Automation and the Workplace: Selected

Labor, Education, and Training Issues

March 1983

NTIS order #PB83-191320
Library of Congress Catalog Card Number 83-600508

For sale by the Superintendent of Documents,

U.S. Government Printing Office, Washington, D.C. 20402
Preface

This Technical Memorandum is an interim product of OTA’s assessment, “Computerized Factory Automation: Employment, Education, and the Workplace.” The assessment is examining the nature and development of automation technologies (such as

robotics, computer-aided design and manufacturing, and automated materials handling,

storage, and retrieval). The assessment is also evaluating the structure and competitive

conduct of industries producing and using programmable automation technologies. Finally, the implications of the production and use of programmable automation for labor

and for education and training activities are being examined. The Joint Economic Committee, the Senate Committee on Labor and Human Resources, the Senate Committee

on Commerce, Science, and Transportation, and the Subcommittee on Labor Standards

of the House Committee on Education and Labor requested the assessment.

This Technical Memorandum discusses procedures for evaluating potential employment change associated with automation, and outlines associated problems. It also provides descriptions of the nature and modes of delivery of education, training, and retraining for persons holding or seeking employment in manufacturing industries. The material

draws in part on the products of a July 1982 OTA workshop on labor markets and

industrial relations and an August 1982 survey commissioned by OTA of education,

training, and retraining activities and trends.

OTA is grateful for the assistance of the assessment advisory panel, the Labor Markets and Industrial Relations Workshop participants, the contractors, and the many

others who provided advice and information. However, OTA assumes full responsibility

for this technical memorandum, which does not necessarily represent the views of individual members of the advisory panel.

 JOHN H. GIBBONS

Director

. . .

Ill
OTA Automation and the Workplace Advisory Panel

Roy Amara, Panel Chairman

President, Institute for the Future

William D. Beeby

President

William Beeby & Associates

Erich Bloch

Vice President, Technical Personnel Development

IBM Corp.

Barbara A. Burns

Manufacturing Technology Group Engineer

Lockheed Georgia

Jack Cahall

Manager, Training and Development

Cincinnati Milacron, Inc.

Dennis Chamot

Assistant Director, Department for

Professional Employees

AFL-CIO

Robert Cole

Project Director, Joint U.S.-Japan Automotive Study

University of Michigan

Alan E. Drane

Manager of Flexible Automated Systems

Emhart Corp.

Audrey Freedman

Chief Labor Economist

The Conference Board, Inc.

Sheldon Friedman

Director, Research Department

United Automobile, Aerospace and Agricultural

Implement Workers of America

Theodore W. Kheel

Of Counsel

Battle, Fowler, Jaffin & Kheel

James F. Lardner

Vice President, Governmental Products and

Component Sales

Deere & Co.

Eli Lustgarten

Vice President

Paine Webber Mitchell Hutchins, Inc.

M. Granger Morgan

Professor, Engineering and Public Policy

Carnegie Mellon University

George J. Poulin

General Vice President

International Association of Machinists and

Aerospace Workers

Bernard M. Sallot

Executive Director, Robot Institute of America

Society of Manufacturing Engineers

Harley Shaiken

Research Fellow, Science, Technology,

and Society Program

Massachusetts Institute of Technology

Kevin G. Snell

Vice President, Forward Planning and Development

Career Works, Inc.

Alfred P. Taylor

Manager, Factory Automation Planning Services

Operation

General Electric Co.

Philippe Villers

President

Automatix, Inc.

Victor C. Walling, Jr.

Coordinator, Business Futures Program

SRI International

Dennis Wisnosky

Vice President, Industrial Systems Group

GCA Corp.

Michael J. Wozny

Professor and Director, Interactive Computer

Graphics Center

Rensselaer Polytechnic Institute

Robert Zager

Vice President for Policy Studies and Technical

Assistance

Work in America Institute

iv
OTA Automation and the Workplace Project Staff

John Andelin, Assistant Director, OTA

Science, Information, and Natural Resources Division

Fred W. Weingarten, Program Manager

Communication and Information Technologies

Marjory S. Blumenthal, Project Director*

Beth A. Brown, Senior Analyst

Jean E. Smith, Analyst

Margaretta McFarland, Research Assistant

Elizabeth A. Emanuel, Administrative Assistant

Shirley A. Gayheart, Secretary

Theresa Richroath, Secretary

Patricia Gleason, Temporary Secretary

Jeanette Contee, Wordprocessor**

Contractors

Veronica F. Nieva and Robert Schrank

Ann Majchrzak, WESTAT

Publishing Staff

John C. Holmes, Publishing Officer

John Bergling Kathie S. Boss Debra M. Datcher Joe Henson

Doreen Cullen Donna Young

*Assistant Project Director until September 1982. Zalman A. Shave]] served as Project Director from April to August 1982.

**Until October 1982.
Labor Markets and Industrial Relations Workshop Participants

Eileen Appelbaum*

Department of Economics

Temple University

William Cooke*

Krannert School of Management

Purdue University

Fay Duchin*

Institute for Economic Analysis

New York University

Alan Fechter

National Science Foundation

Sheldon Friedman

United Automobile Workers of America

Louis Jacobson*

Center for Naval Analyses

Robert Levy*

Center for Naval Analyses

Michael Pilot

Bureau of Labor Statistics

Markley Roberts*

AFL-CIO

Myron Roomkin

Kellogg School of Management

Northwestern University

Jim Smith

The Rand Corp.

Robert Westbrook

Communications Workers of America

● Contributed papers appear in app. C.

vi
Contents

Chapter Page

l. Introduction and Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Programmable Automation Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Dimensions of Technological Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2. Labor Markets and Working Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Potential for Occupational Change: An Overview.. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Occupational Change Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Beyond Hardware and Software: Other Factors To Reconsidered . . . . . . . . . . . . . . 19

Labor Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Working Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3. Education, Training, and Retraining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Technology and Manufacturing: Changes in Instructional Requirements . . . . . . . . . . 35

New Types of Instructional Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Survey of Current Views of Education, Training, and Retraining Requirements . . . 41

Selected Critical Issues for Instructional Programing . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Appendixes

A. Survey Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

B. Industrial Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

C. Papers Prepared for Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

List of Tables

Table No. Page

l. A Comparison of 1980 and 1970 Decennial Census Occupational Categories . . . . . . . 13

2. Comparison of Robot v. Human Skills and Characteristics . . . . . . . . . . . . . . . . . . . . . . . 16

3. Noninstitutional Population and the Labor Force, 1929-82. . . . . . . . . . . . . . . . . . . . . . . . 23

4. Wage and Salary Workers in Nonagricultural Establishments, 1929-82 . . . . . . . . . . . . . 24

S. Labor Management Committees on Industrial Relations Issues, Safety,

and Productivity by Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Figure

Figure No, Page

l. Bureau of Labor Statistics Employment Projections System . . . . . . . . . . . . . . . . . . . . . . . 18

vii
Chapter 1

Introduction and Concepts
Chapter 1

Introduction and Concepts

OVERVIEW

Programmable automation technologies are attracting attention as outgrowths of the evolution

of computer and communications technologies

and as instruments of potentially far-reaching

change in the operations, structure, competitiveness, and hiring patterns of many industries, particularly in manufacturing. While popular recognition of programmable automation seems to be

confined mostly to one of its forms (robotics),

programmable automation comprises other types

of hardware, software, and systems. * The family of programmable automation technologies, as

applied in manufacturing, is the subject of an

Office of Technology Assessment (OTA) study,

“Computerized Factory Automation: Employment, Education, and the Workplace, ” which is

scheduled to be completed in late 1983. This

technical memorandum, which is an interim product of that assessment, presents a set of concepts

and background materials that are fundamental

to the analysis of the labor and education and

training implications of programmable automation technology.

The OTA assessment is examining the development and production of programmable automation technologies and their use in discrete product

fabrication and assembly. It is examining the application of programmable automation to the entire manufacturing process, from design through

production. The assessment is concerned in part

with the economic and social aspects of the production and use of programmable automation,

including:

• impacts on the types and mix of products

manufactured,

● the structure and competitive behavior of

manufacturing industries,

*Besides robotics, programmable automation includes computeraided design (CAD) and manufacturing (CAM), computer-aided

process planning (CAPP), automated materials handling (AMH),

and automated storage and retrieval systems (AS/RS).

●

●

the numbers and skill mix of people

employed in manufacturing, working conditions in manufacturing jobs, and

the education and training requirements implied by growth in the production and use

of programmable automation.

Although it addresses the potential of programmable automation for the manufacturing sector

as a whole, the assessment highlights implications

for the transportation equipment, industrial

machinery, and electronics industries, where the

greatest impacts may occur in the next 10 to 15

years.

Early work on this assessment revealed that

analysis of employment change depends critically on methodology, while analysis of instructional

requirements demands appreciation of the existing

nature of, and delivery system for, education and

training. These fundamental issues are the subjects of this technical memorandum. The remainder of this introduction provides a brief review

of the evolution of programmable automation and

sets out several factors that influence the social

and economic consequences of new technologies.

The labor chapter (ch. 2) discusses methodology

and provides background material useful for evaluating employment and working environment

changes. It draws on the products of a workshop

held by OTA in July 1982, where questions concerning the analysis of labor markets and industrial relations were debated. The education and

training chapter (ch. 3) examines the current status

of education and training provided by schools,

labor, industry, and others to persons holding or

aiming to hold jobs in manufacturing industries.

It draws on the results of an August 1982 survey

conducted by an OTA contractor.

‘Exploratory Wcwkshop on the Social Lmpacts of Robotics—A

Background Paper (Washington, D. C.: U.S. Congress, Office of

Technology Assessment, February 1982), OTA-BP-CIT-11.

3
4

PROGRAMMABLE AUTOMATION TECHNOLOGIES

Programmable automation may be viewed as

the latest development in a long process of enhancing and augmenting human labor with various devices. Throughout history, people have

combined human effort and skill directly with the

cutting and shaping abilities of tools. With the

development of machines, people drew on mechanical and other external sources of power,

reducing the amount of human effort, and to some

extent skill, needed for production. Automation,

in turn, represents an advance over simple machines consisting of a transfer of skills and efforts

for operating and controlling equipment and systems from people to machines. Conventional

automation has improved production efficiency

where automated machinery has been tailored to

specific applications and devoted to the production of single products produced in large quantities. Programmable automation, which weds

computer and data-communications capabilities

to conventional machine abilities, increases the

amount of process control possible by machines

and makes possible the use of single pieces of

equipment and systems for multiple applications.

This flexibility may make programmable automation more economical than conventional automation across a range of applications from large volume production to production of small batches

of products. Consequently, differences between

large-scale, batch, and even custom production

techniques may diminish and traditional ways of

thinking about manufacturing may ultimately

change.

Programmable automation technologies are not

new, at least in concept; they have been introduced and refined over the past two to three decades. Many date the launch of programmable

automation to the mid-1950's, when numerical

control (NC) for machine tools (currently considered as part of computer-aided manufacturing)

was developed and commercialized. The intervening years have seen growth in the capabilities and

use of NC, the introduction of industrial robotics

in the 1960’s, and the initial applications of computer technology to manufacturing design, production, planning, and analysis in the 1960’s and

1970’s. During this period, capabilities and applications for programmable automation have

grown, while associated unit costs—at least for

the computer aspects—have declined.2 The technologies and their potential markets appear to

have developed sufficiently to lead many manufacturing industry analysts to anticipate substantial growth in the production and use of programmable automation in the 1980’s and 1990’s.

However, current use of programmable automation is limited. For example, while the Robot Institute of America reports that less than 5,000 robots

were believed to be in use in the United States in

1981, the National Machine Tool Builders’ Association reports that over 2.6 million machine tools

were in use in U.S. metalworking industries alone

by the late 1970’s.3 4

At this time it is possible to identify four attributes of programmable automation, as compared

with conventional automation, that may have

major ramifications for labor and for education

and

•

●

●

●

training:

capacity for information processing as well

as physical work, in connection with such

processes as planning, routing, design, fabrication, assembly, monitoring, and diagnosing process problems;

capacity for quality enhancement, through

reliability, precision, and adaptive control of

the production process;

capacity for application to the production of

a diverse mix of products, through reprogrammability; and

capacity for integrating production equipment and systems with each other and with

design, analysis, inventory, and other aspects

of the manufacturing process.

These attributes will influence: 1) the types and

the range of human skills and other abilities that

can be replaced by machines, 2) the types of new

2“SME Golden Anniversary Issue, 1932-1982: A Review of Manufacturing and the Society Which Guides Its Progress,” Manufacturing

Engineering, January 1982.

3 WorMvi& Robotics Survey and Directory (Dearborn, Mich.:

Robot Institute of America, 1982).

4Ekonomic Hmdbook of the Machine Tool Industry (McLean,

Va.: National Machine Tool Builders Association, 1982-83).
5

applications within which both men and machines

can be combined, 3) the types of skills required

to produce programmable automation, 4) the

types of products (existing and new) for which

programmable automation may be used, 5) the

costs of producing given quantities of different

products, and 6) the organization and management of the manufacturing process. Consequently, they may give rise to changes in the numbers

and types of people employed, and therefore

changes in requirements for education, training,

and retraining. Distinctive attributes of programmable automation will also influence the working environment of people employed in manufacturing. How much,

DIMENSIONS

ment patterns and working environments change

will depend on how automated equipment and

systems are designed and implemented.

Conventional automation and other types of

manufacturing technologies have traditionally affected—both positively and negatively—the employment and working environment of manual

workers. Because of its capacities for performing

information processing work and for integrating

the manufacturing process, programmable automation may also have significant impact on other

types of workers, the so-called white- and graycollar workers, including managers.

an-d in what ways employOF TECHNOLOGICAL CHANGE

It is possible to relate the emerging capabilities

of programmable automation technologies to

changes in employment, and therefore education

and training requirements, in the abstract. However, the effects of programmable automation on

labor overall, as well as the experiences of specific

groups of people, depend on how programmable

automation technologies are implemented. The

development and implementation of programmable automation, or other new technologies, can

be appraised according to three factors: 1) the rate

of technological change, 2) the nature of the

change, and 3) the pattern of technological diffusion associated with programmable automation.

These factors, which reflect a combination of technological and industrial/economic factors, are

central to assessments of the social and economic

impacts of new technology. They are reviewed

briefly below.

Rate of Technological Change

There are two components to the rate of technological change, the rate at which new technologies are created and the rate at which they are

adopted by users. For appraising the impacts of

technology on employment and related education

and training needs, the rate of adoption is key;

it determines whether changes in requirements for

different types of labor affect primarily existing

or future/prospective employees. Although new

technologies may be created at varying rates, the

conventional view among economists is that the

use of new technologies spreads relatively slowly. It is commonly assumed that firms adopt new

technology in a rational fashion, meaning that

they strive to use the most affordable processes

to avoid the costs of prematurely scrapping facilities and to adapt technologies to their individual

needs. This view implies that, since firms typically

do not adopt each technological advance as soon

as it is developed and since firms experience some

normal level of employee turnover, employees are

not (repeatedly) subject to catastrophic displacement. A more elaborate presentation of this view

and a discussion of supporting research can be

found in a paper by L. Jacobson and R. Levy, appendix C.

Although the notion that there is a lag between

the introduction of a new technology and its widespread use is commonly recognized, there is disagreement as to whether recent innovations based

on microelectronics technology have or will continue to spread as slowly as previous ones. For

example, innovation and associated employment

change in the printing industry have proceeded

quicker than the conventional view might lead one

to expect.
6

In West Germany, for example, employment

among printers dropped by 21.3 percent between

1970 and 1977, while productivity per hour rose

by 43.5 percent . . . .5

There is also evidence that the use of computers

has spread relatively rapidly, a phenomenon that

has prompted many scientists and engineers to

take steps to refine their skills. A discussion of

technological diffusion and its impacts on scientists and engineers is found in a paper by W.

Cooke, appendix C. Additional material on engineering education is presented in chapter 3 of this

technical memorandum.

A central question for an analysis of the social

and economic impacts of programmable automation is whether programmable automation is likely to spread especially rapidly among firms and

industries, and why. Answering that question requires appraising the influence of the international

nature of markets producing and using programmable automation and the influence of cyclical

and structural change in the U.S. economy on the

rates of adoption and production of programmable automation in the United States.

Nature of Technological Change

The way in which technological change affects

employment and instructional requirements depends on the nature of the technology. The aspects

of the technology that are relevant to an examination of labor impacts fall into three categories:

1. process v. product technology,

2. embodied v. disembodied technology, and

3. capital intensity of technology.

Process technologies are technologies of production, while product technologies pertain to the

attributes of a finished product. Programmable

automation, which comprises a set of goods and

services used by businesses to make other products, has elements of both, but is primarily regarded as process technology.

The product-process distinction is important

because, historically, process changes have been

more likely to affect employment adversely than

‘Cohn Norman, Microekwtronics at Work: I+oductivity and Jobs

in the World Economy, Worldwatch Paper 39, October 1980.

have product changes. New products (such as programmable automation equipment and systems)

create new markets and new sources of employment (although net employment growth depends

on whether—and when—new products replace

older ones). New processes, however, are often

adopted because they are considered efficient,

using l%wer resources than older processes to yield

a product of given quality. * If the conserved resource is labor, a company adopting a more efficient process will need fewer employees for a fixed

output level. If the company faces a mature market for its end product (i.e., sales volume is not

likely to grow significantly), overall employment

will fall, but, if the company faces a growing market, it might experience stable or growing employment. Also, some new processes may be adopted

to improve product quality without necessarily

diminishing company employment. Discussions

of programmable automation in the trade and

business press typically note its potential for both

efficiency and quality enhancement. These discussions, which separate quality gains from cost

reductions, recognize that process improvements

may facilitate output growth but do not assure

that companies can sell larger volumes of output.

Embodied technologies are associated with

physical entities such as pieces of equipment. For

example, mechanical adding machines and electronic calculators embody different technologies

to perform the same functions. Disembodied

(sometimes called soft) technologies constitute

ways of organizing and managing production that

are not locked into tangible items. An example

is the just-in-time system of inventory management, wherein suppliers deliver materials for virtually immediate use (rather than interim storage).

The contrast between embodied and disembodied

technologies is important for appraisal of programmable automation because the spread and

the ultimate utility of programmable automation

is linked to associated changes in the organization of production and the structure of companies

and industries.

In evaluating embodied technologies used in

manufacturing and elsewhere, it is important to

● Sometimes this characteristic is referred to as productivity improvement, since productivity refers to the amount of output derived

per unit of input to production.
7

recognize that disembodied technologies can often

complement or even substitute for them. For this

reason, comparing counts of different types of

equipment (e.g., robots) used by different countries may be misleading. As comparisons of automobile production in the United States and abroad

reveal, it is possible to produce the same product

using equipment and systems that differ in sophistication under different principles of organization

and management. Also, because embodied and

disembodied technologies are combined in production, simple attributions of employment or

working environment variations to changes in

equipment and systems are hazardous; they ignore the role of management, organization of production, and other “soft” factors.

Capital intensity refers to the amount of investment in plant and equipment needed to produce

a given level of capacity, relative to the amount

of other inputs, such as labor. A capital-using

change in technology is defined to be one that requires more investment to produce a unit of product than the original technology; a capital-saving

change, one that requires less investment; and a

capital-neutral change, one that requires the same

investment per unit of product.

Generalizations about how programmable automation may affect capital intensity in different

manufacturing applications are difficult to make

at this time because of limited experience with the

technologies and uncertainty about the evolution

of the technologies and their markets. However,

an understanding of the capital intensity aspects

of programmable automation is important for

understanding the long-term employment and

wage impacts of programmable automation. In

brief, capital intensity affects the flexibility employers have for accommodating different employment and wage levels, given company levels

of sales volume and of output per worker. * The

ramifications of varying levels of capital intensity

are examined in a paper by E. Appelbaum, appendix C.

*Capital intensity may also affect the distribution of wealth generated through production—a shift to capital-using technologies may,

for example, be associated with growth in profits (return on capital)

relative to wages. Changes in the distribution of wealth in turn may

affect employment and wage levels because those realizing income

as profits may spend and invest in different markets than those realizing income as wages.

Pattern of Technology Diffusion

The impacts of programmable automation technologies will depend on where they are used as

well as when. New technologies may spread within industries among all firms, among firms in only

certain industry segments, or among large or leading firms only. They may be used in isolated industries, interdependent industries, and/or in industries in different sectors of the economy. The

impact of programmable automation technologies

on employment (and therefore on instructional

requirements) in the United States will depend,

in particular, on global trends in the production

and use of programmable automation, since the

markets for automation and for many products

made with it are international.

Preliminary observations presented by industry

and labor representatives and technology analysts

at the 1981 OTA Exploratory Workshop on the

Social Impacts of Robotics and elsewhere indicate

that programmable automation may eventually

be diffused more broadly than conventional automation. b While conventional automation has been

applied primarily in large-volume or mass-production manufacturing industries, programmable

automation offers potential value to use in smaller volume, batch manufacturing applications,

which are the majority of manufacturing applications. 7 Whereas conventional automation is devoted to production of single products, programmable automation equipment and systems can be

adapted, through reprograming, for production

of multiple products, each of which may be desired in limited quantities. Since production equipment and systems themselves are often manufactured in small-quantity batches, their manufacture

may be automated.

Finally, equipment and systems similar (and in

some cases identical) to those used for programmable automation in manufacturing are being

adopted in nonmanufacturing settings, with multiple impacts on employment opportunities. A possible consequence of the spread of office automation, for example, is a decline in the growth rate

of clerical employment. On the other hand, large

bExploratory Workshop on the Social Impacts of Robotics-A

Background Paper, op. cit,

71bid.
8

investments in office automation equipment and automation equipment is imported. The implicasystems imply potential employment gains in tions of the pattern of technology diffusion for

manufacturing industries supplying office automa- employment in different sectors of the economy

tion, although who benefits from such employ- are discussed in a paper by E. Appelbaum, appenment gains depends on the extent to which office dix C.
Chapter 2

Labor Markets and
Chapter 2

Labor Markets and Working Environment

INTRODUCTION

The use of computers in manufacturing has

aroused concern since the late 1950’s and early

1960’s, when awareness of the potential of computer technology began to emerge and when applications of more conventional automated manufacturing were accelerating. During that period

public interest in the social ramifications of

automation and computers was greater in Europe

than in the United States. However, official U.S.

concern led to the formation in 1965 of a special

Federal study commission, the National Commission on Technology, Automation, and Economic

Progress, charged with the tasks of: 1) assessing

the effects, role, and pace of technological change;

2) describing changes in employment demands

and working conditions associated with technological change; 3) defining “unmet community and

human needs” that technology can help to meet;

and 4) identifying policy options for implementing new technologies. After meeting for a year,

the Commission issued a report that foreshadows

contemporary discussions of job displacement,

changing working conditions, and instructional

needs.

From the 1950’s through today, labor-related

concerns associated with automation and computers have tended to fall into three not-whollydistinct categories: 1) labor markets or employment, 2) working environment (job content and

occupational safety and health), and 3) industrial

or labor-management relations. Of these three

categories, labor market issues have been most

salient in popular (and political) discussions of

automation, because employment is widely seen

as reflecting the economic vitality of a country

or region. By contrast, working environment issues may be more subtle and more likely to be

appreciated by those groups of people in direct

contact with specific working environments. Finally, industrial relations both influence and are

influenced by changes in labor markets and working environments that are associated with new

technology and other factors.

In order to analyze the labor market implications of programmable automation, it is necessary

to be able to measure and forecast the degree and

types of changes in employment that may accompany the spread of this technology. The variety

of claims as to the eventual employment impacts

of programmable automation that are being publicized by the media suggests that such evaluations

are straightforward. However, there appears to

be no accepted methodology for making such employment forecasts reliably, a problem that was

emphasized in debates among participants of the

OTA Labor Markets and Industrial Relations

Workshop. This technical memorandum points

out some of the shortcomings of many publicized

forecasts and some of the requirements for satisfactory forecasts.

POTENTIAL FOR OCCUPATIONAL CHANGE: AN OVERVIEW

A first step in measuring or forecasting how

programmable automation or other new technologies may affect employment-by occupation and

industry-is to assess: 1) how programmable

automation affects the activities performed by

people working in user industries and occupations, and 2) what types of activities maybe performed by people engaged in producing automated equipment and systems. Unfortunately,

there are few empirical data describing relevant

activities. Moreover, what data may exist (e.g.,

in case studies) may have little general value

because early programmable automation applications have been limited in number compared to

applications of other types of equipment and systems, and they have been tailored to individual

company needs. Early applications also are likely to be different from later applications involv11
12

ing more sophisticated equipment and systems,

especially since future applications are expected

to feature greater computer integration of production and other company activities.

At this time it appears that the range of activities undertaken by manufacturing firms and vulnerable to change in connection with programmable automation is not limited to the fabrication

and assembly of products. Employment that may

be directly affected by the production and use of

programmable automation is associated with a

wide range of activities, including research and

development; the design, fabrication, assembly,

distribution, and servicing of products; and management.

Production Activities. —The types of new activities associated with production of programmable automation, as compared with production

of conventional factory equipment, are those that

pertain to its computerization aspects, namely the

development, distribution, and/or adaptation of

computer hardware and software. Computerization, or more broadly a shift to microelectronics

from mechanical or electromechanical components, may also alter other activities associated

with production of programmable automation.

For example, the use of microelectronic components affects fabrication and assembly techniques, in part because individual microelectronic

components can often do the work of multiple

mechanical ones.1 Finally, like the production of

conventional equipment, production of programmable automation also entails applications engineering, technical support, installation, sales, and

clerical activities.

Use Activities.—Activities associated with the

use of programmable automation are broadly

similar to those associated with the production

of programmable automation, since both production and use of programmable automation are

manufacturing endeavors. Nevertheless, variation

among user industries (including users who also

produce programmable automation) by size and

by nature of product will determine the specific

types of tasks and occupations affected among

users. The types of tasks that maybe created with

the use of programmable automation also pertain

to computerization (e.g., programing, maintenance of electronic equipment, and data base

management). The types of tasks that may be

eliminated are those tasks sensitive to the internalization of information flows (e.g., for certain clerical operating and supervisory tasks), or to the

replacement of physical labor (e.g., for welding,

assembling, materials handling, and drafting).

IRoy Rothwell and Walter Zegveld, Technical Change and Employment (New York: St. Martin’s Press, 1979).

OCCUPATIONAL CHANGE FORECASTING

Historically, attempts to forecast detailed

changes in occupational employment have met

with limited success. As the Bureau of Labor Statistics (BLS) has noted in evaluating its own

forecasts, it is easier to predict directions of change

for broad categories of employees than magnitudes of change for relatively specific groups. This

situation is unfortunate, since the more detailed

the occupational differentiation, the more precise

may be the evaluation of employment variation

among occupations and industries and therefore

the identification of people who may benefit or

be harmed by technological change.2

‘Max L. Carey and Kevin Kasunic, “Evaluating the 1980 Projections of Occupational Employment, ” Monthly Labor Review, July

1982.

Note that in practice, very detailed occupational analyses may

be less accurate than more aggregated analyses because of nonsamThe occupations of people who maybe directly affected by the spread of programmable

automation include professional specialty; executive, administrative, and managerial; technicians

and related support; machine operators, assemblers, and inspectors; precision production, craft,

and repair; and handlers, equipment cleaners,

helpers, and laborers. Table 1 contains the full

current and prior lists of major census occupational groups. While this set of categories can be

used to describe the occupational mix of any industry and the labor force as a whole, it is too

broad to describe more than gross shifts in ocpling errors in occupational title classification and analysis. See

Harvey Goldstein, “Occupational Employment Projections for Labor

Market Areas: An Analysis of Alternative Approaches” (Washington

D. C.: U.S. Department of Labor, 1981), R&D Monograph 80.
13

Table 1.—A Comparison of 1980 and 1970 Decennial Census Occupational Categories

1980 1970

Broadest groupings

Managerial and professional specialty White-collar

Technical, sales, and administrative support Blue-collar

Service Service

Precision production, craft, and repair Farm

Operators, fabricators, and laborers

Farming, forestry, and fishing

Major occupational groups

Executive, administrative, and managerial Professional and technical

Professional specialty Managers and administrators, except farm

Technicians and related support Sales

Sales Clerical

Administrative support, including clerical Craft and kindred

Private household Operatives, except transport

Protective service Nonfarm laborers

Service, except private household and protective service Private household

Precision production, craft, and repair Other service workers

Machine operators, assemblers, and inspectors Farmers and farm managers

Transportation and material moving Farm laborers and supervisors

Handlers, equipment cleaners, helpers, and laborers

Farming, forestry, and fishing

SOURCE: John E. Bregger, “Labor Force Data From CPS to Undergo Revision in January 1983,” A40rrth/y Labor 17evlew, November 1982.

cupational proportions. Within each category,

hundreds of occupations can be differentiated.

Aggregating occupational categories may result

in uncertainty about future change in such detailed

occupations as “robot technician, ” where the

specific designation falls within a broader category, such as science and engineering technicians.

Another cost of aggregation is generality—the

average pattern of change within an industry may

not correspond to actual changes experienced by

individual companies or people, in part because

individual companies vary in their use of employees with very specific skills, as well as in their use

of production technologies. However, even a detailed occupational breakdown may mask changes

in job content that may arise with new technology.

Most analyses of employment change use aggregated occupational descriptions because collection and manipulation of more detailed occupational data are costly, and because the most detailed descriptions fall easily out of date. Many

experts believe that analysts have been handicapped by the kinds of data available. For example, the most recent edition of the Dictionary of

Occupational Titles (DOT), which describes

200,000 occupations, was published 6 years ago

in 1977 (the previous edition was published in

1965). The DOT does not contain an entry for

“robot technician, ” and the most similar entry,

“automated equipment engineer technician,” refers

to an individual who works with machinery producing items from paper or cardboard stock (as

opposed to metal, plastic, or other materials with

which robots or other forms of programmable

automation might be used).

How can the effects of programmable automation on employment levels and distribution among

occupations be gaged? Already, there are many

estimates of the overall and occupational employment impacts of programmable automation appearing in the trade, popular, and business

presses. Examples include the following:

Automotive industry sources say the general

formula is that 1.7 jobs are lost for every robot

introduced. 3

“Automation will cause a 20 to 25 percent decline in the factory work force over the next decade, ” says Thomas G. Gunn, managing director

of Arthur D. Little’s computer integrated manufacturing group. An internal study done by GE

shows that it is now technologically possible for

the company to replace half of its 37,000 assem3

Joyce Price, “With Robots On the Way, GM Workers Worry,”

The (Baltimore) News American, Sept. 27, 1982.
14

bly workers with machines. Company officials

are quick to point out that they have no plans

to do that and where GE is automating existing

plants—at Erie, for instance—it is retraining the

displaced workers. Sometimes extensive automation also creates new jobs even as it destroys

others. The new automated parts factory in

Florence, Ky., for example, will allow Yamazaki

to expand production at its manned machine-tool

assembly plant nearby; 100 workers will be hired

to fill the new jobs.4

Experts estimate that on the order of 45 million

existing jobs—45 percent of all jobs, since there

are about 100 million people at work—could be

affected by factory and office automation. Much

of the impact will occur before the year 2000. . .

The United Auto Workers, one of few unions that

tries to anticipate automation expects its auto industry membership to drop to 800,000 from 1

million between 1978 and 1990, even assuming

a 1.8 percent annual increase in domestic auto

sales . . . . Harvey L. Poppel, a senior vicepresident with Booz, Allen& Hamilton, Inc., estimates that 38 million of more than 50 million existing white-collar jobs eventually may be affected by automation. Paul A. Strassmann, vicepresident of strategic planning for Xerox Corp.’s

Information Products Group, predicts that 20

million to 30 million of these jobs will be affected

by 1990.5

Forecasting is, at its best, imprecise. However,

the impact of robotics will definitely mean the

elimination of some blue-collar jobs and the creation of jobs that didn’t exist as recently as 10 years

ago. It’s estimated that there are currently 10,000

workers involved in robotics in some form or another throughout the world. That includes everyone from assembly line workers to designers, engineers, company presidents, clerical help, maintenance people and all of the support necessary

for a young, developing industry.’

The above sources have derived their estimates

through various means. The estimation procedures used appear to fall into two categories:

“engineering” and “economic.” Both categories

derive labor requirements from other phenomena:

‘The Factory of the Future,” Business Week, Sept. 6, 1982.

“’Changing 45 Million Jobs,” Business Week, Aug. 3, 1981.

bJoel Weber, “Can Robots Do a Better Job?” D&B Reports, January/February 1980.

equipment in the former category, and demand

for finished products in the latter. These procedures are reviewed below to illustrate how limited

current understanding and modeling of programmable automation employment impacts really are.

Engineering Estimates

Engineering estimate is the term used in this

report to refer to an estimate based more or less

exclusively on technical aspects of technological

change. Although engineering analyses may be

used to support economic analyses of employment

change, they are frequently used on their own.

Most of the employment (or, in particular, unemployment) estimates cited in popular discussions

of programmable automation seem to be of this

type.

Engineering estimates are made by describing

the capabilities (for physical and mental work) of

new automation technologies, projecting capability improvement over time, comparing the capabilities to tasks performed by humans, relating human tasks to different occupations, and deriving

the number of jobs, by occupation, that could be

assumed by new and future improved types of

equipment. This is done by comparing guesses as

to the percentages of work that could be transferred to programmable automation with counts

of the numbers of people currently doing that

work. For example, the employment impact of a

welding robot might be estimated by identifying

the types of welds the robot can perform, measuring the number of welds the robot can perform

in a given period of time, and calculating the

number of “jobs” that might be displaced by comparing the number of robots needed to achieve

a given volume of welds with the number of human welders who could achieve the same volume

of welds, given contemporary hiring patterns.

Projected improvements in robot welding, or

other changes in the basic assumptions can be accommodated by modifying the calculations.

Similar calculations are used to derive the employment requirements for producing the supply

of robots necessary to achieve a given level of

displacement —estimate the type of tasks required

to produce robots, the number of tasks of each

type required per robot, the allocation of robot-
15

production tasks between humans and equipment,

and combine with the number of robots desired

in a given period to forecast producer employment requirements.

Shortcomings of Engineering Estimates

The engineering approach is easily understood,

adaptable to different assumptions, and useful as

a first step in estimating the potential employment

impacts of programmable automation. However,

it has many limitations—in its application, if not

its concept—which are largely functions of the

narrowness of the technological and/or economic

assumptions chosen. Shortcomings of engineering

estimates may include some or all of the following:

●

●

●

These estimates are easily confounded by errors in projecting future technological

capabilities. Although providing a range of

assumptions may improve the usefulness of

the estimates, there remains a problem of inability to foresee all possible developments,

especially in new technologies.

Both the development and the analysis of

automation technologies (conventional and

also programmable) often rely heavily on

point-by-point comparisons of electronic and

mechanical capabilities with human capabilities, an orientation that lends itself to

calculations of how and where automation

equipment and systems may replace or substitute for human activities. See table 2.

However, this orientation fails to capture the

potential for programmable automation either to perform jobs in ways other than simulation of human behavior, or to perform

jobs that are poorly done or not done at all

by humans because of human limitations.

This failure may lead to overestimation or

underestimation of job displacement.

Engineering estimates may be misleading because they tend to yield a “technically” ideal

mix of humans and equipment, while the actual mix may reflect complex management

and implementation considerations that are

independent of the capabilities of specific

equipment or systems. For example, managers mav be motivated out of risk aversion .

●

to provide redundant capabilities in the form

of “extra” workers (or overskilled workers)

to provide manual performance backup or

monitoring services, at least in the short term

when programmable automation is relatively

unfamiliar. Varying assumptions about the

mix of humans and equipment would ease

this problem.

Engineering estimates are frequently based on

current or recent labor force characteristics.

This practice assumes that users will buy and

use programmable automation to serve relatively constant production needs, and that

workers will seek different jobs at constant

rates. However, the job displacement and

creation consequences of programmable automation will depend not only on how programmable automation affects the number

and type of tasks per worker, but also on

how sales volume and the mix of products—

which determine the total number of tasks

done at all—change. These quantities may

vary in response to factors other than technological change, such as shifts in consumer

tastes. In addition, the employment consequences of programmable automation will

depend on the numbers and types of people

willing and able to work at different types

of jobs, which also may vary independently

of technology.

Engineering analyses are useful for identifying

the types of people (excluding, perhaps, managers)

who may be affected by programmable automation. As currently used, they are often too simplistic to provide realistic estimates of industry or

economywide employment change. The chief

problem with available engineering estimates of

national employment impacts seems to be a lack

of consideration for variations in economic conditions, trade patterns, and labor supply, although

these factors probably could be accommodated

by engineering analyses. Nevertheless, the engineering approach provides a framework that can

be used to evaluate the employment consequences

of alternative strategies for implementing programmable automation, and a mechanism for

evaluating specific variations in production processes.
16

Table 2.—Comparison of Robot v. Human Skills and Characteristics

Robot a Human

A Act/on and manipulation

1. Manipulation abilities

a. One or more arms. Automatic hand change is

possible.

b. Incremental usefulness per each additional arm can be

designed to be relatively higher than in humans.

c. Requires the same amount of feedback throughout

operation.

d. Movement time related to distance moved by speed,

acceleration and deceleration, and will increase with higher

accuracy requirements.

●

B. Brain and control

1. Computational capability

a.

b.

c.

d.

e.

f.

g.

h.

i.

J

k.

1.

2.

a.

b.

c.

d.

3.

a.

b.

E

3.

a.

b.

c.

d.

Fast, e.g., up to 10 K bitrdsec for a small minicomputer control.

Not affected by meaning and connotation of signals.

No valuation of quality of information unless provided by

program.

Error detection depends on program.

Very good computational and algorithmic capability by

computer.

Negligible time lag.

Ability to accept information is very high, limited only by

the channel rate.

Good ability to select and execute responses.

No compatibility limitation.

If programmable—not difficult to reprogram.

Random program selection can be provided.

Command repertoire limited by computer compiler

or control scheme.

Memory

Memory capacity from 20 commands to 2,000 commands,

and can be extended by secondary memory such as cassettes.

Memory partitioning can be used to improve efficiency.

Can forget completely but only on command.

“Skills” must be specified in programs.

Intelligence

No judgment ability of unanticipated events.

Decisionmaking limited by computer program.

Miscellaneous factors

Training

Requires training by teaching and programing by an

experienced human.

Training doesn’t have to be individualized.

No need to retrain once the program taught is correct.

Immediate transfer of skills (“zeroing”) can be provided.

4. Social and psychological needs

None

5. Individual differences

Only if designed to be different.

a.

b.

c.

d.

● ●

a.

b.

c.

d.

e.

f.

g.

h.

i.

j

k.

1.

a.

b.

c.

d.

e.

f.

a.

b.

● *

● *

a.

b.

c.

d.

e.

f.

a.

b.

Two arms—two legs—multipurpose hands.

Two hands cannot operate independently.

Feedback requirements (type and quantity) change with

practice—initially relatively higher than robot; visual feedback

dominates other sources of feedback.

Movement time and accuracy governed by Fitts law. High

precision movements may interfere with calculation processes.

Slow—5 bits/see.

Affected by meaning and connotation of signals.

Evaluates reliability of information.

Good error detection/correction at cost of redundancy.

Heuristic rather than algorithmic.

Time lags increased, 1 to 3 sec.

Limited ability to accept information (10 to 20 bits/sec).

Very limited response selection/execution (l/see); responses

may be “grouped” with practice.

Subject to various compatibility effects (RR, SR, SS).

Difficult to reprogram.

Various sequence/transfer effects.

Command repertoire limited by experience and training.

No indication of capacity imitation.

Not applicable.

Directed forgetting very limited.

Memory contains basic skills accumulated by experience.

Slow storage access/retrieval.

Very limited working register = 5 items.

Can use judgment to deal with unpredicted problems.

Can anticipate problems.

Requires human teacher

Usually individualized is best.

Retraining often needed due to forgetting.

Zeroing usually not possible.

Very costly.

Not everyone can be taught.

Emotional sensitivity to task structure—simplified/enriched;

whole/part.

Social value effects.

100 to 150 percent variation may be expected.

a~obot ~armeter “alue~ are cited from currently available Industrial robot literature.

SOURCE: Nof, Knight, and Salvendy, “Effective Utilization of Industrial Robots—A Job and Skills Analysis Approach,” AHE TransactIons, vol. 12, No. 3, September 19S0.
77

Economic Estimates

Economic estimates is the term that will be used

in this technical memorandum to refer to projections based on macroeconomic models. Economic

estimates are better than engineering estimates for

projecting aggregate changes in employment patterns because they are inherently more comprehensive. On the other hand, economic estimates may not be practical or useful for gaging

possible employment change at the company level

because they tend to be highly aggregated.

Economic estimates are made by explicitly evaluating several factors, in addition to technology,

that impinge on employment demands. For example, prices and production levels of goods and

services are typically considered, taking into account, in turn, the forces that affect these factors,

such as international trade and projected shifts in

the relative strengths of different sectors of the

economy. Economic estimates place substantial

emphasis on descriptions of employers in terms

of different sectors of the economy and different

industries within sectors. They rely on engineering analyses for descriptions of alternative effects

of technologies on industry requirements for such

production inputs as labor (by occupation), equipment, and materials.

Economic estimates of employment change are

made using mathematical models of production

functions, which describe how different inputs to

production are combined to yield a given level

of output. Some models pertain to single industries, while other, more elaborate models also take

into account the interactions among industries.

The most detailed economic estimates come from

large-scale models, in particular those based on

so-called input-output (I-O) models, which encompass entire (regional, national, or global)

economies. Technologies are defined in I-O models as the structure—number, type, and proportions—of inputs associated with the production

of a unit of output of a given product.

The employment forecasts (total and by occupation) of the BLS draw on large-scale economic

modeling. They are generated with an I-O model

of the U.S. economy in combination with other

models that forecast change in the labor force and

in the level and pattern of economic activity. Also

included are descriptions of staffing patterns (the

mix by proportion of different types of workers)

for each industry included, obtained from periodic

surveys. Since the BLS estimates are widely used,

and since the procedures are substantially similar

to procedures used by others who forecast with

large-scale economic models (indeed, other models

often use the same data), a description of the outlines of BLS forecasting procedures can serve as

a description of economic employment forecasting

procedures in general (although individual models

and procedures do differ in their details). *

Figure 1 shows the different computational elements that contribute to BLS forecasts. The first

set of procedures is the projection of labor force

characteristics. The second set of procedures is the

projection of overall economic activity and resulting gross national product. These projections require estimation of the types and volumes of

goods and services the economy can produce or

supply in both private and public sectors, and

those that will be demanded by the public and

private sectors. The third set of procedures translates overall economic projections into projections

of industry activity, allocating estimated consumer spending among product groups and allocating

products to producing industries. Estimated gross

private domestic investment is in turn allocated

between changes in business inventories and investments in construction (residential and nonresidential) and producer-durable goods (e.g., machinery and tools). The fourth set of procedures

translates projections of industry output into projections of industry employment. This is done by

a combination of procedures for estimating labor

productivity (defined as output per unit of labor

input) and weekly hours of work for each industry.

The final set of procedures yields projections

of employment by occupation and by industry.

It combines descriptions of staffing patterns obtained by periodic surveys with estimates of the

*Note that BLS has recently contracted with Chase Econometrics

Associates, Inc., to use the Chase macroeconomic model to develop

projections of aggregate economic activity, using assumptions and

variables chosen by BLS. This arrangement will supplement in-house

BLS modeling and analysis.

98-951 0 - 83 - 4
18

Figure 1 .—Bureau of Labor Statistics Employment

Projections System

“- - -:- 

Census Age/sex/race

population “ forc e ‘

 projections rates, .: 

,

I

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, “BLS Economic Growth Model System Used for Projections to 1990.”

number of jobs per industry. All of these procedures are described in detail in the BLS publication, BLSEconomic Growth Model System Used

for Projections to 1990, April 1982.

Shortcomings of Economic Estimates

As the description of the BLS procedures shows,

large-scale economic models can take into account

the growth and decline of different industries, the

likelihood that individual industries adopting new

technologies may maintain or increase output levels, and the responsiveness of industry employment levels to industry technology change. This

framework prevents overattributing employment

changes to single influences such as technology

change, as it shows the consequences of combinations of influences. In their detail, however, the

validity of the projections generated depends on

the assumptions that underlie the formulation and

operation of each aspect of the model and the integration of the different aspects. Moreover, the use

of large-scale economic models carries the risk of

oversimplifying complex processes and conveying

an impression of greater analytical thoroughness

than may actually exist.

