machining job titles, including 19 NC jobs, for 2,504 machine shop observations. The. Dictionary of Occupational Titles {DOT) sup- plies job analysis data on skill ...
7
\
NUMERICALLY CONTROLLED MACHINE TOOLS AND WORKER SKILLS JEFFREY H. KEEFE*
This study investigates the impact of the spread of numerically controlled machine tools on the average skill level of workers in the nonelectrical machinery industry in the United States. Analyzing data from the Industry Wage Surveys of Machinery Manufacturers to trace
changes in the skill levels of 57 machining jobs, the author finds that 30 vears of the spread of numerically controlled machine tools has resulted in either a very small (1%) reduction in skill levels or no significant change at all, depending on the measure of skill change used. This result supports neither the position of the "post-industrialists," who have argued that this new technology raises overall machine shop skill levels, nor the position of "labor process" theorists, who have argued that it results in deskilling.
OR over a decade, numerically controlled machine tools have been at the center of a sociological debate about the changing relationship between automated process technologies and skill requirements of work. On one side of the controversy, the labor process theorists {Braverman 1974; Noble 1984; Shaiken 1984) view new process technologies as both labor-saving and skill-displacing. They suggest that by transferring skill from workers to machines, management is gaining greater control over the production process with a less expensive and more compliant work force. For workers, these theorists argue, the new technologies
F
* 1 he author is Assistant Professor of Industrial Relations and Human Resources. Institute of Management and Labor Relations, Rtitgers University. He thanks Stephen Barley, Adrienne Eaton, Harry Katz, Mary Ellen Kelley. Douglas Kruse, and David Lipsky for useful comments on earlier drafts of this paper; George Stelluto. Assistant Conunissioner, Office of Wages and Industrial Relations, Bureau of Labor Statistics, for making the Industry Wage Surveys available; and, especially, Carl Barsky, for constructing the microdata set.
mean deskilling, degradation, and craft fragmentation. On the other side of the debate, the post-industrialists (Bell 1973; Blauner 1964: Faunce 1965; Hirschhorn 1984) view new automated process technologies as destroying physically repetitive and onerous tasks, replacing them with more highly conceptual and socially connected activity. They argue that the new technologies re-create skilled work by altering the nature of work and eliminating unskilled jobs (Adler and Borys 1986; Hirschhorn 1984). According to the post-industrialists, training, preparation, and learning are emerging as the core elements of newly created work, and the end result of technological change is a work force that is more skilled and relieved of drudgery. Although the relationship between the adoption of numerical control (NC) and shop floor skill levels has been intensely debated, there has been no systematic effort to test the competing theories. Fhis study is the first to formulate and test propositions that are derived from the
Industrial and luihor Relations Review, Vol. 44, No. 3 (April 1991).
0019-7939/91/4403 501.00
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bv Cortiell University.
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INDUSTRIAL AND LABOR RELATIONS REVIEW
competing theories using a large independently collected data set. To examine the effects of the adoption of NC on machine shop skill levels, 1 use data from three hidmtry Wage Surveys (IWS) of Machinery Manufacturing (SIC 35) for the years 1975, 1981, and 1983, which provide employment, wage, and gender information on 57 highly detailed machining job titles, including 19 NC jobs, for 2,504 machine shop observations. The Dictionary of Occupational Titles {DOT) sup-
plies job analysis data on skill requirements for 88 highly detailed DOT\oh titles that are matched to the 57 IWS machine shop job titles. A skill index is constructed by applying principal components analysis to 11 DOT variables that measure the substantive complexity of the 57 machine shop jobs. The skill index captures both the physical and conceptual aspects of the human-machine interf^ace and incorporates measures of specific and general training. In addition to directly measuring changes in skill associated with the use of NCs, this study also uses machine shop wages as an alternative measure of skill change. What Is Numerical Control? Numerical control is a generic term describing machine control technologies that use numerically coded instructions to guide machining operations. A numerically controlled machine tool has (1) specifically equipped motors to guide the cutting process and (2) a controller. The controller receives numerical commands and translates them into electrical impulses that operate the motors, guiding the metal cutting process.' ' NC technologies were evolving for a quarter o( a century before there was a broad movement toward the adoption of NC in the machinery industry (Hicks 1986; 89). The genesis of NC has been directly linked to the advances in electronic devices from the transistor to the microprocessor. In 1975, the first successful application of a microprocessor to control a numerically controlled machine tool was accomplished. Wiih the application of microprocessors, the cost of numerically controlled machine tools began to drop dramatically and utilization increased. Machine
