5
Implications for Occupational Analysis Systems

The changes in work that we have described in this book pose two major challenges to occupational analysis systems. First, the external and organizational contexts and the content of work are changing. Second, the full scope and direction of the changes are not well known because, in part, we lack the data needed to track and assess the consequences of the changes that are occurring. What is needed is an occupational analysis system that tracks changes in the nature of work in a way that assists in both projecting future conditions and designing new jobs. This is what we mean by forward-looking, using historical data to both project the future and influence design decisions.

In this chapter, we therefore explore two questions: How can occupational analysis systems support efforts to both track and assess the changes in work occurring now and in the future? And how can occupational analysis systems support organizational and individual planning, counseling, and decision-making processes to adapt to these changes and achieve the outcomes from work that are critical to them?

More specifically, we ask: What are the implications of the changing world of work for occupational analysis tools and methods, and for occupational structures? How can occupational



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5 Implications for Occupational Analysis Systems The changes in work that we have described in this book pose two major challenges to occupational analysis systems. First, the external and organizational contexts and the content of work are changing. Second, the full scope and direction of the changes are not well known because, in part, we lack the data needed to track and assess the consequences of the changes that are occurring. What is needed is an occupational analysis system that tracks changes in the nature of work in a way that assists in both projecting future conditions and designing new jobs. This is what we mean by forward-looking, using historical data to both project the future and influence design decisions. In this chapter, we therefore explore two questions: How can occupational analysis systems support efforts to both track and assess the changes in work occurring now and in the future? And how can occupational analysis systems support organizational and individual planning, counseling, and decision-making processes to adapt to these changes and achieve the outcomes from work that are critical to them? More specifically, we ask: What are the implications of the changing world of work for occupational analysis tools and methods, and for occupational structures? How can occupational

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analysis and occupational structures best remain current, relevant, and useful? Can the changes be successfully addressed by existing systems? If not, how must occupational analysis and classification systems be designed to better address the changes? What are the implications (if any) of the failure of occupational analysis to adapt to changes at work for the performance of the institutions that use occupational analysis? Occupational analysis refers to the tools and methods used to describe and label work, positions, jobs, and occupations. Among the products of occupational analysis is an occupational category system, or an occupational structure. Our use of these terms is closest to that of organizational and industrial psychology, and it is somewhat different from the use of terms by other communities and disciplines. For example, sociologists and economists would ascribe additional meaning to the term occupational structure, including patterns of occupational recruitment and retention and inter- and intragenerational patterns of occupational mobility. For us, occupational classification has two general meanings: (a) the act of classifying positions, jobs, or occupations into an existing occupational category system and (b) the set of occupational categories in an occupational category system. Occupational structures reflect the nature of work, its organization, employment relationships, demographics, and other factors. They also reflect their intended purposes and influence, both directly and indirectly, the variety of outcomes depicted in Figure 1.1. Occupational structures are the lenses through which we categorize and view the system of work. Over time, they also help shape the system of work by providing the labels and categories that we use to bundle tasks and duties into positions, jobs, and occupations—in effect telling analysts, employers, and recruiters what is salient about work and what is not. For example, organizations that rely heavily on existing occupational classification systems and categories for the recruitment of personnel may be less likely to identify new task mixtures in their existing job structure, and also less likely to import new occupational distinctions from other organizations in the same industry. Similarly, organizations that use category systems that afford little or no attention to teamwork features of work organization may lag in the adoption of such structures and the moni-

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toring of their effectiveness. Occupational structures serve a defining function that tends to be backward-looking, reflecting what existed in the past, rather than forward-looking, reflecting trends in the changing organization of work. Choices of methodology and technology for occupational analysis are guided by theories of work and occupations, represented most clearly by existing occupational structures. The primary consideration in occupational analysis is to devise a system that provides a basis for understanding the world of work, one that is grounded in this reality. Any arbitrary structure will not suffice. The relevance of a system issues from its connection to reality (recognizing that the structure and the reality can never be fully separated), along with the extent to which it serves its intended purposes. To the extent that reality is changing or undergoing significant shifts, an occupational analysis system should be able to measure the change, and the categories should reflect it. We begin our discussion with a brief history of occupational analysis systems; then we describe contributions of two different types of systems—descriptive and enumerative—and assess how well the most current system under development performs the two key functions of occupational analysis: tracking changes in work and supporting employment decisions and career counseling. In our analysis we examine the extent to which existing and prototype systems of occupational analysis systematically address the major themes of heterogeneity detailed throughout this volume: increasing heterogeneity of the workforce, increasingly fluid boundaries between who performs which jobs, and the increasing range of choices around how work is organized and structured. History The development and evolution of occupational analysis systems has been closely tied to wars and other major social changes. Most observers note that occupational analysis systems evolved principally in response to one or another practical personnel problem, and they have often involved a key role for government in their initiation and definition (see Primoff and Fine, 1988; Mitchell

