Download Science and Technology Development and more Study notes Library science in PDF only on Docsity!
How science and technology developments
impact employment and education
Abstract
A better understanding of how developments in science and technology influence the creation of new occupations and subsequent changes in educational programs can help decision makers at all levels of our society. As a result of research and development efforts, innovations are achieved, resulting in the creation of new occupations and the demand for employees with expertise in these new areas. To fulfill the demand, universities and colleges often revise their programs to address these needs. Several data sources are described in this paper that might help us to explore the relationship between advancements in industry, emerging occupations, and educational changes over time. job analysis emerging occupations R&D funding population projections In this paper, I explore how one might understand the way advances in science, engineering, mathematics, and technology impact employment and education, with the ultimate goal of possibly predicting when these changes are likely to occur. The overall concept is that new developments in science and technology become widely applied in industries as they are expanded and improved upon. This results in a demand by employers for expertise in the new areas and often results in new occupations being defined. It is usually at this point that universities and colleges revise their programs to address the need by employers to fill new occupational specialties. For example, the demand by employers for expertise in big data, predictive analytics, and machine learning in the past 5 y or so has prompted many universities to create degree programs in data science. I describe several data sources in this paper, most of which come from the US Federal Government. We need information on all stages of the process over time (i.e., advances in science and technology, changes in employment and industry, and new degree and certificate programs at universities) to understand the historical trends and how the separate pieces interact. The most useful information will likely come from changes in occupation and industry, which should be reflected in classifications systems like the North American Industry Classification System (NAICS) and the Standard Occupational Classification (SOC) system. Thus, I describe these systems in this article. The United States has provided federal funding for research and development (R&D) for many years, with the largest shares going to the Department of Defense, Department of Health and
Human Services, Department of Energy, National Science Foundation (NSF), NASA, Department of Agriculture, and Department of Commerce ( 1 ). We can use information about government funding programs, published timelines for disciplines (e.g., computer science, statistics, mathematics, science), and data from the National Center for Science and Engineering Statistics to establish the historical trends in science and technology developments. Throughout the article, I propose some research directions to explain these interactions based on historical trends and changes in science and technology developments, occupations, and university environments.
Employment Projections from the Bureau of Labor Statistics
The Bureau of Labor Statistics (BLS) has been publishing employment projections since 1960, with the goal of providing information on career opportunities to students, jobseekers, and policy makers. Every 2 y, the BLS publishes projected employment 10 y into the future for over 300 different industries and 800 occupations. The latest projections for the period 2016–2026 were published in October of 2017 ( 2 ). I describe the process here because it informs our concept of the interactions between industry and occupational employment. The employment projection process involves a series of six major modeling steps, as illustrated in Fig. 1 ( 3 ). Each of these steps is based on different models, processes, and associated assumptions ( 4 ). It is important to note that important assumptions are made at the different modeling steps, such as the full employment assumption in the macromodel used for aggregate economy projections. All modeling assumptions are clearly described by the BLS ( 4 ). Download figure Open in new tab Download powerpoint Fig. 1. These six steps provide an overview of the process the BLS uses to project employment 10 y into the future. We can think of these as four major aspects of the process: ( i ) the size of the future population determines the labor force, ( ii ) the labor force drives the possible size of the future economy, ( iii ) certain industry and employment levels will be needed to achieve the future
for projected employment are nonfarm payroll employment, labor productivity, and GDP. These variables constrain the industry output and employment projections. Industry Projections. The projected demand is a key factor in determining future jobs. In this step, the projections of final demand from the macroeconomic model of the economy are disaggregated into detailed categories. These are used to estimate the types of commodities purchased within each of these categories. The output is a final demand matrix, where the rows correspond to demand categories and the columns represent commodity groups. This results in a detailed distribution of the GDP, which provides the demand component of an interindustry model of the economy. The GDP looks at sales to final purchasers and not at the intermediate purchases required to create the final product. For example, the GDP would include the purchase of a car, but not the steel used to build it. The input-output (I-O) model in this step of the process yields an industry- level estimate of the output and employment required to produce a given level of GDP. The I-O model requires four tables. The use table shows the use of commodities by industry, and the make table indicates the commodity output of each industry. These are converted to coefficient form and then used to derive the direct requirements table and the market share table, respectively. The direct requirements table shows how industry uses commodities in its production process, and the market share table indicates the commodity output of each industry. A relationship derived by the Bureau of Economic Analysis converts a projection of commodity demand into a projection of industry output, using the direct requirements and market share tables, as shown here: where g is a vector of domestic industry output by sector, B is the direct requirements table, D is the market shares table, and e is a vector of final demand by commodity sector. The employment required to produce the projected industry output is determined next. Industry output, industry wage rate relative to output price, and time are used in a regression model to estimate hours worked by industry. Average weekly hours for each industry are also estimated as a function of time and the unemployment rate in this modeling step. These data on hours are used to derive wage and salary employment by industry. Occupational Employment. The BLS produces occupational employment projections in this final step and publishes them in the National Employment Matrix. This matrix provides information on employment in detailed occupations within wage and salary industries and for different classes of workers. These are counts of nonfarm wage and salary jobs (the largest group), self-employed workers, agricultural industry workers, and workers in private households. This information is provided for the base year and the target year.
