Statistical Needs for a Changing U.S.

Economy

September 1989

NTIS order #PB90-126954
Recommended Citation:

U.S. Congress, Office of Technology Assessment, Statistical Needs for a Changing U.S.

Economy—Background Paper, OTA-BP-E-58 (Washington, DC: U.S. Government Printing

Office, September 1989).

Library of Congress Catalog Card Number 89-600753

For sale by the Superintendent of Documents

U.S. Government Printing Office, Washington, DC 20402-9325

(Order form can be found in the back of this report.)
Foreword

In 1988 the Office of Technology Assessment published Technology and the American

Economic Transition outlining ways that new technologies have redefined options for

stimulating economic growth, Several Committees of the Congress had asked OTA to step

back from its analysis of specific technologies and describe the combined effect of the changes

on the living standards of different American households, on jobs, and on America’s position

in the world economy. These questions required OTA to address some very basic questions

about the way the economy operates and could operate in the future. The research made

extensive use of statistical series from many private and public sources. In many cases

standard statistical measures failed to indicate important dimensions of change. Improvements

in choice and quality in areas like recreation were often unmeasured while increased spending

for health often did not represent an increase in the quality of the amenity purchased.

The Subcommittee on Government Information and Regulation of the Senate Committee

on Governmental Affairs asked OTA to use the experience gained in the transition study to

provide a perspective on areas where better data would improve economic policy analysis. We

found that many dimensions of growth and change were not well tracked by the existing

statistical system. The problems are greatest where change is most evident: the introduction

of radically new technologies—like computers and telecommunications equipment, the

impact of international trade on the domestic economy, the value of education and training as

an input to industry, techniques for evaluating the quality of heath-care and other services. In

virtually all cases, the statistical agencies are aware of the problems and are making efforts

to correct them. We find, however, that their efforts are hampered by a lack of effective

coordination and management-a role that the Office of Management and Budget has the

authority to oversee.

Some of the problems identified in this study are old issues that recent events have made

much more important, such as tracking the effects of international trade or finding ways to

adjust service expenditures for inflation. Many of the problems are forced on us by technical

change, evident in the difficulty associated with tracking quality changes in computers and

other information equipment. In many cases the problems we identify have no easy resolution

and the Nation will need to face the fact that uncertainties in key areas exist and, in some cases,

are increasing. We also point out some long-standing problems that should not be forgotten.

For example, there has never been a coordinated way to report statistics on the quality of life

in America.

The background paper does not attempt to provide a comprehensive critique of national

statistics and does not introduce new research designed to solve the technical problems. It is,

instead, designed to show how defects in the existing statistical system can limit our

understanding of key economic issues and to demonstrate the ways that better management

and coordination of America’s statistical agencies can lead to concrete improvements.

OTA acknowledges the generous help of the reviewers and contributors who gave their

time to ensure the accuracy and completeness of this report. OTA, however, remains solely

responsible for the contents of this background paper.

IUs cmge~~, office of ~~o]og As=sment, Technology and the American Economic Tradition ” choices for t~

F~e;OTA-TEr-283 (Washington, DC: U.S. Government Printing Office, May 1988).

iii
OTA Project Staff

Lionel S. Johns, Assistant Director, OTA

Energy, Materials, and International Security Division

Peter D. Blair, Energy and Materials Program Manager

Henry Kelly, Project Director1

Andrew Wyckoff, Analyst

Administrative Staff

Lillian Chapman Linda Long Phyllis Brumfield
Reviewers and Contributors

Catherine Anmington

Consultant

Martin N. Baily

The Brookings Institution

Mark Baribeau

John Hancock Financial Services

Anthony J. Barkume

Bureau of Labor Statistics

U.S. Department of Labor

James Bonnen

Michigan State University

Edwin R. Dean

Bureau of Labor Statistics

U.S. Department of Labor

Edward Denisen

The Brookings Institution

Faye Duchin

Institute for Economic Analysis

New York University

Paula Duggan

Northeast-Midwest Institute

Joe Duncan

Dun & Bradstreet Corp.

Maria Eli

Coalition of Service Industries

Audrey Freeman

The Conference Board

Norman Frumkin

Consultant

Norman Glickman

Center for Urban Policy Research

Rutgers University

Edward Goldfield

Committee on National Statistics

National Academy of Sciences

Gordon Green

Bureau of Census

U.S. Department of Commerce

Hermann Havermann

Office of Information and

Regulatory Affairs

Office of Management and Budget

Roger Herriot

Bureau of Census

U.S. Department of Commerce

Chuck Hulten

American Enterprise Institute

Philip Israilevich

Federal Reserve Bank of Chicago

Sidney Jones

Department of the Treasury

Thomas Juster

University of Michigan

Arnold Packer

Hudson Institute

Rick Kasten

Congressional Budget Office

Charles Leedman

Organization for Economic

Cooperation and Development

Linda LeGande

Congressional Research Service

Dan Luria

Industrial Technology Institute

Sid Marcus

Bureau of the Census

U.S. Department of Commerce

Larry Mishel

The Economic Policy Institute

J.R. Norsworthy

Rensselaer Polytechnic Institute

Robert Parker

Bureau of Economic Analysis

U.S. Department of Commerce

Bruce Phillips

The Small Business Administration

Karen Polenske

Massachusetts Institute

of Technology

Mellisa Pollak

National Science Foundation

Barry A. Rappaport

Bureau of the Census

U.S. Department of Commerce
Katrina Reut

Bureau of Labor Statistics

U.S. Department of Labor

Gordon Richards

National Association of Manufacturers

Marc Ross

University of Michigan

Isabel Sawhill

Urban Institute

Courtney Slater

Slater Hall Information Products

Thomas R. Tibbetts

Bureau of Labor Statistics

U.S. Department of Labor

Raymond Vernon

Harvard University

Louisa Waldstein

Klein & Co.

Katherine Wallman

Council of Professional Associations

on Federal Statistics

Bruce Walter

Bureau of the Census

U.S. Department of Commerce

Amy Walton

Jet Propulsion Laboratory

David Weil

Klein & Co.

Margaret Wigglesworth

Coalition of Service Industries

Roberton Williams

Congressional Budget Office

Gaylord Worden

Bureau of the Census

U.S. Department of Commerce

Allan H. Young

Bureau of Economic Analysis

U.S. Department of Commerce

NOTE: OTA is grateful for the valuable assistance and thoughtful critiques provided by the reviewers and conrnbutors. The views

expressed in this OTA background paper, however, are the sole responsibility of the Office of Technology Assessment.

vi
Contents

Page

Part I: Overview of the Federal Statistical System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

ANSWERS TO BASIC QUESTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

STATISTICS FOR A CHANGING ECONOMY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Part II: How Well Can We Answer the Basic Questions? . . . . . . . . . . . . . . . . . . . . . . . . 7

A. How Rapidly is the U.S. Economy Growing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

B. Which Businesses Are Responsible for Growth and Has Growth in the Complexity of the Networks Connecting Different Kinds of Businesses Changed the

Interdependence of Businesses? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

C. What Is the Impact of Intenational Trade undomestic Producers

and Consumers? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

D. What Capital and Labor Inputs Are Purchased by Domestic Producers? . . . . . . . 25

E. How Productively Do Domestic Producers Use Inputs? . . . . . . . . . . . . . . . . . . . . . 28

F. How Has the Corporate Structure of the U.S. Economy Changed? . . . . . . . . . . . . 31

G. How Does Growth Affect Incomes and Income Distribution? . . . . . . . . . . . . . . . . 34

Part III: Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Boxes

Box Page

A. The Impact of One Economic Statistic: The CPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

B. The National Income and Product Accounts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

C. Deflator Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

D. Classifying Businesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

E. Japan’s International Input/Output Model Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Figures

Figure Page

l. Index of GNP Growth and the Use of Energy and Materials in the United States . . . . . . 8

2. Products Carried Per Supermarket. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3. Shares of GNP in Constant 1982 Dollars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

. 4. Exports and Imports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5. Imports Used to Produce Amenity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

6. Investment in Information Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

7. Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

8. Mergers and Takeovers, 1972-1986 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Table

Table Page

l. Direct Funding for Major Statistical Programs Fiscal Year 1989 . . . . . . . . . . . . . . . . . . . . 2

2. Distribution of the GNP in 1987 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3. BLS Deflator Series for Gross Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Overview

Part I

of the Federal Statistical System

Good public policy demands good information.

There may be disagreement about the wisdom of

different Federal programs but there is little dispute

over the need for adequate data to inform the debate.

The information generated by the $2 billion spent

this year by Federal agencies on statistical programs

is a key resource for government policy makers as

well as for private investors, public interest groups,

academic researched, and labor organizations (table

1).1 Government statistics play a key role in evaluating and implementing legislation and are often used

as indexes in private contracts (see box A).2 Table 1

does not include a large, hidden cost of national

statistics: the time invested by the individuals and

businesses that provide the basic data. These costs

obviously must be carefully considered in reviewing

any proposed changes in statistical efforts.

U.S. national statistics are acknowledged to be

among the best in the world. But the U.S. economy

is changing in ways that make documenting economic performance much more difficult. Business

success today rests heavily on efficient management

of new technologies and a grasp of the international

marketplace. Competitiveness relies on quality,

timeliness, and sensitivity to diverse markets. The

most important inputs purchased by a business may

be research and engineering information and the

skills and education of its employees. Many of these

factors are extremely difficult to measure.

The new dimensions of growth and change have

also challenged traditional approaches to economic

growth policy. Policies that may have effectively

encouraged growth in an era of little international

trade may be ineffective or even counterproductive

in today’s global economy. Economic policy will

require the best possible measurement of the factors

critical for growth and an awareness of areas where

uncertainty prevails. Serving the new needs of

policymakers in a time of change will require a

coordinated response of the Nation’s statistical

agencies. The present management of the statistical

agencies makes such a response difficult.

The fault does not lie primarily in the management of individual statistical agencies. These organizations are painfully aware of the problems. The

greatest problem appears to be the absence of any

central place in government where basic questions

about priorities in statistics are being asked, and the

lack of effective coordination among statistical

agencies.

ANSWERS TO BASIC QUESTIONS

The most basic questions of economic policy will

endure, regardless of the transformations that affect

the economy. We will always need to monitor

changes in American living standards, determine

whether access to the benefits of economic growth

are more or less evenly shared, and estimate how the

United States compares with other countries. But

economic change has made it difficult to address

even these fundamental questions with precision.

This paper examines eight basic questions and our

ability to answer them with currently available

statistics:

A.

B.

c.

D.

E.

F.

How rapidly is the U.S. economy growing?

Which businesses are responsible for this

growth, and has growth in the complexity of

the networks connecting different kinds of

businesses changed the interdependence of

businesses?

What is the impact of international trade on

domestic producers, workers, and consumers?

What capital and labor inputs are purchased

by domestic producers?

How productively do domestic producers use

inputs?

How does the way U.S. businesses are organized affect economic growth? (i.e., whatare

the relative contributions of different sized

establishments and firms? what is the effect of

mergers and acquisitions?)

I It sho~d bC no~ that the btiget for fiscal year 1989 is half-a-billion dollars larger than usual because of expenditures for the decennial Casm.

See table 1.

ZFor exmple, ~ fi=~ yew 1984, 87 ~ent of all F~er~ grants-in-aid to State and local governments were distributed by form~a us~g F~er~

statistics, compared with an estimated two-thirds in fiscal 1975. See U.S. Congress, General &counting Office, Granf Formufas A Carafog of Federal

Aid to States and bca/ities, GAO/HRD-87-28 (Washington, DC: U.S. Government Printing Office March 1987), p. 10. In terms of contracts, 2(X) billion

to 300 billion dollars’ worth of contracts are keyed to the Producer Price Index (PPI) through inflation adjustment clauses.

–l–
2

Table 1—Direct Funding for Major Statistical

Programs Fiscal Year 1989 (estimate)

Millions of dollars

Commerce . . . . . . . . . . . . . . . . . . . . . . . .

Census . . . . . . . . . . . . . . . . . . . . . . . . .

Decennial . . . . . . . . . . . . . . . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . .

Bureau of Economic Analysis . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . . .

HAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Centers for Disease Control . . . . . . . .

National Cancer Institute . . . . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . . .

Agriculture . . . . . . . . . . . . . . . . . . . . . . . .

Soil Conservation Service . . . . . . . . . .

National Agricultural Statistics

service . . . . . . . . . . . . . . . . . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . . . .

Labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bureau of Labor Statistics . . . . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . . .

Interior . . . . . . . . . . . . . . . . . . . . . . . . . . . .

U.S. Geological Service . . . . . . . . . . .

Other . . . . . . . .. . . . . . . . . . . . . . .

Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Environmental Protection Agency . . . . .

Education . . . . . . . . . . . . . . . . . . . . . . . . .

Transportation . . . . . . . . . . . . . . . . . . . . .

Justice . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Treasury . . . . . . . . . . . . . . . . . . . . . . . . . .

HUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Defense . . . . . . . . . . . . . . . . . . . . . . . . . . .

Other agenciesa. . . . . . . . . . . . . . . . . . . .

Total . . . . . . . . . . . . . . . . . . . . . . . . . . .

Total without decennial census . . . . .

$ 705.8

646.2

434.8

211.4

24.7

34.9

290.5

129,0

51.0

110.5

242.3

83.1

64.1

95.1

226.5

190.4

36.1

119.5

78.9

40.6

90.8

69.3

36.1

32.7

30.4

28.2

14.1

8.3

85.7

$1,979.8

$1,545.0

aAgenoy for Mematiinal Development, Consumer Produet Safety Commission, Equal Employment Opportunity Commission, Federal Emergency ManagementAgenoy, Federai Home Losn Bank Board, National

#bronautiea and Space Administration, National Science Foundation,

Small Businese Administration, National Seienoe Foundation.

SOURCE: Office of Management and Budge~”Statiitical Programs of the

UnitedStatesGovemmenL” 1999.

G.

H.

How does growth affect incomes and income

distribution?

How does growth translate into real improvements in living standards such as better health,

an increase in real choice and quality of

products, or rewarding employment opportunities?

Part II of this report examines the data and the

statistical apparatus that is in place to answer these

questions endpoints out a number of deficiencies.

The last question is not addressed as a separate topic

but is touched on throughout. A proper way to

address this question remains a major challenge for

all statistical work.

The material that follows reviews some of the

administrative problems that have contributed to the

problem. In many cases, of course, the statistical

agencies recognize the problems but there are no

easy answers (it is much easier for a report like this

one to identify faults than to suggest concrete

remedies). Better management can not guarantee

improvements but can make the search for solutions

more productive, and make better use of existing

resources.

It is OTA’s hope that this report can provide

guidelines for pursuing important improvements in

statistical data gathering and analysis, providing a

better understanding of the American economy.

STATISTICS FOR A CHANGING

ECONOMY

While the decentralized system of statistical

collection and analysis in the Federal Government

has many strengths, the system suffers from the

absence of any central organization able to develop

a coherent strategy for adjusting to the challenges

presented by today’s economy. The Office of

Information and Regulatory Affairs of the Office of

Management and Budget was charged with establishing statistical policy and coordinating statistical

efforts in the United States in the 1980 Paperwork

Reduction Act.3 It has not performed this role

effectively. 4 While individual statistical agencies

have made efforts to work together and solicit the

opinions of data users, the absence of effective OMB

leadership has left critical gaps. There is no national,

systematic effort to articulate priorities in statistics

and match budgets to these priorities, to anticipate

future needs, to translate the complex and often

conflicting objectives of data consumers into a

practical set of tasks, or to ensure that the work of

individual statistical agencies is adequately coordinated.

In particular:

. There is a pressing need for an organization

where fundamental statistical priorities are

s~bli~ L~~ 96-511.

4A nm~ of ~r ~vlews have exmin~ deficie~ies in OMB’S management of the statistical agencies. See, for example, The Federul Startiticaf

System: ]980 rQ 1985, a report prepared by the Baseline Data Corp. for the Congressional Research Sewice, November 1984, pp. 46-67.
Box A—The Impact of One Economic

Statistic: The CPI

The Consumer Price Index (CPI) measures the

increase or decrease in the total price of a set of

goods and services (a “market basket”) representative of a consumer’s purchases. Constructed as an

indicator of inflation, the CPI has a direct effect on

nearly every citizen in the United States. At least 8.5

million workers are covered by collective bargaining contracts that link wage rates to changes in the

CPI. The payments made to 38 million Social

Security beneficiaries, 3.5 million retired military

and civil service employees and survivors, 20

million food stamp recipients, and 23 million

children who eat lunch at school are also linked to

the CPI by law. The Economic Recovery Tax Act

of 1981 uses the CPI to prevent inflation-induced

tax rate increases (bracket creep).l All told, the

Bureau of Labor Statistics estimates that an increase

of one percentage point in the CPI could add nearly

$5 billion to the Federal budget.2

IU.S. DqJ art.mem of Labor, Bureau of Labor Statics, BLS

Handbook of Methods, Butletm 2134-2, April 1964, pp. 5-6.

%hlculatcd by the Ot%ce of Mmagement and Budget for fiscal year

1986. see Estimorty of the Honorable Janet Norwood before the

Sutxmmittec m Govcrnrnen I Irt.formatim and Regulatiq Cornrnittex

on Govcmmcttd Affairs, U.S. Senate, June 12, 1969, p. 2.

periodically reexamined in light of the new

needs of public and business analysts. The

continuing underemphasis on service industries is a clear symptom of this problem. Hard

pressed by the demands of mandated publication schedules, the statistical agencies have

little time and few resources to do basic

research or ask hard questions about priorities.

This problem has been exacerbated by budget

cuts. While it has a mandate to perform this

task, OMB has not given it much priority and

has dedicated few resources to the effort.

● An effort is needed to evaluate whether statistical efforts match the significance of the problem. For historical reasons, the U.S. Department of Agriculture spends 6.7 times more on

statistics than the U.S. Department of Education. It seems unlikely that this is the right ratio

●

given the transformation underway in the

economy. Even worse, no organization in either

the executive branch or the Congress has

assumed responsibility for asking whether it is

the right ratio.

The work of the statistical agencies should be

more closely coordinated. Much of the output

of statistical agencies depends on careful coordination of work in several different agencies.

The Bureau of Economic Analysis (BEA), for

example, takes price indices from the Bureau of

Labor Statistics (BLS), and incorporates data

from the Bureau of the Census to develop data

about inflation that is, in turn, used by BLS to

develop productivity series. Such networks

must respond to new priorities in carefully

coordinated ways. Budget reductions in one

agency can have complex effects on the performance of the integrated system. A clear view

of the integrated needs of the statistical agencies is essential if either OMB or the Congress

is to make well-informed judgments about

budget priorities. A coordinated approach to

analysis of the burden statistics impose on users

might also reveal ways to produce better data

without increasing the burden on respondents.

Coordination also requires difficult judgments about how to handle confidential data.

Opportunities for using available data without

compromising confidentiality may be missed

because clearances are not well managed, For

example, the BEA staff is not cleared for access

to confidential Census data. With adequate

management it may be possible to develop

tables that facilitate linking data series (e.g., by

providing aggregate statistics about employment by establishment size classification) or

provide for direct links that do not compromise

confidentiality.

Many organizations collect statistics for

specialized regulatory programs. They have no

mandate to contribute to a coherent national

statistical program. For example, elimination

of Civil Aeronautics Board or the Interstate

Commerce Commission data occurred without

the realization that the data provided key

5~~~ony of ~~ay sl~~ ~fo~ ~ JO~[ ~~jc c~i[~, U.S. WWCSS, ~~g Qualify Oflhe ~~h~’$ Ecottornk Slutiszics, ~CW@

before the Joint Economic Committee, Mar. 17 and Apr. 17, 1986, p. 50; and U.S. Congress, GeneraJ Accounting Office, The Bureau of Ecortom”c

Anaiysis Should Lad Eflorts To /wq)rove GNP Estimate, GAO/GGD-83-l (Washington, DC: U.S. Government Printing Office, k. 27, 1982), pp.