Several questions have been raised about the

assumptions used in large-scale economic forecasting models. The following list of some of the

shortcomings of economic estimates reflects concerns raised by participants at the OTA Labor

Markets and Industrial Relations Workshop, who

debated whether economic models could adequately evaluate the impacts of programmable

automation on employment. It also reflects concerns raised by others regarding economic modeling in general and modeling of technological

change impacts in particular.

● Labor Supply. The growth of the labor force

and change in labor force participation rates

of specific groups depend in complex ways

on demographic and economic factors. These

relationships may not be captured in economic models which project labor supply and

industrial output profiles separately. * Also,

variations in the quality, rather than the

quantity, of available labor maybe beyond

*BLS is currently working to improve its treatment of demographic

and economic influences on the labor force.
19

●

●

the scope of contemporary large-scale economic models. Consequently, the output of

large-scale economic models may best be

viewed as projected demands rather than

employment levels, per se.

Technological Change. It is unclear how well

large-scale models account for changes in

equipment technologies. Although the common practice of projecting future capital

stock by extrapolating from past use of plant

and equipment and past descriptions of industries and products suggests that economic

models may be unable to capture the impacts

of nontraditional equipment, experts disagree

as to whether measures of specific new technology attributes are necessary for deriving

economic estimates of employment change.

See papers by L. Jacobson/R. Levy and F.

Duchin, appendix C. In addition, economic

models typically are constructed using the

assumption that technological change is

adopted to reduce unit costs, although it may

also be adopted for other reasons (e.g., to

meet health or pollution standards) leading

to cost increases.

Staffing Patterns. Employment change due

to reorganization of production associated

with programmable automation may not be

captured where occupational employment is

projected using staffing patterns derived from

prior practices. Similarly, changes in occupational content may not be accounted for.

BLS, for example, has found that many of

the largest errors in its past estimates of occupational employment “resulted primarily

from misestimates of industry-occupational

staffing patterns.”7 The development of adequate staffing patterns would appear to require engineering analyses that take into account possible variations in the implementation of programmable automation, alternative levels of integration of manufacturing

activities, and alternative approaches to accommodating existing company work forces.

Like engineering estimates, economic estimates

have several shortcomings. However, while engineering estimates tend to highlight job displacement impacts of new technology, economic estimates are better suited for evaluating whether persons displaced from particular industries may find

job opportunities in other industries requiring

their skills, and therefore whether job displacement is likely to be associated with unemployment. How well they do this depends on how well

they capture the different components of the economy and their interactions. Similarly, while engineering estimates may establish new needs for individuals with certain skills, economic estimates

may more readily provide perspective on economywide demand for such individuals and therefore whether demand for certain skills or occupations is likely to exceed or fall short of supply.

These differences arise because economic analyses

as a rule model the interactions among segments

of the economy, while engineering analyses do

not, even though they may apply to the nationwide use of a technology. However, valid inferences regarding future unemployment and labor

shortages require that engineering analysis, economic and industry analysis, and labor supply

analysis be considered together.

‘Carey and Kasunic, op. cit.

BEYOND HARDWARE AND SOFTWARE:

OTHER FACTORS TO BE CONSIDERED

In general, satisfactory projections of the mag- among industries, and in the overall mix of emnitude and distribution of employment shifts asso- ployment opportunities in the economy. These

ciated with programmable automation should changes will reflect the basic parameters described

take into account a variety of factors that contrib- in the introduction (rate, nature, and diffusion

ute to the direct and indirect effects of the new pattern of technological change) and also the intechnology. Among these are changes in the orga- fluence of institutional factors such as labor-mannization of production, in the level of output agement agreements and norms, which affect the
20

rate and manner of application of new technologies. Labor-management relations are examined

in appendix B.

Organization of Production

Change in the mix and volume of activities

among users of programmable automation will

depend on alteration of the organization of production (and concomitant changes in product

lines) that may occur as a result of its use. As

discussed in chapter 1, it is anticipated that the

spread of programmable automation will involve

both technologies embodied in automated equipment and systems and disembodied technologies

in the form of organization and management

changes. These changes may be most pronounced

in small-volume or batch production settings:

For a long time the functional layout in batch

production, that is, all machines of the same kind

are gathered in groups, has been as natural as the

transfer line in mass production. Through the

functional layout, machine utilization can be kept

high, but at the expense of complex routing of

parts through the shop and large buffers and inventories. . . . In the new manufacturing methods the main principle is to organize the factory

according to product-oriented layouts. All machines needed to produce one product or one set

of products are grouped together in a “subfactory,” sometimes with its own administration.

Each worker in product-oriented layouts attends

several machines. In the functional layout we can

with some simplification say that the materials

wait for the machines while the machines in the

product-oriented layout wait for the materials.

The lead time (defined as the total time needed

for material to be processed into a finished product) can thereby be reduced dramatically.8

Production may also be reorganized between

facilities, as programmable automation facilitates

regional and even international reorganization of

production activities. For example, Ford Motor

Co.’s Erika project (which resulted in the Escort

and Lynx cars in the United States and similar cars

in other markets) used “the largest collection of

computer design hardware under one roof” to

‘The Promotion of Robotics and CAD/CAM in Sweden,” report

from the Computers and Electronics Commission, Ministry of Industry (Sweden), 1981.

pool U.S. and European product design and analysis efforts, eliminating separate parallel efforts

on different continents.9 Although there has been

much speculation among technology and industry

analysts about potential employment effects of

production reorganization, little reorganization

appears to have taken place, in part because business management has either failed to understand

or resisted such change, and in part because the

integration aspects of programmable automation

appear insufficiently developed.l”

Output Level

The employment consequences of programmable automation production and use depend not

only on the mix of manufacturing activities, but

also on production volume for both automation

and end products made with it. Since programmable automation will be sought by both new

users and customers previously using other types

of equipment, production volume should be evaluated by taking into account possible reductions

in volume of other, older technology equipment

and systems. This offset problem is generally

recognized in evaluating the impacts of microelectronics-based (and other) technologies found in

both new products and new production processes.

(I)t is clear that microelectronic technologies

will create jobs in those industries manufacturing novel electronic products. The $4 billion now

being lavished on electronic watches, calculators,

games, and other microelectronic products has

spawned a whole industry that did not even exist a decade ago. According to a projection by

. . . Arthur D. Little, the manufacture of these

items, together with computers and other electronic equipment, could create about 1 million

new jobs between 1977 and 1987 in the United

States and Western Europe combined. About 1.5

million people are now employed in the electronics industry in the United States. But these jobs

will not represent net additions to the work force,

for they will be offset to some extent by job losses

in the manufacture of goods with which the new

microelectronics-based products are competing.11

‘Automotive News, Feb. 15, 1980.

IO%e for example: Bela Gold, “CAM Sets New Rules for Production,” Harvard Business Review, November-December 1982.

llcolin Noman, ‘~icrWIWtronim at Work: Productivity and

Jobs in the World Economy,” Worldwatch Paper 39, October 1980.
21

The net effects of programmable automation

on user employment will depend on the effect it

has on end-product prices and on foreign trade,

product specialization, and other conditions in

user markets in the United States and abroad.

These factors, together with technology and general economic conditions, determine growth in

domestic company sales volume.

Employment Opportunity Mix

Overall, employment effects of programmable

automation will also depend on changes in employment opportunities throughout the economy.

EconomyWide changes in employment activities

depend in part on the pattern of diffusion of programmable automation and in part on the pattern of change in the mix of products available.

LABOR SUPPLY

While employment demands may change because of the characteristics of programmable automation technologies and of industries producing

and using them, change in employment (and unemployment) patterns also depends on the characteristics of the supply of labor: who is available

to do the work offered by employers, how able

people are to do different types of work, and

whether there are too many or too few people

with different abilities to do the work offered. The

following is a brief overview of labor supply attributes and concerns.

Demography

The number of people willing and able to work,

usually counted between the ages of 16 and 65,

depends on several factors, including natural population growth, * immigration and emigration patterns, public health conditions, the age structure

of the population (the proportions by age), and

the willingness of people to work, given the levels

of available wages and salaries and alternative

sources of income. The overall size, growth rate,

● Natural population growth reflects mortality and in particular

fertility (childbearing) rates, both of which may vary geographically,

and among subgroups.

Thus, although the apparent long-term decline in

the manufacturing share of total U.S. employment

(which began prior to widespread use of programmable automation) reflects the adoption of laborsaving technologies, the slow long-term growth

in the absolute level of U.S. manufacturing employment illustrates the importance of sales volume and market growth (including the introduction of new products). It can be misleading to

evaluate the employment impacts of new processes from the perspective of a constant mix of

finished products because the number as well as

the mix of goods and services provided to both

producers and consumers is dynamic. Such evaluations are common, however, because the existing

mix of products is known, while future product

arrays are not.

and age structure of the population are important

measures of the availability of people in gross

numbers to do work using particular technologies

to support a given level of economic activity. Attitudes toward work and other social factors,

which vary among geographic areas and ethnic

groups, contribute to the actual numbers and

types of people participating in the labor force.

Age structure and fertility patterns are particularly important influences on the makeup of the

labor force. Fertility patterns, in combination with

economic conditions and social norms, influence

the labor force participation of women as well as

the age structure of the population. The earlier

and more frequently women give birth, for example, the younger the population is likely to be

and the greater the (eventual) influx of new entrants to the labor force. Delays in and decreases

in the incidence of marriage and childbearing over

the past two decades have been causing the U.S.

population to age by reducing the proportion of

children. The age structure, in turn, influences:

1) the proportion of the population which is too

young and/or too old to work and therefore dependent on the economic activity of the workingage population, 2) the overall rate of population

growth, and 3) the numbers of new entrants to
22

the labor force. Consequently, differences in age

structure among countries influence national differences in employment patterns, preferences, and

policies. The Japanese, for example, are reported

to have shown early interest in programmable

automation in part because “aging” of their population limited the supply of young workers.12

The composition of the American population

has shifted toward older age groups more slowly

than that of the Japanese population, but the supply of new entrants to the labor force is expected

to begin a long-term decline in the 1980’s. Federal

projections of the U.S. population through the

year 2050 show the number of teenagers to peak

in 1980. The U.S. elderly population is expected

to grow from the 1980 level of 25.7 million to 67.1

million by 2050, increasing from 11 to 21.7 percent of the population .13 Unless the propensity of

the elderly to work increases dramatically, this

population shift will reduce the overall exposure

of the U.S. labor force to job displacement, and

it may eventually increase demand for laborsaving technologies.

Qualitative Attributes

Other characteristics of the labor force important to understanding employment trends are

qualitative. They include level, type, and quality

of education or training; skills; and preferences

regarding different types of work. Education and

training are important determinants of skills and

therefore of the types of work individuals can do.

However, educational attainment is an imprecise

measure of the qualities of workers, since skills

can be obtained through means other than formal instruction. A discussion of education, training, and retraining can be found in chapter 3.

Occupational Structure

The characteristics of the labor force, together

with the array of jobs available, contribute to the

occupational structure of an economy-the distribution of workers among occupations. Labor

IZG. K. Hutchinson, “Flexible Manufacturing Systems in Japan”

(Milwaukee, Wis.: University of Wisconsin Management Research

Center, November 1977).

‘3Robert Pear, “Population Drop Predicted in U.S.,” New York

Times, Nov. 9, 1982.

force attributes, and occupational structure in particular, change over time with changes in demography and with changes in social norms, both of

which reflect economic conditions. For example,

the absolute and relative growth in service sector employment has been associated with the

growth in female labor force participation.

Key attributes of the 1980’s labor force in the

United States include growing proportions of older workers, relatively large proportions of women

and minorities, relatively large proportions of

college-educated workers, and declining numbers

of people willing to work in low-level occupations.14 Tables 3 and 4 display basic characteristics of the U.S. labor force.

It is important to note that, as long as different

groups don’t radically change their propensities

to seek employment, it is relatively easy to describe the physical characteristics of the labor

force 10 to 12 years into the future since these people have already been born. However, describing future occupational preferences and distribution is less straightforward, since there are many

paths—not all measurable—for moving into different jobs and occupations and many alternative

paths into, out of, and through the labor force.

Adaptability of Labor

A key issue in evaluating the adaptability of

the labor force to changing labor demands—and

therefore the likelihood of unemployment as a

consequence of technology change-is the willingness and ability of people to perform different

types of jobs if the jobs they have held, or would

prefer to hold, become unavailable. Because this

flexibility depends in part on “objective” worker

traits such as specialized skills, and in part on

“subjective” traits, such as personal preferences

for certain kinds of jobs, it is difficult to evaluate

the true fit between labor supply and labor demand in the wake of circumstances such as technology change that alter employment requirements. A poor fit may be revealed in under-

“StW, for example, Howard N. Fullerton, “HOW AccurateWere

Projections of the 1980 Labor Force?” Monthly Labor Review, July

1982.
23

Table 3.–Noninstitutional Population and the Labor Force, 1929-82

(monthly data seasonally adjusted, except as noted)

I 1

Civilian labor force

NoninstiCivilian Iabor force

Unemploy- participation rate r

menl rate

[percent

of civilian.

labor Total Males Females

Year of month tutional Arme d

Popula- Forces Employment

tio n

I

Total UnemTotal Agri- 

: ’

Nonagr, ployment

Cultural cultural

forcej I

Thousands of pesons 14 years 01 age and over Percent

1929 32

1933

1939

1940

1941

1942

1943

1944

1945

)946

1947

1947

1948

1949

1950

1951

1952

19533

1954

1955

1956

1957

1958

1959

260

250

370

I M

3970

9020

11410

1 I 440

3450

1590

Thous

1591

1456

1616

1649

3098

3593

3547

3350

3048

2856

2799

2636

2551

2514

2572

2827

2137

2138

2722

3122

3446

3534

3 506

3188

2816

2449

2326

2229

2180

2144

2133

2117

2088

2102

2142

2179

2081

2086

2090

2092

2088

2092

49180

51590

55230

55640

55910

56410

55540

5A 630

53860

57520

60168

Is of person

47630

38760

45750

47520

50350

53750

54470

53960

52820

55250

57812

s 16 ye,

57038

58343

57651

10450 37180

10090 28670

9610 36140

1 550

12830

9480

8120

5%0

26-50

1070

670

 MO

2270

2356

2311

2276

3637

3288

2055

1 ea3

1834

3532

2852

2750

2859

4 SJ32

3740

3852

4714

3911

4070

3786

3366

2875

2975

2811

2832

4093

5016

4882

4365

5156

7929

7406

6991

6202

6137

7631

8273

10678

6674

6674

6717

7348

7970

8063

8353

8287

8044

8111

8029

7796

8 C48

8032

7967

7860

8133

8047

7854

8053

8271

8673

9025

9389

100380

101520

102610

103660

104630

105530

106520

107608

103418

104527

105611

106645

107721

108823

110601

Ill 671

112732

113811

115065

116363

117881

119759

121343

122981

125154

127224

129236

131180

133319

135562

137841

140272

143033

146574

149423

152349

153333

158294

161166

164027

166951

169848

172272

I 144.1

168625

168846

169073

169289

169494

169735

249

172

146 557

9 9 560

47 572

19 5s7

12 586

I

572

4; ‘ 558

39 I 568

837

843

856

864

870

848

826

840

9540 37980

9100 41250

9250 44500

9080 45390

8950 45010

282

287

313

360

365

8580 44240

8320 46930

8256 49557

359

312

310

of age and Over

49148

50714

49993

51758

53235

53749

54919

53904

55722

57514

58123

57450

59065

60 318

60546

61759

63076

M 782

66726

68915

70527

72103

74 2%

59350

60621

61286

7890

7629

7658

7160

6726

6500

626o

6205

6450

6283

5947

5586

5565

39 583 864

38 588 866

59 589 864

318

327

331

53 592 864

33 593 845

30 590 & 3

29 589 & O

55 588 855

44 593 ‘ 853

41 600 855

43 596 848

68 59 ~ 842

55 593 837

55 594 833

67 593 829

55 588 820

57 587 814

52 587 810

45 589 807 393

592 804 403

: ! 596 804 411

36 596 8 0 1 416

35 601 798 427

62208 58918

62017 59961

62138 M 250

63015 61179

63643 60109

65023 62170

66552 63799

64929 I 6A 071

67639 63036

68369 6-4630

339

346

347

344

346

357

369

: ;

371

377

38 1

379

383

387

1960 ]

1961

1962>

1963

1964

69628 65778 i 5458

70459 65746 5200

70614 667021 4944

71833 67762 4687

73091 69305, 4523

74455 71088 4361

75770 72895 3,979

77347 74372 3844 ‘

78737 75920 3817

80734 77902 36Q6

1965

1966

1967

1968

1969

1970

1971

1972 >

1973J

1974

1975

1916

1977

19781

1979

82771 78678

84387 79367

87034 82153

89429 (33:

91949

3463

3394

3484

3470

3515

75215

75972

78669

81594

83279

82438

85421

88734

92 661

95477

95938

’37 030

96125

96559

96516

96265

95886

95537

95460

95485

95582

95656

95965

96, 180

96,164,

96 544 

96803 

97148

97487 

97597

97033

97428 

97313

96746

96981

96840

6458

96309

96328

6230

96128

96548

96310

96143

96254

96180

95763

95670

95682

49 J34 797,

59 Q 2 79 I

604 789

: 438 788

56 1 3 7 8 7

433

434

439

447

457

85 1 2 779 ‘

77 616 775

623 777 

1 632 719 

58 637 778,

1 638 774 

639 7 7 0

! 640 166,

93775 85846 3408

96158 88752 3331

99009 92017 3283

102251 048 3387

M 962 98824 3347

463

473

484

500

5439

1980

1981

1982

1069401 9303 3364

108670 CKI 397 3368

110204 9526 3401

1 5

521

526

1980

dn 106546 99872 3313

106637 99963 3387

106394 99677 4 1 2

106552 99204 3318

106892 98922 3385

106832 98769 3309

63

63

63

69

75

75

640 777

639 778

637 775

637 774

639 716

637 775

516

1 5

513

514

515

514

Feb

M a r

’Apr,

May

June

July

A u g

Sept

Oct

Ncv,

Dec

198:

Jan

Feb

Mar

Ap!

May

170 03C ~ 2099

170217 2114

170419 2121

170624 2121

170814 2119

171 Oc 7 2124

98816 3331

98829 3247

S$ 104 3448

99327 3362

99567 3387

99650 3486

78

77

75

75

75

73

638 775

637 773

637 773

638 773

638 773

636 770

515

515

514

516

516

516

171229 2125

171400 ‘ 2121

171581 2128

171770 2129

171956 2127

172172 2,131

172385 2139

172559 2160

172758 2165

172966 215 8

173155 2158

173330 2164

S9 964 3420

lrll 143 3340

ICWI 504 3356

10 I 006 3519

100968 3371

100393 3360

75 i

74

731

72

7 5

7 4

639 773

639 772

640 773

642 774

642 ‘ 774

638 767

51 8

520

521

523

524

June 521

)Uly

Aug

Sept

Oct Nov

O H

19732

la.

Feb

M a r

AP{

May

June

July

A u g

Sepf

Oct

Nov

Oec

100748,

100 7C4

100104

100355

100229

99677

3320,

3396

3358

3374

3389

3219

72

74

; :

83

86

6381 768

638 769

635 767

638 767

639 768

637 767

521

521

517

522

523

520

86 636 765

88 638 766

90 638 765

93 639 766

94 642 770

95 6-40 765

173495 2159

173657 2168

173843 2175 /

174020 2176

174201 2175

174364 2173

99688

99695

99597

99484

99994

99681

99588

99683

99543

99176

99136

99093

3tltUtll

3379

3367

3367

3356

3446

3371

3445

9346 ~

966 9

9881

10256

10384

1046 6

10828 ~

10931

11315

Ii 576

11906

12036

521

523

523

524

527

527

174544 2180

174707 2196

174889 2198

175069 2188

175238 2180

175380 ~ 2182

98 ~ 641 765

766

1:? ;; 768,

105 M 1 761

107 642 768

108 642 766

529

529

528

527

528

530

342’3

3363

3413

3466

3 4)1

‘Not seasonally adjusted

‘Cwlllan labor force as percent of clvdlan non!

‘Not strictly comparable with earlier data due to populatl I adjustments as follows” Begmrung 1953, mtroduct!on of 1950

census data added about 600,000 to population and about )0,000 to labor force, total employment, and agricultural employment Begumn 1960, Incluslon of Alaska and Hawall added about 500,000 to population, about 300,000 to labor torce,

and about 240, 00 to nonagricultural employment 8 Begmnmg 1962, mtroduct!on of 1960 census data reduced populahon

by about 50,000 and labor force and employment by about 200,000 Begmnmg 1972, Introduction of 1970 census data

added about 800,000 to cwdlan nonmstltullonal population and about 333,000 to labor force and employment A subsequent adjustment based on 1970 census m March 1973 added 60,000 to Iatwr force and 10 employment Beglnrung 1978,

changes m samplmg and estlmat!on procedures mlroduced mto the household survey added about 250,000 fo labor force

and to employment Unemployment levels and rates were not significantly affected

II DoDulatlon

SOURCE Oeparfment of Labor Bureau of Labor Statlstlcs
24

Table 4.–Wage and Salary Workers in Nonagricultural Establishments, 1929-82

(thousands of persons; monthly data seasonally adjusted)

‘ — T Manufacturing

Mmrsg

1,087

744

854

W;

992

925

892

836

862

955

994

930

901

929

898

; ; :

792

822

;3;

732

712

672

650

635

634

:3;

613

606

619

623

609

628

642

;3;

779

813

851

958

1,027

1,132

1,122

1,102

1,113

1,124

978

985

1,137

1,164

1,180

1,192

1,195

1,202

1,206

1,201

1,203

1,197

1,182

1,152

1,124

1,100

1,086

1,075

1,058

1,051

1,036

Finance,

insurance,

arrd

real

estate ——.——

1,494

1,280

1,447

1,485

1,525

1,509

1,481

1,461

1,481

1,675

1,728

1,800

1,828

1,888

1,956

2,035

2,111

2,200

2,2!38

2,389

2,438

2,481

2,549

2,629

2,688

2,754

;,:8;

2;977

3,058

3,185

3,337

3,512

3,645

3,772

3,908

4,046

4,148

4,165

4,271

4,467

4,724

4,975

5,160

5,301

5,350

5,252

5,264

5,270

5,286

5,295

5,302

5,311

5,319

5,328

5,325

5,324

5,331

5,326

5,326

5,336

5,335

5,342

5,352

5,359

5,360

5,367

5,357

5,362

5,376

—

Services

;,gg;

3:502

3,665

3,905

4,066

4,130

4,145

4,222

4,697

5,025

5,181

5,240

5,357

5,547

5,699

5,835

5,969

6,240

6,497

6,708

;,;;;

7,378

7,620

7,982

8,277

8,660

9,036

9,498

10,045

10,567

11,169

11,548

11,797

12,276

12,857

13,441

y:;

15:303

16,252

17,112

17,890

18,592

19,000

18,352

18,382

18,414

18,480

18,517

18,556

18,615

18,654

18,707

18,773

18,815

18,834

18,831

18,867

18,904

18,929

18,963

18,988

19,042

19,048

19,084

19c074

19,125

19,143

Government Total

wage

and

salary

workers

rransport~~jn

publlc

uthtles

Year or

month Non.

jurable

goods

—..—

5,564

5,622

6,225

6,458

6,518

6,472

6,450

6,962

7,159

7,256

6,953

7,147

7,304

7,284

7,438

7,185

7,341

7,411

7,321

7,116

7,303

7,337

7,256

7,373

7,380

7,458

7,656

7,930

8,007

8,155

8,272

;,;:;

8:102

8,262

8,152

7,635

7,920

8,086

8,231

8,280

8,098

~,;::

8,051

8,051

8,054

8,074

8,095

8,088

8,113

8,083

8,083

8,038

8,002

7,952

7,895

7,879

7,829

7,794

;,;;;

7,680

;,:;;

7:659

7,628

7,601

Federal

533

565

905

996

1,340

2,213

2,905

2,928

2,808

2,254

1,892

1,863

1,908

1,928

2,302

2,420

2,305

2,188

2,187

2,209

2,217

2,191

2,233

2,270

2,279

2,340

2,358

2,348

2,378

2,564

2,719

2,737

2,758

2,731

2,696

2,684

2,663

2,724

2,748

2,733

2,727

2,753

2,773

2,866

2,772

2,733

2,798

2,789

2,780

2,774

2,776

2,777

2,775

2,769

2,764

2,757

2,749

2,756

2,141

2,737

2,736

2,730

2,728

2,739

2,737

2,739

2,734

2,723

2,726

2,728

—

Construction State

and

local

Durable

goods Total

——— — -t

1929

193 3

1939.

31,324

23,699

30,603

10,702

7,397

10,278

10,985

13,192

15,280

17,602

17,328

15,524

14,703

15,545

y:

15,241

16,393

16,632

17,549

16,314

16,882

17,243

17,174

15,945

16,675

16,796

16,326

16,853

16,995

17,274

18,062

19,214

;;,4:[

20:167

19,367

y:;

20:154

20,077

18,323

18,997

19,682

20,505

21,040

20,285

;;JU;

20,171

20,148

20,197

20,275

20,332

20,334

20,379

20,311

20,267

20,097

19,903

19,676

19,517

19,454

19,319

19,169

19,115

18,930

18,813

18,672

18,572

18,325

18,183

18,134

3,916

2,672

2,936

3,038

3,274

3,460

3,647

3,829

3,906

4,061

4,166

4,189

4,001

4,034

4,226

4,248

4,290

4,084

4,141

4,244

4,241

3,976

4,011

4,004

3,903

3,906

3,903

3,951

4,036

4,158

4,268

4,318

4,442

4,515

4,476

4,541

4,656

4,725

4,542

4,582

4,713

4,923

5,136

5,146

y;

5,139

5,145

5,153

5,163

5,158

5,162

5,168

5,168

5,181

5,162

5,150

5,128

5,125

5,115

5,100

5,094

5,101

5,078

5,044

:,:;;

5;007

4,994

4,979

1,;;;

1,165

1,311

1,814

2,198

1,587

1,108

1,147

1,683

2,009

2,198

2,194

2,364

2,637

2,668

2,659

2,646

2,839

3,039

2,962

2,817

3,004

2,926

2,859

2,948

3,010

3,097

3,232

3,317

3,248

3,350

3,575

3,588

3,704

3,889

4,097

$:;:

3;576

3,851

4,229

4,463

4,346

4,176

3,912

4,315

4,240

4,267

4,281

4,223

4,185

4,175

4,146

4,124

4,101

4,071

4,026

3,966

3,974

3,934

3,938

3,988

3,940

3,927

3,899

3,883

3,856

3,848

3,818

2,532

2,601

3,090

3,206

3,320

3,270

3,175

:,::;

3;341

3,582

3,787

3,948

4,098

4,087

4,188

4,340

4,563

;,;;;

5:399

5,648

5,850

6,083

6,315

6,550

6,868

7,248

7,696

8,220

8,672

9,102

9,437

9,823

10,185

10,649

11,068

11,446

11,937

12,138

;;,:;:

13:174

13,375

13,253

13,051

13,400

13,410

13,371

13,354

13,302

13,243

13,189

13,125

13,140

13,160

13,159

13,161

13,123

13,113

13,123

13,122

13,125

13,093

12,898

12,933

13,029

13,019

~yl;

4,715

5,363

6,968

8,823

11,084

1:,:;:

7;742

8,385

8,326

7,489

8,094

9,089

9,349

10,110

9,129

9,541

9,833

9,855

8,829

9,373

9,459

9,070

9,480

9,616

9,816

10,405

11,282

11,439

11,626

11,895

11,208

10,636

11,049

11,891

11,925

10,688

11,077

11,597

12,274

12,760

12,187

12,1 1?

11,114

12,120

12,097

p:

12:237

12,246

12,266

12,228

12,184

12,059

11,901

11,724

11,622

11,575

11,490

11,375

11,332

11,203

11,133

10,993

10,900

10,666

10,555

10,533

194 0

1941

1942

1943

194 4

1945

194 6 ; 194 7

194 8

1949

32,361

36,539

40,106

42,434

41,864

40.374

41,652

43,857

44,866

43,754

1950

1951 .“

1952

1953

1954

1955. ‘

1956

1957

1958

1959

1960

196 1

1962

1963

1964

1965

1966

p%: .“

1969

45,197

47,819

48,793

50,202

48,990

50,641

52,369

52,853

51,324

53,268

54,189

53,999

55!549

56,653

58,283

60,765

63,901

65,803

67,897

70,384

1970

197 1

1972

1973

1974

197 5

1976

197 7

1978

1979 i

70,880

71,214

73,675

76,790

78,265

76,945

79,382

82,47J

86,697

89,823

1980

1981

1982 p

1981

Jan ,

Feb

Mar .’

Ar

iay

June

July

Aug

Sept

Oct

Nov

Dec

1982

;;:. ‘

Mar .“

WY

June

Jul y

Aug $ Sept

Oct

NOVP

Dec P

90,406

91,105

89,619

90,909

90,913

91,014

91,099

91,131

91,286

91,396

91,322

91.363

91,224

90,996

90,642

89,535

89,312

89,267

88,860

88,684

88,518 —.

Source Department of Labor, Bureau of Labor Statlstlcs
25

employment* and unemployment, and in labor

shortages.

Labor shortages exist where a sufficient number

of particular types of people are unavailable for

work at prevailing wages. Concern has been expressed by people in industry and in government

about the economic effects of shortages in highly

skilled craft and technical occupations, from machinists to certain types of engineers.** Alleged

shortages have been cited as a motivation for in-

● For example, according to BLS many college graduates during

the 1970’s took jobs not requiring college degrees.

● *The extent of current and possibIe future labor shortages that

may affect the development or diffusion of programmable automation is unclear. Among the reasons that shortages are hard to measure

are the following: 1) Federal programs do not collect occupational

shortage statistics (due to cost and data reliability problems),

2) available data do not accurately capture employee mobility within

and between occupations, 3) occupational classifications among firms

and Federal statistical programs are inconsistent, and 4) employer

and union surveys tend to be statistically unreliable. A recent analysis

by BLS found after evaluating data from several sources that a

machinist shortage could be neither established nor disproved. 15

15Neal H. Ro~ntha], “shortages of Machinists: An Evaluation

of the Information,” Monthly Labor Review, July 1982.

WORKING ENVIRONMENT

Introduction

Programmable automation may change not

only the numbers and types of people working

in manufacturing, but also the circumstances of

work—what may be called the working environment. The ways in which programmable automation is applied will determine how it affects the

working environment. This discussion of the

potential implications of programmable automation for the working environment will address

some of the issues concerning worker safety and

health, human factors, job content, and structure

of work.

Expressions of concern about the effects of technology on the conditions of work have increased

in the United States over the past two decades.

For a long time it was assumed by management

that the benefits of more efficient production

achieved through the introduction of new technologies far outweighed any negative effects on the

work force. In other words, the assumption was

vestments in automation, and also in retraining.

While retraining can ease shortages by increasing the supply of skilled workers, raising wages

is another method of stimulating supply, although

employers are often unwilling or unable to do this.

Note that, for skills that take years to develop,

instituting training programs (or raising wages)

will not eliminate a shortage immediately.

A satisfactory analysis of labor supply issues

associated with programmable automation should

address such issues as contrasts in the composition of the U.S. labor force with that of other

countries producing and using programmable

automation, and the extent to which the production and use of programmable automation are influenced by labor shortages. Such issues are fundamental to the identification of components of

the U.S. labor force that may be particularly

helped or harmed by the spread of programmable

automation, and the determination as to whether

anticipated changes in the U.S. labor force are

likely to cushion or exacerbate impacts that might

arise from programmable automation.

that people could always adapt in some way to

the requirements imposed by the technology.l6

As in other countries, concerns about workplace conditions contributed to the growth of the

labor movement in the United States. Since the

mid-1960’s, changing social and economic environments, characterized by an emerging awareness of individual rights and well-being, increased

worker dissatisfaction, and declining productivity,

have increased the importance of the working environment to both management and government,

as well as labor. Workplace issues in manufacturing are currently being addressed in a number

of ways, such as: 1) emphasis on human factors

in the design of manufacturing equipment; 2) innovations in the structure of work; 3) increased

cooperation between management and labor in

solving workplace problems; and 4) a variety of

1eJoe] A. Fadem, “Automation and Work Design in the United

States,” in Memational Comparative Study on Automation and

Work lki.gn, International Labour Office, Geneva, January 1982,

p. 25.

98-951 0 - 83 - 5
26

experiments in worker participation (such as qual- 5. These developments have met with varying

ity control circles and quality of working life pro- degrees of success and commitment from managegrams) intended to give workers greater input into ment and labor. Nevertheless, they are part of the

decisions directly affecting their jobs. See table backdrop to the spread of programmable automaTable 5.—Labor-Management Committees on Industrial Relations Issues, Safety, and Productivity by Industry

(agreements covering 1,000 workers or more, Jan. 1, 1980)

Labor-management committees on—

Industrial relations

All agreements issues a

Safety b Productivityc

Industry Agreements Workers Agreements Workers Agreements Workers Agreements Workers

750

79

8

11

31

11

17

42

15

36

15

14

G

88

41

81

83

112

11

9

800

16

62

80

81

12

123

31

66

327

2

io2i15 0

234,200

21,800

28,850

207,900

17,100

23,100

65,000

31,600

61,700

25,500

68,850

23,100

93,600

460,600

97,000

242,150

323,750

957,100

27,650

14,600

456&650

169,050

469,550

620,000

210,700

23,900

405,200

148,300

323,450

1,195,000

3.500

All industries. . . . . . . . . . . . 1.550 659,800 - 6 0 245,400 - ‘

-

Manufacturing. . . . . . . . . . . . .

Food, kindred products . . . . .

Tobacco manufacturing. . . . .

Textile mill products . . . . . . .

Apparel . . . . . . . . . . . . . . . . . . .

Lumber, wood products. . . . .

Furniture, fixtures. . . . . . . . . .

Paper, allied products . . . . . .

Printing and publishing . . . . .

Chemicals . . . . . . . . . . . . . . . .

Petroleum refining . . . . . . . . .

Rubber and plastics ., . . . . . .

Leather products . . . . . . . . . .

Stone, clay, and glass . . . . . .

Primary metals . . . . . . . . . . . .

Fabricated metals. . . . . . . . . .

Nonelectrical machinery . . . .

Electrical machinery. . . . . . . .

Transportation equipment . . .

Instruments . . . . . . . . . . . . . . .

Miscellaneous

manufacturing . . . . . . . . . . .

Nonmanufacturing . . . . . . . . .

Mining, crude petroleum,

and naturai gas . . . . . . . . . .

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

Communications. . . . . . . . . . .

Utilities, electric and gas . . .

Wholesaie trade . . . . . . . . . . .

Retail trade . . . . . . . . . . . . . . .

Hotels and restaurants . . . . .

Services . . . . . . . . . . . . . . . . . .

Construction . . . . . . . . . . . . . .

Miscellaneous

nonmanufacturing. . . . . . . .

aA labor-management committee on industrial relations issues is a joint committee which studies Issues, fOr OXW@% subcontracting, seniority, and wa9e incentives,

away from the deadlines of bargaining and makes recommendations to the negotiators. It also may be referred to as a “prebargaining” or “continuous bargaining”

committee. It should not be confused with labor-management committees which meat periodically to discuss and resolve grievances and in-plant problems.

bA Iabor.management safety committee is a joint committ~ which meets periodically to discuss safety problems, to work out solutions, and to itTIpbfTlOIIt SafOty

39

6

—

—

—

4

1

1

1

1 —

4—

1

7

2

4

3

3

1

—

21

1* 150

25,500

—

—

—

4,850

1,000

1,100

1,000

1,200

—

29,250

—

1,000

40,150

3,200

10,350

8,200

20,000

1,350

2

1

4

2

2

2

—

5

3

—

6,000

9,000

45,000

4,300

3,950

2,200 —

22,100

4,700

—

572

35

—

7

8

18

3

21

10

14

2

26

76

25

48

42

68

4

6

159

13

22

37

35

1

10

34

—

2,867,850

1,835,550

140,400

—

1,200

1,000

9,950

7,400

27,650

10,800

30,850

18,900

68,850

3,200

66,550

429,700

66,150

141,800

130,300

656,150

16,700

8,000

1,032,300

161,200

289,400

316,050

108,050

1,050

19,050

10,000

8,800

118,700

81 1,091,350

58

5—

—

—

1

1

1

2

1 —

2

—

—

33

3

2

—

7—

—

23

845,300

69,700

—

—

—

1,000

1,000

1,200

9,100

2,000 —

16,450

—

—

316,850

5,050

2,100

—

420,850

—

246,050

3

12

1

2

—

—

—

2

3

—

10,100

208,350

1,550

4,900

—

—

—

3,650

17,500

—

programs in the plant.

CA Iabor.management committee on productivity iS a joint committee which meets periodically to discuss in-plant production problems and to work Out methods O f

improving the quantity and quality of production.

dExcludeg railroads and airlines.

NOTE: Nonadditlve.

SOURCE: U.S. Department of Labor, “Characteristics of Major Collective Bargaining Agreements, January 1, 19S0.”
27

tion, and will influence how these technologies

are implemented and how they affect the overall

conditions of work.

Occupational Safety and Health

Occupational safety and health issues may be

clearer than others associated with programmable

automation. For example, the application of robots to painting and welding tasks is widely

acknowledged as a measure that reduces worker

exposure to occupational hazards by removing

workers from the hazards. However, the use of

robots and other forms of programmable automation may give rise to workplace hazards that are

new and perhaps unanticipated.

The hazards associated with programmable

automation are likely to be similar to those associated with industrial machinery, video display

terminals (VDTs), and other types of equipment.

With the introduction of programmable automation, there may be a shift of occupational safety

and health concerns in manufacturing away from

those directly involving machinery toward VDTrelated issues. VDTs will become more numerous

in manufacturing, and one possible outcome of

the spread of programmable automation is an increase in the percentage of manufacturing workers

using VDTs and a decrease in the percentage

operating machinery. The eyestrain, stress, and

back, neck, and shoulder problems recently documented by the National Institute for Occupational

Safety and Health among workers who use VDTs

for extended periods of time may become a problem for those using computer-aided design and

manufacturing (CAD/CAM) systems.17

Unlike many older manufacturing technologies,

programmable automation technologies are being

developed in an era of greater awareness of occupational safety and health issues. Part of that context includes a body of Occupational Safety and

Health Administration (OSHA) standards as well

as a sophisticated set of nongovernmental technical standards. The applicability of current OSHA

standards to the use of programmable automation will depend on the type of industry or nature

“’’Health Hazards for Office Workers: An Overview of Problems

and Solutions in Occupational Health in the Office, ” Working

Women Educational Fund, 1981, p. 22.

of the operation involved. It is unclear whether

or not programmable automation may give rise

to a need for further OSHA standards.

Human Factors

Programmable automation may change the

way job performance is evaluated in manufacturing. The computer and communications capabilities of programmable automation permit the recording and monitoring in remote locations of

many aspects of equipment and system utilization,

such as the number of operations performed per

minute or per hour. Such monitoring would provide management with more information than

individual piece counts conducted at the end of

a day or week and other traditional measures of

performance. Although sophisticated monitoring

functions are not a necessary feature of programmable automation products, their possible use

may reduce worker discretion in performing tasks

and raise levels of stress among workers. Such

results have been observed where office automation has been implemented with sophisticated

monitoring features, such as tabulation of keystroke-per-minute rates. 18 On the other hand, if

programmable automation requires fewer workers

per machine, it may reduce the amount of direct

personal supervision required.

Many of the effects, both physical and psychological, of programmable automation on people

in the workplace will depend on the care and

thought that go into the basic design of automated

equipment and systems, and on whether the designers are concerned about human factors issues.

Consideration of human factors involves first analyzing the roles people will play in a working environment using programmable automation, and

then “examining such human factors engineering

issues as design, procedurization, and protection.”19 Design engineers who do not work on the

shop floor or in other manufacturing settings may

not be sufficiently sensitive to the physiological

‘

s

Judith Gregory, Testimony for 9 to 5, National Association of

Working Women, Hearings, House Subcommittee on Education and

Labor of the Committee on Education and Labor, June 23, 1982,

p. 10.

I~H. MCI]vaine parsons and Greg P. Kearsley, “Human Factors

and Robotics: Current Status and Future Prospects, ” Human Resources Research Organization, Alexandria, Va., prepared for U.S.

Army Human Engineering Laboratory, October 1981, p. 13.
28

and psychological needs of the user. Whether or

not the user is involved in the design process may

determine to what extent the human needs of

manufacturing personnel will be translated into

equipment and systems designs.20

Although worker involvement in the design

process would seem logical on the surface, it may

also present a dilemma for manufacturing employees. While on the one hand their participation

could improve the consideration of human factors, it could also facilitate the design of equipment that may eliminate jobs. This dilemma may

inhibit the full participation of many workers in

such activities as quality control circles and quality of working life programs.

Job Content

Programmable automation may affect job content in a number of ways and its impact on skill

requirements is likely to be highly variable. By

design, automated equipment and systems may

alter the skills required for certain aspects of the

production process, but the implications for specific jobs (e.g., in terms of the number and variety of tasks comprised) depend on how programmable automation is implemented. The impacts

of programmable automation on skill levels are

uncertain. While some jobs clearly will require a

higher level of skill, others may require a lesser

level, largely because much of the process-control

decisionmaking may be incorporated into computer-controlled equipment and systems. It is

unclear at this time whether the effects on skill

levels are inherent in the programmable automation technology, or the extent to which innovative

use provides a choice. Whether programmable

automation will provide jobs that are more stimulating and satisfying overall than those in traditional manufacturing environments is uncertain.

However, it is unlikely that all programmable

automation jobs will provide more challenge,

variety, and responsibility-nor does everyone require it.21 There will probably always be monoto2oFadem, op. Cit., p. 51.

21Enc Tfist, ‘The Evolution of Socio-Technical Systems: A COI’Iceptual Framework and an Action Research Program, ” Occasional

Paper No. 2, Ontario Quality of Working Life Center, June 1981,

p. 32.

nous jobs, and many workers accept this in return

for such other benefits as fair wages and job

security .22

Depending on how tasks are organized, programmable automation may allow an increase in

the variety of tasks a worker performs.

Therais also a close relation between the manufacturing technology chosen and the organization of work. However, technology is not the

single determinant, so there is no specific organization corresponding to the use of a CAD/CAM

system. Organizational philosophy has a predominant role, for example if one believes in

complementary specialization of skills or in

overlapping skills. The CAD/CAM may be a

loyal servant to any work organization, provided

that those who design and adapt the system know

what they want.23

A restructuring of work in which both technical

and human considerations are given equal treatment could offset the negative effects of changing skill requirements that may arise where old

patterns of work organization persist.

Programmable automation may lead to changing roles and responsibilities at all levels, affecting both the nature of jobs and the distribution

of power. The difficulties of reorganizing companies are well recognized. For example, change

in the hierarchical structure (and thus control)

brought about by the introduction of new technology may meet with resistance from those who

might lose some authority .24 Consultants and

trade and professional associations concerned

with programmable automation have devoted

much attention to the management challenges of

successful use of programmable automation over

the past few years. Clearly, management planning, practices, and policies will be key factors

in how the introduction, implementation, and

operation of programmable automation affects

the overall working environment.

~ZSar A. ~Vitan and Clifford M. Johnson, t%?comi T.~O14?~~5 on

Work (Kalamazoo, Mich.: W. E, Upjohn Institute for Employment

Research, 1982), p. 212.

23’The Promotion of Robotics and CAD/CAM in Sweden,” report

from the Computers and Electronics Commission, Ministry of Industry (Sweden), 1981.

ZtRo~rt Schrank, Ten Thousand Working Days (Cambridge,

Mass.: The MIT Press, 1978), p. 221.
Chapter 3

Education, Training,

and Retraining
Chapter 3

Education, Training, and Retraining

INTRODUCTION

At various stages in U.S. history, changes in

workplace operations and procedures in all sectors of the economy have resulted in changes in

education, training, and retraining requirements

for those employed or preparing for employment.

