states,20 and

another for the 6 fdera.1 districts and territories. Errors are analyzed using the percent error [(Actual minus Forecast) divided by Actual]. As the forecast is

subtracted from the actual, a negative error means the

forecast exceeded actual expenditures.

A shown in Table 5, states have negative valued

errors in 7 of 14 years, and positive valued emors in the

remaining 7, Cumulatively, the error is negative for

the first 7 reported years, and positive for the remaining 7 years. Table 6 shows that more than 50°/0 of

I states have negative errors in 6 years, while they have

positive errors in the 8; however, cumulative errors are

negative in 8 of 14 years.

Table 5 1

States Federal

Year UnwAvg CurnAvg UnwAvg CumAvg

1982 -4,7Y0 -4.7V0 21.6?40 21.6%

1983 -5.3Y0 -5.070 12.8% 17.2!40

1984 -4.9V0 -5.OYO 54.7% 29.7V0

1985 -3.6Y0 -4.6Y0 53.7% 35.7%

1986 -0.3?40 -3.7Y0 -0.4Yo 28.5%

1987 8.l% -1.8’ZO -0.270 23.7%

1988 8.9’%0 -0.2Y0 9.5’?40 21.7%

1989 6.6% 0.6% 13.1’%0 20.6%

1990 8.4?40 1.5% -2.8?40 18.070

1991 23.8’Mo 3 .7’ZO 43.9% 20.6%

1992 35.5?40 6.6% 43.2% 22.6’ZO

1993 6.8V0 6.6’% 46.0% 24.6%

1994 -6.2Y0 5.6% 78. l% 28.7V0

1995 -1.5Y0 5.l% 7.970 27,2%

JnwAvg. = Unweighed average.

;urWlvg. = Cumulative average.

‘ercent error 24 months rxior to fiscal vear end.

EVALUATION OF HYPOTHESES

These hypotheses are too numerous to filly evaluate in this paper. Further, for many operationalization

may be problematic. The following discussion discusses evidence concerning some of these hypotheses:

Hypothesis 1: States will exhibit an asymmetrical

loss function with a preference for overestimation.

Table 6 I

States I

Federal

Year ‘A <=0 Cum. YO ‘%0 <=0 Cum. YO I

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

71.4%

79.6’%

74.0%

78.OVO

46,0V0

20.0%

22.0%

22.0%

22.OVO

8.OVO

4.0’%

38.0%

80.0’%0

71.4%

75.5%

75.0%

75.8%

69.8?4

61.4%

55.7%

51.5’Ya

48.2Ya

44.2%

40.5’?4a

40.3%

43.4%

40.070

0.0?40

20.0’%0

16.7’%0

83.3?40

66.7%

16.7V0

33.3%

66.7%

33.3%

16.7%

50.OVO

0.0’%0

20.OVO

20.OYO

19.OVO

33.370

39.4V0

35.9%

35.6?40

39.2’?40

38.6%

36.5%

37.7%

34.7%

19951 62.0% 44.7YOI O.0% 32.<l%

These results appear to imply that state forecasting

of Medicaid expenditures is unbiased. However, this

understanding may be incorrect. Medicaid has experienced differing phases of policy activity. During the

early 1980s, policy making activity was relatively low.

However, in the mid-l 980s the fixleral government began to engage in extensive Medicaid policy making,

including several expansions of eligibility for children

and pregnant women, welfare reform that expanded

10 Massachusetts has two distinct programs, these are combined in this

analysis,

289
Medicaid eligibility, Medicare reform that extended

Medicaid eligibility to low income Medicare beneficiaries, and broadening of the minimum coverage requirements for children. In part, the states responded

to these changes by incorporating even more sewices

under Medicaid - off loading the cost of those seMces

from programs fimded solely with state funds - and by

using provider tax and/or donation programs that have

the effect of increasing the effective federal share of

total program costs. Most such policy making resulted

in huge expenditure increases over ve~ short horizons.

It is unlikely that these policies would be reflected in

forecasts discussed here.

Table 7 and Table 8 show comparative data at 12

months before the end of the fiscal year (the last forecast before any portion of the actual expenditures are

experienced), The number of years with positive and

negative errors is similar to those in Table 5 and Table 6-7 negative average errors and 7 positive

(Table 7); and 8 years with more than 50% of states

with negative errors and 6 with fewer than 50% (Table

8). However, the cumulative columns reveal a bias towards fiegative errors. Table 7 shows only 5 years in

which the cumulative errors are positive, as compared

with 9 years of negative cumulative errors. Table 8

shows only 1 year where the cumulative percent of

states with negative errors is below 50%. Over this 14

year period the cumulative percent of states with negative forecasting errors is 53.370.21

These results are consistent with the broader view

that budget officials are risk averse. Overestimation of

expenditures serves the same ends as underestimation

of revenue, to establish a cushion against higher risk

error. While errors that lead to surpluses maybe

viewed unfavorably by those who could have allocated

finds to other purposes. The alternative of shortfalls

can lead to financial crisis. This analysis supports the

view that Medicaid forecasters are more averse to financial crisis.

It is interesting that these results are not found

with the fderal districts and territories. These data

demonstrate a bias for underestimation. The unweighed average forecast error for these six districts at

the beginning of fiscal years is positive for 10 of 14

years with the cumulative average error positive 13 of

14 years (Table 7). While there are an equal number

of years in which the majority of these districts make

negative and positive forecasts at this horizon, cumulatively over 14 years, 47.6% of federal district fore2 1 Ibis result maybe afkted by beginning arbitrarily in 1982, the f~

year of ti availability,

casts overestimate expenditures (Table 8). Federal

districts do not have the same bias towards overestimation as states, and they maybe biased towards underestimation. No explanation of this alternative bias

is available at this time.

Table 7 I

Yea

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

I States I Federal I

UnwAvg CurnAvg

-5.0%

-2.lYO

-5.0%

-2.OYO

0.2V0

5.1%

2.4%

0.8%

2.9%

11.5’%

4.8%

-4.lYO

-4.3Y0

-O. l%

-5.0%

“3.5%

-4.0%

-3.5%

-2.8%

-1.5%

-0.9%

-0.7%

-0.3%

0.9%

1.2%

0.8%

0.4%

0.4%

UnwAvg

-6.3%

13.0%

33.3%

1.3%

-4.0%

0.6%

8.8%

-4.9%

-3.5%

41.0%

38.5%

43.8YQ

24.8%

1.3Ya

CumAvg

-6.3Y0

3.4%

13.3!X0

10.3%

7.5%

6.3%

6.7%

5.2%

4.3%

7.970

10.7VO

13.5%

14.3%

13.4%

JnwAvg. = Unweighed average.

hnAvg. = Cumulative average.

>ercent error 12 months mior to fiscal vear end.

1 Table 8 I

II StatesI

Federal

Year Yo <=0 cum. Yo 70 <=0 cum. Yo I

1981

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

I

75.5%

73.5V0

88.0%

58.0%

52.0%

30.0’%0

36.0%

42.0%

34.0’%0

22.0%

28.0%

84.0%

70.0%

75.5% 80.OVO

74.5?40 20.0’%

79.1% 16.7%

73.7% 33.3%

69.4% 83.3%

62.8% 66.7%

58.9% 33.3%

56.8% 66,7%

54.2% 66.7%

51.0% 33.3%

48.9% 16.7%

51.8% 50.0%

53.2% 16.7%

80.0%

50.0%

37.5%

36.4%

46.4%

50.0%

47.570

50.0’%

51.9%

50.070

46.9%

47. l%

44.7%

I 53.3%1 83.3% 47.6V0

Hypothesis 2: States manipulate fiscal yearend accounting data to improve foreeast outcomes

State motivation for this practk rests with the fact

that, in the case of Medicaid, the forecast coincides

290
with the budget. State officials may be motivated to

ensure that the actual expenditures coincide with

planned expenditures. In the case of Medicaid, ordinary fiscal management may not be sufficient to attain

such results. Most Medicaid expditures are made

through claims processing, not discretionary or quasidiscretionary expenditures. In most states, claims

processing is automated. So, adjusting year end expenditures to match budget plans would involve causing claims processing to accelerate or decelerate.