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and Driskill, 1996). For example, following the Civil War, the early attempts at civil service reform were aimed at more orderly placement of people in federal jobs to overcome the political spoils system (Primoff and Fine, 1988). At about the same time, the U.S. Census Bureau began to do more formal grouping and analysis of occupational titles, beyond mere listing (National Research Council, 1980). In their historical account, Mitchell and Driskill (1996) noted the widespread misuse of personnel during World War I, due primarily to a lack of definition of job requirements. The response of the U.S. Army was to commission leading psychologists to improve personnel testing and placement. Among the results were the Army Alpha and Beta tests for selecting and classifying recruits, as well as other occupational analysis efforts after the war aimed at improving the match between people and jobs. In the period between the two world wars, there also were major developments in occupational analysis and category systems. The U.S. Civil Service Commission launched major new efforts in the early 1920s to analyze a comprehensive set of jobs and occupations in terms of their duties, requirements, and advancement prospects (Mitchell and Driskill, 1996). The Wagner-Peyser Act of 1933, passed by Congress at the depths of the Great Depression, established the U.S. Employment Service with the basic aim of helping workers find suitable jobs. The act also established an extensive occupational research program. This research endeavor, closely coordinated with the Social Science Research Council and the National Research Council, eventually produced the first edition of the Dictionary of Occupational Titles, with subsequent editions produced by the Employment Service in 1949, 1965, 1977, and 1991. During this period, key research communities in industrial psychology were formed, including such figures as Sidney Fine, Ernest McCormick, Ernest Primoff, and Carroll Shartle, individuals who would later develop occupational analysis methodologies that substantially inform the current state of the art. Occupational analysis methodologies underwent further developments in and around World War II in both the military and the civilian sectors, as well as in their intersection. For example, during World War II the War Manpower Commission could sanction firms for labor pirating (Jacoby, 1985:262). That commission

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also asked firms in the civilian sector to classify jobs into the categories defined in the recently released Dictionary of Occupational Titles (DOT). The goals included providing better training and more systematic ways to transfer labor to needed areas. Social and behavioral scientists who did occupational analysis played key roles in the development of more sophisticated personnel selection, training, and promotion systems in occupations ranging from Air Force pilots to Navy submarine crews. Similarly in the civilian sector, by 1947 an estimated 20 percent of U.S. industrial firms used employment tests for hiring and placement (Jacoby, 1985). The development of occupational analysis after World War II is interwoven with: (a) the dramatic expansion of higher education, including the number of sociologists and economists specializing in the analysis of occupations and labor markets, and research and practice communities in industrial and organizational psychology, vocational guidance, and employment training; (b) the expansion of survey research activities facilitated by improved sampling and item analysis procedures as well as by the advent of statistical data processing via computers; and (c) the expansion of employers' efforts to systematize hiring, training, promotion, and compensation systems. Most of the major systems described in this chapter had their origins in this period. That includes the DOT, the Standard Occupational Classification System (SOC), the Occupational and Employment Statistics Classification System (OES), and the Military Occupational Specialties (MOS) system as it is underpinned by the Comprehensive Occupational Data Analysis Program (CODAP), which was not fully implemented until the 1960s (Mitchell and Driskill, 1996). Beginning in the late 1950s, a number of occupational analysis systems were developed for use in the private sector, many of which built on work completed in the military and government sectors (Fleishman, 1967, 1992; McCormick et al., 1972; McCormick, 1979; Cunningham et al., 1971; Cunningham, 1988). The SOC has recently undergone revision. The new system will be used by all federal agencies to collect occupational data; it will provide the occupational classification system for the 2000 census; and it will be used for coding jobs in the latest revision of

