Patterns of Advanced Technology Adoption and Manufacturing Performance
Beede, David N., Young, Kan H., Business Economics
Until recently, evidence for the contributions of technology to jobs, productivity, and earnings was based on highly aggregated country- or industry-level data (e.g., see Fagerberg 1994), on relatively small-sample surveys of manufacturing plants or firms, or on anecdotal evidence. However, a large data set collected at the plant level in the 1988 Survey of Manufacturing Technology provides information on how seventeen specific advanced technologies (see Table 1) are used in approximately 10,000 plants in five manufacturing industry groups: fabricated metal products; industrial and commercial machinery and computer equipment; electronic and other electric equipment and components except computer equipment; transportation equipment; and instruments and related products (SIC 34-38). Researchers at the Census Bureau's Center for Economic Studies (CES) have augmented these technology adoption data with the plant performance data for 1982 and 1987 from CES's Longitudinal Research Database.(1) The resulting data for nearly 7,000 plants are the basis of the present study.
CES researchers used the same data set in earlier studies of advanced technologies and plant performance, e.g., Dunne and Schmitz (1995). In addition, Doms, Dunne, and Roberts (1994) supplement these data with data from the 1991 Standard Statistical Establishment List (SSEL). Doms, Dunne, and Troske (1994) add data from the Worker-Employer Characteristics Database (WECD). The WECD matches employee data from the 1990 Census of Population to establishment-level data (from the 1987 Census of Manufactures) on their presumed workplaces. McGuckin, Streitwieser, and Doms (1996) used the 1993 Survey of Manufacturing Technology (which was similar to the 1988 survey) and linked it to data from the 1992 Census of Manufactures and the 1988 Survey of Manufacturing Technology. Because these studies are closely related to the present study, we briefly review them below (see Alexander 1994).
Doms, Dunne, and Roberts (1994) find that plants that adopted more of the SMT technologies experienced higher rates of employment growth and lower closure rates than otherwise similar plants using fewer technologies. The analysis controls for variables that may be associated with employment growth and plant survival, such as productivity and capital-labor ratio.(2)
Dunne and Schmitz (1995) find that the "most technology intensive" plants (i. e., those plants that used six or more of the seventeen SMT technologies) paid wage premiums of about 16 percent to their production workers and 8 percent to their nonproduction workers, compared with otherwise similar plants. Their regression analysis suggests that the technologies explain up to 60 percent of the estimated wage premium paid by large plants. They also find that the most technology-intensive plants employed relatively more nonproduction workers than otherwise similar plants.
Some evidence suggests that the technologies are complements to human capital but still have a positive association with wages. In particular, Doms, Dunne, and Troske (1994) find that including data on workers' education and occupation in regressions similar to those estimated by Dunne and Schmitz diminished but did not eliminate the positive and statistically significant association between technology adoption and wages. Their study also analyzes the association between advanced technology adoption and skilled-worker employment shares.
McGuckin, Streitwieser, and Doms (1996) found that regression analysis of labor productivity levels using the 1993 Survey of Manufacturing Technology data yielded results "remarkably similar" to those based on analysis of 1988 data. The authors found only a modest net increase in adoptions of the seventeen Survey of Manufacturing Technologies between 1988 and 1993, most of which were concentrated in computer-aided design and associated technologies and in local area networks (see also U.S. Bureau of the Census 1994, p. …