Medicaid programs are operated by state agencies.

Executives of these agencies are responsive to state officials, including state governors and state legislators,

because they report to these officials. So, the motivation to appear correct would be a future of state budgeting. There is no particular advantage of manipulating expenditures reported to the federal government to

achieve the illusion of forecast accuracy. Presumably,

the illusion is achieved by either delaying or accelerating payments in the last quarter of the fiscal year,

With a mirror image change in expenditures in the next

fiscal quarter. So, the illusion should appear in data

from those states whose fiscal year coincides with the

fderal fiscal year, but should be absent from data with

other fiscal years.

1 Table 9 I

Average

I

States Ohs.

Error i %

lStates: I I I I

April 6.55% 1 14

Jul y 6.54% 46 643

Combined 6.54’?40 47 657

September 4.45 % 1 14

October 5.46% 2 28

Combined 5.12’XO . 3 42

lFederal Districts I 20.9VOI 61 841

To evaluate this hypothesis absolute percent error

for each state are pooled across the 14 years and then

averaged within groups for each fist’al year end. Federal districts are reported separately. The absolute error is evaluated because the direction of error is not at

issue. The errors are pooled because of the low number

of states whose fiscal year coincides with the federal

fiscal year. There are 46 states with fiscal years beginning in July, 1 beginning in April (New York), 1 beginning in September (Texas), and two begiming in

October (Michigan and Alabama). The ftieral fiscal

year begins in October. The pooled observations are

not independent, so no statistical analysis is attempted.

For the 47 states with fiscal yeaK beginning more

than a month off of the beginning of the federal fiscal

year, the average absolute error for 647 separate fiscal

years (Arizona is not reported for 1982) is 6.54Y0. If

Texas (fiscal year beginning in September) is included

with this group, the average drops to 6. 50°/0. For the

two states with fiscal years matching the fdera.1 fiscal

year, the average absolute error for 28 separate fiscal

years is 5.46?40. If Texas is included with this group,

the average drops to 5. 12Y0. Thus, the range of difference be~een these errors is between 1.04°/0 and 1.42°/0

depending on which group Texas is included with.

These results weakly support the view that states manipulate year end activities to create the illusion of

budgetary accuracy.

Hypothesis 3: Forecasts are more accurate when

forecast models more explicitly reflect the elements

generating the forecasted series.

Data are not available to evaluate each of the 10

sub-hypotheses enumerated. However, two subhypotheses can be evaluated. The first sub-hypothesis

specifics that forecasts will be more accurate when

states ex@icitly account for the trantiormation between

service date and payment date. This sub-hypothesis is

evaluated using the pooled 14 year absolute forecast

errors as discussed with the seccmd hypothesis. Identification of states who explicitly account for service date

to payment date transformation is found in data determined by Lower. Results are shown in Table 10. States

who focus on “payment date” do not attempt to evahlate sewice date (accrual) events, while those who forecast “service date” do. (No federal districts are reported in the Lower study.) A third gToup of states

evaluate service date data some times. Based on these

pooled errors, there is no reason to anticipate that accounting for the sexvice date events improves forecast

accuracy.

Table 10 I

I I Average States

Error I

Payment Date 6,10% 28

Semite Date 6. 13% 9

Mixed 7.61?40 6

However, it maybe that the time period between

the earlier forecasts and the date of the Lower suxvey

invalidates the evaluation using these pooled errors.

Table 11 shows comparable results with errors pooled

291
Hypothesis 4: Forecast accuracy is affected by numerous specific technical elements

of the forecast.

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

.

T

Percent Error By Months Before the End of the Fiscal Year

+—1982

—X—198 3

—x—1984

-==-- 1985

~1988

~1987

~1988

-0.11
from 1992 through 1995. As with Table 10, there is no

evidence that forecast models that account for service

date events are more accurate than those that do not.

Table 11

Average States

Error

Paymen{ Date 5.7570 28

Service Date 6.10% 9

Mixed 6.23% 6

The third sub-hypothesis specifies that forecasts

made within the iia.mework of the recommended PUC

model will be more accurate than those that are not.

This hypothesis is of special concern as HCFA requires

states to produce and submit data on the HCFA-37 that

reflects the PUC model, whether or not they use this

model. If the hypothesis is incorrect, states may be required to conduct unnecessary analyses and forecasts

for the sole purpose of completing arduous papenvork

required by HCFA.

The evaluation of this hypothesis is based on the

same 4 year pooled errors as used in Table 11. Identification of states that use the PUC model is based on

preliminary data horn a sumey of state Medicaid agencies occurring in 1997. At the time of this paper, the

su.my has received 35 responses out of 56 Medicaid

programs. Results are shown in Table 12. Based on

these results, there is no mtidence that use of the PUC

model improves forecast accuracy.

1 Table 12 I

I I Average States I

Error

use Puc 5.65% 13

lDonot UsePUC I 5.81% 201

[Federal I 8.97% 21

Hypothesis 4: Forecast accuracy is affected by numerous specific technical elements of the forecast

Most aspects discussed here are still to be evaluated. Figure 1 demonstrates the relationship between

forecast accuracy and time. Each line demonstrates the

change in forecast error over the period beginning with

the first forecast submitted to HCFA and ending with

the fiscal year end. Data do not always converge to

zero because the HCFA-37 was not required to match

the HCFA-64 (accounting data) for past periods prior

to 1992. It is not very surprising that forecasts become

more accurate as the forecast horizon diminishes. It is

interesting how little accuracy improves over time in

many years. The chart shows only minimal improvement in average error in forecasts for 1982 through

1986, 1994, and 1995. These results tise because of

relative accurate forecasts at the longest horizons. A

review of state specific data (not shown here) reveals

that accuracy in earlier periods arises from the cancellation of forecast emors beween states, and that there

is a convergence towards forecast accuracy over time.

CONCLUSION

This paper is a interim report of research in progress. The report shows that Medicaid forecast data can

be used to evaluate applied forecasting. Some tentative

conclusions include:

State forecasters use asymmetrical loss fimctions

in selecting forecasts to report. Forecasters are more

averse to underestimation of expmi.itures than to overestimation.

Federal district and territory forecasters are not

averse to underestimation and maybe averse to overestimation.

States may manipulate fiscal year end activities to

ensure the amcy of fiscal plans.

There is no evidence the explicitly accounting for

some details of data generating events - specifically,

the t.ransfonnation between sewice date events and

payment date events - results in increased forecasting

accuracy.

There is no evidence that the HCFA recommended

Price x Utilization x Caseload model of data generation

produces more accurate forecasts.

Forecast accuracy is associated with length of horizon.

These results support the view that fMther analysis

of these data will generate other interesting findings

that can improve the understanding of applied forecasting.

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Methods: Results of a Smey of State Medicaid Budget Officers,” Conference Paper,

Human Services Finance Officers, September,

1993.

Wdakis, S, A. hderso~ R. Carbone, R Fildes, M.

Hibo~ R. Lewandowski, J. Newton, E.

