The Origins of Technology-Skill Complementarity
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Goldin, C., and L. F. Katz. 1998. “The Origins of Technology-Skill
Complementarity.” The Quarterly Journal of Economics 113 (3)
(August 1): 693–732. doi:10.1162/003355398555720.
Published Version doi:10.1162/003355398555720
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NBER WORKING PAPER SERIES
THE ORIGINS OF TECHNOLOGY-SKILL
COMPLEMENTARITY
Claudia Goldin
Lawrence F, Katz
NBER Working Paper 5657
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
July 1996
A shorter version of an earlier draft of this paper was presented at the ABA Session on Technology,
Human Capital, and the Wage Structure, January 1996, San Francisco, CA, and will appear in the
American Economic Review, Papers and Proceedings, (May 1996) as “Technology, Skill, and the
Wage Structure: Insights from the Past.” Steve Allen was the discussant and we thank him for his
comments.
Arthur Woolf generously provided us with his sample of 4-digit SIC-code manufacturing data for
1909 to 1929. Seminar participants at Harvard University, Johns Hopkins University, M. I.T.,
Cornell University, UCLA, the University of Chicago, and Northwestern University offered useful
advice. The critical comments of John Dunlop, Edward Glaeser, and Andrei Shleifer have made this
abetter paper, although it will be obvious to them that we have not yet been able to implement all
of their suggestions. Bridget Chen and Mireille Jacobson provided research assistance. We thank
all those mentioned. This paper is part of NBER’s research programs in the Development of the
American Economy, and Labor Studies. Any opinions expressed are those of the authors and not
those of the National Bureau of Economic Research,
@ 1996 by Claudia Goldin and Lawrence F. Katz. All rights reserved. Short sections of text, not
to exceed two paragraphs, may be quoted without explicit permission provided that full credit,
including O notice, is given to the source.
NBER Working Paper 5657
July 1996
THE ORIGINS OF TECHNOLOGY-SKILL
COMPLEMENTARITY
ABSTRACT
Current concern with relationships among particular technologies, capital, and the wage
structure motivates this study of the origins of technology-skill complementarily in manufacturing.
We offer evidence of the existence of technology-skill and capital-skill (relative) complementarities
from 1909 to 1929, and suggest that they were associated with continuous-process and batch
methods and the adoption of electric motors. Industries that used more capital per worker and a
greater proportion of their horsepower in the form of purchased electricity employed relatively more
educated blue-collar workers in 1940 and paid their blue-collar workers substantially more from
1909 to 1929. We also infer capital-skill complementarily using the wage-bill for non-production
workers and find that the relationship was as large from 1909-19 as it has been recently. Finally, we
link our findings to those on the high-school movement(1910 to 1940). The rapid increase in the
supply of skills from 1910 to 1940 may have prevented rising inequality with technological change.
Claudia Goldin Lawrence F. Katz
Department of Economics Department of Economics
Harvard University Harvard University
Cambridge, MA 02138 Cambridge, MA 02138
and NBER and NBER
The recent coincidence of computertiation and a widening wage structure has led many
to conclude that new technologies and human capital are relative complements (e.g., Bound and
Johnson 1992). A related literature has demonstrated that physical capital and skill are relative
complements today and in the recent past,l Thus capital deepening may also have increased the
relative demand for the educated, serving further to stretch the wage structure. According to
this view, only large injections of education can reduce the impact of technology on inequality.z
The possibility that such a relationship exists today has prompted a widely-noted conjecture that
technological progress and skill have always been relative complements.3
Physical capital and more advanced technology are now regarded as the relative
complements of human capital, but have they been so for the past two centuries? Some answers
have already been provided. A literature on the bias to technological change across history
challenges the view that physical and human capital were relative complements throughout the
1 The empirical research covering the post-World War II period, beginning with Griliches (1969) and
summarized by Hamermesh (1993), supports the capital-skill and technology-skill complementarily
hypotheses. Bartel and Lichtenberg (1987) find, from a panel of manufacturing industries from 1960 to
1980, that the implementation of new technologies, proxied by the age of the capital stock, increases the
share of highly-educated labor in total labor cost, Doms, Dunne, and Troske (1995) show that U.S.
manufacturing plants utilizing more-advanced technologies employ more educated workers. Berman,
Bound, and Griliches (1994) conclude that skill-upgrading across US. manufacturing industries during
the last thirty years is positively associated with the rate of growth of the capital-output ratio and with
the level of investment in R&D and computers. Berndt, Morrison, and Rosenblum (1992) reach a similar
conclusion. Autor, Katz, and Krueger (1996) find a substantial and growing wage premium associated
with computer use despite large increases in the supply of workers with computer skills. They also find
greater shifts towards college-educated workers in industries with more rapid growth in employee
computer usage (see also Krueger 1993). Fallen and Layard (1975) confirm the capital-skill
complementarily hypothesis using cross-country data at both the aggregate and sectoral levels, Positive
correlations have also been found between the utilization of formal company training, and technological
change and sectoral capital intensity (Bartel and Sicherman 1995; Mincer 1989)
2 Tinbergen (1975), for example, has characterized the evolution of the wage structure as a “race
between technological development and access to education.” Direct intervention in wage setting, of
course, can also alter the wage structrue.
3 By skill we mean higher levels of education, ability, or job training. When we use the term
technology-skill or capital-skill complementarily we mean that skilled or more educated labor is more
complementary with new technology or physical capital than is unskilled or less educated labor.
1
industrial past. Many of the major technological advances of the nineteenth century, according
to the more recent literature, substituted physical capital, raw materials, and unskilled labor, as
a group, for highly-skilled artisans.4 Rather than being the relative complement to human skill,
physical capital was, for some time, a relative complement of raw materials and together, with
unskilled labor, substituted for highly-skilled individuals (Cain and Paterson 1986; James and
Skinner 1985).5 T’he prototypical example is that of the gunmaker. Cheap raw materials in
America fostered the use of wood lathes and displaced hand fitting in the production of gun
stocks by skilled woodworkers (Hounshell 1984). Similarly, the cordwainer once made shoes by
hand with practically no physical capital. The intricate division of labor of the shoe factory
began, and its later mechanization completed, the transition from artisan to unskilled operative.b
The butcher, baker, smith, and glass blower were also skilled artisans whose occupations were
profoundly altered by the factory system, capital, and mechanization.7
If, as the literature now claims, technological advance and human skill were not always
4 Throughout this paper we use the term artisan to mean a worker who produces virtually the entire
good.