Changes in instructional requirements for manufacturing-related work have been particularly

dramatic. In some instances, they have been so

extensive and widespread that they have triggered

changes in the structures of institutions and

organizations engaged in the delivery of education, training, and retraining services or the

emergence of new instructional providers. For example, new production techniques introduced

during the Industrial Revolution had much to do

with the creation of a system of free public education, since large-scale production and continued

industrial expansion required a literate work force

capable of functioning on production lines, supervising manufacturing operations, keeping administrative records, and performing other functions.

During this period, both industry and the labor

movement became involved in the design and implementation of instructional programs to address

short-term and special needs they felt could not

be met either within a system of general instruction or through the public and private vocational

programs that were emerging.1

Another era of substantial industrial change

occurred in the 1960’s, when the aerospace/defense industry underwent tremendous expansion

and mechanization as a result of a concern over

national defense and a national commitment to

manned space exploration. Under provisions of

the National Defense Education Act,2 an example of legislation that led to the establishment of

national policy for certain forms of occupational

IFor additional information on the history of education and training in the United States, see Mormationa] Technology and Its Impact on American Education (Washington, D. C.: U.S. Congress,

Office of Technology Assessment, November 1982), OTA-CIT-187.

‘National Defense Education Act of 1958 (Public Law 85-864, Sept.

2, 1958); National Defense Education Act Amendments, 1964 (Public

Law 88-665).

instruction, U.S. educational institutions were

charged with coordinating efforts to prepare

thousands of individuals for careers in science,

engineering, and related fields. Training in these

fields was considered necessary in order to

develop the expertise and the technological base

essential to creating a strong system of national

defense and to meeting the challenges of space.

Rapid technological change in aerospace/defense

and other industries affected by these nationwide

efforts required the continued involvement of

business and labor in specialized instruction, to

ensure that skill levels advanced at the same rate

as applications of new machinery. However,

when national priorities changed, this cross-sector

commitment to linking advances in technology

with the upgrading of skills within aerospace/defense and related industries disappeared. Since

that time, the development of the human resource

for these industries has been approached in more

parochial ways by business, labor, educators, and

government.

In these and other instances of changing education and training requirements, three factors have

impeded the development of a coordinated, flexible system for occupational instruction in the

United States. First, the absence of long-range,

public and private projections of skill requirements, particularly those that highlight changes

in skill levels and in core skill requirements within

occupations, has hindered the development and

delivery of instructional programs before industrial demand reaches critical proportions. * For example, the Electronic Industries Association reports that within the software technology field,

certain highly specialized skills possessed by electrical engineers and computer scientists are interchangeable. Although not captured in more for-

● There are those who would argue that establishment of a coordinated, flexible system for occupational instruction in which projections of national as well as regional demand are taken into account is not the best approach and that actual rather than projected

demand within local labor markets should determine public and private sector human resource development activities.

31
32

ma] projections, this interchangeability will affect

recruitment strategies and, in turn, supply within

both occupational groups. The net result may be

that current and projected shortages of computer

science graduates may also be seen in electrical

engineering and yet may not be reflected in formal occupational projections. * Second, a history

of responding to changing industrial skills requirements as crises arise has perpetuated a fragmented

approach to education and training. Little or no

planning or coordination of efforts takes place

among traditional educators, business, labor, government, and others. Third, rapid technological

change has placed great strain on educators as

they attempt to adapt instruction to the requirements of new technology, while at the same time

they address other changes in instructional needs.

The application of programmable automation

in manufacturing operations has the potential to

trigger widespread changes in education and training requirements. Robots and other forms of programmable equipment and systems may change

the organization of the manufacturing process, the

character of the production line, the occupational

mix, and the human-machine relationship. The

utilization of programmable automation, depending on its impact on employment levels within

specific occupations, may also necessitate the

retraining of individuals for occupations in other

sectors.

This section of the technical memorandum examines the changing role of education, training,

and retraining in the United States; describes how

industry and labor engage in instructional services

delivery; presents some current views held by representatives of industry, labor, and the educational community concerning changes in instructional requirements and providers; and outlines

selected critical issues for those engaged in instructional design and delivery, in light of possible

widespread use of programmable automation.

This picture of education, training, and retrain-

“Alternatively, it could be argued that electrical engineers, given

an adequate supply, could be recruited to fill computer science jobs

and eliminate projected shortages for that occupational group, but

not in ways that could be foreseen by current methods of formal

projection. In addition, recruitment for computer science jobs from

the ranks of individuals with liberal arts training such as music and

foreign languages, a practice that has proven quite successful, is not

captured in formal projections of demand for those disciplines.

ing, when viewed in light of present and future

trends in the labor market, should facilitate the

identification of new opportunities, problems, and

issues in education and training policy.**

Changing Role of Education,

Training, and Retraining

Formal instruction has always been viewed as

an important part of the human development

process in the United States. Since the colonial

period, a variety of institutions and organizations

have been established to deliver education and/or

training services to the general public or to special

segments of the population. Some of these institutions and organizations consider the provision of

instructional services their primary mission;

others, such as corporations and labor unions,

view education and/or training as one of a number of activities in which they are engaged. The

recent OTA study, Informational Technology and

Its Impact on American Education, found that

today, instructional services are available from

an even wider variety of sources, including electronic-based services delivered directly to the

home.3

As U.S. economic and social conditions have

changed over the years, the role of education and

training in the lives of all citizens has changed as

well. Formal instruction was once viewed as a luxury that was unavailable to a large percentage of

the population. Then, after a system for public

education was established, the role of instruction

became the initial preparation of young people

to assume responsibilities as productive numbers

of society. In the 1980’s, instruction has come to

● *A number of investigations are now underway in the private

sector that could considerably improve the understanding of changing education, training, and retraining requirements in general and

requirements related to the utilization of programmable automation in manufacturing in particular. One such effort is a survey of

education and training representatives in 1,000 corporations, conducted by TrainingMagazine, designed to identify current instructional needs and in-house program content. A second survey, initiated by the Society of Manufacturing Engineers (SME), has been

designed to elicit from a sample of the SME membership, as well

as from selected educators, views on instructional requirements that

may stem from the application of computer-aided design (CAD) and

computer-aided manufacturing (CAM). Knowledge derived from

these investigations and others in progress will be incorporated into

the final report.

31nformational Technology and Its Impact on American Education, op. cit.
33

be seen as a lifelong process that enables individuals of all ages to cope with economic and

social change.

Questions concerning who receives instruction,

who determines the content, who provides the instruction, what modes of delivery are utilized,

how much instruction costs, and who pays for

it have received considerable discussion throughout modem U.S. history and have served to shape

national education and training policy. Education

and training for work and who should provide

them have been controversial subjects since the

Industrial Revolution.

Participation in Instructional Programs

Due to a variety of forces, including the accelerating rate of technological change and growth

in foreign trade, some workers, especially those

in manufacturing environments, are finding that

their skills are not adequate either to continue to

perform their current jobs or, if they are displaced,

to secure new jobs. Others may find that they are

overskilled for their positions, due to the introduction of computer-based equipment and systems

as well as other workplace changes. Changes in

skill requirements and skill levels are affecting all

manufacturing occupations, from the production

line worker to the professional engineer. In

response, many individuals are seeking additional

training in order to keep pace with technological

and economic change, although it is unclear from

available data which occupational groups they

may represent and whether they are predominantly white- or blue-collar. The relative quality of

the instruction, and therefore its usefulness, is also

difficult to determine from the available data. The

Current Population Survey’s Special Survey of

Participation in Adult Education revealed that

over 21 million persons 17 years old and over,

or some 13 percent of the adult U.S. population,

participated in adult education programs in 1981.

An analysis of enrollments in 37,381 courses revealed that approximately 60 percent had participated for job-related reasons. In 9,260 cases,

courses were provided by employers; in another

12,287 cases, employers paid enrollment fees for

courses delivered outside the company. Professional and technical workers comprised the largest

percentage of those enrolled—some 30 percent

98-951 0 - 83 - 6

(there is no distinction made in the survey as to

whether respondents are salaried or hourly employees). About 54 percent of the participants

were under 35 years of age; 12 percent were over

55.

4

Industry and Labor as

Instructional Providers

Since the mid-19th century, both business and

the labor movement have contributed to or participated in the design and delivery of instructional

programs. Formal, in-house instruction is more

common in larger business and labor organizations. At present business and labor both sponsor a variety of employee education and training

activities, such as secondary-level remedial

courses, traditional apprenticeships, and postsecondary degree and nondegree programs.

The American Society for Training and Development estimates that U.S. industry now spends

approximately $40 billion annually on education

and training programs for employees. The estimate excludes instructor fees or other administrative costs, such as equipment and enrollee

travel expenses. Although rather dated, the results

of a study of corporate-based training and education conducted by the Conference Board in 1974-

75 indicated that in 1973-74, 75 percent of the 610

firms responding provided some in-house courses,

89 percent had tuition aid or refund programs,

and 74 percent sponsored the enrollment of

selected employees, usually managers and professionals, in courses offered outside the company

during working hours. * The Conference Board

estimated that in firms with 500 or more employees, with a combined employee base of 32 million,

about 3.7 million, or 11 percent, were enrolled

in in-house courses during working hours and

another 2 percent (or 700,000) were enrolled during nonworking hours. Participation was more

common for salaried than for hourly employees.

The survey also showed that firms with less than

1,000 employees relied more on hiring trained individuals and on informal, on-the-job training.’

“’Participation in Adult Education, May 1981, ” National Center

for Education Statistics Early Release, June 1982.

*Roughly 1.3 million employees in responding companies were

taking advantage of tuition assistance programs.

‘Seymour Lusterrnan, Education in Industry (New York: The Conference Board, Inc., 1977), pp. 11-12.
34

This finding is consistent with other evidence that

firms with up to 500 employees depend on informal, on-the-job training for all types of staff, since

formal instruction programs are often too expensive for businesses of this size. b

Labor unions and labor organizations are also

active sponsors and providers of employee education and training. Historically, unions have promoted liberal arts education in addition to more

narrowly focused occupational education. Labor

unions and labor organizations have been a strong

force in shaping the popular view of education

as the key to social and economic advancement.7

Like some companies, unions sponsor 2- and

4-year degree programs at community colleges,

colleges and universities, as well as single courses

in labor studies. Unions also cosponsor apprenticeship programs with industry, providing specialized training in a skilled trade, craft or occupation at the worksite, and on-the-job instruction.

According to Bureau of Labor Statistics (BLS)

figures, at the close of 1979 there were 323,866

persons enrolled in apprenticeship programs. Unpublished BLS estimates of apprenticeship enrollments are 320,000 persons in 1980, 316,000 in

1981, and 287,000 in 1982. While the drop in enrollment reflects reduced public and private funding levels, unions report there is no evidence to

suggest that interest in apprenticeship has declined. Reductions in U.S. Employment and

Training Administration apprenticeship and

preapprenticeship grants to individual labor

unions, labor organizations such as the AFL-CIO’s

Human Resources Development Institute, and

community-based organizations such as the National Urban League, have diminished recruitment

for and enrollment in apprenticeships. Deteriorating economic conditions within industries providing apprenticeship opportunities may be

another factor in declining enrollments.

Manufacturing-Related Instruction

Management and labor have been the major

providers of employee instruction beyond initial

bInterview with Jerome Pelaquin, Chairman, Technical and Skills

Training Special Interest Group, American Society for Training and

Development, July 1982.

‘Paul E. Barton, Workhk Transitions: The Adult Learning Connection (New York: McGraw-Hill, 1982), ch. 7.

occupational preparation, since public sponsorship of training and retraining of noneconomically

disadvantaged adults has been limited. The extent

to which individuals working in manufacturing

participate in education, training, and retraining

programs offered by industry, labor unions, or

other private or public providers is unknown.

However, within corporate-based instruction as

a whole, fewer courses are designed specifically

for production line workers (excluding apprenticeship) than any other occupational group. Training industry representatives suggest that corporate

instructional efforts have not previously concentrated on technical training other than in programs

provided for engineering or data processing personnel. * This situation may change as U.S. manufacturing firms become familiar with new design

and production technologies.

Another reason that technical and skills training do not receive more emphasis in corporatebased instructional programs may be their relative

complexity, which requires demonstration of skill

as well as knowledge transfer. This implies that

technical courses and programs must emphasize

hands-on practice and include a performance test

to ensure mastery of skills.8 Technical and skills

training also require instructors with an understanding of current manufacturing processes as

well as in-depth knowledge of the subject matter. These requirements result in high costs for

establishing and maintaining an instructional program. In nonunion facilities, work-related instruction may depend on the advantages companies

see in providing continuing education experiences

in-house or through tuition reimbursement plans.

Different views exist as to how much emphasis

unions place on instruction beyond apprenticeship. Some suggest that such instruction has

heretofore not been a great concern of union

members who therefore have addressed it in labormanagement agreements in only a general way,

through tuition reimbursement provisions. How-

“Technical training is the term commonly used to describe workrelated instruction for individuals who perform technical procedures

or who work in environments where technologies have been applied.

‘Stanley J. Holden, “Business, Industry and Labor: Linkages Between Training and Employment,” in -lob Training for Youth Robert

E. Taylor, Howard Rosen, Frank C. Pratzner (eds.) (Columbus,

Ohio: The National Center for Research in Vocational Education,

The Ohio State University, 1982), p. 360.
35

ever, since the late 1960’s, layoffs in auto, steel, future emphasis on education and training benefits

and other industries have given rise to agreements in labor-management agreements.9

featuring education and training benefits. One indicator of expanded worklife learning may be ‘Barton, op. cit., pp. 125-126.

TECHNOLOGY AND MANUFACTURING: CHANGES IN

INSTRUCTIONAL REQUIREMENTS

Discussions of the impacts of automation on

education and training for the American worker

are hardly new. The National Commission on

Technology, Automation, and Economic Progress (hereafter “Automation Commission”), in its

1966 report, noted shifts in skill requirements occurring during that decade.l0 The report cites rapidly increasing employment levels of the highly

skilled, as manifested in a technical work force

that had grown from 6.6 percent of the total in

1947 to 12.2 percent of the total in 1964, and significant shifts during the same period from manual

to white-collar work. The Commission report

notes the trend toward more formal schooling,

particularly higher education, as well as the growing education gap between the skilled and the unskilled. The Commission observed:

The encouragement of an adaptable labor force

fostered through education and training is second

in importance only to the provision of adequate

employment opportunities in the facilitation of

adjustment to technological and other change . . .

We wish to emphasize at the onset that we regard

the goals of education as far transcending economic objectives. These goals go beyond economic progress to the development of individuals

as persons and as responsible citizens. A clear

division of education into its “economic” and

“noneconomic” aspects is impossible . . . From

the purely economic point of view, education has

three principal effects: I) it can increase the versatility y and adaptability of people with respect to

change; 2) it can open up increasing opportunity

to persons who might otherwise have difficulty

in finding and holding employment; and 3) it can

increase the productivity of workers at any level

of skill or ability. Though education is much more

than a means of economic progress, it is a decisive

IOTw~o]oa and the Amerjcan E20nomy: Report of the National

Commission on Technology, Automation and E20nomic Progress,

vol. 1, February 1966.

factor in the economic advancement of any country.11

Commission members acknowledged growth in

corporate-based employee instructional programs,

and they considered widespread basic skills deficiencies an impediment to future economic

growth. Among their recommendations were:

●

●

●

●

●

provision of quality compensatory education

to all who need it;

improvement of “quality and quantity” of

primary and secondary education, especially in economically depressed areas, in order

to achieve equity of access and equity of

opportunity;

universal high school graduation;

deferral of vocational training until after high

school, to ensure that individuals receive a

general education to prepare them for subsequent occupational education and to instill

an appreciation for education as a continuing process” . . . indispensable for continued

adaptability in a changing world . . . ;“

availability of education, training, and

retraining to individuals throughout their

lives.

The Commission also proposed a nationwide system of public education lasting 14 years with

direct links between high school curricula and

those of community colleges and technical schools

designed to prepare individuals for technical and

paraprofessional careers.l2

Education, Training, and

Economic Growth

The concerns of the Automation Commission

have reemerged in various forms. While the

“Ibid.

121bid, pp. 4s-47.
36

Automation Commission’s report focused on the

role of education and training as a complement

to technological change in stimulating national

economic development, more recent studies focus

on education and training and new technology as

factors in regional, State, and local economic

growth. A 1982 study published by the NortheastMidwest Institute cites basic skills deficiencies as

a critical problem already depressing economic

growth rates in the Northeast and Midwest and

threatening U.S. participation in international

markets. That study recommends a unified policy

for training, retraining, and skills upgrading for

all workers.13

Other observations on the relationship between

education, training, and economic growth are

being made on the State level, as public and

private groups explore the relationship of human

resource development and continued economic

advancement in their respective geographic areas.

A Connecticut Business and Industry Association

study has found that an appropriately trained

work force is the strongest influence on location

decisions of advanced technology companies and

is critical to expanding that State’s electronic

economy. That study recommends: 1) diverting

resources within Connecticut education institutions to programs that graduate individuals qualified to enter high technology industries, as well

as 2) publicizing existing, in-State continuing

education programs for working, corporate-based

professionals.14 Another study, conducted for the

New York State Science and Technology Foundation, found that universities could participate in

State economic development through cooperative

university/industrial education programs, cooperative university/industry research and development programs and improved responsiveness to

unique industry needs.15

In a series of recent papers on higher education

and technological innovation, the New England

IJPat Choate, ~e~OO@ the American Work Force TOwarda ~ational Training Strategy (Washington, D. C.: Northeast-Midwest Institute, July 1982).

14An~ Wingate and H.Craig Leroy, E@h Techology ~dustries

and Future Jobs in Comecticut (Business and Industry Association,

December 1981), p. 10.

15 Special Report V: The Higher Education System in New York

and Its Potential Role in Economic Llwelopment, prepared for New

York State Science and Technology Foundation by BattelIeColumbus Division, April 1982.

Board of Higher Education responded to charges

of unresponsiveness made by business leaders and

others concerned about changes in occupational

supply and demand by redefining the problem as

a need on the part of educators for:

. . . hard numbers on the regional supply of

trained personnel, and correspondingly, projections of demand based on reasonably firm business plans . . . , a clearly assumed responsibility y

for the regular collection of such statistics and for

the underwriting of expenses associated with continuing projects of this nature, (and) an organizational structure whose mission is to gain consensus from leaders of the business, education and

governmental communities on the regional

needs . . . and on the appropriate goals and

strategies by which they can be attained. Plans

for implementation would, ideally, include a clear

demarcation between short- and long-term

issues .16

Technological Literacy

It is possible that the United States is entering

an era in which the potential for mechanization

in the factory and the office will dramatically alter

work force skill requirements. This will require

employees and individuals preparing to enter the

job market to enhance skills and/or to develop

new ones. The OTA study, Informational Technology and Its Impact on American Education,

found that in order to function as citizens in an

information-based society that is driven in large

part by technological innovation, individuals

must have knowledge of the computer as a tool

for managing and providing access to massive

amounts of information. This need to understand

the applications of computer technology has resulted in a modified definition of basic literacy

that includes familiarity with the computer.

“Technological literacy” is now a common term

used to describe a level of understanding of technology in its various forms that goes beyond a

familiarity with the computer. Experts suggest that

technological literacy will soon be required of all

members of the work force, as broader and more

extensive applications of information technology

are made in offices and plants. Widespread tech1“’Engineering and Technological Education in New England—

Part II: Alternatives for the Eighties,” issues in Planning and Policymaking, December 1981.
37

nological literacy may be hard to achieve, however, since about one-fifth of the U.S. population

has yet to master the basic skills of reading, writing, and arithmetic.17

Industry representatives have expressed growing disillusionment with the lack of employability skills in entry-level workers with educational

preparation through the graduate level. They define employability as an individual’s understanding of the basic rules of the workplace, including

the need to report for work, to arrive on time,

to stay with a job for a reasonable period, and

to demonstrate competence in the basic skills. This

has led many companies to increase their involvement in education on the local and even national

levels and to establish more in-house corporate

education and training systems.18 Labor unions

and labor organizations have also been vocal in

their concerns about basic skills deficiencies in

“’’Ahead: A Nation of Illiterates?” U.S. News and WorldReport,

May 17, 1982; National Center for Education Statistics.

18Jam= Campbe]], “Emp]oyers Expect the Best, ” reprinted from

Vocational Education Journal of the American Vocational Association, vol. 55, No. 8, October 1981.

NEW TYPES OF INSTRUCTIONAL

As a result of research performed to date, OTA

has identified several instructional programs to

prepare individuals to function in computer-automated manufacturing environments. It is too early

to say whether or not the establishment of these

programs constitutes the beginning of a trend, or

to make qualitative evaluations, but the existence

of these programs does indicate that some business, labor, and government representatives are

aware of a skills gap in manufacturing firms where

programmable automation has already been applied. It is important to note, however, that these

programs are scattered; by no means do they constitute a coordinated attempt by the public and

private sectors to address the problem of a potential widespread skills gap. At this stage in the investigation, it appears that the evolution of these

and similar programs is occurring in traditional,

uneven fashion, and that the capacities of educational institutions and other instructional providers would fall severely short of the potential

those seeking apprenticeships. One union educational representative has found weak communications and reasoning skills common among trainees

today. Many union locals establish close working relationships with school districts to improve

basic skills, while national and international labor

organizations address these problems by working with national education groups.

Although many elementary and secondary

schools, both public and private, are placing

renewed emphasis on basic skills development,

and many adult education programs offer remedial courses in math, reading, and writing,

these programs reach only some of the individuals

who need this type of instruction. In addition,

public school systems are hampered in modifying and strengthening curricula as a result of lower

levels of Federal funding and reduced State and

local tax revenues in many areas. These conditions complicate the process of developing strong

basic skills and technological literacy among those

preparing for entry into, and those already in, the

work force.

PROGRAMS

demand for skills development and upgrading that

may be associated with the widespread adoption

of programmable automation.

Secondary-Level Programs

Since the establishment of a public education

system, local school districts have attempted to

develop secondary-level programs that achieve

two distinct ends: 1) the preparation of some individuals for direct entry into the work force immediately after graduation; and 2) the preparation of others planning to enter college who require a strong foundation of knowledge on which

to base more advanced instruction. Since lifelong

learning is likely to become necessary for all members of the labor force, these objectives are becoming blurred. For example, in some high

schools serving areas where programmable automation is now being produced or used in manufacturing, there are indications that students not
38

going on to college are receiving more attention

than in the past, and that career exploration for

high technology careers is recognized as important for all students, regardless of their postgraduation plans.

The State of Michigan, due to its economic dependence on auto and truck manufacturing, has

been hit hard by massive layoffs over the past few

years. With high unemployment among manufacturing workers in the region, some local high

schools in southeastern Michigan have been looking for new career opportunities for which they

can begin to prepare their students.

Several Michigan school systems, including

Oakland County, have added introductory robotics courses to their curricula. These courses give

students an opportunity to learn first-hand about

robotics technology and to explore career opportunities within this manufacturing-related field.

Students learn to operate simple, tabletop, electric robots, which are provided to the school

systems by local robot manufacturers, or build

their own robots. In some cases, courses include

site visits to local auto manufacturing plants to

observe robot applications in welding and painting. There are no prerequisites for juniors and

seniors who wish to enroll. It is important to note

that these courses do not purport to develop

entry-level job skills in students, but are offered

simply as an opportunity to develop some measure of career awareness in high technology. Also,

there are no formal placement services provided

and at present no links to more advanced robotics

technology instruction.

An experiment is underway in Oakland County, where segments of a successful summer high

school robotics course have been incorporated

into the curricula of several regional vocationaltechnical centers. Courses offered through these

centers are open to high school students as well

as adults who wish to explore interests and career

options in the field of robotics.

Retraining for Skilled and

Semiskilled Occupations

Unions and others concerned about the potential social impacts of the use of programmable

automation have been active in promoting the

need for retraining programs for skilled and semiskilled occupations. They regard education and

training as tools for strengthening the job security

of and alternative job opportunities for their members. Through collective bargaining and other

means, unions are looking for ways to influence

who is trained and what is taught in retraining

programs. In particular, unions are looking at

ways to have more control over in-plant training to upgrade skills and to modify standard tuition refund programs to provide members with

more opportunities to participate in education and

training programs outside the workplace .19 This

position is in keeping with provisions included in

selected agreements of the 1960’s, when earlier

forms of manufacturing automation were applied

in steel, electronics, and aerospace firms.20

The United Auto Workers (UAW) and the International Association of Machinists (IAM) are

among the most active unions in promoting technology-related education, training, and retraining opportunities for their respective memberships. Within 1982 agreements UAW reached with

Ford Motor Co., General Motors, and International Harvester, there are provisions for training and retraining programs for current employees

as well as those laid off. In addition, each contract calls for the establishment of a joint unionmanagement employee development and training

committee through which special instructional assistance will be provided to members who are displaced by new technologies, new techniques of

production and “ . . . shifts in customer preference. ” Employees—both skilled and semiskilled

—are covered under other provisions of the agreements. They are eligible to participate in upgrade

training designed to sharpen job skills and to provide updates on the state of the art of technology

being utilized in their plants.21 22 23

“’’Retraining: The Need for Flexibility,” in Silicon, Satellites and

Robots: The Impacts of Technological Change on the Workplace

(Washington, D. C.: Department for Professional Employees, AFLCIO, 1979), pp. 44-45!

20S= Rwent C’o]]=tive Bargaining and Technological change

(Washington, D. C.: U.S. Department of Labor, Bureau of Labor

Statistics, March 1964), BLS Report No. 266.

‘

]

’’Programs Set Up for Training and Retraining,” UAW-GM Report, March 1982, p. 11.

22’’T’raining, Retraining Plan to Cover Ford Workers,” UAW-Ford

Report, February 1982, p. 7.

Z3CJA w-~temationa] Mw-vester: Highlights of the New Agree

ment-1982-1984 (Detroit, Mich.: UAW Agricultural Implement Department, April 1982).
39

The Ford agreement called for the establishment

of a National Development and Training Center,

where staff on loan from the union and the company will promote training, retraining, and other

skills development opportunities for current and

displaced workers. * Two projects were launched

by the Center in August 1982: a National Vocational Retraining Assistance Plan, which provides

prepaid financial assistance of up to $1,000 per

year to workers on layoff who wish to undertake

self-directed, formal education or retraining; and

Targeted Vocational Retraining Projects, highly

specialized retraining activities designed to

develop skills for use in new or existing occupations in which there are documented worker shortages. The Vocational Retraining Projects would

be limited to geographic areas where established

educational institutions and vocational training

programs are not already providing such instructional opportunities. The Center also hopes to

stimulate similar, publicly funded efforts in areas

of the country where Ford workers are on layoff

and might be eligible to participate.24

IAM initiated in the 1950’s an annual electronics

industry conference, known since 1968 as the Electronics and New Technology Conference, during

which national staff and representatives of IAM

union locals discuss issues that arise from the use

of manufacturing technologies. In 1960, IAM

began the practice of preparing a manual of model

contract language that included provisions for use

in dealing with in-plant technological change.

1AM model contract language on training benefits

calls for instruction during working hours at company expense and at prevailing wage rates. It also

states that senior employees should have first

claim on training opportunities and suggests that

management should be required to train employees for jobs not necessarily associated with new

technology, in cases where “ . . . either the new

technology requires substantially fewer workers

or present employees are not capable of successful

● The Center, temporarily located at Ford World Headquarters

in Dearborn, Mich., will move to its permanent headquarters at

Henry Ford Community College (Dearborn) in 1983.

“’’National Vocational Retraining Assistance Plan and Other Projects for Certain Employees on Layoff, ” UAW-%d Emp)oyee De

velopment and Training Program Bulletin, Aug. 20, 1982.

retraining. “25 At an IAM-sponsored Scientists and

Engineers Conference, held in the union’s Placid

Harbor Training Center in June 1982, members

expressed concern that training and retraining provisions in contracts address instructional procedures as well as content.

Retraining the Displaced

A comprehensive review of documentation representing over 20 years of plant-closing experience

revealed that retraining programs are of greater

benefit to displaced workers who are younger,

have slightly more formal education, and have

achieved some level of financial security. Even

among displaced individuals who possessed these

characteristics, only about 15 percent participated

in retraining, due to inadequate financial assistance during the training period.26 These findings

suggest that some new approaches to retraining

the displaced should be developed that increase

the utility of instruction and its availability to

workers of all ages, with varying amounts of formal education and different degrees of financial

security.

Although the public perception is that industry

is one of the chief sources of sponsorship for retraining of displaced workers, in the past it has

sponsored few retraining efforts. In some cases,

the communities surrounding plants lacked alternative career opportunities for which instruction

could be provided; in others, workers expected

to be called back to their old jobs and resisted taking advantage of instructional and placement opportunities; in still other cases, economic conditions that led a company to close a plant made

the cost of retraining prohibitive. Although

retraining activities authorized under the Comprehensive Employment and Training Act (CETA)

and the Trade Readjustment Assistance Act (TRA)

ZSL=lie E. Nu]ty, “Ca~ Studies of IAM Local Experiences With

the Introduction of New Technologies, ” in Labor and Technology:

Union Responses to Changing Environments (University Park, Pa.:

Department of Labor Studies, Pennsylvania State University, 1982),

pp. 115-139,

26J eame p. Gordus, paul Jarley, and Louis A. Ferman, r~n~ C]osings and Economic Dislocation (Kalamazoo, Mich.: W. E. Upjohn

Institute for Employment Research, 1981).
40

have been criticized, these Federal programs represent the majority of resources that have been utilized to prepare displaced workers for new careers. *

Two recent examples of retraining efforts

funded under CETA illustrate the potential for

retraining some of the displaced for new, technology-related occupations. The first, a pilot project made possible through a $300,000 Department

of Labor discretionary grant to UAW, is designed

to retrain 400 displaced auto workers for occupations in demand within the aerospace/defense industry. The first phase of the project, an assessment of the potential for skills transfer from jobs

performed within the auto industry to the new

positions within aerospace/defense, has already

been completed. Other products of the grant include two retraining programs, which will be developed by combining components of existing retraining packages. 27 Although the project does not

train individuals solely for technology-related

positions, a UAW spokesperson indicated that

many of the new aerospace jobs involve working

with automated equipment and therefore related

skills requirements will be addressed in the retraining packages to be developed. Implementation of

the training process now awaits Federal funding

or sponsorship by the aerospace/defense industry.

A second CETA-funded project, initiated by the

Warren County, Mich., prime sponsor, is a 40-

week robotic technician program, which qualifies

18 displaced auto workers, machinists, and others

who completed the course to assume new careers

within the auto industry, in local robotics firms,

or in other companies using robots.**

*The recently enacted Job Training Partnership Act, which replaces CETA, authorizes the expenditure of Federal funds for employment and training of dislocated workers. CETA will operate

during fiscal year 1983 at a $2.8 billion funding level, while programs authorized under the Training Partnership Act are established

(tiployment and Training Reporter, Nov. 19, 1982).

“’’Assessment of the Reemployment Opportunities for Unemployed Automobile Workers in the Aerospace/Defense Industry,”

proposal prepared for the U.S. Department of Labor by the United

Auto Workers, 1982.

● *As stipulated in Public Law 95-524, CETA amendments of 1978,

a prime sponsor may be” . . . a State; a unit of general local government which has a population of 100,000 or more . . . ; a consortium of units of general local government . . . ; . . . program grantees serving rural areas having a high level of unemployment . . . ;

and any unit of general local government previously designated as

a prime sponsor under the provisions of this Act . . . , regardless

of population decline. ”

Instruction for Technician= Level

Occupations

Although technicians emerged as an occupational group within the field of engineering in the

1920’s, the availability and application of technology in manufacturing has increased the demand for and the popularity of this occupation.

Technicians who are trained in the use of computer-aided drafting systems are now in great demand within aerospace and other industries.28

Technician instruction is typically a 2-year

associate degree program, although other, more

concentrated approaches to program delivery are

becoming more common, such as the one initiated

in Warren County, Mich. The electromechanical

technician curriculum, which combines two formerly distinct engineering specialties, is viewed

by some educators and industry representatives

as an excellent foundation for careers that require

knowledge of programmable automation.

Community colleges in various areas of the

country are currently offering electromechanical

technician programs, sometimes called robotics

technician programs by the institutions in order

to capitalize on general public awareness of this

form of programmable automation. The State of

Georgia began offering an electromechanical curriculum in its community colleges in 1982. Several

community colleges in Michigan have offered electromechanical programs for the past few years.

In general, curricula are designed to prepare

enrollees to perform installation, maintenance,

repair, and programing functions. At present,

however, no standardized performance criteria

exist for electromechanical technicians, so the content and emphasis of these programs vary considerably.

Engineering Education

The utilization of programmable automation

has had an observable effect on initial and continuing education for engineers. CAD, which enables faster design and analysis, is now common

in the aerospace and auto industries. Selected

engineering schools are working with industry to

“’’The Engineering of a Revolution: Computer Now Designer’s

Tool,” The Atlanta Constitution, Oct. 24, 1982.
41

add CAD instruction to their curricula. Boeing

Commercial Airplane Co. in Seattle, Wash., has

established at the request of local universities (e.g.,

Washington State) CAD laboratories adjacent to

engineering school campuses. These labs provide

students with opportunities to work with Boeing

aircraft data bases when they are not being utilized

by Boeing personnel. The program is voluntary,

but students receive university credit for participating.

On the national level, a 1981 grant from the

National Science Foundation’s Directorate for

Science and Engineering Education (now the

Office for Science and Engineering Education)

established the College CAD/CAM Consortium

as a nonprofit group dedicated to the development

of CAD/CAM curriculum and the improvement

of CAD/CAM instruction. Twelve engineering

schools, including Carnegie-Mellon University

and Rensselaer Polytechnic Institute, were founding members.29

29?7w College CAD/CM Consortium: An Overview (Cluwlottesville, Va.: University of Virginia, School of Engineering and Applied Science, January 1981).

SURVEY OF CURRENT VIEWS OF EDUCATION, TRAINING,

AND RETRAINING REQUIREMENTS

There has been little or no information gathered

on how representatives of the key groups involved

in or affected by the manufacturing automation

process—producers of the equipment and systems; users of the equipment and systems; and

various groups in the work force—view the potential retooling of the operations with which they

are associated. In addition, no national readings

have been taken of current views held by these

groups on education, training, and retraining requirements associated with the use of programmable automation. In order to supplement available information of this type, and in so doing get

a better sense of the climate in which automation

is occurring, OTA commissioned structured telephone interviews with a sample of representatives

of firms within the electric and electronics equipment, industrial machinery, and transportation

equipment industries (industries in which firms

are especially likely to use programmable automation). OTA also contacted producers of programmable automation equipment and systems, as well

as educators and others familiar with the instructional design process. A total of 506 interviews

were completed in July and August 1982. * In this

section of the technical memorandum, a summary

of selected survey findings is presented. A descrip-

*The term users refers to firms applying programmable automation; the term producers refers to firms producing programmable

automation; and the term others refers to educators and others involved with education and training.

tion of the survey methodology and sample size

are included in appendix A.

Education and Training:

Users and Producers

The survey found that 40 percent of the representative manufacturing plants contacted utilized

some form of programmable automation, and of

this number, only 22 percent sponsored or conducted education and training for new technology. Among plants currently not offering education and training of this type, only 18 percent indicated any plans to implement programs in the

future. Low benefits relative to costs was by far

most commonly cited by user firm representatives

as a barrier to the establishment of instructional

programs for new technology. The low levels of

current and anticipated direct involvement in education and training for new technology is particularly notable in light of the nearly unanimous

view expressed by users, producers, and others

that the users should bear the costs for new technology instruction. This seems to indicate that

while users may be willing to pay for instruction

delivered by vendors, educational institutions,

and others, few are planning to establish their

own, in-house programs. Another possible interpretation of the low levels of in-house instruction

among users might be that changes brought about

through the utilization of programmable automa98-951 0 - 83 - 7
42

tion thus far have not been sufficient to warrant

the establishment of formal instructional systems.

Instruction Available Through Producers

In contrast to the low proportion of users who

sponsored and conducted education and training

for new technology, a very high proportion of

producers (93 percent) provide such instruction

for their customers. Manufacturers within the industry groups polled appeared to depend on producers for design and delivery of new technology-related instruction. Results indicate that vendors or producers of programmable automation

equipment were more heavily used for instruction

than were training industry/management consultants, traditional educational institutions, proprietary schools, unions, and government-sponsored

instructional programs such as CETA.

The nature and scope of instruction currently

offered by producers, however, seems to be quite

limited. Over 80 percent provide only single

courses, and few provide series of courses. Furthermore, only about one-third of the producers

felt that vendors or manufacturers of computerautomated equipment and systems were currently

ready to provide the necessary education and

training. One can speculate that the producers

who work closely with new technology understand the education and training implications of

implementing their technology, but are currently

only providing part of what they consider is required. Producers may be providing limited services for a variety of other reasons, including cost

factors, customer demand, and their views of the

responsibilities of other institutions (particularly

users) in providing additional training.

Occupational Coverage and

Content Coverage

Both users and producers reported generally

broad occupational coverage in the instructional

programs on new technology that they provided,

although there was considerable variation in the

extent to which occupations were covered. The

majority of both users and producers sponsored

or conducted programs for various types of shopfloor staff (e.g., assemblers, handlers, loaders,

equipment operators), repair and maintenance

staff, engineers, programmers, and supervisors or

managers. Apparently, the impact of programmable automation on a wide array of occupations

is recognized by industry.

Broad occupational coverage was not accompanied by breadth in instructional content. The

primary content of current education and training programs appears to reflect traditional topics

addressed in technical training; e.g., machine

operation, safety procedures, and maintenance.

Current instructional programs focus least on the

basic skills—reading, writing, and arithmetic—

and basic physical science. The survey results suggest that manufacturers assume that these needs

should be met in ways other than in instructional

programs they devise.

Government Role in New

Technology Instruction

Survey results show a lack of receptivity to

government involvement in instruction for new

technology by both users and producers. As noted

earlier, government-sponsored instructional systems such as CETA were generally considered not

ready to provide such training and were not expected to become ready within 10 years. When

asked about possible sources of funding for education and training for new technology, only about

half of the respondents in both groups indicated

that Federal or State and local government funding was desirable, while funding from all other

sources, particularly private sector user industries

and foundations, was endorsed by at least three

quarters of the respondents. In contrast, a great

majority of the others (the group that included

educators and Federal and State officials) endorsed

government as a funding source. *

● It is not clear whether these responses reflect popular political

views, attitudes toward government intervention in general, or actual

preferences for private control of instruction for high technology.

In any case, it is unlikely that respondents had in mind all forms

of Federal, State, or local support (e.g., funding of colleges, universities, and research efforts), although it is not possible to determine this from the present data. Nevertheless, the consistency of

responses in the user and producer groups may provide some guidance for determining the nature of the government role in instruction for computer-automated manufacturing. It is likely that indirect

or less visible forms of government intervention would be more acceptable to industry than more direct forms of intervention, such

as the provision of education and training services (e.g., CETA programs) or direct subsidies to industry for worker retraining.
43

SELECTED CRITICAL ISSUES FOR INSTRUCTIONAL PROGRAMING

Current views of representatives from industry,

labor, the educational community and government are consistent with other indicators discussed

earlier in this technical memorandum in suggesting

that training and retraining requirements for programmable automation are, at this point, poorly

defined. Even within specific geographic areas,

programs initiated to address changing instructional requirements do not in the aggregate represent a coordinated approach to defining instructional needs associated with new industrial processes. While it is too soon to know how widespread applications of programmable automation

will be, there is little evidence that any sector—

including private industry-is seriously considering the long-range implications of possible widespread use on occupational skills requirements and

current instructional capacities.

There are a number of pressing issues facing

those who operate instructional systems, in the

event that widespread utilization of programmable automation occurs. Among them are:

1.

2.

3.

4.

how and by whom the need for technological literacy will be addressed,

types of short-range and long-term counseling and instructional systems,

initiation of appropriate curriculum design

processes, and

funding sources for curriculum design and

implementation.
Appendixes
Appendix A

Survey Methodology

Introduction and Overview

In support of the Automation and the Workplace

study undertaken by the congressional Office of Technology Assessment, Westat conducted a survey to

identify education and training requirements inherent

in the use of programmable automation in manufacturing settings. The survey describes current levels of utilization of programmable automation, as well as existing instructional opportunities focused on this form

of technology, and elicits various opinions related to

current and anticipated education and training needs

resulting from applications of computer-based automation.

Survey data were collected by the Westat Telephone

Research Center on three samples: 1) users, 2) producers of computer-automated technologies in manufacturing, and 3) a diverse sample of knowledgeable

others. The surveys used structured instruments developed for the study. Data were collected by telephone

interviews over a 2-week period in August 1982.

Methodology

This section briefly describes the methodology used

for the survey. The first part describes sampling procedures, the second describes data collection instruments and methodology, and the third describes data

analysis procedures.

Sample

Three groups were contacted for this study: users

and producers of computer-automated equipment and

systems, and others, a diverse group of individuals involved in instruction for employees in computer-automated manufacturing environments or for individuals

preparing for careers in such settings. Formal sampling

procedures were used only for the user group, and for

a subset of the others. Details of the procedures are

described below.

USERS

Sampling Frame. —The sample of users was composed of manufacturing establishments from industries

identified as currently using or likely to use computerprogrammable equipment and systems in the near future. An establishment was defined as an individual

location of company. This location might constitute

a division, subsidiary, plant, branch, or the entire

company.

Three major manufacturing industries were represented: transportation equipment manufacturing, electric and electronic equipment manufacturing, and industrial and metalworking machinery manufacturing.

For each of these three major industries, specific standard industrial classifications (SIC) were selected based

on two criteria: 1) proportion of total employees in

industry accounted for by establishments within the

SIC code; and 2) likelihood of establishments within

the SIC code using computer-automated technology.

SIC codes meeting the second criteria were selected

based on judgments of project staff, as well as OTA

Automation Study Advisory Panel members. The selected SIC codes for the transportation equipment industry account for 76 percent of the total workers

employed in the industry; SIC codes in electric and

electronic equipment manufacturing account for 59

percent of the total employees; and the SIC codes in

industrial and metalworking machinery account for

41 percent.

The data source for constructing the frames for the

three user samples was National Business Lists (NBL),

a firm which compiles a national list of most types of

establishments, including manufacturing and commercial. The NBL lists rely heavily on the Dun & Bradstreet directory of establishments, supplemented by

NBL’s own sources.

Sampling Methodology.—A probability sample of

users in the three industries was selected from the NBL

lists using a two-stage sampling approach. This sampling procedure involved stratification by size and regional location, and included as selections with certainty a small number of establishments known to use

computer-automated equipment for manufacturing.

These were included to assure a minimum of current

users within the sample to provide an adequate basis

for analysis of this subgroup.

The first step in a two-stage sampling procedure entailed compiling a list of approximately 5,000 establishments from the NBL master file in the three major

industry groups specified earlier. The purpose of “oversampling” establishments at this initial stage was to

obtain a sufficiently large sample for examining the

size distribution of establishment by SIC group for

subsequent use in deriving appropriate (and more nearly optimal) sampling rates. Since larger establishments

account for a larger share of the work force while ac47
48

counting for a smaller share of the total number of

establishments, selection of the initial sample from

NBL was stratified by establishment size. The size

strata used by the initial sampling were:

Small: 1-99 employees

Medium: 100-499 employees

Large: 500 or more employees

Furthermore, to take these size differences into account

in the sampling, large establishments were sampled at

a higher rate than small ones. Therefore, the initial

sample consisted of all of the large establishments in

NBL for each of the three industries, one-half of the

medium-sized establishments and one-tenth of the

small establishments. Since the listings of establishments in the NBL file were geographically sorted within each of the three size classes, and the samples were

selected systematically (using a random start), the

method of sample selection simplicity included stratification by geographic region. These proportions

yielded 5,128 total establishments in the initial sample.

In the second stage of the sampling procedure, the

5,128 establishments were further stratified by industry

type, establishment size, and regional location. Regional

location was defined by the four regions delineated by

the U.S. Census Bureau (i.e., Northeast, North Central, West, and South). In addition to the three major

size strata defined above, the “small” size class was

further subdivided into two classes for sampling (1-20

and 20-99). This more detailed stratification by size

permitted a more nearly optional allocation of the sample cases to the various strata. This stratification

yielded 48 different cells in which establishments were

placed for sampling.

To determine the appropriate sampling rates to select the second-stage sample, three options were considered:

1. The sample could be allocated to each cell in proportion to the total number of establishments in

that cell.

