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Introduction
"... the widespread introduction of new technology has brought new employment
opportunities and rising relative wages to those with the highest levels of human
capital. However, this new technology has also helped to bring about higher than
normal job losses, particularly among unskilled workers, and put a premium on
being able to adapt to new workplace challenges." Introduction to Chapter 15,
Modern Labor Economics, 7th Ed. Ehrenberg and Smith
Understanding how the introduction of new technology impacts firms and in turn impacts
workers has increasingly become important in the past two decades — particularly understanding the dynamic consequences of firms' decision to invest in advanced technology such as computers. Yet little is known about this interaction — measures of human capital at the firm level have been very limited, detailed firm–level measures of technology are difficult to obtain in general and especially for service sector businesses, and longitudinal data on firms are not widely available. This paper uses new data which remedies many of these deficiencies to provide a detailed examination of these issues for all sectors of the economy: first by documenting how the demand for human capital has changed within and between businesses and then by using firm level data to examine the link between changes in technology and the demand for human capital. We take a broad view of changes in technology in this context — we are interested in observable changes in physical capital with an emphasis on the role of advanced technology such as computers and changes in intangible capital such as organizational and business practices.
Our ability to investigate these issues is due to access to a new longitudinal employer–employee dataset and methods being developed at the U.S. Census Bureau. These data and our approach have a number of advantages relative to the existing literature. First, since we have data on the virtual universe of workers and firms and their associated transitions, we exploit the new techniques pioneered by Abowd and Kramarz and Margolis (1999) to measure human capital of workers and in turn to measure the human capital at individual firms. In addition, we are able to exploit Economic Census data on firms that includes substantial amounts of information about the inputs and outputs used by individual firms. These data provide a basis for characterizing differences in technology across businesses. Moreover, the data span all sectors of the economy, which enables us to test whether the relation between technology and human capital differs for different types of firms and different types of industries. Such a distinction can be particularly important in differentiating between the manufacturing and service sectors. In goods producing industries, for example, firms combine a variety of inputs — physical capital, materials, and human capital — in a variety of different ways to produce some physical output. In service industries, the same inputs enter into the production process, but the service is fundamentally delivered by the human capital — and hence human capital differences yield a form of product differentiation. Finally, the longitudinal component of the data enables us to capture the dynamic evolution of the demand for human capital.
Our ability to use longitudinal linked employer–employee data thus represents a
considerable advance over earlier work, since most related work has used either industry level
data, typically in manufacturing, and/or very crude measures of human capital at the micro/industry level, and/or data on individuals that has very limited information on the firms at which workers are employed. Berman, Bound and Griliches (1996), for example, used 4–digit manufacturing data to examine changing demand for skills in response to changes in technology, and were forced to use the ratio of non–production to production workers as a measure of skill. Dunne, Haltiwanger and Troske (1997) were also forced to use the same crude measure of skill in exploring similar issues using plant—level data for manufacturing.1 Data on individuals has been used extensively, of course, to study the impact of technology on the demand for skilled workers (e.g., Autor, Katz and Krueger, 1998) but such data inherently miss some important features of the relationship. For one, the growing literature on firm dynamics makes clear that there is tremendous between—firm heterogeneity in choices of technology (see, e.g., Doms, Dunne and Troske, 1997, Dunne, Haltiwanger, and Troske, 1997, and Haltiwanger, Lane and Spletzer, 2000). As such, between–firm variation is very useful, however, the differences across firms are important beyond providing a source of variation. The differences between firms raise questions about the nature and evolution of the adoption of new technologies and in turn the impact on workers. It has become increasingly clear that the adoption of new technologies is a noisy, complex process at the micro level with considerable trial and error and associated entry and exit of businesses and reallocation of jobs. The churning of businesses and, in turn, workers is thus a critical feature of the relation between changes in technology and changes in the demand for human capital because there are substantial implications for the allocation of human capital across businesses. Longitudinal matched employer–employee data are required to investigate the nature of these dynamic interactions between firms and workers.
With these introductory remarks in mind, we examine the following key questions in this
paper.
- How has the distribution and allocation of human capital changed in the overall economy? Are the observed aggregate change broadly based, or are they confined to specific industries or even specific firms within specific industries?
- How do changes occur? Do new firms, with different levels of human capital, supplant old firms? Or do continuing firms adjust their current workforce? Or do high technology firms expand employment, and in the process, "crowd out" employment in lower technology firms?
- Why do changes occur? What types of observable changes in technology are associated drive changes in human capital? Do changes in technology have more than just "first moment effects" on skill intensity — affecting both skill intensity and skill dispersion
(Kremer and Maskin (2000)?
1 Our data do have some limitations relative to the data used in these studies. We only have data for the 1990s and for this version of the paper the data are confined to the universe of businesses and workers in one state — Illinois.
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