The BLS explores several factors that can affect the demand for an occupation within an industry. These include technological innovation, changes in production methods, replacement of a product, and more. It is interesting to note that the BLS also models and estimates the number of job openings resulting from separations due to employees migrating to other positions or leaving the labor force and includes this information in the National Employment Matrix.
Industry and Technology Developments
NAIC System. Changes in industry demand and technological innovations are important factors affecting future occupational employment, as we saw in the previous section. Furthermore, the projected employment published by the BLS is given for detailed industries and occupations. Thus, I describe the industry classification systems used by the BLS and other federal agencies. These systems provide a framework for assigning codes to establishments, allowing for consistent data collection and analyses of economic statistics in industries over time. Federal statistical agencies used the Standard Industrial Classification (SIC) system in 1939 when it was first published by the former Bureau of the Budget, which is now the Office of Management and Budget (OMB). Like all classification systems, it was updated periodically. However, economic changes, such as the emerging services-oriented economy, increased use of computers, rapidly evolving technology, and globalization, motivated the need to change the industry classification system. In 1992, the OMB created the Economic Classification Policy Committee to develop a new industry classification system. The committee worked with statistical agencies in Canada and Mexico to develop the NAICS. In contrast to the SIC system, this system was based on production, which eliminated definitional differences and focused on emerging economic activity. The NAICS was first introduced in 1997 partly to account for the increase of services relative to manufacturing, which needed to be accounted for in an industry coding system. The NAICS is reviewed periodically to reflect changes in the North American economies ( 7 , 8 ). The NAICS uses a six-digit hierarchical coding system. It categorizes economic activity into 20 industry sectors. These sectors can be grouped into those that are mainly goods-producing or services-providing sectors. As an example, a sampling of NAICS codes at the two-digit level is shown in Fig. 2. Economic analyses often use finer detailed NAICS codes at the three-digit or even the six-digit level.
federal R&D dollars ( 9 ). The DoD funding agency that has been around the longest is the Office of Naval Research (ONR) ( 10 ). The ONR was established in 1946 to continue the collaboration between government, academia, and industry started during World War II, which resulted in many technological innovations. It is interesting to note that the ONR predates the NSF, which was founded in 1950. I discuss how to gather historical information on federal funding for R&D using the ONR as an example. A research agenda and calls for proposals are published in solicitations or broad agency announcements. Current and past solicitations for their programs are available on the ONR website ( 11 ) and should be similarly available on other funding agencies’ websites. There are two other potentially useful sources of data on funding: the Small Business Innovation Research (SBIR) program and the Multidisciplinary University Research Initiative (MURI). These two programs are associated with all arms of the military, not just the Navy. The MURIs are large efforts funding interdisciplinary teams of academic researchers. The topics are proposed annually by DoD program officers and are selected based on their potential for producing dual-use technologies critical for national defense and commercial applications. The MURI awards are typically funded at a much higher level than single-investigator awards to foster innovations and to accelerate the research. The SBIR program provides funding to small businesses to support and stimulate technological innovation in industry. Like the MURI program, SBIR topics are developed by program officers in participating federal agencies ( 12 ), and they reflect opportunities to further develop and commercialize advancements in research and technology. SBIR topics for the past 10 y are available on the web ( 13 ). Business and industry also fund R&D. Data on these investments have been collected via the Business R&D and Innovation Survey, which is a survey conducted by the Census Bureau for the National Center for Science and Engineering Statistics ( 14 ). This is an annual survey of companies in manufacturing and nonmanufacturing industries. The survey provides information on funding levels, type of funding, employment, occupations, innovations, and intellectual property for various NAICS levels. Timelines in Science and Technology. Timelines for major advancements in various disciplines, such as mathematics, statistics, computer science, physics, and engineering, can also be informative. These exist on the web, and a simple search will provide many resources and timelines. However, web-based sources can be unreliable and error-prone, so it is wise to use data from government agencies, reputable companies, and professional associations. Other potential sources of information on how science and technology change over time are the professional associations, such as the AAAS, the Association for Computing Machinery, and the American Physical Society. These large professional societies often have groups that focus on specific topic areas in their discipline. The historical development of these groups can provide a timeline of advancements in science and technology. For example, the American Statistical
Association (ASA) has sections focusing on specific areas or applications of statistics. These sections are usually established once there are sufficient advancements in the area and membership to justify the section. Fig. 3 shows when ASA sections were established and includes some interesting milestones along the way.