Parzen, and R. Winkler, “The accuracy of extrapolation (time series) methods: results of a

forecasting competition,” Journal of Forecasting, 1 (1982): 111-153.

Makridakis, Spyros, Chris Chatfield, Michele Hibon,

Michael Lawrence, Terence Mills, Keith Oral,

and LeRoy F. Simmons, “The M.2-

competition: A real-time judgmentally based

forecasting study,” International Journal of

Forecasting, 5 (1989): 381-391.

McKusic~ David “Forecasting Techniques and Budgetary Issues of State Medicaid Programs,”

Falls Church, Virginia, September 30, 1980.

Muse, Donald N. “Estimating Costs and Savings from

Preventive Child Health Proposals,” The

George Washington University, 1993.

National Association of State Budget Officers, “The

Medicaid Estimate,” [Washington, 1991].

National Association of State Budget Officers, “1995

State Expenditure Report,” ~ashington),

April, 1996.

OtWe of Technology Assessmen~ “Understanding Estimates of the Impact of Health Reform on the

Federal Budget,” published in CCHPulse:

The Resource Guide to Health Care Reform

(section 9076) 1994.

Ratner, Jonathan, “MEDICAID Decline in Spending

Growth Due to a Combination of Factors,”

Testimony Before the Committee on Finance,

U.S. Senate, (Washington, DC: GAO), March

4, 1997.

Rich, Spencer, “Bad Statistics Cited in Policy Miscalculations,” Washington Post, August 6, 1991.

Rodgers, Robert and Philip Joyce, “The Effect of Undeflorecasting on the Accuracy or Revenue

Forecasts by State Government,” Public Administration Review 56 (1996): 48-56.

Scanlon, William J. “MEDICAID Recent Spending

Experience an the Administration’s Proposed

Program Reform: Testimony Before the Subcommittee on Health and Environmen~

Committee on Commerce., House of Representatives, (Washingto~ DC: GAO), March

11, 1997.

Trapnell, Gordon R “Best Practices Guide for Preparation of Medicaid Budget Estimates,” Actuarial Research Corporation [1994].

Trapnell, Gordo~ “Assessment of Cment Medicaid

Forecasting Procedures,” Annandale, Virginia, Actuarial Research corporatio~ June

1991.

294
Consumer Health Accounts: What Can They Add to Medicare Policy Analysis?

R.M. Monaco, INFORUM, University of Maryland

J.H. Phelps, OHNS, office of the Actuary, HCFA1

Introduction”

Over the past few years the Office of the Actuary

of the Health care Financing Administration (HHS)

and the Inforum group at the University of Maryland

have been investigating the relationships between the

health care sector and the rest of the economy. One

path of research has emphasized the fiscal

consequences of large spending increases for federal

medical programs, like Medicare. Beginning in 1995,

outlays for hospital insurance (Medicare, part A)

exceeded earmarked revenues and projections suggest

that outlays and revenues will continue to diverge by

ever-widening amounts. 2 Actuarial projections of

growth in Medicare part B. which is financed out of

general revenues, also show growth faster than federal

tax revenues. When Medicare spending rises faster

than federal tax revenues, any or all of three

consequences occur:

1. The federal deficit rises.

2. Other spending programs are cut so the deficit does

not rise.

3. Payroll or other taxes are raised, so that other

spending programs can remain on their original funding

paths without increasing the deficit.

All of these occurrences have macroeconomic

consequences, and influence the industrial structure of

employment and output. Thus, in an indirect way,

federal outlays related to medical care can have a large

influence on a wide variety of industries. In Monaco

and Phelps {1995) we showed that a large increase in

the federal deficit from increases in Medicare spending

shills economic activity away from industries with high

measured productivity, like manufacturing and

agriculture, to industries with low measured

productivity, like health care and business services. In

addition, our research has shown that the combined

projected social insurance deficits using SSA/HCFA

middle-case scenarios over the next 50 years would

require a pay-as-you-go payroll tax rate of 32 percent

in 2050 to keep the federal budget balanced. This is

more than double the cunent rate (Monaco and Phelps

(1997)).

Actuarial projections of Medicare spending rise

for two distinct reasons (Monaco, Phelps, and Mulvey

(1996)). First, in the short run (through about 201 1),

the projected Medicare “deficit” is driven primarily by

rapidly-rising spending per beneficiary. After 2011,

actuarial projections of growth in spending per

beneficiary slows, but a rapid rise in the number of

beneficiaries from retirement of the Baby Boomers

results in continued growth in Medicare spending.~ The

recent budget compromise has moved the projected

Medicare Part A trust fund insolvency date from 2001

to 2007. This was done partly by reducing provider

payments per beneficiary--at least partially addressing

the first problem. However, the improvement in the

outlook of the Part A trust fund was accomplished

primarily by reassigning home health liabilities from

Part A of the program to Part B. While this improved

Part A solvency, it did not reduce the projected overall

Medicare deficit. Little was done to address the longerterm financing problems arising from the coming

demographic shift. Presumably, longer-term issues will

be addressed by the yet-to-be-appointed Medicare

commission. Thus, even with the recent reductions in

the near-term path of Medicare spending, it is clear that

Medicare policy will remain an important issue

throughout the next several years.

What Determines Medicare Spending?

The Medicare program was designed to cover a

significant portion of the cost of medical care for the

elderly (over 64) and those with disabilities or endstage renal disease. Broadly speaking, Part A of

Medicare covers hospital services, while Part B covers

physician services. Conceptually, Medicare is an

insurance program that, after some deductibles and

copayments paid by the beneficiary, directly reimburses

providers for each kind of treatment.4 The important

point for our purpose is that the amount of Medicare

spending in any year is not “capped” at any level.

Given the reimbursement structure, Medicare outlays

are actually determined by the usage of covered

Medicare services by the beneficiary population. In

short, Medicare outlays are determined by the demand

for medical services.

In most of our research, we have concentrated on

analyzing the economy-wide implications of projected

295

m-’
Medicare outlay paths without specifically forecasting

the Medicare outlay paths ourselves. Typically, we

have used projections of Medicare outlays from the

Office of the Actuary (HCFA), which we put into a

model as exogenous assumptions. We then ran the

model under different assumptions concerning

feedback between the health sector and the rest of the

economy, and compared the results of the various

simulations.

The model we’ve used and developed is a

comprehensive, consistent, structural econometric

model estimated on historical data from about 1960

through 1994, with a simulation horizon of one to fifty

years. In general, the model accounts for relationships

among macroeconomic variables such as government

deficits, interest rates, exchange rates, inflation, and

unemployment, as well as the input-output relationships

among various industries that make up the economy.

For example, the model accounts for the tendency of

larger government deficits to be accompanied by higher

real interest rates and exchanges rates, leading to lower

spending on autos, new houses, and agricultural goods

(especially sensitive to exchange rates). It also

accounts for the reduction in the amount of steel

production in the economy that accompanies the

reduction in automobile and construction activity, as

well as the reduction in the output of the chemical

industry as a result of the reduction in fertilizer

demand that accompanies the reduction in agricultural

production. 5

A key part of the model is the complete consumer

demand system, which estimates consumer spending by

80 types of goods/services, allowing for income effects,

demographic effects, own price, and cross-price effects

(henceforth, goods and services will be called

“goods”). We refer to a complete consumer demand

system to emphasize that the system accounts for all

consumer spending, rather than just a part. The

theoretical basis for the demand work is standard

consumer demand theory. Consumer demand theory

begins with a utility function, which translates amounts

spent on each good into consumer satisfaction. As a

quick review, the theory holds that consumers seek to

maximize their total utility by allocating spending

across all goods subject to a budget constraint. The

theory makes a few other key assumptions as well: (1)

consumers prefer more of a good to less of a good, (2)

consumers have complete preferences, that is, they can

compare and rank all combinations of goods, and (3)

consumer preferences are transitive. In addition,

assuming spending on all other goods remains the

same, additional equal amounts of spending on any

single good eventually yield a smaller and smaller

amount of satisfaction. This last concept is the

traditional “diminishing marginal utility” assumption.b

In the typical empirical implementation of the

complete demand system, the demand for medical

services is treated no differently from other goods.