5 James and Skinner (1985) divide industries in 1850 into two categories: “skilled” (e.g., woodworking)
and “unskilled” (e.g., clothing). They find that in both “skilled” and “unskilled” industries raw materials
were the relative complements of physical capital, although the effect was greater in the “skilled” sector.
More importantly for the capital-skill complementarily hypothesis is that skilled labor was the better
substitute for capital in its sector than was unskilled labor in its. Thus an increase in capital (or raw
materials) would have decreased the relative demand for skilled labor. Cain and Paterson (1986) do not
consider skill differences but find, similar to James and Skinner, that capital and raw materials were
relative complements and that both together substituted for labor.
In the framework we provide below, capital and skilled labor are complements in the creation of
workable machinery which is then used together with unskilled labor to produce output. We use, here,
the language of the existing literature rather than that of our framework.
bAccording to Sokoloff (1986) the initial deskilling involved little capital and mechanization and was
of the Smithian pin-factory variety. Landes (1972) takes an opposing view.
7 Braverman (1974), among others, argues that industrialization and mechanization sewed to deskill
a host of artisanal trades and reduce the relative earnings of craftsmen. Yet Williamson and Lindert
(1980), in their innovative book on historical trends in inequality, rely heavily on capital-skill
complementarily to generate, in their C.G.E. model, rising inequality with economic development during
the nineteenth century,
2
relative complements, when did they become so? We argue that technology-skill and capitalskill
complementarily emerged across the manufacturing sector as particular technologies, known
as batch and continuous-process methods of production, spread.a Reinforctig the trend was
another technological change: the switch to purchased electricityy from steam and water-power
energy sources.
Our interest, like that of many we cite, concerns how technological change affects the
relative demand for skills. Because technological change is often accompanied by increases in
capital intensity, capital has often been the focus. But many major advances are associated with
particular types of capital, like computers are today and electric motors were in the past. As our
measure of technological change we will often use capital intensity, although we also consider
electricity use and particular types of production processes.
Manufacturing production shifted, during the past 150 years, from artisanal shops to
factories (1830s to 1880s) and later assembly-lines (early 1900s), then from factories to continuous
and batch processes (1890s and beyond), and more recently to robotized assembly lines. The
technological shifts did not affect all products the same, and some were never manufactured by
more than one of the technologies. But manufacturing, as a whole, progressed in this fashion.9
a Batch methods were common to various production processes such as those in chemicals, certain
nonferrous products, some foods (e.g., butter, sugar), liquors, beer, and a host of other products that are
liquid. Late in the nineteenth century and continuing to ours, continuous-process methods were pioneered
in products requiring little assembly and having few or no moving parts, such as oats, flour, canned foods
(eg., condensed milk, soup), soap, film, paper, matches, and cigarettes (Chandler 1977).
9 See, for example, Atack (1987) and Sokoloff (1984) concerning the transition from the artisanal shop
to the factory in the nineteenth century. Both make the important point that the transition was slow in
some industries. It depended not just on technological change (often simply changes in the organization
of work) but also on decreases in transport costs. In some industries (e.g., boots and shoes, clothing,
furniture, leather, meatpacking, tobacco) a significant minority of value added was produced in artisanal
shops (< 7 employees with no power source) even as late as 1870. And in some (e.g., saddle~), the
majority of value added came from artisanal shops in 1870. Even though there were economies from
larger scale for much of manufacturing, transport costs were still too high to justify a complete shift in
the mode of production across the entire United States.
3
Even though the transition from the artisanal shop to the factory may have increased the
capital-output ratio, it most likely decreased the overall demand for skilled relative to unskilled
labor. The technological advances that prompted the shift from the factory to continuous-process
methods increased the relative demand for skilled labor while increasing the capital-output ratio.
Thus the shift from the factory to continuous-process or batch methods may have produced
manufacturing-wide capital-skill (technology-skill) complementarily. Electrification and the
adoption of unit-drive machines reinforced the change through the automation of hauling and
conveying operations.]o
Few products went through all the stages we describe, but those that did provide
illuminating examples, Automobile production began in large artisanal shops, Like the carriages
that preceded them, automobiles were assembled by groups of artisans who hand-fitted the
various pieces.ll Technological advances led to standardized and completely interchangeable
parts that were then assembled in factories by scores of less-skilled workers, while skilled
craftsmen constructed, installed, and maintained the machines. It was only a matter of time for
the assembly line to be innovated, as it was at Ford in 1913. Much later, the robotized assembly
line was created using relatively fewer less-skilled operatives and more skilled machinecrewmen.
In the history of automobile production, the first technological advances reduced the
relative demand for skilled labor but later advances increased it.
We explore the transition to technology-skill complementarily, believed to be in full
blossom today. tir central point is that the shift to batch and continuous- process methods, as
well as the rise in purchased electricityy, led to an increase in the relative demand for higher-
10Jerome (1934) illustrates this point for many industries.
11 Braverman (1974) quotes Eli Chinoy: “Final assembly, for example, had originally been a highly
skilled job. Each car was put together in one spot by a number of all-around mechanics” (p. 146). See
also Hounshell (1984).
4
skilled and educated workers. Reinforcing the shift in manufacturing was an increased demand
for educated labor to sell, install, and service technologically-advanced products.12
A Framework to Understand CapitaI-Skill and Technology-Skill Complementarity
In considering our argument, it is useful to envision manufacturing as having two distinct
stages: a machine-installation, machine-maintenance segment (termed “capital-maintenance”) and
a production or assembly portion (termed “production”), The two stages together comprise
“manufacturing.” Capital and educated (skilled) labor, we will argue, are always complements
in the machine-maintenance segment of manufacturing for any technology. Machinists, for
example, are needed to install machinery and make it run.13 The “workable” capital created by
skilled labor and raw capital is then used by unskilled labor to create the final product in the
production or assembly segment of manufacturing.
