Information Technology and the U.S. Workforce Where Are We and Where Do We Go from Here? (2017) / Chapter Skim
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It offers evaluations of what is known, notes open questions to be addressed, and identifies promising research pathways moving forward.
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A particular set of algorithms, called deep neural networks, have been a driver of recent advances in areas such as computer vision, speech recognition, and text analysis. The increasing generation of online data is expected to further fuel the development of these machine learning systems.
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Computer performance continues to improve via advances in hardware parallelism, hardware specialization, and enhanced programming languages. Beyond speedup, a broad range of progress has been seen in important technologies such as the mobile Internet, the Internet of Things, cloud computing and storage, AI, robotics, virtual and augmented reality, and machine learning.
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There is evidence that the diffusion and successful adoption of IT advances is time- and resource-intensive, producing a lag, possibly measured in years or even decades, between technological advances and resulting productivity growth.2 Emerging evidence suggests that this diffusion is increasingly uneven, leading to bigger productivity gaps between frontier firms and those in the middle of the distribution.3 Income and wealth inequality has increased over the past 20 years in the United States, with median family income stagnating while incomes rose significantly for the top 1 percent; significant disparities also exist among the other 99 percent, largely correlated to a rising premium of education. The share of wealth owned by the bottom 80 percent has fallen 1 J.G.
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Not only is the traditional employment model changing, but nontraditional models are increasingly facilitated by technology. For traditional firms, despite a burst of new start-up companies in many areas, statistics suggest 4 For this and other statistics on wealth inequality, see E.N.
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This partly reflects our inadequate understanding of the complex interactions among technologies themselves combined with the skills, organizations, institutions, policies, and human preferences in society. 7 Measured either by total job creation plus job destruction, or by total hires plus separations, or by geographic mobility of workers.
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See Udacity, 2016, "About Us," Udacity, https://www.udacity.com/us, accessed May 2016. 12 Such sources include data from the Current Establishment Survey, the Quarterly Cen sus of Employment and Wages, the Current Population Survey, the Decennial Census, the American Community Survey, the Job Openings and Labor Turnover Survey, and the Business Employment Dynamics and Business Dynamic Statistics data.
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Although current digital platforms for on-demand work directly involve less than 1 percent of the workforce, they display significant growth potential.
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improved, ongoing tracking of workforce and technology developments would be of great value for informing public policies, organizational choices, and education and training strategies. A RESEARCH AGENDA Federal agencies or other organizations that sponsor research or collect data relevant to technology and the workforce should establish a sustained, multidisciplinary research program in order to address the many important yet unanswered questions about how technology is changing, might change, or could help to shape the nature of work and the U.S.
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Such research could focus on the following objectives: • Develop, refine, and test improved strategies for classifying technological capabilities in terms of the human skills and tasks they can or could replace. • Identify key indicators that could signal the extent of the impact of developments in a given technological field.
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Research tracking and mapping the changing labor and skills demands in specific industries and occupational fields over time, along with regional variations and associated policy implications, could provide insights into such trends. This research could evaluate the extent of IT diffusion into different occupational fields.
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Because education will significantly determine the success of the United States in responding to the changing workplace, a better understanding of effective strategies is critical. While the United States has a poor track record of predicting future workforce skills demands, some insight can be gained from how skill demands are currently changing.
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• Developing new data sources and methods by creating new partnerships to provide researchers access to private-sector data, including new strategies for collecting and using born-digital data from multiple public and private sources and developing appropriate machine-learning and data-mining approaches to analyze this data. Research could also be conducted on providing alternate, more frequently updatable, and potentially even automated methods of obtaining information typically generated through cost-, labor-, and time-intensive survey methods.
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Advances in IT and automation will present opportunities to boost America's overall income and wealth, improve health care, shorten the workweek, provide more job flexibility, enhance educational opportunities, develop new goods and services, and increase product safety and reliability. These same advances could also lead to growing inequality and decreased job stability, increasing demands on workers to change jobs, or major changes in business organization.
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