Between 1978 and 1983 the number of numerically controlled machine tools in use doubled. Such machines represented 4.7% of the American machine tool stock in 1983.2 Tsjc offers several advantages over conventional machine operations, such as increased accuracy, repeatability, versatility, and reduced production time and tool costs (Childs 1982:147-74). NC is potentially both a capital- and labor-saving technology; an NC operator can process up to four or five times more parts than can a conventional operator. The purchasers of machine tools are concentrated in four major industries: fabricated metals (SIC 34), non-electrical machinery (SIC 35), electrical machinery (SIC 36), and transportation equipment (SIC 37). The non-electrical machinery industry (the subject of this study) is the single largest user of NC, with approximately 50% of the stock of numerically controlled machine tools (U.S. Department of commerce 1982). The NC Controversy and the Sociology of Technological Change According to the labor process perspective (Braverman 1974; Shaiken 1984; Noble 1986), the major function of modern technology is the progressive eliminatools with computerized numerical control (CNC) are usually equipped with a screen and a keyboard for writing or editing NC programs at the machine. A related CNC development is direct numerical control (DNC), in which a central computer is used to store programs and modifications, which can be called up by the operator. The CNC controller allows the operator to edit the program at the machine, rather than sending a tape back to the programmer in a computer room for modifications. Also, by avoiding the use of tapes, CNC and DNC operations are more reliable than machines with ordinary NC (BLS 1982:24). ^ This figure is probably an under-estimate for two reasons. First, firms tend to retain their old machine tools, thereby inflating the total stock figure. Second, numerically controlled machine tools are used more intensively than conventional machines and account for much more of the value added than their number indicates. Some experts estimate that possibly as much as half of the parts manufactured in machine shops are made on equipment with NC (OTA 1984:58)
WORKER SKILLS
tion of productive activity directly under the control of workers. By separating conception from execution, by fragmenting jobs and skills, and by embodying human skill in machinery, management gains control over the shop floor. Management uses machines not only to raise the productivity of labor, but to control the pace of work and to centralize decisionmaking. Small-batch machine shop production historically has presented technical obstacles to management control. Not until recently has there existed a technical means to gain control in machine shops: "The mechanical solution to the problem has taken the form of numerical control" (Braverman 1974.187). NC is used to divide the machine process among separate operatives, each requiring less training and commanding a lower wage than the skilled machinist (Braverman 1974; 200). The machinist thus becomes "a monitor rather than a participant in the production process" (Shaiken 1984:67). In the NC skill controversy, the labor process theorists are joined by industry analysts in concluding that NC lowers skill levels. The groups disagree, however, about why there is a skill reduction. Government and industry representatives maintain that there exists a chronic shortage of skilled machinists (Rosenthal 1982), which has induced a shift to NC. They argue that because NC is used as a substitute for skilled workers, it lowers the average skill level required to operate a machine shop (Childs 1982; National Academy of Engineering 1983; U.S. Congress, Office of Technology Assessment 1984; BLS 1982). According to the National Academy of Engineering, the declining number of skilled machinists "is a force pushing for new machine tools with greater simplicity of operation and control" (1983:26). These analysts suggest that the lower skill content of NC operator jobs can be attributed to two factors: (1) the pre-planning and encoding of the process on tape, and (2) the three- to five-fold increase in the amount of time the operator spends tending a numerically controlled machine tool as compared to
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tending a conventional machine tool. By reversing the relationship between machine operating time and other activities, NC can reduce the skill required or the entire operation (Childs 1982). Both the labor process and industry perspectives predict that NC diffusion diminishes both conceptual and manual machining skills and that NC jobs require less training than conventional machinist jobs. It is the reduction of the conceptual component of skill, however, that is central to the labor process perspective (Armstrong 1988). By displacing and thereby reducing the conceptual tasks in jobs, management increases its control over work and workers lose their autonomy. In contrast to the deskilling prediction, the post-industrial thesis maintains that technological change aimed at increased productivity requires higher levels and broader varieties of skill from the work force (Kerr et al. 1960; also, see Spenner 1982 and Barley 1988 for reviews).^ The post-industrialists maintain that new microelectronic process technologies create skilled tasks, alter the relationship between conceptual and sentient skill, and eliminate unskilled work (Adler and Borys 1986; Attewell 1987; Hirschhorn 1984; Zuboff 1988). They suggest that conceptual activity replaces physical labor, and continuous learning becomes the core of new work. Automation eliminates routin^ Consistent with the post-industrial upgrading thesis, neoclassical research on capital labor substitution, using production and cost functions, has shown that skilled labor and physical capital are complements (Hamermesh and Grant 1979; Hamermesh 1984). Capital has been found to be a substitute for unskilled labor. This conclusion is supported by Rumberger (1981) and Spenner (1979). Both studies, comparing jobs in 3rd and 4th editions of the Dictionary of Occupational Titles, concluded that em-
ployment growth has favored more skilled jobs and that [he overall average skill requirements for jobs have been narrowing. These studies have been criticized by Cain and Treiman because of the lack of independence between the DOT editions: ' The fact is that each edition of the DOT has incorporated a substantial portion of the preceding edition" (Cain and Treiman 1981:272). Both neoclassical and DOT occupational studies support the post-industrial upgrading thesis.