58-61.
4

●

●

information to BEA’s income and product

accounts.5 There is reason to suspect that these

organizations and nonstatistical operations like

OMB’s own Office of Federal Contracts and

Procurement, the General Services Administration (for data about Federal purchases), and the

Internal Revenue Service (IRS) could provide

valuable data at comparatively little incremental cost if some care were taken to achieve

government-wide efficiencies.

The task of coordinating agency statistical

work was also assigned to OMB by the 1980

Paperwork Reduction Act. Again, the task has

not been given a high priority.

Improved management could make the production of data more efficient and faster. A

coordinated set of industrial codes, use of a

common business list as a sampling frame (i.e.,

the Business Directory List), and more diligent

efforts to use data improvements emanating

from other agencies (e.g., BEA’s use of BLS

deflators in international trade and selected

services industries) could improve data quality

while possibly lowering costs.

There is no formal mechanism that ensures that

the needs of consumers of national statistics

are reflected in the priorities of the national

statistical system taken as a whole. Consumers

of data frequently are often forced to work

around deficiencies in statistics making heroic

assumptions in order to satisfy the pressing

demands for policy or business analysis. They

may be forced to use data that are a decade out

of date or rely on private databases that were

not designed for sensitive policy work. The

creation of an institutionalized feedback loop,

connecting consumers to producers, would

make the statistical agencies more aware of

deficiencies in the data they produce. The

report prepared for Economic Policy Council in

1987 by Wendy Gramm and Robert Ortner

●

●

suggested the creation of such a system but

little has been accomplished.6

In its absence, the BEA, the Bureau of

Census and other statistical agencies assemble

advisory groups, but in many cases the complexity of the Federal statistical effort makes it

difficult for data users to translate their needs

into specific recommendations for individual

agencies.7 The feasibility or the cost consequences of different priorities are not easily

estimated. With budget constraints, a compromise must be struck between forcing policy

analysts to use data that may be many years old

or using data that is published comparatively

rapidly but might not be as complete or detailed

as other users would like.8 These and other

trade-offs are difficult to address at the level of

individual agencies. A government-wide perspective is needed.

Better use of modern computational and communication equipment would contribute to

productivity, The computational systems available to BEA, BLS, and other major statistical

services appear to lag far behind the systems

available to many o the business service

industries that rely heavily on government data.

Few Federal agencies have adequate distributed computer workstations or state-of-the-art

local area networks. This is a major barrier to

productivity (and perhaps to attracting people

who expect to be able to use modem equipment).

The growing interdependence of economies

around the world has increased the need for

international cooperative efforts in statistical

work. Greater efforts are needed to coordinate

U.S. and foreign data, and to identify areas

where cooperative research projects in statistical methods would be beneficial. Cooperative

efforts are most obviously needed in the

statistics of international trade. The United

%st.imony of Courtenay Slatcr before the Joint Economic Committee. U.S. Congress, The Qucafity of the Natiwt’s Economic Stutisrics, Hearings

before the Joint Economic Committee, Mar. 17 and Apr. i7. 1986, p. 50; and U.S. Congress, General Accounting Office, The Bureau of Economic

Anafysis Should Lead Eflorts To hnprove GNP Estimate, GAO/GGD-83-l (Washington, DC: U.S. Government Printing Office, Dec. 27, 1982), pp.

58-61.

6s= ~c c$R~ ~the Wofing Group on ~@~lty of ~onomic Sttistics” to tie &onomic Policy Council chaired by Wendy Gramm ~d Rokt

Ormer, April 1987, p. 8.

TSi&ey L. JOB, “staying on ‘l@ of the Numbers,” The Broo&ings Review, Spling 1988, p. 38.

8~ isw~g of @fiinWda~ ~~d in ~cn~l ~ ongoing ~vi~~ p~ess m ~dit)onal data become available. Such MI effort woldd involve

an expansion of the current program’s resources.
5

●

Nations has already taken a number of steps to

coordinate statistical methods and categories.

For example, a new U.N. group is working on

international service sector statistics and the

harmonized commodity classification system

is being implemented. But more can be done.

Many of the problems inherent in U.S. statistical agencies are faced by all advanced nations

and much could be gained from joint research

projects.

Efforts should be made to ease the reporting

burden and increase the timeliness of the data

by taking advantage of commercial computer

and communication technologies. It should be

possible to improve techniques for electronic

gathering of statistics, possibly by allowing

companies to transfer data electronically from

standard accounting software. For example, the

IRS now has a pilot program that allows the

electronic filing of tax returns, cutting the

processing time by 2 or 3 weeks and reducing

errors by a factor of 10.9 BLS is currently

testing touch-tone and voice reporting of establishment data; current results show a significant

improvement in the timeliness of the data. The

installation of a similar system for industrybased surveys and censuses should reduce the

reporting burden on the firms and streamline

collection efforts at the statistical agencies.

Efforts in this direction require long lead times,

and extensive coordination with representatives of the firms that will be affected.

It should also be possible to make changes in

the way data are delivered. (See OTA’s report

Informing the Nation for a more detailed

discussion on information dissemination. *O)

Steps have been taken-the Department of

Commerce now has an electronic bulletin board

and issues some of its data in a floppy-disk

format, the U.S. Geological Survey issues some

of its data on a compact-disk, and BLS and the

Census Bureau make some data available on

floppy disks—but more remains to be done.

The proliferation of computers and powerful

software has meant that not only has the

medium for using data tilted towards an elec-

●

●

tronic format, but that the number and variety

of users has grown. This shift needs to be

weighed against the continued strong demand

for printed statistical information.

Greater effort needs to be made in coordinating

statistical work describing changes in the

goods and services available to individual

households with the rest of national economic

accounting. BEA does a heroic job in collecting

and coordinating statistics from the many

agencies with data relevant to the standard

national accounting framework. But no group

is asked to coordinate statistics in a way that

provides an integrated look at the way economic change affects different types of households. Many statistics are available on changes

in the quality of health care, access to transportation, and quality in education. The statistical

system lacks an organization which is charged

with ensuring that a complete and balanced

picture is available from this data and that links

can be drawn between changes in aggregate

levels of spending, changes in household spending, and changes in the quality of such things as

health care, education, and transportation available to households. Without such a coordinated

effort, it proves very difficult to provide a

balanced view of the way economic change

has, and may, affect the welfare of different

American households.

Coupled with this is the need for an organization capable of addressing many of the basic

challenges presented by an economy in transition. These challenges require a coordinated,

patient, and systematic effort to match resources to new demands. They require an

organization with the scope to translate emerging priorities into a practical plan for action and

the power to ensure that this plan is enacted.

Better management of existing resources could

undoubtedly improve the quality of and usefulness

of U.S. statistics. But there is a limit to the efficiency

gains possible—even with improvements in technology; data collection and compilation is an

extremely labor-intensive task, Given the challenges

presented by the transformation underway in the

9J~y RO~nfeld, ‘me Electronic Taxtnan,” PC World, April 1987, p. 187.

l~os. (Imgw, Offi@ of ~~o]ow A~~e~t, ]nfOm”~8 1~ NatiO~ Fe&r~ [~or~rion D@emi~lOn in an EkCtrOrlk Age, OTA-CIT-396

(Washington, DC: U.S. Government Printing Office, October 1988).
6

Nation’s economy, more resources may well be overlooked. The cost of a poorly run government

needed simply to maintain the quality of existing program may be many times higher than the cost of

statistical series. Saving money by reducing statisti- improvements to statistical agencies. Unlike other

cal budgets can be shortsighted if inadequate data

lead to poor management of public programs or government purchases that can be postponed, statisprivate investments. Important opportunities for tics cannot be turned off and on—once a gap is

growth may be missed and important dangers created it cannot be easily eliminated.
Part II

How Well Can We Answer the Basic Questions?

The remainder of this paper examines the statistics available to address a set of important economic

policy issues. The discussion is designed to outline

some of the challenges faced by the statistical

agencies in providing answers to even the most basic

economic questions and survey some of the more

important problems that have emerged in recent

years resulting from inadequate planning, budgets,

or coordination. Some of the problems identified are

not new, some may be difficult or impossible to

resolve completely given even the most perfect

system. The purpose of this discussion is not to

propose specific solutions to the problems raised but

to demonstrate that important problems exist and

that there is a need for a coordinated effort to address

them.

A. How Rapidly is the U.S. Economy Growing?

New technology, the pressures of highly competitive domestic and international markets, and changes

in the tastes and values of the American market have

changed the direction of economic growth in basic

ways. The number of pounds of materials and the

total amount of energy used by the economy did not

increase significantly between 1977 and 1987 even

though the total output of the economy measured by

the real Gross National Product (GNP) increased 28

percent (see figure 1).1 The products produced by the

economy has obviously taken the form of adding

more and more value to a given amount of basic

materials. Much of this value is difficult to measure

with the precision possible in a economy dominated

by raw materials. Growth must be measured not only

in terms of the number of items produced, but by

changes in the quality of products ranging from optic

fiber cables to fresh produce. In turn, quality should

reflect the growth in the variety of products offered,

and the extent to which people are able to purchase

products well tailored to their specific tastes and

interests. For example, the magazine publication

industry, once dominated by large national journals,

now has some 11,500 titles.2 The problem of

adjusting for quality becomes more complicated

when the product being produced is a service: how

do you measure quality changes in legal services?

Tracking an economy where growth depends on

qualitative factors is obviously more difficult than

tracking growth when output is easily weighed or

counted. It is necessary to acknowledge the fact that

the precision with which we measure economic

growth is likely to decline even given the most

heroic efforts by statistical agencies. But policies

designed to encourage economic growth need to be

made with the best possible description of the areas

where growth is likely to be important.

Measuring Real Economic Growth

The primary tool for measuring changes in the

size of the economy is the Gross National Product

Accounts (GNP) (see box B). The GNP estimates

produced by the Bureau of Economic Analysis

(BEA) have been the subject of a number of reviews,

and improvements are constantly being made,3 BEA

received a total of 75 recommendations from various

groups and commissions and was able to implement

51 of them.4 BEA maintains a long list of additional

improvements they would like to incorporate into

the accounts.

Two kinds of improvements to the GNP have been

discussed over the years. The first deal with the basic

structure and coverage of the accounts—e.g. should

government spending be treated as consumption or

should government spending on roads, airports,

research, or education be considered an investments should the value of economic activity that

occurs outside of the formal marketplace such as

Iu.s, Conwess, office of T&~oIoU Asses~ent, Technology and the American Economic Traruition ” Choices for the Future, OTA-TET-283

(Wa.shngton, DC: U.S. Government Printing Office, May 1988), p. 277.

ZU.S. ~p~cnt of Commerw, BWeau of the Census, Statistical Abstract of the United States, 1989, table 913, p. 549.

3MOS of tie rwent studies we reviewed in c.s. Carson and C. Jaszi, “The Use of National Income ~d product Acomts for Public Policy: OU

Successes and Failures,” U.S. Department of Commerce, Bureau of Economic Analysis, Staff Paper No. 43.

Am ~oups ~c~u&d the Nation~ ~comts Review Committ=, the Contributors to the Rerrospecf and Prospect, the GNp Data improvement

Project, and the Round Table of GNP Users. See Allan H. Young “Evaluation of the GNP Estimates,” Surve}’ of Current Business, AugusI 1987, p. 20.

5S= R, Ruggle~ ad ND. Rugg]es, “~te~atcd fionomi~ Accounts for the UIUted States, 1947 -80,” Survey of cUfrCnt B~inew May 1982; R.

Eisner, ‘The Total Incomes System of Accounts,” Survey of Current Business, January 1985; and Anthony S. Campagna, Macroeconomics Theory and

Policy (Boston, MA: Houghton Mifflin, 1974), ch. 1, pp. 7-21,

-7–
8

Figure I-Index of GNP Growth and the Use of Energy

and Materials in the United States

150 (100’ 1973)

140

130

1973 1975 1977 1979 1981 1983 1985 1987

— GNP (1982$ ) Primary energy

— Electricity (kWh ) Materials (Ibs)

SOURCE: R. Williams, E. l-arson, and M. Ross, “Materials, Affluencs and

Energy Uaa,- AnmJa/ Review of Erregy, No. 12, 19S7, pp.

99-144.

housework or illegal activities be included in the

national accounts?6

The oil crises of the 1970s and growing concern

about environmental issues in the 1980s has led to

concern that the formal national accounts do not

properly reflect changes in natural resource assets or

environmental quality and thereby give a misleading

view of changes in real national wealth.7 It would be

possible to maintain statistical series that would

track resource and environmental issues as “addenda” to the traditional accounts. More precise

tracking of resource and environmental changes

would require statistics on the inputs and outputs of

different production technologies not available within

standard statistical series.8

The existing statistical system makes it extremely

difficult to anticipate the potential impact of emerging technologies. Statistics document changes in

average businesses inputs and outputs but provide

little information about the performance of facilities

using new technology. The accounts do not distinguish between capital investments that simply replace obsolete or worn equipment from capital

investments that represent real growth or replacement of old technologies with new.9 These limitations makes analysis designed to show the net

impact of new technology on employment, profitability. job quality, energy use and other factors

difficult to track.

A second class of improvement, which will be the

primary focus of this discussion, deals with more

technical issues—how accurate are records maintained within the existing accounting framework.

The problem of improving, or indeed even of

maintaining the quality of the GNP accounts has

been made more difficult in recent years for a

number of reasons:

●

●

●

Rapid changes in the quality of goods (especially in computers and other information

equipment) and rapid growth of service industries makes the problem of adjusting for inflation increasingly difficult.

Rapid increases in the number of comparatively

small manufacturing establishments (many of

which may be subsidiaries of large Fins) and

increases in the role of service businesses

(service businesses have always been comparatively small establishments), have made census

counts more difficult. It is easier and less

expensive to obtain accurate data from a small

number of large establishments than a large

number of small ones—if only because the

larger establishments keep more precise records.

In some instances, data formerly available from

accounts provided to regulators in businesses

like trucking, railroads, and airlines now must

%Mol S. Carson, “The Underground Economy: An Introduction, ’’Survey of Current Business, May, pp. 21-37, and July 1984, pp. 106-1 17; Frank

de hmiw, “An htdirect IMmique for Measuring the Underground Economy: A Note on Revised Data,” Survey of Current Business, September 1986,

pp. 21-22; Joel F. Houston, “The Underground Economy: A Troubling Issue for Policy makers,” Business Review, September-@to&r 1987, pp, 3-12;

and James D. Smith, “Measuring the Informal Economy, “ The Annals of the American Academy, vol. 493, September 1987, pp. 83-99.

7S= Ro&fl Re~t~, “wsst~g AWts: Na~~ ResNces in the National Income Accounts” (Washington DC: World Resources ~mitute. 1989);

“A System of National Accounts,” U.N. Statistical Papers Series F-2, 1968; U.N. Department of Economic and Social A.ffsirs, “Provisional International

Guidelines on the National and Seetoral Balsnee Sheets and Reconciliation Accounts of the System of National Accounts.” Statistical Papers Series M60,

1979; U.N. Statistical Office, “Future Directions for Work on the System of National Accounts,” 1979.

sFaye Duehin, “Frsmewmk for the Ewluation of Scenarios for the Conversion of Biological Materials and Wastes to Useful Products: An

Input-output Approach,” presented at the joint session of the American Economics Assoeiatiort/American Association for the Advancement of Science,

New York, Dee. 29, 1988.

9Faye ~ch~, 1a&~ysing s~twd fimge in the &xmomy,’’l~ut-OutputA na,lysis: CurrentDeveloPments, M. Ciaschini (d.) (bndon: Chwmm

and Hall, 1988).
9

Box B—The National Income and Product Accounts

The U.S. GNP accounts are constructed in two ways: (i) the “product accounts” that measure the value of all

products and services sold for final consumption by households and the government, as business investment (gross

private domestic investment), and net exports (exports less imports), and; (ii) the “income accounts” that measure

the value of all income earned as wages, benefits, profits, and the like (see tab. 2).1

In principle, the income and product accounts both sum to the GNP. In practice, adjustments need to be made

to achieve balance. The product estimates are generally considered most reliable. Even after both sides of the

accounts are adjusted using a variety of data sources a small “statistical discrepancy” remains. It was 0.2 percent

of the GNP in 1987 and appears to be declining slowly.2

It is necessary to recognize that the GNP accounts were never intended to be a complete tool for describing

the economy and its limitations must be recognized. Many activities of enormous value do not appear in the accounts

(i.e., the value of education received from parents at home, environmental damage resulting from economic activity)

largely because they occur outside of the formal marketplace. GNP accounts no longer provide any information

about the way income is distributed among households—in fact the GNP can grow while the real income of many

groups declines.3

ICarol S. Carson, ‘The History of the U.S. National Income and ProducI Accounts: Development of an Analy~cal Tcwl,” Rewew of

Income and Weal[h, June 1975. Carol S. Carson,’ ’GNP: An Overview of Source Data and Estimadng Merhmts,” Swwey of Current Bu.rmess,

July 1987.

2A.H. Youg, “Evduatmn of the GNP Estunmes,” Survey of Curren/ Business, August 1987, pp. 20-21.

3~ he 19703, & BEA had a Progm of non.market ~pec~ of ~onomic wellbeing ~d prexn[d busehold income dMtIhAtlOll ss a

part of GNP accounts, but both were el iminated because of budget cuts m the late 1970s and early 1980s.

Table 2—Distribution of the GNP in 1987 to the BEA, which maintains the GNP ac-

(percent of total) counts. 10

Income accounts:

Compensation of employees . . . . . . . . . . . . . . . . .

Property-type incomea 

. . . . . . . . . . . . . . . . . . . . . . .

Depreciation b 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Other c

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Statistical discrepancy . . . . . . . . . . . . . . . . . . . . . .

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

product accounts:

Personal consumption expenditures . . . . . . . . . . .

Government purchases of goods and services. . .

Gross private fixed investment. . . . . . . . . . . . . . . .

Net exports of goods and services . . . . . . . . . . . .

59.2

22.0

10.6

8.2

-0.2

100.0

66.7

20.4

15.7

-2. 8

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0

%oprtetor’s income, rental income, corporate profits, net interest.

bc~lt~ ~nsumption allowances with capital consumption adjustment.

Technical Measurement Issues

Adjusting for Inflation—The question of how to

adjust for inflation is probably the most difficult to

solve. Yearly measurements of the value of goods

and services purchased cannot provide an adequate

measure of economic growth. In order to obtain a

consistent comparison over time, products valued in

current dollars must be revalued into a constant set

of prices that adjust not only for the changes in

prices, but also for changes in the quality of

products.

cBu&lness ti~sf& ~yments, indirect busks tax and nontax-llablllty b3S

subsidies less current surplus of government enteqmses. The Bureau of Labor Statistics provides the basic

SOURCE: U.S. Department of Commerca, Bureau of Eonomic Analysis, data for inflation adjustments primarily by using the

National Income and Product *ounts. results of three major surveys (box C); each of which

has its share of drawbacks. Some are chronic while

be obtained from trade associations or census others result from changes in the economy.

surveys.