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the DOT, known as O*NET™. Although there is no external crisis, such as war or depression, driving the development of O*NET, this new system offers a response to many of the weaknesses in previous systems. At present, O*NET™ is at the prototype stage of development; moving it to an operational system will require a strong sponsor. In the committee's judgment, O*NET™ holds significant promise for dealing with changing work contexts and content. In a later section of this chapter, we provide a detailed description of its key features. Appendix A presents additional details and a discussion of its prototype development and evaluation. Types of Occupational Analysis Systems A useful way to organize a review of occupational analysis systems is to distinguish between systems that emphasize occupational categories and subsequent enumeration, and systems that emphasize descriptive analysis of the content of work. The characteristics of these two types of systems are quite different; the differences arise because their purposes, historical roots, and methods of development have differed. Wootton succinctly states this (1993:3–9): As was the case for the U.S. [Dictionary of Occupational Titles], other national dictionaries [descriptive analysis systems] were developed from the "bottom up" through expensive and extensive job analyses, mostly with enterprises. Specification of detailed occupations was weighted towards manufacturing and production occupations that were dominant in the early post-war period. Some countries, particularly English speaking nations, tended to borrow other countries' dictionaries before they had their own. Initial versions of dictionaries usually were developed between the 1940's and the early 1970's. Most statistical occupational classification systems [enumerative] were developed to serve the needs of population censuses. These structures usually were developed from the "top down" according to analytical principles. Occupational categories tend to be fewer in number and broader than those in dictionaries, and they provide little information other than occupational title, alternate titles, and a brief description of tasks. Occupational coding is based on these items. Aggregation principles often appear to be heterogeneous

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within the same system—a mix of tasks performed, function, industry, and education or training required. Figure 5.1 presents an abstract model that shows a useful way to conceptualize the relationship between descriptive analytic systems and category/enumerative systems. The rows in this matrix represent occupational categories. These categories can and do vary in their specificity, representing quite specific jobs in some systems (e.g., police detective) and relatively broad occupational families in others (e.g., public safety occupations). The columns in the matrix represent requirements or other characteristics that are descriptive of the categories or rows, for example, the knowledge and skills required by occupations (geography, biology, negotiation, troubleshooting), characteristics of the environment of occupations (work schedule, indoors/outdoors, presence of hazardous materials), and work activities completed in occupations (analyzing data or information, handling and moving objects, assisting and caring for others). The cell entries (e.g., cell 1,1; cell 3,2) are numerical values or other information that denotes the standing of each occupation on each attribute. For example, if attribute 1 is "presence of hazardous materials" and category 1 is "police detective," then the value in cell 1,1 might be a rating of the frequency with which police detectives encounter hazardous materials in the course of doing their jobs.     Occupational Attributes (e.g., required work activities, skills, knowledge, number of incumbents, compensation)    Occupational Categories (e.g., occupations, occupational families)                 Att. 1 Att. 2 Att. 3 . . . . . Att. ZZZ    Cat. 1 Cell 1,1 Cell 1,2 Cell 1,3   Cell 1,ZZZ    Cat. 2 Cell 2,1 Cell 2,2 Cell 2,3    Cell 2,ZZZ    Cat. 3 Cell 3,1 Cell 3,2 Cell 3,3    Cell 3,ZZZ    .             .             .             Cat. N Cell N,1 Cell N,2 Cell N,3    Cell N,ZZZ Figure 5.1 Matrix depicting conceptual relationship between categories of occupations and attributes of occupations.

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By conceptualizing the category/enumerative and descriptive analytic systems as the rows and columns, respectively, of a matrix, a more complete and integrated view of occupational analysis and structure is possible. Furthermore, if a hierarchy is imposed on both the rows and columns of such a matrix, allowing the aggregation and assembly at differing levels of specificity, then it is possible to think of a complete occupational analysis/structure system that can meet varying user needs. Our discussion is divided into three sections. The first section presents an overview of the descriptive/analytic systems; the second presents category and enumerative systems; and the third presents systems that combine the enumerative and descriptive approaches (e.g., DOT, O*NET™, MOS). Appendix B presents additional details of most of the systems mentioned. Descriptive Analytic Systems As noted above, descriptive analytic systems have as their primary purpose the detailed description of occupations in terms of a number of attributes. These systems often do not concern themselves at all, or only secondarily, with a particular structure or category system of occupations. The systems discussed below are primarily descriptive, borrowing their categorical structures from elsewhere or creating such structures for particular applied purposes, sometimes employing the descriptive data obtained through their system to create the structure. Table 5.1 contains summary information about six illustrative descriptive analytic systems. These systems, taken together, represent the long stream of research on occupational analysis arising out of the Great Depression and World War II eras, as well as more recent developments that attempt to capitalize on the lessons learned through that research. They illustrate the utility of descriptive analytic systems that are appropriate for a large proportion, if not all, occupations through the use of a set of common attributes for describing occupations. These systems rely on a variety of data sources described in Appendix B. They are listed below: Position Analysis Questionnaire: This is perhaps the best-known example of a worker-oriented job analysis technique