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INDUSTRIAL AND LABOR RELATIONS REVIEW
ized tasks, which are supplanted by work of greater complexity (Blauner 1964). Systematic learning must be instituted to prepare workers to intervene in moments of unexpected but inevitable failure in automatic systems (Hirschhorn 1984). Some post-industrialists forecast a shift to a system of production based on flexible specialization (Piore and Sabel 1984). For machine shops, NC provides the technical basis for flexible specialization by expanding the capacity of machine tools for quick and complex product changes in response to changing market conditions. A flexible product market strategy, in turn, requires a labor force with integrated skills capable of making rapid adjustments (Piore and Sabel 1984; Hirschhorn 1984; Adler and Borys 1986). According to the post-industriahsts, the introduction of NC should raise overall machine shop skill levels by increasing the demand for conceptual skills, eliminating routinized work, permitting greater autonomy, and requiring more formal training. In contrast, the labor process perspective predicts that NC will reduce the physical and conceptual complexity of work, require less training, and, by transferring the conceptual component of work to an NC controller, cause workers to lose autonomy on the job. Both perspectives agree, however, that NC will diminish the physical complexity of machine shop jobs. To test the competing predictions about the implications of NC, the skill levels of 57 machining jobs contained in the Industry Wage Surveys (IWS)
of
Machinery
Manufacturing are evaluated and aggregated into a machine shop skill index. Changes in occupational composition cause the index to change. Using the IWS data, I model machine shop skill change as a function of NC utilization, controlling for other variables. Data Three surveys conducted by the Bureau of Labor Statistics and reported in the Industry Wage Survey: Machinery Manufac-
turingXlWS) for winter 1974-75, January 1981, and November 1983 are used to test
the skill consequences of NC adoption. A total of 57 detailed machining job classifications were surveyed, covering a total of 131,084 machining workers. In addition, the IWS supplies establishment information on machine shop employment, training programs, union status, wages, gender, NC usage, and 4-digit SIC. The surveys were conducted in 23 metropolitan areas that have high concentrations of machining establishments (BLS 1985:1). The surveys were restricted, for the most part, to establishments employing 50 or more workers (BLS 1985:79). Also included, however, were establishments employing only 8 to 49 workers that were primarily manufacturing special dies and tools, die sets, jigs and fixtures, or machine tool accessories and measuring devices (SICs 3544 and 3545). A total of 2,504 establishment observations are contained in these three crosssections, and 558 establishment matches have been made by cross-section pairs, 268 between 1974-75 and 1981 and 290 between 1981 and 1983. These panel data allow fixed-effect models to be estimated. A fixed-effect model eliminates the influence of specific establishment effects and bias from constant omitted variables. By focusing on the changes in NC and skill, the fixed-effect models also solve potential heterogeneity problems that could arise if the NC shops are systematically ex ante either high-skill or low-skill machine shops. To test the competing predictions about the implication of NC, a skill measure that can capture the changing conceptual complexity of work is constructed. Measuring Skill In the industrial sociology literature, there is ostensible agreement that skill refers to job complexity (Form 1987). In a comprehensive review of the literattire, Spenner (1983) concluded that job-based skill has been most often associated with two dimensions, substantive complexity and autonomy, which have been found to be highly correlated (r = 0.50-0.70) (Form 1987; Kohn and Schooler 1983).