● Perhaps the most vexing problem arises from

. During the past few years there has been an 8 changes in the quality of the products offered

percent real reduction in the funding available for sale. Price adjustments work well only

lo~ fjw~ yea 1978, BEA’S budget (exc]u~ng ~~sfers) wss $16.2 million (in 1980$). Ln 1986 the budget WaS $14.9 million. U.S. Coni9ess, Gen~al

Accounting Office, “R&D Funding: Tbe Department of Education in Perspective” (GAOEEMD-88-18FS), May 1988. PP. 9-11. For a more detail~

discussion of the budgets of the statistical agencies, see National Association of Business Economists, “Report of the Statistics Committee of the National

Association of Business Economists,” February 1988, pp. 10-15, 25.
10

Box C—Deflator Series

● The “Consumer Price Index” (CPI) is used to compare prices of a fixed “market-basket” of products and

services purchased by consumers-about two-thirds of the GNP. Surveyors are given a precise list of

products, an item might be a jar of peanut butter, and check the prices of these items in a scientifically

selected set of retail establishments throughout the country. Changes in the prices of the selected products

are used to estimate price changes in most areas of consumer spending. They are also used to estimate price

changes for many areas of government spending. * The ratio of priced changed for a product in a given year

to the price charged in a baseline year (e.g., 1982) is called the “deflator” for that year.

● The “Producer Price Index” (PPI) is used to adjust the value of most products purchased as an investment

by businesses and business inventory changes. This index uses techniques similar to those used in the CPI

but in this case surveyors ask individual businesses for the prices of their products.

● The “International Price Program” measure price changes for products imported and exported from the

united states.

Separate series are provided for housing and other structures and for some other products. The

PPI is used to deflate some government purchases of goods and services for which a reasonable analogy can

be found in the private sector and which are not covered by BEA price series on defense expenditures and

compensation paid to government employees.2

IIW u,s. Nfi~ -C~d fi~~[ &XOUXMS: Revised Estimates,” Survey o/Curren( Bwiness, July 1988, table 7, pp. 31-33.

21bid

when quality changes occur comparatively variety of different types of peanut butter in

slowly. ‘But in today's economy, growth must

increasingly be measured in terms of product

quality and consumer choice.

Quality adjustments are comparatively easy

when a new feature is added to a familiar

product. If a new car is sold with white

side-wall tires as standard equipment, for

example, the Consumer Price Index (CPI) can

be adjusted to reflect the extra marginal cost of

these tires. Other kinds of quality adjustments

are more difficult-both conceptually and mechanically. Electronic products are perhaps the

most striking example of change. New kinds of

television receivers, home computers, telephones, and a variety of other devices redefine

consumer electronic markets yearly.

Even more troubling, the inflation adjustments

miss an important dimension of quality that

seems to have proven appeal to the American

market: the value of growing choices available

to consumers. While the BLS surveyor accurately assesses price changes in the 12-ounce

jar of peanut butter, a grocery store customer

may be selecting the jar from an enormous

1989 ‘while having few choices in 1983. For

example, large groceries have also introduced

many other amenities (salad bars, bakeries, fish

specialties, etc.) without significant increases

in product cost. Is this an improvement in

quality? Consumers and grocery store owners

seem to think so, “Generic brand” offerings

have declined while stores offering 20,000 or

more different products have prospered (see

figure 2).11 The value of variety embodied in

grocery purchases is not captured by the BLS

surveyors. As a result, BLS may measure a

price increase while in fact the real cost of the

bundle of goods and services purchased at

groceries may have decreased because sophisticated technology and management techniques

make variety increasingly inexpensive.

. It has become increasingly difficult to develop

good measures of changes in the quality of

products purchased as capital equipment by

businesses (7 percent of GNP). In particular,

new information equipment presents the most

serious challenges. Working with IBM, BEA

has attempted to find a way to adjust the prices

11- N , B&ly ~d Ro~~ J. Gordon, “me productivity Slowdown, Me~~ment ISSUCS, and the ExplosioII of COmpWiX power,” Brookings

Papers on Economic Aaivify, vol. 2, 1988, p. 412.
Figure 2—Products Carried Per Supermarket

Thousands

2 0

16

10

0

1948 1967 1967 1972 1977 1982 1986

SOURCE: M. Baily and R. Gordon, The Productivity Sbwdown, Measurement ksues, and the Explosion of Computer Power,= Brookings

Pspws on Economic Activity, vol. 2, 1988, p. 413.

of computers for quality changes.12 The deflator uses an index not strictly tied to the price of

products, but instead makes adjustments by

comparing specific characteristics over time,

such as how many million of instructions are

executed per second (MIPS) or how memory

capacity has changed, to get an indication of the

change in the value of computer power.13 Not

surprisingly the change has been dramatic. For

example, the price of one megabyte of main

memory fell by a factor of 20 between 1972 and

1984.14 Changes of this magnitude have a large

affect on the deflator. While the deflator for all

producer durable equipment (PDE) was 1.078

in 1987 (1982=1.00), the deflator for the

“office, computing, and accounting machinery” (OCAM) category was 0.55.15 This single

deflator has an enormous effect on the “real”

purchases of PDE measured, the real growth of

GNP, and the productivity growth rate of

manufacturing. For example, PDE in constant

●

1982 dollars increased at an annual growth rate

of 13.8 percent between 1983 and 1987. If the

OCAM deflator had been the same as the

average of the other parts of the PDE deflator,

growth would have been only 4.3 percent.16

The process used to reflect quality improvements in computers has not been extended to

other high-technology equipment like semiconductors and communications equipment,

although work is beginning. As a result, the

output price deflators for microelectronics actually rose from 1972 to 1982 while the index

for computers fell drastically .17 This inconsistency could lead a researcher unaware of the

problem to erroneously conclude that productivity gains in the computer industry were

achieved without, or in spite of, corresponding

gains in the semiconductor industry.18 Similar

problems exist in many areas where new

products are radically different than the ones

they replace.

Measuring changes in the quality of computers,

however, can seem easy in comparison with the

challenge of measuring changes in the quality

of services. Services are a growing fraction of

the GNP before adjustments are made for

inflation. Changes in the quality of health care

(10 to 11 percent of the GNP) and education (7

percent of the GNP) are poorly measured or not

measured at all. Fundamental conceptual problems must be confronted in developing deflators for these sectors. BLS has research projects

underway in health care and other difficult

service sectors, A deflator for health care

should, in principle, adjust for changes in the

quality of care received. But while it may be

possible to develop a deflator for a specific

medical test or a specific set of medical

IZR. ale, Y.C. cttm, J.A. Bsrquin-stollctnan, E. Dulberger, N. Helvacian, and J.H, Hedge, *’@ality-Adjusted Price bdexes for COrnPUter ~mes~~

and Sekctcd Peripheral Equipment,” WV9 of Current Buriness, January 1986, pp. 41-50; David W. CartWright, ‘

( Lrnproved Deflation of Purchases of

computa’s,” ~wey of Current Business, March 1986; and David W. Cartwright and Scott D. Smith, ‘Deflators for Purchases of Computers in GNP:

Revised and Extended Estimates, 1983 -1988,” WV9 of Current Bwiness, November 1988, pp. 22-23.

13c01e, et ~., op. cit., pp. 41-50.

Iak, et al., op. cit., p. XT.

15u-s.-mt of berm, B~u of ~~c Analysis, National income ~d product Acc~ts, ~blc 5.7.

l~~id., tables 5.6 ~ 5.7.

17s~w.~ J~g ~J-R. N~~fiy, “sc~e ~~ies, ~~g *CS Snd ~ws~~ ~~uctivity Grow: A Study of ~hnolOgy k the

Us. Micnx kctronics and Computer Industries,” Wchnical Report 02-88, Center for Science and ‘Ikdmology Policy, School of Management, Rensselaer

Polytechnic lttstitute, August 1988, p, 13.

18Ibid., p. 12.
12

procedures, it is difficult to measure whether

the patient’s health has benefited from additional tests that may be administered.

. Changes in the quality of structures (8.4 percent

of the GNP), particularly nonresidential structures, are measured very poorly .19 The value of

new housing is adjusted using 10 measures of

housing quality.20 The value of nonresidential

structures is adjusted for inflation using a

mixture of the residential deflator and a standard set of building inputs.21 There are many

reasons to believe that the construction deflators prepared in this way do not provide an

accurate measure of changes in product quality.

The residential deflator, for example, does

not reflect the addition of new amenities such

as dishwashers, energy-efficient improvements,

and landscaping which have become common.

22 The use of a standard mix of labor,

material, and equipment inputs, is used to

represent nonresidential buildings ranging from

warehouses to hospitals has obvious deficiencies. Changes in quality and productivity improvements are not incorporated. The Canadians, who do a much more thorough job of

measuring construction quality, estimate that

the price of construction products rose only 3.5

percent more than the Canadian GNP average

between 1967 and 1986 while the U.S. estimates show construction prices rose 15 percent

more than the U.S. average. 23 Because the price

index is so high, output of the construction

industry tends to be over adjusted for inflation,

resulting in an underestimate of real output.

Adjustments for inflation can lead to misleading measures of growth rates. For example, the

deflator for computers is so much lower than

the deflator for the economy, a significant

fraction of the percentage growth of GNP in

constant dollars results from the methods,

particularly the selection of a fixed base year,

used to adjust for inflation-not an increase in

current spending for computers. The deflators

can thereby create a distorted view of growth

rates. For example, growth in producer durable

equipment between 1982 and 1988 measured in

constant 1987 dollars averaged 5.9 percent per

year but was 8.4 percent per year when

measured in constant 1982 dollars. The BEA is

examining alternative ways to express GNP

growth in preparation for the comprehensive

revision scheduled for 1990.24 One way to

avoid distortions due to different base-year

weights is to compute growth rates by determining the constant dollar growth rate for each

product separately and weighting each product

by the average current dollar sales of each

product for the first year.25

It would also be useful to develop explicit

“addendum” accounts that report changes in

production and use of physical commodities

whenever such data is available. Many of these

series are already maintained to develop deflator series. Published series consistent with the

national accounts would provide a view of

changes in demand for energy, materials, and

other countable products and services that

would be a valuable addition to “constant

dollar” measures.

1- ~Oblern is Cornplicti by the fact that structures are very heterogeneous products produced by a diverse industry composed of gener~ly sm~l

fins. See Committee on Construction Productivity, Building Research Board, Commission on Engineering and ‘fkchnical Systems, National Research

Council, Con$trucdonProducrivity (Washington DC: National Academy of Sciences Press, 1986); and P. PiePer, ‘The Measurement of Structures Prices:

Retrospect ad Prospect,” 50th Anniversary Conference on Research in Income and Wealth, NBER, May 12-14, 1988.

%oorq number of stories, number of bathroom, presence of central air-conditioning, type of psrking facility, type of foundation, geographic

region, metropolitan location, presence of fireplaces, and lot size. See “price Index of New One-Family Houses Sold” (Bureau of the Census), various

issues.

zl~ ~~ns~ction G., a l~ge builder of c.ommerci~ and industrial structures, estimates the cost plus profit using a standard mix Of inputs.

See “Revised Deflators for New Construction, 1947-73,” Survey of(krenr Business, August 1974.

ootnote 11, pp. 402-406. 22B~]y~ Gordon, op. Cjt.g f

mB~ly ~ @rdon, op. cit., footnote 11, PP. @2~.

ZqAIlm H. yo~, “Alternative Measures of Real GNP,” Survey o~Currenf Business, April 1989, PP. 27-34.

MS= N~O~ ~come and Product Accounts, op. cit. footnote 15, table 8.1.
1 3

Box D-Classifying Businesses

Measuring the output of industries is contingent upon defining the industry itself. The fundamental identifying

system for industry classifications is the Standard Industrial Classification (SIC) Code-the official existence of

an industry. After 15 years, several false starts, and the input of over a thousand public and private opinions, the

SIC was revised in 1987.

Nevertheless, several observers think that this effort was incremental in nature and that a more complete

overhaul is needed.l The concern is that emerging industries that typically enjoy tremendous growth can not be

tracked because they are not identified by the SIC system. Instead their growth is lumped into a broader category

that obscures the source of the change or a category that is a grab bag of leftovers such as SIC 7389 “Miscellaneous

Business Services” which includes everything from meter readers to yacht brokers.

Even though the 1987 SIC revision created three new 4-digit categories for computer equipment like storage

devices, terminals, and computer equipment n.e.c. (not elsewhere classified), it still left ail computer manufacturing

(minis, micros, and mainframes) under one 4-digit SIC. Similarly all eating places (McDonalds to the 21 Club) are

under one 4-digit SIC (no change from 1972). Meanwhile, footwear gets broken into rubber and non-rubber

categories (at the 3-digit level) and then under non-rubber footwear there are four, four-digit categories: men’s

footwear (except athletic), women’s footwear (except athletic), house slippers, and footwear, except rubber n.e.c.2

Although attempts to retain consistency over time are important, additional detail for large and growing industries

at the 4-digit level seems warranted.

INati~ As=iatim of Fhsiness hmomists, “Report of the Staustics Comtm [tee of the Nationat Association of Business Ecmmrnk,” February 1988,

p. 17; Statunent of Courten ay Slater before the Subcommi [tee on Govemrnenl Lnfotma[ion and Regulation, Commi ttee on Govemtne ntal Affairs, U.S. Senate, May

15, 1989, p. 5; National Academy of Scicnees, Committee on National Statistics, Statidcs Abcwf Service Industries (Washington, DC: National Academy of

Sciences), 19S6, p. 10.

@ffkx of Management and Budget, Standard Industrial Classifiiatwn Manual, 1987 (WashingtorL DC: U.S. Gov emment Printing Office).

B. Which Businesses Are Responsible for for future growth, and it is needed to understand the

Growth and Has Growth in the Complexity of way businesses operate together as parts of complex

the Networks Connecting Different “Kinds of

Businesses Changed the Interdependence of

Businesses?

While many difficulties are encountered in measuring the gross output of a modem economy, even

greater problems are faced when attempts are made

to trace this output to the activities of different kinds

of businesses. Just defining and classifying a business is a difficult task (see box D). The contribution

each business type makes to GNP is important for a

number of reasons: it is needed to assess rates of

innovation and productivity growth in different

types of business, it is needed to understand which

kinds of economic activity are likely to be the basis

production networks .40

Data published by BEA indicate that the share of

the GNP provided by natural resource industries,

such as farming and mining, has decreased sharply

since 1950 while output from the service sector,

particularly services that play a transactional role in

the economy such as finance, communication, and

business services, has increased (see figure 3).27

BEA’s data show that manufacturing’s contribution to GNP has stayed remarkably stable at roughly

20 to 22 percent of the GNP over the 36-year period

from 1950 to 1986 when measured in constant 1982

dollars. 28 This estimate has been the subject of a

~Jerome A. Mark, “Problems Encountered ift Measurin g Single- and Mtdtifactor Productivity,” Monrtsfy Lu60r Review, December 1986, p. 6; and

Edwin Dean and Kent Kunze, “Recent Changes in the Growth of U.S. Multifactor Productivity,” IUottrhly Lubor Review, May 1988, p. 20.

zTBusin~s ~i~s aS defm~ in the irtput/owput accounts includes activities such as consulting, law, advertising, and cOmp@r WViCe5. to name

a few.

28National ~come and Product Accounts, op. cit., foomote 15, table 6.2.
14

Figure 3-Shares of GNP in Constant 1982 Dollars

Percent of GNP

2 5

20“ Manufacturing

Transactions

I

10 I

Natural resources I

considerable amount of criticism during the past

year.

29 Manufacturing’s stability appears to be

inconsistent with other events in the economy such

as a huge trade deficit in manufactured goods,

lagging investment in plants and equipment, and the

loss of 2 million manufacturing jobs between 1979

and 1986.30 BEA has responded to some of the

criticisms and has undertaken an effort to revise the

constant dollar value-added by industry series

(termed gross product originating) .31 Although firm

conclusions cannot be drawn about biases in the

present series until the revised series is available, it

appears likely that manufacturing’s growth in output

from 1979 to 1985 will be revised downward.32

Assuming that the gross national product of the

economy is properly measured, overestimating the

contribution of one business sector (e.g., manufacturing) necessarily is balanced by an underestimate

of the contribution of other sectors (e.g., business

services).

Understanding Linkages

Tracing the source of growth in the economy to

individual business sectors requires an ability to

describe the complex business networks that now

operate in virtually every part of the economy. The

transformation of the American economy can be

seen in the growing complexity of these networks

and the service businesses needed for their efficient

operation. 33 Understanding the complex patterns of

interdependence that result from these networks is

critical for understanding the way national policies

can affect economic performance. Imports that

affect manufacturing industries have a strong indirect effect on the service firms that supply these

industries. The prosperity of services and manufacturing depends as never before on the quality of

infrastructures like communications and a responsive transportation system.

In an effort to cut costs many firms have begun to

specialize and purchase products and services from

other specialized fins. Manufacturing firms may,

for example, purchase legal, bookkeeping, or janitorial services from outside suppliers rather than

performing these activities “in-house.”34 As a result

of this growth in subcontracting, and the wider

geographical dispersion that it entails, service sector

businesses have thrived because of the increased

need for financing, legal assistance, consulting

services, communication, transportation, and wholesale and retail trade. These transactional and distributional service sectors represent the two fastest

growing parts of the economy over the past two

decades. Separately, each of the two groups contributes more to the GNP than all of manufacturing.

The basic source of information about the way

businesses depend on each other is the input/output

Wechnologyandthe Ameriean Economic Tramition: Choices for the Future, op. cit., footnote 1, pp. 168-175; Lawrence R, Mishel, “Manufacturing

Numbers: How Inaccurate Statistics Conceal U.S. Industrial Deeline” (Washington, DC: Economic Policy Institute, April 1988); Edward F, Denisen,

Estimates of Productivity Change by lndust?y (Washington, DC: The Brookings Institution, 1989); Robcn Kutmcr, “U.S. Industry is Wasting

Away—But Official F@ures DuI’t Show It,” Business Week, May 16, 1988; “The Factory Rebound maybe More Fantasy Than Fact,” Business Week,

Dee. 12, 1988, p. 98., and Anthony Harris, “Figures Calculated to Deceive,” Financial Times, July 11, 1988, p. 11; Toddi L. Gufncr, “U.S. Economic

Statistics Off the Mark,” National Journal, Sept. 3, 1988, p. 2200.

%Lawrenec R. Mishcl, ‘The Late Great Debate on Deindustrialization,” Challenge, Janwq/February 1989, p. 35.

31U.S ~~atof c~em, Bureau of Economic Analysis. “Gross Product by Industry: Comments on Recent Criticisms,” Survey of Current

Bwiness, July 1988, p. 132.

32~id. p. 133; ~~el (1989), ~, ci~, fm~~ 30, p. ~, Baily and Gordon, op. Cit,, footnote 11, p. 367; and Deniscm, op. cit., footnote 29, p. 23 and

p. 37.

~JTeC~~~ ~t~ tiric~ Economic Transition, op. cit., chs. 44 and 5, pp. 143-177.

34John T~&t@, *C~u= Senlces ~d~es: Wy ~e They Growing SO Rapidly?” Monthly f,ubor Review, December 1987, pp. 314.
15

(I/O) tables, compiled by BEA. These tables show

what each of 537 industries purchase from other

industries. Input/output data provide an essential

tool for tracking the effects of new technologies;

they alone provide a detailed description of how new

technologies affect the inputs needed by different

businesses. I/O tables also provide a key tool for

monitoring the performance of the complex business

networks that are coming to dominate the U.S.

economy, showing how the value of products sold to

final consumers combine the skills and technologies

of the retail, wholesale, transport, production, and

natural resource industries. They also provide a key

insight into the way the rapidly expanding service

industries are used by other businesses.

Since so much detailed information is needed, it

takes many years to create input/output tables using

current methods. The most current “benchmark”

table, called a benchmark because it is largely based

on quinquennial industrial censuses, dates to 1977

and was published in 1984. The 1982 benchmark

table will not be published until later in 1989. Unless

something changes we will be using the recession

year of 1982 as a “benchmark” until 1994.

