The Skill Content Of Recent Technological Change: An


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THE SKILL CONTENT OF RECENT TECHNOLOGICAL
CHANGE: AN EMPIRICAL EXPLORATION*
DAVID H. AUTOR FRANK LEVY
RICHARD J. MURNANE
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 problemsolving 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 nd 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.
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 rms, 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 explain-
* We thank the Alfred P. Sloan Foundation, the Russell Sage Foundation, and the MIT-Ford Research Collaboration for nancial support and Kokkeong Puah, Detelina Vasileva, and especially Melissa S. Kearny for research assistance. We are indebted to Daron Acemoglu, Joshua Angrist, Lex Borghans, Nicole Fortin, Edward Glaeser, Lawrence Katz, Kevin Lang, Thomas Lemieux, Sendhil Mullainathan, Richard Nelson, Kathryn Shaw, Marika Tatsutani, Bas ter Weel, three anonymous referees, and numerous seminar participants for excellent suggestions. We thank Randy Davis of the Massachusetts Institute of Technology Arti-
cial Intelligence Laboratory and Peter Szolovits of the Massachusetts Institute of Technology Laboratory for Computer Science for clarifying issues in arti cial intelligence, and Michael Handel for providing key data sources and expert advice on use of the Dictionary of Occupational Titles.
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 rm and plant level shifts. Katz and Autor [1999] summarize this literature. Card and DiNardo [2002] offer a critique.
© 2003 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. The Quarterly Journal of Economics, November 2003
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ing 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-de ned 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

2. Simon [1960] provides the rst treatment of this question with which we are
familiar, and his essay introduces many of the ideas explored here. Other early works 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.

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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. A unique virtue of this database is that it permits us to analyze changes in task input within industries, education groups, and occupations—phenomena that are normally unobservable. By measuring the tasks performed in jobs rather than the educational credentials of workers performing those jobs, we believe our study supplies a missing conceptual and empirical link in the economic literature on technical change and skill demand.
Our analysis provides four main pieces of evidence supporting our model.
1) Commencing in the 1970s, labor input of routine cognitive and manual tasks in the U. S. economy declined, and labor input of nonroutine analytic and interactive tasks rose.
2) Shifts in labor input favoring nonroutine and against routine tasks were concentrated in rapidly computerizing industries. These shifts were small and insigni cant in the precomputer decade of the 1960s, and accelerated in each subsequent decade.
3) The substitution away from routine and toward nonroutine labor input was not primarily accounted for by educational upgrading; rather, task shifts are pervasive at all educational levels.
4) Paralleling the within-industry task shifts, occupations undergoing rapid computerization reduced input of routine cognitive tasks and increased input of nonroutine cognitive tasks.
We consider a number of economic and purely mechanical alternative explanations for our results. Two supply side factors that we study in particular are the rising educational attainment of the workforce and the rising human capital and labor force attachment of women— both of which could potentially generate shifts in job task composition independent of demand shifts. As we show below, the task shifts that we document—and their

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associations with the adoption of computer technology—are as pervasive within gender, education, and occupation groups as between, indicating that these supply side forces are not the primary explanation for our results.
We begin by presenting our informal “task model” describing how computerization affects the tasks that workers and machines perform. We next formalize this model in a production framework to develop empirical implications for task demand at the industry and occupation level. Subsequent sections describe our data sources and test the model’s main implications. Drawing together the empirical strands, we nally assess the extent to which changes in the task composition can account for recent demand shifts favoring more educated workers. This exercise shows that estimated task shifts are economically large, underscoring the potential of the conceptual model to reconcile key facts.

I. THE TASK MODEL
We begin by conceptualizing work from a “machine’s-eye” view as a series of tasks to be performed, such as moving an object, performing a calculation, communicating a piece of information, or resolving a discrepancy. Our model asks: which of these tasks can be performed by a computer? A general answer is found by examining what is arguably the rst digital computer, the Jacquard Loom of 1801. Jacquard’s invention was a machine for weaving fabrics with inlaid patterns speci ed by a program punched onto cards and fed into the loom. Some programs were quite sophisticated; one surviving example uses more than 10,000 cards to weave a black and white silk portrait of Jacquard himself.3 Two centuries later, the electronic descendants of Jacquard’s loom share with it two intrinsic traits. First, they rapidly and accurately perform repetitive tasks that are deterministically speci ed by stored instructions (programs) that designate unambiguously what actions the machine will perform at each contingency to achieve the desired result. Second, computers are “symbolic processors,” acting upon abstract representations of information such as binary numbers or, in the loom’s case, punched cards.
Spurred by a more than trillionfold decline in the real price of

3. The Jacquard loom was also the inspiration for Charles Babbage’s analytical engine and Herman Hollerith’s punch card reader, used to process the 1910 United States Census.