The adoption of a particular technology may increase or decrease the capital to output
ratio. Whether or not its adoption increases or decreases the relative demand for skilled workers
will generally depend on the degree to which the machine-maintenance portion’s demand for
skilled labor is offset by the production process’s demand for unskilled labor. We outline a more
12 In the past ten years individuals who sell computer software and hardware have changed from
nerds to slick sales personnel. Similarly, in the 1920s those who sold radios had to deal with the “radio
nuts,” as they were called, but not much later, radio sales personnel were hired to increase sales, not
fraternize with the wireless amateurs. We do not, in this paper, consider the impact technology had on
skill demands in other than the manufacturing sector.
13Machine-maintenance, however, may itself have a life-cycle, particularly for the factory or assenlblyline
technology: “In the first stages of such mechanization when machines are somewhat crude, their
operation requires the watchful care of skilled machinists, Likewise, to make those improvements which
increase machine efficiency, competent machinists are employed to observe the machines in operation
under practical working conditions. But, as machines increase in importance, they must be further
improved in efficiency so as to require little attention and a minimum number of stoppages for repairs
or overhauling. This increasing efficiency of the machine itself tends, in the long run, to eliminate much
of the work of that large corps of machinists which was required when machines were first installed to
displace handworkers” (Anderson and Davidson 1940, p. 228).
5
formal framework showing that the immediate effects of the shift from the artisanal shop to the
factory were probably an increase in the capital-output ratio and a decrease in the demand for
skilled relative to unskilled labor. The shift from the factory to continuous-process (or batch)
methods, however, raised the capital-output ratio but increased the relative demand for skilled
labor. Within any of the technologies, however, an increase in the ratio of the unskilled to
skilled wage will induce an increase in the capital-output ratio, the capital-labor ratio, and the
relative demand for skilled labor. That is, we hypothesize that capital and skill are always
relative complements within any technology, But we believe that shifts in technology, rather
than changes in relative factor prices, have been more important across history in altering
capital-output and capital-labor ratios.
Our framework posits three types of technologies (and one that has two phases): artisanal
shop or hand production (H), the factory (F) which in its more technologically-advanced stage
is called the assembly line (A), and a continuous-process or batch (C) technology. There are
three inputs: raw capital (K), skilled or educated labor (L,), and unskilled labor (LU), with
corresponding prices r, w, and WU The manufacturing process contains two distinct portions:
(1) raw capital must be installed and, later, serviced (“capital maintenance”) and (2) goods must
be assembled or created (“production”). All workers in the “production” segment are unskilled,
whereas all workers in the “capital-maintenance” portion are skilled.
The “capital maintenance” portion of manufacturing is Leontief and, thus, physical capital
and human capital are always strictly complements in the creation of usable, workable capital for
all technologies. We will label the usable, workable capital as K*.14 The production portion of
manufacturing uses K* and Lu to manufacture Q and is Cobb-Douglas. Thus the creation of K*
14We also assume, realistically, that K+ cannot be shifted about from one technology to another but
is specific to the technology for which it was created.
6
is a separable part of the production of Q.15
The same factor proportions are needed to create K“ in the F (or A) and C technologies,
but the H technology requires a larger amount of L, relative to K (see Figure 1, part A). In the
production of Q, however, the H, F (or A), and C production functions differ in factor intensity
(see Figure 1, Part B). Production of Q using the H technology is the most intensive in K“, next
is C, and last is F (or A). That is, artisanal or hand production has the highest skilled-labor to
capital ratio, relative to the other production methods, in the creation of workable capital. And
when its workable capital is used in the production process it has the greatest output elasticity.
The factory and continuous-process technologies require a higher capital to skilled labor ratio
in the creation of workable capital, but both have a lower output elasticity for workable capital
in the production process than does the artisanal shop, the factory system having the lowest.
More formally, we assume that K’ is generated by:
K* = ti(k]i “L, /kki “K) i= H, F,A, C (1)
such that ~l~,A,c> kl~ and kk~,*,C< lk~ , where 1/kl i , for example, is the unit skilled-labor
requirement for K* creation in production process i. Q production is, likewise, given by:
Q = yi ~(Lu) U (K*) (1 -U) i= H, F,A, C (2)
such that a~ < ac < ~F,* .
Even though skilled labor and capital are strictly complements in capital-maintenance for
all technologies, as in eq. (l), capital-skill complementarily need not be observed for
15Our framework produces results that are qualitatively similar to those of a more general production
function such as a two-level CES of the form:
Q=Ai[%(K*)pi+ (l-~) Lupi] l/pi pi<l
K* = [ pi (K)
ei l/ei
‘i+(l-~i)L6 ] gi<l
as long as pi > 6i for all i. We analyze the special case for which pi = O and 6i = -m.
7
manufacturing as a whole as technology advances (by which we mean a movement from
artisanal production to the factory and eventually to continuous-process methods).lG The reason
can be seen with reference to Figure 1 (see also Table 1). Part B of Figure 1 is drawn so that all
isoquants represent equal amounts of Q. It has also been drawn for a very special case of K“
production, one in which the average cost of K* for all technologies is the same (and thus for
which the isocost lines in Part B are parallel). If r*i is the average cost of K* for technology i, the
condition for which r* is equal for the H and F (or A or C) technologies is:
r/ws = [(kl~)-l - (kl~)-l]/[(k~~)-l - (~kF)-l] (3)
The condition, although not necessary, makes the geometry far sirnpler.17 Without it, however,
we will need another, less restrictive, condition for one of the results.
Initially, say in time period O,consider the factory system to be in its infancy, continuous
processes have not yet been invented, and the artisanal shop is the dominant mode of
production. We begin, therefore, at point HO, Note that just because K* is relatively high does
not mean that much raw capital per unit of output is used. As can be seen in Part A of Figure
1, the H technology uses relatively more L. than do the other technologies in producing K*.
Technological change eventually makes the factory system the dominant mode of Q
production, as can be seen by the Hicks-neutral technical change from FOto F1 (resulting from
an increase in yF). As this type of technological change proceeds, firms will eventually switch
from the H to the F technology (just beyond F1 in Figure 1, Part B). The shift from artisanal
shops to factories initially causes an increase in (K/Q), in (K/L~), probably also in K/(L~ + Lu),
16By a technological advance from artisanal production to the factory system we mean an increase in
~Frelative to y~. The appearance of continuous or batch processes, likewise, comes from an increase in
yc relative to yF.
17If the r’s were different, the isocost lines would not be parallel. The condition simply states that
the degree to which the cost of a unit of K exceeds that for a unit of L, must equal the degree to which
F conserves on ~, relative to H, compared with how much H conserves on K, relative to F.
