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Skilled technical occupationsâ€"defined as occupations that require a high level of knowledge in a technical domain but do not require a bachelor's degree for entryâ€"are a key component of the U.S. economy. In response to globalization and advances in science and technology, American firms are demanding workers with greater proficiency in literacy and numeracy, as well as strong interpersonal, technical, and problem-solving skills. However, employer surveys and industry and government reports have raised concerns that the nation may not have an adequate supply of skilled technical workers to achieve its competitiveness and economic growth objectives. In response to the broader need for policy information and advice, Building America's Skilled Technical Workforce examines the coverage, effectiveness, flexibility, and coordination of the policies and various programs that prepare Americans for skilled technical jobs. This report provides action-oriented recommendations for improving the American system of technical education, training, and certification.
Information about the characteristics of jobs and the individuals who fill them is valuable for career guidance, reemployment counseling, workforce development, human resource management, and other purposes. To meet these needs, the U.S. Department of Labor (DOL) in 1998 launched the Occupational Information Network (O*NET), which consists of a content model-a framework for organizing occupational data-and an electronic database. The O*NET content model includes hundreds of descriptors of work and workers organized into domains, such as skills, knowledge, and work activities. Data are collected using a classification system that organizes job titles into 1,102 occupations. The National Center for O*NET Development (the O*NET Center) continually collects data related to these occupations. In 2008, DOL requested the National Academies to review O*NET and consider its future directions. In response, the present volume inventories and evaluates the uses of O*NET; explores the linkage of O*NET with the Standard Occupational Classification System and other data sets; and identifies ways to improve O*NET, particularly in the areas of cost-effectiveness, efficiency, and currency.