BEA produces input/output tables for years between benchmarks using a variety of approximation

techniques. Until the past few months, the “updated

annual tables” were published 6 years after the year

for which they apply (e.g., the 1983 table was

published in 1989).35 Partly in an effort to fully

integrate the gross product originating series with

the I/O tables, the BEA has accelerated the process

of constructing annual I/O tables and a 1986 table

will be published late in 1989.

Although more up-to-date, the annual input/

output accounts suffer from an industrial classification scheme that has a strong manufacturing bias. Of

the 85 industries, 52 are dedicated to manufacturing,

15 are services, 12 are in natural resource, and 6 are

“other.” This occurs even though only a fifth of the

GNP is attributable to manufacturing. The end result

is great detail on the production of wooden boxes,

about two-one hundredths of a percent of GNP,

while the private health, education, and social

service industries, about 8 percent of GNP, are

lumped together into one category. Obviously, these

classifications present a severe constraint on analysis.

Another limitation with the more timely annual

tables is that they are forced to use the 1977

benchmark table as the basis for scaling, limiting the

ability to track areas where the economy has

changed rapidly. Unfortunately these are often

precisely the areas where most policy analysis

focuses. Difficulties in tracking the role of services

is an important example. The fastest growing intermediate input in the economy, and particularly to

manufacturing, is the purchase of a group of services

collectively called business services, which contains

services like accounting, advertising, legal help,

computer services, and temporary help services. The

1982 input/output table shows business services as

the third largest intermediate input to manufacturing—

above commodities like steel, rubber, paper, and

transportation. 36

The basic data source on intermediate inputs for

the manufacturing sector, the quinquennial Census

of Manufacturing, does not collect data on purchased business services. The smaller, sample-based

Annual Survey of Manufacturing collects data on

only a few purchased services such as repair and

communication services once every five years. This

means that a number of approximation techniques

were used even to establish the benchmark 1977

input/output table. Attempts to scale up from this

benchmark to a more recent year are highly approximate.

37 It is extremely difficult to track many

technical changes resulting from greater purchases

of services by businesses. For example, employment

in temporary help agencies (one component of the

1/0 business services sector) grew by 70 percent

from 1982 to 1984. 38 This “out-sourcing” has

35u.s. ~artmcmt of commerce, Bureau of Economic Analysis, Survey of Current Business, “Annual Input-Output kcounts of the U.S. Economy,

1983,” Fe- 1989, pp. 21-36; U.S. Depsrtmatt of Commerce, Bureau of Economic Analysis, Survey of Current Business, “Annual Input-Output

Accourttsof tk U.S. Economy, 1982,” April 1988; U.S. Department of Commerce, Bureau of Economic Analysis, Survey of Current Business, “InputOutput Accotmts of tbe U.S. Economy, 1981,” Jsnusry 1987.

Mu+S.~-at of c~~,B~~ of ~nomic ~ysis, Suney @Curren( B~iness, “AnntI~ Input-output Accounts of the U.S. )%OnOmy,

1982,” April 1988, pp. 3146.

37’’Gross Roduct by Industry: Comments on Recent (Micisms.” op. cit., footnote 31, p. 132.

38* L. C=y ~~m L. H~]~er, “Employment Groin in ~~mporq Help ~dlls~,” Monthly hhr Review, April 1986, p. 37.
16

obviously reshaped business networks and affected

the difference between sales and value-added contributed by the business, but the impact is almost

impossible to trace. The fast growth of business

services means that it is likely that they have not

been fully accounted for, causing an overestimate of

manufacturing’s contribution to GNP that has grown

in severity recently .39

Data on the services used by businesses at the

establishment level are difficult for Census to obtain

because services like accounting and advertising are

often purchased by corporate headquarters while the

questionnaires go to individual establishments. The

managers in the establishment often do not know

how much corporate advertising is done in their

interest. This problem could be reduced if the

information was collected at the headquarters of the

firm and then allocated to the fro’s individual

establishments through an imputation scheme.

There are also defects in the way the input/output

statistics can track the performance of the new kinds

of transportation systems required by a flexible,

highly interconnected economy.

40 One important

source of data is a Census Bureau product called the

Commodity Transportation Survey which measured

what types of manufacturing output were transported by a particular mode of transportation: truck,

rail, air, or water. As the economy shifts toward a

system of flexible production networks that relies

more on “just-in-time” inventories, quick reactions

to competitors, and better responses to consumer

demand, transportation data like those provided in

the Commodity Transportation Survey (CTS) are of

considerable importance in tracking this change.41

Due to methodological problems, the 1982 CTS was

postponed to 1983 and conducted in a modified

form. Because of the deficiencies found in the

quality, the 1983 CTS was never published.42 The

1987 CTS was canceled due to methodological

problems and budget constraints.43

The input/output tables are important for many

reasons other than computing accurate estimates of

value-added in each type of business. They are also

used extensively in preparing detailed tables throughout the National Income and Product Account.44 The

Bureau of Labor Statistics relies on input/output data

to generate its industry-level multifactor productivity series,45 construct the producer price index,26 and

estimates of what occupations will be in demand in

the future.47 Input/output is at the heart of the

Department of Agriculture’s projections of agricultural output and the Department of Energy’s estimates of energy use.

Since input/output statistics are so important, and

BEA tables are often many years out of date, private

analytical firms and other Federal statistical agencies have developed their own updated tables using

a variety of methods. The Bureau of Labor Statistics

estimates its own updates of input/output tables

independently of BEA. Because of this independent

effort there are disagreements between BLS and

39&tim, op. cit., fOOmOti 299 P. 47”

@U.S. Congress, General Accounting Office, The Bureau of Economic Analysti Skndd Lead Eforts to Improve GNP Estimate, GAO/OGD-83-l

(Washington, DC: U.S. Government Printing Office, Dec. 27, 1982), p. 58.

41Ro&~ H. Hay- ad R~ch~dr~ hikm~, ‘Manufacturing’s tisis: New lldmologies, Obsolete ~gSIIk~i0f15,” Harvard Business Review,

September-October 1988, pp. 77-85.

42u.s, cm-,~luof ~~oloaA=ment, Tr~portatwn of ~a~rd~~ Afaterids, OTA-SET’-3@$ (Washington, DC: U.S. Government

Printing Offke, July 1986), p. 44.

4SK.R. polem~, “RelevmU of U.S. Re@on~ Statistics,” pm~nt~ at the ~eric~ Association for the Advancement of Science, Jan. 19, 1989, p.

3.

‘Warwn, op. cit., foomote 6, p. 112.

qS~w~ km ~d Kent K-, *’R~ent ~anges in the Growth of U.S. Multifactor Productivity,” A40nthty Labor Review, May 1988, p. 20.

46A-G. clm ad Willlm D, Thom~, “New wei@t s~t~ king us~ in producer Price lndcx,’’~ont~fy Lubor Review, Augu~ 1987, p. 12; d

Robert Gaddie and Maureen Zoner, “New Stage of Process Price System Developed for the Producer Price Index,” Mwtthly Lubor Review, April 1988,

pp.3-16.

dTu,s. ~mmt of~, Bweau of ~rs~stics, B~&o~mic GrowlhMOdelSYSlern UsedforProjectio~ to 1990, Bulletin 2112, April 1982,

p. 2,
17

BEA estimates of industrial output.48 The Forest

Service, a division of U.S. Department of Agriculture, creates another set of updated input/output

tables for its own use (IMPLAN). 49 Even the

Department of Commerce, the source of the input/

output tables, contracts out to consultants for updated input/output tables.50

In an era when computers are easier to use and

vastly more powerful, delays in creating input/

output tables should be growing shorter. Significant

problems remain in data communication. Until 1985

the massive amounts of data from the Bureau of the

Census needed to construct the input/output accounts were delivered to the Bureau of Economic

Analysis in printed form. Every number had to be

reentered by hand and rechecked. In the last few

years computer tapes have been delivered to BEA.

Unfortunately the data are still not in the form

needed by BEA, but instead, are simply a digital

representation of the pages that formerly appeared in

printed form. The tapes contain tables with lines,

headings, and notes, which must be removed in a

laborious process. After the irrelevant characters are

removed, the arduous process of converting the data

to forms useful for input/output work can begin. As

the process proceeds, it is often discovered that some

data items may not be provided by Census for

reasons of confidentiality. In part because the

Census does not have money set aside for retabulation, BEA can seldom get additional information

from the Census Bureau (e.g., data aggregated in a

way that does not reveal confidential information).

Other nations manage to produce detailed input/

output data much faster than we do—though possibly with less accuracy-in part because 1/0 plays a

more fundamental role in policy making in these

countries. Japan already has input/output tables

based on data collected in 1985. And the Japanese

government is involved in creating an input/output

model for a major portion of the international

enconomy.

51(See box E for a description of this

effort.) The United Kingdom has a benchmark table

for 1984. China is about to complete a 1987 table.52

Computing Business Output by Sector

Each business in a network delivering a final

product or service to a consumer adds some value

(value-added) to the product. The sum of “value-

-added” in all businesses in the United States equals

the GNP. The input/output tables described above

provide the basic tool needed to see how much value

each business in the economy contributes in constant

dollar terms.53

The value-added by a business can be computed

by subtracting the value of all products (both goods

and services) purchased by a business from total

sales (or gross output) .54 In this sense, value-added

is a better indicator of performance than sales (gross

output) because it shows the contribution made by

the company-not the aggregate value of the company’s contribution and the value of inputs produced

by suppliers. An automobile company can, for

example, decide to purchase components from

abroad instead of producing them internally. Its sales

4SF~r~x~Pl~, ~ BLS 1982 ti~of ~souqut fortheoffice, computing, Snd ~uting mwhines industry (SIC 3572,3573,3574,3576, d

3579) was over a billion dollars less than the BEA 1982 estimate. Other discrepancies occur in industries such as eating and drinking places (SIC 58)

and non-metallic minerals, except fuels (SIC 14), but exact SIC matches between the two series are difficult. U.S. Department of Commerce, Bureau

ofi%onomic Analysis, Survey occurrent Business, “Annual Input-Output Accounts of the U.S. Economy, 1982,” April 1988, p. 35, and U.S. Department

of Labor, Bureau of Labor Statistics, Ofilce of Economic Growth, “Output and Employment Database,” January 1988. A similar discrepancy occurs

between Federal Reseme Board estimates of production (the Index of Industrial Production) and those issue-d by Department of Commerce (sales adjusted

for inventories). See Jeffkey A. Miron and Stephen P. Zeldes, “Production, Sales, and the Change in Inventories: An Identity that Doesn’t Add Up,”

Working paper No. 2765, National Bureau of Economic Research, Cambridge, MA., November 1988.

4!l~1f E. Siv- ~d D~el E. Chww]le, *’A com~son of ~tu~ c~~sin Employment ~d kcornc With predictions Using IMPLAN Models,”

presented at the Western Regional Science Association, Feb. 19-22, 1989, San Diego, CA, p. 4.

S~swr A. Davis, “~rnbutions of Exports to U.S. Employment: 1980 -1987,” U.S. Department Of commerce, ~~rnatioti Tr~~inis~tion!

Trade Research Division, Rojea DTR4)14-89, March 1989, p. 22; and Ken Young, Ann Lawson, and Jennifer Duncan, U.S. Department of Commerce,

Office of Business Analysis, ‘Trade Ripples Across U.S. Industries,” January 1986, p. 9.

SIM$ Sam, J~’s Mini- of ~t~~m~ Trtie ~d ~u~, “compilation of ~ kt~tion~ hput-output lhble,” paper presented at the OECD

Workshop on Intonational I-O Tables and performance Analysis of Structural Adjustment, Dec. 14, 1988, Paris, France.

52K.R. po]~e, ’’Relev~ceof U.S. Re@on~ St~stics,’’paPr pre~nt~ at~~eric~ A~iationfortie Advancement of Science, Jan. 19,1989,

p. 7.

ss’’Gross ~Xt ~ l.nd~~: comments on Recent Criticisms,” op. cit., footnote 31, p. 132.

~N~on~ ~~y of ScienWs, tit=~ N~o~ Stmstics, Me~ure~~ ~/nterPret@n of pr~~ffv~, (W~iXtgtort, ~: National

Academy of Sciences, 1979), p. 65.

20-014 0 - 89 - 2
18

BOX E—Japan’s International Input/Output Model Project

In an effort to build an analytical capability for analyzing “bilateral and multilateral economic issues and

conflicts,””analyzing the economic impact of international economic activities,” and “clarifying the magnitude of

international interdependence,” the Japanese are engaged in creating an international input/output table.

At the cost of a million dollars a year for 6 years, the Ministry of International Trade and Industry (MITI)

directed project will construct a series of I/O tables that connect the economies of Japan, the United States, the

United Kingdom, France, the Federal Republic of Germany, South Korea, Malaysia, Singapore, Thailand, the

Philippines, Indonesia, China and Taiwan.1

It’s scheduled to be completed in 1992. Because the model is based on 1985 data, data for the United States

had to be estimated by a private contractor. Similarly, since the United States does not have data on the use of

imports by industry-an important characteristic of the model-the Japanese had to estimate it by surveying

Japanese firms about which U.S. industries buy which type of goods from them. In the case of the developing Pacific

Rim countries who in some cases lack a strong statistical system, the Japanese are collecting and organizing the data

as a part of Japan’s foreign aid to those countries.

The Japanese have stated that they will use the input/output tables to:

. make international comparisons of industrial structures,

● evaluate the results of a given country’s protectionism,

. determine the economic effects of direct overseas investment,

. analyze the impact of changing crude oil prices, and

. evaluate the effect of fostering the development of new industries in specific countries.2

Obviously, the data could bean important competitive tool in identifying and targeting key industries. As the

world gets increasingly carved up into large trading-blocks (i.e., U.S. and Canada and Europe’s 1992 agreement),

the input/output tables could also be used to evaluate the costs and benefits of a Pacific Rim trading agreement.

llt ~ ~~~~ ~~ Jap~ ~$1 million ~ y- on ~S intmtiml effort which is a supplement to Iheir main domestic effort, Lhe 1989 fkd

year budget for the cmtire U.S. inputbtttput effat (BEA’s Interindustsy Division) was $1.4 milliom

%. Sate, Japan’s Ministry of Intemadonal Trade and Industry, “Compilation of an International Input-output Table,” paper presented at he OECD

workshop on IutuMtt“OMI I-O Tables and Pedamuscc AnaJysis of Structural Ac@strmmt, Dec. 14, 1988, Pans, France, p. 2,3.

could remain unchanged while its domestic value- diate inputs” in complex ways. On close examinaadded and employment declines. (In fact, about 70 tion, there are deficiencies in much of the data that

percent of the value of the components of Chrysler’s are currently used. The largest of these include

vehicles are manufactured by outside suppliers.55) problems with:

Similarly, the company could decide to purchase

advertising, payroll accounting, software develop- ●

ment, and other services from specialty firms instead

of doing this work with internal staff. This could also

leave total auto sales unchanged while reducing

value-added and employment in the automobile ●

industry.

Value-added in constant dollars can be computed ●

by subtracting an industry’s intermediate inputs

from its output after both have been adjusted for

inflation. Doing this with precision obviously redata showing the links connecting different

kinds of businesses (including data showing

what businesses purchase from other businesses)

the way products and services are adjusted for

inflation (many of these problems have already

been discussed)

the way imported intermediate inputs are treated

(this issue is treated in greater detail in the next

section on international trade).

quires an enormous amount of data. It also requires Data limitations in these areas make it extremely

high quality data for the entire economy since difficult to trace national output to specific busibusinesses depend on each other through “interme- nesses with much precision--particularly when it is

sSKeVi~ ~~~, “fici~ ~~~g by ~ U.S. A~~obile ~us~,” report prepared for the Congressional Research Service, Washington, DC,

Nov. 8, 1985, p. 2.
important to remove the effects of inflation. In many

cases it is necessary to use “gross output” or sales

instead of value-added in analysis of specific industries; this is the case with estimates of industrial

productivity. 56

Adjusting for Inflation —In principle, the value-

-added in each type of business can be computed

using input/output tables. The inputs purchased by

each business are deflated separately and the total

deflated value of intermediate inputs is subtracted

from a deflated level of industry sales (or gross

output). This technique, called “double-deflation,”

is recognized as a preferred method by the Department of Commerce because of its use of a consistent

set of price indexes.57 The problem of adjusting total

GNP accounts for inflation were discussed earlier.

Developing deflators for value-added by industry

(also referred to as gross product originating)

compounds the problem since much more detailed

and extensive information is needed. Services are a

major problem since about two-thirds of all services

are purchased by businesses as intermediate inputs

and not by households or the government as a final

product. 58

In practice, however, incomplete data mean this

technique can only be used in 29 percent of the 1986

GNP—the manufacturing, farm, and construction

sectors. 59 A variety of scaling techniques and other

methods are used for the rest of the economy.60

Again, services are the most difficult area. The

intermediate inputs of service industries are very

poorly documented from survey-based series.61 For

many kinds of services, no direct data on intermediate inputs are available.

Even after the current dollar data are compiled,

adjusting for inflation is difficult because of the lack

of adequate price series for services. BEA calculates

deflators for several service sectors by extrapolation

using jobs as a proxy for increases in quantity. By

definition, this means that no labor productivity

growth can occur, resulting in an overstatement of

the rise of prices in this industry and a subsequent

underestimate of the quantity of services purchased

as inputs.62 The underestimate of inputs means that

the value-added is overstated.63

Estimates of value-added in construction suffer

both from poor estimates of total industry output in

constant dollars (for reasons discussed earlier) and

from poor estimates of inputs. Since there is no detail

on prefabrication by construction suppliers, inputs

are double counted. For example, inputs such as

wood are counted once when they are purchased by

a business making prefabricated (pre-hung) doors

and are counted again for a second time when the

prefabricated doors are purchased as an input by a

construction firm building a house.64 This results in

an over counting of inputs and subsequently an

underestimate of construction’s value-added.

BLS has a deflator series for the gross output of a

number of service businesses (some not completely

incorporated into the BEA accounts) and has research projects underway in a number of areas

(communications, semiconductors, computer programming services, medical services, and banking).

Double-deflation is also limited by the fact that

deflators for gross output are available only for 72

percent of the GNP (table 3). BLS computes

deflators for a number of service industries not

presently used by BEA. The services for which

precise deflator series are available, however, are

primarily those where output is comparatively easy

to quantify (e.g., electricity). In other service sectors

attempts are made to count or quantify output using

measures such as the number of checks processed or

%Me~~e~# & Interpretation Of Productivity, Op. Cit., fOOtnOtC 54, P. 67.

57~1(3 ~~, “Grosa Roduct by Industry,” Survey of Current Buriness, vol. 67, No. 4, April 1987, p. 27.

SSU.S. ~p~at Ofcomerce, B~~ of &onomlc ~~ysis, ~~rvey ofc~~renfB~i~ess. 1984, “mC InpUt-GutpUt Structure of the U.S. Economy:

1977,” vol. 64, No. 5 (hhy), pp. 42-84.

59c~c-~ ~m N~on~ ~ome~~uct &XXXIMS, op. cit., foo~om 15, table 6.1.

@Peterson, op. cit., foomote 57, p. 27,

61~ ~me ex~nt ~is a rat ad growing probl~ Since a good pofiion of intermti~ ~rvke kput data usd to come from records kept on

regulated industries that have been or are being deregulated.

~~mn, op. cit., footnote 29, p. 52.

s3’’Grosa Product by Industry: Comments on Recent Criticisms,” op. cit., footnote 31, p. 133.

64BA1Y ad Gord~, op. cit., fOOtnOU 11 ~ P.
20

Table 3—BLS Deflator Series for Gross Output

Percent of

GNP

covered

Gross output deflators based on price indices . . . . 72

Manufacturing

Mining

Some Services

Gross output deflated by an index based on 6

hours or cost indexes . . . . . . . . . . . . . . . . . . . .

Nonresidential structures etc.