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computing power [Nordhaus 2001], engineers have become vastly more pro cient at applying the loom’s basic capabilities—rapid execution of stored instructions—to a panoply of tasks. How does this advance affect the task composition of human work? The answer depends both upon how computers substitute for or complement workers in carrying out speci c tasks, and how these tasks substitute for one another. We illustrate these cases by considering the application of computers to routine and nonroutine cognitive and manual tasks.
In our usage, a task is “routine” if it can be accomplished by machines following explicit programmed rules. Many manual tasks that workers used to perform, such as monitoring the temperature of a steel nishing line or moving a windshield into place on an assembly line, t this description. Because these tasks require methodical repetition of an unwavering procedure, they can be exhaustively speci ed with programmed instructions and performed by machines.
A problem that arises with many commonplace manual and cognitive tasks, however, is that the procedures for accomplishing them are not well understood. As Polanyi [1966] observed, “We can know more than we can tell [p. 4] . . . The skill of a driver cannot be replaced by a thorough schooling in the theory of the motorcar; the knowledge I have of my own body differs altogether from the knowledge of its physiology; and the rules of rhyming and prosody do not tell me what a poem told me, without any knowledge of its rules [p. 20].” We refer to tasks tting Polanyi’s description as “nonroutine,” that is, tasks for which the rules are not suf ciently well understood to be speci ed in computer code and executed by machines. Navigating a car through city traf c or deciphering the scrawled handwriting on a personal check— minor undertakings for most adults—are not routine tasks by our de nition (see Beamish, Levy, and Murnane [1999] and Autor, Levy and Murnane [2002] for examples). The reason is that these tasks require visual and motor processing capabilities that cannot at present be described in terms of a set of programmable rules [Pinker 1997].4
Our conceptual model suggests that, because of its declining cost, computer-controlled machinery should have substantially

4. If a manual task is performed in a well-controlled environment, however, it can often be automated despite the seeming need for adaptive visual or manual processing. As Simon [1960] observed, environmental control is a substitute for
exibility.

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substituted for workers in performing routine manual tasks. This phenomenon is not novel. Substitution of machinery for repetitive human labor has been a thrust of technological change throughout the Industrial Revolution [Hounshell 1985; Mokyr 1990; Goldin and Katz 1998]. By increasing the feasibility of machine substitution for repetitive human tasks, computerization furthers—and perhaps accelerates—this long-prevailing trend.
The advent of computerization also marks a qualitative enlargement in the set of tasks that machines can perform. Because computers can perform symbolic processing—storing, retrieving, and acting upon information—they augment or supplant human cognition in a large set of information-processing tasks that historically were not amenable to mechanization. Over the last three decades, computers have substituted for the calculating, coordinating, and communicating functions of bookkeepers, cashiers, telephone operators, and other handlers of repetitive information-processing tasks [Bresnahan 1999].
This substitution marks an important reversal. Previous generations of high technology capital sharply increased demand for human input of routine information-processing tasks, as seen in the rapid rise of the clerking occupation in the nineteenth century [Chandler 1977; Goldin and Katz 1995]. Like these technologies, computerization augments demand for clerical and information-processing tasks. But in contrast to its nineteenth century predecessors, it permits these tasks to be automated.
The capability of computers to substitute for workers in carrying out cognitive tasks is limited, however. Tasks demanding exibility, creativity, generalized problem-solving, and complex communications—what we call nonroutine cognitive tasks—do not (yet) lend themselves to computerization [Bresnahan 1999]. At present, the need for explicit programmed instructions appears a binding constraint. There are very few computer-based technologies that can draw inferences from models, solve novel problems, or form persuasive arguments.5 In the words of computer scientist Patrick Winston

5. It is, however, a fallacy to assume that a computer must reproduce all of the functions of a human to perform a task traditionally done by workers. For example,
Automatic Teller Machines have supplanted many bank teller functions, although they cannot verify signatures or make polite conversation while tallying change. Closely related, computer capital may substitute for the routine components of
predominantly nonroutine tasks, e.g., on-board computers that direct taxi cabs. What is required for our conceptual model is that the routine and nonroutine tasks embodied in a job are imperfect substitutes. Consequently, a decline in the price of accom-
plishing routine tasks does not eliminate demand for nonroutine tasks.