8
and, most importantly, a decrease in L./ (L, + LU).lB In the more general case of our framework,
in which the isocost lines in Part B are not parallel for the H and F technologies, a weaker
condition than that given by eq. (3) is required for an increase in the relative demand for
unskilled labor to emerge from the shift to the factory. The condition requires that the share of
unskilled labor in total product for the factory technology be sufficiently larger than the share
for the artisanal shop technology. It is likely that such a condition held.”
Additioml Hicks-neutral technical change shifts the F isoquant to A, possibly through
an organizational innovation: the assembly line. The shift does not change the capital-labor ratio,
nor the relative demand for skiUed labor, but it does lower the capital-output ratio.
The shift of most interest to us is the appearance of another technology, that of
continuous-process methods, given by isoquant C.20 Although its capital maintenance technology
is identical to that of the A or F processes, it has a higher output elasticity with respect to K*
than do the A or F processes (~ < ~~,*). Its eventual adoption will increase (K/Q), K/(L~ + Lu),
and L,/ (L, + LU). That is, with its adoption we will observe capital-stil complementarily and
la See Table 1 for the various conditions. We are considering here a small factor-neutral technological
change around F,. Greater technological change, as in the movement to A, can decrease K/Q.
19More formally, the condition is that: (r’~/{~) < [(uE/u~)] . [(1 - u~)/(1 - a~)] - (L1~/ll~) Intuitively,
the condition for the shift from H to F to decrease the relative demand for skilled labor, given Q, will not
occur if (r”~/r*~)is too high. The cost of installed capital will be relatively high in the H sector when the
cost of skilled labor is great in comparison with raw capital. When that occurs, the H sector will have an
incentive to conserve on expensive K* in the production of Q, thus substituting unskilled labor for K*.
But the condition will hold, even when ({”/{~) is high, if u~ is sufficiently low in comparison with u~.
The shift to the F technology will, then, still increase the relative demand for unskilled labor because the
marginal product of ~ is lower in the H technology (given Q and L.) The effect is further helped by
the assumption that X1~> 11~. It does seem reasonable that, for given Q and ~, the marginal product of unskilled
labor was lower in the artisanal shop mode of production compared with the factory. After all, the reason for having
the factory system, with its more intricate division of labor, was to increase the marginal product of unskilled labor.
20The shift from A to C could arise from exogenous technological advance (growth in y= relative to
Y*, ~) or such change could have been fostered by an increase in the relative price of unskilled labor
(increase in wu/r*), as may have occurred with the expansion of high schools after c.191O and with the
ending of open immigration in the 1920s.
9
technology-skill complementarit y, at fl”ven factor prices.21
Note that the introduction of electricity and unit-drive in many factories had effects
similar to those arising from a shift to continuous-process technology. The mechanization of the
hauling and conveying of materials decreased the relative demand for unskilled labor, and the
faster and hotter ruining of machinery required relatively more machine-maintenance persomel.
Technology-Skill and Capital-Skill Complementarity: 1909 to 1940
We turn now to the historical record to observe when capital and skill, or technology and
skill, became relative complements. Because capital is more easily measured than technology
and its use facilitates comparisons with the more recent period, we will often focus on capitalskill
complementarily. But we are also able to gauge technological advance in the past by the
use of purchased electricity. The U.S. censuses of manufactures for 1909 and 1919 contain
information on the capital stock, but that for 1929, important in our measure of technology by
purchased electricity use, does not. Employment is not disaggregated into subgroups of blue-collar
workers, except by sex and by child versus adult.22 And we do not have direct evidence
on the average skill or education of workers in those years, although white-collar workers were
2] Although we do not have a fourth input -- raw materials -- it is easy to compare our framework
with that in James and Skinner (1984). Their skilled sector is our artisanal sector, and it shifts from the
H to the F technology, thereby increasing K/Q, probably also K/L, but decreasing the fraction of the work
force that is skilled, The cause of the technological shift in their case is the lower relative price of raw
materials in the United States versus Britain.
22Capital was also inquired of in 1914, but the question on the capital stock ended in 1919. In 1939
a question was asked on capital investment. For a discussion of the accuracy and consistency of the
capital stock variable and for its defense, see Creamer et al. (1960, appendix A). Capital was to include
only the owned portions of land, buildings, machinery, tools, inventories, and working capital (e. g., cash)
and to be assessed at book value.
10
grouped separately.23 Beginning in 1940 the U.S. census of population asked information on
highest grade completed, industry, and occupation. Using all these sources, and the narrative
historical record, we explore the origins of capital-skill and technology-skill complementarily.24
The 1940 census allows us to identify industries that used disproportionate numbers of
higher-educated (e.g., high-school graduate) workers in blue-collar occupations, and the census
of manufactures can reveal whether “high-education” industries were more capital- or electricityintensive
from 1909 to 1929. We find, looking across narrowly-defined industries in 1909 and
1919, that “high-education” industries, categorized on the basis of 1940 data, were both more
capital- and electricity-intensive. They also paid their production workers more in 1909, 1919,
and 1929, suggesting that these workers were more skilled in the 1910s and 1920s.
Many of the industries we characterize as capital-intensive and “high-education” have
been classified by others (e.g., Chandler 1977) as using continuous-processor batch technologies.
The evidence for the 1909 to 1929 period is consistent with the notion that the transition from
the factory (or assembly-line) to these processes increased the relative demand for skilled
workers and thus can be viewed as a candidate for the origins of economy-wide capital-skill
complementarit y. me previous transition, from the artisanal shop to the factory, involved just
the opposite force. Many of the industries that remained craft in nature (e.g., engraving, jewelry,
23The groups were proprietors and firm members; salaried officers of corporations; superintendents
managers, engineers, and other technical experts; and clerks, stenographers, salesmen, and other salaried
employees.
24Several data sets culled from the U.S. censuses of manufactures are used. We assembled data for
1909 and 1919 and matched them by (pseudo) 4-digit SIC industry using the 1909 industries. Similar data,
covering 1909, 1914, 1919, and 1929, were generously provided by Arthur Woolf (see Woolf 1980). They
contain information on power sources, omit some detail on wage-earners, and are not matched over time
by industry, We matched the 1929 industries in the Woolf data to our 1909 and 1919 industries and added
the missing information on wage-earners. Across the three years (1909, 1919, and 1929) we can match 228
(pseudo) 4-digit industries covering gg~. of employment in those years. The 1909 and 1919 census data
surveyed all firms with more than $500 in gross output; that for 1929 used a $5000 cutoff point. There
appear to be no important biases as a result (see Fabricant 1940).