No BLS deflator series. . . . . . . . . . . . . . . . . . . . . . .

General government . . . . . . . . . . . . . . . . . . . . . . (10)

Owner-occupied housing . . . . . . . . . . . . . . . . . . .

Rest of the world . . . . . . . . . . . . . . . . . . . . . . . . . . . (1)

Households and institutions . . . . . . . . . . . . . . . . . . . (4)

NOTE: Totat may not be 100 pement due to rounding.

SOURCE: J.A. Mark, “Problema Encountamd in Measuring Single- and

Multtfdor Produetivlty,” *fh& LdXW /?ev&w, Deeember

1-, p. 5.

the number of operations performed.65 But these

techniques have clear limitations in times of rapid

technical change.66

Adjusting for Trade—Another difficulty in assigning value-added to different industries is the fact

that the current input/output tables do not distinguish between imported and domestically produced

products used as intermediate inputs. Imported

inputs are recorded as a category of final demand.

This accounting convention is adequate for aggregate measures of GNP. But without additional

information the data cannot be used to track many

important effects of international trade on the U.S.

economy-such as the way trade affects the output

of different kinds of businesses.

First, BEA uses the deflator for domestic industries to adjust intermediate inputs purchased even

though some of these inputs are imported.67 This had

the effect of underestimating the value of intermediate inputs when the dollar was strong in the

mid- 1980s and underestimating their value when the

dollar weakened. During the mid 1980s, using faster

rising domestic price series on imported inputs, the

level of intermediate inputs was overstated and the

level of value-added or output was understated.68

BEA estimates that correcting for this problem

would drop the growth rate of manufacturing by half

a percent or more per year from 1979 to 1985.69 That

correction is nearly equivalent to eliminating the

increase in output of the electrical equipment

industry—the second largest contributor to manufacturing’s growth over the period.70

Second, some products purchased “intermediate

inputs” by businesses are in fact products produced

by foreign subsidiaries. In many cases U.S. components are shipped abroad for assembly and then

reimported for final testing and sales. This kind of

production is encouraged by Items 806 and 807 of

the U.S. Tariff Code requiring duties only on the

difference between the value of components exported and products imported by a multinational

firm. The Census Bureau reports that many firms fail

to report inputs received from overseas affiliates as

costs of business.71 As a result, the value of the input

gets credited as value that was added domestically,

overstating the true amount of U.S. production in

that industry .72 Although this problem has always

existed, the upsurge in intermediate inputs coming

from foreign sources makes this a growing problem.

And potentially it is a large problem. In 1985, nearly

a third of all U.S. exports were exports from U.S.

companies to overseas affiliates. Over a fifth of all

imports to the United States came from these

overseas U.S. affiliates.73 For some industries like

65J~e A. M-‘o~~~~tivi~ in ~i~~~~es: ~BLS Expakmce,” paper presented at the Symposium on Technology ~d tk

services Induntrtca“ , Hanover, NH, August 1987, p. ~.

66Fm ~-p]e, ~nw~ of ~=tions p~~ fails to include developments such as increases in branch btiingm~ ~ssible ~u@ ~

widespread uae of automatic teller machines (ATMS). Baily and Gordon, op. cit., footnote 11, pp. 399-400.

67~ ~~m~ ~is occurs in he defl~on of ~~eum Where the imported price iS used.

% decline in the vak of the dollar since 1985 should have the reverse effect of underestimating the change in manufacturing’s output.

w~m -t~ ~d~: COIIMXICIUS on Recent &hicisms,” op. cit., footnote 31, p. 132.

7~me1~~mew, whi~ ~cl~~cmw~r indu~, wss the largest contributor. National Income and product Wounts, OP. Cit-,

footnote 15, table 6.2.

71~ d- ~~g ~f~ ~ is tie c~~ of ~uf~~m.~ Joht’I P. @VO1’d, “ Possible Improvements in Industrial Statistics for 1987,” paper

-Mto tk -MtiSOV Cmmittee of the Americart Economic Association and the American Marketing Association at the Joint A&iwry

committee, Oct. 9-1o, 1986, p. 5.

72~~.

73J~ ~~~~~ ~T* Num~~ Hi~,” ~ross t~ hard, October 1987, p. 12.
21

1 6

10

6

0

- 6

- 1 0

-16

- 2 0

Figure 4-Exports and Imports (percent of GNP in constant 1982 dollars)

Percrnt of GNP

1

u

I

i I 1 I I 1 1 I

I 1 I I I i I I I I I I I I I I I I I I I I I I I I [ I I I

1960 1966 1960 1966 1970 1976 1980 1986

SOURCE: U.S. Department of Commeree, Bureau of Economc Analysis, National Income and Product Accounts, tables 1.2 and 4.4.

semiconductors where potentially half of the value

of the chip is added overseas in the assembly

process, this could be an especially important

problem. 74

C. What Is the Impact of International Trade

on Domestic Producers and Consumers?

The speed by which the U.S. economy has

become immersed in international trade and accumulated a huge trade deficit is a striking example of

an economy in transition. The share of GNP held by

exports has grown by almost a third from 1970 to

1986; import penetration increased by 76 percent.

(See figure 4.) From the end of World War II to 1983

the amount of imports purchased on a per capita

basis (real 1982 dollars) slowly crept from $300 to

$1,500. By 1987, the amount was $2,300----a 50

percent increase in 4 years. This phenomenon is not

limited to a few select products but now affects

virtually every industry.

Global production networks have redefined the

nature of trade. Direct foreign investment75 by the

U.S. investors overseas increased by 43 percent

between 1980 and 1987; foreign investment in the

United States tripled during the same time.76 There

has been particularly sharp growth of worldwide

production associated with direct investment leading to an upsurge in “intrafirm” trade where a

7~ovoni, op. cit., f(XXllOtC 7 ] * P. 3’

75~fm~ ss owning 10 percent or more of an enterprise.

76u~ome]y, ~is &ta ~~v~l~le only in Cment ~IIMS, a Shortcoming ~SCUS~ ~low. !$CX J~eS K. Jackson, “American Direct Investment

Abroad,” Congressional Research Service, Aug. 8, 1988, p. 2.
22

division of a multinational corporation trades with

another affiliate.77

It is obviously important that we understand how

foreign trade influences the performance of the

domestic economy. For example, the effects of trade

on jobs, inflation, and our dependence on foreign

sources, are the root of many policy decisions.

While data about international trade have improved

significantly in recent years with the development of

improved reporting and good deflator series, more

improvement is possible. The absence of any detail

about trade in the input/output accounts, for example, means that an uncomfortable number of assumptions must be made to see which industries are

affected directly and indirectly by changes in the

U.S. trade position.78 The explosive growth in the

volume and complexity of trade makes this a major

challenge. And there is reason to believe the quality

of trade statistics may decline in the next few years.

Trade data are largely a by-product of data

collected for administrative or regulatory purposes .79

The administrative need associated with trade data

is the assignment of duties, tariffs, or quotas on

particular products from particular countries. But

increasingly, trade between countries have become

more free, undermining the motivation of Customs

to collect data on those products.80 From 1970 to

1986, the share of imports (based on value) subject

to duties has fallen by 50 percent.81 Data on U.S.

trade with Canada (about 20 percent of all U.S.

trade) may become very unreliable when tariff

barriers between the two nations are removed. Data

on trade with individual members of the European

Economic Community (EEC) may be difficult to

obtain once internal EEC tariff barriers are removed

at the end of 1992.82 New mechanisms for collecting

trade data must then be found to prevent the quality

of trade statistics from declining. Some form of

international cooperation on trade statistics is likely

to be desirable (possibly essential) to prevent a

major deterioration in the quality of data.

Tracking Trade Volume

When the number of export and import documents

more than doubles over a decade, reaching 15

million in 1987, just reporting the level of exports

and imports becomes a daunting task.83 This huge

jump in the volume of transactions has contributed

to deficiencies in the data. For instance, in the

mid-1980s the dramatic increase in imports made it

impossible for the government to collect accurate

monthly trade statistics. So much time was required

to process trade data that in 1985 anywhere from 35

to 53 percent of the official import statistics in any

single month actually represented a “carryover”

from previous months.84

Monthly trade data are, of course, also needed to

measure GNP accurately. The discovery of one

carryover forced the estimate of growth in the gross

national product (GNP) during the last quarter of

1984 to be revised downward-from 4.3 to 0.6

percent.85 Another revision of GNP tied to trade data

occurred in the second quarter of 1986 where the

new estimate reversed the direction of growth from

a positive rate to a negative one, resulting in the first

quarter of negative growth in the current economic

expansion-a fact that went largely unnoticed.86

Obviously, revisions of such magnitude can send a

nJ~S*h~c, “~@a-Fi~ TrA: h Update,” The New England Economic Rw2?w, May-June 1987, p. 47.

78w K. Yomg,~Law~n, md Jennifer Dman, “Trade Ripples Across U.S. Industries,” wot’king paper, U.S. DePment of c~e~t ‘ffi=

of Business Analysis, Januaty 1986, pp. 57-61, or ttte appendix of Technology and the American Econornic Tramition, OP. cit.. foomo~ 1 v for ex~Pl~

of assumptions employed to circumvent this ~bletn, pp. 469470.

79N~on~ ACAY Of ~ie~es, Coanmhtee on National statistics, “proposal for a Panel Study on Foreign Trade Statistics,” July 1987, p. 6.

~eport on ‘me Working Group on h Quality of Economic Statistics,” op. cit., footnote 6, part 1, p. 10.

S]U.S. ~pmat of ~~eme, B-u of tie -u,s~fi:icuf Ab~tract of the u@ed ~tu~e~, ]9&7, p. 778, l’tile 1351, ~ @ant W. Gardner

and Kent P. Kimbrou~ ‘The Behavior of U.S. Tariff Rates,” The American Economic Review, March 1989, vol. 79, No. 1, pp. 212-214.

sxJ~ph W< ~cm, ● ~e s~ti~cs @er: S@tiCS and 1992 in Europe,” Business ECOnOrrU”CS, JIIIY 1989, pp. 52-53.

83U.S. Congtlxa, General Accounting Offke, Merchandise Trade Statistics: Some Observations, GAO~CE-89-lBR, (Washington, DC: U.S.

Governtnent Printing Office, April 1989), p. 40.

~Slater, op. cit., footnote 5, PWt 1. P. 53.

Ssslwr, op. cit., foomote 5, part 1. p. 53.

86

U.S. ~mat of co-=,B~u of ~~ic~~ysis, Sumey Of c~re~ f?~i~ss, July 1988 and July 1986, table 1.2.
23

false signal to policy makers about the strength or

weakness of the economy .87

The Bureau of the Census is aware of these

shortcomings and improvements are gradually being

made by automating reporting and reducing “carryovers” by releasing trade figures 45 days after the

close of the month. By July of 1988, import and

export carryovers had been reduced to 5 percent of

the value of imports and exports.88 But nagging

problems remain.

An illustration is provided by looking at the gaps

in data between the world’s two largest bilateral

trading partners: Canada and the United States. U.S.

exports to Canada are consistently underestimated.89

In 1986 the U.S. trade deficit with Canada was

revised downward by 42 percent from $22.9 to$13.3

billion.90 Since June 1987, U.S. exports to Canada

are based on the more relaible Canadian reports of

imports from the United States.91 These data are

likely to become less accurate if, as is likely,

Canadian interest in precise tracking declines in a

free-trade regime.

A report by the General Accounting Office (GAO)

on Merchandise Trade Statistics pointed out many

other discrepancies in trade data. Over the last 13

years GAO found that U.S. export data consistently

fell short of foreign import data.92 Overall, the

shortfalls rose from $5.7 billion in 1975 to $18.7

billion in 1986, falling to 13 billion in 1987 after the

use of Canadian data was implemented.93 The report

concluded that: “ . . . there is a strong possibility

that U.S. exports are not fully counted; as a result,

the U.S. merchandise trade deficit possibly has been

overstated for the past several years. ’94

Trade in Production Networks

The growth in investments around the globe is

indicative of the emergence of worldwide production networks. For example, the assembly of the

Ford Escort involves glass from Canada, fan belts

from Denmark, radiators from Spain, steering

wheels from England, and tires from Norway. In all,

parts are made or assembled in 16 countries on three

different continents.95 In 1985, more than a quarter

of all Digital Equipment Corporation’s (DEC) sales

were sales to its overseas affiliates.% These global

production networks transcend the idea of the

sovereign state, obscuring the notion of what is

1313upposedly, ti overly optil~lc early estimate of 1984 GNP fooled the Federal Reseme Bank into limiting the expansion of the money supply,

helping to push the value of the dollar to its 1985 high mark. Testimony of Courtenay Slater before the Joint Economic Committee. Slater, op. cit.,

footnote 5, part 1, p. 94.

EEMerctie Tr~e Statistics: Some Observatwns, op. cit., foomote 83, p. 22.

89s1a~r, op. cit., foomote 5* X

 1 

~ P“ 56.

%lational Academy of Sciences, Committee on National Statistics, “Proposal for a Panel Study on Foreign Trade Statistics,” p. 6.

g~Merc_e Tr~e Stattitics: Some Observatwm, op. cit., footnote 83. p. 42.

92w q~y hcjdd the Un]tti !Natcs’ m@r trading partners: Canada, Japan, West Germany, France, The United Kingdom, and the Netherlands.

The Netherlands was the only country where U.S. exports were not consistently short of recorded imports from the United States. Sec h4erchundise Trade

Statistics: Some Observatiaw op. cit., footnote 83, p. 30.

gJMerc-e Tra& st~~tics,’ Some Observations, op. cit., footnote *3, pp. 30-32.

g4Me?cWe T?~e Statistics: Some Observations, op. cit., foomo~ 83. p. 2.

95Bq BIUCSWM and Bennett Harrison, The Deindustrialization of America (New York, NY: Basic Books), 1982, p. 177.

%Hein, op. cit., footnote 73! P. 12
24

meant by foreign trade.97 It is as if GM’s sourcing of

parts from a division in New York to an assembly

plant in Michigan was considered international

trade. Certainly, international, intrafirm transactions

are a much different variety of trade than what is

connoted by the traditional meaning of trade. This

changing nature of trade requires a reexamination of

how the statistics are collected, presented, and

interpreted.

For example, when the International Trade Commission (ITC) tries to calculate the import penetration rate of the computer industry, how should it treat

the large intrafirm imports from DEC’s Scotland

plant to its Maynard, Massachusetts plant? Is this a

threat to the U.S. industry? Should the sale price of

a Chrysler Mini Van complete with a Mitsubishi

engine (24 percent of Mitsubishi is owned by

Chrysler) be counted as domestic production?98 Tied

to this is the question of whether or not these

intrafirm transactions are being priced at the “armslength” market rates or at internal, “transfer” prices?

The Internal Revenue Service under section 482 of

the Internal Revenue Code requires that these

transactions be valued at market rates, but in many

cases the product being sold is a unique component

that does not have a corollary in the market. This

problem is even more vexing in the cases of

intangible, intermediate service sector products. The

BLS does not include intracompany transfer prices

in its international price series, preferring instead to

include only a subset of intrafirrn prices designated

as being arms-length. In any case, if in fact these

intrafirm transactions are not priced at market rates

then our knowledge about the “true” level of imports

and exports is diminished further.

Existing data makes it difficult to trace the effects

of imports or exports through the economy. Making

some reasonable assumptions about how trade is

used as an intermediate input, it is possible to see

that imported commodities affect businesses throughout the economy (figure 5).99 Without more precise

data, however, it is impossible to make firm estiFigure 5-imports Used to Produce Amenity

(directly and indirectly)

Transportation

Clothing & pers care

Federal defense

Exports

Recreation leisure

Food

Housing

Government (n.o.c.)

Health

Personal buo. & comm

Education

o 5 10 16 20 26

Percent of spending on unonlty

SOURCE: U.S. Congress, Offke of Technology Assessment, Tmhr?ology

and the American Economic Transition: Choices for the Future,

OTA-TET-283 (Washington, DC: U.S. Government Printing

Office, May 1968), p. 296.

mates of the problems that might be created by a

sudden change in trade--such as a change in the

import quotas of steel on the U.S. auto industry, a

disruption of the oil supply because of a war in the

middle east, or the effect low-priced or “dumped”

semiconductors would have on the U.S. computer

industry. It is even difficult to determine the

vulnerability of the Department of Defense (DoD) to

import disruptions since DoD purchases products

are at the end of long and complex production

networks. 100

The Census Bureau is attempting to ask more

detailed questions about imports used in production,

but in many cases the establishments simply do not

know the national origin of the products they

purchase since the products have passed through

many middlemen.

Direct Investment

Shifts in the Capital Account— the value of U.S.

assets owned overseas net of foreign owned assets in

the United States—are also distorted by a failure to

‘27s= ROlXII B. Reich, “Mcm&s Only,” The New Republic, June 26, 1989, pp. 14-18.

98 Autmotive News, 1987 M~ket Data B~k ~ cit~in office of ~ho&y Assessment, Technology @d the American Econor?dc Transition, Op.

cit., footnote 1, part 1, p. 326.

99s= Tec~fo~ ad the krican Economic Transition, op. cit., foomote 1, pm 1, p. 469.

1OOs= us, ConmSS, offIce of ~hnolo~ As~ssm~nt, Holding the Edge, OTA-ISC-421, Aptil 1989.
25

Figure 6—Investment In Information Equipment

(percent of all producer’s durable equipment)

4 0

90

20

10

0

1960 1966 1960 1966 1070 1976 1060 1906

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

National Irteome and Product keounts, table 5.7.

adjust for changes in value over time.101 Direct

investments by the United States overseas and by

foreigners in the United States102 are valued at book

value-the price paid for at the time of purchase. No

attempt is made to adjust for inflation. Since 70

percent of U.S. direct investments owned overseas

were purchased by 1980 at relatively low prices

while over two-thirds of foreign direct investments

in the United States were acquired after 1980 at

higher prices, the net position of the United States is

greatly underestimated.103Estimates indicate that

the failure to adjust for inflation undervalues U.S.

overseas investments by $200 billion to $400

billion. 104 In terms of policy, this undervaluation

means that the rate of return associated with those

foreign assets has been grossly overestimated and

the alarm over the United States becoming the

largest net debtor in the world might be misplaced.105 In addition to a more accurate valuation

process, researchers argue that more detailed data on

inputs, type of labor employed, and layoffs are

needed for a more complete analysis of the effect of

foreign direct investment on the U.S. economy.l06

D. What Capital and Labor Inputs Are

Purchased by Domestic Producers?

Up to this point this discussion has focused almost

entirely on different ways of measuring the output of

the economy and how to assign this output to the

activities of different kinds of domestic products. It

is important, however, to determine the extent to

which the economy is growing only because of

increases in the amounts of labor and capital and the

extent to which growth which can be traced to better

management or use of technology. Measuring the

productivity of the economy, or output per unit of

input, requires good information about both inputs

and outputs.

Rapid economic changes have always been associated with major changes in capital investment.

While investment in steam engines, railroads, telephones, and electric generating facilities dominated

earlier periods of economic change, the most critical

investments in the present period seem to involve

information and intelligence—both human and artificial. It proves difficult to measure either kind of

input.

A sharp growth in purchases of information

equipment is documented in the income and product

accounts. Over 40 percent of all business investment

in durable equipment is now in “information processing” equipment—double the 1979 share and four

times the 1972 share (see figure 6). ’07 It is difficult

to know how precise these measures are. The

loi~ Norm~ j. G]ickm~ ad hugISS p. Woodwud, The NW Competitors (New York, NY: Basic Books), 1989, Appendix A; Roben E. Lip=y,

“Changing Patterns of Internatiomd Investment In and By the United States,” National Bureau of Economic Research Working Paper No. 2240, May

1987, p. 47; and “Measuring the U.S. International Investment Position,” Survey of Currerat Business, June 1989, p. 40.

l~D~t inves~ent ~ def~ed ~ “ . . . the ownership, acquisition, or establishment directly or indirectly by a person-individual association,

corporation, government, etc.— of 10 percent or more of the voting securities of a foreign enterprise.” See James K. Jackson, “American Direct

Investments Abroad: How Much are ‘hey Worth?” Congressional Research Service, July 25, 1988, p. 1.