Consumer spending for medical services is assumed to

be a function of the relative price of medical care and

the total income to be allocated, which includes

government cash and non-cash transfers. In most cases,

other determinants are allowed as well. For example,

in the demand system currently used by our macroeconomic model, consumers decide how much hospital

services, physician services, etc. they demand on the

basis of their income, the prices they must pay, and the

age-structure of the population. Thus, we allow for the

fact that older age groups tend to consume more

medical services, all other things equal.

In many implementations of complete consumer

demand systems, medical services spending is divided

into several categories. In the current version of our

model, there are separate categories for hospital

services, physician services, nursing home services, and

dentists and other medical practitioners. One

innovation in our ongoing line of research allowed

Medicare policy to affect the level of consumer demand

for medical care goods (Janoska ( 1997)). The view

embodied in our current consumer spending equations

is that a policy decision to raise or lower Medicare

spending influences the consumer demand for m-edical

services by raising or lowering the prices faced by a

group of consumers. Thus, when the implied

government subsidy for medical care is made more

generous, holding everything else equal, we reduce the

price of hospital services facing consumers. As a

result, Medicare beneficiaries typically tend to

consume more hospital services. Our estimated

parameters suggest that, as a generalization, the ownprice elasticity across all of the medical services

categories (after trying to account for the effect of

Medicare) is around 1, i.e. a 1 percent increase in the

price of medical services relative to all other goods

reduces demand by about 1 percent.

When a complete consumer demand system is

embedded in a larger, comprehensive general

equilibrium model, the model can be used to generate

forecasts of Medicare outlays, while simultaneously

allowing for the feedback effects that rising Medicare

outlays generate on the rest of the economy (described

above). The model can also be used to evaluate

impacts of other projections of Medicare spending by

296
overriding endogenous consumer health spending.

With its endogenous demand system, the model can

indicate of the path of Medicare spending over the next

fifty years, accounting for the rising number of people

over age 64, the rising levels of income, and the

assumed “generosity” of the Medicare program, while

simultaneously allowing for the feedback between

Medicare outlays and the rest of the economy.

t

Data Problems in Measuring the Medical Services

Sector

Simply applying consumer demand theory to the

available national accounts consumer spending data is

fairly straightforward and has proved very useful for

policy analysis and forecasting in nonmedical areas.

However, lately we and several other researchers have

begun to question whether the data typically used to

estimate a system of complete demand equations meet

the requirements for a proper application of standard

theory. Questions have arisen especially in the area of

consumer health spending, where the current structure

of national account data emphasizes which providers

receive consumers’ dollars, rather than what actually is

provided. To consider the question more fully, it is

useful to consider briefly how the consumer spending

data in the national accounts are derived.

Consumer spending data most often used by

researchers estimating complete consumer demand

systems appear in the published National Income and

Product Accounts (NIPA).7 The tables show the

composition of consumer spending by type of

expenditure; for example, consumer spending on new

cars, used cars, food, clothes, jewelry, barbers’

services. hospital services, physician services, dentist

services, etc. The data underlying these estimates of

consumer spending by good/service are, for the most

part, actually collected from the institutions receiving

the dollars, e.g. W-al-Mart, Home Depot, barbershops,

hospitals, and nursing homes. These institutions are

actually consumer-goods providers. The data in table

2.4 (consumer spending by type of expenditure) is

constructed by knowing what these providers typically

sell, supplemented by information from other consumer

surveys and trade association data.~

In most of the cases, data are only available for

nominal spending. Few indicators of inflation-adjusted

consumer spending are available directly. For example,

the “benchmark” estimate of consumer spending on

physician services in nominal dollars comes from the

Census of Service Industries. Between benchmarks,

data are from an annual sutvey of services and from

HCFA data, and for quarterly data, supplemented by a

“judgmental trend.”9 To calculate real consumer

spending by type of expenditure, the nominal figures

are deflated by price indexes. For the vast majority of

the expenditures, the relevant price indexes are

consumer prices. To calculate the “real” value of

consumer spending on physician services, the nominal

estimates prepared from the above-mentioned sources

are divided by the consumer price index for physician

services.

No data set is perfect. The key question in using

any data set is simply whether the data are good enough

to support the structure that will be built on them. Our

current concern with the NIPA consumer spending data

as a support for consumer demand theory centers on

two somewhat-related ideas. First, conceptually,

medical services data in the NIPA, like almost all

services data in the NIPA, tell us where the consumer

dollar is spent, not what the consumer dollar buys.

Secondly, recent research has suggested that consumer

price indexes for medical care have substantially

overestimated the amount of inflation in medical care,

and so have underestimated the amount of real services

provided by medical care practitioners.’” Each of these

problems potentially has an enormous effect on the

quality of the conclusions that can be obtained by a

demand system approach to medical care consumption,

and, by implication, to all consumer spending.

Spending on Consumer Goods versus Spending at

Consumer-Goods Providers

The national accounts tell us where the consumer

dollar for health goes -- which providers get the money.

It does not, like data for some other goods,

immediately tell us what consumers bought. In this

sense, hospital services are treated the same way as

barbers’ services. In both cases, the “good” purchased

by the consumer is really measured by the spending

that occurs in a particular kind of “store.” This

distinction poses problems for consumer demand

theory, which is almost always stated in terms of the

demand for distinct, separable goods and services that

each yield distinct bundles of satisfaction. Thus, the

available data do not exactly match the requirements of

theory.

Pragmatically, when researchers apply consumer

demand theory to existing time-series of inflationadjusted consumer spending by good, they generally

assume that using data on where consumers spend their

money is a close approximation to the kinds of goods

that are actually bought. For the most part, this is true

297

ml-.
and we would not expect our price and income

elasticities to be exceptionally sensitive to small

deviations of the data from the “true” data. Common

sense allows us to be reasonably sure that substituting

spending at barbershops for the true “haircut” category

does little violence to the estimated price and income

elasticities for haircuts. This is because, in general, we

think we have a fairly good idea of the type of service

provided in the barber’s shop. The possible range of

prices for haircuts, and the range of personal

appearance services -- shaves, perms, pedicure,

manicure, etc. -- is really quite narrow. We are willing

to believe or assume that, given a constant consumer

utility fimction, a real dollar of barbers’ services

conveys a given amount of satisfaction over time. In

other words, we are willing to believe that the

satisfaction derived from any particular good purchased

at a barber’s shop is quite close to the average amount

of satisfaction across all possible goods purchased at a

barber’s shop. it

These implicit assumptions aren’t quite as tenable

for consumer spending data for hospital and physician

services. Here the range of possible services is very

wide. The range suggests that a dollar’s worth of

hospital spending may be associated with varying

degrees of satisfaction, depending on exactly what the

dollar bought. For example, a dollar’s worth of

satisfaction may be quite large as part of a visit the

emergency room to have a tetanus shot, or very small,

as apart of a complicated operation. Thus, it is hard to

be content, as we are in the case of barbers’ services, to

argue that the “average” amount of satisfaction from a

dollar spent on hospital services is reasonably

representative of the satisfaction derived from a dollar

spent on any arbitrary good purchased at a hospital.’2

Beyond these reservations, applying consumer

demand theory to the medical services categories in the

national accounts violates a basic part of the paradigm.