2. The samples could be allocated to each cell in proportion to the total employment in that cell.

3. The samples could be allocated to each cell in proportion to some function of employment, say

square root of employment.

The implication of the first option was to have large

numbers of small establishments and few large establishments since most manufacturing establishments

have fewer than 500 employees. This would be desirable for estimation of counts of establishments, but

would not be sufficient for estimation of magnitude

variables such as employment.

The implication of the second option was to have

large numbers of large establishments and very few

small establishments, since manufacturing establishments of 500 or more employees account for most of

the work force. This would be approximately optimum

for estimation of magnitude variables (in particular,

those correlated with employment), but would be less

efficient for estimates of the numbers of establishments.

The final option, which combines the first and second options by sampling with probabilities proportionate to the square root of employment, distributes

the numbers of establishments somewhat more evenly

across cells. This last option was selected because it

provided a better basis for making comparisons between the different size classes, in addition to being

reasonably efficient for estimating both magnitude and

count variables.

Sampling Methodology .—Establishments from the

user sampling frame were screened to eliminate from

the user samples those not meeting the following criteria:

1. establishments must be performing manufacturing functions at the location contacted (purely administrative facilities were dropped); and

2. establishments must be able to identify an individual within the first three referrals during the

phone call who can answer selected key questions. (Those unable to do so were treated as nonresponses. )

Selection of the second-stage sample, therefore, was

based on the assumption that there would be extensive

dropouts due to ineligibility and nonresponse. From

the initial sample of 5,000 user establishments, 200

establishments were drawn from each user industry

group for a total of 600 sample establishments. These

were allocated to the various size strata in proportion

to the aggregate measure of size based on the square

root of employment. This sample included 18 establishments that were selected with certainty in addition

to the 600 selected establishments. The sample allocation of the noncertainties in each user industry group

by size class is shown in table A-1.

Detailed records were kept of establishments failing

to meet these criteria as well as refusals and nonresponses. Table A-2 shows the distribution of the initially sampled cases and the final number of completed

interviews by size strata and region.

PRODUCERS

Sampling Methodology. —The producer group was

composed of companies who manufacture and/or sell

programmable equipment to U.S. manufacturing industries. The compilation of a list of producer companies was no simple task, since no such lists were

readily available. An intensive search to identify com-
49

Table A-l. -Sample Allocation for User Groups

Number of MOSa based on square

Size class (employment) establishments in NBL root of employment Sample allocation

Transportation equipment

1-19 . . . . . . . . . . . . . . . . . 2,070 5,430 30

20-99 . . . . . . . . . . . . . . . . 1,030 7,050 40

100-499 . . . . . . . . . . . . . . 596 9,020 50

500+ . . . . . . . . . . . . . . . . 287 14,969 80

Total. . . . . . . . . . . . . . . 3,983 36,469 200

Electrical and electronic machinery

1-19 . . . . . . . . . . . . . . . . . 4,520 11,440 40

20-99 . . . . . . . . . . . . . . . . 2,090 14,510 50

100-499 . . . . . . . . . . . . . . 1,244 18,742 60

500+ . . . . . . . . . . . . . . . . 363 14,240 50

Total. . . . . . . . . . . . . . . 8,217 58,932 200

Machinery manufacturers

1-19 . . . . . . . . . . . . . . . . . 12,620 30,900 60

20-99 . . . . . . . . . . . . . . . . 3,590 22,450 50

100-499 . . . . . . . . . . . . . . 1,296 19,092 50

500+ . . . . . . . . . . . . . . . . 318 12,005 40

Total . . . . . . . . . . . . . . . 17,824 84,447 200

aMea~ure@f.~ize. F~~a~ivensizeclagg,theaggregateMOSwascomputeda9S ‘}Ei, where Eiistheaverage employment

slzeof all establishments inthe SICmouP and size class based on 1979 CO@YBUS~rW-3SS Patterns, and where the summation extends over all establishments-in the NBL frame.

SOURCE:Westat.

Table A-2.—Stratification of User Establishments

and Costs of Initially Sampled Cases and

Completed Interviewsa

Northeast North Central South West

Transportation region

1-19 . . . . . . . . . . . 4 : 2

20-99 . . . . . . . . . . 8 : 4

100-499 . . . . . . . . 8 : 3

500 or more. . . . 12 : 6

Electrical/electronic region

1-19 . . . . . . . . . . . 12 : 3

20-99 . . . . . . . . . . 18 : 9

100-499 . . . . . . . . 22 : 11

500 or more. . . . 16 : 9

Machinery region

1-19 . . . . . . . . . . . 12 : 5

20-99 . . . . . . . . . . 10:5

100-499 . . . . . . . . 10:5

5000r more.... 8: 7

8 : 1

14 : 8

26 : 16

42 : 24

8 : 3

10: 4

14 : 7

12 : 8

28 : 13

28: 9

26 : 10

20: 12

6:1 12: 4

6:3 12: 4

8:8 8:2

16:11 10:6

8:4 12: 4

8:4 14: 8

12:4 12: 7

10:6 12: 6

10:4 10: 3

10:8 6: 2

10:3 4: 1

8:6 4:1

aThe number on the left in each cell shows the inltlai Sample count, and the

number on the right shows the number of completed interviews. The number

of completed interviews shown in this table does not include the nine completed

users which were sampled with cerlainty, since these were not preassigned to

a size class.

SOURCE: Westat.

panics who were involved in manufacturing or selling

computer-automated equipment was conducted by

OTA, with assistance from Westat & Hadron, a subcontractor. The list used in this study was constructed

from a variety of sources, including rosters of exhibitors at conventions on computer-automated manufacturing, lists from organizations such as the Robotics

Institute of America, trade publications citing companies involved with such products, and personal contacts with relevant companies. The final list consisted

of 203 producers, and is considered to be a fairly good

approximation of the actual universe of companies

producing computer-automated equipment for manufacturing in the United States.

Producers were contacted in random order until 101

companies had completed interviews.

OTHERS

Sampling Frame.—The others group was composed

of individuals who have had experience in designing

and/or delivering and/or evaluating formal instruction for employees operating in computer-automated

or conventional manufacturing environments. These

others were selected because of their pertinent expertise and/or because they represented institutions (e.g.,

unions) whose opinions are important to consider in

formulating policy in this area. A list of 280 others was

compiled by OTA. The list was composed of six subgroups:

●

●

Traditional educational institutions (e.g., colleges

and universities, community colleges, technical

schools);

proprietary educational institutions (private, profitseeking, trade and technical schools that operate

on the secondary and postsecondary level);

98-951 0 - 83 - 8
——

50

● labor unions and labor organizations;

● training industry representatives (individual consultants and training firm representatives);

● State and local agency representatives (e.g., vocational education and economic development agencies); and

. miscellaneous others (e.g., Federal Government

and trade association representatives, individual

scholars, and experts).

Sampling Methodology. —Representatives of traditional educational institutions were randomly sampled,

while attempts were made to contact all those in the

other five subgroups. The initial goal of obtaining 25

interviews from the traditional education subgroup

and 75 from the remaining subgroups had to be revised, due to nonresponse rates among the five other

subgroups. Actual portions are presented below.

Final Samples and Response Rates

A total of 506 interviews were completed for the

study. There were 303 users (105 in transportation

equipment, 98 in electric and electronic equipment,

and 100 in industrial and metalworking machinery),

101 producers, and 102 others. In the others sample,

there were 34 traditional educators, 11 educators from

proprietary educational institutions, 13 union representatives, 2 representatives of the training industry,

17 representatives of State and local agencies, and 25

“others.”

The response rates obtained (defined as the number

of completed interviews plus refusals) were 82 percent

overall, 76 percent for the users, 89 percent for the producers, and 95 percent for the others. The completion

rates (defined as the number of completed interviews

divided by all completes plus all incomplete) were

somewhat lower, due to unknowledgeable, unavailable, or nonlocatable respondents. Table A-3 summarizes the final completion status of the telephone

surveys conducted with further explanations of various

completion statuses in table A-4.

Data Collection Instruments and Methodology

SURVEY INSTRUMENTS

Three telephone survey instruments (for users, producers, and others) were developed for the study. The

instruments were closed-ended in format—i.e., response options were provided for most of the questions. A core set of questions was asked three groups,

along with additional questions designed specifically

for each group. The instruments were designed to require approximately 15 to 20 minutes per interview.

In general, the instruments were designed to obtain

information about the extent and nature of the involvement of the respondents with programmable automation technology, their involvement with education and

training focused on the application of various forms

of programmable automation in manufacturing settings, and their opinions about a variety of issues related to such instruction. In addition, questions on

basic background characteristics (e.g., size of the work

force) were also included in the instruments.

Table A-5 presents the major topics covered by the

three survey instruments. The greatest number of questions were asked of the users, although most topics

were covered in the three instruments. One major difTable A-3.—Final Response and Completion, Statuses of Telephone Surveys

Users

Status codes Group 1 Group 2 Group 3 All users Producers Others Total survey sample

Complete . . . . . . . . . . . . . . . . . . . . . . .

Admin Hdqtrs. . . . . . . . . . . . . . . . . . . .

Not working . . . . . . . . . . . . . . . . . . . . .

No answer . . . . . . . . . . . . . . . . . . . . . .

No new tech. . . . . . . . . . . . . . . . . . . . .

No E&T . . . . . . . . . . . . . . . . . . . . . . . . .

Duplicate . . . . . . . . . . . . . . . . . . . . . . .

Final refusal . . . . . . . . . . . . . . . . . . . . .

Not available . . . . . . . . . . . . . . . . . . . .

Not locatable . . . . . . . . . . . . . . . . . . . .

E&T knowledge . . . . . . . . . . . . . . . . . .

Response ratesa 

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

Completion ratesb 

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

105

32

11

1 —

—

1

31

11—

—

77 ”/0

71 “/0

98

21

9

11—

—

2

28

13

21

1

780/o

61 0/0

100 303

22 75

11 31

12 24 — —

—

74”!0

620/o

—

3

94

37

35

1

76%

650/o

101

1

11

—

39—

7

12

23

9—

890/o

70”!0

102

—

—

—

—

3

7

5

14

5

—

95%

81 0/0

506

76

42

24

39

3

17

111

74

49

1

820/o

68 ”/0

a

Response rate = No. completes x 100

(No. completes) + (No. final refusals)

b

Completion rate = No. completes x 100 (No. completes) + (No. noncompletes)

SOURCE: Westat.
51

Table A-4.—Definitions of Completion, Ineligible,

and Nonresponse Status Codes

/. Completion

A. Complete(C)-completed entire interview. A complete

means all pertinent questions have been answered.

//. Inteligibility (The following categories of respondents were

screened out of the survey.)

A.

B.

c.

D.

E.

F.

Admin hdqtrs (l) —user establishment is an administrative headquarters which does not perform a

manufacturing function.

Not working (NW) —phone number is not in service

and, after calling directory assistance, there is no new

listing for that facility.

No answer (NA)—there is no answer after three attempts at different times on different days.

No new tech (N L) —producers only; if producer

establishment is not manufacturing or selling new

technology included in the survey.

No E&T (S2)—others only; if respondent represents

a traditional educational institution, proprietary educational institution, or a training firm, which does not

have an education and training program.

Duplicate (OA)—duplicate respondent.

///. Noncompletion (The following categories of respondents

are included in computation of a completion rate.)

A.

B.

c.

Final refusal (R B) —respondent refuses the interview

or breaks off interview.

Not avai/ab/e (0)—respondent was not available during field period.

Not locatable (Sl)—appropriate respondent was not

located after three referrals or the respondent was not

knowledgeable about new technology for his/her

establishment.

SOURCE: Westat.

ference should be noted in the questions on education

and training in the three instruments. Users were asked

about the education and training provided to their own

employees. In contrast, producers and others were

asked about the instruction they provide to customers

or clientele, not their own staff.

The instruments were developed in several stages by

Westat & Hadron staff, in collaboration with OTA.

At the start of the reseach effort, 12 in-depth interviews

were conducted, in person and on the telephone by

Westat & Hadron staff, to identify important issues

and develop possible questions and response options

for the instrument, A topic guide was developed for

these interviews.

Based on these preliminary interviews, a list of draft

questions and response options was submitted to

OTA. Comments from OTA staff and further interviews by Westat & Hadron staff were used to refine

and shorten the questionnaires.

The instruments were pretested by the Westat Telephone Center on a small number of respondents, and

further minor changes resulted from the pretests.

Data Analysis

SAMPLE WEIGHTS

Since disproportionate sampling procedures were

employed in drawing the user samples—i.e., different

sampling ratios were used for the different strata—

Table A-5.—Topics in Survey Instruments

User Producer Other

Background

. Year founded . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Gross sales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Work force or clientele characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

Computer automation

● Use, production, or sale of new technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Extent of computerization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Computerized integration of equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

Education and training (E&T)

• Presence of general E&T, and E&T for new technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Priority given to setting up E&T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Barriers to setting up new technology instruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

. Work force/clientele percentage who received or will need E&T . . . . . . . . . . . . . . . . . . . . . . . . . . X

• Number of instructors for new technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Forms of instruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Sources for designing/delivering instruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Target occupational groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

• Skill and knowledge areas covered. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

• Policies and opportunities on E&T outside the company. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

Opinions

● Current and future readiness of institutions to provide instruction . . . . . . . . . . . . . . . . . . . . . . . . X

● Options for institutional collaboration on E&T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

● Sources of funding for E&T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

SOURCE: Westat.
52

mechanisms to equalize the differential probabilities

of selection attached to establishments from the different strata were required. Such weighting adjustments are necessary in cases where generalizations are

made from the sample to a larger sampling frame or

universe, and to take into account nonresponse.

Weights were applied only to the user sample and

the others. Weights were not necessary for the producers, since they did not constitute a sample from a larger

universe of such firms. Users were given weights such

that the sample represented an estimated 24,142 active and eligible establishments in the NBL frame—

and the other sample was weighted to represent the

280 others in the original sampling list. The estimated

24,142 active and eligible user establishments was obtained by summing up the weights of the responding

establishments in the sample and compares with about

30,000 establishments in the designated SIC groups in

the NBL frame.

The sampling weights for the OTA samples (user

and other groups) were computed from the formula:

where

Whi = The weight for establishment i in

stratum h (for a particular group).

nh = The total number of establishments (in

the frame) in stratum h.

Nh = The number of establishments in stratum

h that were finally sampled.

n ‘h = The number of eligible and responding

establishments in stratum h.

n “h = The number of eligible but nonresponding

establishments in stratum h.

The factor, (n ‘h + n“h)/n ‘h, in the above expression

represents an upward adjustment for total questionnaire nonresponse. The weight for any given establishment depends on the stratum (and group) from which

the establishment was sampled, but is uniform for all

responding establishments in a particular stratum.

Weights for the establishments selected with certainty would be 1.0 if there were no nonresponding cases,

and otherwise exceed 1.0 by a factor representing an

adjustment for nonresponse.

Table A-6.—Sampling Weights for Estimation

Sampling stratum

Respondent group Certainty < 20 employees 20-99 employees 100-499 employees 500+ employees

User 1 (transportation) . . . . . . . . . . . . . . 2.5 155.2 42.8 18.4 4.8

User 2 (electric and electronic). . . . . . . 4.0 248.4 74.5 36.1 10,0

User 3 (industrial and metalworking) . . 1.0 419.0 136.6 52.2 10.3

Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Noncertainties a

: 4.7

aA~ lndlCat~ In ‘(oth~~~)r ~eCt\On In text, all trgdltlonal educato~ except for educational Institutions were Included in the 9~Ple with certaintY. Educational ‘institutions

were sampled at a fixed rate of about 1 prior to adjustments for nonresponse.

SOURCE: Westat.
Appendix B

Industrial Relations

Overview

The activities, institutions, and circumstances of industrial, or labor-management, relations influence the

implementation of new technology and its consequences within firms and industries. In particular, they

contribute to employment patterns and workplace

conditions that might not arise with technology change

and market forces alone. Therefore, an understanding

of industrial relations is necessary for understanding

not only how programmable automation may affect

company and industry employment and wage levels;

but also how job content, promotion paths, and workplace conditions may change with programmable automation; and why employees and management in different companies and industries may have different experiences with technological change.

Despite the important role of industrial relations in

the U.S. economy, the analysis of industrial relations

tends to be relatively imprecise and experiential. As

one participant in the OTA Labor Markets and Industrial Relations Workshop put it, there seem to be more

“ad hoc-cries” than true theories for explaining industrial relations phenomena. Further complicating an

evaluation of industrial relations issues are the differences in approach taken by different analysts. For example, most labor economists and so-called industrial

relationists tend to regard workers and managers as

having opposing interests, with workers striving to

minimize work effort and maximize compensation,

and managers striving to minimize cost and maximize

production. Most organizational behaviorists and organizational development specialists tend, by contrast,

to regard workers and managers as sharing basically

similar interests that stem from their association with

the same organizations. The former group tends to

focus on the setting of wages and other “economic”

issues, while the latter group tends to focus on job

satisfaction and performance, supervisory relationships, and job design. *

A final, but critical, factor complicating attempts

at precise analysis of industrial relations issues is the

fact that rhetoric that tends to exaggerate conflict between labor and management can obscure the actual

circumstances of industrial relations, particularly in

unionized settings. According to some observers, rheIPeter Feuille and Hoyt N. Wheeler, ‘Will the Real Industrial Conflict please

Stand Up?” in U.S. industrial Relations 19s@19&.): A Critical Assessment,

Jack Steiber, et al. (eds. ) (Madison, Wis.: Industrial Relations Research Association 1981).

torical hostility between organized labor and management has been especially high during the last few years:

. . . (W)e are witnessing a continuation of this recent

high level of rhetorical hostility between labor and management compared to the situation that prevailed during most of the 1950-80 period. In addition, . . . the

one-sidedness of our (and the traditional) definition of

conflict as worker action shows a tendency to obfuscate

the reality of conflict between managers and workers,

for it leads us to reject aggressive action by managemerit. z

This rhetoric, amplified by the news media in the context of deteriorating economic conditions, may bias

public opinion against organized labor, despite the lack

of objective analysis of the contributions of both labor

and management activities to current economic conditions.

The popular, and even the research, view of industrial relations tends to focus on unionized settings,

since unions (and employee associations that function

similarly) serve to focus and articulate the concerns

of workers both at the workplace and in the community, although only a portion of U.S. companies and

workers are unionized. The union-nonunion distinction is misleading, however, because labor-management relations fall into a spectrum that includes intermediate arrangements containing greater and lesser

numbers of pure union-like and nonunion-like attributes. The principal difference between the union and

the nonunion setting is that in the nonunion setting,

management typically imposes job descriptions, wage

levels, working conditions, and technological change

unilaterally, while in the union setting, many of the

terms of the workplace are jointly set by labor and

management through a negotiation process. Therefore,

the role or conduct of labor is as important as that of

management in the unionized setting.

Unions are of particular, but not exclusive, interest

to a study of the impacts of programmable automation because workers in many of the occupations and

industries where programmable automation is expected to have the greatest impacts are especially likely

to belong to unions. Unions whose members will be

exposed to programmable automation include those

representing workers in metalworking manufacturing

industries, such as the United Auto Workers, the International Association of Machinists (IAM), the International Brotherhood of Electrical Workers, and

others that are listed in a paper by W. Cooke, appen2

1bid.

53
54

dix C. Although the median size for national unions

is around 25,000 members, several unions representing manufacturing workers are among the largest, with

memberships between 100,000 and 1.5 million.3 See

table B-1. While unions may influence the adoption

of programmable automation and its impacts on their

members, the adoption of programmable automation

may in turn affect the strengths and abilities of unions

insofar as job content, numbers of different types of

workers, wage levels, and job satisfaction levels

change. How unions change as programmable automation is adopted has implications for both the spread

of automation and the characteristics of industrial

relations.

In addition to unions, and to the various entities that

influence labor-management relations in unionized settings (e.g., the National Labor Relations Board, the

Federal Mediation and Conciliation Service, arbitrators, and the courts), there are other institutions that

shape industrial relations in both unionized and nonunionized settings and that may influence the adoption of programmable automation and its impacts.

These include labor-management committees (insti1

IJ. S. Bureau of Labor Statistics, Directory of National Unions and Employee Associations, 1980.

tuted in both unionized and nonunionized settings),

and government regulatory agencies such as the Occupational Safety and Health Administration and the

Equal Employment Opportunity Commission.

The remainder of this section will provide a brief

description of the collective bargaining process and

outline some of the issues facing labor organizations

and management in the context of the spread of programmable automation. Union and management attitudes and practices regarding education and training

and working environment issues are addressed elsewhere in this report. Industrial relations in nonunion

settings is not addressed in this technical memorandum.

Legal/Regulatory Framework

The central feature of labor-management relations

in the unionized setting is collective bargaining, the

process of negotiating the terms and conditions of

work that will be codified in a contract that may apply for a period of 1 to 3 or more years. Guidelines

for collective bargaining governing the processes of

unionization and selection of worker representatives,

procedures for bargaining and resolving disputes, and

Table B-1 .—National Unions and Employee Associations Reporting 100,000 Members or More, 1978a

Members Members

Organizationb

(in thousands) Organizationb

(in thousands)

Unions:

Teamsters (Ind.) ... , . . . . . . . . . . . . . . . . . . . 1,924

Automobile Workers (Ind.). . . . . . . . . . . . . . . 1,499

Steelworkers . . . . . . . . . . . . . . . . . . . . . . . . . . 1,286

State, County. . . . . . . . . . . . . . . . . . . . . . . . . . 1,020

Electrical (IBEW) . . . . . . . . . . . . . . . . . . . . . . . 1,012

Machinists . . . . . . . . . . . . . . . . . . . . . . . . . . . . 921

Carpenters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769

Retail Clerks . . . . . . . . . . . . . . . . . . . . . . . . . . 736

Service Employees . . . . . . . . . . . . . . . . . . . . . 625

Laborers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610

Communications Workers . . . . . . . . . . . . . . . 508

Clothing and Textile Workers . . . . . . . . . . . . 501

Meat Cutters . . . . . . . . . . . . . . . . . . . . . . . . . .

Teachers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500

Operating Engineers . . . . . . . . . . . . . . . . . . . 412

Hotel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

Ladies’ Garment . . . . . . . . . . . . . . . . . . . . . . . 348

Plumbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337

Musicians. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330

Mine Workers (Ind.) . . . . . . . . . . . . . . . . . . . . 308

Paperworkers. . . . . . . . . . . . . . . . . . . . . . . . . . 284

Government (AFGE) . . . . . . . . . . . . . . . . . . . . 266

Electrical (IUE). . . . . . . . . . . . . . . . . . . . . . . . . 255

Postal Workers . . . . . . . . . . . . . . . . . . . . . . . . 246

Letter Carriers . . . . . . . . . . . . . . . . . . . . . . . . . 227

Unions:—Continued

Government (NAGE) (Ind.) . . . . . . . . . . . . . . . 200

Railway Clerks. . . . . . . . . . . . . . . . . . . . . . . . . 200

Rubber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

Retail, Wholesale . . . . . . . . . . . . . . . . . . . . . . 198

Painters . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . 190

Oil, Chemical . . . . . . . . . . . . . . . . . . . . . . . . . . 180

Fire Fighters . . . . . . . . . . . . . . . . . . . . . . . . . . 176

Transportation Union . . . . . . . . . . . . . . . . . . . 176

Iron Workers . . . . . . . . . . . . . . . . . . . . . . . . . . 175

Bakery, Confectionery, Tobacco . . . . . . . . . 167

Electrical (UE) (Ind.) . . . . . . . . . . . . . . . . . . . . 166

Sheet Metal . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Transit Union. . . . . . . . . . . . . . . . . . . . . . . . . . 154

Boilermakers . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Transport Workers . . . . . . . . . . . . . . . . . . . . . 130

Printing and Graphic . . . . . . . . . . . . . . . . . . . 120

Maintenance of Way . . . . . . . . . . . . . . . . . . . 119

Woodworkers. . . . . . . . . . . . . . . . . . . . . . . . . . 118

Office . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Associations:

National Education Association. . . . . . . . . . 1,696

Nurses Association . . . . . . . . . . . . . . . . . . . . 187

Classified School Employees . . . . . . . . . . . . 150

Police . . . . . . . . . . . . . . . . . . . . ., . . . . . . . . . . 140

California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

aBmw on ~epofl~ t. the Bureau. All Unlong not identiflad as (Ind.) are affiliated with the AFL-CIO.

bFor ~erger~ and changes since 1978, see app. ‘

SOURCE: U.S. Department of Labor, “Directory of National Unions and Employee Associations, 1979.”
55

the sanctioning of unfair labor practices on the part

of both management and labor, are found in several

pieces of Federal legislation: 1) the National Labor

Relations Act (Wagner Act/NLRA) of 1935, which established the National Labor Relations Board (NLRB)

for labor practices rulemaking, investigation, and

dispute-adjudication; and 2) its amendments promulgated in 1947 (Taft-Hartley Act) and 1959 (LandrumGriffin Act).4 The statutory framework for collective

bargaining has remained unchanged since 1959, although attempts at legal reform were made unsuccessfully in the late 1970’s.

Labor contracts can have enormous influence on

how programmable automation affects existing and

future workers in unionized firms. What kind of influence they have depends on what is included in the contracts, how the contracts are administered, and how

NLRB, arbitrators, and courts interpret provisions subject to dispute.

The NLRA established that “wages, hours, and other

terms and conditions of employment” constitute mandatory bargaining material. NLRB has interpreted this

provision to mean that labor and management may

negotiate over issues in two categories, one category

of issues for which bargaining is mandatory, and one

category of issues for which bargaining is permissible

but not mandatory. NLRB and court rulings on the

adoption of (conventional) automation through the

1970’s generally imposed a requirement to bargain as

to the effects of automation on workers, but not on

the decision of whether and when to introduce automation.

Automation and the Law

Past NLRB and court rulings have generally treated

the decision to automate as protected by “managerial

rights” established in labor contracts. The breadth of

the managerial rights protection depends on the language of the contract and its interpretation, given management’s other obligations. Managerial rights have

been construed to apply (in the absence of proven antiunion conduct) to the control of the production process, including the making of changes in property,

plant, and equipment associated with production. Although changes in property, plant, and equipment can

affect the terms and conditions of employment, and

can, especially in the long term, lead to reductions in

company employment levels, NLRB rulings to date imply that employers need not bargain where new technology “does not deprive employees of jobs, work opportunities, or otherwise cause a real change in working conditions” immediately. s Similarly, arbitration

’29 U. SC. sec. 151-167 (1964).

‘Joseph Manners, “New Technology and the Law, ” notes for remarks presented at IAM Electronics and New Technology Conference, Sept. 21, 1982.

rulings regarding the interpretation of existing contracts suggest that management is accorded broad discretion for implementing new technology, altering

work rules, and reallocating work between employees

in the bargaining unit and others as a result of technological change, in the absence of specific contract

language governing such changes.’

Both the language of NLRA and past rulings of

NLRB and the courts leave unanswered many questions regarding the scope and timing of bargaining to

which an employer is obligated regarding the adoption of new technology in general and programmable

automation in particular.7 Consequently, in the absence of new legislation, the development of clearer

standards for collective bargaining regarding programmable automation would appear to await the passage

of time and the development of precedents through

NLRB and court rulings. The development of precedent, in turn, will depend in part on the changing

membership of the NLRB which is comprised of presidential appointees serving 5-year terms.8 Additional

discussion of the role of NLRB may be found in a paper

by W. Cooke, appendix C.

Contract Language

Existing contracts vary greatly in the degree to which

they can influence the adoption of programmable

automation or its effects. The substantive focus of

most labor contracts has historically been on such matters as wages and hours, work rules and labor grades,

and procedures for grievance resolution. Indeed, a

government survey of labor contracts covering at least

1,000 workers that were in effect at the beginning of

1980 indicates concern over only one issue directly

relevant to the adoption of programmable automation

—advance notice of technological change. See table

B-2. The general lack of specificity of past contracts

with respect to technological change suggests that most

unionized workers are preoccupied with the so-called

bread and butter” issues of wages and hours and that

they may accept management’s responsibility to make

and implement decisions necessary to keep the company financially healthy and competitive—except, perhaps, where those decisions can be clearly linked to

threats to job security. The infrequency of specific

language regarding technological change may also

reflect a lack of appreciation on the part of workers

of how technological change may affect employment

6Doris B. McLaughlin, ‘The Impact of Labor Unions on the Rate and Direction of Technological Innovation,” report prepared for the National Science

Foundation (Detroit, Mich.: Wayne State University, Institute of Labor and

Industrial Relations, 1979); and Manners, op. cit.

7“Notes: Automation and Collective Bargaining,” Harvard Law Review,

84, 1971.

‘Robert S. Greenberger, “Reagan NLRB Tilts Toward Management, ” Wall

Street Journal, Aug. 2, 1982.
56

Table B.2.—Major Collective Bargaining Agreements Advance Notice Provisions by Industry

(agreements covering 1,000 workers or more, January 1, 1980)

Requiring advance notice

Plant shutdown

All agreements Total Layoff or relocation Technological change

Industry Agreements Workers Agreements Workers Agreements Workers Agreements Workers Agreements Workers

’750

79

8

11

31

11

17

42

15

36

15

14

11

35

88

41

81

63

112

11

9

16

62

80

81

12

123

31

88

327

2

6,593,800

3,025,150

234,200

21,800

26,850

207,900

17,100

23,100

85,000

31,800

61,700

25,500

68,850

23,100

93,600

460,800

97,000

242,150

323,750

957,100

27,850

14,800

3,588,850

189,050

489,550

620,000

210,700

23,900

405,200

148,300

323,450

1,195,000

3.500

796

499

45

8

7

11

4

11

14

25

9

12

5

28

48

35

85

81

76

11

4

297

6

24

63

53

8

82

12

26

21

—

3,689,100

2,202,350

159,900

21,800

21,000

118,000

6,700

13,100

34,900

30,800

36,850

15,500

52,500

9,750

63,050

193,600

67,150

212,100

259,850

810,150

27,850

6,200

1,488,750

148,200

128,800

492,450

155,800

18,250

304,450

51,050

115,800

72,354) —

Ail Industries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.550 682 2,986,70 0 lg 1,201,650

Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 504.950 713,950

Food, kindred products . . . . . . . . . . . . . . . . . . . .

Tobacco manufacturing. . . . . . . . . . . . . . . . . . . .

Textile mill products . . . . . . . . . . . . . . . . . . . . . .

Apparel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Lumber, wood products . . . . . . . . . . . . . . . . . . .

Furniture, fixtures. . . . . . . . . . . . . . . . . . . . . . . . .

Paper, allied products . . . . . . . . . . . . . . . . . . . . .

Printing and publishing . . . . . . . . . . . . . . . . . . . .

Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Petroleum refining . . . . . . . . . . . . . . . . . . . . . . . .

Rubber and plastics. . . . . . . . . . . . . . . . . . . . . . .

Leather products . . . . . . . . . . . . . . . . . . . . . . . . .

Stone, clay, and glass . . . . . . . . . . . . . . . . . . . . .

Primary metals . . . . . . . . . . . . . . . . . . . . . . . . . . .

Fabricated metals. . . . . . . . . . . . . . . . . . . . . . . . .

Nonelctrician machinery . . . . . . . . . . . . . . . . . . .

Electrical machinery . . . . . . . . . . . . . . . . . . . . . .

Transportation equipment . . . . . . . . . . . . . . . . . .

Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Miscellaneous manufacturing . . . . . . . . . . . . . .

Nonmanufacturing . . . . . . . . . . . . . . . . . . . . . . . . . .

Mining, crude petroleum, and natural gas . . . .

a Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . .

Communications. . . . . . . . . . . . . . . . . . . . . . . . . .

Utilities, electric, and gas . . . . . . . . . . . . . . . . . .

Wholesale trade . . . . . . . . . . . . . . . . . . . . . . . . . .

Retail trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Hotels and restaurants . . . . . . . . . . . . . . . . . . . .

Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Miscellaneous nonmanufacturing . . . . . . . . . . .

NOTE: Nonedditive.

aExcludes railroeds and airlines.

SOURCE: U.S. Department of Laboc ”Characteristics of Major Collective Bargaining Agreements, Januatyl, 1960:’ May 1961.

and working conditions, and/or an inability of unions

to negotiate successfully for such language.

Overall, the scope of labor contracts began to expand beyond traditional provisions in the 1960’s in

response to technological change, growth in foreign

competition, and growth in the practice of subcontracting work to both domestic, and particularly foreign, firms. Clauses in the following areas, which may

be relevant to the adoption of programmable automation, have become more common during the past two

decades:

● Job and Wage Security. Retraining (for whom,

who pays); layoff, transfer, and relocation procedures; “red-circling’’( maintenance) of wages of

persons transferred to lower paying jobs; severance payments; early retirement.

• Technology Change. Advance notice; consultation; establishment of labor-management advisory committees.

In 1966 the Automation Commission endorsed the

practice of advance notice of technological change as

a measure that the private sector could take to facilitate

adjustments in the labor market, together with explicit

431

36

5

2

2

4

9

10

12

23

8

9

3

21

43

32

84

59

74

11

4

251

4

16

61

50

5

84

10

22

19—

1,756,750

80,000

16,100

2,300

6,200

6,700

10,800

17,950

28,400

34,450

13,000

20,250

6,300

67,750

128,550

79,750

21O,900I

188,300

807,200

27,650

6,200

1,229,950

133,200

56,950

463,650

135,350

11,200

226,500

32,250

08,150

54,700 —

14 89;900

6 11,800

1 5,000

2 18,200

1 l,500

2 2,300

9 15,250

3 3,300

2 3,800

6 11,800

4 34,050

1 1,100

15 51,150

7 75,850

6 11,800

11 50,150

10 99,700

5 13,550

3 5,550

— —

42 204,250

— —

10 78.950

3 16,250

2 3,750

1 1,550

17 48,000

1 1,000

5 31,500

3 21,250

— —

12—

6

8—

—

9

9

5

1

2

2

7

3—

7

:

—

—

61

3

5

7

9

2

43

3

9—

—

32;650

—

19,800

96,800

—

—

17,100

24,S00

9,000

1,700

23,450

3,450

17,350

8,800

—

12,650

9,000

437,400

—

—

487,700

140,000

23,650

67,450

36,450

5,500

148,950

21,750

45,950

—

—

advance planning by companies for attrition and other

internal work force adjustments. g A comparison of

contract scope in the mid-1960’s and the early 1980’s

is provided in a paper by M. Roberts, appendix C.

Additional areas for labor contract change in connection with programmable automation include modification of work hours (currently included in some contracts as a means of adapting to periods of slack business), specific triggers for reopening negotiations

before contracts formally expire, procedures for reclassifying workers, definition of and assignment of work

to the bargaining unit, and involvement of labor representatives in planning, design, and purchase decisions for automated systems. Whether, when, and how

labor contracts accommodate the adoption of automation will depend on many factors, such as the duration of the current concessionary bargaining trend and

the weight given to technological change relative to

other concerns by both labor and management. IAM,

for example, appears to attach great weight to tech-

‘Techno]o~ and the AmerJc“ an lkonomy, report of the U.S. National Commission on Technology, Automation, and Fconomic Progress, February 1980.
57

nological change, especially automation, as a bargaining issue; it has included technological change provisions in model contract language it has developed since

the 1960’s. In 1982, two IAM locals engaged in longterm strikes over proposed work-rule changes associated with programmable automation.l0

A key question with regard to the impacts of programmable automation on industrial relations among

unionized firms is whether the collective bargaining

framework is adequate for meeting needs of both labor

and management with respect to programmable automation. At this time, there does not appear to be empirical data suitable for evaluating how programmable

automation may affect industrial relations, and vice

versa. Participants in the OTA Labor Markets and Industrial Relations Workshop appeared to agree that

collective bargaining can accommodate new needs

associated with programmable automation, although

some participants maintained that the resiliency of collective bargaining depends in part on how the relative

bargaining power of unions and management changes

in response to new technology and to other factors.

A discussion of relative bargaining power is provided

in a paper by W. Cooke, appendix C.

1OMari]yn chase, “wOrh Rule Changes Sought, ” American Metal Market/

Metalworking News, Oct. 25, 1982.

Institutional Change

The overall bargaining power of unions relative to

management and the overall role played by unions in

the transition to new manufacturing technologies, including programmable automation, depend on the extent of union representation and on the response of

unions to specific aspects of programmable automation (and other new technologies). Factors influencing

union representation and union responses to new technology are outlined below.

Union representation is largely a function of numerical strength. Changes in the numerical strength of the

labor movement as a whole are widely acknowledged.

Although membership in labor organizations has

grown, the proportion of the labor force that is

organized and the rate of growth of union membership have both declined during recent decades, and

unions have been less successful in arranging and winning elections. Moreover, unions have become less successful in overcoming recertification efforts in the past

few years. See figure B-1.

Factors Influencing Union Representation

The erosion of overall union representation has been

attributed to many factors, including changes in emFigure B-1 .—Change in Union Representation Over Time

Chart 1. Membership of national unions, 1930-78 a

20

18

16

14

12

10

8

6

4

2

o

1930 35 40 45 50 55 60 65 70 757880

Chart 3. Union membership as a percent of total labor force

and of employees in nonagricultural establishments,

1930-78a

4U

I Percent of employees i n

nonagricultural establishments

28 -

-

24 -

 20 “

labor force

 1 6

8 “

1930 35 40 45 50 55 60 65 70 75788

aExciude9 Can-ian membership but includes rnernbers in other areas outside the United States. Members of AFL-CIO directly affiliated IOGd UniOnS are alSO included.

Members of single-firm and local unaffiliated unions are excluded. For the years 194&52, midpoints of membership estimates w-hich were expressed as ranges were used.

SOURCE. U.S Department of Labor, “Directory of National Unions and Employee Associations, 1979. ”
58

ployer practices (as a factor enhancing employer effectiveness in avoiding unionization), relocation of production, structural change in the economy, and proliferation of new parties to industrial relations activities.

It is uncertain, however, whether the overall economic

strength of unions has declined commensurately.

Modern personnel practices may diminish the incentive of workers to organize where management provides grievance procedures, complaint channels, company information, fair compensation, and other services or benefits that unions have been instrumental in

launching at unionized firms. Personnel practices have

improved as a result of growth in government regulation of employment conditions, growth in business

school training of managers, increased attention of

business school curricula to human resource management, and other factors.11 One industrial relations

analyst relates change in employer practices to the

spread among managers of the view that “unions exist as a reflection of management failures, ” although

he notes that such generalizations tend to be unmerited, reflecting doctrine rather than analysis of specific

situations. 12 A review of the industrial relations literature shows that this characterization appears to be accepted by many academic observers of industrial relations trends.

The shift in location of production from unionized

to nonunionized regions in the United States, and from

the United States to other countries, has also diminished the union presence in the workplace. Locational

shifts occur for many reasons, most related to costs,

and in some cases including a desire by management

to evade unions.13 Where locational shifts involve

plant closings, unions can gain political support

through community opposition to closings. * On the

other hand, management develops political support

(though not necessarily at the local level) by relating

locational and other decisions to business strategy for

maintaining competitiveness. Although “competitiveness” has become a battle cry in rhetorical wars between unions and employers, the true extent of the effect of unions on industrial competitiveness, and the

soundness of that rationale for relocating production

facilities away from unionized areas, are uncertain.

Another important factor in observed erosion of

union representation is structural change in the economy. In brief, growth in service industry relative to

manufacturing employment, and growth of public sec1lD, ~~ Mills, ‘~an=ment Performance, ” and Fred K. Foulk=, ‘large

Non Unionized Employers, “ in U.S. Mustrial Relations 19s01980: A Critical Assessment, Jack Steiber, et al. (eds. ) (Madison, Wis.: Industrial Relations Research Association, 1981).

‘

z

Ibid.

I’Ibid.

● Point debated in 1982 OTA Labor Markets and Industrial Relations Workshop.

tor relative to private sector employment have increased the proportion of employment opportunities

in occupations and industries with traditionally limited union representation. See figure B-2. Moreover,

growth in electronics and other so-called “high tech”

industries which have little union representation relative to traditional manufacturing has also reduced the

proportion of employment in unionized industries (although unionized, traditional manufacturing industries

employ more people than high-tech industries). * The

continuation of these divisions between predominantly

union and nonunion industries and sectors is uncertain.

Finally, several new parties have entered the industrial relations arena in the past two to three decades.

First, the use of consultants who specialize in personnel management and in combating unions and the establishment of labor-management committees have

grown among both unionized and nonunionized

firms. ** Although the legality of labor-management

committees in unionized firms has been questioned (as

possibly unfair employer interference in the bargaining process), and although some unions regard committees as conflicting with the bargaining process,

many committees have been established through collective bargaining, and legal problems are being

resolved. * * * The long-term impacts of labor-manage-

● A BLS analysis conducted for the Joint Economic Committee notes that

high-tech industries account for 4.6 percent of total wage and salary employment. By contrast, all manufacturing industry wage and salary jobs comprise about 22 percent of the total.

‘ ● This point was raised at the OTA Labor Market and Industrial Relations Workshop and in a roundtable reported in Fortune magazine, Sept. 20,

1982.

● **Point discussed at the OTA Labor Market and Industrial Relations

Workshop.

Figure B-2.—Job Growth for Major Occupational

Categories Under Alternative Economic Projections,

1978-90

20

High

trend I

15

:

 1 0

5

0

- 1

White-collar Blue-collar Service Farmworkers

workers workers workers

SOURCE: Bureau of Labor Statistics, “Monthly Labor Review,” August 1981.
59

ment committees on union-management relations are

unclear, since existing committees differ in focus (e.g.,

training, quality control) and scope, and since the current increase in interest in committees seems linked at

least in part to current economic conditions and import levels.

Second, new regulations and regulatory bodies began to influence labor-management relations in the

areas of occupational safety and health protection and

equal opportunity in hiring and promotion in the

1960’s and 1970’s, beginning with the 1969 Coal Mine

Health and Safety Act and continuing with the 1970

Occupational Health and Safety Act and the 1972

Equal Employment Opportunity Act.14 New regulations served to force changes in union practices, including contract modification. Some observers believe that

government regulation of hiring, promotion, and occupational health and safety practices may have undermined the value of collective bargaining in those areas,

by establishing new complaint mechanisms for workers outside the traditional industrial relations framework, and placing an emphasis on concerns of the individual worker rather than the bargaining unit .15 Occupational health and safety regulations, in particular,

may also affect unions by promoting technology

change in general and automation in particular. And,

l~pub]ic Law 91.173, public L.aW 91-596, and Public Law 92-261, respectively.

IJphy]]ls A, Wallis and James W. Driscoll, “Social Issues in Cokctive Bargaining, ” in V, S. industrial Relations 19s01980: A Gitical Assessment, Jack

Steiber, et al. (eds, ) (Madison, Wis.: Industrial Relations Research Association, 1981).

as noted earlier, regulations motivated improvement

in personnel management.

Programmable automation may present opportunities or liabilities for labor organizations. How labor

organizations are affected by programmable automation depends on how the equipment and systems are

developed and implemented, and on where they are

used. To develop an understanding of how programmable automation may affect labor organizations, a

variety of issues should be addressed, such as the

aspects of programmable automation design and implementation that may be fundamental to union (and

other labor) responses to programmable automation,

the degree to which workers consider programmable

automation design and implementation characteristics

to be inevitable or negotiable, and, in particular, the

impact of programmable automation on the organizing base for unions.

While unions are perceived as representing primarily

production workers, the application of programmable

automation to all aspects of the manufacturing process, including nonproduction activities such as drafting and inventory control, may broaden the base of

workers interested in organizing. Already, scientific/

engineering and clerical unions have been formed,

serving constituencies which may be especially vulnerable to technological change in the future. Whether

nonproduction workers do organize at higher rates,

and if they do, whether they join unions dominated

by production workers or separate labor organizations, may be important factors in determining how

labor organizations influence the spread of programmable automation and moderate its impacts.
Appendix C

Papers Prepared for Workshop

The following papers were prepared as background

materials for the OTA Labor Markets and Industrial

Relations Workshop which was held July 27, 1982. 3.

Their content and conclusions are the sole responsibility of the authors and do not necessarily reflect the 4.

views of OTA.

1. “The Economics of Technical Progress: Labor

Issues Arising From the Spread of Programmable 5.

Automation Technologies,” by Eileen Appelbaum. 6.

2. “Assessing the Future Impacts on Employment of

Technological Change: An Input-Output Approach,” by Faye Duchin.