11
clocks and watches) had far lower capital intensity but higher skill (education) per worker than
did the majority of the industries that shifted to factory-style production techniques.
Capital and skill, measured by the utilization of non-production workers, appear
complementary across manufacturing. We find for 1909-19, using a methodology similar to that
in Berman, Bound, and Griliches (1994), that the change in the proportion of an industry’s wage
bill going to white-collar workers was positively related to the change in both capital-intensity
and purchased electricity. And the coefficient measuring capital-skill complementarit y for 1909-
19 is larger than for 1959-69 and 1969-79 and almost as great as that for 1979-89.
Educated Labor in Blue-Collar Occupations
The 1940 U.S. census of population contains the earliest evidence regarding the
educational attainment of Arnericm workers by industry and occupation.25 Using these data,
we explore whether the blue-collar labor force varied in its schooling by industry. We limit the
analysis of the 1940 data to males, 18 to 34-years old in blue-collar occupations (craft, operatives,
laborers, service) working in the manufacturing sector.2G The age limitation is imposed because
the educational attainment reported by older Americans in the 1940 census appears overstated
(Goldin and Katz 1996), Secondary schooling, moreover, increased greatly in the 1920s and
1930s, and thus the educations of older Americans would have been largely untouched by these
changes. We first restrict the analysis to blue-collar occupations because questions concerning
capital-skill complementarily often revolve around the skill content of production workers.
x The 1940 U.S. pop ulation census was the first to ask the educational attainment. Only three state
censuses did so prior to 1940 (Iowa 1915, 1925; South Dakota 1915). Small samples exist that contain data
on education and occupation, and the 1934-36 Consumer Expenditure Survey does as well. But none is
national in scope, unbiased in the population sampled, and large, with the possible exception of the Iowa
data which we are currently collecting.
26Our substantive findings are very similar if we look, instead, at the educational attainment of all
male workers or at all blue-collar workers, rather than restrict attention, as we do, to 18 to 34 year old
blue-collar males.
12
Among all 18 to 34-year old, male blue-collar manufacturing workers in the 1940 PUMS
(Public Use Microdata Sample), 27.4% stated they completed 12 years of schooling and most of
them were probably graduates of high schools.z’ As a fraction of the total blue-collar work force,
the more educated were disproportionately found in certain industries. The industries,
moreover, fit the categorization we offered previously, for many were continuous-process and
batch technologies (petroleum refining, dairy products, paints and varnishes, nonferrous metals)
or were high-technology industries and those producing relatively new goods (aircraft, business
machinery, scientific and photographic equipment),2a
We list, in Table 2, industries by the percentage of their blue-collar male workers (18 to
34 years old) who were high-school graduates, giving those in the top and bottom 2070 by
employment. 29 The industries divide into two categories. At the low end of the education
spectrum are the products of the first industrial revolution and those of construction: cotton,
woolen, and silk textiles, boots and shoes, lumber, stone, clay, and cement. At the other end are
the newer goods and services of the second industrial revolution (e.g., chemicals, petroleum),
as well as many in the machine-producing group. Also in this category are some that were
produced in settings similar to the artisanal shop: clocks, watches, jewelry, and even aircraft.
27The sample is restricted to the currently-employed. In 1940 more than 10’%oof those in the labor
force were unemployed and an additional 4% were on work-relief. Most, rather than all, of those who
claimed to complete 12 years of school graduated from high school because, even as late as 1940, some
had been educated in ungraded schools, some may have been left back in school, and some, in this period
of soaring education levels, may have overstated their school attainment, as we believe was the case for
older cohorts (see Goldin and Katz 1995a).
26The list would probably include many others in the batch and continuous-process group if the 1940
census tabulated finer categories of industries, e.g., distilled liquors, pharmaceuticals. Industries are
defined as high-technology if a large percentage of their total labor force were engineers, chemists, and
other scientific personnel, similar to currently used definitions.
29The PUMS gives all 3-digit industries and some of the larger 4-digit ones. In the sectors we analyze
there were 61 separate industrial categories (omitting miscellaneous).
13
Finally, there is that perennial among high-education industries: printing and publishing.’”
Before making further sense of the findings regarding high and low-education industries,
we must address whether the results have been generated simply by geographic differences in
both education and industry or by age differences in employment and educational attainment.
Also of interest is whether particular occupations determine the groupings. If, for example, only
machinists, a highly educated group, work in aircraft, then aircraft will be a high-education
industry, We have regressed the percentage high school on a full set of age, state, and
occupation (craft, operative, laborer, service) dummies and find that the residuals by industry
retain an almost identical ranking to that of the original data. That is, state of current residence,
age, and occupation do not have major effects on the educational ranking of the industry. Even
though, for example, aircraft was located in high-education states and employed large numbers
of machinists, its other blue-collar workers were also more highly educated and aircraft
employed disproportionately more high-education workers even in lower-education states.
Another possibility is that the distribution of educated workers by industry in 1940 was
a product of the depression. Firms that employed high-educated workers, according to this
logic, would, in the face of decreased demand, have fired low-educated workers and replaced
them temporarily with their higher-skilled employees. Such occupational changes are well
documented for the depression years. But we find that even in 1950 and 1960 most of the higheducation
industries and sectors we identify in 1940 remain high-educated. Our categorization
aoThe results do not differ substantially if we considered years of education instead of the percentage
graduating from high school. Although we do not here present evidence for sectors other than
manufacturing, they display similar patterns. More educated blue collar workers in the communications,
transportation, and public utilities sectors were found in telephone, wire & radio, air transportation,
petroleum & gasoline pipe lines, electric light & power, and radio broadcasting & television. In retail
trade new products and those that were time-sensitive or more valuable per unit were sold, delivered, and
serviced by high school graduates, For example, drivers for jewelry stores and drug stores were more
educated than other drivers. Blue-collar workers in radio stores and even gas-station attendants were far
more educated than the average blue-collar worker.