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TABLE 5.1 Type of Jobs, Descriptors, and Applications for Some Illustrative Descriptive Analytic Systems Name Types of Jobs Types of Descriptors Applications (lists are illustrative, not exhaustive) Position Analysis Questionnaire (PAQ) All Information input; mental processes; work output; relationships with others; job context; job demands Selection of employees; job evaluation, grouping and design; performance appraisal; position classification; job matching Fleishman Job Analysis Survey (F-JAS) All Abilities—cognitive, physical, psychomotor, sensory/perceptual and social-interactive; job skills and knowledge Job descriptions; selection of employees; classification (people into jobs); performance appraisal General Work Inventory (GWI) All Activities—sensory, information-based, physical, interpersonal; general mental and physical requirements; work conditions; job benefits Job description and grouping; selection and placement of employees Common Metric Questionnaire (CMQ) All Background; contacts with people decision-making; physical and mechanical activities; work setting Job description and evaluation; performance appraisal; position classification Multipurpose Occupational Analysis Systems Inventory-Closed Ended (MOSAIC) All federal jobs Tasks; competencies; personal and organizational styles Position description; position classification; selection of employees Work Profiling System (WPS) Managerial or professional; service or administrative; manual or technical Job tasks; job context Selection and placement of employees; performance appraisal; job design, description, and classification NOTE: This table is adapted with permission from Peterson and Jeanneret, 1997:36–44.

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(McCormick et al., 1969). It has a long history of development and research and its strengths and weaknesses are well known. The questionnaire has 187 items listing work behaviors and job elements at a level of abstraction that permits work to be described across a broad range of occupations. Fleishman Job Analysis System: This system is likewise based on a long history of research, primarily in the area of human ability testing (Fleishman and Quaintance, 1984). A unique contribution of this system was the development of behaviorally anchored rating scales to assist subject-matter experts (job incumbents, supervisors, or other persons knowledgeable about the jobs to be rated) in estimating the amount of each ability required to perform a job or job task (Fleishman, 1992). General Work Inventory: This inventory grew out of the research on the occupational analysis inventory (Cunningham et al., 1990; Cunningham, 1988) and had its origins primarily in occupational education and guidance. Common Metric Questionnaire: This is a more recently developed "worker-oriented" job analysis instrument intended to apply to a broad range of jobs and to overcome some of the perceived inadequacies of earlier systems, particularly the relatively difficult reading level of descriptor items and the relative (as opposed to absolute) nature of the ratings obtained for jobs (Harvey, 1991). Multipurpose Occupational Systems Analysis Inventory-Closed Ended (MOSAIC): This system, recently developed by the Office of Personnel Management, is designed to collect and distribute data about tasks and competencies (combinations of knowledge, skills, and abilities) for occupations within large occupational families (Gregory and Park, 1992). Work Profiling System: This system likewise uses instruments tailored to occupational families rather than a single instrument intended to apply across all jobs in the workforce (Saville and Holdsworth Ltd. USA, Inc., 1990). Category/Enumerative Systems This section describes international systems, national systems outside the United States, and major U.S. systems. In the discus-

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sion of U.S. systems, we focus attention on the current revision plans for the Standard Occupational Classification. This revision promises a number of important advances over existing systems, including the provision for assimilating new occupations into the system on an ongoing basis. International Standard Classification of Occupations The International Standard Classification of Occupations (ISCO) has three objectives: (1) to facilitate international communication about occupations through its use internationally; (2) to provide international occupational data for research, decision making, and other activities; and (3) to serve as a model, but not a replacement, for countries developing or revising their national occupation classifications. The development of this structure was based on the recommendations and decisions of the Thirteenth and Fourteenth International Conferences of Labour Statisticians, held at the International Labour Office, Geneva, in 1982 and 1987. The underlying source data consist of population censuses, statistical surveys, and administrative records maintained at the national level. ISCO-88 is the 1988 revision of the 1968 version of the classification system (International Labour Office, 1990). The ISCO system uses two key concepts: job and skill. Job is defined as "a set of tasks and duties executed, or meant to be executed, by one person." Skill is defined as "the ability to carry out the tasks and duties of a given job." The ISCO-88 structure is hierarchical, with 10 major groups at the top, 28 submajor groups, 116 minor groups, and 390 unit groups. Although ISCO-88 was not intended to be the single structure that would fit all nations, many nations have adopted the ISCO system with little or no modification (Elias, 1993). Western nations have tended to make more substantial modifications to ISCO-88 or to devise their own structures (Wootton, 1993). Australia, Canada, the United Kingdom, and the Netherlands are primary examples. Table 5.2 presents comparative information about ISCO-88 and its adaptation for use in those countries. Shown are the key classification concepts, the number of levels in the hierarchical ladder, and the number of occupational groups in each level. All of the systems have fewer than a dozen broad

