2003 TheSkillContentofRecentTechnolo

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Subject Headings: Technical Change; Technological Unemployment; Job Skill Demand; Dictionary of Occupational Titles.

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2013

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Abstract

We apply an understanding of what computers do to study how computerization alters job skill demands. We argue that computer capital (1) substitutes for workers in performing cognitive and manual tasks that can be accomplished by following explicit rules; and (2) complements workers in performing nonroutine problem-solving and complex communications tasks. Provided that these tasks are imperfect substitutes, our model implies measurable changes in the composition of job tasks, which we explore using representative data on task input for 1960 to 1998. We find that within industries, occupations, and education groups, computerization is associated with reduced labor input of routine manual and routine cognitive tasks and increased labor input of nonroutine cognitive tasks. Translating task shifts into education demand, the model can explain 60 percent of the estimated relative demand shift favoring college labor during 1970 to 1998. Task changes within nominally identical occupations account for almost half of this impact.

1. Introduction

A wealth of quantitative and case-study evidence documents a striking correlation between the adoption of computer-based technologies and the increased use of college-educated labor within detailed industries, within firms, and across plants within industries.[1] This robust correlation is frequently interpreted as evidence of skill-biased technical change. Yet, as critics point out, this interpretation merely labels the correlation without explaining its cause. It fails to answer the question of what it is that computers do — or what it is that people do with computers — that causes educated workers to be relatively more in demand. This paper proposes an answer to this question. We formalize and test a simple theory of how the rapid adoption of computer technology — spurred by precipitous real price declines — changes the tasks performed by workers at their jobs and ultimately the demand for human skills. Our approach builds on an intuitive set of observations offered by organizational theorists, computer scientists, and most recently economists about what computers do — that is, the tasks they are best suited to accomplish — and how these capabilities complement or substitute for human skills in workplace settings.[2] The simple observations that undergird our analysis are (1) that computer capital substitutes for workers in carrying out a limited and well-defined set of cognitive and manual activities, those that can be accomplished by following explicit rules (what we term “routine tasks”); and (2) that computer capital complements workers in carrying out problem-solving and complex communication activities (“nonroutine” tasks). (See Table I for examples.) Provided that routine and nonroutine tasks are imperfect substitutes, these observations imply measurable changes in the task composition of jobs, which we test below.

To answer the core questions of our paper, the ideal experiment would provide two identical autarkic economies, one facing a dramatic decline in the price of computing power and the other not. By contrasting these economies, it would be straightforward to assess how computerization reshapes the task composition of work and hence the structure of labor demand. Because this experiment is not available, we develop a simple economic model to predict how demand for workplace tasks responds to an economywide decline in the price of computer capital. The model predicts that industries and occupations that are initially intensive in labor input of routine tasks will make relatively larger investments in computer capital as its price declines. These industries and occupations will reduce labor input of routine tasks, for which computer capital substitutes, and increase demand for nonroutine task input, which computer capital complements. In net, these forces will raise relative demand for highly educated workers, who hold comparative advantage in nonroutine versus routine tasks.

To test these predictions, we pair representative data on job task requirements from the Dictionary of Occupational Titles (DOT) with samples of employed workers from the Census and Current Population Survey to form a consistent panel of industry and occupational task input over the four-decade period from 1960 to 1998.

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Footnotes

  1. 1. Berman, Bound, and Griliches (1994), Autor, Katz, and Krueger (1998), Machin and Van Reenen (1998), Berman, Bound, and Machin (1998, 2000), and Gera, Gu, and Lin (2001) present evidence on industry level demand shifts from the United States, OECD, Canada, and other developed and developing countries. Levy and Murnane (1996), Doms, Dunne, and Troske (1997), and [[Bresnahan, Brynjolfsson, and Hitt (2002) provide evidence on firm and plant level shifts. [[Katz and Autor [1999]] ] summarize this literature. Card and DiNardo (2002) offer a critique.
  2. 2. Simon [1960] provides the first treatment of this question with which we are familiar, and his essay introduces many of the ideas explored here. Other earlyworks include Drucker [1954] and Nelson and Winter [1982]. Adler [1986], Orr (1996), and Zuboff (1988) discuss what computers and related technology do in the workplace, but do not consider economic implications. Acemoglu (1998), Goldin and Katz (1998), Bresnahan (1999), Bartel, Ichniowski, and Shaw (2000), Lindbeck and Snower (2000), Lang (2002), and Bresnahan, Brynjolfsson, and Hitt (2002) provide economic analyses of why technology and human capital are complementary.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2003 TheSkillContentofRecentTechnoloDavid H. Autor
Frank Levy
Richard J. Murnane
The Skill Content of Recent Technological Change: An Empirical Exploration10.1162/0033553033225528012003
AuthorDavid H. Autor +, Frank Levy + and Richard J Murnane +
doi10.1162/003355303322552801 +
titleThe Skill Content of Recent Technological Change: An Empirical Exploration +
year2003 +