14
does not appear to be merely a product of the depression.
A related issue is whether firms paid their higher-educated, blue-collar workers more in
1940. We find that the rate of return to a year of education in a standard earnings equation, for
male blue-collar workers (white, 18 to 34-years old), was 8.37..31 The return for an equivalent
group of ordinary white-collar workers was 8.970. Even within the blue-collar group there was
a substantial return to education, suggesting that the industrial
merely a by-product of the depression, Of more importance to
the education of blue-collar workers.
distribution of workers was not
us, it suggests that firms valued
Certain industries, therefore, employed more highly-educated blue-collar workers in 1940.
The industries were apparently technologically advanced, frequently of recent origin, often
producing goods previously unknown to
continuous-process or batch methods .32
consumers, and using technologies characterized by
But what do these findings signify for capital-skill
complementarily?
To investigate capital-education
merged the education data by industry
complementarily in the 1909 to 1940 period, we have
from the 1940 census with that on industry attributes
from the 1909, 1919, and 1929 censuses of manufactures,33 The 1940 census categories are
considerably broader than are those from the censuses of manufactures, which contain about
four times as many industries, and we have aggregated the earlier data to conform to the 1940
31The coefficient on years of schooling is .083 in a (log) weekly earnings equation that also includes
potential-experience and its square. The return is 77. with a full set of state dummies (data source is the
1/100, 1940 PUMS).
‘2 It should be noted that even though many of the high-education industries were of recent origin,
we base our education data only on workers 18 to 34 years old. Differences in the industries could not,
therefore, be entirely due to the fact that younger workers were more educated than older workers.
m We rely heavily on U.S. Department of Commerce, Bureau of the Census (1940) for the
categorization of the 1909 and 1919 industries, but also the insights of Fabricant (1940) concerning
products that underwent substantial change.
15
categories. We regress the educational level of 18 to 34-year old male blue-collar workers in 1940
on the capital to labor ratio (in 1909 and 1919), a measure of electrification (averaged over 1909,
1919, and 1929), and various controls for worker and industry characteristics.
The ratio of capital to wage-earners in 1909 and 1919 is positively related to the education
of workers by industry in 1940 (see Table 3, COIS.1, 2), The effect, moreover, is economically
significant, Increasing the capital to labor ratio by the equivalent of its difference between the
lumber & timber industry and oleomargarine in 1909 increases the high school graduation rate
by 7 percentage points or by zs~. in 1940.’4 Thus more capital-intensive industries employed a
more highly-educated lab or force some twenty years later.
We find similar effects with respect to the use of purchased electricityy, entered either as
the fraction of horsepower that was run by purchased electricity or as the log of the electricity
variable per wage earner (COIS.3, 4). The effect, moreover, is obtained even when the growth
of industry employment from 1909 to 1929 is held constant. Production-worker skill was not
simply determined by industry growth and the resulting newness of plant and equipment.
Rather, the usage of purchased electricity affected the relative demand for skill independent of
industry growth. An 18 percentage point increase in the purchased electricity share of
horsepower (a one standard deviation change) is associated with a 3.6 percentage point increase
in the share of young blue-collar workers who were high school graduates. Note that increases
in total horsepower did not have the same effect. The type of power, not simply the amount,
affected the skiU level. There are important reasons for this.
Purchased electricity and separate motors enabled many industries to automate conveying
34 (Capital/wage-eamers) in lumber & timber was $1693, and was $5871 in oleomargarine in 1909,
Oleomargarine was produced using a continuous-process technology, whereas lumber & timber was
mainly factory production. The 25~0 figure comes from the fact that 27.47. of all blue-collar males (18 to
34 years old) working in manufacturing were high school graduates in 1940.
16
and hauling operations and thereby eliminate substantial numbers of laborers. Many industries
were prompted to introduce these labor-saving methods with the onset of World War I, and
chief among them were iron & steel, brick manufacturing, pottery, Portland cement, pulp &
paper, rubber tires & tubes, slaughtering&meat packing, lumber manufacture & wood-working,
and mining (Jerome 1934; Nelson 1987; Nye 1990).35 Ample and cheap electricity rendered
feasible the production of various materials, such as aluminum and other electrochemical, that
disproportionately used skilled labor. Cheap electricity also encouraged a more intensive use
of machines thereby increasing demand for the skilled personnel who maintained them.3G
Note that we do not mean to imply any direct relationship between the individual
workers in 1909 (or 1919) and 1940. In fact, education levels in Table 3 refer to men 18 to 34years
old in 1940 who could not possibly have been at work in 1909. We are claiming, however,
that there is something about these industries that increased the value of secondary-school
education during the 1909 to 1940 period. Secondary schooling was just beginning to affect the
education of the masses in the 1910s but by the mid-1920s to mid-1930s some 30% to 50°/0of
youths, depending on region, had high school diplomas (Goldin 1994). Skilled workers in 1909
and 1919 were probably replaced in subsequent decades by young high-school graduates.
35 In iron and steel “the proportion of common laborers was cut approximately in half from 1910 to
1931. The evidence is unmistakable,” notes Jerome, “that recent progress has eliminated unskilled labor
to a much greater extent than other grades” (1934, p. 63). In all these industries, the use of conveyors,
traveling cranes, jitneys, carriers, industrial trucks, and other handling devices reduced the relative
demand for unskilled labor. The changes, moreover, were evident as early as 1916 to 1920.
36The role of electricity in increasing the relative demand for skilled workers is complex. Nye (1990)
concludes that electricity increased the relative demand for skill but recognizes the large number of effects.
“As the electrified factory evolved it required a different mix of labor and management more middle
management; more engineers and technicians; fewer artisanal workers; and a more complex grading of
worker skills, with many more semiskilled laborers and far fewer unskilled workers . Boy mule drivers
in coal mines, carriers in tire factories, or shovelers of raw materials in steel mills saw their work taken
over by electric locomotives, conveyors, and cranes as a few skilled men using expensive machines did
work formerly perfomed by a mass of the unskilled (pp. 234-35). See also Du Boff (1964) and Devine
(1983) on the transition from mechanical to electric drive and the introduction of group and unit drive
motors.
17
Formal education may have substituted for on-the-job training and formal apprenticeships.