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Substantive complexity reflects the level, scope, and integration of mental, interpersonal, and manipulative tasks required in a job. Indices of substantive complexity are generally considered superior to autonomy measures and have become accepted measures of skill (Form 1987). One indicator of substantive complexity is the total pieparation time a job requires for an average worker to attain an average level of performance, which includes years of general education, specific vocational training, and on-the-job training (Form 1987). In this study, the skill index measures the substantive complexity of machine shop jobs, focusing on the humanmachine interface and incorporating measures of specific and general training. 1 he
across machining jobs.' Three more variables are dropped from the analysis because of their skewed sampling distributions. Ten of the 12 remaining variables measure the conceptual, perceptual, and training proficiency aspects of skill. (An appendix is available from the author that provides a detailed discussion of how the skill index is developed. For descriptions and defmitions of the DOT variables, see Appendix A.) The skill index is constructed by applying principal components analysis to 12 DOT variables matched to the 57 machine shop job titles."^ Table 1 reports the results. Eleven variables loaded on one component, and the variable People loaded separately.'^ Variations in People were
Dictionary of Occupational Titles {DOT) pro-
'^ In the sample of machining job title matches, the aptitude ratings for Clerical Perception, Eye-HandFoot-Coordination, and Color Discrimination contained no variation across machine shop jobs and were therefore dropped from further analysis. Physical demands are the physical requirements made on the worker in performing the job. The components of the DOFs Physical Demands are strength, climbing, stooping, reaching, fingering, seeing, talking, and hearing (DOL 1972:9). Machine shop employment requires medium strength and the ability to reach, finger, and see. Since the measurement of Physical Demands also contained no variation across job titles, it too was dropped from further analysis.
vides the single best source for measuring substantive complexity (Form 1987; Kohn and Schooler 1983; Spenner 1983), although there are acknowledged shortcomings (Miller et al. 1980; Cain and Treiman 1981). The Dictionary of Occupational Titles
(fourth edition. December 1977) provides job descriptions and job analysis data for 88 DOT machine shop job titles that are matched with the 57 IWS job titles and descriptions.^ Skill is measured by DOT variables that scale Worker Functions. Aptitudes, General Educatiotial Development, Specific Vocational Training, and Physical Demands (U.S. Department of Labor 1972). When variables that contain no variation are excluded, a total of 15 remain to explain tbe variation of skill ^ The DOT supplies ratings on 46 job characteristics—three worker function variables (Data, People, and Thingi), four measures of iraiiiing (GED and SVP), eleven aptitudes, ten temperaments, five interest factors, six physical demands, and seven working condition variables — rated by the Department of Labor's job analysts for 12,099 jobs in the United States. The DDL (1972;^) defines a job lor DOT purposes as "a group of positions which are identical with respect to their major or significant tasks and sufficiently alike to justify tbeir being covered by a single analysis." A machine-readable tape was used to obtain the ratings for most of the machining jobs. Robert Vincent, the director of research for the U.S. Employment Service, New England Region, supplied information on six jobs not listed on the tape.