‘The Effect of Technical Change on Labor,” by

Louis Jadobson and Robert Levy.

“Programmable Automation: Its Effect on the

Scientific-Engineering Labor Market,” by William

N. Cooke.

“Technology and Labor,” by Markley Roberts.

“Labor-Management Relations in an Era of Programmable Automation,” by William’N. Cooke.

THE ECONOMICS OF TECHNICAL PROGRESS: LABOR ISSUES

ARISING FROM THE SPREAD OF PROGRAMMABLE

AUTOMATION TECHNOLOGIES

by Eileen Appelbaum*

Department of Economics

Temple University

July 27, 1982

One of the specific objectives of this workshop is

to place alternative analytic methods which may be

used to make inferences about programmable automation technologies and labor market issues in perspective. A second objective is to specify the information

requirements associated with the formulation of appropriate policies and legislative initiatives. The two

objectives are related in that the choice of variables

for study as well as the specification of the behavioral

relations among them is dictated by the system of analysis that is utilized. My remarks are directed to the

second set of questions which this panel will be considering, subsumed under the heading, “To what extent are the production and use of programmable automation likely to result in unemployment of current

workers and job displacement?” My view is that an

adequate system of analysis is indispensable to an

evaluation of the questions raised in this section and

to the design of studies capable of providing reliable

information.

The first section of this paper is a critique of the

standard economic analysis of technology and employ-

● The author is a member of the Working Group on Reindustrialization

at the University of Massachusetts (Amherst) and participates in its Subgroup

on Microelectronics and Work Process. She is indebted to her colleagues for

valuable discussions, though she is solely responsible for the content of this

paper and any errors in it.

ment. This view probably exercises some influence in

the policy community and elsewhere. Its conclusions

are remarkably sanguine. They may or may not prove

to be true; but the analysis itself is faulty and the conclusions are not supported by it. I will keep my comments brief, and will limit them to demonstrating the

most critical weaknesses of this approach. In the next

and most important section I will proceed to the constructive task of developing the economics of technical

progress to encompass the issues of interest to this

panel. The analysis presented in this section will, I

believe, make clear the existence of important relationships that are not otherwise obvious, and will provide

qualitatively new insights in the analysis of the

employment issues. The final section will indicate the

kinds of studies that will best serve to increase our

knowledge of the effects of this latest round of automation on workers, jobs, and employment.

Critique of the Standard Economic

Analysis of Technology

The analytical methodology employed by economists to analyze the effects of the introduction of

robots on wages and employment of workers in the

affected industries and upon employment generally

60
61

tends to trivialize what is a complex question, and offers little policy guidance. This does not appear to

create any difficulties, mainly because the analysis

leads economists to the conclusion that, apart from

a short-term need for retraining, workers face no problems. Thus, standard analysis suggests that robotics:

1) will have a positive impact on wage levels, 2) will

probably tend to reduce rather than increase unemployment in the long run, and 3) will stimulate total

employment even in the industry introducing the

robots.1 The analysis on which these conclusions are

based can only be characterized as glib and superficial.

In analyzing the wage and employment effects of

introducing robots, a typical approach taken by economists is to present a hypothetical situation. One

typical presentation compares the case in which a

widget factory adds one additional worker to its

existing production line and increases output by 4 units

a day to the case in which the firm buys several robots

and hires one additional worker to oversee them, thus

increasing output by 10 units a day. At $20 per widget,

the author concludes that the value of a worker’s marginal product has increased from $80 to $200 and that

the firm, which previously would have been unable

to pay any worker more than $80 will now be forced

by competition to pay this new worker something near

the $200 figure. This type of example, though common, is peculiar; and the argument behind it is logically incorrect.

It is strange that in this example the robots do not

replace any previously employed workers (welders,

painters, machinists, etc.), but are simply added to an

existing production process. It’s an unusual technology, indeed, that simply adds two or three robots to

an existing process without materially affecting that

process. As no workers have been replaced, utilization of the robots simply increases employment by one

skilled worker because the robot requires maintenance

and oversight. Economists making this argument

sometimes conclude that the worker hired will be a

skilled worker. Now, robot-fixer may be a more skilled

job than painter or welder, but robot-minder is not.

With only two or three robots, the firm is likely to

contract out for robot maintenance, increasing employment in the contracting firm by considerably less

than one skilled worker. The worker hired by the firm

using only two or three robots is more likely to be a

relatively unskilled adjunct to the robot. By making

the robot overseer both robot-minder and robot-fixer,

this approach manages to evade entirely the issues in-

‘See, for example, Richard K. Vedder, “Robotics and the Economy, ” a staff

study prepared for the Joint Economic Committee, Congress of the United

States, Mar. 26, 1982 (Washington, D. C.: U.S. Government Printing Office,

1982).

volved in the fact that some skills will be downgraded

even as others are increased.

Moreover, the argument is logically incorrect. In

going from the first case to the second, an increase of

capital (two or three robots) as well as an increase of

labor (one robot-fixer) has been slipped into the example. Capital and labor have both increased, and

consequently it is not possible to speak of an increase

in the marginal product of labor. Nor can the firm afford to use the total increase in productivity to increase

wages. What this argument appears to ignore is that

with an increase in wages for the newly hired worker

from $80 to $200, the rate of profit on capital has

decreased. The amount of capital has increased by two

or three robots, but all the increase in output has gone

to pay the wages of the robot-fixer. Consequently,

gross profits are unchanged. As a result, the ratio of

net profits to capital (the rate of profit on capital) has

decreased. An even more serious consequence is that,

with gross profits unchanged, the firm will be unable

to amortize the new robots. How will it replace them

as they wear out’?

Economists sometimes use oligopolistic industries

like auto and steel as illustrative examples in considering what will happen to the price of, and demand for,

output. In these examples, price and quantity sold of

steel and cars are determined by impersonal market

forces. Auto and steel firms exercise no control over

market price and, in particular, they do not view price

as a strategic variable which they can manipulate to

restore profitability after the introduction of robots.

Nor are steel or auto workers capable of bargaining

for higher money wages as their productivity increases.

Thus, the introduction of robots reduces production

costs, increases supply, and ultimately causes prices

to decline and the quantity demanded to increase. The

problems with this analysis when applied to industries

like steel and autos are myriad. If firms exercise market

power to increase profit, or workers bargain for wage

increases, or the new technology requires greater investment, or the new technology is likely to become

obsolete in a short time as robots become more sophisticated and hence has higher amortization costs, then

the decline in production costs will be damped, supply need not shift very much, prices need not fall very

much, and the quantity demanded need not be much

affected. Real wages and/or profits will increase and

demand may very well be affected, but simple textbook models of supply and demand are not sufficient

for analyzing industries that are highly unionized,

capital intensive, and in which firms are able to exercise market power. An alternative analysis which poses

very different questions and possibilities, is a prerequisite to research.
62

On one point standard economic analysis is correct:

there is no stopping the introduction of robots. Even

if it should turn out that employment is adversely affected and the rate of profit on capitaJ is approximately

the same after the introduction of robots as it was

before, firms cannot avoid trouble by never adopting

the new technology. So long as some firms (in this

case, Japanese or European firms) are going to introduce the new technique, U.S. firms will have to do

likewise or find they are unable to compete in world

markets. In the absence of competition, firms have

sometimes delayed the introduction of cost saving

techniques to protect the value of existing capital. In

the presence of competition, delay risks loss of sales

and, hence, of jobs. U.S. firms will ultimately have

to adopt robotics and related technologies. If they do

so later rather than sooner, they will lose whatever

advantages accrue to having been among the leaders.

The Economics of Technical Progress:

Labor Issues Arising From the Spread of

Programmable Automation Technologies*

Programmable automation technologies are introduced in order to reduce manufacturing cost per unit

of revenue. That is, as a result of robotics, firms expect their net receipts from a given outlay to be higher

than they would have been otherwise. A successful innovation reduces costs—saving labor cost, or saving

capital cost, or expanding the resource base and thus

saving resource cost. An innovation may be worth

making even if it raises one element of cost provided

that it reduces other elements more. Thus, programmable automation technologies will be introduced

because they reduce labor costs by increasing output

per man-hour or by allowing the substitution of

cheaper for higher paid labor, or because they reduce

amortization charges by embodying major advances

in technique in new plant and equipment, thus reducing the risk that the capital stock will become prematurely obsolete and extending its probable useful

life. Such changes in technique maybe cost saving even

if they increase capital costs. The effect of the new

technologies on wages and employment will vary,

however, depending on whether they are capital saving or capital using. Capital costs are increased, and

the new technique may be characterized as capital

using, if the firm requires higher levels of investment

in order to achieve a given increase in capacity. It may

This argument owes much to Joan Robinson, “Notes on the Economics

of Technical Progress,” Z% Generalisation of the General Theory and Other

Essays (New York: St, Martin’s Press, 1979 (1952)). The importance of capital

saving technology in economic development was suggested to me by Professor Thomas Hughes of the University of Pennsylvania.

be characterized as capital saving if less investment will

achieve a given increase in capacity, and it may be

characterized as neutral if the amount of investment

required to achieve a given increase in capacity is the

same with the old and new techniques.

Whether they are capital using or capital saving,

programmable automation technologies are labor saving. A saving in labor cost may be due to an increase

in output per man-hour with the type of labor (i.e.,

the distribution of skill levels) unchanged, or it may

be due to the substitution of less skilled labor for more

skilled. It will, thus, be necessary to consider several

cases.

Wage and Employment Effects

Programmable automation technologies are likely

to spread throughout the manufacturing sector, though

they may have fewer applications in some industries

than in others. Consider first what will happen if these

technologies are capital saving (or neutral) and, in addition, are labor saving due to an increase in output

per man-hour and not due to the deskilling of work.

If money wage rates are unchanged following the introduction of robots and related technologies, then the

effect of these innovations depends on what happens

to the price of manufactured goods. If the price of these

products is unchanged, the net profit per unit of output and net profit per unit of capital will be increased.

Real wages will be unchanged. If the prices of these

products decline, as a result perhaps of the strength

of foreign competition for the domestic market, then

real wage rates will rise. The increase in real wages

will be greatest when competition is strong enough to

force prices down to the point at which net profit per

unit of capital is the same after the change in technology as it was before the change. Lesser price decreases

imply an increase in the rate of profit and smaller increases in real wages. An alternative scenario is one

in which money wage rates increase in line with productivity advances so that unit labor costs are constant while prices are constant or increase. Again, so

long as competition is sufficient to assure that prices

increase by less than wages, real wages will increase.

The extent of the increase in real wages will depend

on the strength of competition in product markets,

which limits the increase in the rate of net profit on

capital following the introduction of the new technique. Provided that competition in product markets

is not entirely absent, real wages of manufacturing

workers will increase following the spread of programmable automation techniques if the proportion of

skilled manufacturing jobs does not decline.

The effect on employment in manufacturing depends, in the first instance, on what happens to effec-
63

tive demand for manufactured commodities and capital goods. To maintain a given level of manufacturing employment, demand for manufactured goods (including robots) must increase in the same proportion

as output per worker. If the new technology is capital

saving (or neutral), then the increase in capital goods

required to maintain a given level of employment will

be less than proportionate (just proportionate) to the

increase in output forthcoming from that employment.

Then the increase in demand (including export demand) for manufactured commodities will have to increase more rapidly than (at the same rate as) output

per head in order to maintain employment constant.

If effective demand for manufactured products does

not grow apace, technological unemployment in manufacturing will result. Keynesian demand management

policies may or may not be capable of increasing effective demand sufficiently to prevent unemployment

in individual industries—it depends on the extent to

which the new technology increases output per

worker. Given worldwide excess capacity in steel production, for example, reliance on demand management

alone is insufficient to restore the U.S. steel industry’s

employment to its 1979 level, even after cost saving

robots are introduced.

A reduction in the workweek with no reduction in

weekly income is an alternative means of maintaining employment in manufacturing. The question that

arises is whether this can be done without raising unit

labor costs or reducing real weekly wages. If the new

technology is capital saving or neutral and doesn’t

reduce the proportion of skilled jobs, the answer is

“yes.” With output per worker increasing and the ratio

of investment to output constant or declining, it is

always feasible to reduce the workweek without a reduction in weekly income or an increase in price. The

increase in labor productivity in an industry may be

shared between a slower increase in weekly income

and a reduction in the workweek for workers in that

industry. * A reduction in the workweek under these

● The following example may clarify this point. Consider a firm that

employs 10 workers at 40 hours a week and produces 100 widgets a week.

The workers are paid $1.00 an hour and the widgets are priced at $5.00 each.

Each worker earns $40 per week. The total wage bill is $400 and total profits

are $100. Now suppose a capital saving advance in technology is introduced

that increases output per worker by 50 percent, and that wages also rise by

50 percent to $1..50 an hour. Suppose that demand for widgets increases, but

not enough to maintain employment of all 10 workers. Suppose that demand

for widgets increases to 120 a week. Following the advance in technology,

this output can be produced with only eight workers. The total wage bill

is now $480 (eight workers working 40 hours at $1.50 an hour). The wage

bill has increased 20 percent. With no increase in price, total profits will increase to $120, also a 20-percent increase. The eight workers are employed

a total of 320 hours a week. If a decision is made to maintain employment

by reducing the workweek, the 10 workers will each work a 32-hour week.

At $1.50 an hour, each worker will earn $48 a week. The workweek has

decreased, labor costs have not increased, real weekly wages have increased

(though more slowly than they otherwise would have), and the increase in

profits is unaffected.

circumstances is always possible. There are at least two

reasons, however, why it may not be practical. First,

a shorter workweek is only possible if prices of the industry’s products do not decline. In the face of international competition for domestic markets, it may not

be possible to meet this condition. Second, consider

what happens when some manufacturing industries are

better able than others to utilize the new technologies,

and thus achieve much greater increases in productivity. If the standard workweek in manufacturing is reduced, the reduction is likely to apply to all manufacturing industries, including those with slower productivity gains. Labor costs and prices in industries where

productivity improves less must rise as a result of the

reduction in the workweek. If the products of such industries are capital goods, they will raise the final price

of the outputs of the technically progressive industries

in which they are used, possibly making them less

competitive in world markets. In any event, the increase in the price of output of industries where the

new automation technologies are less applicable retards

the rise in real wages of workers. Thus, a reduction

in the weekly hours of manufacturing workers with

no reduction in weekly income can most easily be undertaken if 1)programmable automation technologies

prove to be capital saving or neutral, 2) real wages of

workers rise because money wages increase in line with

productivity increases while prices remain constant

(and not because money wages remain constant while

prices decline), and 3) the technologies are widely

spread among manufacturing firms and not much

more highly concentrated in some than in others.

If programmable automation technologies are capital using rather than capital saving or neutral, the

discussion of the effect on real wages has to be

modified somewhat. Again, if money wages and prices

are both unchanged, net profit per unit of output will

increase. Whether any increase in the rate of profit on

capital and/or real wages is possible depends on

whether the increase in the amount of capital required

per unit of output and in net profit per unit of output

are proportionate. That is, it depends on whether the

saving in labor cost is completely offset by the increase

in capital costs. If it is not entirely offset, then provided competition in product markets is not entirely

absent, real wages of labor will rise. The increase,

however, will be more moderate than if the technology were capital saving or neutral. The reason for this

is straightforward: If the new technologies are capital

using, a larger share of gross output will be required

for amortization. The increase in net output available

to be divided between increases in real wages and in

the rate of profit is consequently smaller. For the same

reason, the possibilities for trading off increases in real

wages for a shorter workweek are more limited.
64

b

Even when demand for manufactured goods grows

in line with the growth of output per worker, a capital

saving technology will not directly increase employment in the capital goods industries. A capital using

technology, however, may have this effect. With investment requirements higher, whether employment

will increase in the capital goods industries depends

on whether these industries have also introduced labor

saving technologies as they shifted from the production of ordinary to programmable automation technologies.

An important motive for the decision of firms to introduce capital using techniques is that such techniques

may substitute machinery for skill, reducing the skill

component of the task, and making possible the substitution of lesser skilled machine-tenders for more

highly skilled craftsmen. * Such developments in technology save labor costs by allowing firms to substitute

cheap labor for more expensive, and bring us to the

third case which must be considered. This type of technical change is, perhaps, more invidious than a change

which saves labor by increasing output per worker

without reducing skill requirements. One reason is that

even if aggregate demand policies could be devised to

maintain reasonable full aggregate employment levels,

skilled manufacturing workers would be displaced by

less skilled workers. If the wages of skilled workers

do not then decline substantially, the new technique

will diffuse through the industry, effectively displacing the skilled workers. A substantial decline in wages

of skilled workers might check the spread of the new

technique, allowing both techniques to operate side by

side. This outcome, however, would probably require

a decline in the wages of skilled workers sufficient to

nearly eliminate the wage differential between skilled

and unskilled workers. Unlike changes in technology

that save labor costs by increasing output per worker,

changes in technology that save labor costs by enabling

firms to substitute cheap, unskilled labor for more

highly skilled workers do not increase real wages.

Whether the rate of profit on capital increases or not,

net profit as a share of output and amortization will

both increase as a result of the introduction of a more

capital using technique. Given the overabundance of

workers available for less skilled jobs in the U.S. economy, and their lack of union organization and bargaining strength, it is unlikely that real wage rates of less

skilled workers will increase. Union organization of

● Such a development has occurred in the last decade in the baked goods

industry. Bakers continued to be classified among craft workers although,

increasingly, the baker’s task has become one of minding, measuring, and

mixing machines, Cheap labor, often female, has been substituted for highly

skilled craft labor as mechanization increased and the job was deskilled.

these workers could change this, of course, by capturing for the less skilled employees some part of the

cost saving associated with the elimination of more

highly skilled jobs. Otherwise, average wage rate in

manufacturing will be reduced and the rate of profit

increased. In any event, high wage jobs will have been

eliminated.

This raises a related issue. As we have just seen,

firms in an industry can improve their cash flow position (net profit plus amortization) by adopting a technique that is capital using but saves labor cost by

allowing the substitution of less skilled labor for more

skilled. For this reason, they may choose such a technique in preference to an alternative available technology that saves labor cost by increasing output per

worker without reducing skill levels since such techniques, in general, increase real wages. Such a choice,

however, may have serious negative ramifications for

further technological change 10 or 20 years in the

future. These negative impacts may affect the competitive position of such firms vis-a-vis foreign competitors who have chosen not to reduce the proportion of skilled workers employed. A hypothetical example, which Peter Albin of CUNY suggests may not

be so far from the truth, may serve to illustrate this

point. Suppose that when a U.S. firm puts in a robotic

installation, it replaces as many as six master

machinists with one programmer plus three entry-level

people whose function is to “load and unload,” keeping things lined up for the robots. In Japan, on the

other hand, let us suppose that master machinists are

retrained and prepared for positions as machinist/programmers. The machinist’s job is transformed but not

downgraded, and the machinist is ready for future

changeovers. The initial cash flow advantage is gained

by the American firms which have a less skilled and

lower paid labor force. However, the man/machine

configuration in U.S. firms is more permanent, less

flexible. In the absence of skilled master machinists,

the opportunities for learning by doing are severely

curtailed. The Japanese in this example, because they

retain their master machinists, need to build less into

the machine, can design less immutable man/machine

configurations, have enhanced opportunities for learning by doing, and have increased opportunities for

continuous technological change. Longrun competitive

advantage would rest with the Japanese. This example suggests that the substitution of less skilled workers

for craft and highly skilled workers, as a means of

holding down costs and increasing profits in the near

future, may be myopic. In the longer period, it could

have serious implications for international competitiveness and manufacturing employment.
65

Changes in Aggregate Employment

Outside Manufacturing

Changes in technology that involve the deskilling

of tasks have a deleterious effect on the employment

possibilities of skilled manufacturing workers, while

those that increase output per worker raise the specter

of technological unemployment. Such unemployment

will materialize if growth in demand lags behind productivity growth. In an open economy, in which imports supply some part of the domestic market, growth

in demand at home and abroad for domestically produced manufactured products will have to outstrip the

productivity gains just to maintain employment constant. Otherwise, as seems likely, employment in U.S.

manufacturing will decline. The question, then, is

whether employment in nonmanufacturing sectors of

the economy will expand and absorb displaced workers as well as new labor force entrants who would

previously have found skilled and semiskilled manufacturing jobs.

Economists are used to thinking of increases in productivity as increasing real income and spending, thus

increasing demand for services and other products, as

well as employment in the industries producing them.

It happens, however, that it matters whether the increase in real income is an increase in real wage rates

or an increase in net profit. An increase in net profit

results in an increase in business saving, and sets up

the conditions for a cyclical decline in employment.

Moreover, the marginal propensity to save and to consume imports is higher among those income groups

receiving dividends than among wage earners, again

with negative implications for U.S. employment. Some

part of the increase in net profits will increase demand

for producer services (e.g., advertising, financial, architectural, or computer services), so that output of

this sector will increase.

An increase in real wages, on the other hand, is likely to generate an increase in demand for the full range

of consumer goods and services. Output will increase,

but the effect on aggregate employment is more uncertain. Two cases need to be considered: the case in

which technology in nonmanufacturing firms is essentially unchanged, and that in which labor saving programmable automation technologies spread through

the nonmanufacturing sectors as well.

Suppose that the application of programmable automation technologies is confined to the manufacturing

sector and does not spread to the two other broad sectors of the economy—other goods producing industries

(agriculture, construction, and mining) and nongoods

producing industries broadly construed as the service

sector. Price declines for some manufactured goods,

as a result of the increase in output per worker, are

likely—in clocks, calculators, home computers, etc.

Typically, however, real wages of manufacturing

workers and profits of manufacturing firms will increase as a result of increases in money wages in line

with productivity and prices that remain largely unchanged, perhaps creeping upward slightly. * With

prices largely unchanged, real wages of nonmanufacturing workers are also unchanged. As manufacturing workers’ spend their higher real incomes, demand

for nonmanufactured goods will increase. The higher

incomes (gross profits and total wage bill, though not

wage rates) generated in these sectors again increase

demand for manufactured goods, other goods, and

services. Since existing technologies in the service sector have a much higher ratio of labor to “output,”

growth in services and consequently in service sector

employment could possibly maintain aggregate employment, increasing the share of service jobs.

The employment outcome is far less optimistic if

unionized workers in construction, mining, transportation, and the public sector, concerned over the

decline in their relative real wage position, bargain for

higher money wages. The resulting rise in prices, especially of housing, will slow the increase in real wage

rates due to the advance in technology in manufacturing, and hence will retard the increase in employment generated by expenditures out of increased wage

income. Moreover, with prices rising the already low

real wages of nonunionized workers in retail trade and

health services, and of many of the clerical workers

found in the service sector, will decline, The resulting

change in relative output prices may stimulate demand

for these services, resulting in a disproportionate increase in such low paying jobs.

It is not realistic, of course, to assume that programmable automation technologies will be limited to the

manufacturing sector. In particular, the spread of

distributed data processing in offices has already begun

and should be well under way by 1990. Automation

in manufacturing had progressed far even before the

introduction of robots and related technologies. Reliable estimates of productivity growth as a result of

these new techniques are not available, though the

guess that is bandied around is that between 4 and 9

percent of the work in manufacturing is suited to such

automation. * * The effect on employment will be less

than 4 to 9 percent since robot support staff—maintenance and repair personnel, programmers, and machine-tenders-will be required. Productivity might increase by as much as 6 percent. These are only guesses,

● Such was approximately the case in the United States from 19s0 to 1964.

If there are price shocks from increases in resource prices (oil, food, etc.),

a wage-price spiral will upset this pattern of real wage gains.

“h-i the Carnegie-Mellon survey, responding firms placed it at 8 percent.

Like most of our information on the subject, this is little more than a guess.
66

of course, but that future productivity gains in

manufacturing will approximate current gains in a

technologically progressive sector like communications, does not seem entirely unreasonable. Unlike

manufacturing, automation in offices is a qualitatively new experience. Mechanization, in which machines

supplement the input of workers, has occurred steadily

with the introduction of typewriters, electric typewriters, and word processors as well as other business

machines. But office automation, in which machines

take over entire worker functions and drastically alter

job content for the remaining workers is a recent

phenomenon. Gradual gains in clerical productivity

since 1948 may, within the decade, be replaced by

rapid advances in output per worker. Again no reliable

estimates of the potential spread of office automation

have been made for the United States. European observors3 suggest that 30 to 40 percent of clerical work

may be suitable for automation, with the impact on

employment in the 20- to 30-percent range. These

guesses (and I emphasize that they are only guesses)

suggest an increase in labor productivity in the office

of about 25 percent. Even if these guesses are too high

by a factor of 2 or 3, they suggest the severity of the

technological unemployment problem that may

emerge if programmable automation technologies

spread through both manufacturing and nonmanufacturing sectors. With so small a fraction of clerical

workers unionized and so large a percentage of them

women, clerical workers are likely to reap but few of

the gains of their higher productivity directly. Major

employers of clerical workers—insurance companies,

accounting firms, advertising agencies, computer service firms, law firms-may lower their rates, raising real

incomes generally and stimulating demand for their

services. Alternatively, net profits will increase if rates

are unchanged. In either case, the rapid growth that

has characterized clerical employment in the last decade is likely to slow, further compounding the problem of providing jobs for new labor force entrants.

Rapid Technological Change and Investment

One other aspect of the relationship between implementation of new technology and employment

should be briefly noted. Older plant in manufacturing industries, auto and steel perhaps, may have been

rendered obsolete more quickly than anticipated. Certainly the step up in the pace of automation has generated a major increase in demand for office equipment. During the period of the changeover to programmable automation and microprocessor-based

technologies, a period which may last 10 to 20 years

“The Job-Killers of Germany,” New Scientist, June 8, 1978.

while more versatile robots and software for office and

other applications are developed, investment in the

capital goods required to replace manufacturing plant

that has had to be prematurely scrapped or to increase

capital-to-labor ratios in offices will generate employment. High capital requirements as offices switch to

electronic equipment and manufacturers use computerized machinery implies employment growth over

the next two decades in industries supplying advanced

capital goods. If these goods are produced in U.S.

plants, growth in manufacturing jobs in these industries will, during this period, partially offset the loss

of manufacturing jobs in industries where the new

technologies are implemented. The full impact of

technological unemployment will not be felt until 2000.

The unknown here is the extent to which this market,

both domestic and international, will be supplied by

firms located in the United States. The Japanese lead

in computerized manufacturing is substantial and its

greater experience in successfully producing and using

robots gives it an advantage in future sales of these

machines. The Yamazaki Machinery Works reports

more than 300 serious inquiries from U.S. companies

alone.4 American firms are better situated in the

markets for electronic office equipment, home computers, electronic toys, but competition from Japan,

West Germany, the Netherlands, and France is intense.5 The growth of U.S. multinational corporations

(MNCS) is a further threat to the future of U.S.

manufacturing jobs in these industries. The widely

scattered manufacturing operations of semiconductor

firms are a case in point.

Multinational Corporations and Employment

The existence of MNCS further complicates the analysis of job loss due to changing technology. When

firms were national entities and not multinational concerns, it was increasing wages of skilled workers within

a country that motivated firms to seek new techniques

that save labor cost by substituting less skilled for more

skilled workers. The existence of MNCS with affiliates

in low wage areas means that such firms have an incentive to invest in techniques that substitute less

skilled Third World workers for American workers so

long as the wage differential between the two groups

of workers exceeds some minimum. When transportation costs for bulky products manufactured in the

Third World were high, even a substantial wage differential might not have induced U.S. firms to move

their fabricating operations abroad. Progressive advances in microminiaturization, lightweight materials,

and transportation technology have changed that.

4New York Times, Dec. 13, 1981, sec. 3.

5Business Week, Dec. 14, 1981, pp. 39-120 and Dec. 21, 1981, pp. 52-54.
67

The ability of the developing countries to compete

in the production and sale of manufactured products

to the United States is only partially the result of indigenous economic development. It results as well

from the growth of U.S. multinationals. The unique

advantage of the MNC in comparison with national

firms is its transnational ability to combine labor and

material resources in the host country with technology and administrative capabilities developed in the

home country. Because labor costs in less developed

countries are so much lower than labor costs in the

United States the possibility exists that increasing

North-South trade will result in increasing specialization in production within countries with the developing countries specializing in the production of an increasing number of manufactured products. The debate over whether steel production, like black and

white televisions, should be phased out in favor of imports emphasizes the immediacy of this concern. Leaving aside the issue of whether the United States should

view industrial structure as an object of policy, formulating economic policies designed to achieve some

structural outcome, let us consider the implications for

employment as technology advances. The export of

manufacturing jobs outside the United States means

that productivity gains will be realized elsewhere. How

much of the increase in productivity will go to increase

the real wages of local workers in American-owned

plants overseas is uncertain. Even if the MNC captures

the entire productivity increase, it may or may not

reduce the price of the product in U.S. markets. A price

reduction would, of course, mean an increase in real

income in the United States, which would raise demand for output and increase employment in this way.

The magnitude of the effect would depend on how

much of the increase in productivity the MNC keeps

in the form of retained earnings (to finance expansion

of its foreign subsidiaries as well as, or in place of, its

American operations) and how much it passed along

in the form of lower prices.

Service Sector Jobs and the Quality of

Employment Opportunities

The analysis of the previous section focused on the

effects of programmable automation technologies on

real wage rates and on aggregate employment. To

discuss the qualitative aspects of service sector employment growth requires that we turn our attention to

how labor markets are currently structured. To begin,

I want to consider the familiar thesis that investment

in human capital will enable the service sector to continue expanding, so much so that it will easily absorb

the workers displaced from manufacturing.’ This hypothesis, relating economic growth to human capital

formation, is a simple extrapolation of the findings of

“growth accounting.” In growth accounting, past economic growth is broken down into its component

sources—quantity and quality of labor inputs, quantity and quality of capital inputs, The great accomplishment of growth accounting has been the identification of the increasing educational attainments of

the labor force as one of the quantitatively most significant sources of growth in output per worker between

1929 and 1969.7 However, growth accounting provides

no theory to explain the nature of the relationship between the quality of labor inputs and economic

growth. All that can be said on the basis of growth

accounting is that investment in human capital and

economic growth both proceeded at an impressive rate

during the period following 1948. It is incorrect to infer that increased schooling caused economic growth

from the association between educational attainment

and the growth in gross national product (GNP). Service sector employment has also grown substantially

during this period, though again cause cannot be inferred from correlation. In considering the ease with

which displaced manufacturing workers can be absorbed by a growing service sector, two points need

to be addressed. The first relates to the sex-labeling

of jobs in the U.S. economy, and the second to the

quality of service sector jobs.

Much of the expansion in service sector employment

in the 1970’s was in jobs for which women are the preferred labor force—retail sales including restaurants

and fast food establishments, and health services.

These are jobs that typically employ women at wages

below the average in manufacturing, and that provide

short hours, few fringe benefits, and little opportunity for advancement. Clerical employment was another

important area of job growth. Here, again, women are

the preferred labor force. Though the jobs are more

varied in terms of responsibilities, opportunities, and

wages paid, they are nevertheless among the poorest

paid and least prestigious of the white-collar occupations. The shift to service sector jobs has largely been

a shift away from jobs employing male workers and

toward jobs employing females. Despite the growth

bEli Ginzberg and George J. Vojta, ‘The Service Sector of the U.S. Economy,” Scientific American 244, March 1981.

The development of growth accounting is best exemplified in the work

of Edward F. Denisen, The Sources of fionomic Growth in the United States

and the Alternatives Befbre Us (New York: Committee for Economic Development, 1%2); Why Growth Rates Differ: Postwar Experience in Nine Western

Countries (Washington, D. C.: The Brookings Institution, 1%7); Accounting

for Llnited States Economic Growth, 1929-69 (Washington, D. C.: The Brookings Institution, 1974); Accounting for Slower &onomic Growth: The United

States in the 1980’s (Washington, D. C.: The Brookings Institution, 1979).
68

in employment, high unemployment rates for women

persisted throughout the decade, suggesting there was

no shortage of female workers available to take such

jobs. Had manufacturing employment decreased by

nearly a million jobs during the last decade instead of

having increased by that amount (measured from its

peak in 1969 to its peak in 1979), with most of the

displaced workers men, it is unclear for which service

sector jobs they might have been retrained.

My second point is that the growth of service sector jobs has meant an increase in both good and poor

jobs. It is evident that the U.S. labor market is

segmented into a primary labor market segment in

which most of the “good” or “acceptable” jobs are

located, and a secondary segment in which most of

the “poor” jobs are to be found.8 A further distinction

can be made between autonomous primary segment

jobs (professionals, like doctors, lawyers, and professors, or craftsmen like plumbers, electricians,

carpenters) and subordinate primary sector jobs (mail

carriers, city transit drivers, steel workers, etc.). The

difference between a subordinate primary sector job

and a secondary sector job is sometimes based on

worker skills and sometimes on nonskill-related job

characteristics. The better jobs are those in which the

employer values steadiness and low turnover and is

willing to reward tenure on the job with promotions

and higher pay, or in which the employee is represented by a union that has won decent wages, a pay

scale that rewards seniority, and protection against arbitrary treatment. Many of the subordinate primary

sector jobs available for male workers are to be found

in the goods producing sector of the economy, where

workers have more union representation than in the

service sector. This is probably the basis for the casual

observation that changes in technology and the shift

to service sector employment is eroding the middle of

the job distribution, making the labor market more

starkly two-tiered with good jobs at the top, poor jobs

at the bottom, and shrinking opportunity in the

middle.

The growth of the service sector has provided both

good jobs and poor ones. Producer services (advertising, architecture, law, management consulting, computer, financial) now account for 19 percent of GNP,

and firms in this industry have been a major source

of good jobs.9 This observation is confirmed by a

Bureau of Labor Statistics (BLS) study of the computer

and data processing services industry (SIC 7372 and

SIC 7374),10 This industry currently employs nearly

‘See Richard C. Edwards, Contested Terrain (New York: Basic Books,

1979); and David Gordon, The Working Poor (Washington, D. C.: Council

on State Planning, 1980).

‘Ginzberg and Vojta, op. cit.

‘“Department of Labor, Bureau of Labor Statistics, “Industry Wage Survey:

Computers and Data Processing Services, ” Bulletin 2028, March 1978.

350,000 workers and is one of the rapidly growing

business services.11 The BLS survey found that approximately 37 percent of the nonsupervisory employees

in this industry are highly paid professional or

technical workers, many of them computer systems

analysts, computer operators, and computer programmers. At the same time, however, the survey found

that office clerical employees account for another 32

percent of employment in this industry. Two-fifths of

the office clerical employees are key entry operators

earning $135 to $175 (Class B) or $150 to $205 (Class

A) in 1978. If we calculate their hourly wage on the

basis of a 35-hour week, they earned between $3.86

and $5.86 an hour. By comparison, average wages in

manufacturing in 1978 were $6.17 an hour. The point

is that service sector employment, like employment in

the U.S. economy generally, is two-tiered. The growth

of this sector has meant an increase in professional,

technical, and managerial jobs at the top; but it has

also meant an increase in clerical, sales, and nonprofessional service jobs at the bottom. In particular, poorly paying jobs in health services have been a major

source of employment growth in the 1970’s.

The number of good jobs has grown but, since 1970,

it has failed to keep pace with the supply of college

graduates. Employment prospects have deteriorated

substantially since the 1950’s and 1960’s.12 The result

has been a credentials inflation in which a college

degree is now a prerequisite for many jobs which could

be done or previously were done by workers with a

high school education. It would be incorrect, therefore,

to conclude that providing an educated work force

would be sufficient to guarantee the growth of jobs

requiring such workers. With good jobs in the service

sector growing more slowly than the supply of college educated labor, what are the prospects for workers

with less schooling who in earlier years would have

found employment in the manufacturing sector? Recent growth in employment opportunities in the bottom tier of the service sector suggests that, unless

clerical and service workers are displaced by technology, aggregate employment will continue to grow. The

available jobs, however, may not provide viable alternatives for workers accustomed to decent wages and

due process—union benefits and protections characteristic of much subordinate primary sector employment in manufacturing. Moreover, sex stereotyping

of clerical and retail sales occupations operate to deny

jobs to displaced male workers even were they to accept the low wages and authority relations typical in

such jobs. Can we specify the jobs for which these

llfiplo~ent ad &m@s, November 1981, table B-2.

IZRichard FrWman (cd.), The Overeducated American (New York:

Academic Press, Inc., 1976).
69

young workers should be trained? The future to which

they should aspire?

Research Needs

It should be evident from the foregoing analysis that

we need detailed, disaggregate data by industry on:

1.

2.

3.

4.

5.

6.

the kinds of work suitable for automation, with

currently available robots and programmable

technologies and with future generations of

robots;

the number of workers likely to be displaced, and

the slowdown in growth or decline in growth of

the labor force;

the demographic and educational characteristics

of the displaced workers;

estimates of the average increase in average labor

productivity, derived from 1. and 2.;

an estimate- of the cost saving to be realized as

a result of introducing the new technology; and

estimates of average labor requirements by occupation, capital requirements by type of plant

and equipment, and energy requirements per unit

of output at a baseline point prior to the introduction of the new technology, and at discrete intervals in the next 10 to 20 years.

Some of this information already exists in engineering cost studies of technical improvements and in other

technical studies done by businesses or consulting

firms, in reports of private research firms, and in case

studies of individual firms. Access to these studies is

sometimes limited and even the published material is

not always easy to obtain. A chief difficulty is that

it has not been organized into a coherent picture that

would enable us to say with confidence what is known

and what remains to be learned about the impact of

programmable technologies. A survey and synthesis

of the existing literature that identifies the gaps in what

is known and is followed up with case studies and surveys taken to fill the spaces seems essential. If such

a study is undertaken, it should address not only employment issues but changes in the nature of work as

well. Changes in work process, management functions,

and social relations within firms should all be studied

for insights into changes in the quality of working

life. ’3 In addition, we need to know how widely used

IJFO~ ~ d~talled exposition of the work process issues involved see Eileen

Appelbaum, Kenneth Flamm, and Leonard Rapping, “A Proposed State of

the Art Survey of the Literature on the Microelectronic Revolution, ” mimeo,

June 1981.

in manufacturing the programmable automation technologies will be. For firms that do not directly employ

robots and related technologies, we need to know

whether they will utilize capital goods produced using

such technologies since this will have the effect of

reducing capital costs in such industries even without

a change in technique (provided, of course, that the

price of the capital goods declines).

Information obtained from such case studies can

usefully be incorporated into an input-output framework in order to obtain estimates of the size of the

labor force, its demographic composition, and its occupational distribution under alternative assumptions

about how the technology develops.14 Final demand

might be treated exogenously so that the percentage

increase in various detailed input requirements per

dollar of aggregate GNP could be calculated. Demand

structures that hold constant, respectively, the physical

and value composition of output might reasonably

provide orders of magnitude for demand effects. Since

the assumptions about the new technologies, or “scenarios, ” are necessarily subject to substantial uncertainty, ranges of plausible technology changes ought

to be tested using the input-output framework to assess

the sensitivity to changes in the scenarios of the qualitative shifts in demand for energy, labor, and capital

inputs.

Input-output analysis refers to changes in physical

quantities of required inputs per unit of output. The

definitions of capital using and capital saving utilized

in this paper depend on the dollar value of investment

per unit of capacity. I am familiar with one study that

examines that concept empirically for the Canadian

economy.l5 That study, which is concerned with unemployment and only touches on the technology

issues, uses measures that are aggregated for the

economy as a whole. The results are not very useful

for the purpose of studying technology, but the

methodology is very straightforward. I have put my

research assistant to work using this technique to look

at particular industries and time periods to determine

whether technology has been capital saving or capital

using.

14A detailed d=ription of this methodology can be found in Eileen

Applbaum, Robert Costrell, Kenneth Flamm, and Leonard A. Rapping, “The

Impact of High Technology on the Structure of Factor Demands and on the

Process of Economic Growth,” mimeo, March 1981.

“Paul Davenport, “Capital-Using Technical Change and Long-Period

Unemployment in Canada, 1947-1981,” journal of Post Keynesian E20nomics,

forthcoming, September 1982.
70

ASSESSING THE FUTURE IMPACTS ON EMPLOYMENT OF

TECHNOLOGICAL CHANGE: AN INPUT-OUTPUT APPROACH

by Faye Duchin

Institute for Economic Analysis

New York University

July 27, 1982

The OTA project proposal on Information Technology, Automation, and the Workplace is an attempt

to anticipate now the likely situation with respect to

employment of the U.S. working-age population in

one or two decades. This paper describes a methodology for quantifying the size and occupational mix

of the employed labor force that will be required as

a result of the progressive introduction into the various

sectors of the economy of new technologies, in particular production and office automation. It makes frequent reference to a model currently being developed

for this purpose at the Institute for Economic Analysis

(IEA) with support from the National Science Foundation, called the IEA model.

The input-output (I-O) approach is now used in, or

rather as a component of, virtually all the well-known,

large-scale models of the U.S. economy like that of

the Bureau of Labor Statistics (BLS) Office of Economic Growth and the proprietary forecasting models

of DRI, Chase Econometrics, Wharton, and many

others. 1-0 is valued in these applications for its ability to disaggregate overall economic activity into sectoral detail and to ensure the consistency of different

assumptions made about separate parts of the economy. In this paper I will concentrate on other aspects

of I-O that are generally not exploited, and I will try

to address the many detailed questions about methodology, assumptions, and data that have been put

to me since I agreed to participate in this workshop.

I will focus on the issues that can be most readily

analyzed at the present time for policy purposes but

I will also indicate the research areas into which the

I-O framework can and should be extended to integrate issues related, for example, to education and

training into the analysis of employment and technological change.

Representing Technology and

Technological Change

The technology used in a particular sector can be

characterized by the mix and amount of each input

required to produce one unit of that sector’s output.

Inputs include raw materials; various types of processed goods and of services; different categories of

machines, tools, and other capital goods; and an assortment of labor skills. A technology is most concretely associated with a single production process, for example use of the open hearth v. the basic oxygen furnace for steelmaking, each requiring a different mix

of inputs. Since one technology displaces others only

gradually, there are generally several distinct processes

in use in a given sector at any particular time and also

several stages of production; the sector’s “technology”

is the weighted average input structure of the various

processes in use. The technological structure of an

n-sector economy in a given year can be described by

three matrices of coefficients: the A matrix (nxn) of

intermediate inputs (or interindustry transactions), the

B matrix (nxn) of capital stock requirements, and the

L matrix (mxn) of labor requirements, assuming m occupational categories. All coefficients are expressed per

unit of output.

This description is not of course assumed to be an

adequate basis for actual production: it is like the list

of ingredients for a recipe which does not include the

directions to the cook. Rather, technology is being

described in terms of the demands placed by a sector

on the rest of the economy.

Technology defined in this way also reflects other

inputs not customarily associated with the choice of

production process: “overhead” in the form of legal

or personnel services or the purchase of an executive

jet. The discussion of scenarios below indicates how

one proceeds to isolate the phenomena of interest within this broad interpretation of technology.

A change in the input structure of a given sector

(where inputs are measured in constant physical or

value units) may reflect any number of underlying factors. In a comparison of statistically compiled, historical I-O tables, it will often be necessary to attempt

to distinguish the impacts of different factors. On the

other hand, one purpose of designing experiments—

or scenarios as they are generally called—is to isolate

the changes of interest to assess their separate impacts.

Choice of Classification Scheme

The A, B, and L matrices describe the state of the

economy at a given time as classified into n produc-
71

ing sectors and m occupations. The number and choice

of categories will depend on the purposes of a particular investigation. (In many practical applications,

the abilities to collect the required information and to

handle large matrices will also constrain the choice.)

Sectors can be described in terms of industries or

commodities: * if each industry produces one commodity or service and no two industries produce the

same commodity or service, the two schemes are the

same. The confounding factors are the presence of:

1. secondary outputs that are marketed; and

2. in-house operations (essentially “captive” production and producer services) that may, or may not,

be the primary output of some other sector. These

are not reported as outputs.

The commodity scheme has the advantage that the

corresponding input structures are more readily interpreted in terms of engineering technology although in

the presence of secondary products the “overhead”

operations may be difficult to allocate to individual

commodities. The IEA model uses essentially an industry classification for practical reasons: the labor

and capital data and price deflators are much more

readily available in that form (for historical reasons).