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[1999]: “The goal of understanding intelligence, from a computational point of view, remains elusive. Reasoning programs still exhibit little or no common sense. Today’s language programs translate simple sentences into database queries, but those language programs are derailed by idioms, metaphors, convoluted syntax, or ungrammatical expressions.”6
The implication of our discussion is that because present computer technology is more substitutable for workers in carrying out routine tasks than nonroutine tasks, it is a relative complement to workers in carrying out nonroutine tasks. From a production function standpoint, outward shifts in the supply of routine informational inputs, both in quantity and quality, increase the marginal productivity of workers performing nonroutine tasks that demand these inputs. For example, comprehensive bibliographic searches increase the quality of legal research and timely market information improves the ef ciency of managerial decision-making. More tangibly, because repetitive, predictable tasks are readily automated, computerization of the workplace raises demand for problem-solving and communications tasks such as responding to discrepancies, improving production processes, and coordinating and managing the activities of others. This changing allocation of tasks was anticipated by Drucker [1954] in the 1950s: “The technological changes now occurring will carry [the Industrial Revolution] a big step further. They will not make human labor super uous. On the contrary, they will require tremendous numbers of highly skilled and highly trained men—managers to think through and plan, highly trained technicians and workers to design the new tools, to produce them, to maintain them, to direct them” [p. 22, brackets added].
Table I provides examples of tasks in each cell of our two-bytwo matrix of workplace tasks (routine versus nonroutine, manual versus information processing) and states our hypothesis about the impact of computerization for each cell. The next section formalizes these ideas and derives empirical implications.7

6. Software that recognizes patterns (e.g., neural networks) or solves prob-
lems based upon inductive reasoning from well-speci ed models is under development. But these technologies have had little role in the “computer revolution” of the last several decades. As one example, current speech recognition software
based on pattern recognition can recognize words and short phrases but can only process rudimentary conversational speech [Zue and Glass 2000].
7. Our focus on task shifts in the process of production within given jobs
overlooks two other potentially complementary avenues by which technical

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TABLE I PREDICTIONS OF TASK MODEL FOR THE IMPACT OF COMPUTERIZATION ON FOUR
CATEGORIES OF WORKPLACE TASKS

Routine tasks

Nonroutine tasks

Analytic and interactive tasks

Examples Computer impact

Record-keeping Calculation Repetitive customer service (e.g., bank teller)
Substantial substitution

Forming/testing hypotheses Medical diagnosis Legal writing Persuading/selling Managing others
Strong complementarities

Manual tasks

Examples Computer impact

Picking or sorting Repetitive assembly
Substantial substitution

Janitorial services Truck driving
Limited opportunities for substitution or complementarity

I.A. The Demand for Routine and Nonroutine Tasks
The informal task framework above implies three postulates about how computer capital interacts with human labor input.
A1. Computer capital is more substitutable for human labor in carrying out routine tasks than nonroutine tasks.
A2. Routine and nonroutine tasks are themselves imperfect substitutes.
A3. Greater intensity of routine inputs increases the marginal productivity of nonroutine inputs.
To develop the formal implications of these assumptions, we write a simple, general equilibrium production model with two
change impacts job task demands. First, innovations in the organization of production reinforce the task-level shifts that we describe above. See Adler [1986], Zuboff [1988], Levy and Murnane [1996], Acemoglu [1999], Bresnahan [1999], Bartel, Ichniowski, and Shaw [2000], Brynjolfsson and Hitt [2000], Lindbeck and Snower [2000], Mobius [2000], Thesmar and Thoenig [2000], Caroli and Van Reenen [2001], Fernandez [2001], Autor, Levy, and Murnane [2002], and Bresnahan, Brynjolfsson, and Hitt [2002] for examples. Second, distinct from our focus on process innovations, Xiang [2002] presents evidence that product innovations over the past 25 years have also raised skill demands.