"^ job titles were matched hy relying on the detailed job descriptions accompanying both the DOT and IWS data sets. When there was more than one DOT job title, variables were weighted by the proportion that each DOT job title contributed to the aggregate IWS title, if that information was known. Otherwise, variahle scores were averaged if there were multiple matches. When there was no D07 joh match for an IWS title, as occurred for several jobs, a job analysis was performed with assistance provided by Robert Vincent of the U.S. Employment Service and discussions with industry experts. An appendix that gives a detailed account of how the skill index was constructed is available on request to the author. ^ Miller et al. (1980:389) construct a measure of substantive complexity using the DOT matched with the 3-digit Census occupational classifications. Overall, the scores and components are very similar. They share Data, GED, SVP, Intelligence, Numerical, and Verbal components. Inchided in the machine shop measure of substantive complexity is Things, Spatial, and Form. Miller et al. include Things in their factor of manual skills; the Spatial and Eorm variables do not load on any factor, and neither does People. These differences can he attributed to the differences in the scope of the measure. "I hings. Spatial,
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Table 1. Skill Index: Principal Components Analysis ot DOT Variables Matched to 57 Industry Wage Survey Machine Shop Job Classifications. Component Landmg Variable^
Data People Things Reason (GED) Math (GED) Language (GED) SVP
Intelligence Verbal Numerical Spatial Form Eigenvalue Percent of Variance Explained
(II
Factor Scores (2)
12 Variables 0.978 -0.015 0.291 0.902 0.965 -0.161 -0.852 0.304 -0.939 0.227 -0.868 0.099 -0.952 -0.035 0.938 -0.019 0.946 0.021 0.934 0.077 0.905 0.130 0.704 0.342 9.200 1.135 76.663 9.456
il)
(2)
0.106 0.032 0.105 - 0.093 -0.102 - 0.094 -0.103 0.102 0.103 0.102 0.098 0.076
-0.013 0.795 -0.142 0.268 0.200 0.087 -0.031 -0.017 0.019 0.068 0.114 0.301
11 Variables 0.978 0.107 0.969 0.106 -0.858 -0.094 -0.945 -0.104 -0.870 - 0.095 SVP -0.951 -0.104 Intelligence 0.937 0.103 Verbal 0.944 0.103 Numerical 0.932 0.102 Spatial 0.902 0.099 Form 0.698 0.077 Eigenvalue 9.124 Percent of Variance Explained 82.943 DOT variable definitions are supplied in the Appendix. Scale is reflected for the skill scores. Data Things Reason (GED) Math (GED) Language (GED)
almost perfectly correlated with NC.^ All NC jobs that require the operator to set up and Form measure capabilities thar are fundamental to performance in a machine shop but are less important to skill ill (he economy as a whole. Kelley (1988), in her study of machine shops, a priori breaks substantive complexity into two correlated (0.68) dimensions. One dimension, measuring judgment and planning, is referred to as conceptual, and the other, measuring the complexity of implementation tasks, is referred to as execution. The results reported in this paper suggest that there is only one dimension to substantive complexity, a finding that is implicitly confirmed by Kelley's two highly correlated dimensions. The Dictionary of Occupational Titles "People"
variable evaluates NG set-up operaror jobs and setter jobs as requiring "Speaking-signaling: Talking with or signalling people to convey or exchange information" (DOL 1972:78), whereas most machine shop jobs simply require "Taking Instructions: Attending to work assignment instructions or orders of supervisor" (DOL 1972:79).
the machine tool scored higher on the People item than the conventional machining johs. Case study evidence supports this higher rating on the social complexity of NC set-up jobs. Shaiken (1983:3, 23) observed that NC brought about greater social interdependence both among workers and between workers and support staff, which is consistent with the postindustrial thesis. The People factor indicates that social complexity is a separate dimension of skill. By excluding People from the skill index, the skill measure is restricted to the conceptual and physical aspects of the human-machine interface. Without a direct measure of the social complexity of work, the skill scores for the NC set-up operator titles are downwardly biased.
WORKER SKILLS The skill index is recomputed using the remaining 11 variables. The first component explains 839? of the sample variance and is the only component with an eigenvalue greater than one. A scree test offers further confirmation that there is only one component worth examining. The reliability of the 11 variables used to construct the skill index is examined by applying Cronbach's alpha, which measures the lower bound of reliability. The alpha of .975 demonstrates that random measurement error is not a problem for the scores of the 57 machine shop jobs analyzed. The skill score for each job title is matched with the job titles in eacb sample establishment and is weighted by the number of workers in each job title,^ The machine shop's average skill score is computed by summing the weighted job title skill scores and by dividing the total skill score by the total number of machining workers in each establishment. This average skill score is referred to as the skill index, which is used in the regression analysis to measure changes in the machine shop skill levels. Since job titles and skill scores are held constant over the period of study, 1975 to 1983, compositional shifts in employment provide the mechanism for changes in skill levels. This approach to measuring skill change has several limitations. First, subtle changes in the use of the machine tools within the highly detailed job titles cannot be assessed or observed. This methodology assumes that the 57 detailed job titles can account for the variations in practices through the classification and reclassification of workers. Case study and survey evidence suggests, however, that there are inter-establishment and intraestablishment variations that cannot be fully captured in this manner (Kelley and Brooks 1988). Second, this method assumes that the DOT job analyses remain constant over this nine-year period. Since ^ Skill scores for each job are multiplied by 10, This step does tiot alter the distribuiioii of .scores, and it makes the magnitudes easier to compare and coiurast with wage data.