‘mJames K. Jackson, “American Direct Investments Abroad: How Much are They Worth?” Congressional Research Setvice, July 25, 1988, p. 2.

l~Jackson, op. cit., footnote 103, p. 10.

lC6Jac~, op. Clt-, fmmae 103, p. I and p. 6. when forms of international investment other than direct investment such m go~dt b~k loans, ad

securitie~h of which have valuation problem~ reviewed for measurement problems, the Department of Commerce contends that it is likely

that the aggregate international investment position has shown a substantial decline” . . . reflecting the large cumulative U.S. current account deficit.”

&e “Messurin g the U.S. International Investment Position,” Survey of Currenr Business, June 1989, p. 40.

l~ljc~m and Woodward, op. cit., footnote 101, p. 280.

lmN~mSI ~~tne and product Accounts, op. cit., foomote 15, table 5.7.
26

difficulty of measuring the constant dollar value of

computer purchases, however, has already been

discussed.

While the inflation adjusted value of the capital

equipment purchased may be difficult to measure

with precision, it seems likely that a significant

change in the composition of what is bought has

occurred. Such a large shift over such a short time

period means that not only has the composition of

capital investment undergone a dramatic change, but

also the nature of the firms that are buying the

equipment. Increasingly, industries not typically

associated with capital equipment, like insurance,

banking, and retailing, are becoming large users. In

1982, department and grocery stores bought more

computers and peripheral equipment than the aircraft and guided missile industry .*” These large

investments in information processing equipment,

all along the chain of production, are indicative of an

economy becoming more responsive to the needs of

customers and the challenges of competitors.109

Accounting Conventions

Changes in the economy have reopened an ancient

debate about whether to count investment in the

education and training of people in the same way

that physical capital is treated. Workers with good

educations, and skilled in adapting to new tasks, are

the most important inputs for most businesses. The

traditional national income and product accounts,

however, continue to treat education as a form of

consumption even though economists have long

understood it as a form ofinvestment.11°

Similar problems are encountered in government

spending. Unlike accounting conventions used for

private businesses, government purchases of capital

goods—roads, airports, public buildings, and other

facilities—continue to be treated as current government expenditures (current accounts) and not investments (capital accounts), even though many of these

facilities obviously provide a critical infrastructure

Figur87-ProductMty (output per hour)

100

SOURCE: U.S. Department of Labor, Bureau of Labor Statmtke, Fkw?dbod( of Labor SkNisfics, Bulletin 2217, June 19S5, table 91.

for economic growth. Since government investments are excluded by definition, figures on net

investment, the stock of capital, and government’s

role as an investor in the economy’s infrastructure

and a holder of assets is obscured. This limits

analysis that attempts to understand the links between types of government investment and economic growth. The United States is the only major

advanced economy that doesn’t separate its government expenditures into current and capital accounts. 111

Although these accounting conventions are not

new, their existence has drawn more attention in an

era of large budget deficits. According to one

estimate, the amount of useful capital investment

made by the government is roughly equal to half of

the $155 billion annual Federal deficit.112 The role

of Federal Government investment in promoting

economic growth also takes on increased importance

as international competitors explicitly base economic policies on government investment strategies.

Industry Purchases of New Capital Equipment

Statistical problems are encountered even using

the most conventional definitions of capital. The

10SU.S. ~-ent of Cmeme, Bureau of Census, Gener~ RcpO~ on lndusfriu/ Organization, October 1986, table 8, p. 294.

1~=11 Johnqon ~d pad R. Lawrence, “Beyond ve~c~ Integrati~-~e Ri~ of tie Hue-Adding p~ershlp,” The Harvard Business Review,

hdy-AuguSt 1988, pp. 94-101.

110H~ bvin, “Mapping the Economics of Education,” Educarwnal Researcher, May 1989, pp. 13-16.

11 l~c~l J. ~skjn, ‘~mtic~ ~d ErnplfiC~ ISSUeS in the M~urernent, Evaluation, and Interpretation of postwar U.S. !$aving,” in &rvings Ond

Capiral Forrnano‘ n, Gerard Adams and Susan Watcher, eds. (Lexington, MA: hxington Books, 1986), p. 19.

1 IZRoben Elsner, ‘*Divergences of Measurement ~d ~eory ad Some Implications for &onomic policy,” The American Ecorwrrtic Review, Mach

1989, p. 5; see Charles L. Schultz.e, “Of Wolves, lkrmites, and Pussycats,” The Brookings Review, Summer 1989, for an opposing view, pp. 26-33.
27

most recent statistics providing a comprehensive

estimate of which businesses purchased which types

of capital equipment (the capital flows tables) date

to 1977.113 Sometime in 1990, the 1982 capital flows

tables will be released, but given the peculiarity of

that recession year, the usefulness of the data on

capital investment will be limited. Even if 1982 were

not a recession year, the fast pace of change in

capital investments that has occurred since 1982

leaves a poor understanding of how the economy has

evolved. For example, from private sources we

know that the number of personal computers in the

workplace has jumped from 1.2 million units in 1981

to nearly 13 million in 1986, but we don’t know

which industries are using them or how.114Recent

criticisms that U.S. industry has not concentrated on

the process side of production 115 and has “hollowedout” 116 need to be analyzed using data that reflect

which industries are purchasing what type of capital

goods and how this equipment is being applied.

Recently, the Bureau of the Census has produced

a report that attempts to rectify this situation for the

manufacturing sector and goes further by breaking

the data down by size and age of the firm, but there

is no provision to make this an ongoing survey or to

extend it to other sectors. 117 Interestingly, the main

funding source for this survey was the Defense

Logistics Agency of the Department of Defense. A

one-time snapshot is useful, but it is static and it does

not begin to answer questions about how investments in capital goods change in an economy that is

increasingly dynamic.

Capital Stock

These issues deal with accounting for new purchases of capital equipment; another, possibly more

fundamental issue, involves accounting for the

existing stock and vintage of existing capital.

Currently, estimates of capital stock are derived

from a perpetual inventory system that assumes an

average useful life, a set retirement distribution, and

a particular efficiency rate.118 Estimates of the

existing stock can be calculated by knowing what

was purchased, when, and how fast it depreciates.

By and large, service lives and discard rates are

based on educated guesses and tax law provisions. 119

Except for nuclear fuel, railroad equipment, and

autos, the service lives for private equipment were

estimated from industry studies conducted during

the 1970s.120 These estimates work reasonably well

in periods of economic tranquility. But unexpected

events like the two oil shocks,121 the severe recession of the early 80s, 122 the advent of fierce foreign

competition, and the arrival of new types of capital

equipment, like computers, have undoubtedly

changed the efficiency, depreciation, and discard

rate of equipment and have caused a radical realignment of relative prices from what prevailed in the

early seventies. 123 The rapid obsolescence of capital

equipment used in the design and creation of

semi-conductors is an example of an important

industry that barely even existed in the early

seventies. A periodic rebenchmarking of the exiting

capital stock would reanchor the perpetual inventory

system and allow a fine tuning of the perpetual

1

‘3Jerry Silverstein, “New Structures and Equipment by Using Industry, 1977,” Survey o~Currenr Business, November 1985, pp. 26-35.

1 ldFu~ Cmputing k. cit~ in tie U.S. ~-ent of commerce, Bureau of the Census, StattiticalAbstractof the United States: 1988, table 1286,

p. 726.

115~c~l L. ~~m ~ Mchad K. ~er, ROIXXI M. SO1OW and the MIT Commission on Industrial productivity, Made in America.” Regairdng

the Productive Edge, (Cambridge, MA: The MIT press), 1989.

11% tem “hollo#+ut*’ refers to the prxtice of contracting out for finished goods and setvices that used to be produced internally, reducing the

function of the company to primarily marketing and distribution. “The Hollow Corporation,” Business Week, Mar. 3, 1986, pp. 57-85.

I ITU.S. Department of commerce, Bureau of Census, Man@cruring Technology Z988, SMT(88)-1, May 1989.

118cha]eS R. H~ten, “~ Me~~rnent of &plt~,” fo~cornlng In tie proc~ings from t~ Confennce on Research in kome and Wealth,

Washington, DC, May 12-14, 1988, edited by Ernst Bemtt and Jack Tnplett, July 1988, p. 44.

119~w~ ~,-. Dmou@, ad MW N~f, “Altemat.ive Measures of Capital Inputs in Japanese Mmufxturing,” foficoming in A

Comparison of Productivity in Japan and the United States, Nationzl Bureau of Economic Research, (University of Chicago Press), p. 3.

1~.s. ~p~mt of Cmeme, B~u of &onomic ~~ysis, ~~ed Preproducibfe Tangfi/e Wealth in the United States, 1925-88, (Washington,

DC: Government Printing Offk.c, 1987), p. xxi.

121s= M- N. B~ly, “pr~uctivlty ~d he Servims of Capital and Labor,” The Brookings p@erS on ECOnO@c Acrivify, vo~.1, 1981.

122s= sum G. Powem, ‘The Role of Capiti ~sc~d~ in Multif~tor Productivity Me~~ement,” Mo~~y Mor Revi~, June 1988, pp. 27-31.

123~ sl~ficmce of Some of ~e= fxtom ~ estimates of capll~ we ~xout~ by Huten et, al. s= ch~]esR. I-fdten, James W. Robertson, ad

Frank C. Wykoff, “Energy Obsolescence, and the Productivity Slowdown, “ in Technology and Capital Formation, edited by Date W. Jorgenson and

Ralph Landau (MIT Press 1989), pp. 225-258.
28

inventory estimation process.124 Canada has recently begun an effort to measure capital stock for

exactly this purpose.125

Education as Investment

Traditional accounting conventions do not treat

expenditures on education or training as a form of

investment.126 This is not a trivial exclusion because

total education expenditures are nearly 10 percent of

the GNP. Currently, data suggest that a company is

investing if it purchases a new machine, but not if it

pays for the employee training needed to use that

machine efficiently. Indeed, the total national investment in education probably exceeds net private

purchases of equipment like machine tools and

computers. Even if we decided to treat education as

an investment, however, we don’t have any clear

idea of how much we are investing. Estimates of

corporate investment in education and training range

from $66 billion to $210 billion.127 GM boasts that

it has the largest, private educational program in the

world. ’n Federal data on training are very incomplete. Estimates of private investment in education

are based primarily on private surveys, which are

acknowledged to be inadequate. 129

If education outlays are to be included in capital

accounting, a number of problems must be overcome. Perhaps most difficult is measuring the value

of education. However, even the most conservative

estimates would more accurately reflect the new

realities of an education-based economy.

Labor Inputs

Data on the actual number of hours worked by

Americans are available from BLS. But all hours are

not equal. Several attempts have been made to adjust

for the “quality” of labor by examining changes in

skills and levels of education.130 BLS is introducing

a new indicator of labor quality.

It is particularly difficult to adjust for the quality

of education delivered. Declines in standardized test

scores during the 1970s and early 1980s seemed to

indicate that an adjustment should be made. Estimates show that growth in “quality adjusted labor

inputs” may be 0.1 to 0.25 percent per year lower

than unadjusted labor inputs during the 1980s.131 It

is important to measure changes in the real education

assets available in the American workforce since

competitiveness of U.S. industry appears to depend

critically on changes in labor quality.

Adjustments for labor quality depend on techniques linking wages to education, age, sex, work

experience, and other factors. Few, if any of the

measures take into account training received on the

job (except to the extent that training is measured by

years of “work experience”). It is difficult to

determine whether higher wages are paid for education because the education was needed on the job or

simply because education was needed to get the job

in the first place. Planned improvements to the

Survey of Income and Program participation (SIPP)

survey (discussed later) may help.

E. How Productively Do Domestic Producers

Use Inputs?

Given accurate information about outputs and

inputs, it is possible to compute changes in the

productivity growth rate (output per unit of labor

input) of the economy. In the absence of productivity

IZAH~~n, op. cit., footnote 118. p. 50.

l~ptir Ko~~~, “’rhe CWIW stock Smey proj~t,” ~u~ey ~fcu~re~t B~iness, May 1985, VO1. 64, No. s, p. 31,

126A s~lw ~~t co~d ~W be m~ about expenditures on health care. See Eisner, op. cit., fOOtnOte 112, p. 10.

lzTRo~r J. wu~ and Sue E. Berryman, “Employer-Sponsored Trti g: Current Status, Future Responsibilities,” prepared for the Conference on

Employer-Sponsored Training, Alexandri& VA, Dec. 1-2, 1988, P. 3.

lzg~ve~=ment, The Atlantic, May 1989, p. 105.

129A.p. cmev~e ~d H. Gol&ein, “~p]oy~ Tr~ing: [ts ch~ging Role @An Analysis of New Data,” American Society fOr Training ~d

Development Press, Washington, DC, 1983 and A.P. Carnevale, testimony before the Subcommittee on Taxation and Debt Management of the U.S.

Senate Fiiance Committee, Washington, DC, Nov. 30, 1987.

13~w~ F. ~wn, Tre~S in A~r~c~n Ecowm”c Growth, 1929-1982 (Washington, DC: The Brookings lnstltuthm), 1985; D. Jor&nsont F.

Gollop, and B. Fraurneni, Productivity and U.S. Economic Growrh (Harvard University Press, 1987), E. Dean, K. Kunze, and L. Rosenbhun,

‘Troduetivity Change and the Measurement of Heterogeneous Labor Inputs,” paper presented at the Conference on New Measurement procedures for

U.S. Agricultural Productivity (Mar. 31-Apr. 1, 1988, Washington DC).

131~~ N- B~y, $’~~uctivi~ ad tie semices of Capital ~d Labor,” Brookings P~ers on ECOnOm”C Activiry, VOI. 1, 1981, p. 49; J. Bishop, “1S

the IkQ Score Decline Responsible for the Productivity Growth Decline?” working paper 87-05 (Cornell University, Jan. 6, 1988).
29

growth, economic output per person and therefore

living standards can only increase over the long term

by increasing inputs—e.g. by having each person

work more hours or by putting more people to work.

Productivity is a key measure of progress in the

economy.

132The output per hour worked in the

business sector of the U.S. economy in 1986 was 60

percent higher than it was in 1960. 133 Each hour

worked therefore produced 60 percent more to be

paid as wages or profits.

Tracking changes in productivity, and helping

explain changes in productivity, is clearly one of the

central goals of the national statistical effort. It has

been very difficult to explain the striking changes in

rates of growth in labor productivity that have

occurred during the past few years. Labor productivity grew an average of 2.9 percent per year between

1948 and 1973, fell to 0.6 percent per year between

1973 and 1979, and increased to 1.4 percent per year

from 1979 to 1986.134 The challenge of explaining

the changes is made more difficult by the fact that

the post 1979 increases in productivity show a very

peculiar feature. Unlike previous periods of productivity growth, the post 1979 growth was dominated

by increases in manufacturing productivity (it averaged 3.5 percent per year between 1979 and 1986)

while productivity growth in other business sector

activities (primarily services) was very slow (0.6

percent per year). 135

A variety of explanations have been offered to

explain the change. Martin Baily and Robert Gordon

suggest that as much as 30 percent of the productivity decline after 1973 is the result of errors in

measuring both outputs and inputs. 136 Even after

granting some errors in measurement, however, a

real decline has clearly occurred. Explanations for

this decline include a surge of less experienced

workers from the baby boom, a decline in spending

for research and development, dramatic shifts in oil

prices that required increased inputs of capital and

labor, and new government regulations that increased labor inputs without increasing sales. 137

The Bureau of Labor Statistics (BLS) provides

three kinds of productivity statistics:

●

●

●

Business Sector Productivity that measures the

value-added in the business sector per hour

worked. 138

Industry Productivity Series that measures

gross output (or sales) produced per unit of

labor for each major industry. 139

Multi-factor Productivity Series that separates

changes in Business Sector output due to

changes in capital and labor inputs from

changes that result from new technologies or

other practices that can increase output without

increased use of capital and labor. A new series

(the KLEMS) provides a more detailed analysis

of the effect of different types of inputs (capital,

labor, energy, materials, and services) on productivity at an industry (2-digit SIC) detail for

the manufacturing sector.

In addition to these productivity series, the BLS

produces a number of “unpublished” series that

cover industries such as construction which are not

IJZRo&~ E. Lit~, Ro&~ Z. ~wrence, and Charles L. Schultze (eds. ), American Living Smnalzrds, (Washington, W: The Brookings hstitution,

1988), p. 178.

~BBSta~tlcal Abstract of the United States, 1989, op. cit., table 659, P. 403.

IJq~niWn, op. cit., foomote 29, p. 3.

135~niWn, op. cit., fOOt.nOte 29, p. 3$

13bB~Iy md Gordon, op. cit., footnote 11, p. 418.

13TS= ~wwd F. ~fimn, “The ~temptlon of Productivity Gro~ in tie Unitd States,” The Economic Journd, VO1. 93, Mach 1983; Herbert

Giersch and Frank Welter, ‘~wards an Explanation of the Productivity Slowdown: An Acceleration-Deceleration Hypotheses,” The Economic Journal,

vol. 93, March 1983; Wayne B. Gray, ‘The Impact of OSHA and EPA Regulation on productivity,” working paper, National Bureau of Economic

Research, Cambridge, MA, July 1984; Assar Lindbeck, ‘The Recent Slowdown of Productivity Growth,” The Econom”c Journa/, vol. 93, March 1983;

Zvi Griliches, ‘*R&D and the Roductivity Slowdown,” The American Economic Review, vol. 70, No. 2, May 1980; Martin N. Baily and Alok K.

Chakrabarti, “Innovation and Productivity in U.S. Industry,” Brookings Papers on Economic Activity, No. 2, 1985; Richard J. Murnarne, “Education

and the Productivity of the Workforce: Imoking Ahead,” in American Living Standara% Robert E. Litan, et. at., (eds. ) (Washington, DC: The Brookings

Institution, 1988), pp. 215-245.

138The bufiness ~{or ~clu&s roughly ~~.q~ers of GNP, 1t excludes output from (,he re~-of-~e-wor]d, general government, output from ptid

employees of household help, nonprofit institutions, the rental values of owner-occupied buildings, and the statisti- cal discrepancy in computing NIPA.

See U.S. Department of Labor, Bureau of Labor Statistics, Handbook of Labor Smtistics, Butletin 2217, June 1985, p. 226,

139us. ~pment of L~r, B~eau of Labor Smtlstics, prod~rivi~ Measures for Se/eC(ed fruf~trles M cove~m~t Services, Bulletin 2296,

February 1988.
30

covered in the published series because of weaknesses associated with the data.

Business Sector Productivity

The broadest and most widely followed measure

of productivity is called business sector productivity.140 The business sector is chosen to include only

the parts of the economy for which BLS has

reasonably good price deflators (e.g., it does not

include any government activities) and is highly

aggregated. Business sector productivity is published only for a few not mutually exclusive sectors:

private non-farm business, manufacturing, durable

and nondurable manufacturing, and nonfinancial

corporations. This high level of aggregation avoids

many of the difficulties encountered when attempts

are made to assign economic output to specific

businesses.

Business sector labor productivity is calculated

using constant dollar estimates of value-added

generated by the “business sector” and estimates of

the total value-added in manufacturing provided by

BEA in its Gross Product Originating series.141 But

there are problems even at this high-level of

aggregation. The difficulty BEA encounters in

estimating value-added in constant dollars was

discussed at length earlier in this paper. A number of

researchers including Baily and Gordon,142 Denison,

143 John Kendrick, 144 Lawrence Mishel,145 and

OTA146 think that manufacturing’s productivity rate

has been overestimated because manufacturing’s

constant dollar value-added is improperly measured.147 If this is true, non-manufacturing’s productivity has been underestimated.