In standard consumer theory, a good maybe a

substitute or complement with any other good,

however, it may not be both. Yet because medical

services data are largely shown by provider, aggregate

consumer spending on hospitals and physicians are

almost certainly both at the same time. For example,

physicians work in hospitals, but bill separately. Thus,

for an operation requiring a hospital stay, physicians

and hospitals are complements. However, there are

also cases in which a series of in-office visits can take

the place of a hospital stay; physicians and hospitals are

substitutes. Similar kinds of examples can be

constructed across all of the categories of national

income account health spending. These problems

appear to be unique to the medical services categories,

owing to the institutional arrangements that have

developed for paying for medical care. If barber shops

billed each patron for using a chair and electricity, etc.,

while the barber billed each patron for “haircutting,”

we would have a similar situation to that of hospitals

and physicians.

The implication of the last issue is exceptionally

serious for good estimates of the price, income and agestructure relationships that underpin the standard

consumer demand approach. Shifts in treatment

regimes across national income account categories will

make it impossible to estimate reliable price, income,

and age-structure relationships. To the extent that

empirical models rely on these parameters to help make

forecasts or are used in policy analysis, the analyses are

based on shaky ground.

The discussion of the last few paragraphs has been

somewhat abstract. However, the key points can be

made quite simply. For many services, national

accounts data substitute data measuring where

consumer spending occurred in the place of what was

actually bought. When the establishment sells a narrow

range of goods, this is not a particularly significant

problem. As the range of goods and their prices

widens, the approximation worsens progressively. The

range of services provided by hospitals, physicians, and

other medical care institutions is exceptionally wide, so

the approximation of provider spending to goods

spending needed for a proper application of consumer

demand theory is likely quite bad.

While some of these criticisms apply to almost all

consumer spending service accounts, two other features

of medical care spending make this categorization

problem especially serious. First, spending is an

extremely large part of the total consumer spending

bundle; about 17 percent in 1996. Thus, because the

health categories comprise a large portion of spending,

forecasts and scenarios based on poor estimates of price

and income elasticities will be influential in the overall

economic analysis. On a related note, because public

funding of medical spending accounts for about a third

of overall health care spending, and has an enormous

influence on the government budget, accurate forecasts

and robust estimates of the underlying demand

parameters are essential to good policy making.

Better Estimates of Medical Prices

It has become popular recently to point out the

limitations of the current medical price indexes in the

298
consumer price index measurement program. The most

findarnental issue facing the measurement of prices in

the health care sector is accounting for the changes in

the quality of the care provided. Conceptually, the

inflation-adjusted spending figures and prices that

should be the basis of price and income elasticities of

the consumer demand approach should be “qualityadjusted.” Consider how the Bureau of Labor Statistics

(BLS) would treat an innovation embodied in a new

car, such as more “bump resistant” bumpers. Because

this improves the car, the CPI for new cars would be

lowered slightly, so that each nominal dollar spent on a

new car would result in a slightly higher value for

“real” new car spending than would have been the case

without the adjustment. The BLS attempts to account

for the improved quality of the car. 13 In general, when

the “quality” of the good in question improves, we

expect that the price measures used to deflate the

nominal purchases (the only available data) will be

calculated in such a way as to make clear that the price

of a constant-quality good is lower.

Although measuring quality change for goods is

hard, measuring quality change for many sewices is

nearly impossible. For the medical services sector, the

task is made even more problematic because of the

tendency to view prices from the perspective of who is

providing services rather than what services are being

provided. Thus, there is a CPI for physician services

and another for hospital services, but not one for, say,

heart attacks. The major problem with even attempting

to measure quality change under the current structure of

the CPI (and NIA consumer spending) in the medical

services sector is that we would need to have some kind

of outcomes data. But hospitals and physicians could

be doing very different things over time, depending on

the disease incidence, the age-structure of the

population, and a host of other factors. Thus, the

“outcomes” from a dollar’s worth of spending in

hospitals would be nearly impossible to measure,

because it depends on what was done at the hospital. It

is, however, the latter kind of CPI -- CPI adjusted for

outcomes quality -- which is most interesting and

usefid from the point of view of consumer demand

theory .14

For many types of services the assumption of little

or no quality change is, to a first approximation,

probably appropriate. In the case of barbers, we do not

take the trouble to inquire about whether the “haircut’”

was successfid or not. We generally assume that they

are all successful (a haircut= a good haircut), or, more

generally, that the probability of a satisfactory haircut is

reasonably constant over time (90 percent of the

haircuts are good). Either is fine for time-series

analysis. But the probability of satisfaction from a

dollar spent on medical services of any type is probably

rising. At the very least, mortality data by disease

suggests this is so. IS For example, between 1968 and

1991, the age-sex-adjusted central death rate declined

from 1079 per hundred thousand to 778.9, a decline of

almost 25 percent. Looking at the ten major causes of

death. the first and third (Heart disease and Vascular

disease) showed annual percentage declines during this

period of 2.2 and 4.3 percent respectively. Only the

fifth and tenth leading causes (Respiratory and Other)

showed any significant increase. The result of these

improvements was that life expectancy at birth for

males increased by 5,3 years and by 4.7 years for

females. The data above offer the strong implication

that, generally speaking, the probability of any dollar

spent on hospital or physician services yielding

satisfaction has risen over time. This is nothing more

than saying that medical care, per dollar spent, is

getting better: an inflation-adjusted dollar spent on

hospital services for the treatment of a disease in 1990

has a greater probability of yielding satisfaction than a

dollar spent in 1980 on the same disease.

To recapitulate, we can state the five major points

in our argument.

1. Federal medical outlays depend on the consumer

demand for medical semices.

2. Comprehensive general equilibrium models --

those incorporating macroeconomic feedbacks, as well

as the relationships among industrial sectors in the

economy -- require a complete system of consumer

spending. This type of model relies on national

account data because it is complete and exhaustive of

consumer spending.

3. There are serious problems applying the standard

consumer demand paradigm to the existing national

accounts data for medical services. Conceptually. the

data by providers is not appropriate for consumer

demand theo~. In addition, even if the data were

categorized in away useful for consumer demand

theory, lack of quality adjustment makes it nearly

impossible to estimate good price and income

elasticities. Together, these problems compromise the

stability of estimated price and income elasticities for

health-care components.

4. Inappropriate income and price elasticities -- even

in a model that captures all relevant feedbacks -- lead to

poor forecasts of consumer demand for medical

299
services.

5. Poor forecasts of the consumer demand for medical

services can lead to poor forecasts of federal medical

services liabilities, and reduce the effectiveness of the

economy-wide approach to analyzing federal medical

services policy. We have concluded that our own

economy-wide approach -- based on NIPA data --

cannot be reasonably used to differentiate the separate

economic effects of medical price changes and the

quantity of medicai care used in the economy.

Another Approach to Accounting for Consumer

Health Spending

As mentioned above, the national account data for

consumer spending on health services focuses on

providers. In contrast, what consumers typically

demand is not hospital or physician services per se, but

treatment for a specific condition. The treatment path

can be quite short and limited to a single provider, like

a single trip to the general practitioner for an

immunization or an antibiotic. However, the treatment

path could be longer and much more complex, as in a

treatment requiring initial in-office physician visits for

diagnosis, an in-patient hospital stay for an operation, a

set of follow-up physician visits, and/or a stay in a

short-stay rehabilitation institution. To try to sidestep

many of the problems posed when the standard

consumer demand paradigm is applied to existing

medical services data in the national accounts, we can

try to recast the accounts by type of disease or

condition treated.