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the new CNC machine tools, which permit shop-fioor programming, became available and began rapidly diffusing during this period, this assumption may not be accurate for NC jobs.'" Third, it is presumed that the variable weightings used in constructing the skill index remain constant, which means that skill is not, as suggested by the post-industrialists, in the process of being redefined. Recognizing these limitations of the skill index for direct measurement of skill, I use, as an alternative, average machine shop wages as an indirect measure of skill. This measure necessarily implies that skill changes can be inferred from movements in wages. According to human capital theory {Becker 1964), relative wages refiect relative worker skills and occupational wage differentials measure occupational skill differentials. Wages need not be isomorphic with skill levels, however, in order for wage changes to refiect differences in the relative skills associated with occupations (Wallace and Kalleberg 1982)." For example, Braverman (1974: 243-47) used wage changes to document deskilling (also see Wallace and Kalleberg 1982). Table 2 provides a summary of the skill scores, wages, and employment of the nine major machining occupational groups, combining the 57 machine shop IWS job titles. (See Appendix B for descriptions of the occupational groups.) Although the wage levels and corresponding skill scores differ substantially in magnitude, the ordinal rankings are remarkably similar. The Pearson correlation between the skill scores and wages for tbe 57 machine shop job titles is 0.912. As expected, machinists and tool and die makers are the most highly skilled workers, followed closely by tool room set-up operators, who are frequently called upon to perform custom work. Next in rank of skill are the conventional set-np operators. '" Since the DOT job descriptions are based on the older NC technology, whatever bias may arise favors the deskilling perspective (Kelley 1988). " Wages may also vary because of differences in working conditions, the effort bargain, the ability to pay, discrimination, organizaiion size, industry wage norms, and unionism.
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INDUSTRIAL AND LABOR RELATIONS REVIEW Table 2. Skill Index, Wages, and Employment by Major Machine Shop Occupations.
Occupation^
Tool &: Die Machinist Tool Room Operator Setter Conv. Setup Operator NC Setup Operator Conv. Operator NC Tender Conv. Tender Total No. of Workers
Employment
Job Title Number
Weighted .Skill Score^
1983 Wage
197^
1981
1983
2 2 6 2 9 9 9 9 9
10.78 10.78 10.19 8.09 7.57 5.50 -0.45 -13.39 -13.58
12.05 11.13 11.15 10.37 10.81 10.86 9.13 8.72 7.86
4,732 2,859 4.437 2,753 21,605 2,496 14,325
3,419 2.682 3,046 2,069 15,648 3,782 10,856
2,782 2,205 1,907 1,374 8,012 2,850 4,309
358
445
5,789 59,364
3,543 45,494
329 983
26,304
^ For descriptions of machine shop occupations, see Appendix B. ''Weighted by job title employment.
who are capable of setting up and operating a single type of machine tool. Below them are the NC set-up operators, who set up and operate a specific type of numerically controlled machine tool, followed by conventional operators, who are capable of simple set-ups but require help for more complicated parts. At the bottom of the machine shop skill hierarchy are the machine tenders, both conventional and NC, who are assisted by set-up workers who perform their set-ups.'^ Consistent with the post-industrial position, the employment numbers by occupational group indicate that most NC operators are expected to set up and operate their own machines, whereas a considerably higher proportion of conventional production operators are not required to set up the machines but only to operate them. The skill scores and wage levels indicate, however, that most conventional machining job classifications require greater skill than the NC set-up operator job does. That finding provides support for the argument of industry representatives and the deskilling theorists that NC jobs require less skill than most conventional machining jobs, and it is consistent with early NC studies that used detailed job analyses to examine the '^ One occupation not included in this analysis that has played a role in the debate is the new job of parts programmer. Data from other sources, however, indicate that there are relatively few workers employed in this occupation, so even a very high skill score for this occupation could have only a minor effect on the overall establishment skill distribution.