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task inputs, routine and nonroutine, that are used to produce output Q, which sells at price one. Because our discussion stresses that computers neither strongly substitute nor strongly complement nonroutine manual tasks, we consider this model to pertain primarily to routine cognitive and routine manual tasks, and nonroutine analytic and nonroutine interactive tasks.
We assume for tractability an aggregate, constant returns to scale Cobb-Douglas production function of the form,

(1)

Q 5 ~LR 1 C!12bLNb , b [ ~0,1!,

where LR and LN are routine and nonroutine labor inputs and C is computer capital, all measured in ef ciency units. Computer capital is supplied perfectly elastically at market price r per ef ciency unit, where r is falling exogenously with time due to technical advances. The declining price of computer capital is the causal force in our model.8
We assume that computer capital and labor are perfect substitutes in carrying out routine tasks. Cobb-Douglas technology further implies that the elasticity of substitution between routine and nonroutine tasks is one, and hence computer capital and nonroutine task inputs are relative complements. While the assumption of perfect substitutability between computer capital and routine task input places assumptions A1 and A2 in bold relief, the only substantive requirement for our model is that computer capital is more substitutable for routine than nonroutine tasks. Observe that routine and nonroutine tasks are qcomplements; the marginal productivity of nonroutine tasks rises with the quantity of routine task input, consistent with assumption A3.9
We assume a large number of income-maximizing workers, each of whom inelastically supplies one unit of labor. Workers have heterogeneous productivity endowments in both routine and nonroutine tasks, with Ei 5 [ri,ni] and 1 $ ri, ni . 0 @ i. A given worker can choose to supply ri ef ciency units of routine task input, ni ef ciency units of nonroutine task input, or any convex

8. Borghans and ter Weel [2002] offer a related model exploring how the

declining price of computer capital affects the diffusion of computers and the

distribution of wages. A key difference is that the tasks performed by computers

and workers are inseparable in the Borghans-ter Weel model. Accordingly, com-

puterization alters wage levels but does not directly change the allocation of

human labor input across task types. This latter point is the focus of our model

and empirical analysis.

9.

Speci

cally, ]2Q/]LN](LR

1 C) 5 b(1

2

b

)

L

b2 N

1

/

(

L

R

1 C)b

. 0.

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combination of the two. Hence, Li 5 [liri, (1 2 li)ni], where 0 # li # 1. These assumptions imply that workers will choose tasks according to comparative advantage as in Roy [1951]. We adopt the Roy framework because it implies that relative task supply will respond elastically to relative wage levels. If, instead, workers were bound to given tasks, the implications of our model for task productivity would be unchanged, but technical progress, re ected by a decline in r, would not generate re-sorting of workers across jobs.
Two main conditions govern market equilibrium in this model. First, given the perfect substitutability of routine tasks and computer capital, the wage per ef ciency unit of routine task input is pinned down by the price of computer capital:10

(2)

wR 5 r.

Second, worker self-selection among occupations—routine versus
nonroutine— clears the labor market. De ne the relative ef ciency of individual i at nonroutine
versus routine tasks as hi 5 ni/ r1. Our assumptions above imply that hi [ (0,`). At the labor market equilibrium, the marginal worker with relative ef ciency units h* is indifferent between
performing routine and nonroutine tasks when

(3)

h* 5 wR/wN.

Individual i supplies routine labor (li 5 1) if hi , h*, and supplies nonroutine labor otherwise (li 5 0).
To quantify labor supply, write the functions g(h), h(h),
which sum population endowments in ef ciency units of routine
and nonroutine tasks, respectively, at each value of h. Hence,
g(h) 5 ¥iri z I[hi , h] and h(h) 5 ¥ini z I[hi $ h], where I[ z ] is the indicator function. We further assume that hi has nonzero support at all hi [ (0,`), so that that h(h) is continuously upward sloping in h, and g(h) is continuously downward sloping.
Assuming that the economy operates on the demand curve,
productive ef ciency requires

]Q

]Q

(4) wR 5 ]LR 5 ~1 2 b!u2b and wN 5 ]LN 5 bu12b,

10. We implicitly assume that the shadow price of nonroutine tasks absent computer capital exceeds r and hence equation (2) holds with equality. In the precomputer era it is likely that wR , r.

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The Skill Content Of Recent Technological Change: An