At the level of sectoral detail of the IEA model (the

85 sectors are identified in Annex 1 along with the 54

occupational categories), industry and commodity

largely coincide; the divergence between the two

necessarily increases with progressive product disaggregation. Practically speaking, any scheme is a compromise between the two,** and it presents no particular conceptual problem to disaggregate some part

of the economy more than others.

For present purposes, let us consider the case of

robots which may be produced for an establishment’s

own use, for sale, or some combination of the two,

In a commodity scheme, robots would constitute a sector which is the sole source of (domestically produced)

robots for all other sectors even, say, the automobile

sector, although the automobile industry might produce most of its own robots. In an industry scheme,

the input structure of every sector that produces some

robots would need to be modified to reflect the production of robots in addition to the creation of a

separate robotics industry. The former is easier to implement (and is the approach chosen for this sector in

the IEA model even though most other sectors are classified by industry). Both representations will lead to

the same conclusions as to the effects of the use of

robots on the economy as a whole and as to the total

(direct plus indirect) labor requirements of each sec-

‘An industry in this usage is the set of establishments that produce a given

principal output: they may in addition produce (the same or different) secondary outputs.

● ● The sector including mills producing two slightly different grades of paper

could be considered an industry with a secondary product. Alternatively,

it could be considered a single commodity.

tor. They will differ in a well-defined way in the computed allocation between direct and indirect requirements of individual sectors.

If the automobile sector produces all of its own

robots, the labor used in producing these robots (the

vector L) is charged directly to the automobile sector

(still L) will be counted as indirect. The vector L itself

can easily be computed given either representation and

allocated in any desired proportions between what are

reported as the direct and indirect labor requirements

of the automobile sector. Eventually it may be important to distinguish between the average input structure of General Motors robots and say, those produced

by Unimation for other users. In this case the single

producing sector would have to be disaggregated into

several, producing different types of robots.

Data

The principal sources of data for the IEA model have

been official government series produced by the Bureau of Economic Analysis (BEA) of the U.S. Department of Commerce (input-output tables and capital

flow tables for 1963, 1967, and 1972) and by BLS (industry-occupation matrices for 1960, 1970, and 1978

and industry price deflators). The sources of these data

are given in Annex 2.* We have relied also on manual

and automated search of the open business and technical literatures, reports of private research organizations, and some interviews and informal surveys.

There is no substitute in terms of scope and comprehensiveness for the official data. At the same time

many of their limitations are well known and have

been described in various places. For example, a great

deal of effort has been expended at our Institute to

render the data tables compatible with respect to

assumptions and classifications.

The official I-O and capital flow tables can be considered accounting type data in terms both of their

basic sources and the methods used in compiling them.

Relatively little use is made of technical sector experts

in evaluating the input structures (i. e., table columns);

this is one objective of the IEA case studies described

below.

There is no concrete concept in the official data of

output units for the so-called service sectors; consequently many of the associated price deflators are

largely arbitrary. ** This situation makes it hazardous

to analyze the changing position of the non-goods-

‘Historical data have been assembled because the model will also be used

to examine the period from about 1960 to the present: they are not used to

extrapolate the structure of the economy in the future from a time series

description of the past.

● ’It might be pointed out in a discussion of price deflators that for other

reasons the official deflator for computers implies no change in real price over

the past two decades. We have instead assumed an average annual decrease

of 10 percent which more accurately reflects the already steep increase in their

use.
7 2

producing sectors, which broadly defined already

employ about two-thirds of the labor force, based on

these tables. In attempting to make some progress in

this area, we have concentrated first on the education

sector: it is a large employer, it is the source of training and retraining, and it is easier to improve than

most of the other service sectors. We have redefined

the unit of output and disaggregated public education

from health care and nonprofit organizations in the

capital flow tables using fragmentary information from

government studies and the other types of sources indicated above.

The capital flow tables compiled by BEA would be

even more useful if sector experts brought more information to bear on the present methods of allocating

a particular capital good among using industries. The

official data on capital stock requirements by using industry distinguish only between plant and equipment.

A detailed breakdown by type of capital based not

only on deduction from flow data, but including direct

survey, should be a high priority objective. We have

constructed very tentative stock matrices for the IEA

model based on the capital flow tables.

The IEA model will make use, through its case studies, of the available business and technical literature

and the data and reports of private organizations,

which contain a wealth of information but require a

great deal of effort to piece together into a coherent

picture. An example of this source of information is

the “composite forecast” prepared in electronic and

hard-copy form by Predicasts, Inc.: an illustrative

table describing robots in the U.S. market is shown

in Annex 2. Not surprisingly a number of vendors of

detailed economic information either use or have expressed active interest in using I-O both as an organizing device and to improve the consistency of a great

quantity of numbers taken from diverse sources.

Information for describing, for example, a robot sector, which had little economic significance in the year

of the most recent official tables (1972), was pieced

together from other sources including the items referenced in the Predicasts forecast mentioned earlier and

informal interviews and written surveys by our own

staff of knowledgeable individuals willing to cooperate, for example the president and the manager of

personnel of a major producer of robots. This type of

survey is also the basis of the so-called ex-ante I-O

tables compiled by the Battelle Memorial Institute in

a more formal and standardized fashion. *

● The Battelle tables are based mainly on surveys of engineers. We have

for various reasons not been able to make use of these tables for ongoing

Institute work but consider them a valuable source of information-and, more

important, a valuable methodology-for use in future work.

Case Studies

Any model that uses an I-O module has at least indirectly to be concerned with the representation of

technology and technological changes, but typically

this concern is indeed indirect. The historical data are

usually accepted as given in the official sources. The

coefficients describing the structure of the economy in

the future are generally estimated by a formal statistical procedure with little effort at interpreting the

technical structure implied by it. In most cases where

an I-O module is used to disaggregate the projections

which have been made by an aggregate econometric

model, technological change is not at the center of

attention.

The case studies being carried out at IEA vary in

scope and depth but have a common purpose: to eva]-

uate and improve the corresponding portions of the

available historical I-O type data (i.e., A, B, and L

matrices) and to project these data to the future on the

basis of alternative assumptions about structural

change in the sector under study.

Two of the case studies carried out at IEA on robotics and education have already been mentioned;

others include the chemical, iron and steel, automobile,

textile, and health care sectors, and telecommunications, office equipment, and computers are in progress.

The robotics study, for example, includes a compilation of current, capital, and labor input vectors for

the U.S. robotics producing sector circa 1990. In addition, it projects the level of robot purchases for 45

likely using sectors. In each using sector, seven occupations in which workers are likely to be displaced by

robots and two which will be required in greater number because of robots, are identified. The percentage

change in labor input requirements (for each occupation in each using sector) is projected under alternative

assumptions. The quantitative output of the case study

is the direct input to the computer programs.

The Dynamic Model

The basic objective of an I-O analysis of the impacts

of technological change on employment is, in operational terms, to quantify the levels of detailed labor

inputs which will be required in a given year to satisfy

any given level and composition of final demand, making alternative assumptions about production technologies (i.e., using alternative A matrices). In an open

static model, the level and composition of final demand, which includes private and public consumption

and investment and net exports, are exogenous; they

are implicitly assumed to be relatively independent of

the technologies in use. Almost all empirical I-O
7 3

analyses have been carried out within an open static

framework.

In a fully closed dynamic model, at the other extreme, the different components of final demand are

explained endogenously. For example, trade flows in

the IEA World I-O Model are parameterized by means

of region- and commodity-specific import coefficients

and export shares, and the levels of imports and exports are thus computed endogenously. (In the open

model one can—at least in principle—using alternative

assumptions concerning the exogenously determined

exports and imports, approximate changes in the structure of foreign trade that are taken care of automatically (i.e., endogenously) in the closed model. )

The present version of the IEA model represents a

significant advance in the endogenous determination

of investment. * In experiments carried out with this

model, investment will be governed by a B matrix associated (in terms of technological considerations) with

each A matrix. Exogenous final demand will exclude

private fixed capital formation which will instead be

computed endogenously to satisfy in level and composition the production of a particular final bill of

goods. This means of course that the labor required

to produce these investment goods will also be appropriately computed,

Scenarios

A scenario is an experiment carried out within the

framework of the model to address a specific question.

The starting point is a description of the I-O structure

of the economy (i. e., A, B, and L matrices) for each

year in the time horizon under examination, and each

scenario consists of specific alternative assumptions

about how that structure (i. e., the individual coefficients in the A, B, and L matrices) might evolve. The

size of the labor force and its composition by occupation as implied by each scenario are then computed

(along with many other variables) and can be compared across scenarios. It is standard practice to define

a baseline scenario for purposes of comparison. In the

IEA model the baseline scenario assumes that the present I-O structure of the economy remains unchanged

in the future.**

Scenarios may differ in their assumptions about virtually any aspect of the economic structure. I will brief-

● A model in which investment is endogenous can be considered a dynamic

model. Many models have been called dynamic; in the dynamic J-O model,

capital flows and stocks are disaggregated by producing and by using sectors, and the framework requires not only intersectoral but also intertemporal consistency in the production and disposition of the highly disaggregate

capital stock.

‘ ● “Present” here means the most recent year for which official accounting

data are available; 1-0 and capital flow tables for 1977 should be available

in the near future,

ly discuss some possible scenarios about the rate of

adoption of automation, the identification of the occupations affected and quantification of the impact on

individual labor coefficients, and the level and composition of final demand, These are the types of scenarios that can be most readily computed at the present time. As the model is progressively “closed” with

respect to other components of final demand, a scenario approach will also be used to represent alternative assumptions about the constraints under which

the economy operates.

In order to represent the use of programmable automation, it is first necessary to distinguish in physical

terms the types of automation equipment in question,

by what combination of existing and new sectors it

will be produced, and the input structure for production. Then, depending on the case, it is necessary to

identify the sectors and/or the operations in which this

equipment may be used and the likely level of use. The

occupations of workers that may use both the automated equipment and that which it complements or

supersedes must be specified, along with other affected

current and capital inputs of the using sector, on a per

unit of output basis. All the assumptions must of

course be quantified. A standard set of assumptions

for 1990 is produced by the associated case study and

“spliced” into the A, B, and L matrices for that year

and for prior years as required: the actual splicing consists of course of replacing equipment and the corresponding input flows, including labor inputs, per

unit of output of a particular sector by new equipment

(e.g., robots) and input flows, including labor, required (also per unit of output) by the new technology. (Note that the incremental output—and labor—

required in prior years to produce the capital in place

in May 1990 will automatically be computed. )

The “standard” assumptions referred to in the preceding paragraph reflect the combination of what we

consider to be the moderate projections of experts dealing with different aspects of, say, robotics. Alternative

scenarios incorporate more extreme views, either

hypothetical or actually expressed by other analysts.

(In later stages of our research, the portions of the case

studies dealing with the future will rely increasingly

on technical factfinding; this work will make possible

the elaboration of more detailed, technical scenarios. )

Sample robotics scenarios for which preliminary

computations have been made include:

• What will be the level and occupational composition of employment in 1990 implied by the standard assumptions of the robotics case study compared to the baseline scenario (assuming in both

cases the same projected 1990 final demand bill

of goods and the same state of the economy in

1980)?
7 4

● What if one additional mechanic is required for

every three robots instead of every six robots in

each using sector?

● What if each using sector acquires twice as many

robots as under the standard assumptions?

Scenarios will also be developed for office automation involving changes in the use of telecommunications equipment, computers, and office equipment by

clerical and certain categories of managerial and professional employees in all sectors. Production automation, involving essentially computers and sensors (as

well as robots), includes process control, computeraided design and manufacturing, inventory control,

and scheduling. While the latter case study is more

complicated, the investigation will follow the same approach. Scenarios will specify values for the amount

of use and the impacts on individual I-O coefficients

of different forms of automation, singly and in combination.

The following type of scenario is included to give

a concrete idea of the range of questions that can be

addressed in the near future with the present model

and expanded effort on the case studies:

• The different case studies deal with the introduction of specific types of automation equipment

into specific operations. Assume that all aspects

(that have been identified) of production and

office automation proceed at a “moderate” pace

over the next 10 or 20 years. How will the size

of the employed labor force evolve? Which occupations will experience the slowest growth (or

greatest decline)? What will be the demand for

computer programmers?

Policy and Research Issues

For policy purposes it is necessary to have a model

that can produce results quickly and inexpensively.

This can be achieved if:

1. there is a methodology for preparing at least certain types of scenarios quickly by human analysts, and

2. the computer software is designed (among other

considerations) to process an entire scenario as

a single input.

The section on scenarios describes the types of scenarios for which these two considerations can be met

most readily. The software design issues are well understood.

The case study approach has been developed to provide the required format for scenarios, and improving the scope and depth of the case studies should be

a high priority. In fact, “micro” studies now sponsored

by the policy community for more general factfinding

purposes could be guided to include a section structuring the information content into a format that

would permit its being used directly by a model such

as the one described.

In subsequent work on technological change, the

IEA model can and should be “closed” with respect

to trade, accomplished by integrating it into the World

Model. This will make it possible to analyze a considerably expanded range of policy scenarios where

U.S. production is directly linked to its trading partners’ economic activities. In the open system, these impacts are instead approximated by hypothesizing

changes in U.S. imports and exports.

From a research point of view, there are many avenues of work that would improve the accuracy of the

projections of the basic model (without even touching

for present purposes on the important area of computational research).

Additional basic research is required to make further conceptual advances toward a fully dynamic I-O

model in addition to the critical need for better data

on capital flows and especially capital stocks, on a

disaggregated basis.

The other important area for further work is the incorporation of a demographic model into the dynamic

I-O model. This would provide the basis for improving and rendering consistent the projected demand for

education and health care as well as other components

of household consumption.
7 5

THE EFFECT OF TECHNICAL CHANGE ON LABOR

by Louis Jacobson and Robert Levy

The Public Research Institute of

the Center for Naval Analyses

July 27, 1982

Summary

Technical change, which makes possible the production of more output from a given amount of resources,

is a major contributor to increases in society’s wellbeing. The very process of change may, however, impose hardships on those who use old and no longer

efficient methods or produce products that are no

longer wanted. The net effect of technical change on

labor is hard to predict. Workers, as consumers, gain

from increases in productivity; they are able to buy

things at lower prices. If they cannot adapt to new production methods (and lose their jobs as a result), they

can end up as net losers,

Not only is the effect of technical change on labor

hard to predict, but technical change itself is hard to

define and measure. It cannot be measured simply as

the installation of new (and different) equipment. Nor

can it be measured solely as the growth of productivity. All three—technical change, new equipment, and

productivity growth—are related. A goal of this paper

is to clarify the relationship.

It is important, at the start, to distinguish technical

change resulting in the appearance of new products

from a change in production processes. To some

degree, this distinction is artificial; programmable

automation equipment is a new product of industries

producing capital goods, but its use represents a new

process in other industries. Both new products and new

processes can affect labor. The development of a new

product —passenger aircraft—and new processes—assembly-line methods of production of automobiles—

led to increases in employment in those and a host of

related industries—home construction, leisure products, etc. At the same time, workers in other industries—horse-breeding, saddle-making, the rail passenger industry-were being squeezed out into other

jobs. Now, changes in production technology, here

and abroad, are leading to a reduction in employment

in auto production and autoworkers are having to

adjust.

We analyzed the effects of technical change on

workers in two steps. The first step is to estimate the

effect of technical change on an industry’s employment. We concentrate on the effect of new process innovations since these are likely to have more concentrated impacts on employment. The next step is to

estimate the effect on individual workers: How many

workers are displaced during the adjustment process?

How long does it take them to find new employment?

How much does displacement affect their earnings?

The Industry Cost Model

To determine industry effects, we use an econometric model of cost in which cost is assumed to depend on the prices of factor inputs, the level of industry

output, and the level of technology. This is the standard analytic approach in economic studies of technical

change. The rate of growth of total cost is composed

of the weighted average of the rates of growth of input prices, a weighted rate of growth of industry output, and the rate of cost reduction due to technical

change. Technical change is therefore only one determinant affecting industry cost; input price changes, for

example, may be more important.

We included five factor inputs—production labor,

nonproduction labor, capital, energy, and materials

in the cost function and imposed no restrictions on

how they can substitute for each other. We assumed

cost minimization and estimated how technical change

has affected the steel, auto, and aluminum industries.

The model was estimated at the four-digit Standard

Industrial Classification (SIC) category, a finer level

of disaggregation than that used in most industry

studies.

One of the advantages of using this kind of model

is that with relatively few variables, much can be

learned about factor substitution, input adjustment,

and the effects of technical change. Data on input

quantities and prices, industry cost and real output,

and the level of technology are almost all that is

needed. Data needed to measure capital and technology is often difficuIt to find, but the model clearly

specifies what is needed for estimation.

Equations in the econometric system illustrate important relationships. For example, equations that express the dependence of each input’s share in total costs

on all input prices, output and technology maybe used

to describe how input-output coefficients, which are

measured similarly to input shares, move over time.

More important, the equation also describes why they
7 6

move. It explains changes in input-output coefficients

in terms of: 1) adoption of new techniques, 2) changes

in input prices, and 3) changes in scale.

For labor, the equation describes how changes in

labor’s share (i.e., the ratio of payroll to total cost)

depend on changes in the wage, other input prices, output, and the level of technology. The effect of any

variable on labor’s share is obtained from the regression estimates. For example, the effects of technical

change, holding prices and output constant, is described as labor saving, using, or neutral if the

parameter on technology is negative, positive, or zero.

Labor-saving technical change means that as technology increases, the share of payroll in total costs goes

down. The estimated equation can then be used to

derive the effect of technical change on the demand

for the quantity of labor.

Another equation in the system maybe used to explain the relationship between the rates of technical

change and productivity growth. Productivity growth

is measured in terms of total factor productivity rather

than labor productivity, which is only a partial measure of input use.

The rate of productivity growth has two components. The first is the rate of technical change. The second depends on the relationship between changes in

industry cost and output changes. Most researchers

assume that, in longrun equilibrium, industry cost and

output change proportionately. This assumption,

called “constant returns to scale, ” is often imposed on

the equations. If this assumption is correct, then this

second component of productivity growth becomes

equal to zero, making the rates of productivity growth

and technical change equivalent. Thus, low rates of

technical change would imply low rates of productivity

growth.

We feel, however, that constant returns to scale is

too strong an assumption. Certain factor inputs, like

capital and nonproduction labor, maybe “fixed;” they

cannot be adjusted quickly without incurring large

cost. Because of these fixed inputs, costs do not change

as much as output. This means that studies that simply impose constant returns to scale, thereby disregarding the second component of productivity growth,

will overstate the effects of technical change.

The Measurement of Industry Technology

Thus far, we have illustrated that technical change

is only one determinant of industry cost changes and

one component of productivity growth. We have not

yet related technical change to the introduction of new

technologies.

To do this in an econometric model required measurement of the level of industry technology. The standard approach is to represent the level by a time trend.

This is satisfactory if changes in technology unfold

regularly and gradually. It is unsatisfactory if new

processes are introduced rapidly, that is, within a relatively short period of time. This distinction is important since sudden or unexpected shifts in production

processes and labor demand may make adjustment difficult for the industry’s work force.

To be as precise as possible, we therefore constructed direct measures of steel and auto technology.

(A measure for aluminum could not be constructed.)

For steel, the measure was based on the use of the basic

oxygen furnace, which is important in the steelmaking process. Technological innovation in the auto industry since World War II has proceeded under the

term “Detroit” automation. The term refers generally

to the substitution of machines for workers in actual

production processes, such as welding. To quantify the

concept of automation, we measure the stock of transfer machines, the basic unit of Detroit automation.

Empirical Results of the Cost Model

In our empirical work on steel and autos, we compared the precise “direct” measure with the simpler

time trend. The findings were about the same; the use

of the direct measure added little precision to the

estimates of the effect of technical change beyond what

we estimated using the time trend. Regardless of how

technology was measured in steel and autos, estimates

of the rate of technical change in the industry were

similar. We found the average rate of technical change

to be virtually O percent in steel. It was just under 1

percent in aluminum, where little new process innovation has been observed, and 1.50 and 2 percent in autos

(the higher figure was for the time trend version).

These findings illustrate the tenuous connection between new process innovation and technical change.

The effects on input demand are similar across industries when the time trend is used to represent technology. We found that technical change was labor saving and capital using. This meant that, holding input

prices and output constant, labor’s share was decreasing over time while capital’s share was increasing by

about the same amount. In autos, in the 1970’s, labor

demand decreased by about 4 to 5 percent a year.

When direct measure was used, the results for steel

were about the same as when the time trend was used.

For autos, there were some differences from the time

trend regression. There were smaller negative effects

on labor and smaller positive effects on capital. One

tentative interpretation of this is that advances in

technology meant that newer capital was more productive than the capital it replaced and that it used

only slightly less labor.
77

In summary, we found that the effects on employment of introducing new technologies, whether measured directly or by a time trend, were indeed negative

but occurred gradually. We did not find evidence of

abrupt changes in the demand for labor. Some of the

empirical estimates differed according to how technology was measured, but the implications for employment adjustment were essentially similar. Given these

similarities, the time and expense of creating direct

measures seem unnecessary. The exploration of other

issues may prove more fruitful in determining how

new technologies affect employment.

As an example, our findings point to an interesting

implication of the generally labor-saving and capitalusing effects of technical change. When the price of

labor increases relative to capital, little short-term

substitution of capital for labor takes place. Over the

long term, however, the use of advanced technologies

allows capital to be less labor intensive and so the

quantity of labor decreases relative to the quantity of

capital. This possible “induced innovation” has been

difficult to identify empirically. Generalizations of our

model may help quantify the link between new technologies and their determinants.

The Effect of Employment Reductions

on Workers

We have taken our analysis further than simply examining the effects of technical change on aggregate

employment. We have also examined:

• the extent to which employment reductions can

be accommodated through attrition rather than

displacement, and

• the earnings losses of displaced workers.

Displacement Findings

Our results are that attrition varies widely across

industries, from about 5 percent per year in high-wage

industries (steel) to about 65 percent in low-wage industries. If high-wage, low-turnover industries, such

as steel and autos, had to reduce employment by 5 percent in a given year, about two-thirds of the reduction could be accommodated by attrition under average conditions. If employment had been increasing

prior to the reduction, attrition would be higher, that

is, more than two-thirds would be attrition. If employment had been falling, attrition would be a lower proportion. This is because attrition is primarily determined by the tenure structure of the industry. Attrition, which is extremely high among recently hired

workers, falls dramatically after a year or two, and

is extremely low thereafter until workers near retirement age. Recent hiring and layoff patterns cause large

swings in the proportion of the work force that is most

likely to leave voluntarily, and this, in turn, causes

swings in the attrition rate by as much as 2 percent.

Aside from their effect on the tenure distribution,

general business conditions (the business cycle) have

little effe6t on attrition. Although other studies show

that the quit rate is sensitive to changes in business conditions (and quits are the major element of attrition),

those studies generally ignore the changes in the tenure

distribution over the business cycle (use imperfect

measures of tenure structure). Instead, they rely on aggregate turnover statistics that show a fall in quits during recession simply because workers who would otherwise quit are laid off.

A 5 percent employment decline cannot be fully offset

by a 5 percent attrition rate. There will be some displacements because the employment change across firms

is not uniform. Even while total employment in the industry is declining some firms will be expanding. Attrition in the firms that are expanding must be replaced by

hiring and obviously cannot count against the net decline.

The dispersion of firms around the mean employment

change is substantial, so that even where employment in

the industry is constant, about 1 percent of the employment industry’s labor force will be undergoing displacement.

The existence of a more-or-less constant background

level of displacement is important. Displacements are

most costly when they are entirely unexpected. If workers

anticipate a nonzero probability of displacement, they

can prepare for that eventuality and reduce its consequences. This appears to be the case in some high-wage

industries such as aerospace, which have major boom and

bust cycles as a result of military and civilian aviation

procurement policies.

Plant closings are a major source of displacements because attrition can do little to offset employment declines

when plants close. Closings are particularly likely when

a firm experiences a sharp decline in demand: below a

certain level of production, it is simply uneconomic to

stay in business.

Earnings Loss Findings

Earnings losses due to displacement are highly correlated with attrition rates—high losses go with low

attrition. This makes good economic sense; if few

workers are leaving voluntarily, this is strong evidence

that there are few good alternative jobs, and a worker

who is forced to leave will have large losses. Displaced

workers experience a transitional period of earnings

losses due to lengthy unemployment (a good part of
7 8

which may be simply waiting until all hope of recall

vanishes*).

When workers begin to search for work, they often

will try out several jobs until they find suitable

employment. Workers’ earnings then begin to rebound

but usually, in cases where initial losses are high, they

never fully catch up with what they would have earned

had they not been displaced. The “permanent” loss is

generally between 7 and 15 percent of annual earnings and amounts to about 50 percent of the total earnings loss. (The total loss in industries such as steel and

autos is generally equal to about 2 year’s earnings. )

The transitional loss is about equal to the permanent

loss, but it lasts only a year or two. The temporary

loss, however, is more likely to be offset by unemployment insurance, SUB, and severance pay.

In analyzing earnings losses, we measured how they

vary with respect to age, tenure, labor market characteristics, and the size of the employment decline.

Again, tenure was a key determinant of the size of the

loss. New hires and workers close to retirement experience small losses, while other workers tend to have

large losses. The largest losses occur among workers

with about 5 years experience. This is because they

have many years over which to accumulate the permanent part of the loss, and because they may be in

a position to assume more responsible positions but

do not yet have the experience needed to convince

another employer that they are ready for such a job.

Labor market characteristics are also important.

Loss in the transitional period can be doubled if

displacement occurs when area unemployment is high

(one standard deviation over average–about 1.3 percentage points). Losses are substantially larger if the

displacement has occurred in a small labor market and

marginally greater if a large number of similar workers

are searching for work. Workers who have been displaced because of a plant shutdown do not have much

larger losses than similar workers displaced under

other circumstances. A shutdown will, however, displace higher tenure workers who have larger than

average losses.

Methodology

This section discusses, in general terms, the methodology used to obtain the above results. The details

are discussed in the reports referenced at the end of

this paper. The basic estimating equations used to examine attrition and earnings loss are quite simple. Attrition is assumed to be a function of worker charac-

● A large fraction of workers laid off eventually are recalled. In fact, temporary unemployment is the largest component in the cost of an employment reduction, but the cost is low per capita, and industrial workers generally

anticipate several episodes of substantial temporary unemployment.

teristics-age, race, sex, tenure, earnings level, and the

rate of change of earnings; plant characteristics—employment level (size), employment trends, whether

part of a multiplant firm, average wage rate, and trend

of wages; and labor market conditions—stage of the

business cycle (current unemployment rate divided by

the rate at the previous trough), size of the labor market, recent growth rate, average wage rate, and measures of industrial diversity.

Earnings are estimated as a function of many of the

same variables used to measure attrition. Prior earnings, however, is the key determinant, since it captures

the way in which a host of determinants, which remain more or less constant over time (such as education, health, and marital status) affect a worker’s earnings potential. The earnings equations are designed to

compare the earnings of displaced workers to those

of similar workers who are not displaced but are initially employed in the same industry. Econometric tests

are applied to ensure that the equation matches the

earnings of the two groups exactly.

The procedure for estimating how employment

changes are distributed across firms in an industry is

more complex. We assume that units use a strict LIFO

(last-in-first-out) displacement rule. Given this assumption, the seniority distribution of displaced workers

is also the cumulative distribution of employment

changes across all the individual units. The actual

seniority distribution is calculated by observing the

tenure of displaced workers and relating tenure to

seniority. This procedure is used to examine displacement under a range of circumstances and then econometric techniques are applied to generalize the results.

Data

All three estimating procedures are carried out with

the same data set—Social Security’s Longitudinal Employer-Employee Data (LEED). These data contain the

age, race, and sex of a continuing 1 percent sample

of the work force, each individual worker’s quarterly

earnings from each employer, and the employer’s industry, location, and firm size. The data cover the

years 1957 through 1975. Job changes are noted by a

change in the employer who files a quarterly earnings

report for the worker.

A key problem is that there is no explicit indicator

of the reason for separation in the data. Thus, we must

infer whether a worker who changed jobs was displaced or left voluntarily (attrition). To measure attrition, we isolated a sample of workers who changed

jobs when employment in their plant (or sometimes

area) was increasing or stable. We assume that under

such circumstances, no displacements were taking

place. The earnings loss due to displacement is esti-
7 9

mated using a simultaneous equation approach that

compares the earnings loss of leavers in firms where

employment is growing or stable to the loss of leavers

in firms where employment is falling. It is thus possible to net out the effect of attrition from the effect of

displacement. In practice, it turns out that holding constant the other factors mentioned earlier, earnings reductions are relatively insensitive to whether displacement or attrition was the reason for the job change;

both the displaced and voluntary leavers have similar

earnings reductions.

The LEED file is not the only data base suitable for

estimating earnings losses. We have also used data

from State employment security agencies (UI offices).

These data include wage records that closely resemble those in the LEED file, but they also include information on the worker’s receipt of unemployment compensation, last day worked, occupation (in some

cases), and the reason for separation (if the worker

claims UI). Thus, these data contain a richer set of

variables. A major value of these data is examining

the extent to which transitional losses are offset by the

receipt of UI. For example, we recently used data of

the type to study TAA (Trade Adjustment Assistance)

recipients in Pennsylvania and concluded that more

than 50 percent of the transitional loss in earnings is

replaced by UI benefits.

Summary and Conclusion

Our research on the effects of technical change on

employment has been aimed at measuring technical

change and its effects on labor in the steel, auto, and

related industries. State-of-the-art econometric models

were used to determine the effect of technical change

on employment and to measure how displaced workers

adjust.

Our basic conclusion about the effect of technical

change on employment is that, while technical change

was labor saving, it occurred at a steady and relatively slow pace. It appears that new technologies were

adopted principally to avoid increases in labor costs.

Detailed description and measurement of the installation of new technologies, such as transfer machines

in autos and the basic oxygen furnace in steel, seems

no better than the time trend in measuring how technical change affects employment. This reinforces our

conclusion that technical change in established industries (process innovation) is slow and steady.

We also linked technical change in one industry to

its effect through factor prices on the other industries.

This allows comprehensive measurement of the net

employment effect of innovation. Although technical

change in steel reduced employment in the steel industry, the change in steel increased employment in

autos by reducing the price of a key input.

Our models have been carefully checked and tested

for sensitivity to key assumptions. They represent a

good way to measure the effect of technical change;

the same methodology can successfully be used in

future research. In particular, we believe tracing the

effects of individual technologies is not worth the high

cost. The effects of changes in consumer preferences,

factor prices, and foreign competition on labor demand are likely to be far more important and can

change far more swiftly than production technology,

as we learned in the 1973 oil embargo. One fruitful

extension of this approach would be to investigate the

determinants of technical change itself, such as the extent changes in factor prices induce factor-saving technical change.

We have also analyzed how employment reductions

affect workers employed in a given industry. Employment reductions are costly to labor primarily when

workers are displaced from established jobs, but reductions that do not result in displacing workers are

almost costless. The work, therefore, addressed how

employment reductions are distributed across plants

and how much of the reductions can be accommodated

by attrition. The cost to displaced workers was also

assessed.

Our basic conclusion is that employment reductions

can be handled largely through attrition. The main

threat of dislocation is from plant closings. Although

major plant closings occur rarely, we calculated that

they are responsible for half the displacements in the

steel industry. Our work on earning losses showed that

when potential losses are large, the attrition rate is low.

Thus, in industries where few workers leave voluntarily, such as steel and autos, major employment

reductions are likely to be very costly and can involve

substantial displacement. On the other hand, employment reduction in high turnover industries, such as textiles and apparel, have minimal adverse effects on

workers.

In terms of future research, further work on attrition is called for. We found that attrition is largely a

function of the tenure structure in an industry, but

other research suggests that cyclical conditions are a

key determinant of attrition. Although these two explanations are not necessarily contradictory (the tenure

structure changes as a result of cyclical hiring and

layoffs), it is important to determine whether bad

business conditions reduce attrition holding tenure

structure constant, If, contrary to our evidence, attrition falls in a recession because general business conditions worsen, we will have overestimated an industry’s ability to adjust to employment reduction without

displacing many workers.
80

A second objective of future research is to explain

why workers, in industries such as steel and autos, suffer such large losses and what government can do

about it. There is a widespread feeling that these losses

are an indication of a failure in the functioning of the

labor market. In the extreme form, it is assumed that

displaced workers “fall off the end of the earth” and

never adjust. Competing explanations are that the

earning reductions represent normal job search costs

plus loss of high wages attributable to unionization or

specific human capital.

If market failure is responsible, an effort should be

made to determine its cause. If specific failures can be

isolated, there is a reasonable chance that government

action can help eliminate them at low cost. If loss of

union protection or human capital are involved, policymakers should recognize the constraints this

implies.

Our evidence is that where losses are large, displaced

workers adapt to new jobs but never regain the earnings level they would have attained had they not lost

their jobs. The adjustment is painful; often 5O percent

of the loss occurs in the first 2 years. The “permanent”

loss is about 7 to 15 percent, about that estimated for

union/nonunion wage differentials. This evidence is

consistent with the hypothesis that losses are due to

loss of union protection or specific human capital. If

this view is correct, a policy that will fully eliminate

the loss must raise the income of an experienced worker about $2,500 a year or about $40,000 in present

value terms. Thus, any government actions to eliminate losses such as training or enhancing unemployment insurance will be expensive.

References

1.

2.

3.

4,

Jacobson, Louis S., “Earnings Losses and Worker

Displacement When Employment Falls in the Steel

Industry” (unpublished Ph.D. dissertation, Northwestern University, August 1977).

Jacobson, Louis S., “Earnings Losses of Workers

Displaced From Manufacturing Industries,” in The

Impact of International Trade and Investment on

Employment, William Dewald (cd. ) (Washington,

D. C.: U.S. Department of Labor, Bureau of International Affairs, 1978).

Jacobson, Louis S., and Jondrow, James, and Levy,

Robert, “Labor Adjustment to Technical Change:

A Multi-Industry Study” (progress report to the National Science Foundation, 1980).

Jondrow, James, Levy, Robert, and Jacobson,

Louis, “Labor Adjustment to Technical Change in

Steel and Related Industries” (paper presented at

American Economic Association meetings held in

Washington, D. C., December 1981).

PROGRAMMABLE AUTOMATION: ITS EFFECT ON THE

SCIENTIFIC= ENGINEERING LABOR MARKET

by William N. Cooke

Krannert Graduate School of Management, Purdue University

July 27, 1982

Adjusting to Technological

Advancement

Introduction: An Analytical Framework

Technological change and innovation play a predominant role in productivity and general economic

growth. They also create shifts and movements in the

supply and demand for labor. In particular, they are

presumed to require the labor force to adjust or respond to demands for alternative knowledge and skills.

Generally, the theoretical construct for determining the

supply and demand for labor (or the impact of technological change on shifts in supply and demand of

labor) is the production function (6). This function

(whether Cobb-Douglas or C. E. S.) expresses how the

final product (output) is a function of inputs (for

simplicity, capital, and labor) and how the inputs are

related. For example, the marginal product produced

by capital or labor is derived by differentiating the production function by either input. As either increases

its marginal product (ceteris paribus), the demand for

its services increases over the other.

The relationship of inputs to outputs, and inputs to

inputs are influenced heavily, however, by the existing

technology. Technological change, in turn, alters these

relationships in several important ways—including

changes in: 1) the efficiency of technology, 2) economies of scale, 3) capital intensity, and 4) elasticity

of substitution. Moreover, technology change either
8 1

is neutral or nonneutral. Neutral changes in technology affect technological efficiency and technologically

determined economies of scale. These alter the relationship of inputs to outputs. Nonneutral changes, on

the other hand, affect the intensity of technology between inputs. An increase in capital intensity is labor

saving when capital is a more rapidly growing factor

of production than labor. “Many economists have the

feeling that technological change has been quite laborsaving, but they generally acknowledge that the evidence is indirect and too weak to permit a clear-cut

judgment” (12).

A further dimension to the production function

analysis is found in what has been termed embodied

and disembodied technological change (17). The

former generally refers to actual physical changes in

the capital equipment being operated and assumes that

it becomes more productive. The latter concept is

usually characterized by managerial or organizational

changes which cause the interaction of capital and

labor to be more productive. Generally speaking the

analysis of programmable automation can be treated

as embodied technological change. In summary, the

production function framework suggests that embodied technological change raises the marginal productivity of capital relative to labor. It does this

through increasing the intensity of technology and the

elasticity of substitution. Thus, everything else constant, the demand for labor shifts. For the most part,

such shifts cause the substitution of capital for labor,

and/or one type of labor for another.

Postschool Education, On-the-Job Training,

and Turnover

Although the production function offers a useful

framework for conceptualizing the impacts of technological change on the demand for labor, our present interests require us to go beyond this framework.

We seek a framework that explains the type and level

of human capital adjustments made by labor as the

demand curve for specific skills is shifted by embodied

technological advances. The widely applied theory of

investment in human capital lends itself well to economic choice modeling and to an analysis of earnings.

The theory takes a labor supply perspective. Workers

make decisions about their labor force behavior in

response to the derived demand for alternative skills

or knowledge. Thus, demand conditions are given and

the supply of workers responds. Human capital theory

maintains that worker response is in the form of income maximization over the working life. Since derived demand is dynamic, labor supply is dynamic.

What may have been a good decision previously may

become a poor one later if the worker does not adjust

to changes in demand. According to human capital

theory the incentive to adjust (or not adjust) primarily is pecuniary. In effect, the theory holds that a person makes an investment of resources in upgrading his

or her productivity. Couched in the marginal productivity thesis, workers get paid the value of their productivity. Consequently, increases in productivity are

assumed to lead to increases in income. Since the investment of resources (primarily foregone earnings) is

costly, individuals weigh the present value of the increased flow of future income against the present value

of the cost of the investment. Income maximizing individuals will make investments in those alternatives

with the highest rates of return.

The theory is an exact interpretation of very inexact

reasoning on the part of individuals. The theory has

received much criticism, especially for its simplicity

of the “economic man” concept (ignoring the nonpecuniary incentives of selecting occupations or furthering education—at least in empirical work). However, the concept of maximizing utility readily fits into

the investment framework. It is the measurement of

nonpecuniary returns that eludes the researcher. The

theoretical framework is a useful one for the present

analysis because it explains behavior as a function of

price (cost and earnings) and it encompasses formal

educational training (both early and later in the

career), on-the-job training (OJT), * quit behavior, and

occupational changes.

Although not fully developed at this point (nor

widely embraced by economists) “implicit contract”

theory promises to improve our understanding of labor

market adjustments (1,2,3,8,9,11). The implicit contract framework addresses better the employer’s part

in the human capital investment decisionmaking. In

particular, it addresses the decision of the firm to provide OJT and to influence turnover. Succinctly, because workers are more risk averse vis a vis employers,

employers are willing to provide more stable employment and less risky OJT investments in exchange for

lower wages. Without developing the theory more

fully here, let me emphasize that an examination of

labor market adjustments to technological change

should consider employer as well as worker decisionmaking.

What kind of alternative choices do workers and

employers make when rapid advancements in technical

knowledge and skills lead to obsolescence? For scientists and engineers (S/Es), these choices include reading

professional and trade literature, participating in workshops and conferences, returning to school (part- or

full-time) (10), investing in formal and/or informal

● Editor’s note: OJT is more broadly defined in this paper than in ch. 3

of the text where it refers only to informal skill acquisition.
82

OJT, and changing jobs. Changing jobs includes both

changing employers and/or changing occupations

(either within or out of S/E occupations). Employers,

likewise, have alternatives for retooling their work

forces experiencing obsolescence. Primarily, these options include providing informal and formal OJT, reassignment of job tasks, and laying off workers with

obsolete human capital (and, in turn, recruiting new

talent).

Investment decisions made to replace obsolete

human capital probably are influenced most strongly

by age, which affects investment behavior in several

ways. In its simplest form the relationship between age

and investments is considered dependent on the time

horizon for receiving returns. Older persons simply invest less because they have fewer years to receive investment returns. For the same reason, employers restrict opportunities for older workers. Secondly, investments will decrease at an increasing rate with age

if older workers become less efficient in producing

human capital. Ben-Porath addresses this possibility

in testing his “neutrality hypothesis. ” The neutrality

hypothesis assumes that the efficiency of human capital production and market production remains constant throughout the career. Any shift in efficiency

towards the production of goods and services in the

product market reduces the likelihood of producing

further human capital.

A third dimension to the relationship between age

and investment probabilities is deterioration. Like

physical capital, human capital can deteriorate over

time as a result of obsolescence and/or depreciation.

According to Rosen (16), obsolescence indicates that

more vintaged knowledge has become outdated by

more recent knowledge. This can be caused by advancements in the sciences, production innovations

which render existing skills useless, and the “increasing abilities of successive generations. ” Depreciation,

on the other hand, indicates a loss of ability by individuals to apply their existing knowledge and skills.

This presumably goes hand in hand with age as physical and mental capacities diminish.

Although most modeling treats obsolescence and depreciation as one (usually a depreciation factor), the

distinction between the two is especially important to

the study of S/Es where technological and scientific

advances make relatively specialized skills obsolete

more quickly. To keep abreast of these advancements

and to at least protect themselves in the labor market,

scientists make further investments in their human

capital stock. In contrast, depreciation reduces the

capacity to apply one’s stock of human capital to production, which reduces the rates of return to further

investments, and, thus reduces investments.

Other important determinants of investment behavior for S/Es include education level, labor market conditions, years of experience, tenure with present

employer, sex, and government contracting. When the

determinants of investment behavior cause disincentives to retooling obsolete skills, S/Es are more likely

to change employers, occupations, industries, and incur spells of involuntary unemployment.

Adjustments by Occupation and Industry

Below are several tables providing frequencies on

the incidence of various adjustments. The figures

presented in these tables are based on the National

Science Foundation’s (NSF) Longitudinal Survey of

Scientists and Engineers (NLSSE). There is no attempt

here to tie these adjustments to technological change.

(See ref. 7 for a more in-depth description and analysis

of these adjustments. )

Table C-1 provides observations on the frequency

of investments in postschool education and OJT by

S/Es. Occupational classifications are based on the

respondent’s self-concept.

Roughly 6 percent of the scientists and 4 percent of

the engineers made formal educational investments in

the same field, whereas about 10 percent of both

samples invested in other fields. Considerably more

investments were made in OJT and in course work provided at the employer’s training school: 19 percent of

the scientists and 18 percent of the engineers invested

in OJT and 16 percent of the scientists and 20 percent

of the engineers took courses at the employer’s training facility.

Table C-2 provides figures on the percent of S/Es

undertaking OJT by selected manufacturing industry.

The incidence of formal OJT ranges from a low of 15

percent of the S/E work force in the fabricated metals

industry to a high of 36 percent in the electronic computer industry. The incidence of reported informal OJT

ranges from a low of approximately 16 percent in the

aircraft industry to a high of 36 percent in the electronics computer industry.