effects of NC (Crossman 1966; Horowitz and Herrnstadt 1966; Hazelhurst, Bradbury, and Cortlett 1969).'^ The impact of NC, however, extends beyond conventional set-up operators. The labor process perspective predicts that all-around machinists are downgraded as a result of NC (Shaiken 1984), whereas the post-industrialists predict that tenders and operators are replaced or upgraded by NC automation (Attewell 1987). To examine the overall impact of NC diffusion on machine shop skills, I use '^ Crossman (1966) studied machining in an aerospace company. Skill was broken down into several components and measured on an ordinal scale. Each component was weighted by the numtier of hours the operator performed the activity. The study concluded that NC jobs required fewer hours of highly skilled work than similar jobs in conventional machine tools. Horowitz and Herrnstadt (1966) compared 170 machine shop jobs on detailed skill measures in the second and third editions {1949 and 1965) of the Dictionary of Occupational Titles. They
concluded that in view of the changing technology, machine shop skill requirements would not rise and perhaps might decline somewhat, but the overall levelof skill would remain relatively high (1966:257). In another study, Hazelhurst, Bradbury, and Corlett (1969) analyzed 16 jobs—eight NC jobs and eight jobs replaced by NC. They found that NC reduces the need for motor skills and perceptual skills related to precision and accuracy of movement and reduces the number of operator decisions, but that it increases the demand for perceptual skills associated with machine monitoring and conceptual skills associated with the interpretation of symbolic information {Hazelhurst, Bradbury, and Corlett 1969). They concluded that the great majority of job skills required by NC are already possessed by conventional operators.
WORKER SKILLS several regression models with the skill index and machine shop wages as dependent variables. The Effects of NC Diffusion on Skill Levels To analyze the impact of NC diffusion on skill levels, linear cross-section and fixed-effect models are estimated with the machine shop skill levels as the dependent variable.'"* The skill index and the log of the average machine shop wage serve as the measures of skill.'^ The independent variable of particular relevance is the proportion of workers operating numerically controlled machine tools, called NC Prop. If the coefficient for NC Prop is positive, then NC is associated with increasing machine shop skill levels, consistent with the post-industrial thesis; if it is negative, then NC is associated with lower average skill levels, consistent with the deskilling hypothesis. A variety of other factors that may influence skill, independent of technological change, need to be controlled for. Size, measured by the number of employees, affects work organization through economies of scale and scope, the formalization of control systems, and the extensiveness of the division of labor. Prior research shows, however, that size is not related to substantive complexity (Kalleberg and Leicht 1986) or skill and variety (Baron and Bielby 1982), although it is associated with higher wages, even after for controlling for education, experience, and occupation. In this study, the variable ''' A fixed-effect model is used to address the potential of an omitted variable bias in the crosssections estimates. If any omitted variable is correlated with any of the explanatory variables (for example, the Union variable), biased estimates will result unless these coefficients are included in the model or differenced out. In a fixed-effect model the omitted variables are treated as being constant over time. The first-difference model, used here, is equivalent to each machine shop having a separate intercept term to control for unobserved sample heterogeneity (see Mundalk 1978). ' ' Wages are adjusted for inflation using the Employment Cost Index.
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Log Size measures size as the natural log of machine shop employment. Craft unions are often viewed as institutions for the advocacy and protection of skilled workers, giving them control over training and the supply of occupationspecific skills. In contrast, it is commonly assumed that industrial unions represent semi-skilled operators and implicitly accept the division of labor that results from scientific management techniques (Piore 1982; Kalleberg and Leicht 1986). The data provide union status at the establishment level by the three major unions in the industry, which permits the testing of the skill effects of craft versus industrial unionism. The International Association of Machinists (IAM) is a craft union that has vigorously resisted scientific management and has drafted a "Technology Bill of Rights" as part of a plan to prevent deskilling associated with new technology, particularly NC (Nulty 1982). The United Automobile Workers (UAW) and the United Steel Workers (USW) are two leading industrial unions. Other unions in this industry are grouped together under the variable name Othunion. Each union is expected to raise wages for institutional reasons other than skill. Establishments differ in the structure of their internal labor markets, which can affect their skill distributions independent of technology. One way internal labor markets vary is in their methods of training (Doeringer and Piore 1971). In machine shops, all-around machinists, tool and die makers, and skilled maintenance personnel are often trained in DOL-certified apprenticeship programs. These programs are associated with craft internal labor market structures. Semiskilled operators, on the other hand, although they may participate in brief formal programs, learn primarily on the job. This method of training is associated with the hierarchical structure of enterprise internal labor markets, with their job ladders and seniority districts. This study controls for variations in the internal labor markets through several establishment-level binary training variables: Apprentice, Ftrain (formal training), and Btrain (both formal and apprentice training).