Looking at spending by condition treated rather

than by where the medical services dollar is spent

appears to resolve many of the issues discussed above.

For example, treatment for diseases of the circulatory

system is a fairly well-defined, if somewhat aggregated

consumer good to which standard consumer theory can

be applied. It is sufficiently well-defined to avoid the

problem of being a substitute and complement with any

other good simultaneously. In short, it is a “good” in a

way that none of the consumer spending categories for

medical services currently in the national accounts are.

Like Triplett (1997), we recommend developing a

matrix that maps health spending by provider into the

treatments that completely exhaust consumer medical

care spending (one category would likely be “all other

spending, not elsewhere classified.”). We would then

use the “treatments” data in the place of the currentlyused provider data to estimate a complete consumer

demand system. This approach would lead to estimates

of the income and price elasticities of treatment for

disease X, where X could be a very aggregated

category (diseases of the circulatory system) or very

disaggregated (acute myocardial infarction). These

elasticities would, in contrast to the currently estimated

price and income elasticities, be economically

meaningful.

Benefits of the “Treatments” Accounts

What benefits do we get from examining health

spending by disease categories rather than provider

categories? First and foremost, the approach should

yield more reliable estimates of important price and

income elasticities on which projections of consumer

medical spending are based. Thus, it is likely that

general equilibrium models would produce better (more

accurate) forecasts of consumer health spending and

Medicare liabilities than they currently can. The

policy-analysis potential of this treatments approach is

quite broad, allowing evaluation of alternative changes

in government subsidies of medical health demand by

the elderly. An additional advantage is that consumer

health spending can be disaggregated into far more

detailed goods than the current groupings of providers

(hospitals, nursing homes, physicians etc.)

Secondly, a treatments approach naturally

facilitates splitting the nominal data into its price and

quantity components. Estimating the quantity and

quality of treatments is a daunting task, but the

treatments approach at least provides an outlet and a

framework for incorporating current research about

treatment efficacy into the national accounts. Several

recent papers suggest that the conceptual and data

problems are manageable with a treatments approach,

although not easily so. Cutler et. al. (1996) discuss the

issues with respect to acute myocardial infarctions

(heart attacks), and show that after accounting for the

improvement in outcomes -- quality change -- the

“price” of treatment has fallen by about one percent a

year since 1984. The flip side is that the “quantity” of

heart attack treatment has risen much more rapidly than

had previously been thought.

The work of Cutler et. al. as well as those cited in

Graboyes (1994) has several important implications: (1)

Medicare spending has probably been more

300
“productive” than had previously been thought, (2) the

“true” cost of living with respect to medical care is

probably much lower than ofilcial estimates suggest,

and (3) measures of real economic activity like GDP

have probably been understated. The treatments

approach allows these implications to be incorporated

into the national accounts data.

The possibility that a different framework for

consumer spending accounts might lead to a different

view of the role of medical care in the economy raises

broader policy issues. Under the current situation,

without knowing the treatment composition of medical

consumption, it is difficult to account for the costs and

benefits of higher levels of medical spending. Thus it

is difficult even to partially answer the most basic

question posed in the early 1990’s health care reform

debates: what is the appropriate amount of health care

spending in the economy? Consider only Medicare

spending for the moment. When we are not sure what

treatments the elderly are demanding, it is hard to

design our policy to improve overall health with some

cost effectiveness. Without some definition of the

“quantity” of treatment and the cost per unit of

quantity, policy is reduced to the very indirect

expedient of targeting only the provider income flows -

- largely the current situation.

But targeting provider income flows can be a poor

mechanism for enhancing the effectiveness of Medicare

policy. The elderly obtain heart bypasses, hip

replacements and the like to treat their health problems.

These health goods are composed of different

combinations of inputs, like drugs, and providers’

services (physicians’ services, inpatient and outpatient

hospital services, nursing homes, and others).

Medicare does not cover all types of health care costs

equally. For example, Medicare does not typically

cover drugs purchased out of the hospital or nursing

home costs. Thus, when policy makers squeeze

Medicare outlays by provider, they are increasing the

direct costs facing consumers differentially across

treatments that require different mixes of covered and

not-covered provider services. Without knowing the

different provider combinations that produce different

health goods, prices of more cost-effective treatment

paths may be raised more than others. An interesting

corollary to this point is that programs that subsidize

some providers and not others are likely to induce a

number of distortions and be economically inefficient.

Focusing on provider payments without also

considering the services provided implicitly promotes

the notion that provider income can be reduced without

reducing the volume of services per beneficiary.

Generally, provider payments are made for services

rendered. The reduction in provider payments means

either the volume of services rendered will be reduced,

or the cost per service rendered will be reduced. Only if

Medicare providers were reaping economic rent (excess

profits), would the reduction in “provider payments”

not lead to a reduction in either the volume or quality of

services rendered. The idea that we can reduce

provider payments without affecting services is

inextricably bound up with the idea that Medicare

providers are earning “excess” income, which we can

reduce without changing their behavior.’s If that is not

true, then changing provider payments changes

provider behavior.

Whether cutting provider payments and services is

a good idea or not depends on an assessment of the

total costs and benefits of the Medicare program. In

other words, if the total costs of the last Medicare dollar

spent were less than the total benefits, then cutting

provider payments is not a good idea. The chief

difficulty with applying this test to Medicare is

developing estimates of the total costs and benefits.

Among the costs to be considered would be the effect

of spending on the federal deficit, and the consequent

effects on interest rates, the value of the dollar,

employment, and production. Also included would be

the forgone benefits of having not spent the dollar on

other programs. Among the benefits to be considered

would be reductions in mortality and morbidity, and,

the investment benefits that accompany learning by

doing. Some of these costs and benefits can be

determined only by looking at the medical sector;

others require a comprehensive, economy-wide view.

In either event, a better framework for evaluating the

medical services sector will provide both better

economy-wide analyses as well as analyses of the

medical care sector alone.

A crucial benefit of the treatments approach is that

it provides a natural conduit for considering disease

incidence, treatment costs, and treatment efficacy. For

forecasting purposes, projecting disease incidence by

age, the costs of each treatment, and the age structure of

the population would take center stage. 17 For example,

projections of a growing group of the very old should

correspond with projections of higher demand for

treatment paths in which nursing homes area very large

input. Thus, a consumer goods-based view of Medicare

spending might encourage a broader view of federal

medical policy. If it were important to reduce future

Medicare spending, we might begin by actually raising

current spending on targeted research at the disease

301

. .
treatment categories accounting for a high percentage

of outlays. Or policies could be designed to encourage

prevention or early detection. For example, diseases of

the circulato~ system accounted for 18 percent of

inpatient hospital Medicare reimbursements in 1995.

One policy might aim to reduce the incidence of such

disease through diet, exercise, and regular tests which

might result in early diagnosis and implementation of

preventive steps.

The benefit of having a consumer treatment

approach is to make these tradeoffs among disease

categories explicit because we will now be able to see

the flow of subsidies to different diseases and their

differing outcomes.. This hardly eases the job of the

policy maker. In fact, by explicitly acknowledging the

differences across consumer health goods categories,

policy making will probably get harder, because the

conflicting resource requirements across treatment

types will be explicit. However, it is important to

remember that these conflicts exist already; they are

simply more diff-icuh to see under the current data

treatment.