Table C-3 provides observations on the frequencies

of voluntary and involuntary turnover by S/Es over

the 1969-72 period. Quit frequencies indicate that agricultural scientists were the least likely to quit during

this period (7 percent) and that physicists had the highest quit percentage (15 percent). Among engineers,

aeronautical/astronautical engineers had the highest

frequency (5 percent) and agricultural scientists had

the lowest frequency (0.4 percent). Among engineers,

aeronautical/astronautical engineers had the highest

permanent layoff incidence (8 percent) and mining/
8 3

Table C-1 .–Frequencies of Postschool Education and On-the-Job Training by

Scientific and Engineering Occupation, 1972-74

Postschool education On-the-job training

Occupation Same field Other field Formal Informal

Scientists:

Agricultural . . . . . . . . . . . . . . . . . . . . . . 0.035 0.055 0,242 0.315

Biological . . . . . . . . . . . . . . . . . . . . . . . . 0.048 0.123 0.055 0.141

Chemist . . . . . . . . . . . . . . . . . . . . . . . . . 0.064 0.070 0.130 0.158

Earth/marine . . . . . . . . . . . . . . . . . . . . . 0.052 0.054 0.212 0.196

Physicist . . . . . . . . . . . . . . . . . . . . . . . . 0.088 0.075 0.076 0.087

Engineers:

Aeronautical/astronautical. . . . . . . . . . 0.052 0.133 0.217 0.156

Agricultural . . . . . . . . . . . . . . . . . . . . . . 0.055 0.055 0.273 0.164

Chemical . . . . . . . . . . . . . . . . . . . . . . . . 0.049 0.090 0.184 0.210

Civil/architectural . . . . . . . . . . . . . . . . . 0.043 0.084 0.153 0.186

Electrical/electronic . . . . . . . . . . . . . . . 0.063 0.106 0.240 0.176

Industrial . . . . . . . . . . . . . . . . . . . . . . . . 0.012 0.129 0.226 0.219

Mechanical . . . . . . . . . . . . . . . . . . . . . . 0.032 0.095 0.189 0.163

Metallurgical/materials. . . . . . . . . . . . . 0.041 0.090 0.178 0.134

Mining/petroleum . . . . . . . . . . . . . . . . . 0.023 0.062 0.229 0.209

Table C-2.—Formal and Informal On-the-Job Training

Frequencies by Selected Major industries, 1972.73

(working full-time year round in same job)

Percent of Percent of

Industry formal OJT informal OJT

Aircraft a

. . . . . . . . . . . . . . . . . . . . 25.4% 15.50/0

Chemicals b . . . . . . . . . . . . . . . . . 22.2 23.6

Electrical machinery . . . . . . . . 30.1 19.7

Electronic apparatus . . . . . . . . 28.0 17.6

Electronic computerese . . . . . . . 46.3 35.5

Fabricated metalsf. . . . . . . . . . . 15.3 17.3

Machinery (except electrical)g . 18.6 17.4

Motor vehiclesh . . . . . . . . . . . . . 31.2 21.8

Ordinance i. . . . . . . . . . . . . . . . . . 31.2 19.3

aAirCraft, aircraft engines, and Parts.

bchemicals and allied products.

cEle~trical machinew, qu~pfllent and supplies for the generation, storage, transformation, transmission, and utilization of electrical energy.

dEIKtronic apparatus, r~io, television, and communication equipment and partS.

OElectronic computers, accounting, calculating and office machinery and equipment.

fabricated metal products (except ordnance, machinew~d transportation

equipment.

gMachinery (except electrical) including engines and turbines, farming and COrlstruction machinery, mining, metalworking, and other manufacturing and service

industry machines.

hMotor vehicles and motor vehicle equipment including trucks, buses, automobiles, railroad engines and cars.

iordnance, including manufacture of arms, ammunition, tanks, and comPlete

guided missiles, space vehicles and equipment.

petroleum engineers had the lowest incidence (2

percent).

Table C-4 reports the percentage of engineers changing occupations during the 1969-72 period. Within the

selected sample, 3.6 percent of engineers changed occupations, and about 54 percent of them shifted into

other engineering occupations. The greatest exodus

was out of aeronautical/astronautical engineering (6.7

percent) and the greatest entrance was into mechanical

Table C-3.—Frequencies by Voluntary and involuntary

Mobility by Scientific and Engineering

Occupations, 1969-72

Percent

Occupation Percent quit Iaidoff

Scientists:

Agricultural . . . . . . . . . . . . . . . . . . 0.0680/0 0.0040/0

Biological . . . . . . . . . . . . . . . . . . . . 0.124 0.018

Chemist . . . . . . . . . . . . . . . . . . . . . 0.095 0.042

Earth/marine . . . . . . . . . . . . . . . . . 0.122 0.034

Physicist . . . . . . . . . . . . . . . . . . . . 0.147 0.053

Engineers:

Aeronautical/astronautical. . . . . . 0.075 0.081

Chemical . . . . . . . . . . . . . . . . . . . . 0.093 0.049

Civil/architectural . . . . . . . . . . . . . 0.179 0.034

Eiectricai/electronic . . . . . . . . . . . 0.143 0.074

industrial . . . . . . . . . . . . . . . . . . . . 0.157 0.066

Mechanical . . . . . . . . . . . . . . . . . . 0.130 0.068

Metallurgical/materials. . . . . . . . . 0.083 0.057

Mining/petroleum . . . . . . . . . . . . . 0.158 0.022

engineering (9.6 percent of those changing occupations). Of all occupation leavers, 73 percent moved

into what appears to be equivalent engineering or

scientific occupations. Another 6 percent shifted

downward to technician jobs, and the remainder

shifted into a wide mix of occupations ranging from

secondary teachers to laborers.

An examination of the incidence of various labor

market adjustments indicates that OJT (both formal

and informal) is more widely experienced across S/E

occupations than any other type of market adjustment.

Although to some degree I am comparing apples to

oranges, the above figures are consistent with the no-
84

Table C-4.—Frequencies of Occupational Change

by Engineering Occupation, 1969=72

Percent

Occupation changed occupations

Aeronautical/astronautical. . . . . . . .

Chemical . . . . . . . . . . . . . . . . . . . . . .

Civil/architectural . . . . . . . . . . . . . . .

Electrical/electronic . . . . . . . . . . . . .

Industrial . . . . . . . . . . . . . . . . . . . . . .

Mechanical . . . . . . . . . . . . . . . . . . . .

Metallurgical/materials. . . . . . . . . . .

Mining/petroleum . . . . . . . . . . . . . . .

0.067%

0.027

0.018

0.039

0.052

0.032

0.036

0.015

tion that OJT plays a predominate role in adjusting

to technological change.

The Case of the Computer Industry

The following section is a brief summary of my recent analysis of human capital adjustments by S/Es

in computer manufacturing (9). The summary begins

with a cursory overview of the structure and growth

of the computer industry in the United States. Subsequently, I present some empirical evidence on the relationship between technological change and OJT.

TECHNOLOGICAL CHANGE IN THE

COMPUTER INDUSTRY

Soma describes the computer industry structure as

it has evolved from the early 1950’s. During the 1950’s

there were three distinct segments: the electrical component manufacturers, the computer manufacturers

(mainframe assemblers, sales, and maintenance), and

the end users. By the 1970’s this structure became much

more complex. For example, some mainframe assemblers integrated vertically into electronic component

manufacturing, and independent leasing and maintenance firms were established along with software

firms, computer utilities, a used computer market, and

peripheral manufacturers. A study of the computer industry, therefore, requires the examination of electrical

component manufacturers, the various types of hardware manufacturers, and software firms. Of course the

users of computer technologies include nearly every

type of enterprise.

Technological change (as defined for present purposes) primarily has been contingent on major improvements in electrical components. The transition

from vacuum tube technology (used in the early 1950’s)

to transistor-based product lines marked a substantial

advance in computer technology. This adoption in

electrical components appears to have underpinned the

second generation of computers—from 1959 to 1964.

The third generation of computers was founded on the

development of integrated circuits which became economically competitive in the mid-1960’s. If a fourth

generation exists, it is not as well defined as the

previous generations. “Most companies have introduced new lines of equipment with improved technology since 1965, but there is no single technical advance

to be used in specifying a new generation. In terms of

circuits, the primary change has been a movement

from integrated circuits to large scale integration . . . “

(5)0

Based on these technologies, technological changes

in computer hardware have varied widely and grown

substantially. Phister plots annually many of these

changes over the 1955-75 period. For example, he

shows that the average internal memory bytes per

general practice system remained roughly constant

from 1955 to 1965 but steadily increased from 1965

to 1974. Other types of measurable innovations include increases in the: storage density of magnetic-core

memories and moving-head files, off-line storage

capacity of magnetic tape and disk pack media, average number of moving-head files, magnetic tape

drives, and terminals per system (among many others).

EMPIRICAL ANALYSIS

Exploratory interviews with personnel managers of

a convenient sample of firms in the computer and electronics industry yielded two important general observations. First, formal OJT plays the predominate role

in human capital adjustments for employed S/Es. Formal OJT typically takes the form of training sessions

ranging in duration from 2 to 3 days to 2 to 3 months,

sometimes on an intensive full-time schedule but usually on a less intensive part-time schedule. The second

major observation from the open-ended interviews is

that employers decide who, when, and how much

training is required to meet planned production and

service goals. According to the sample of personnel

managers, employees who survive the industry take

the training as required—’’its just part of the job.”

It is hypothesized that rapid technological advancement in the computer industry requires considerable

OJT to keep abreast of rapid obsolescence in human

capital. The probability of OJT in any given year,

therefore, is a direct function of technological change.

As a general index of technological change, the change

in the rate that new computers (including minicomputers) are introduced annually is employed in the

probability model below. By using an index of the

change in the rate of technological advancement, I am

arguing that employers respond to noticeable shifts in

deciding to provide OJT. The larger the shift upward

in the introduction of new technology, the larger the

probability that OJT is provided. Conversely, the
8 5

larger the shift downward, the larger the reduction in

OJT requirements.

Annual observations on S/Es in the computer industry are drawn from the NLSSE. Observations are

pooled for the 1972-75 period for which the incidence

of formal OJT is reported in the survey.

Restricting the sample to S/Es working full-time year

round for the entire 1972-75 period, let P (OJT)ij be

the probability of receiving formal OJT in year i by

individual j. The cumulative logistic function:

P (OJT)ij = 1/1 + e- ( Bij X + Uij)

is estimated by maximum likelihood. P (OJT)ij = 1,

if OJT was made in year i by individual j, O otherwise;

Uij is the error term, and

Bij X = BOij + Blij) (NMODELS) + BziJ (CTOTREV)

+ B3ij (AGE) + B4ij (BS) + B5ij (MS) + B6ij (PHD) +

B7ij (OCCUP) + B8ij (TENURE)

where:

NMODELS =

CROTREV =

AGE =

BS .

MS =

PHD =

occur =

TENURE =

rate of change in number of new computer

models introduced in year i-3

percentage change in annual total industry

revenue in year i-1

age in year i for individual j

1 if had bachelors degree in year i for individual j

1 if had masters degree in year i for individual j

1 if had Ph.D. in year i for individual j

zero-one dummy variables for engineers,

computer specialists, and managers (scientists

and others in benchmark)

years employed in job in year i for individual j

Once a new innovation is introduced to the market,

there is a delay before it becomes widely adopted.

Thus, obsolescence and the need for retraining becomes a function of that adoption delay. Knowing the

appropriate lag of the change in the rate of introduction of new computers is problematic. Unfortunately

there is little empirical evidence about the rate of diffusion of other technological advancements on which

to base a judgment. Work by Mansfield, et al. (13),

however shows that the introduction of numerically

controlled machine tools has taken anywhere from 6

to 15 years to become widely adopted by major user

industries. Since technological advancement in the

computer industry has been particularly rapid, one

would infer that the lag is considerably shorter in the

computer industry than in other industries. In order

to estimate the most “appropriate looking” lag, the

model is tested against 1- to 5-year lags on the

NMODELS variable.

RESULTS

The results of the estimated probability model are

reported in table C-5. The partial derivatives of P

Table C-S.—Estimation of the On-the-Job Training

Probability Model, 1972-75

Variable Mean Coefficient a t-value

NMODELS . . . . . . . . . . . . . . . –2.75 0.004 1.811

CTOTREV . . . . . . . . . . . . . . . . 6.50 –0.007 –0.979

AGE . . . . . . . . . . . . . . . . . . . . . 39.25 –0.015 –3.555

BS . . . . . . . . . . . . . . . . . . . . . . 0.59 0.157 1.291

MS. . . . . . . . . . . . . . . . . . . . . . 0.30 0.128 0.998

PHD . . . . . . . . . . . . . . . . . . . . . 0.03 0.667 3.226

Computer specialist . . . . . . . 0.35 0.266 3.985

Manager . . . . . . . . . . . . . . . . . 0.12 0.179 1.951

Other. . . . . . . . . . . . . . . . . . . . 0.04 0.442 2.810

TENURE . . . . . . . . . . . . . . . . . 6.63 0.010 1.333

Intercept. . . . . . . . . . . . . . . . . 0.173 0.885

Chi square , . . . . . . . . . . . . . . 45.55 (10 d. f.)

N . . . . . . . . . . . . . . . . . . . . . . . 324.00

aThe c~fficients reported above are B (~) (1 – $), where @ iS the lo9it eStimate

and ~ is the mean probability of on-the-job training (= 0.386).

(PJT)ij with respect to each variable are given, evaluated at the mean P (OJT) ( = 0.386). Using a 3-year

lag on the rate of change in the number of new models

introduced annually (NMODELS), a significant

positive relationship with the probability of OJT is

found. The increased probability in OJT in 1972

(where the rate of change between 1968 and 1971 =

–25), is estimated to be as much as 15 percentage

points. Given that the mean probability of investing

during the 4-year period under consideration (1972 to

1975) is 0.39, the impact of technological change is apparently considerable.

No significant relationship is found between the percentage change in total revenue and the probability

of OJT. This holds regardless of the lag employed.

Age shows the expected negative relationship, where

the partial derivative is –0.015 per year of age; significant at the 0.01 level. As a point estimate, this indicates

that a 40 year old S/E would be less likely to receive

OJT than an identical 30 year old by as much as 15

percentage points. Further tests to examine the linearity

of the relationship (i.e., using age in log and quadratic

forms) support the inference that the training-age profile is downwardly linear.

Only for Ph.D.s does the probability of OJT differ

by level of education. But here the estimate is peculiarly large, albeit, highly significant. Given the size of

the estimate (0.667) and the fact that only 0.03 of the

sample are Ph.D.s, I find it difficult to place much faith

in the estimate, On the other hand, the results suggests

that Ph.D.s are especially prone to receiving formal

OJT—perhaps because their functions are so closely

tied to generating technological change.

The set of occupational dummy variables shows that

there are substantial differences among occupations.

Since any occupational classification must be compared to all others (i.e., all others, including engineers,

are in the benchmark), it is impossible to estimate the
86

differences between any two occupations. Yet it can

be inferred that computer specialists and managers retrain more frequently than engineers.

Finally, TENURE obtains an unexpected positive

sign but it is insignificant at conventional levels of confidence. The positive sign does suggest, however, that

OJT is more likely received later than earlier in one’s

tenure.

CONCLUSIONS

Using a pooled cross-section time series logit probability model, it was found that technological change

has a substantial impact on the probability of receiving OJT during any given year. The results are limited,

however, and must be treated as first approximations.

First, the time period under study is limited to 1972

to 1975 because of data limitations—both with respect

to individual data and available indices of technological change. Second, the pooling technique suffers from

a potential violation of the assumption of independence. That is, if an important variable correlated with

the dependent variable is omitted at the cross-section,

pooling that omitted variable (in effect) over time results in autocorrelated error. Although the estimates

would be unbiased, the standard errors would be understated.

As a first approximation, (and bearing in mind the

above limitations) the results support the consensus

of a sample of personnel managers in the computer

industry that formal OJT plays in a crucial role in

adapting the S/E labor force to changing production

functions. Furthermore, the model estimates that the

lag between the introduction of new computer models

and subsequent training is approximately 3 years on

average.

A similar empirical analysis of the linkage between

postschool educational investments and technological

change yielded no significant relationship. Because

technological change in the computer industry is so

rapid and universities cannot afford to provide training based on expensive technologies, continuing education programs do not provide a viable alternative of

adjustment to technological change—at least for S/Es

in the computer industry.

Programmable Automation and

Labor Saving Adjustments

What Can We Expect?

The above analysis of OJT indicates that the acquisition of new skills and knowledge is widespread for the

S/E labor force. My impression is that the professional

S/E labor force is probably the most highly adaptable

segment of our labor force. By the very nature of S/E

educational training, workers are well equipped to

make adjustments to outdated skills and knowledge.

Indeed one of the primary roles of the S/E labor force

is to advance our knowledge and pursue the goal of

technological and innovative improvements.

Consequently, growth in programmable automation

in manufacturing is unlikely to cause considerable

disruption for the experienced S/E labor force. Adaptation to technological and innovative advance caused

by programmable automation will come in the form

of formal and informal OJT. The more rapid the developments in programmable automation, the greater

will be the extent of OJT and the less likely formal

education will play an important role in providing experienced S/Es with new skills and knowledge. This

latter conclusion is based on what has been observed

in the computer industry. Universities in general simply do not have the resources for accumulating and

developing the necessary physical capital to train S/Es

in the latest technologies. Because of limited resources,

research and development (R&D) in programmable

automation unfortunately will greatly limit universities

from providing students with advanced training,

which in turn will slow the reindustrialization of U.S.

manufacturing.

Therefore, the potential fly in the ointment is the

lack of resources available to universities to play lead

roles in R&D and the education of new S/Es. The

problem is that newly trained S/E graduates will not

provide the manufacturing industry with cutting-edge

talent. Instead, the industry will need to provide

substantial training to new S/E entrants. Consequently, instead of recruiting S/Es with skills capable of

satisfying immediate technical needs, manufacturing

firms will experience a lag in recruiting S/Es to implement and improve new programmable automation operating objectives.

To aggravate the problem, private industry appears

to be recruiting some of the best talent in S/E Ph.D.s;

not only newly minted Ph.D.s but also experienced

educators. The heart of this problem lies in the

substantial differences between current salaries of

Ph.D.s in universities visa vis private enterprise. This

brain drain from the universities implies that opportunities for more widespread and advanced educational training will be retarded.

The above scenario suggests that although programmable automation will be ready technically for diffusion throughout manufacturing, the S/E labor market

will not have the requisite skills to implement the technology-at least in the short run. This will hamper diffusion of programmable automation and any immediate expected improvements in productivity attributable to programmable automation.
8 7

Research Needs

If my best guess about what to expect is reasonably

accurate, then appropriate social policy should focus

on providing universities with the necessary resources

to establish educational curricula in programmable

automation. Additional resources can come from government and/or private industry. The primary research need is to examine the current and planned

R&D and educational activities of higher education.

The

1.

2.

3.

4.

5.

basic questions to investigate are:

What programs focusing on programmable automation have been established? We know, for example, that several major universities have established research centers in programmable automation; including Stanford, Purdue, Carnegie-Mellon, Massachusetts Institute of Technology, and

the University of New Hampshire (among

others).

Where have universities received resources for

these programs? The above-mentioned universities have relied primarily on NSF grants, donations from corporations, and university budgets.

Are universities gearing up for R&D and educational programs in programmable automation?

What resource limitations are the universities

facing?

To what extent is private industry attracting top

Ph.D. talent? Is this recruitment causing shortrun

bottlenecks in developing programmable automation curricula and R&D activities? How can universities maintain their S/E faculty in light of low

relative salaries?

Answers to these questions are important not only

to government policymakers but also to higher education administrators and manufacturing executives.

Thus, I would recommend that OTA investigate the

answers to the above set of questions. A survey of

major educational institutions could provide OTA

with reliable information to evaluate the above scenario. If such an assessment warrants considerable interest by the U.S. Government, then a task force composed of administrators from higher education, executives from manufacturing, and officials from government should be established. The purpose of the task

force would be to design and coordinate educational

efforts to train future S/Es in programmable automation.

In the next section I briefly overview several data

collection efforts by NSF (Division of Science Resources Studies). Ongoing efforts by NSF in collecting

and analyzing data about S/E labor markets are extensive and in depth. They are potentially very suitable

for collecting the type of information I have suggested

above. Although I am not recommending at this point

any additional research, data collection efforts by NSF

also provide the most useful data source for monitoring how well the S/E labor force adjusts during an era

of programmable automation—both in terms of new

entrants and experienced S/Es.

National Science Foundation Data Bases (see NSF)

SURVEY OF SCIENTIFIC AND ENGINEERING

EXPENDITURES AT UNIVERSITIES AND COLLEGES

This survey is conducted biennially to provide information on three areas of academic spending for

scientific activities: 1) R&D budgets, 2) expenditures

for departmental research and instruction, and 3) capital expenditures.

SPECIAL SURVEYS

NSF also conducts special surveys. Prominent

among these is the Higher Education Panel Survey

which is conducted by the American Council of Education. The survey is conducted several times a year with

the primary objective of providing quick responses to

current policy questions relevant to S/E labor markets.

(This survey may be especially well suited for the type

of quick evaluation suggested in “Research Needs”

above. )

A second example is the survey of Scientific Equipment in Academic Institutions. Its purpose is to measure the adequacy and utility of available equipment.

SURVEY OF SCIENTIFIC AND ENGINEERING

PERSONNEL EMPLOYED AT UNIVERSITIES

AND COLLEGES

This survey collects data about academic S/Es by

field of employment and primary function.

SURVEY OF GRADUATE SCIENCE STUDENTS

AND POST-DOCTORALS

The objective of this survey is to obtain data on the

characteristics of graduate science and engineering

enrollment at the departmental level. Data from this

survey provide a base for assessing the relationship between financial support and shifts in graduate enrollments.

THE NATIONAL LONGITUDINAL SURVEY OF

SCIENTISTS AND ENGINEERS

This biennial survey is a continuing longitudinal effort to maintain a comprehensive picture of the development and utilization of individuals who were part

of the S/E labor force in 1969. The survey elicits information about human capital investments, earnings,

and employment/reemployment experiences.
88

THE NATIONAL SURVEY OF RECENT SCIENCE AND

ENGINEERING GRADUATES

This survey is conducted biennially, furnishing information on graduates in science and engineering

fields; including data on employment, earnings, and

other labor market experiences.

THE NATIONAL SURVEY OF

DOCTORATE RECIPIENTS

This is a biennial survey with the primary objective

of estimating the national supply and utilization of

doctoral S/Es.

References

1.

2.

3.

4.

5.

6.

7.

8.

Azariadis, Costas, “Implicit Contracts and Underemployment Equilibrium, ” ]ourna] of Political

Economy, vol. 83, December 1975, pp. 1183-1202.

Baily, Martin N., ‘Wages and Employment Under

Uncertain Demand,” Review of Economic Studies,

vol. 411, January 1974, pp. 37-50.

Baily, Martin N., “On the Theory of Layoffs and

Unemployment,” Econometrica, vol. 45, No. 5,

July 1977, pp. 1043-1063.

Ben-Porath, Y., “The Production of Human Capital Over Time, ” in Education, Income and Human

Capita], Lee Hansen (cd. ) (New York: NBER,

1970), pp. 129-147.

Brock, G., The U.S. Computer Industry (Cambridge, Mass.: Ballinger Publishing Co., 1975).

Brown, M., On the Theory and Measurement of

Technological Change (Cambridge, Mass: MIT

University Press, 1966).

Cooke, William N., Labor Market Adjustments by

Scientists and Engineers: Probabilities and Outcomes (report to NSF, 1980).

Cooke, William N., “Permanent Layoff Probabilities: What’s Implicit in the Contract, ” Industrial

Relations, vol. 20, spring 1981.

TECHNOLOGY AND LABOR

by Markley Roberts

AFL-CIO

July 27, 1982

9.

10.

11.

12.

13.

14.

Cooke, William N., Human Capital Adjustments

to Technological Change in the Computer Industry: The Case of Scientists and Engineers (report

to NSF, 1981).

Cooke, William N., and Cobern, Morris, “PartTime Post-School Investments in Education and

Their Impact on Earnings Growth for Engineers,”

in Human Resources and Employment in the Developed Countries: International Economy and

Employment, Proceedings of the 6th World Congress of the International Economic Association

Meetings, forthcoming.

Gordon, Donald F., “A Neo-Classical Theory of

Keynesian Unemployment,” Economic Inquiry,

vol. 12, December 1974, pp. 431-459.

Mansfield, E., The Economics of Technological

Change (New York: W. W. Norton & Co., Inc.,

1968).

Mansfield, E., et al., The Production and Application of New Industrial Technology (New York: W.

W. Norton & Co., Inc., 1977).

National Science Foundation, Scientific and Technical Personnel Data System: Strategy and Design,

proceedings of a conference, October 1979,

15. Phister, M., Data Processing Technology and Economics (Santa Monica, Calif.: Santa Monica Publishing Co., 1976).

16. Rosen, S., “Measuring the Obsolescence of Knowledge, ” in Education, Income and Human Behavior,

T. Juster (cd.) (New York: NBER, 1975).

17. Solow, R., “Investment and Technical Progress,”

in K. J. Arrow, et. al. (eds. ), Mathematical Methods in the Social Sciences (Stanford, 1960).

18. Soma, J. T., The Computer Industry (Lexington,

Mass.: D. C. Heath & Co., 1976).

19. Somers, G., E. Cushman, and N. Weinberg (eds.),

Adjusting to Technological Change (New York:

Harper & Row, 1963).

Technology changes the way goods and services are Therefore, workers and their unions have a direct and

produced and distributed—and for all its potential vital interest in how technology is introduced in the

benefits, including creation of new jobs, technology workplace—to make sure people get priority over

also has destructive effects on workers and their jobs. technology, to make human values prevail.
8 9

Technology often involves labor-saving operations—increased production with the same number or

fewer workers. This may wipe out many existing jobs.

It may raise new dangers to workers’ safety and health.

Of course, new jobs may also be created. New protections may be achieved for workers’ safety and

health. But the impact of new technology is often to

eliminate some jobs, change the job content of others,

change skill requirements, and change the flow of

work.

Technology often causes changes in industry location—shutdowns of departments and entire plants and

shifts to new locations in suburban or outlying areas

and sometimes overseas. No industry is immune to

such changes, which are constantly shifting the structure of skills, occupations, jobs, and earnings of

American workers.

Collective Bargaining

Collective bargaining holds a vitally important role

in meeting the challenges, opportunities, and dangers

of new technology. There is much to be learned from

past experience in collective bargaining. The flexibility of this institution, the American system of labormanagement bargaining at the plant, company, and

industry level, helps workers negotiate and settle with

employers on reasonable and humane protections for

workers against the potentially adverse effects of jobdestroying technological innovation. Mature collective

bargaining relationships between labor and management provide more opportunities and a sound basis

for special labor-management committees to deal with

adjustment to technological change within the framework of collective bargaining.

Collective bargaining can help democratize labormanagement relations and humanize the workplace

and work itself, including the impact of new technology on workers’ jobs and earnings. Collective bargaining can provide cushions to soften the adverse impact

on workers by setting up adjustment procedures and

programs at the workplace. In a full employment economy—linked with adequate employment services, employment and training programs, and unemployment

compensation—the disruption of workers’ lives and

the job displacement resulting from technological

change can be minimized.

Historically, unions have responded in a number of

ways to the introduction of new technology. In 1960,

Sumner Slichter, James J. Healy, and Robert Livernash, reported that major determinants of union policies toward technological change are:l

‘Sumner Slichter, James J. Healy, and Robert Livernash, The Impact of

Cofkctive Bargaining on Management (Washington, D. C.: The Brookings

Institution, 1960), ch, 12, “Union Policies Toward Technological Change.”

1. the nature of the union, meaning specifically

whether it is a craft or industrial union;

2. the economic condition of the industry or the enterprise, or occupation, whether it is expanding

or contracting, whether the industry is highIy

competitive or not;

3. the nature of the technological change, the effect

on jobs and on the bargaining unit, the effect on

workers’ skills and job responsibilities; and

4. the stage of development of the technological

change and the stage of development of union

policy toward the technological change.

Slichter, Healy, and Livernash distinguish five principal policies that unions adopt when faced with technological change: 1) willing acceptance, 2) opposition,

3) competition, 4) encouragement, and 5) adjustment

with an effort to control use of the new technology.

They note:

The most usual policy of unions toward technological change is willing acceptance. This happens in the

numerous cases in which the technological change

makes little difference in the kind and degree of skill

required and has little immediate effect on the number

of jobs. But the gain in productivity from the change

may make it attractive by giving labor improved opportunity to bargain for wage increases. Unions may

be led by favorable bargaining opportunities to accept

willingly technological changes that involve a mixture

of advantages and disadvantages. Thus, the bargaining advantages that accompany a change requiring

greater skill may lead to willing acceptance even though

it greatly reduces the number of jobs.

Slichter, Healy, and Livernash go on to point out

that no national union in recent years has destroyed

itself by fighting technological change.

Nor is there record of any union in recent years being

able to prevent technological change by opposing it—

though many unions have retarded recent changes temporarily and locally. Union wage policies appear to have

been partly responsible for stimulating technological

change under some circumstances and may have affected the distribution of gains. Three principal effects

have been produced by union policies toward technological changes:

(1)

(2)

(3)

They have tended to give to the holders of jobs

on the new machines or new processes somewhat higher wages relative to other workers in

the same plant—in other words, they have

tended to introduce distortions in the wage

structure of the plant.

They have tended to a slight extent to cause the

new techniques to be operated with excessive

crews and under make-work rules.

They have considerably eased the hardship of

displacement, partly by forcing managements

to do advance planning in the introduction of

technological changes and partly by giving displaced workmen an opportunity to qualify for

other jobs.
Using the approach developed by Slichter, Healy,

and Livernash to distinguish the five principal attitudes

that unions take toward technological change, Doris

McLaughlin of the University of Michigan made a survey of union officials, management, and mediators and

arbitrators on the impact of labor unions on the rate

and direction of technological innovation.

The McLaughlin report2 found that willing acceptance was the most common response American labor

unions make to the introduction of new technology.

The next most common response was initial opposition, but this was followed by adjustment, so that, in

the long run, willing acceptance or adjustment were,

by far, most common.

A negative union response to the introduction of

technological change was invariably the result of belief

that acceptance would have an adverse effect on a large

or important segment of the union’s membership. If

the employer convinced the union’s leaders that their

members would not be adversely affected, or that

those who were adversely affected would receive some

off-setting benefit, union opposition disappeared.

The three most important variables in determining

union reactions, in order of importance, were:

1. the state of the economy,

2. union leaders’ perception of the inevitability or

necessity for the change, and

3. the nature of the industry.

McLaughlin noted that, depending on union perception of these three variables, a fourth variable-where

decisionmaking power lay—became crucial. If the international union held the decisionmaking power, a

decision on how to react to the new technology would

be made only on consideration of the first three

variables. However, if decisionmaking power lay with

local union leaders, three more variables became relevant:

4. how local union leaders perceive the impact of

the new technology on the bargaining unit,

5. how local union leaders perceive the “quid pro

quo” offered by the employer to the affected

union members, and

6. how local union leaders perceive the impact on

those union members left in the unit after the new

technology is introduced.

Third-party action by mediators, arbitrators, or

judges did not seem to affect the outcome, according

to the report, but did appear to affect the process by

which unions and management reached accommodation to the effects of the new technology. These thirdparty agents, as outsiders, serve a useful function in

‘Doris B. McLaughlin, The Impact of Unions on the Rate and Direction

of Technological Innovation (Detroit, Mich.: Institute of Labor and Industrial

Relations, University of Michigan–Wayne State University, February 1979),

report to the National Science Foundation, grant PRA 77-15268.

taking the heat off local union leaders “when otherwise politically delicate decisions need to be made with

regard to the introduction of new technology, ” the

report states.

Surprisingly, labor unions are not the major stumbling block to new technology and higher productivity, McLaughlin concludes, but “employer representatives, particularly at the middle management level,

were often cited as constituting the real barrier to the

introduction and effective use of technological

innovation. ”

In 1964, the Bureau of Labor Statistics reported3 that

some of the major labor-management efforts to protect against the effects of new technology have

included:

1.

2,

3.

4.

5.

6.

guarantees against job or income loss and, in

some cases, against loss of supplementary benefits

for varying periods,

compensation for employees who lose their jobs,

guaranteed income for workers required to take

lower paying jobs,

provisions for retraining,

provisions for transfer to other plants and payment of relocation expenses, and

agreements to provide workers with notice of

plant closings or other major changes.

Some agreements have established joint labormanagement committees to recommend methods of

providing for workers affected by automation. The

report concluded that:

These arrangements typically are combined with provisions for retention of workers with greatest seniority, but in a limited number of cases, efforts are made

to spread work among larger numbers of employees or

to encourage early retirement of workers with relatively high seniority.

In 1966, the Automation Commission called attention to the need for private sector efforts to facilitate

adjustment to technological change including reliance

on attrition, an advance notice early warning system,

job counseling and job-finding assistance, training and

retraining. The Commission noted the rationality of

using the seniority principle in the case of layoffs and

the seriousness of the need for pension and health benefits to continue during periods of unemployment.

They also pointed out that technological improvements can bring more flexibility to work schedules and

more leisure to employees through reduced hours of

work per day, per week and per year,

3Cokctive Bargain@ and Technological Change (Washington, D. C.: U.S.

Department of Labor, Bureau of Labor Statistics, March 1964), BLS report

No. 266.

‘Technology and the American fionomy, vol. I (Washington, D. C.: National Commission on Technology, Automation, and Economic Progress,

February 1966).
9 1

The record of collective bargaining response to technological change offers many examples of both success and failure, the Commission noted:

Collective bargaining has proved to be an excellent

vehicle for the effective management to change; it permits those directly affected by the change to deal with

it firsthand and with a familiarity that takes into account peculiarities and problems peculiar to an enterprise. Especially in recent years, some managements and

unions, occasionally but not usually with the help of

outsiders, have developed, with varying degrees of ingenuity and success, plans to facilitate change.”

But the Commission warned:

Despite its many successes, collective bargaining has

often failed, and sometimes has failed spectacularly,

to deal effectively or even responsibly with the management of change. It has been argued, not unreasonably,

that the failures are the fault of the parties, not of the

system.

Procedurally, the process of collective bargaining on

basic issues has tended to stagnate during the life of

the agreement and to accelerate frantically in an atmosphere of crisis immediately preceding contract renewal.

Happily, employers and unions in a number of industries are abandoning this pattern in favor of more or

less continuous discussion. Basic issues such as adjustment to technological change cannot be resolved, however, by a small team of negotiators working themselves

into a state of physical and mental exhaustion for a few

months every 2 or 3 years. These issues must be dealt

with patiently, carefully, and above all, continuously,

until satisfactory solutions emerge. This kind of bargaining calls for ability of the highest caliber on the part

of leaders of both labor and management.

In the 15 years since the Automation Commission’s

report, with generally slow economic growth and recessions in 1969-70, 1973-75, 1980, and 1981-82, economic conditions have not been conducive to easy adjustments to technological change. The impact of new

technology has become much more pervasive in the

1980’s than it was in the 1960’s.

It must be emphasized that it is easier to deal with

adverse effects of technological change in a general

economic climate of full employment. National economic policies must aim at full employment for a variety of economic, social, and moral reasons. Among

those reasons we must recognize the need to facilitate

successful and humane adjustments to job-destroying

technology in both the private and public sectors.

Much progress has already been achieved through

collective bargaining. For example, a 1981 Bureau of

Labor Statistics study,5 updating a similar 1966-67

‘U.S. Department of Labor, Bureau of Labor Statistics, Major Collective

Bargaining Agreements: Plant Movement, lnte~lant Trans& and Relocation Allowances, Bulletin 1425-20, July 1981. The 1966-67 study was reported

in U.S. Department of Labor, Bureau of Labor Statistics, Major Collective

Bargaining Agreements: Plant Movement, Transfer, and Relocation

Allowances, Bulletin 1425, July 1969.

study, presents a wide range of contract language and

statistical summaries of contract language on plant

movement, plant transfer, and relocation allowances,

many of which relate to the effects of technological

change. Agreements limiting plant movement rose

from 22 percent in the 1966-67 survey to 36 percent

in the 1980-81 survey of some 1,600 contracts, while

worker coverage rose from 38 to 49 percent. InterPlant

transfer provisions increased from 32 to 35 percent and

worker coverage went from 46 to 49 percent. Agreements dealing with relocation allowances increased

from 34 to 41 percent while worker coverage went up

from 60 to 65 percent.

On the issues of the major technological change,

work transfer, or plant closings, some major contracts

have a variety of provisions.’ For example, the United

Auto Workers (UAW) -General Motors contract provides for advance notice to the union in cases of technology-related permanent layoffs, and negotiation of

rights related to plant closing, department closing, and

company transfer of work. Workers have the right to

training for a new job in cases of technology-related

permanent layoff. In the case of plant closings, department closings and transfer of work, workers have the

right to bump to another job in the same plant, transfer

to a replacement facility, or transfer to a new plant.

They will receive preferential hiring at another plant,

keep seniority with respect to fringe benefits, get moving expenses up to $1,355, take layoff with recall

rights, and get severance pay.

The United Steel Workers’ contract with Kennecott

Copper includes a no-layoff clause and attrition protection for workers affected by technology changes

which will permanently eliminate their jobs. Under this

contract, workers have the right to bump to another

job in the same plant or in another plant. The Transit

Workers’ contract with the New York City transit system and the Newspaper Guild’s contract with the New

York Times also have no-layoff contract protection.

The Steel Workers’ contract with American Can Co.

calls for a 12-month advance notice of permanent layoffs related to technological change. The United Food

and Commercial Workers’ contract with Armour calls

for 6-month notice, and the Guild-New York Times

agreement calls for 4 months. There are contracts with

advance notice requirements as short as 7 days and

contracts with advance notice requirements, but no

specified time period.

A broad range of labor-management cooperation is

already included in many other labor-management

agreements with negotiated specific procedures for adjusting to technological change.

bThe following contract provisions are listed in Industrial Union Department, AFL-CIO, Comparative Survey of Major Collective Bargaining Agree

ments, Manufacturing and Non-Manufacturing March 1979, December 1979.
9 2

One method to ease the human costs of new technology is to assure advance information to workers

and their unions about management plans for future

innovation which will affect workers with job loss or

other serious problems. Major technology changes

result from management decisions taken long before

the new technology is actually introduced, often years

earlier. Certainly there should be long advance notice

before any technological change which results in layoffs or plant shutdown. The failure of management

to institute worker safety-health and environmental

protections should not be the way workers learn about

intended plant shutdowns or major layoffs.

An “early warning system” of advance notice helps

make it possible to ease the problems of affected workers. Such “early warning” provisions have long been

standard in many union contracts. With advance notice and labor-management cooperation, workers can

look for or train for a new job, perhaps with the same

employer in the same plant or at another location,

Employer-paid retraining is an important part of any

adjustment-to-innovation program.

There are other methods and techniques for labormanagement cooperation to cushion adverse effects

from changing technology. These include income

maintenance with work and/or pay guarantees. One

way is through “no-layoff” attrition to reduce the work

force by natural turnover, deaths, retirements, and

voluntary quits, thus protecting the jobs and earnings

of those workers who remain with the company. Of

course, attrition alone is not an adequate solution.

“Red circle” earnings protection for workers downgraded through no fault of their own attaches a wage

rate to an individual instead of to the job itself and

thus protects workers against loss of income which

might result from innovation-induced downgrading.

Seniority is a key principle in protecting workers

against layoffs and downgradings. This rewards long

service, but does much more—properly reflecting the

worker’s investment in the job and the company’s investment in the worker. Early retirement is an option

that older workers should have available when major

technological change wipes out their jobs. But the option should be available as a free choice, not as a requirement. Many older workers cannot afford to retire

early and others prefer to continue working.

Transfer and relocation rights and mobility assistance to workers are other ways to provide job and income protection. Within-plant and interplant transfers,

relocation assistance, severance pay, pension rights

and seniority protections and supplemental unemployment benefits can all help cushion adverse effects on

workers and their families when industrial innovation

occurs.

Shorter workweeks and reduced time per year on

the job, including longer paid vacations and sabbatical

leaves, also can ease the negative employment effects

of technology.

Electrical machinery manufacturing is an industry

where extensive use of robots is expected in the future.

The June 1982 General Electric agreement with the International Union of Electrical Workers includes these

protections for workers who lose jobs to robots and

automation: 7

A production employee whose job is directly eliminated by a transfer of work, the introduction of a robot

or of an automated manufacturing machine and who

is entitled to transfer or displace to another job shall

basically retain the rate of the eliminated job for a period of up to 26 weeks.

The company shall give the union advance notice of

a minimum of six months of plant closing or transfer

of work and of a minimum of 60 days of the installation of robots or automated manufacturing machine

for production.

An employee who is terminated because of a plant

closing will be assisted to find new jobs and learn new

skills under an employment assistance program which

will include job counseling as well as job information

services.

An employee with two or more years of service who

is terminated as a result of a plant closing will be entitled to receive education and retraining assistance, including reimbursement of $1,800 for authorized education expenses.

Obviously these provisions do not constitute total

protection but they offer some protection and some

help to displaced workers.

Public Policy

More information is needed on the effects of changing technology on workers. Federal action is needed

to set up a clearinghouse to gather information on a

continuing basis on innovation and technological

change and its effects on the welfare of the American

people, on jobs, skills, training needs, and industry

location. Few economic studies of the impact of technological change exist because there is no systematic

data-gathering relating to the changing technology of

American production. With more and better information, public and private adjustment programs can better avoid needless human hardship and suffering which

too often result from the disruptive impact of changing technology.

Through this clearinghouse, the Federal Government could provide unions and employers with comprehensive information and service, upon request, to

‘Bureau of National Affairs, Daily Labor Report, June 29, 1982.
9 3

help develop labor-management solutions for the complex problems related to the impact of technological

change at the workplace.

Technology-caused economic dislocation and other

kinds of dislocation—including plant shutdowns

caused by corporate merger mania and by recession,

job loss from trade policies and production shifts away

from defense-related industry-require cooperative

labor-management efforts and also national programs

to deal with these complex problems. Further exploration is needed of a variety of such programs, including

proposals dealing with plant shutdowns and plant relocation and with reconversion of defense-related industry.

Occupational training and retraining may perhaps

help displaced workers acquire new skills and new

jobs—but such new jobs may be at lower skill levels

and at lower pay. Furthermore, the loss of an industry

and the skills and know-how that go with that industry

diminish the essential diversity and pluralism required

for a healthy economy and healthy society.

In mid-1982 Congress was moving to approve new

federally supported job training legislation, but the

scope of the program is too small to help the millions

of workers who have lost their jobs to technological

change and economy shifts. The recession has sharpened union pressures to get retraining commitments

from employers.8

Workers who lose their jobs because of plant closings may not be able to find new ones or maybe forced

to work at reduced pay. Family life is often disrupted.

The mental and physical health of displaced workers

often declines at a rapid rate. Research over a 13-year

period indicates that the suicide rate among workers

displaced by plant closings is almost 30 times the national average.9

Such workers also suffer a far higher

than average incidence of heart disease, hypertension

and other ailments.

Bills to deal with this grave economic and social

problem have been introduced in Congress .’” Although

these bills differ in some respects, they would do much

to counteract the devastating effects of shutdowns and

relocations. Unfortunately, they do not address the

problems caused by the relocation of governmental

facilities. Among other things, these bills would:

‘For example, see “Retraining Displaced Workers: Too Little, Too Late?”

Week, JU]Y 19, 1982, pp. 178-185.

‘Barry Bluestone and Bennett Harrison, Capital and Communities: The

Causes and Consequences of Private Disinvestment (Washington, D. C.: Progressive Alliance, 1980), pp. 78-82. The health-unemployment link is one of

the most clearly documented social research conclusions, e.g., Harvey Brenner, Estimating the Costs of National lkonomic Policy Implications for Mental

and Physical ffeahh, and Criminal Aggression, Joint Economic Committee,

U.S. Congress, Oct. 26, 1976.

IoFor example, ~gbth Cong. bills H.R. 5040, introduced by Congre=man

Ford of Michigan; S. 1608, introduced by Senator Riegle of Michigan; S. 1609,

introduced by Senator Williams of New Jersey; and S. 2400, introduced by

Senator Metzenbaum of Ohio.

1.

2.

3.

4.

require firms to provide advance notice of their

intentions to close or relocate a major facility;

advocate programs to support troubled businesses, including incentives to promote employee

ownership;

call for the issuance of economic impact statements and Federal investigation of the circumstance; and

require employers, whenever existing jobs cannot be saved, to provide minimal protections to

their workers in such matters as transfer rights,

relocation expenses, severance pay, pension protection, health care, and job training.

Three states—Wisconsin, Maine, and Michigan—

have laws relating to plant shutdowns, and some 15

other States have proposals pending with State labor

organizations pressing for action on protective plant

shutdown legislation at the State level. However, because of “competitive laxity” among the States in their

efforts to attract new business and “runaway” business,

Federal legislation with national plant closings standards is essential,

Unfortunately, since reporting on plant closings is

voluntary, the U.S. Government does not have centralized, comprehensive information on this important

social and economic issue. We don’t even know

whether most plant closings are related to technology

changes or tax incentives, to short- or to long-term

economic pressures.

For labor it is crucially important to require employers to recognize their responsibilities to their employees

and their communities before they shut down a plant

and to provide economic protections to workers and

their families who must suffer the consequence of too

hasty corporate action. There is nothing radical or

unusual about national legislation requiring advance

notice and other worker-community protections. In

other nations, private business firms—including affiliates and subsidiaries of many American firms—find

they can live with laws requiring advance notice and

other protections for workers and communities against

the adverse effects of economic dislocation and plant

shutdowns.