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expanded contents of questionnaires (e.g., new or additional knowledge, skill, and ability), mode of data collection (paper and pencil questionnaires versus computer-administered or Internet-administered), and studies designed to further illustrate the validity and utility of O*NET™ data for various applications. Practical issues concerning reading level and length of O*NET™ questionnaires should also be further investigated from the stand-point of gathering incumbent data for the nearly 1,100 occupations remaining. Scientific Quality of the Enabling Potential of the System Because of its relational database structure, O*NET™ not only can assist in new job design/redesign but also can serve as an analytic framework for monitoring changes in the nature of work. Looking to the long-term future, one can envision a decentralized data collection design in which a multitude of individual users from across the economy—perhaps each with different purposes and level of use of the system—might contribute incumbent data on job descriptors organized into a system for identifying new jobs and work arrangements and regularly updating the information in the database. Dramatic changes in the uses of information technology in the workplace and at home increasingly bring such a system closer to the present. As with the 1998 SOC, accommodating new occupational information that has implications for content and structural changes to an existing database is a technical challenge that should be addressed by a standing O*NET™ Revision Policy Committee. We advise that such a mechanism be established. Coordination with Other Analysis and Classification Systems At present there appears reasonable prospect of the coordinated use of a single occupational classification system (the 1998 revised SOC) by various federal agencies, one that would be seamlessly interleaved with O*NET™. The committee fully endorses such coordination. For mappings or cross-walks to other systems (i.e., historical, international, private-sector), it is important to know the scientific quality of the cross-walks and the locus

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of responsibility for their generation, maintenance, and quality control. Military and Civilian Systems The military and civilian occupational analysis and classification systems are presently integrated through the use of cross-walks. The revised SOC more fully integrates military occupations into the civilian sector. However, the descriptive information on military occupations is largely restricted to lengthy lists of discrete tasks that do not generalize across occupations. Some work has been completed in the Air Force using more generalized job descriptors like those in O*NET™ to analyze military occupations (Ballentine et al., 1992; Cunningham et al., 1996). O*NET™, if used by the military services, could serve as a common language to describe military and civilian occupations, enhancing the ability to move personnel smoothly across both sectors in cases of national emergency, or even for the relatively more common task of placing veterans into civilian occupations. Some questions surrounding this issue include: How well does O*NET™ meet military needs going into the next century? What additions (or deletions) to O*NET™ would be required to make it a practical and valuable tool for the military? What synergies can be attained by using the same system in military and civilian sectors? Ownership, Control, and Liability Who will own and maintain quality control over future editions of O*NET™? A federal entity? A private but federally funded entity? A private entity? What are the legal implications—if occupational classification technologies are misused, who will be liable? What are the costs of fully developing and maintaining O*NET™? Who will pay? What are the ownership, privacy, and liability implications for users and for applications that are based on O*NET™ databases and methodology? Answering these questions will be part of the full implementation of the system. We urge the U.S. Department of Labor to address and resolve these issues soon.