A Small Beginning

Recasting the accounts this way is not an

exceptionally novel idea (see Triplett (1997) for related

arguments) . Several researchers have distributed total

personal health care spending by disease/condition.

The seminal work by Cooper and Rice (1976) has

been extended by Hodgson (1984, 1997)). Table 1

shows the distribution of total U.S. personal health care

spending over 18 aggregated ICD-9-CM categories for

1980 and 1995. For a longer historical perspective,

data are also presented for 1972, but, since they use an

earlier ICD-7 coding system, the data are not strictly

comparable for all categories. It is also important to

note that the share of spending that was not allocated

varies across all three time periods. The major source

of unallocated costs comes from physicians and other

professional services and nursing home care.

The 18 ICD-9-CM categories show a fairly high

degree of concentration of spending and a high degree

of stability of spending shares and their trends over

time. Together, these five categories (Diseases of the

Nervous, Circulatory, and Digestive systems, Injury

and poisoning and Mental disorders) account for 55

percent of all spending, with individual shares ranging

from 8-to- 17 percent. Even a cursory look indicates

some interesting characteristics that suggest

amenability to forecasting.

Circulatory system diseases has the largest share

(16.9 percent in 1995) and the largest increase in share

( 14.5 percent in 1972) of any of the top five categories.

In 1980, the only year with separate over-65 data,

approximately two-thirds of spending on circulatory

diseases go to the over 65 population, accounting for

almost 30 percent of the total spending for this group.’8

On the other hand, the data for digestive diseases in

Table 1 show a different picture. Digestive diseases

accounted for a declining share of spending , from 14.8

percent in 1972 to 11.4 percent in 1995. Digestive

diseases are a somewhat special case, however, since

they are heavily influenced by trends in dental spending

(accounting for 48 percent of digestive-disease

spending in 1995). Only one-sixth of total spending on

digestive diseases was for the over-65 population. The

different age characteristics of circulatory and digestive

diseases suggest that spending growth for these

categories will be decidedly different as the Baby

Boomers age.

Relative price movements will also affect the

evolution of spending in these categories. Relative

prices are affected by productivity (technology) and

government subsidies. It is interesting to note that the

Medicare subsidy of physician and hospital services is

more important in circulatory diseases--where these

services account for 65 percent of spending--than

digestive diseases where these services account for 44

percent (see Table 2). The declining share of digestive

-disease spending over the last 15 years might be partly

explained by a relatively smaller Medicare subsidy for

the digestive-disease category.

Although there was some movement in nominal

spending shares reflecting other underlying factors, the

relative stability of shares during a period when

personal health spending had an annual growth rate of

nearly 10 percent is worth noting. Forecasting

consumer decisions requires that spending on disease

categories first be deflated to constant dollars. This

deflation, along with adjustment for demographic

changes, should allow unique, disease-specific trends to

emerge.

Table 2 displays input shares from the drug,

hospital, and physician industries for the 18 disease

treatment categories. The percentages accounted for by

302
these “inputs” vary greatly across treatment categories.

In 1995. hospitals accounted for only 20 percent of

total spending on diseases of the nervous system, but 65

percent of spending on neoplasms. Physicians played a

small role (10- 11 percent) for mental disorders and

diseases of the digestive system, but they accounted for

a higher share of spending in diseases of the nervous

system (32 percent) and the respiratory system (28.7

percent). Th~ share of drug inputs varied from 1.5 to

13 percent in 1995 and was substantially lower than in

1972 and 1980 for most categories. Between 1972 and

1995. drug input shares rose only for mental disorders

and diseases of the digestive system. About half the

categories had declining drug shares of 3-5 percentage

points. while in four categories, drug shares declined

by more than 10 percentage points. These declines are

what would be predicted by the structure of the

Medicare subsidy. Since spending on drugs is the leastsubsidized input. treatment programs using more

hospital and physician inputs present lower relative

prices to the consumer. Since some inputs are more

amenable to technological change (i.e. drugs) this type

of distortion may be having unintended dynamic

consequences.

Where Do We Go From Here?

While this preliminary picture is interesting, we

clearly have a long way to go in the development of

consumer health goods accounts. There are two

directions that can be simultaneously pursued. First,

we can setup a set of accounts for 1992 through 1995

making use of the large amount of data that are already

available. A key data source for this recent data on the

over-65 population is HCFA’S Medicare Current

Beneficiary Survey (MCBS). This is a rich data source

that was not available to researchers constructing the

previous data on spending by disease. Then we could

apply the Hodgson methodology, data from the

National Center for Health Statistics’ Hospital

Discharge Survey, and the National Health Accounts

produced by HCFA to develop estimates of spending

by disease treatment for the under-65 population.

Coincident with the development of nominal accounts

would be work to begin developing price deflators for

disease categories. One very important question is

which disease categorization scheme to use. Medicare

data are available at both the DRG (diagnosis related

group) level, or the ICD-9 groupings. Data for the nonelderiy, however, are available only under the ICD-9

definitions. The level of disaggregation for the disease

categories is also important. Fortunately, if the healthconsumption accounts are built from micro files, there

is a considerable amount of disaggregation available.

A more immediate route for developing diseasetreatment accounts would simply begin from the three

available data points shown here. Rough estimates of

deflators could be created using already existing studies

where they are available. These preliminary time series

would be used in a complete consumer demand system

to estimate price and income elasticities and to forecast

consumer health spending. The resulting income and

price elasticities could then be compared with studies

using the currently available NIPA data. While only a

first approximation, these elasticity estimates and

forecasts would be quite interesting in themselves and

help determine whether there is indeed an empirical

reason to more carefully develop other historical

series.

Conclusion

In this paper, we have argued that if a traditional

consumer demand approach is to be used to estimate

key parameters for forecasting and policy analysis, the

current NIPA data structure for health care spending

will have to be changed. The issues involved appear to

be: (1) the current set of accounts do not measure

“goods”, but where spending occurs, and (2) that focus

makes it difllcult to adequately adjust for quality in

medical care treatment. We have noted that other

analysts have developed a disease-treatment data set

which can serve as the basis for ongoing work, and

provide the examples to develop a richer data

environment. We believe that a disease-treatment

approach in the NIPA would tie together several

disparate strands of work on disease incidence, quality

of treatment, consumer health care spending, and

federal budget policy analysis. and improve the

underlying data structures on which federal medical

care policy rest.

The issues of how health spending should grow is

critically important not only to policy makers, but to

the general public. How much life expectancy can

continue to be increased and at what cost are essential

pieces of information for the public to make informed

decisions about how much to spend. Ultimately, the

question of whether to build health consumption

accounts rests on their potential contribution to these

issues.

End notes

1. This work was partially supported by HCFA

Contract 500-93-0007. We gratefully acknowledge

HCFA’S financial support as well as helpful comments

from Katie Levit, Mark Freeland and Art Sensenig of

303
the Office of National Health Statistics, HCFA and

Margaret McCarthy of Inforum. This paper does not

represent the position of the Health Care Financing

Administration. Any errors are ours.

2. Board of Trustees, Federal Hospital Insurance

Trust Fund (1997), p 30.

3. Board of Trustees, Federal Hospital Insurance

Trust Fund (1997), p. 52

q. A clear, simple source explaining Medicare

reimbursement is Waid ( 1997).

5. Monaco (1997) contains a description of the

model and a comparative discussion of this model type,

known as an interindustry-macro (IM) model, macro

and AGE-CGE models.

6. This is a caricature of consumer demand theory.

See any undergraduate intermediate macroeconomics

textbook for a more rigorous treatment, like Nicholson

(1988) or Pindyck and Rubinfeld (1989).