Firms in 1909, 1919, and 1929 also appear to have paid significantly more for educated
(skilled) workers. The average blue-collar wage in any of the years is strongly and positively
related to the education level of the industry’s workers in 1940. The coefficient on years of
schooling is 1270 in 1909, 107o in 1919, and 17,570 in 1929, as can be seen in the Table 4
regressions of the (log) average wage on average years of schooling among blue-collar workers.
Although it is tempting to interpret the coefficients as rates of return to an individual’s
education, they are, instead, a combination of that and the return to working in an industry
having more educated workers. Using data sets containing more complete information, such as
the censuses beginning with 1940, one can reconstruct the “poorer” data we are forced to use and
measure both the individual and industry effects. Almost identical magnitudes are obtained to
those we present for 1929 even though the return to an individual’s years of education is
considerably less.37 In light of these comparisons we interpret the coefficients for 1909 to 1929
as indicating that a return to education for blue-collar jobs existed, but that we cannot be certain
of its magnitude.3S
We also analyze, in Table 5, the relationship between average earnings per wage earner
37We construct a similar regression using micro-level data from the 1940 PUMS by aggregating across
industries, The analogous regression of the log(average wage, production workers) by industry on
average years of schooling of male blue-collar workers, 18-34 years old, in that industry (and the
percentage female of blue-collar workers) yields a coefficient of .185 (se. .013). In contrast, a micro-level
regression of individual Iog(eamings) on own schooling (plus a female dummy) yields a coefficient of ,040
(se. .0004). It should be noted that because an experience variable is not included, the coefficient on
education is lower than in regressions to measure the return to education.
3g The wage regressions in Table 4 (and those in Table 5) use consistent data on average annual
earnings of blue-collar workers by industry for 1909, 1919, and 1929. Because hours of work may have
varied in a manner that could bias the results, we have also estimated these regressions (for 1909 and
1919) using hourly earnings, where hours worked in each industry is estimated from a distribution of
hours. The results are robust to the choice of hourly or annual data.
18
and the capital and electricity variables in 1909, 1919, and 1929.39 We find, consistent with the
previous tables, a positive correlation between the capital to labor ratio and wages and, similarly,
a positive effect between wages and the percentage of all horsepower coming from electricity.
Because World War I caused a transitory compression in production-worker wages, we prefer
to concentrate on the 1909 and 1929 coefficients. The magnitudes implied by the coefficients are
substantial, particularly with regard to purchased electricityy use. The difference, for example,
between the capital to labor ratios in the oleomargarine and lumber& timber industries implies
a 5% premium in wages for oleomargarine; the difference in their purchased electricity use
implies a 23~0 wage difference .40
The wage premia we measure are largely due, we believe, to compositional effects; a
greater proportion of more skilled workers were found in industries with more capital per
worker and with a greater percentage of industry horsepower coming from purchased electricity.
Another interpretation is that the wage differentials reflect premia for identically-skilled
individuals working in more capital- and electricity-intensive industries in which there was
greater worker bargaining power and managerial discretion (e.g., Slichter 1950). That may well
be the case. But the strong correlation between wages in the 1909 to 1929 period and education
in 1940, by industry, convinces us
That high-school graduate
that the compositional effect matters.41
blue-collar workers were employed in particular industries
(more capital-intensive ones, those producing newer goods) and were paid substantially
may come as a surprise to readers of the historical literature on the subject. Rarely
39We use the 1919 capital/labor ratios for 1929.
more,
is the
40The wage premia use the 1929 coefficients. The actual difference in wages is 337.. Oleomargarine
had a capital to labor ratio in 1919 of $8759 and 69,4% of its horsepower came from purchased electricity;
the numbers for lumber & timber are $3028 and 27.5Y0,
41 We are currently looking for direct evidence on the matter of skill composition.
19
education of production workers mentioned. Yet there is narrative evidence, complementing our
empirical findings, on the cognitive skills valued in various trades. High-school graduates were
sought because they could read manuals and blueprints, knew about chemistry and electricity,
could do algebra and solve formulas, and, we surmise, could more effectively converse with nonproduction
workers in high-technology industries.42 They were hired into particular skilled
occupations but also for more ordinary positions in certain “high education” industries,
Capital-Skill Complementarily and Non-Production Workers
The data for 1909 and 1919 allow another test of the capital-skill complementarily
hypothesis, as well as a comparison with more recent estimates, by examining whether the nonproduction
worker share of total labor costs increased along with the ratio of capital to output.43
The motivation is the notion that the relative size of the non-production worker group captures
the average amount of skill required across all workers. More engineers and chemists, according
to this logic, necessitate more educated and skilled production workers (see, e.g., Allen 1996).
Continuous-process and batch methods according to Chandler (1977) required more managerial
and professional employees, relative to production workers, and our estimations above are
evidence that such processes also required relatively more skilled blue-collar workers.
42 See, fOr exampler
the descriptions of positions in electrical machinery, glass, medicinal
manufacturing, paint and varnish, and the printing trades in U.S. Department of Labor (various years,
1918-1921). Those that required some years of high school or a high school diploma commanded general
cognitive skills such as “good judgment,” “skilled in free-hand drawing, “ “special ability to interpret
drawings, “ “be familiar with the chemical formulas,” “general knowledge of chemicals used,” “be able to
mix the chemicals.” More technical skills were also valued such as “knowledge of electricity” and “of
electric wire sizes and insulation, “ “technical knowledge of the properties of glass,” “general knowledge
of photography.” Printing establishments required that some be “well versed in grammar, spelling,
punctuation and other essentials of a thorough knowledge of English;’ and noted that “an elementary
knowledge of Latin and Greek is helpful.”
N Chiswick (1979) addresses a somewhat different question using cross-state aggregate manufacturing
data for both 1909 and 1919 and fails to find much evidence of aggregate capital-skill complementarily
(where “skilled” workers are salaried officers and managers). Our data are for the same years, but are
disaggregated by industry, and we analyze the composition of skill demand within detailed industries.
20
The estimating equation comes from a model in which capital, over each ten-year
horizon, is considered the quasi-fixed factor and non-production (skilled or educated) and
production (unskilled) labor are the variable factors.~ If the variable cost function (total labor
cost
cost
function) for industry j is translog and production exhibits
minimization produces an equation for the non-production
(S) in time f of the form:
Sj, = ~ + $jt + ~~(wn/wp) jt + ~j~(K/Q) jt
constant returns to scale, then
labor share of total labor costs
(4)
where w. (wP) is the wage of non-production (production) workers, (K/Q) is the capital to
output ratio, and $j measures the rate of skill-biased technological change in industry j.