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Moving from Prototype to an Operational System As we have emphasized elsewhere in this chapter, moving O*NET™ from a prototype to a fully operational system is a significant undertaking. Data collection is paramount in this regard. Some of the more important issues in data collection include identifying samples of establishments and data sources (incumbents, analysts, or others); securing cooperation from establishments and sources; accounting for important contextual variations within occupations; and appropriately aggregating data for use in the O*NET™ database. These issues and data collection, in general, must be approached in a systematic manner. Clearly, there is a requirement for a long-range plan that identifies which occupations will be targeted for data collection at what times, both for collecting the original O*NET™ data and for updating that data on a regular basis. Such a schedule can be difficult to adhere to, but it seems essential to do so if the database is to maintain integrity and credibility with users. Probability sampling of establishments and data sources is the most desirable method for ensuring the representativeness of the obtained data, and it is perhaps the best way to ensure that contextual variations are accounted for in the data. However, simple probability sampling approaches may not always be feasible. For example, the distribution of members of some newer occupations across establishments may not be well known. Also, as we note elsewhere, some occupations may not be easily accessed through establishments because individuals in those occupations are primarily self-employed. More targeted approaches may be necessary in such cases. Such approaches could include the use of unions, professional associations, business groups, or other institutions to assist in identifying samples and data sources and in securing cooperation from members of the occupation. Given that random sampling, stratified random sampling, and nonrandom targeted sampling approaches are likely to be used and that, at any given point in time, data sources may include occupational incumbents, supervisors, job analysts, or some other type of occupational expert, it seems important that extreme care be taken in both aggregating data to the individual occupational level and in labeling the approaches taken and sources used in the collection of data for occupations.

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There are approximately 1,100 occupations anticipated in the O*NET™ database if, as planned, the Standard Occupational Classification forms the basis for the O*NET™ occupational classification scheme. Using the standard of five years for currency of occupational information, it becomes clear that the populating and maintenance of the O*NET™ database is an enormous undertaking. If all occupational information is outdated after five years, then data for about 220 occupations must be collected and integrated annually. Of course, it may be that many occupations will not require updating on this frequency, but others may require more frequent updating. It seems that a systematic method of monitoring changes in occupations to identify "out-of-sequence" needs for updating, as well as a regular schedule for updates, would be essential. All of these activities are both technically and practically feasible given adequate resources. An essential element is securing the cooperation of establishments and data sources identified as possessing the knowledge to provide data for O*NET™. Usually, a job analysis is carried out for an organization that, for one reason or another, wishes to obtain information immediately. This ensures a fairly high level of cooperation from establishment(s) and people in the organization knowledgeable about the occupation or occupations that are the foci of the analysis. Such is not the case for the O*NET™ database. It is a national database intended for widespread use by a variety of users and, as such, constitutes an extremely valuable resource for many institutions, organizations, and individuals. However, for each individual establishment and data source, randomly or otherwise selected and asked to cooperate in providing data, there is no immediate payoff for offering the access to data sources and time taken to provide information. In a few years, the incentive to cooperate may be the visible utility of the O*NET™ information in a variety of applications useful to schools, government organizations, private-sector businesses, individuals, unions, and others. Unfortunately, that situation does not yet exist. Therefore, it may be necessary to consider a variety of more immediate incentives to encourage cooperation—for example, money, provision of services such as customized data analysis and reports, or symbolic recognition of some sort (e.g., an O*NET™ all-star organization).

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New Technological Horizons As of this writing, O*NET™ has just been released in its first operational version, called O*NET™ 98. The U.S. Department of Labor anticipates that O*NET™ 98 will enable DOT users to prepare for O*NET™ in the 21st century, when it will formally replace the DOT. The capabilities of O*NET™ as an electronic repository of occupational information, in conjunction with the Internet as a global communications medium, expand the potential of the computer software industry to serve the needs of users of occupational information through the development of highly customized applications interface software. It is also possible, and therefore likely, that the technologies that will facilitate access of the user community to O*NET™ will also be capable of providing information to the Department of Labor (or other O*NET™ maintenance entities) about the uses of O*NET™ data. These technologies could also be used in data gathering to update O*NET™'s databases. Throughout this volume we have repeatedly emphasized the need for up-to-date occupational information systems that serve the needs of job seekers, career counselors, training specialists, public-and private-sector employers, and state and federal labor and manpower specialists. Occupational information that is broadly encompassing of work as it is performed by a substantial majority of the American workforce, coupled with user-friendly technologies that make the information accessible to users, will facilitate achievement of such objectives as designing new jobs and redesigning existing ones, combining similar jobs or splitting overly complex ones, creating teams and cross-training members, and maintaining systems for staffing, training, and compensation. This new technical potential may fundamentally alter what is accomplished with occupational information, how it is accomplished, and by whom. Furthermore, technology is likely to keep changing how these systems run and who runs them. For example, making occupational analysis systems available over the Internet may change the way employers recruit and screen candidates, and it may change who does the recruiting and screening. America's Job Bank is an example of an employment service sponsored by a partnership between the Department of Labor and the