7. The standard data source is the Survey of Current

Business. Detailed annual consumer spending tables

usually appear in the issues published just after annual

revisions. The nominal data appear in NIPA Table 2.4;

“inflation-adjusted” data are shown in billions of chainweighted 1992 dollars appear in table 2.5. The most

recent annual numbers appear in the July 1997 issue of

the Survey. Data are also available through the Internet

(http://www.bea.com/).

8. In “benchmark” years this is true. In other years,

and for quarterly data, consumer spending at the

published detail level is “moved forward” by the

movements in closely related data series that are

available in a more timely fashion, like retail sales data

9. This information is taken from Personal

Consumption Expenditures, Methodology Papers: U.S.

National Income and Product Accounts (June 1990), p

23.

10. See The Boskin Commission Report and other

papers on the overstatement of the CPI.

11. As a corollary, we are willing to believe that the

average “price” of all possible goods purchased at a

barber’s shop is reasonably representative of all prices

of all possible goods purchased at a barber’s shop.

12. Likewise, it is hard to argue that the average

“price” for hospital services is representative of the

price of any arbitrary good purchased at a hospital.

13. There has been a veritable explosion of research

and commentary on measuring quality change in the

CPI, partially brought on by the findings of the Boskin

Commission. Some key papers on the issue include

Moulton (1996).

14. It is interesting to note that producer price indexes

for hospitals have recently been developed that attempt

to price “treatments” as opposed to “room charges” as

is done in the current CPI. The inflation rate for

hospitals using the PPI measure is substantially lower

than that using the CPI measure.

15. SSA Population Area Projections: 1996,

Actuarial Study #110 p. 8-18.

16. This is the definition of economic rent.

17. Some of this work is already being done (see

OASDI Actuarial Note 107).

18. Hodgson (1984) developed a separate

dissaggregation for people over age 65 for 1980, which

will also soon be available for 1995, but the basic idea

is amply illustrated with these numbers.

References

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Insurance and Disability Insurance Trust Funds

(1997) 1997 Annual Report of the Board of

Trustees of the Fe&ral Olc-Age and Survivors

Insurance and Disability Insurance Trwt Funds.

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304
Cooper, Barbara S. and Dorothy P. Rice (1976).

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Cutler, David M., Mark McClellan, Joseph P.

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Data: A User’s Gui&. 3rd Ed., Federal Reserve

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1980,” Health Care Financing Review. Volume

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Expenditures, 1994, ” Health Care Financing

Review, 17 (3), pp.205-242.

McCarthy, Margaret B. (1991), “LIFT: INFORUM’s

Model of the U.S. Economy, ” Econonu”c Systems

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Price Index: What is the Evidence?” Journal of

Econonu”c Perspectives, 10 (4), Fall, pp. 159-178.

Momco, Ralph M. (1997), “A Brief Review of

Alternative Approaches to Inter-Sectoral Policy

Analysis and Forecasting. ” Unpublished

manuscript.

Fe&ral Forecasters Conference-1994. NCES 97-

341, PP 235-248.

Monaco, Ralph M., and John Phelps (1995), “Health

Care Prices, The Federal Budget, And Economic

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Dryden Press, Fort Worth.

Pindyck, Robert S., and Daniel L. Rubinfeld. (1989)

Microecononu”cs. Macmillan Publishing

Company, New York.

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Actuary , ‘Social Security Area Population

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Triplett, Jack E. (1997), What’s Different about

Health? Human Repair and Car Repair in National

Accounts,” Unpublished manuscript.

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and Medicaid, ” available at Internet site

http: //www.hcfa.gov/

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Ten Years: The Need to Lmok Further,” in U.S.

Department of Education, National Center for

Educational Statistics. me Eighth Fe&ral

Forecasters Conference-1996 and me Seventh

305
Distribution of Personal Health Care Expenditures by Major

Disease Category (ICD-9-CM), percent of health spending

Major Disease Categories

Infectious and parasitic diseases

Neoplasms

Endocrine, Nutritional and metabolic diseases

Diseases of the blood and blood forming organs

Mental disorders

Diseases of the nervous system and sense organs

Diseases of the Circulatory system

Diseases of the respiratory system

Diseases of the digestive system

Diseases of the genitourinary system

Complications of pregnancy, childbirth and the puerperium

Diseases of the skin and subcutaneous tissue

Diseases of the Musculoskeletal system

Congenital anomalies

Injury and poisoning

Certain conditions originating in the perinatal period

Symptoms, signs and ill defined conditions

Supplementary classifications

Other

Unallocated

CODE

001-139

140-239

240-279

280-289

290-319

320-389

390-459

460-519

520-579

580-629

630-676

680-709

710-739

740-759

800-999

760-779

780-799

v() 1-v82

1972

1.9

5.1

4.6

0.7

9.3

7.9

14.5

7.9

14.8

5.9

3.5

2.0

4.8

0.5

6.8

9.8

16.8

1980

2.1

6.2

3.5

0.5

9.3

8.0

15.1

7.9

14.5

6.0

2.8

6.2

0.6

8.8

1.8

1.0

5.6

1995

2.2

5.4

4.3

0.6

9.5

8.4

16.9

7.8

11.4

4.8

0.5

2.4

6.4

0.6

9.1

0.4

3.0

6.3

12.0

Sources: Cooper and Rice (1976), Hodgson ( 1984), and Hodgson (forthcoming).

306
Hospital, Physician and Dmg Shares of Spending

HOSPITAL SHARE PHYSICIAN SHARE DRUG SHARE

hdajodheasefategories

TOTAL

Infectious and pamsitic diseases

Neoplasms

Endocrine, Nutritional and metabolic diseases

Diseases of the blood and blood forming organs

Mental disorders

Diseases of the nemous system and sense organs

Dkeases of the Circulatory system

Diseases of the mpiratory system

Diseases of the digestive system

Diseases of the genitourinary system

CODE

001-139

140-239

240-279

280-289

290-319

320-389

390459

460-519

520-579

580429

1972

45

47

76

27

46

75

17

48

42

36

65

Complications of pregnancy. childbirth and the 630-676 89

pueq)erium

Diseases of the skin and subcutaneous tissue 680-709 32

Diseases of the Musculoskeletai system 710-739 46

Congenital anomalies 740-759 82

Injury and poisoning 800-999 61

Certain conditions originating in the perinatal period 760-779

Symptoms, signs and ill defined conditions 780-799

Supplementary classifications vO1-V82

Unallocated II

1980

46

48

67

44

62

63

25

50

49

36

57

26

46

65

60

43

78

I 995

45.8

53.4

65.5

43.3

54

57.8

20.1

50.8

50.5

32

47.5

59.7

34. I

40.8

54. I

56.3

75.6

32.7

43.6

1972

22

24

14

38

31

10

22

15

31

8

24

6

43

21

12

24

58

1980

21

34

23

27

26

10

26

18

31

12

32

49

28

27

25

37

17

I 995

23.5

27.8

22.1

20.8

20.7

10.4

32.7

14.2

28.7

11.4

29.8

37.9

36.5

27.3

25.2

27.1

15.1

36. I

49

I 772 . 1980

II 9

14 18

5 5

25 13

16 II

6 5

10 12

12 7

25 17

4 3

13 II

3

23 24

12 11

2 7

7 10

20

17 5

I 995

7

10.7

2.2

13.1

3.3

8.1

5.2

8.8

12.4

5.4

7.5

1.6

12.4

8. I

0.4

1.4

0.1

8.3

;.7

SoUnxs: Cooper and Rice ( i 976), Hodgson ( 1984), and Hodgson (forthcoming).