In terms of international comparisons, Sweden requires 6 months notice where more than 100 workers

are involved, 4 months notice where 26 to 100 workers

are involved, and 2 months notice where 5 to 25 workers are involved. Under Swedish law, no dismissals

may take place until the unions have been contacted

and granted an opportunity to negotiate concerning

the issues and consequences of the dismissals. In the

United Kingdom, 90-day notices must be given where

100 or more workers are involved and 30 days in

plants employing 10 to 99 workers. Failure to communicate with the unions and to give the appropriate
94

notice can make the employer liable for continuing pay

of the workers during the required notice period. In

France, Greece, and the Netherlands, prior to making

large-scale dismissals, the firm must have permission

of the government to lay off the workers and in actual

practice the advance notice period is as long as half

a year to a year depending on the specific circumstances.

These examples indicate that advance notice is a

practice with which firms can live. It must also be

remembered that in most foreign countries the benefits

paid workers are generally two-thirds of lost earnings

for up to 1 year after the layoff.

Unfortunately, in the United States, there area number of tax advantages provided for corporations which

close down even viable, moneymaking plants. Congress should look into these plant closings very carefully to determine if there is indeed an array of tax incentives encouraging businesses to close down plants. Legislation must be created which will stop such incentives and will prevent tax-related plant shutdowns.

Legislation must also be created which will establish

basic job and income protections for workers and protection of workers’ pension and health care and other

benefits, to deal in an effective and humane way with

the economic and social dislocation resulting from

plant closings.

Industrial Democracy

The potential for misusing technology is great, but

the possibility of human progress through the wise and

humane use of technology is equally great. The opportunity for new technology to be introduced with minimal social disruption will be greatly enhanced if workers and employers have an equal opportunity for discussion and joint decisionmaking on the subjects of

changing technology and the quality of working life.

Collective bargaining, an established institution in

our democratic society, has been a fair and workable

process for joint labor-management decisions on

wages, working conditions, and other major issues.

It is therefore a logical mechanism for increasing the

involvement of workers in such areas of decisionmaking as adjustment to new technology.

New technology and rising expectations are forcing

transformations in the workplace. Applications of new

technology should be humane for workers as well as

profitable to business. Human and social values must

be more highly valued in the production process, not

only when the process is producing goods and services,

but also when it is producing cultural and social values

and leisure and unemployment. The human desire for

greater autonomy and greater participation in decisionmaking on the shop floor, in the corporate boardroom, and in national economic policymaking must

receive higher priority. Improvements in the “quality

of work life” (QWL) include a broad range of issues,

such as better occupational safety and health, as well

as work-organization, work environment, and longrun

investment, employment and training decisions, and

the introduction of new technology. These QWL issues

are logical subjects for joint labor-management negotiation and decision. But employers must not use QWL

as a disguise for union-busting.

Irving Bluestone, a former UAW vice president, has

been a strong proponent of increased worker participation in corporate decisions. He warns:ll

The joint union-management programs that are in

existence have not yet proven themselves in any permanent sense. They must be subject constantly to review and change as management, the union, and the

workers learn by doing. Although it is not possible to

set forth a precise blueprint to ensure the successful participation of workers in the decision-making process,

experience already indicates certain criteria that are

basic:

●

●

●

●

●

●

●

●

 ✎

The programs should be voluntary. Workers must

have the free opportunity to decide whether or not

to participate in the program. To order compulsion is to invite resistance and failure.

Workers should be assured that their participation

in decision-making will not erode their job security

or that of their fellow workers, that they will not

be subject to ‘speed up’ by reason of it, and that

the program will not violate their rights under the

collective bargaining agreement.

Workers should genuinely experience that they are

not simply adjuncts to the tool, but that they are

bent toward being creative, innovative, and inventive plays a significant role in the production (or

service) process.

Job functions should be engineered to fit the worker; the current system is designed to make the worker fit the job on the theory that this is a more efficient production system and that, in any event, economic gain is the worker’s only reason for working.

This theory is wrong on both counts.

The worker should be assured the widest possible

latitude of self-management, responsibility, and opportunity for use of “brainpower.” Gimmickry and

manipulation of the worker must not be employed.

The changes in job content and the added responsibility and involvement in decisionmaking should

be accompanied by an effective reward system.

Workers should be able to foresee opportunities for

growth in their work, and for promotion.

The role of workers in the business should enable

them to relate to the products being produced or

the services being rendered, and to their meaning

in society; in a broader sense, it should also enable

them to relate constructively to their role in society.

lllWi% BIuestone, Ch. 12, “Emerging Trends in Collective Bargaining, ” in

Work in America: The Decade Ahead Clark Kerr and Jerome M. Resow

(cd.) (New York: 1979), Van Nostrand Reinhold, pp. 249-50.
95

Quality of Work Life

The conflict theory of labor relations is the soundest

basis for worker representation, worker participation,

and worker gains. Conflict is institutionalized in our

political system. Conflict is institutionalized in our

legal system. Conflict is institutionalized in our economic system. And we have institutionalized conflict

in labor-management relations through the American

system of collective bargaining.

But the adversary role, which is appropriate to the

conflict of collective bargaining, should be limited to

the period of negotiation—and during the life of the

contract, the adversary relationship can very logically and appropriately be replaced by cooperation aimed

at maximizing the potential success of the enterprise,

the company, or the establishment. The labor relations

cycle should be one of periods of conflict during the

negotiating period followed by the longer contract period of cooperation.

Collective bargaining is basic and fundamental to

honest labor-management cooperation—and such cooperation can be mutual self-help supplement to collective bargaining. Committees that exist outside collective bargaining or try to take over the process of

collective bargaining are not to be trusted.

Labor unions today jointly participate with management in thousands of safety committees, apprenticeship committees, communitywide labor-management committees, quality of work life committees,

quality circles and other joint labor-management efforts. As a result, labor-management committees are

joining together to deal with matters of mutual interest

such as foreign trade; Federal, State, and local programs; community philanthropic purposes; and revitalization and strengthening of their industry and their

community as well as those committees which give

workers a direct voice in the issues of the workplace,

including investment and innovation with new technology.

But, only the collective bargaining stature of their

unions establishes workers as real partners in those

labor-management committees. Any action that weakens a union, distorts the balance in its relationship to

management, or its ability to represent its membership, will damage that union’s ability and desire to participate in committees of any kind with a particular

management.

Any program which strengthens the union’s ability

to grapple with the new issues union members want

addressed, including technology change issues, any

program which holds out real promise for the expansion of workplace democracy, ought to be grasped,

minutely examined for flaws, reshaped as necessary,

polished, and put in place—and then watched very

carefully.

QWL programs in the United States have taken

many different forms and appeared in many different

guises-participatory management, employee involvement, shop-floor democracy, consultation schemes,

labor-management committees, quality circles,

autonomous work groups, QWL teams, profit-sharing

incentive structures, etc.

As a tool used toward labor’s basic goals, these

QWL programs can develop skill improvement programs, more flexible working schedules, greater job

security and promotional opportunities, along with

many other matters of great importance to the members we represent. So, other things being equal, unions

have every reason to encourage and cooperate in any

enterprise that will work to those constructive ends,

for the benefit of workers and management alike.

But hard experience has taught us to look for pitfalls. Too many employers are more interested in programs that offer cosmetic changes that try to fool the

workers into believing that management really cares

about them, in spite of low pay and bad working conditions.

Too many union-busting “consultants” are promoting these QWL programs as an alternative to

worker participation through trade unionism. That

way, without the protection of a union contract, any

concessions to workers can be revoked as easily as they

were given. At best, the QWL group concept poses

a problem to the labor movement because of the potential that exists for management to penetrate and influence small, informal work groups and bust unions.

For strong unions, able to insist on an equal and active voice in how the QWL program works and able,

if necessary, to veto actions that aim at subverting its

bargaining position, these are not insuperable problems. That accounts for the general acceptance of

QWL programs by such dominant and secure unions

as the Auto Workers, Steel Workers, and Communications Workers. Even they have sometimes had to take

strong action to prevent their employers from using

the programs for company propaganda in bargaining

situations.

Other unions are in a more difficult posture. They

have organized only a piece of the action—the other

facilities of the same firm unorganized, under contract

with another union, or a mixture of both. Here management more often controls the introduction and implementation of technology and QWL programs.

Sometimes, in fact, they move ahead to new technology with major layoffs or plant closings with barely

a nod to the union.

In doing so, they establish a carefully orchestrated

organizational and communication link with the em-
ployees that can bypass or attempt to supplant the

union. In part, this accounts for the more antagonistic

response of unions which have only bits and parts of

different firms and thus more limited bargaining leverage.

QWL programs, under whatever name, can be of

tremendous help in facilitating the dealing with the

larger issues of collective bargaining, including wages

and working conditions and the job impact of new

technology. At the same time, QWL programs can deal

with other less visible but basic issues that affect the

individual at the workplace.

Labor has no intention of allowing management to

co-opt any of these basic issues. But dealing with QWL

programs will present our unions with immense problems of educating our members—training and retraining of shop stewards and business agents; giving attention to the overall coordination of QWL programs

plant by plant, employer by employer, and individual

by individual; and developing at national staff levels

the technical expertise to assist in the negotiation of

QWL programs and in their development and maintenance; and resolving problems relating to sharing

technology’s benefits; and deciding what are necessary

agreements and conditions before entering into QWL

programs.

Every union must continue in every way possible

to assert its rights and the rights of its members to acceptance as legitimate equals in a partnership with

management, with collective bargaining as the essential foundation for labor-management cooperation.

We recognize the valuable contribution that properly constituted and equally balanced labor-management

programs can make in fulfilling the American trade

union member’s desire for individual recognition, dignity, safety, quality of work life, and job security.

To that end, unions will cooperate with managements that recognize and support the right of workers

freely to join unions of their choice and who demonstrate willingness to work with unions as equal partners in all areas that affect their members’ interests,

including the impact of technology.

But unions will reject, as a dangerous fraud, all efforts to use specious programs and rigged committees

to undermine unions, divert attention from the real

needs of workers, and weaken enforcement of the Nation’s labor laws.

Conclusion

Workers and their unions have reasonable, understandable, and legitimate concerns about loss of jobs,

loss of income, and loss of life and health. If these concerns are met adequately and effectively, workers will

be much more willing to accept and adjust to changing technology.

There are no simple solutions to the task of protecting workers against the adverse impacts of changing

technology. In thousands of labor-management contracts covering millions of workers in both the public

and private sectors, unions and management have

adopted a wide variety of provisions to cushion workers against these adverse impacts. These provisions fall

into a few general categories—job protection, income

protection, safety and health protection, retraining,

and relocation assistance. The specifics include attrition or no-layoff protection, early warning of technological change, seniority protections, early retirement

opportunities, “red circle” pay protection, shorter

workweeks or work-years, relocation rights to follow

transferred operations, severance pay, negotiated

safety-health protections supplementing safety-health

laws and regulations, and many other specific labormanagement collectively bargained responses to technological change.

Without full collective bargaining—no matter how

enlightened or benevolent management may be—

working men and women simply don’t have a sense

of participation in the basic decisions which govern

their jobs, their income and their lives. Collective

bargaining is essential to help workers share the

benefits of technological progress and help workers to

meet the challenge of technological change with a

minimum of social and human dislocation.
9 7

LABOR= MANAGEMENT RELATIONS IN AN ERA OF

PROGRAMMABLE AUTOMATION

by William N. Cooke

Krannert School of Management, Purdue University

July 27, 1982

Introduction

Currently we know very little about the impact of

programmable automation on labor-management relations. One gets the general impression that the impact

to date has been relatively small, except on a handful

of occupations and industries. The potential for

dramatic growth in the utilization of programmable

automation, however, is generally acknowledged, albeit, the timing of rapid diffusion remains iffy. The

potential for growth raises a number of important

questions about labor-management adjustments during an era of programmable automation. First, we

must ask: What shapes the decisions of employers to

invest in programmable automation? Will unions impede the diffusion of programmable automation and

to what degree (if any)? What impact will unions have

on the lag between the introduction of commercially

available programmable automation and its diffusion

in manufacturing? Simultaneously, we must ask: What

will be the degree of displacement of workers? What

happens to displaced workers? What proportion will

be retrained by employers? How many will be laid off?

What kind of changes in work rules will unions seek?

What kind of changes will unions gain? Will whitecollar workers seek union representations?

Answers to these questions require in-depth

research. Toward developing a research design, I begin

with an overview of the current utilization of robotics

in manufacturing and the extent of collective bargaining. I then discuss several collective bargaining issues

relevant to an understanding of labor-management relations and the utilization of robotics. Subsequently,

I lay out a research agenda, including a theoretical explanation of collective bargaining, implicit model

specifications, and data collection.

Programmable Automation and

Collective Bargaining in Manufacturing

Although programmable automation encompasses

more than robotics, it is this form of programmable

automation that most directly impacts on blue-collar

work forces and, thus, existing union-management

relations. From a research design perspective, worker

displacement caused by the introduction of robots can

be more precisely quantified than say, for example,

displacement caused by the implementation of computer-aided design techniques. Furthermore, firms and

plants utilizing robots can be identified.

Currently, robots are capable of performing tasks

associated with a handful of occupations. According

to the Robotics Institute of America (RIA), there were

fewer than 5,000 robots in use in the United States in

1980. Table C-6 reports the estimated number of

robots by broad occupational application (17).

The type of broad occupational categories most vulnerable to replacement by robot applications are heavily concentrated in the metalworking industries (1).

Table C-7 lists the basic metalworking industries by

2-digit Standard Industrial Classification (SIC) code,

the number of unions representing workers within an

industry, and the estimated union membership by percentage category.

It can be seen readily that metalworking manufacturing is heavily unionized and by a fairly large number of different unions. Table C-8 identifies some of

the national unions that have been active in organizing drives in metalworking industries during 1980.

Time restraints in preparing this report have not

allowed me to investigate the scope of contractual

agreements pertaining to programmable automation.

However, using the Bureau of Labor Statistics’ (BLS)

Table C-6.–Robot Usage and Identification of Applications, 1980

Machine loading, Painting/

Welding unloading Foundry finishing Assembly Other

Number of robots . . . . . . . . . . 1,500 850 840 540 100 600
98

Table C-7.—ldentlfication of Metalworking Industries

and Extent of Unionization

Percent in

Number of industry

lndusty SIC unions unionized

Primary metals . . . . . . . . . . . 33 50-75

Fabricated metals. . . . . . . . . 34 28 50-75

Machinery (except

electrical) . . . . . . . . . . . . . . 35 16 25-50

Electrical/electronic

equipment . . . . . . . . . . . . . 36 14 50-75

Transportation equipment. . 37 16 75-1oo

SOURCE: Bureau of Labor Statlstlcs, LXrectorY of fW/orra/ Urt/orK? and Hr@oyee

Assoclat\ons, 197fi Bulletin 1837.

Table C-8.-Unions Representing Workers in

Metalworking Manufacturing Industriesa

1. Allied Industrial Workers of America (AIW)

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

—

Aluminum Workers international Union (AWU)

Automobile, Aerospace, and Agricultural implement

Workers of America (UAW)

Boilermakers, Iron Shipbuilders, Blacksmiths, Forgers,

and Helpers; International Union (BBF)

Carpenters and Joiners of America; United Brotherhood

of (CJA)

Chemical Workers Union; international (ICW)

Clothing and Textile Workers of America; Amalgamated

(ACTW)

Communications Workers of America (CWA)

Electrical Workers; International Brotherhood of (IBEW)

Electrical, Radio, and Machine Workers of America United

(UE)

Electrical, Radio, and Machine Workers of America; International Union of (IUE)

Engineers; international Union of Operating (IUOE)

Furniture Workers of America; United (FWW)

iron Workers; international Association of Bridge Structural and Ornamental (BSOIW)

Laborers; international Union of (LIU)

Longshoremen’s and Warehousemen’s Union; international (ILWU)

Machinists and Aerospace Workers; international

Association of (IAM)

Marine and Shipbuilding Workers of America; industrial

Union of (IUMSW)

Metal Polishers, Buffers, Platers, and Helpers; international Union (MPBP)

Molders and Allied Workers Union; International (i MAW)

Oil, Chemical, and Atomic Workers; international Union

of (OCAW)

Painters and Allied Trades; international Union of (IUPAT)

Paperworkers; United international Union (UPI)

Plumbing and Pipe Fitting industry; United Association

of Journeymen and Apprentices (PPF)

Service Employees; international Union (SEIU)

Sheet Metal Workers’ international Association (SMW)

Steelworkers of America; United (USA)

Teamsters, Chauffeurs, Warehousemen, and Helpers of

America; International Brotherhood (TCWH)

~he above list of unions was taken from NLRB union election files. The unions

(and others) were involved [n union representation elections in 1980 in the

metalworking industries (SiC 33-38).

data base on union contracts (Characteristics of Major

Collective Bargain@ Agreements), an examination of

recent contracts negotiated by unions in manufacturing would reveal how unions alter the scope of collective bargaining as programmable automation becomes

more widely applied.

A glimpse at contracts and various publications of

the UAW, the USW, the IUE, and the IAM indicate

that joint union-management committees have been

established to address the general issue of productivity and the specific issue of technological change. A central purpose of these committees is to address in advance impending technological change: how to prepare

for changes in the production process and how to mitigate its impact on the work force. Below I discuss the

set of alternatives that unions and employers are likely to consider. One can only imagine that the scope

of contractual agreements will change as more workplaces apply programmable automation to the production process. I propose below that we examine these

contracts and the undertakings of joint union-management committees in order to understand more fully

how labor-management relations cope with the application of programmable automation.

Current Collective Bargaining:

Relevant Issues

In the following section I discuss several issues that

are especially relevant to understanding union-management relations. My intention is to describe briefly

some important parameters that help set the stage for

my subsequent discussion of collective bargaining in

an era of programmable automation.

Concession Bargaining

The current mood and trend in collective bargaining in manufacturing differs substantially from the

past. Due to a deep recession and growing international competition, unions and employers have begun

making concessions in wages, benefits, and work rules.

Concessions by unions have covered wage and benefit

packages and, to some degree, restrictive work practices. Concession bargaining is being witnessed in

many places of employment—well beyond the highly

publicized automobile, agricultural implement, rubber, electronic machinery, and ongoing steel negotiations. A recent mid-May poll by Louis Harris & Associates of 600 large corporations found that 26 percent of the unionized firms had obtained wage and

benefit concessions in recent negotiations (4).

Employers, likewise, have had to make concessions—albeit types of concessions that do not lend
99

themselves to easy pecuniary estimation. These concessions have included more union and worker input

into management decisions and a variety of security

provisions (e.g., added SUBS and reversals or postponement of plant closure decisions).

Several points can be made about the current concession bargaining. First, the recession itself would not

have precipitated these concessions. The recession

merely brought the longrun ills of some major industries to a head. The surge of foreign competition

is the cause of the ongoing decline in much of U.S.

manufacturing. Consequently, collective bargaining is

beginning to address the long-term livelihood of many

industries.

Second, concessions are being made by both unions

and employers. This suggests that unions in these manufacturing industries are not ipso facto in weaker

bargaining positions than employers. Instead, the industry is in a state of demise and, consequently, both

employers and unions need to change standard operating procedures.

Third, job security has become the major bargaining chip. Rank and file have insisted on additional

forms of job security (or monetary cushions to displacement) as the quid pro quo for concessions. Job

security issues will continue to hold the limelight in

further bargaining—primarily due to the longrun impact on existing employment in manufacturing; both

in the potential reduction of existing jobs and in requiring major changes in the structure of work.

Programmable automation, therefore, will be

viewed by labor much like foreign competition is

viewed—as a threat to longrun employment. Job

security, in turn, will be the bargaining chip for

cooperation with management in restructuring hardhit industries. In order to maximize the utilization of

programmable automation, unionized employers will

have to higgle and haggle over job security provisions.

Legal Requirements of Collective Bargaining and

the Issue of Programmable Automation

Union-management relations have been shaped substantially by the National Labor Relations Act (NLRA)

and any departure from the legal duty to bargain in

good faith is highly unlikely. The National Labor Relations Board (NLRB) is the regulatory agency charged

with the interpretation (along with the courts) and application of NLRA. It has laid down the ground rules

(albeit foggy ones at times) to protect both parties from

unfair labor practices.

In particular, the NLRB has attempted to delineate

mandatory bargaining subjects (i.e., the subjects and

issues for which the parties must negotiate in good

faith). Good faith bargaining requires discussion but

not necessarily concession to either party’s demands.

However, the NLRB generally has interpreted the lack

of compromise as evidence of bad faith bargaining.

Mandatory subjects fall under the heading of wages,

hours, and other terms and conditions of employment.

Ever since the landmark Supreme Court case of Fibreboard Paper Products v. NLRB (1964), the NLRB has

interpreted such issues as subcontracting and plant

closures as mandatory bargaining subjects. Until 1972

(Summit Tool Co. Case), employers had the legal responsibility to negotiate in good faith about decisions

to subcontract (where such subcontracted work was

previously done by current employees) and close

plants. The Summit Tool Co. Case appears to allow

employers to make unilateral decisions to close plants

but still requires employers to negotiate in good faith

about the displacement effects of plant closures.

The question to raise is, what will the NLRB require

in terms of good faith bargaining when workers face

displacement by programmable automation? It appears that if employers close plants and resort to subcontracting previous in-house production, they will be

obligated to negotiate with unions—at least about the

impact of these decisions on labor. It is difficult to

second-guess the NLRB since: 1) it is not clear to what

extent employers will close plants and/or subcontract,

2) the complexity of cases leads to unclear precedent

and, thus, case-by-case resolution, and 3) the changing make-up of the Board leads to inconsistency in the

interpretation of unfair labor practices (10).

Although it is unclear what impact the NLRB will

have in resolving disputes over programmable automation, it is clear that NLRB governance of labor-management relations will help shape labor-management

relations affected by programmable automation. It is

also clear (given the enormous workload of the NLRB)

that the typical long delay in NLRB conflict resolution will add to the transitory problems we might anticipate as we move into an era of programmable automation.

Union Organizing Activity

Table C-9 provides a description of union election

activity since 1950. Column 1 shows that the percent

of workers unionized in the nonagricultural labor force

has declined steadily since 1955—from a high of 33

percent to the present low of approximately 24 percent. Part of this decline is attributable to a long-term

drop in union success in representation elections; from

a high of winning 75 percent of elections in 1950 to

the current low of winning only 46 percent of elections.

Concurrent with increased election losses has been a

substantial drop in the average size of work units

holding elections; from 519 workers in 1950 to only
100

Table C-9.—Annual Observations on Selected Parameters of

Union Representation Elections

(1) (2) (3) (4) (5)

Percent of the

nonagricultural

labor force Percent of Average Percent Percent

Year unionized elections wonb

unit sizeb manufacturing consent b

1950. . . . . . . . . . 31.5 74.5 158.5 39.0 (c)

1955 . . . . . . . . . . 33.0 65.3 122.4 38.6 42.6

1960 . . . . . . . . . . 31.5 58.6 75.9 36.6 42.2

1965 . . . . . . . . . . 28.5 60.2 70.0 35.6 46.9

1970 . . . . . . . . . . 27.3 55.2 75.4 33.2 26.5

1971 . . . . . . . . . . 27.0 53.2 70.1 31.8 23.1

1972 . . . . . . . . . . 26.4 53.6 66.3 31.6 20.2

1973. . . . . . . . . . 25.9 51.1 57.8 31.8 16.3

1974. . . . . . . . . . 25.8 50.0 61.5 31.2 14,7

1975. . . . . . . . . . 25.3 48.2 66.3 29.4 11.6

1976. . . . . . . . . . 24.5 48.1 55.0 29.4 10.4

1977. . . . . . . . . . 24.1 46.0 60.2 29.2 8.9

1978. . . . . . . . . . 24.0 46.0 57.3 29.2 7.9

aDataare for calendar years.

bDataareforflscal years.

cNo comparable flgureavallable for Im.

SOURCES: Columns2,3,and 5aretaken fromvarlousannual reports oftheNLRB.Column4 Isadoptedfromthe 1979Handbook of Labor Statlstlcs. Column 1 IS adoptad from the Directoty of National Unions and Employee Assoclatlons,

1979.

57 workers in 1978. Obviously, this has had a substantial affect on total union membership. Column 4 presents the percent of workers employed in manufacturing, The loss in manufacturing employment is widely

cited as a leading cause in the decline of union membership, primarily because manufacturing has been a historic stronghold for the union movement. A recent

study of union elections over the 1970-78 period shows

that roughly 28 percent of all private sector elections

were held in manufacturing industries and that, on

average, workers were more likely to vote against

union representation (all other things held constant)

than their counterparts in nonmanufacturing industries

(19).

Stepped-up employer resistance, however, has perhaps had the greatest impact on union representation

elections. Employers, for example, rarely consent to

union elections today. Instead, employers have been

campaigning actively against union representation. For

instance, using the percent of elections consented to

by employers, column 5 shows that consent elections

have dropped dramatically from a high of 47 percent

in 1965 to under 8 percent by 1978. Recent studies find

a large negative and highly significant relationship between not consenting to elections and those election

outcomes (9,19).

Another area of interest is white-collar unionization.

Chamberlain, et al. (7), report that as of 1976, roughly

18 percent of the membership in national unions was

white-collar (professional-technical, clerical, and sales

workers). That figure represents an increase in whitecollar unionization in recent years. For instance, in

1960 only 12 percent of national union membership

was white-collar. This reflects an increase from

2,200,000 union members in 1960 to 3,850,000 in 1976.

If membership in employee associations is added to

union membership, the proportion of members from

white-collar occupations becomes 27 percent. Cooke

(9) also finds in an analysis of private sector elections

in 1979 that both professional-technical and clericalsales work groups were much more likely to vote for

union representation than blue-collar workers voting

in representation elections. However, it should also be

noted that only 13 percent of 1979 elections involved

primarily white-collar work groups.

Several important points can be drawn from the

above discussion. First, the union movement in the

private sector has been experiencing a long-term

decline in its relative power–at least as proxied by

membership figures. Second, the relative bargaining

power in manufacturing has experienced the greatest

slippage. Third, employer resistance to further union

organizing has increased dramatically over the last 15

years. Finally, white-collar unionization has been increasing and remains a potential growth area for the

union movement.
101

Collective Bargaining in an Era of

Programmable Automation:

A Research Agenda

Absent one of those funny little crystal balls, the

task of anticipating the impact of programmable automation on labor-management relations will require

more in-depth and sophisticated research than is currently available. In the following section I sketch out

a general theory of collective bargaining. I then raise

a set of questions about the impact of union-management relations. Subsequently, I develop a set of implicit models to answer these questions. Finally, I

discuss data collection issues and propose a research

plan.

A General Theory of Collective Bargaining

Dunlop (11) advocates the use of a systems framework in the general analysis of union-management

relationships. His framework encompasses three

broadly defined actors: 1) workers and their representatives, 2) management, and 3) interested government

agencies (the NLRB for present purpose). These actors

interact within a set of environmental constraints (technological, economic, and sociopolitical) to establish

the rules of the work relationship; both pecuniary and

nonpecuniary. Although Dunlop’s framework is based

on a historically observed set of relationships, he fails

to breathe any life into the framework. A suitable

theory, however, requires some underlying motivator(s) that helps explain and predict behavior. My

general intention here is to utilize Dunlop’s set of actors, but make them dynamic and explain their interaction within the environmental context.

Theoretical analyses of negotiations are based on the

conceptual idea that bargaining “power” determines

the outcome(s) of negotiations or, more generally,

union-management relations. Concerned primarily

with the ability of unions to raise wages, Pigou and

Hicks (16,13) define bargaining power as the ability

of unions to increase wages above competitive wage

levels. This concept of bargaining power has become

popularly known among economists as the union’s

“monopoly power” to raise wages (20). Chamberlain

and Kuhn (8) suggest a broader definition of power

that encompasses more than wage gains: power is the

ability to secure some agreement that otherwise would

not be granted. This latter definition is more appropriate for our present purpose. In the simplest context,

unions (employers) use power to force employees

(unions) to agree to sets of work rules.

Since power is primarily the means of attaining some

underlying goal(s), one needs to examine more closely the central goals of the parties. It would seem

reasonable to believe that unions, employers, and

government agencies act under some premise of utility maximization. Although utility maximization offers an underlying motivator (i.e., maximizing behavior), it does not lead us to what actually motivates parties; except “whatever motivates the parties” (i.e., utility). Unless the researcher assumes what utility represents (e. g., profits, wages, etc. ), the researcher cannot model very well any cause-effect relationships.

Since the analysis of industrial relations issues encompasses economic, sociological, political, and psychological relationships, we need to establish a motivating

principle which is amenable to incorporating such a

complex array of factors.

I argue here that unions, employers, and the NLRB

attempt to “optimize control” over employment relations. For unions (acting as the voice of workers), optimization of control over employment relations includes, for example, increasing job security and

minimizing employer discretion with respect to wages

and benefits, work assignments, displacement, and

worker discipline. Optimization of control, however,

does not mean that unions want complete responsibility for managing employment relations. Instead, unions

want to maximize control up to the point where their

members’ employment prospects are not jeopardized

through massive layoffs or business closures. In contrast to union optimization of control, employers want

near unilateral control of all decisions affecting

employment relations. Obviously, unionized employers have given up substantial control of the work rules.

The optimization thesis holds that unions and employers are motivated to wrestle back as much control as

possible; which implies that any state of equilibrium

is short-lived. Such an optimization principle, therefore, places unions and employers in a natural state

of conflict (although not necessarily in a destructive

one).

During negotiations both unions and employers

draw on their respective bargaining power to wrestle

control from one another. Under the thesis that bargaining power determines negotiation outcomes, the

sources of bargaining power must be examined. But

first, bargaining power should be defined in relative

terms since union and employer willingness to use

potential power is a function of the perceived net gain

(i.e., total benefit minus total cost) associated with

using that potential power. For instance, a union’s willingness to endure a strike to force an employer to agree

to a change in a contract is dependent on the perceived

net gain to the union of such a strike. Likewise, an employer’s willingness to take a strike to avert signing

the change in contract is dependent on the perceived

net gain associated with taking a strike. In defining
1 0 2

relative power, Chamberlain, Cullen, and Lewis (7)

argue that:

Only if the cost to management of not agreeing to the

union’s terms exceeds the cost of agreeing with them,

and if the cost to the union of not agreeing to management’s terms is less than the cost of agreeing to them,

does the union’s bargaining power surpass that of the

management.

Such a definition of relative power is dependent on

the size of the demand, however. For example, if a

union’s demand is for a 30-percent increase in “costs”

to the employer, the union’s relative power is less than

if the demand is only 10 percent. This implies that

unions and employers will be more successful in negotiating contract changes as the cost of change

becomes smaller.

The sources of relative power are determined by an

array of economic, technical, legal, organizational,

and sociopolitical factors. As any factor changes (or

differs across firms), the relative bargaining power

changes (or differs across firms). The set of implicit

models described below are derived by focusing on

parameters of the various sources of relative power.

This set of structural equations is designed to provide

answers to some basic questions about union-management relationships in an era of programmable automation.

Model Specification: Implicit Models

At this stage of the analysis I formulate several implicit models; models that begin to establish the conceptual understanding of cause-effect relationships between the diffusion of programmable automation and

union-management relations. Explicit empirical specifications detailing functional form and variable measurement will require considerable additional effort.

Based on a theory of control optimization, any

change in the work rules attributable to programmable

automation will be a function of the employer’s bargaining power visa vis the union’s bargaining power.

Stated algebraically,

Work Rules = f (POWm/POWu)

where POWm = power of management and POWU

= power of union. As discussed above, the relative

power of the parties depends on the sources of power

and the perceived cost of the change in work rules.

Thus,

POWm/POWU = f (sources of power + cost of change)

The utilization or diffusion of programmable automation (which can be viewed as a change in work rules),

therefore, becomes a function of relative power:

Diffusion = f (Powm/Powu)

Similarly, a union’s response to the utilization of programmable automation is a function of relative power:

Union Resp. = f (Powm /Po wu

)

Several of the questions raised in the introduction

basically ask: how will unions influence the diffusion

of programmable automation? In effect, we want to

test the hypothesis that a union’s response influences

diffusion. The dependent variable, therefore, is the diffusion of programmable automation. Diffusion can be

evaluated as: 1) the decision to invest, or 2) some

measure of the amount of utilization (e.g., number of

robots), and/or 3) the lag in diffusion. The dependent variable can be evaluated at firm or industrywide

levels.

Our theory implies that the diffusion of programmable automation increases as the relative power of

management rises above the relative power of the

union, and conversely. Since relative power is a function of the perceived cost of the change of work rules

plus sources of power, relative power varies as the cost

of the pending change varies and as the parameters

of the sources of power vary. Consequently, our analysis focuses on the cost of proposed changes in work

rules as well as the parameters of sources of power.

We can begin by postulating that the diffusion of

programmable automation is a function of: 1) the

union response to proposed or anticipated utilization

of programmable automation, plus 2) a vector (X) of

other variables.

Diffusion = f (union response, X)

Our theory says that the union response will be a function of the cost of proposed changes and its source of

power.

Union Resp. = f (cost of changes + sources of power)

Thus, everything else constant, as the cost to a union

of the application of programmable automation increases, the more negative the response of the union.

As discussed in the section about concession bargaining, job security becomes the central issue and the

primary bargaining chip in negotiations. At the extreme, for example, if displaced workers are laid off

(and say without severance or relocation pay), the cost

to the union in membership and status is quite high.

Of course, the greater such layoffs, the greater the cost

to the union. Under these circumstances the union response will be strongly negative to the diffusion of

programmable automation. At the other extreme,

where, for example, all displaced workers are retrained

or placed in alternative and equivalent jobs, the cost

to the union is negligible: causing no negative response

by the union (except perhaps to seek the establishment

of a joint union-management committee to prepare for

future diffusion).
1 0 3

Under the negative response scenario, (and again,

everything else constant) the union will attempt to

negotiate work rules protecting its membership. Thus,

the union will attempt to increase the cost to the

employer of making changes in working rules. One

can imagine a wide range of proposals at the bargaining table, including: 1) guarantees of employment (perhaps tied to years of seniority); 2) restrictions on timing of the implementation of programmable automation (e.g., implementation would be tied to normal

work force attrition or requirements of advanced

notice); 3) retraining requirements; 4) postponement

of plant closures and subcontracting; 5) cushions to

layoffs (e.g., severance, relocation, or SUB payments,

and early retirement schemes); 6) reduction in work

hours; and 7) union shop requirements covering newly created jobs.

Holding constant the cost of management’s proposed changes in work rules, we can now consider the

sources of power that determine the union’s ability to

negotiate protective work rules. It is hypothesized that

the greater the union’s power, the more successful it

will be in negotiating protective work rules, and the

more costly it becomes for management to change

work rules (i. e., utilize programmable automation).

Sources of power are gained or lost according to

variation in the economic and technological context

of the firm, union organizational strength, legal constraints, and the sociopolitical environment. With

respect to the economic context, the power of the

union varies with: 1) labor costs (e.g., wages, labor

cost per unit produced, wage bill/total cost, etc.);

2) the product market (profits, sales, industry concentration, insulation from foreign competition, etc.); and

3) the labor market (employment growth in firm or

industry, layoffs, unemployment, etc. ) As an illustration, in a firm or industry experiencing a loss in sales,

declining profits and employment, and a growing

threat from international competition, the union’s

sources of power are diminished as well as its ability

to shift the cost of work rules to the employer.

With respect to the technological context, the more

the production process is amenable to the utilization

of programmable automation and/or the less strategic

the work force in the production process, the less the

power available to the union. This relationship is

analogous, in part, to the study of union strike activity, whereby union strike activity is reduced as the production process can be manned temporarily by nonunion supervisors and workers.

Everything else constant, organizational strength

plays a role in determining the union’s strength.

Worker unity in supporting leadership initiatives, the

willingness of workers to endure strikes, the financial

resources of the union, membership size and extent of

organization, and negotiation skills are all positively

related to union power.

The NLRB also plays a potential role in shaping the

union’s power. The impact of plant closures and subcontracting on the work force, for example, are mandatory bargaining issues. If the NLRB interprets its current legal precedent about displacement to also include

displacement caused by the introduction of programmable automation, then the union’s power to negotiate

protective work rules is enhanced.

Finally, both employers and unions are sensitive to

the sociopolitical climate. If public opinion supports

notions of “job rights” and “employer responsibility”

for displacement, then unions will draw on this support in negotiating with firms. Sociopolitical support

for union initiatives will depend in large part on the

impact of displacement on a given community. Communities have played, for instance, an active role in

keeping some plants open.

In summary, the impact of unions on the diffusion

of programmable automation depends on the cost to

the union of its implementation and union sources of

power. Likewise, the diffusion of programmable automation depends on the power of the employer (which

is a function of the cost to the employer of changes

in the work rules and the employer’s sources of power).

The cost to the employer of utilizing programmable

automation can be treated as a standard investment

decision. The rate of return is obviously reduced as

unions are able to increase the cost of utilization (i. e.,

more expensive work rules). The ability of employers

to keep the investment cost down is a function of their

power sources. The sources of power are determined

from the same set of economic, technological, organizational, legal, and sociopolitical parameters that

determines the power of the union. Parameters that

yield power to the union generally take power away

from the employer, and conversely, Through negotiations over work rules, the parties will eventually sign

a contract that generally reflects compromises by both,

and which theoretically reflect the relative power of

the parties.

The types of renegotiated work rules will also reflect

the preferences of the parties. For example, unions and

employers may be willing to trade regular wages and

fringes for greater security provisions like retraining

and relocation pay, since job security becomes a primary concern. The types of outcomes, for labor, therefore, are tied to relative bargaining power and preferences of the parties. One can imagine a wide variation in outcomes among unionized firms.

It seems reasonably clear that as the capabilities of

programmable automation increase and as the price
104

per unit drops, there will be a substantial impact on

unionized workers in selected occupations, industries,

and localities. It would not be feasible for employers

to retrain substantial portions of their work forces.

Consequently, unions will attempt to bargain work

rules that cushion the layoff experience of large

numbers of workers. It also seems reasonably evident

that as the diffusion of programmable automation progresses rapidly in nonunionized establishments, the

longrun threat to unionized groups of losses in employment due to noncompetitive work rules will diminish

the sources of power for unions. As such, it becomes

less likely that unionized employers will agree to protective work rules.

Finally, let me briefly address the issue of union

organizing activity of white-collar workers. First, the

diffusion of programmable automation (especially in

the form of computer-aided design and manufacturing) also threatens existing white-collar jobs. The desire

for job security and due process are important factors

in work group decisions to unionize. As noted in the

section “Union Organizing Activity, ” white-collar

unionization has been increasing and remains a potential growth area for the union movement. Second,

unions which can organize both blue- and white-collar

workers, obviously increase their sources of bargaining power to negotiate protective work rules. Thus,

in firms and industries with the greatest potential for

white-collar displacement by programmable automation, the likelihood that white-collar work groups will

unionize increases. One can note, for example, that

white-collar workers at General Motors very recently

have shown considerable interest in attaining UAW

representation primarily, it appears, because of declining job security.

Data Analysis and Research Plan

Our knowledge of the relationship between the diffusion of programmable automation and labor-management relations is very limited. We simply have no

adequate empirical information to make reliable

judgments about the impact of unions on the diffusion of programmable automation, nor the impact of

programmable automation on union-management relationships and worker displacement. In part this can

be attributed to the modest and welcomed encroachment of programmable automation to date. However,

if the type of projections of programmable automation utilization made by RIA are reasonably accurate

(a threefold increase for 1985 over 1980, and twentyfold increase by 199o), the need to increase our

knowledge on this subject is highly warranted.

I suggest that we examine existing evidence in light

of the implicit models described above. The first step

would be to formulate a set of tentative explicit empirical models derived from the above implicit modeling. Once the critical (and measurable) variables are

identified and the models are written in appropriate

functional form, we can begin efforts to obtain the

necessary observations to test the models.

The information we seek covers: 1) the extent of

utilization and how employers weigh the reaction of

unions and/or workers in making investment decisions; 2) the negotiation experience (i.e., the changes

in work rules); 3) the parameters of the sources of

power for employers and unions; and 4) the impact

on the work force (e.g., numbers of workers retrained,

relocated, or laid off, etc. ) Toward this end we need

the cooperation of employers and unions. Unfortunately it is not clear at this point how much cooperation we will receive. The second step, therefore, calls

for a limited exploratory effort to test the waters. In

testing the waters, we can make a judgment of the likelihood of successfully gathering the necessary data.

Furthermore, we will learn: 1) what data on the variables in the tentative explicit models can be collected,

and 2) what additional parameters should be considered in our hypothesis testing.

If the second step indicates that further data collection is feasible, then as a third step we need to identify the users of programmable automation; and ideally, potential users. Given that in 1980 there were about

5,OOO robots installed, we have a fairly large population of programmable automation applications. RIA

is probably the best source of information on users and

potential users in manufacturing. After identifying the

population of users and potential users, we can then

begin the process of selecting a representative sample.

A small population of users, however, would allow

us to examine the entire population.

Finally, once the data have been collected, the explicit equations can be estimated. Only at that point

should inferences be drawn, projections made, and

policy scenarios evaluated.

References and Selected Bibliography

1.

2.

3.

4.

5.

Ayres, Robert, and Miller, Steve, “Industrial

Robots on the Line,” Technology Review, vol. 85,

No. 4, May/June 1982, pp. 34-46.

Ayres, Robert, “Robotics, CAM, and Industrial

Productivity,” NationalProductivity Review, vol.

1, No. 1, winter 1981-82, pp. 42-60.

Business Week, “Robots Join the Labor Force,”

June 9, 1980, pp. 62-76.

Business Week, “Concessionary Bargaining,” June

14, 1982, pp. 66-81.

Business Week, “Retraining Displaced Workers:

Too Little, Too Late?” July 19, 1981, p. 178.
105

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

Carnegie-Mellon University, “The Impacts of

Robotics on the Workforce and Workplace,” manuscript, June 14, 1981.

Chamberlain, Neil W., Cullen, Donald E., and

Lewis, David, The Labor Sector (New York:

McGraw-Hill Co., 1980).

Chamberlain, Neil W., and Kuhn, James W., Collective Bargaining (New York: McGraw-Hill Co.,

1965).

Cooke, William N., “The Collective Decision to

Unionize: Theory and Evidence, ” Industrial and

Labor Relations Review, forthcoming, April 1983.

Cooke, WiIliam N., and Gautschi, Frederick, III,

“Political Bias in NLRB Unfair Labor Practice Decisions, ” Industrial and Labor Relations Review, vol.

35, No. 4, July 1982, pp. 539-549.

Dunlop, John T., industrial Relations Systems

(New York: Holt, Reinhart, & Winston, 1958).

Extejt, Marian, “The Impact of Robotic Technology on Human Resource Management: A Preliminary Investigation, ” working paper, University of Pittsburgh, 1982.

Hicks, John R., The Theory of Wages (New York:

MacMillan, 1932).

McLaughlin, Doris B., “The Impact of Labor

Unions on the Rate and Direction of Technological

Innovation, ” manuscript, University of MichiganWayne State University, 1979.

Office of Technology Assessment, U.S. Congress,

Exploratory Workshop on the Social Impacts of

Robotics—A Background Paper, OTA-BPCIT-11, February 1982.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

Pigou, A. C., Economics of Welfare (London:

MacMillan), 1938.

Robotics Institute of America, Worldwide

Robotics Survey and Directory (Dearborn, Mich.:

1982).

Robotics Today, “Surveys Reveal Robot Population and Trends,” February 1982, pp. 79-80.

Seeber, Ronald L., and Cooke, William N., “The

Decline in Union Success in NLRB Representation

Elections,” Industrial Relations, forthcoming,

winter 1983.

Simons, Henry C., Economic Policy for a Free

Society (Chicago: University of Chicago Press,

1948).

Somers, Gerald G., Cushman, Edward L., and

Weinberg, Nat (eds.), Adjusting to Technological

Change (New York: Harper & Row Publishers,

1963)

Time, “The Robot Revolution, ” Dec. 9, 1980, pp.

72-83.

Wall Street Journal “Steel-Collar Jobs: As Robot

Age Arrives, Labor Seeks Protection,” Oct. 26,

1981.

Wall Street Journal, “The Big Revolution on the

Factory Floor,” July 12, 1982.

Zager, Robert, “A Working Model,” New York

Times, Sept. 7, 1981.

Zager, Robert, “T’he Problem of Job Obsolescence:

Working It Out at River Works, ” Monthly Labor

Review, July 1978, pp. 29-32.

0