Industry Productivity

The problem of computing value-added can be

avoided if labor productivity is defined to be gross

business output (or sales) divided by hours worked.

In this case it is only necessary to develop a deflator

for the products sold by an industry. The difficulty

with using gross output as a measure of a business’

contribution to economic activity has already been

discussed in the section on “Computing Business

Output by Sector.” A potential problem with gross

output is that it includes the value of intermediate

inputs produced by suppliers and the value-added by

the firm. Thus artificial boosts in labor productivity

could occur if a firm kept gross output steady while

increasing suppliers’ products and decreasing labor

that used to be used to make those products

internally. BLS claims that it does not publish series

for industries where the intermediate inputs have

changed so significantly that they would distort the

results.

The industry labor productivity series covers

mining and manufacturing industries in extensive 3

and 4 digit SIC (Standard Industrial Classification)

detail, but does not have any published productivity

data on any segment of the construction industry and

the published data covers less than half of the jobs

in private sector service industries.148 The services

that are included tend to be industries whose output

can be quantified, such as ton miles in the railroad

transportation industry.149 This means that impor1~ b~~ ~tor iul~es ~@y ~qu~m of GNP. II cxcl~es o~put f~rn tie ~St-of-tie-world, general government, output from paid

employees of housebold help, nonprofit institutiotm the rental values of owner-occupied buildings, and the statishcal discrepancy in computing NIPA.

see Us. Departmen t of Labor, Bureau of Labor Statistics, Handbook ofl.abor Statistics, Bulletin 2217, June 1985, p. 226.

141u.s+ ~=ntof~r,B~au of ~bor s~tistics, Handb~~k of Met~&, Bul]etin 2134-1, (w&in@on, DC: U.S. Government Printing Offii%

1982), p. 94.

14zBaily and Gordon, op. cit., footnote 11, p. 420.

lqs~w~ Fe ~Wn, Es-es of pr~~tiv@ Ctinge @ /~~(w~hgton, DC: The Brookings Institution, 1989), p. 58.

144Jo~ w. K~&& “sfi~ s~~ ~IIctiMty, B~iness Economics, April 1987, p, 23.

145~~nW R.-1, *C~uf~_ Numb: How ~~~ate Statistics ~~e~ U.S. ~dusrn~ ~li~” (Washington, ~: &OnOIItiC pOliCy

Institute, April 1988), p. 57.

l~u.s. congress, Ofnce of ‘lkchnology Assessment, Paying the Bill: Man@cturing and America’s Trade Deficit, OTA- ITE-390 (Washington DC:

U.S. Government Rinting Office, June 1988), p. 46.

147* & ~~~ on 6*C~pU@ BUS&SS -t by s~mr” for a mom complete discussion of this mismeasurement.

14S~riv~~m~le 1 ofPr~~dvi~Me~~esforSe/ectedlnd~t~es ~Gover~~Se~ices, f3~letin 2*%, February 1988 ad U.S. ~-CIM

of Commerce, Bureau of Economic Analysis, National Income and Roduct Accounts, op. cit., footnote 15, table 6.1OB.

l@Je~e A. h4ark, ’’Measuring Producu“vity in Service Industries: llte BLS Experience, ’’Resented at the Symposium on ‘lkchnology and the Services

Mustries, Hanover, NH, August 1987.
31

tant and quickly growing service industries whose

output is difficult to measure, such as business

services, health care, and private education, do not

have published productivity estimates.

Left with this gap, researchers either calculate

their own productivity estimates using BEA’s National Income and Product Accounts data (value-

-added divided by persons or hours) or unofficial,

unpublished BLS estimates. In sectors like business

services and banking, inflation adjusted gross product originating (value-added by industry) is estimated using changes in employee hours as an

indicator of how the quantity of output in this sector

has changed.150 Because labor productivity is calculated by dividing an industry’s total output by the

total number of hours needed to generate the output,

this process of estimating output through the use of

hours essentially puts hours in both the numerator

and the denominator of the ratio, canceling each

other out. The result is that labor productivity

increases are practically eliminated by definition

algebraically. *s* This might help explain why this

sector’s productivity has been flat even though the

service sector has made substantial investments in

productivity enhancing capital equipment, like computers. It is because of these limitations that the BLS

publishes detailed productivity indexes only for

selected industries.

Nevertheless, the widely followed business sector

productivity series does indirectly make use of this

limited, extrapolated data because many of the

constant-productivity services like business services

are large and growing inputs into other sectors,

particularly manufacturing.152 Because of this and

other factors, Denisen estimates that the productivity of farms and manufacturing has not only been

overstated, but has been increasingly overstated over

time .

153

Multifactor Productivity

The third measure of productivity produced by the

Bureau of Labor Statistics is the multifactor series.

Favored by most economists, the multifactor series

does not ascribe all changes in output to one factor

such as labor, but rather breaks it down into broad

categories of inputs. The series published on a

regular schedule traces changes in business sector

productivity to changes in multifactor, labor, and

capital productivity. A new series (KLEMS) add

energy, materials, and purchased services productivity for all manufacturing industries. Whatever

growth that can not be attributed to these factors

reflects a more qualitative change such as a shift in

technology or different management. Although, this

series is conceptually a large improvement, it suffers

from some of the same problems that plague the

other productivity and output measures; particularly

the lack of detailed data on purchased services.

F. How Has the Corporate Structure of the

U.S. Economy Changed?

Sophisticated technology, regulatory changes, an

active merger wave, a severe recession and the need

to participate in intricate marketing and financial

networks, have led to a realignment of the structure

of America’s enterprises.

While it is difficult to provide precise documentation, there is clear evidence that the growth of large

firms is increasingly built around the aggregation of

large numbers of comparatively specialized small

establishments. 154 Many large firms,l55 including

AT&T, DuPont, General Motors, Hewlett-Packard,

IBM, Martin Marietta, NCR, 3M, and Xerox claim

that they are reorganizing operations to encourage

more entrepreneurial behavior on the part of individuals and establishments. 156

15M10 ~tem,”~product by Indusq,” Survey of Current B~iness, April 1987, p. 27.

~slK+ck op. cit., f~tnote 144, p. 20 and Bady and Gordon, op. cit., footnote 11, pp. 394 and 397.

lS2T=he~er, op. cit., fOOtnOtC 3A. P. 34.

153&jwad F. ~~n, ESt~eS @p~o&tiv@ Chnge by /ndus~ (w~n~on, DC: The Brookings Institution), 1989, p. 55.

15~~ ~~j~at is ~e sm~lest tit in which b~iness ~tivi~ is conduc~. It CUI rcpxnt the entirety of the business activity within a business,

making that business a sole-establishment enterprise, or it can be one of hundreds of establishments (branches, subsidiaries, and corporate headquarters)

that make up a muki-establishrnent enterprise.

155w tm fm Rfem to tie entfi c~ra~ f~i]y or enterprise which can be made up of numerous establishments.

156jmeS Brim ~m, ~$~w~g ~matlon: c~moll~ ~am,” ~anar~ Business Revi~, ~y/June, 1985, p. 80 ~d R.M. KMter, “The Alt~k

on Pay,” The Harvard Bainess Review, vol. 65, No. 2, March-April 1987, pp. 60-67; and Michael Piore and Charles Sable, The Second industrial Divide

(New York, NY: Basic Books), 1984.
32

Small firms traditionally flourish during periods

of rapid transition, since the bureaucratic inertia of

large firms may blind them to opportunities in places

where none were expected. Computers and communications technology are providing opportunities for

small enterprises to hook into larger networks of

production. Sophisticated production technologies

capable of tailoring products to specialized markets

without a significant sacrifice in productivity or

increase in cost can vastly diminish the values of

economies of scale-benefiting small businesses.

When the trends are combined there appears to be

a convergence in structure as firms in sectors that are

traditionally fragmented (farming, physicians, and

home construction firms) become amalgamated into

larger firms while sectors that are traditionally

highly concentrated, like automobile production,

bear less resemblance to Ford’s enormous Rouge

River Plant (where wood and iron went in one end

and cars rolled out the other) and more like the

craft-based production that preceded heavy automation. This observation parallels the popular notion

that advances in production technologies have

reduced the need for large production units, but have

simultaneously made it possible to connect and

manage many different units under one corporate

structure. 157

The data needed to track these changes and their

impacts are spotty at best. Even answering more

mundane industrial organization questions, such as

how mergers and acquisitions have changed the

makeup of the U.S. economy, whether small firms

are the source of a disproportionate amount of new

jobs, or what the levels of new business startups and

closures are, requires elaborate methodological contortions. A complete data base of U.S. business

establishments organized into their corporate families that researchers can study to observe some of the

dynamics discussed above does not exist. The

constant churning of the economy makes this a

difficult task.

Currently, there are two primary data sets that

show the corporate structure of the U.S. economy

over time: the Small Business Administration’s

(SBA) Small Business Data Base (SBDB)158 and the

Enterprise Statistics file compiled by the Bureau of

the Census. 159 Both of these data sources suffer from

limitations that restrict their usefulness.

The Enterprise Statistics data series is a file built

from establishment level data collected from the

economic censuses (e.g., Census of Manufacturing).

The data are converted from the establishment level

to the enterprise level through use of another Census

data file, (The Report of Organizations), which takes

a snap-shot of the enterprise at a specific time.160

Because the data are cross-sectional and not longitudinal, it is impossible to understand the dynamics

underlying changes in employment or sales. Questions such as whether the employment growth was

due to the birth of new firms or the expansion of old

firms cannot be answered. Collected in five-year

increments, the Enterprise Statistics currently available dates back to 1982. In addition, the data do not

provide links that allow longitudinal tracking and

lack coverage in some of the fastest growing

segments of the economy, such as transportation,

communication, finance, insurance, and real estate. 161

157s= Russcll Johnston and Paul R, Lawrence, op. cit., footnote 109, pp. W- 101.

15EM SBDB C~SCS of ~vti~ ~temlat~ files. TWO are implicitly referred to here: USEEM (U.S. Establishment and Enterprise Microdata) ~d

USELM (United States Establishment Longitudinal Microdata). See U.S Small Business Administration, Handbook of Small Business Data, 1988,

(Washington, DC: U.S. Governmental Printing Office), 1988.

15gSeve~ o~= @~Wsfa business level &a ~ exist, such u unemployment ~sur~ (UI) ~ords, ~gitudin~ Employee-Employer Data

(LEED), Statistics of income, the hmgitudinal Research Database (LRD), and the Standard Statistical Establishment list (SSEL). These data sounxs

are not included as primary data sets because they have either been discontinued, are subject to disclosure provisions, cover only one sector such as

manufacturing, or lack fiudamental data of interest to researchers such as employment or the enterprise structure. See Handbook of Srnd Business Data,

1988, op. cit., footnote 158, pp. 10-17.

1~.s. ~pmatof Co-erW,B~~of~ ~us, ]982 Enferpr~e s~tistics: Gener~ReportonI~~trial organization, OCtOkr 1986, p. 31 ].

161- fA1tito cover ~me ~ims of ~~miw indus~ should & ~~eviat~ when we Census B~au exp~ds i~ industria~ wnsws in 1992. h

prior years, the Census Bureau could not include these sectors because they were in regulated industries. For a comparison of the two data sources, see

U.S. Small Business Administration, Handbook of SmaJIB~iness Data, 1988, (Washington, DC: U.S. Government Printing Office), op. cit., foomote

158, pp. 26-79, and Candee S. Harris, “Handbook of Small Business Data,” report to the Small Business Administration, January 1983, p. 8.
33

Under a mandate from Congress,162 the Small

Business Administration started in 1979 to develop

a database built from private Dun & Bradstreet

Dun’s Market Identifier (DMI) data that roughly

cover all private businesses with employees.l63 A

private data source was used so that individual firms

could be analyzed without violating confidentiality

restrictions associated with public data. Organized

into their respective corporate enterprises, the firms

are linked over time for the even years from 1976 to

1986, exposing the evolution of the business as it

gains and loses jobs and establishments, shifts lines

of business, and relocates geographically. The births

of new businesses appear alongside deaths. The

problem is that the Dun & Bradstreet (D&B) data

were collected for the purpose of assigning credit

ratings and creating other business products, not for

academic economic analysis. Nor was it ever intended to be organized on a longitudinal enterprise

basis. As a result, the data underreport branches of

multiestablishment firms since most credit decisions

are made at the corporate headquarters or subsidiary

level. 164 Thus the true corporate structure from an

establishment viewpoint is obscured. The database

does not include firms without employees, such as

the self-employed, who represent about 9 percent of

the labor force and roughly 60 percent of all

businesses.165 Other problems include the fact that it

can take several years for D&B to include new firms

(especially smaller ones) in its file—possibly missing a large number of firms that go in and out of

business during that time.166 Likewise, firms, especially smaller ones, are not consistently updated,

creating possible distortions in calculations of

growth.

167 When these problems are added to

clerical errors that were found, nearly half of the

establishments in the full SBA USEEM database

were deemed inadequate for tracking employment

over time. To correct for this, editing, imputing, and

weighting schemes had to be devised. 168

Depending on how one adjusts for these deficiencies, researchers using Dun & Bradstreet data can

get significantly different results. One of the early

users of D&B data, David Birch, states in his new

book, Job Creation in America, that firms with less

than 20 employees created 88 percent of all the new

jobs between 1981 and 1985.169 But Mr. Birch,

whose work has attracted a lot of criticism, asserts

that: “Anybody can make it come out any way they

want. ’’170

SBA analyses using a database built on Dun &

Bradstreet data conclude that only 36,5 percent of

new jobs came from firms with less than 20

employees between 1982 and 1986.171

The lack of a comprehensive longitudinal database on corporate structure means that questions

concerning the roles of small versus large business

in generating economic growth are answered with

great imprecision-if they are answered at all.

Studies about the source of job growth, as discussed

above, are controversial. Research that looks at the

correlation between productivity and firm size yield

ambiguous results.172 The amount of business startups and closures—a seemingly basic indicator of the

direction of the economy-is unavailable with any

162~ Stil B-s Economic Policy Act of 1980, Public Law %- 302.

lbs~~l~ti~ity RXtiCtiOIIS prewmxl the SBA from using detailed Census data.

164~ 1978, *R Wm a 15 million employ= ~ffme~ ~tw~n tie tot~ employment re~~ by tie corporate tops of the fimls ~d the Stlm of the

firms establishment employment. Dun & Bradstrcet says that it has recently improved its coverage of branch establishments. See A.L. Walton, “How

Small Businesses Contribute to Job Generation—The Pitfalls of a Sesmingly Simple Question,” National Science Foundation, prepared for the 1983

Conference on Industrial Science and lkchnologicat Innovation, Evanston, IL, May 2-4, 1983 and The State of Small Business, March 1983, p. 290.

~~Htiook Of small Business, 1988, op. cit., footnote 158, P. 7.

166Bmce D. ~~lps~B- A. Kfihhoff, “F~~ion, Grofi ad s~iv~: Small Firm Dynamics in the U.S. EConomy,” Srnaff Business

Economics, vol. 1, 1989, pp. 66-67; Sue Birley, “Finding the New Firm,” Academy of Management Proceedings ’84, 44th Amual Nkxting, Boston,

MA., Aug. 12-15, 1984, p. 67; Douglas P. Handler, “Business Demographics,” a study by Dun & Bradstrcet, p. 7.

167A.L. w~tm, “How f$m~] Busj~~scon~bute to Job Generatjon_The pitf~ls of a s~mtigly simple Question,” National Science Foundation,

paper prepared for the 1983 Conference on Industrial Science and ‘Ikdnological Innovation, Evanston, IL, May 2-4, 1983, pp. 10-14.

168s=H~mkofs~lBwiN5s Da~, 1988, ~. cite, foo~ote 158, p. 2 ~d T’he S~te OfSnaUBu.siraem (Washington, DC: U.S. Government printing

Office) May 1985, p. 425.

I@David Bjrch, Job Creation in America (New York, NY: The Free Press, 1987), p. 16.

ITODavid w-l ~d Buck Brown, “me Hyping of Sm~]-Firm Job GrOW~,” ~~e Wui/ street ~Ourw/, NOV, 8, 1988, SCCtk)n B, p. 1.

l?l”~e Hyp~g of Sm~-Firm Job Growth,” The Wail Street JoIund, NOV. 8, 1988, p. B7.

ITzSteva L~g~en, Fi~ Repo~ t. t~ s~//B~i~ss A&irusmmon on Firm Size and Productivity, (W~inlYon, DC: U.S. Government printing

Of- fice), Sqtcmbcr 1982 and Stahrl W. Edrnunds, ‘Organizational Size and Efficiency in the U.S.,” The Antitrust Bulletin, Fall 1981, pp. 507-519.
34

sort of precision.173 More abstract, but crucial

questions, such as the role of organizational structure in promoting innovation, cannot be definitively

answered.174 This uncertainty hinders and possibly

misleads the development of economic growth

policies.

The difficulty associated with developing a complete list of businesses cannot be understated. Dun&

Bradstreet estimates that “ . . . several hundred

thousand businesses see a change in their status

through starting or discontinuing operations or

through M&A [mergers and acquisitions] activity. “

175 A potential solution to this problem exists

through use of the Standard Statistical Establishment List (SSEL) that is used by the Census Bureau

or the unemployment insurance data collected by the

BLS. Currently both databases provide a sampling

frame for the agencies’ surveys which could possibly be used for other purposes such as examining

changes in corporate structure. Consisting of the

address, SIC code, sales, and employment of nearly

every business establishment in the country, the

SSEL provides fairly complete data on the way

business organize to form the economy; although

efforts would be required to organize the file into a

corporate structure. But the use of detailed Census

data trigger confidentiality limitations that restrict

their use by outsiders and even by other statistical

agencies like BLS. 176 Because of these restrictions

BLS has developed its own business list from

unemployment-insurance administrative lists that

have extensive coverage of small businesses.177

External use might be possible if the data are

aggregated in a way that protects the confidentiality

of respondents.

G. How Does Growth Affect Incomes and

Income Distribution?

All the discussion thus far has been directed at

national averages. Aggregate data on growth of

national income and wealth say nothing about who

benefits from this growth. There are clear indications that the benefits of growth have been enjoyed

primarily by the wealthiest families in the United

States since 1979.178 The gap separating those able

to benefit from the transformation of the American

economy (primarily people with good educations)

and those left behind (primarily those with poor

educations and families headed by single women) is

growing. 179This trend has a direct effect on theU.S.

labor force because it means that about a fifth of all

children now live in poverty and may enter the work

force disadvantaged by this experience.

Information about the effects of economic change

on income distribution are important for policies

ranging from changes in the personal income tax to

changes in welfare policies to identifying who are

the poor. The bipartisan welfare reform program

recently enacted drew heavily on data showing how

many people remained in welfare programs for long

periods of time.

Data linking incomes to individuals and households come primarily from four sources: the Statistics of Income, the Current Population Survey, the

Survey of Income and Program Participation, and

the Consumer Expenditure Survey. Of the four, the

most widely used for studying patterns of income

distribution is the Census Bureau’s Current Population Survey (CPS) because of its rich demographic

detail on households. While most wage income is

reported in the CPS, income from dividends, interlT3H@~k of s~l B~i~ss Dw, 19~, op. cit., foomote 158,pp. 21-22.

lT4~ c.~a,j. cl~k, ~d LOG. ~~, UMW~yw~ ~Techm”c~ ]~va~n (WeSp~ m: Greenwood FWXS, 1982); W.M. Cohen, R.C.

bvin, and D.C. Mowery, “Firm Size and R&D Intensity: A Re-examination, “National Bureau of Economic Research (Cmbridge. MA). w~ing p-r

No. 2205; and L. Rmuuzky, et. al., The Process of Technofogicaf Innovation: Reviewing che Literature (Washington, DC: U.S. Government Printing

Office, 1983).