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state-operated Public Employment Service that has been available on the Internet for several years (at www.ajb.dni.us). The number of Internet sites that provide similar services, targeted to particular geographic regions, occupations, and industries, is proliferating rapidly (e.g., the ComputerJobs Store, Inc., sponsors a web site for those seeking jobs in the computer industry at www.computerjobs.com). As we discuss in detail in Chapter 6, similar challenges face the U.S. military as it adapts its occupational structures to new technologies and changing missions. For example, the U.S. Army task lists are 50 percent shorter than in the past, and they are now updated by training school personnel rather than Army Research Institute staff. The committee anticipates that the availability of O*NET™ will accelerate the development of applications software available from private-sector vendors to serve the needs of the user community. It seems likely that increasing use of O*NET™ databases will increase pressure on the Department of Labor to complete the full database and maintain its currency. It also seems likely that occupational software applications will proliferate, with competitive pressures of the marketplace determining product success. We note that the same phenomenon occurred for products based on the DOT; however, because of the much greater scope of work descriptors of O*NET™ and its electronic medium, we expect that the number and variety of occupational software products available within a few years will be much greater than in the past. Research and developmental work are needed in both the public and private sectors before these predictions become reality. However, the advent of O*NET™ and the Internet make some version of this scenario virtually inevitable. We conclude with speculations about how O*NET™, when coupled with applications software, could satisfy user needs and, in so doing, contribute to national economic development. Specifically, we provide two brief illustrations of how O*NET™-based occupational information technology could be used to address today's workforce challenges. Consider the cases of Sal Carpinella and Stan Adamchick, two workers displaced by a defense shipyard in Philadelphia. They found their way into new jobs with the help of career search and

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job placement software available at the career transition center on base. If Sal and Stan were to face a similar situation again, they could, in the not too distant future, conduct their career searches with their home computers and the Internet. Sal, who wished to remain an electrician, could request a listing of job openings for electricians in the Philadelphia area. He could also search by O*NET™ occupational code number, or SOC code, to produce a somewhat broader range of options (jobs that require the skills of an electrician but that have a different title would also appear) and could filter the solution further according to employer requirements (e.g., amount of related work experience required, work schedule), job characteristics (e.g., whether the work is performed in a team context, the type of equipment to work on), or other features of work described by the hundreds of work descriptors in O*NET™. Once suitable prospective employers were identified, Sal could electronically transmit his resume to them. Stan's use of the same Internet occupational database might be quite different than Sal's, due to his desire to change career directions. Stan could explore his options by filtering the database for occupations that match his interests (e.g., thinking creatively) and skills (e.g., troubleshooting problems with electrical systems). He could then investigate the occupations that meet his criteria in great detail, browsing the work descriptors for each occupation. Finally, he could explore the job prospects in his geographic area by searching, as did Sal, for employers with job openings in his chosen field. Consider another scenario, the case of Tom Johnson, a business unit manager of an auto plant. His company has evolved its vehicle assembly process from a traditional assembly line operation with narrowly defined sequential jobs to team-based assembly with closely coordinated jobs. Workers are now organized into assembly teams and cross-trained to perform a broader array of tasks. This process redesign has paid off in fewer defects per vehicle. Tom is contemplating additional changes by linking technical support staff (helper-mechanics, industrial engineering technicians) more closely to teams, perhaps using a modified matrix model. He is uncertain whether it would be more efficient to add competencies to assembly teams by adding members with de-

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sired skills, or to expand the skill sets of team members via training. Tom and his assistant Jill Turbanski use a software application based on O*NET™ to help answer his questions. They begin by identifying the jobs in the O*NET™ database that correspond most closely to the assembly team and staff support jobs, considering such factors as title, tasks performed, and tools and equipment used. Examination of the broad array of descriptors in the O*NET™ database, however, leads to a discussion between Tom and Jill about which factors are most important to consider in deciding between the alternatives. Jill suggests that a job matching function in the database can be used to explore the similarities and differences between the jobs. Before running the match, they decide to limit the factors included to the specific knowledge, skills, and abilities required for each job. They know that the tasks performed by incumbents of each job are different (hence the idea to add capabilities to the team), so there is no point in comparing jobs on tasks performed. Likewise, there is little need to consider other more general types of work descriptors for this purpose, such as generalized work activities and personality and interest variables, as the issue is work process redesign rather than staffing or vocational counseling. Comparing jobs on knowledge, skill, and ability requirements is the appropriate level of detail, as it addresses the capabilities of the workers to perform the work. Tom and Jill focus on the top six factors in each category for each job, with factors ranked according to level or amount of each required. In a nutshell, this analysis reveals that the similarities between the assembler and helper-mechanic jobs are much greater than those between the assembler and industrial engineering technician jobs. Assembler and helper jobs require similar amounts of knowledge of mechanics (i.e., machines and tools), engineering and technology (i.e., uses of equipment, tools, and mechanical devices), building and construction (i.e., materials, methods, and appropriate tools), and mathematics (i.e., numbers, their operations and interrelationships). Likewise, they correspond closely on the skill levels required in the areas of operation and control (i.e., controlling operations of equipment), installation (i.e., installing equipment, machines, wiring, or programs), and equipment selection (i.e., determining the kind of tools and