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Another way internal labor markets vary is by gender typing of occupations. Previous research by Baron and Bielby (1982) shows that determinants of job skills are different for men and women. Women are expected to be overly represented in the low-skill occupations and to receive lower wages. The variable Female measures the proportion of machining workers who are women and is expected to be negatively related to both skill and wages. Each machine tool has a different likelihood of being numerically controlled."' Mills, lathes, and drills are significantly more likely to be numerically controlled than grinders. Skill requirements also vary by machine tool. To prevent a spurious correlation between skill and NC because of the type of machine tool using NC, eight machine tool control variables are used in the equations to measure the proportion of workers operating each specific machine tool. The four-firm Concentration Ratio, measured at the four-digit SIC level, is taken from the Census of Manufactures to
control for market structure and managerial discretion in selecting a work organization. The four regions of the Department of Labor are used to control for regional differences, and the year of the survey controls for the business cycle influence on skill and wages. Table 3 provides names, definitions, means, and standard deviations for the variables used in this study. NC Impact on Skill Levels: Regression Results The results of the skill index regressions, using two linear cross-sectional models and two fixed-effect models, are reported in Table 4. All estimates indicate that the higher the proportion of NC workers, the lower the average machine shop skill level. A complete conversion of a firm from nil to total use of NC is predicted to result in a two- to three-point Data are in an appendix availahle on request to the author.
decline in the skill index. In this sample, 6% of the machining workers in the average machine shop operated a numerically controlled machine tool. Accordingly, the diffusion of NC has resulted in a 0.12 decline in the skill index, which translates into a less than 1% reduction in the average machine shop's skill level attributable to NC. A one standard deviation increase in the diffusion of NC (0.13) would cause a decline in the skill index in the range of 1% to 1.57%. In both the cross-sections and fixedeffect estimates, size is negatively related to skill levels, probably because of the more extensive division of labor in larger machine shops. Small machine shops are more likely to rely upon all-around machinists and tool and die makers to serve custom markets. Training, particularly apprenticeship training, is associated with significantly higher skill levels. As expected, the higher the proportion of f^emale workers, the lower the skill level. Female workers constitute only 39? of the machining operatives surveyed and are concentrated in the machine-tending occupations. Surprisingly, union status has little impact on skill levels. Although the three major unions are associated with higher skill levels, none of the coefficients are significantly different from zero. The other unions in this industry represent lower-skilled workers. These findings suggest that in this industry the distinction between industrial and craft unionism is not particularly meaningful. Finally, a higher industry concentration ratio is associated with a lower average skill level, which may be partially capturing a more extensive division of labor. Table 5 reports the wage equation estimates. The coefficient estimates on NC Prop are small to trivial and, more important, NC Prop is not a significant predictor of machine shop wages. This finding is robust across models. Given the relatively large cross-section sample size, this finding of insignificance has considerable power (Cohen 1977). Analysis of over 99 out of 100 samples of this size would be able to detect an extremely small effect of NC on wages if it were present. Thus, if
WORKER SKILLS
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Table 3. Skill and Numerically Controlled Machine Tools (SIC 35): Variable Definitions and Descriptive Statistics. Variable Name
Definition
Mean
(Std. Dev.)
NC Prop Skill Log Wage Log Size IAM UAW USW Othunion Apprentice Ftrain Btrain Female Concentration 1981 1983 Observations
Percent of machine tool users operating NC machines Machine shop average skill level Log of machine shop's real average wage Log of machine shop employment At least 50% of plant work force is in IAM bargaining unit At least 50% of plant work force is in UAW bargaining unit At least 50% of plant work force is in USW bargaining unit At least 50% of plant work force is in other bargaining unit 1 if plant has apprentice training 1 if plant has formal training program 1 if plant has formal and apprentice training Proportion of female machine operators Concentration ratio for 4*digit SIC 1 if year 1981, otherwise 0 1 if year 1983, otherwise 0 Number of establishments
0.06 4.33 2.34 2.96 0.13 0.09 0.08 0.16 0.19 0.09 0.05 0.03 0.22 0.31 0.31 2,504
(0.13) (5.16) (0.20) (1.39) (0.33) (0.28) (0.27) (0.37) (0.39) (0.29) (0.23) (0.11)