175 Hmdl=, op. cit., foomote 166, p. 10.

176Eff~~ ~ le~l~ve]y ~~~fi&nti~~ limitations have met witi opposition. See National Associatmn of Business EcOnOmi sts, “Report

of the Statistics Ccxmm“ttce of the National Associatkm of Business Economists,” February 1988, p. 18.

177 Sl*r, op. cit., foomote 5S ~1* P“ ‘“

178s. ~iger, p, G@~h~, ~ Eugene Smolensky, “How & Rich Have F-, IWS-8T,” The American ECOtIOmiC Review, VO1. 79, No.2, hhy

1989, PP. 310-314. US. COngrCSS, Congrcasioti BIJ@t Off@, The C~n8ing Dis~~~n Of Feder~ T~S: 1~75-~~v %* 1987”

179s= U.S. *P, conmlm~B-t offi~, Trends in FamL/y [~ome; ]970-1986 (Washington, D, C,: U.S. Government Printing office),

February 1988, p. xviii; U.S. House of Representatives, Committee on ways and M=w ChM~en ~nfovero! May Z 1985) P. 601; ad U.S. COn81WISS

Congressional Rcsearch Service, “Economic Benefits of Education,” Dec. 13, 1988.
35

est, rents and other types of property-type income

are very poorly reported. Estimates indicate that

even after some imputations are made, only half of

some categories of income (e.g., interest and dividend) may be reported. The survey also does not

fully capture the income of the very wealthy.

In an effort to improve the accuracy and the

response rates, the survey does not ask the wealthy

to report their exact income, but instead simply to

state whether their income exceeds a certain threshold such as “$300 thousand or above” giving no

information about how much is earned in excess of

the threshold.180 The data available to the public has

an even more restrictive threshold of $100 thousand.

This not only leads to a significant underestimate of

the income of high income households which appear

to be a growing segment of the population, but also

frustrates accurate comparisons of the income distribution over time since the threshold level has been

increased erratically over the past 15 years. 181

Because this level changes over time, researchers

trying to analyze expenditure patterns on the basis of

income and estimates of household income growth

and inequality, particularly for this upper group, are

constrained. To get around the problem researchers

usually throw out the top group, leading to an

underestimate of the inequality in income.182

Some of the confusion surrounding the so-called

“Missing Middle” income distribution question,

where it is alleged that the middle class is shrinking

as the lower and upper classes grow, involves

limitations with the CPS. A report from the Congressional Budget Offie, which made creative use of

IRS records to correct for this upper-income problem, found that all of the inflation-adjusted, after-tax

income growth between 1977 to 1988 occurred in

the upper 10 percent of all families (see figure 8).183

Efforts to track growth that exclude this upper

income group are obviously severely hampered.

A potential solution would be for the Census

Bureau to assign a mean or median income level for

this top group rather than the threshold level. This

estimate could be derived through matching the CPS

to another data source (e.g., IRS Statistics of Income

Figure 8—Mergers and Takeovers,

1

2

3

4

5

6

7

8

9

10

Top 5%

Top 1%

(constant 1982 dollars)

1972-1986

O 5 10 15 20 25 30 35 40

Percent of All Family Income

NOTE: Assumes Corporate Tax Allocated to Captal Income,

SOURCE: Julius Allen, ‘Corporate Takeovers: A Survey of Recent Developments,”U.S. Congressional Research .%rwca report No 87-726-

E, Washington, DC, Aug. 6, 1987.

data) that does not suffer the same limitations. The

characteristics of the data used to accomplish this

task and necessary confidentiality restrictions require that it be done at a very aggregated level.

The other sources for income data include:

The Statistics of Income (SOI) published by the

Internal Revenue Service consists primarily of

income data, providing good detail on the

source of income (wages, interest, dividends,

etc.), but lacks explanatory demographic data

and information on people who do not file tax

returns.

The Survey of Income and Program participation (SIPP) is designed to provide ‘more detailed information about the income households receive from all sources—particularly

noncash transfer benefits. Unlike the CPS, the

SIPP monitors changes in the income of

individual families over a number of years and

data are available on a monthly basis. It also

attempts to provide a measure of the wealth of

households, not just current income.

I%ssetting of an iwome threshold is commonly referred to as “top-coding.”

181u.s. HOU~ of Rep~~n~vm, Cornmitt= on Ways and Means, Ck”fdren in POVer~, op. Cit., fOOtnOte 179, p. 599.

1821bid., p. 599.

183u.s. Cmms, ~c cm=s~m~ B~get office, The c~g~ng Dislribu~n Of Fe&raJ T~es. )975-)990, &tO&r 1987, table A-2, p. 85,
36

●

●

The Consumer Expenditure Survey provides

information about household purchases and

income for a small sample of families each year

(see the next section for more details).

A small sample of households has been followed by the-University of Michigan since the

mid 1970s. Known as the Panel Study of

Income Dynamics, the series is a rich longitudinal database but suffers from a small sample

size and data inconsistencies.

While the data available from these sources can be

used to outline basic changes in income distribution,

the limitations noted greatly limit the analysis

possible. Changes in the wealth and income of

wealthy households is not well reported. Sample

sizes in SIPP and other longitudinal studies are so

small that many small but important demographic

groups are not represented in large enough numbers

to be statistically significant (e.g., nonwhite welfare

mothers, or the very old). Information about employer benefits is not well reported either by

employers or by individuals. What is reported is of

uneven quality. Efforts to link total employer

contributions to benefits enjoyed by different kinds

of households, are only in the experimental stages. 184

As mentioned before, a potential solution to some

of these shortcomings involves matching these data

files to other data sources. This process could

compensate for some data deficiencies, but suffers

from the fact that much of this data was collected

from respondents with an assurance of confidentiality. Conforming to these confidentiality restrictions

can limit the usefulness of the matching technique. 185

Other Problem Areas

This report has only touched on data designed to

address gross measures of economic change. A

number of other areas clearly deserve attention.

These include:

The redundancy of two business directory lists

for sampling purposes should be eliminated.

Currently, two nonfarm business lists exist as

sampling frames for establishment censuses

and surveys. The Bureau of the Census uses the

Standard Statistical List (SSEL) and BLS uses

its business directory list compiled from Unemployment Insurance (UI) data. The existence of

two lists means that data is not strictly comparable and that an expensive duplication of effort

occurs. Although the President’s Economic

Policy Group recommended that BLS serve as

the agency in charge of compiling a common

list,186 efforts have been stymied by a lack of

legislation that amends the confidentiality law

allowing BLS access to the Census data.

Data on scientific and engineering manpower

also suffer from weaknesses. BLS surveys

report only half of the scientists and engineers

reported by NSF while NSF reports only 60

percent of the engineers counted by the Bureau

of the Census.187

While extensive data are available on regional

economic activity, comparatively little work is

done to analyze or present the data in ways that

would allow states or regions to reproduce

national analyses. Regional input/output data

are constructed with painful deliberation.188

BEA has long published personal income

estimates by state and county and has recently

introduced an “experimental” data series on

Gross State Product. As businesses focus on

niche markets and as economic development

issues increasingly fall into the hands of the

states, there is a need to add to databases of

regional economic statistics. Significant economies seem to exist by having the Federal

government undertake this task rather than

having the individual states do their own data

collection.

The two standard measures of net savings—

BEA’s NIPA measure and the Federal Reserve

Iud~ ~mW B-u h= exP~a~ wi~ coll~~g ~ta from emp]oyers of respondents of SIPP and hopes IO do so on a larger scale in the future.

lass= RW= H-it, ~~r Bowie, Daniel Kasprzyk, and Sheldon Ha~r) “Enhanced Demographic-Economic Data Sets,” Survey of Current

Business, November 1988, pp.44+8 and Walter Y. Oi, “How Valuable Are Matched Data Files,” Survey of Current Business, November 1988, pp. 49-50.

18&”R~ of tie Wofing ~upon he @~ity of &onomic s~istics to the Economic pOliCy COllIICil,” April 1987 and “Implementation of the

Working Group on the Quality of Economic Statistics Rexomrnendations,” March 1987.

107s= N~~~ ~~my of Science, Sumeylng the Nation’s Scietiists ~Engi~ers: A Data System for tk 1990s, 1989 ~d U.S. Congress, Office

of Ikchnology Asseasmen t, Preparing for Science and Engineering Careers: Field-Level Profiles, Staff Paper, Jan. 21, 1987.

lsspole~e, op. cit., foomote 43, p. 3.
37

●

●

●

Board’s flow-of-funds saving measure-lead

to conflicting estimates.189 The NIPA “incomeless-outlays” method faces the problem of

subtracting two large numbers.190 Thus errors

in either end up as errors in the residual which

is the estimate of savings. Flow-of-funds savings estimates are actually built up from data on

assets and liabilities which face new problems

because of the many new savings instruments

made possible by a deregulated financial indusQ .

Nagging problems remain in treatment of the

“underground economy” and possible under

count of inner-city residents in the decennial

census.

191

Time-Use Accounting needs to be improved.

The way Americans spend their time is becoming a critical part of economic analysis since

major changes are occurring in the things

formally counted as a part of the economy.

Many activities formerly done with unpaid

household time (child care, cooking, care for

the elderly) are now purchased while capital

equipment in the home (microwaves, VCRs)

substitutes for services that might otherwise

have been purchased. Changes in time use

provide a sensitive way to measure changes in

the overall performance of the national economy. It is clear, for example, that Americans

have purchased economic growth in recent

years by sacrificing free time. The National

Science Foundation funded small time-use

surveys in 1965 and 1975, but budget cuts made

a 1985 survey impossible. Fortunately, a private corporation, AT&T, provided the bulk of

funding for a similar time-use study that year

based on many of the same categories as the

earlier research, and has recently agreed to

make most of the data available to the public. 192

Timely data are needed on the bridge used to

convert consumer spending to business output.193 A critical link between the world familiar to consumers (where things like pizza and

automobiles are bought in retail stores) and the

world of economic statistics comes in the form

of an obscure table called the bridge table in the

jargon of input/output accounting. These tables

are used to translate a dollar spent (say on a

pizza) into economic output in standard business categories (e.g., $10 in pizza purchases

might result in $2 for the grocery store, $0.50

for the trucking and warehousing company,

$0.50 for insurance, $5 for food manufacturing

companies and the rest for farmers). Since it

was assumed that the ratios in these tables

would change only slowly (retailing was never

considered a progressive industry) there was

little point in devoting a lot of attention to

updating them. Our work convinces us, however, that the new production networks necessarily alter the chain of production that includes

transportation and retail operations. This is

precisely where the inventory reductions occur

when information equipment is used to improve the flow of products.

Research and Development data by industry

and product type have not been reported in

much detail. The National Science Foundation

has attempted to correct many of the deficiencies (e.g. no summary data are published for

construction, services, and some manufacturing) and will shortly publish much more

complete data. Many problems remain, however, because of incomplete reporting and

because research and development conducted

at the corporate level is difficult to assign to

activities by individual establishments that

may be owned by the corporation.

One of the weakest links in the chain of

statistics occurs between the highly detailed

demographic statistics on household spending

patterns contained in the Consumer Expenditure Survey (CSX) and consumer spending

information available in other data series like

those that appear in NIPA (the first item in the

right-hand column of table 1). Administered to

IWB~~, opt Cit., fm~o~ I I 1, pp. 15-20, and F. & heUW “Conflicting Measures of mva~ savings, “Survey of Current Business, November 1984.

~~t]ays include consumer expenditures, taxes, and interest payments made to b~inwws.

191JWI F. Hou~~, “The Un&rground Economy: A Troubling kue fOr policymakers, “ Business Review, September-October 1987, pp. 3-12; and

Barbara A. Bailar, “Finding Those the Census Missed,” Technology Review, May/June 1988, pp. 22-24.

19’2~ Natio~ sci~~ Foundation provided some funding for the 1985 time-use smey.

193s=, U.S. ~ment of Commerce, Bureau of Economic Andysls, “The hput-Output Structure of the U.S, Economy, 1977,” Survey of Current

Business, May 1984, table B.
38

5,000 household (consumer units) a year, the

CSX has since 1980 been conducted on an

ongoing (annual and quarterly) basis-prior to

that time it was done about every 10 years. The

primary purpose of the CSX is to provide

weights for the Consumer Price Index. The

relatively small sample size limits any detailed

attempt to figure out how or why different type

of consumers buy what they do; by the time you

slice the data by expenditure category, type of

household, and income level, the divisions

have become so fine that further attempts to

analyze the data by variables such as age, sex,

or region are impossible because the results

quickly become unreliable or “statistically

insignificant.” This is frustrating when advances in information technology have allowed

a much finer targeting of products to consumers

and have in-turn allowed consumers much

more flexibility in tailoring products to their

needs. This so-called “niching” phenomenon is

difficult to track given the limits to public data.

In a broader sense, many surveys linking spending

to consumer amenity are not well coordinated with

the national accounts. It is all but impossible to link

spending patterns to measures of the quality of the

amenities that are the ultimate result of the spending.

A number of surveys are taken that could, in

principle, be coordinated with the consumer expenditure surveys to help make some of these connections. They could also be designed with a more

comprehensive view to documenting changes in the

quality of American life and understanding the

nature of economic change instead of focusing

narrowly on the programmatic issues for which they

were initially designed.
Part III

Conclusion

Accurate, complete, and timely statistics are

critical tools for effective public policy and business

management. The perplexing nature of the economic

changes underway in today’s economy have, however, increased the difficulty of providing precise

measures of change and growth. This background

paper evaluates problems confronted in answering

some of the most basic economic questions.

Some of the difficulties are longstanding. For

example, there has always been a need to get a more

accurate estimate of the output of the construction

industry. Some are old problems that have become

much more important as the economy changes.

Growth in service businesses makes it impossible to

ignore defects in the way productivity in services is

estimated. And economic change has created some

entirely new problems-such as the emergence of

complex international production networks, and the

need to understand the way information is used as an

input in the economy.

The statistical agencies must continue to reevaluate their methods in view of new needs of public and

private data consumers. Although this background

paper has focused on the shortcomings, a number of

improvements have occurred. Some of the most

important recent developments include: the newly

revised Standard Industrial Classification, BEA’s

development of a computer deflator, BEA’s Gross

State Product series, BEA’s revision of the Gross

Product Originating series, the Bureau of the Census’ Survey of Income and Program Participation,

Census Bureau’s Longitudinal Research Database,

the BLS International Price Program, and the new

BLS Multifactor Productivity series. These improvements indicate that the system can react to new

policy-oriented needs, especially if the resources to

do so are available.

These and the many other innovations underway

are commendable, but to be effective they and other

planned improvements need to be part of a coordinated program that sets priorities, develops a coordinated response, and evaluates how well the needs are

being met. This function was given to the Office of

Management and Budget. But for many years OMB

has elected to take a very narrow view of that

responsibility and has not fulfilled its larger mission

in this area.

–39-

This paper makes no attempt to provide a comprehensive critique of national statistics nor does it

attempt to offer comprehensive solutions. This

important task needs to be undertaken on a regular

basis by the Federal statistical agencies themselves

acting under the guidance of OMB. We have

identified a number of areas where a coordinated

response is clearly needed. These include:

●

●

●

●

Develop better techniques for evaluating real

(i.e., constant dollar) growth in areas where

most growth involves changes in quality or

capability. This typically includes areas where

technology is redefining the nature of the

product in fundamental ways. Improvements

would include expanded efforts in accounting

for growth in manufacturing areas like computers, semiconductors, communication equipment, advanced machine tools, etc. It would

also find a way to measure improvements in

quality that occur as firms make more timely

deliveries to suppliers or offer consumers a

wider variety of products, Without accurate

measures of changes in quality, policy makers

have a distorted view of where real growth is

occurring in the economy.

Improve techniques for evaluating real growth

in services. This means developing better

methods for recognizing that the quality of

education, health care, financial services, software development, and other services can

change. Lacking such methods, real growth and

productivity change for many services is underestimated, obscuring the innovations that are

occurring in these sectors.

Strengthen methods used to show the way

purchased services are used as an input in the

economy, particularly by manufacturing. By

underestimating the real value of purchased

services, the contribution of manufacturing

operations may be overestimated and thereby

mask problems in some industries and lead to

a misunderstanding of the value of service

industries.

Improve the methods used to track imported

products through the U.S. economy. Errors that

may underestimate the value of the foreign

inputs purchased by domestic businesses may

overestimate the contribution of domestic fins,

especially in the manufacturing sector. An
40

●

●

●

●

●

inability to trace imported products through the

economy makes it difficult to estimate the

vulnerabilities or the competitive strength of

different industries.

Develop better methods to monitor the construction industry. The health of this important

business, particularly the nonresidential segment is difficult to track using current methods

of measuring outputs, inputs, and productivity.

Improve methods of measuring investment in

education and training as well as the quality of

these services. Since worker education and

training has become a critical input to virtually

every business it is important to measure its

role as precisely as possible. Virtually nothing

is known about corporate investment in training

and only crude estimates exist of the practical

knowledge of people in the work force.

Establish better methods for monitoring changes

in the size and scope of firms and establishments. Major changes are taking place in the

size of individual establishments and in the

number and kinds of products produced in an

individual establishment. Likewise, major changes

have occurred in the number, size, and type of

establishments owned by a single firm. Policymakers concerned about trends in the sources

of job generation, the effect of mergers and

acquisitions, or the regional shifts of industry,

need to understand the nature of such changes

with greater precision.

Improve methods for measuring changes in the

distribution of income. It appears that income

distribution in the United States has changed

substantially in the recent past. A significant

amount of change has occurred in households

with very high and very low incomes. Neither

group is well tracked by existing data series.

Policy designed to affect such changes needs to

be informed by better data--particularly data

that shows changes in the income history of

individual households. Welfare policy, tax policy, and a variety of Federal expenditure

programs are strongly influenced by data in this

area.

Develop methods for tracking the effect of new

technologies. This could involve more timely

input/output series and a capital-flows table

with an improved set of business categories.

Such data is critical to tracing linkages between

●

industries and understanding the connection

between management of technology and economic growth.

Improve methods for tracking standard national economic accounts and other measures

of economic well-being in an integrated way.

The national system of accounts is designed

primarily to provide support to Federal macroeconomic policy makers. They were not designed to provide a macroeconomic view of

changes affecting particular industries or a

complete perspective of changes in the welfare

of Americans. But by necessity, the accounts

serve as a crucial resource for informing policy

decisions in areas ranging from energy policy

to social welfare policy. Policy analysis inmost

areas requires combining this data with statistics in areas like environmental quality, resource depletion, income distribution, and

health.

These links could be much more clearly understood given a more integrated way of reporting

economic progress in the United States that uses

both the national accounts and other measures of

social change. Such reports could also provide a

systematic view of the quality and completeness of

information not easily reported in economic accounts.

Few of these problems have easy solutions. They

all require a commitment to a long-term process and

management committed to making the system work

well as a whole. In some cases it will require

additional investments in computational equipment—

few of the statistical agencies have adequate computational facilities. It may also involve a concerted

effort to ensure that enough young people are trained

to take jobs in the statistical agencies and that the

agencies are in a position to attract a continuing flow

of new talent.

An adequate response to these challenges also

requires coordinated approaches to budgeting and

undoubtedly more money. The need for resources,

however, cannot be established without a clearer

view of the needs and priorities of the system taken

as a whole. Such a perspective is not now available

from any source. It is clear, however, that the price

paid for public policy mistakes that stem from

defects in national statistics can be many times

higher than the entire national statistical budget.