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equipment needed). Finally, they require similar amounts of ability in manual dexterity (i.e., making coordinated hand movements) and near vision (i.e., seeing details at close range). There is little correspondence between the knowledge, skills, or abilities required of assemblers and industrial engineering technicians. Tom decides to increase the equipment maintenance and repair skills of the assembler teams by training selected team members. He increases the capabilities of the teams to monitor and optimize their work processes by designating an industrial engineering technician as a part-time member of each team. Before instituting these changes, Jill will gather local data on specific knowledge and skills of both assembler and engineering tech jobs at the plant to supplement O*NET™ information. These data will be used both as a check on O*NET™ results and, being more detailed, in designing the equipment maintenance training program for assemblers. Final Comment It may be that O*NET™ and the SOC will receive adequate human and financial resources and will fulfill the objectives that they have been designed to meet. Even if they do not, however, we believe the nation needs an accurate, current occupational classification system that encompasses both occupational categories (the SOC role) and occupational attributes (the O'NET™ role) as depicted in Figure 5.1. Individuals and institutions need the information in such a system to plan their futures in the changing world of work, and the development and maintenance of the information in the system is unlikely to be successfully achieved by the private sector. The private sector will no doubt do a superb job of shaping the information resident in the system to meet a wide variety of anticipated and unanticipated consumer needs, but the resources necessary to collect, screen, and integrate occupational data into the system in a timely fashion are properly found in the public sector. A timely and flexible national occupational information system, although a monumental undertaking, is an indispensable public resource and should be supported by public funds. If these data collection and quality control functions are left to

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the private sector, it is the committee's opinion that the result will be a patchwork of databases of uneven or unknown quality. Larger, better-financed private-sector organizations or industry sectors may develop occupational information systems to meet their needs, but these systems would, naturally enough, cover only those occupations and descriptive variables that would be of most current interest to them. They may, again understandably, maintain proprietary control over the information to retain a competitive edge. Smaller organizations and sectors may have no systems at all. Trying to put together a national occupational database by building on such a collection of independently developed databases is doomed to failure. Developing and maintaining a common, national occupational database allows private organizations and individuals to apply their resources to augment, enhance, and build on the national information to compete in a global marketplace. Competition is not eliminated, it is likely to be raised to another level—a good thing for the entire country. One missing element in the present vision of an occupational information system is a closer tie-in of the day-to-day labor transactions and the occupational information system. If daily recruiting, hiring, and firing activity could be linked with occupational categories and, in turn, with the associated skills, abilities, and other attributes of the categories, then trends in desired or required occupations and occupational attributes could be more dynamically monitored. Furthermore, historical data could be accumulated that would be extremely useful for disentangling relatively minor or momentary trends from longer-term shifts in the world of work, something that we have shown to be a difficult undertaking. It seems to us that many of the pieces for such a linkage are already or nearly in place. To achieve a dynamic, accurate occupational information system, some compromises will no doubt need to be made between breadth of coverage (numbers of occupational categories), depth of coverage (number and types of attributes of occupations), and currency of information (frequency of updating). One possible compromise that strikes us as attractive is to forgo the routine, random sampling of small-frequency occupations or occupations that are extremely difficult to access. The cost per bit of informa-

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tion may be extremely high for such occupations. Such occupations should not be ignored, especially not those that are thought to be emerging or fast-growing occupations, but more directed efforts at data collection that are less costly should be considered. For example, it might be possible to sample from locations where the occupations are known to exist in some numbers and to use matrix sampling of occupational attributes (rather than collecting data on all attributes). Making predictions about the future is hazardous in any field, and the future of occupations is no exception. However, the existence of an accurate, comprehensive, current occupational database, especially if it is linked to labor transactions as mentioned above, should provide an empirical base for making statistical, algorithmic projections as well as a solid footing for subjective estimates by occupational experts of all stripes.