Differencing eq. (4) to eliminate industry fixed-effects, yields the following estimating equation
for the change in the non-production share of the wage bill in industry j (under the assumptions
that ~j and pj do not vary across industries or, more realistically, that the average coefficients
across industries are being estimated):
dSjt = PO+ ~ldln(wn/wP)j, + ~zdh(K/Q)jL + ~~ (5)
The coefficient ~1 is greater than or less than O depending on whether the elasticity of
substitution between non-production and production labor is less than or greater than 1. The
condition ~z >0 implies capital-skill complementarily; POcaptures the cross-industry average of
the skill-bias to technical change and (PO+ ~,) is the industry-specific bias to technical change
plus measurement error. The intuitive interpretation of ~, >0 is that when capital and skilled
labor are relative complements, industries experiencing a greater increase in capital intensity are
those with a larger increase in
We estimate eq. (5) as
the non-production worker share of total labor costs.
well as a modification that drops the relative wage variable,
44See Berman, Bound, and Griliches (1994) and Brown and Christensen (1981). The approach is used,
as well, by Bartel and Lichtenberg (1987). Our findings are not sensitive to whether we include materials
as an additional variable factor of production.
21
because cross-sectional wage variation could largely reflect skill differences, not exogenous wage
variation.45 The data are matched at the (pseudo) 4-digit SIC industry level -- there are 256--
for 1909 and 1919, the last year until 1957 that the census of manufactures inquired about the
capital stock. Non-production workers were listed in the four groups: proprietors, officers,
managers (often including professionals), and clericals (often including other salaried workers),
and their share of manufacturing employment increased from 13.9~0 in 1909 to 15,7~0 in 1919.
Among the data issues we had to confront are the treatment of proprietor earnings and the
choice of output and capital deflators,~ In some industries (e.g., bakeries), proprietors were a
large fraction of all workers and probably perfomed managerial and clerical duties. We have
chosen to exclude their income from the wage bill,47 Our input (capital) and output (shipments;
value added, viz., shipments minus materials cost) variables are both nominal measures. In the
absence of industry-specific output prices and materials and investment deflators, we use the
aggregate WPI for the 1909 and 1919 period and, for consistency, the aggregate PPI for finished
goods in the 1959 to 1989 comparison period.4a
The results for 1909 to 1919, given in Table 6, reveal that the coefficient on the change in
45Another reason not to include the wage ratio when estimating eq. (5) is that the estimates will then
suffer from a version of division bias, because WP appears in the denominators of both the dependent
variable and the wage ratio measure. Following Berman, Bound, and Griliches (1994), we also add an
output term to account for cyclical differences in the extent to which non-production and production labor
are quasi-fixed factors. The inclusion of din(Q) also allows the production function to be non-homothetic.
46The weighting of the regression is another data issue. Because classification errors are more of a
problem for small industries we weight by the wage bill share. Qualitatively similar results obtain
without weights, and median regressions (LAD estimates) without weights also yield similar results.
47Two methods of accounting for proprietor earnings can be offered: one imputes their earnings as
if they earned the average non-production wage in the census, and the second assumes that proprietor
income entirely reflects returns to capital and should not be included in the overall wage bill (it was not
supposed to be included). We present the results under the latter assumption; they are robust to either.
4’ We can test the sensitivity of applying industry-specific versus aggregate deflators using the 1959
to 1989 data, We find that the results regarding the capital-skill complementarily coefficient (~z) are robust
to the choice of deflator.
22
the (log) capital-output ratio (~2) is consistently positive and significant regardless of other
controls and independent of the output measure (shipments or value added) .49 Inclusion of the
change in the relative wage reduces the coefficient somewhat, but far less so when we purge the
wage differential of certain compositional effects.50 We also find that the inclusion of a measure
of technological advance -- the change in the percentage of horsepower run by purchased
electricity -- does not noticeably affect the coefficient on dln(K/Q) and is itself positive.
How large we think the effects in Table 6 are will depend on their magnitude in more
recent years when many have concluded on the basis of such regressions that there is capitalskill
complementarily. We present such a comparison in Table 7. The first two columns repeat
the results from COIS.(1) and (2) in Table 6. The remaintig six give nearly identical estimations
for 1959-69, 1969-79, and 1979-89. The striking finding is that the coefficient on the change in
the (log) capital-output ratio (~~ is larger for the 1909-19 period than more recently. Only one
coefficient -- that for 1979-89 in the estimation with the change in shipments variable -- is larger.
The comparison of the results for 1909-19 with those more recently suggests a strong
movement during the 1910s toward capital-skill complementarily. We do not yet know whether
the shift toward capital-skill complementarit y preceded the 1910s, nor can we discern whether
it continued beyond.51 But the historical evidence suggests that the 1910s were a critical
moment in the history of American industry when new goods and new processes were
introduced as never before. Moreover, the new goods and processes were associated with
49Qualitatively similar results are obtained by using the employment share of non-production workers
as the dependent variable. We prefer the cost share measure because it implicitly adjusts for changes in
relative labor quality and hours of work.
50To mitigate the influence of compositional effects on changes in the sectoral skill premia, we use the
clerical wage for the non-production workers and we adjust the change in the skill premium for changes
in percent female and child among production workers (see CO1.7 of Table 6}.
51We might be able to analyze the role of capital before 1909 but not between 1919 and the early 1950s.
23
relatively more educated and skilled labor, as we showed above.
Technological Change and Capital-Skill Complementarily: Historical Insights and Conclusion
The goal of this paper was to locate the origtis, and thus the sources, of technology-skill
and capital-skill complementarily in manufacturing. We have demonstrated that such relative
complementarities existed from 1909 to 1929 and were associated with technologies such as
continuous and batch processes and the adoption of electric motors. To motivate the empirical
work, we devised a framework in which capital is a relative complement of skilled labor in the
creation of workable capital but in which unskilled labor is used with workable capital to create
output. Manufacturing-wide capital-skill complementarily can arise from shifts between
technologies. Our evidence pertains mainly to the period since 1909 and the transition from the
factory to continuous and batch processes and electric motors; we can say little about the earlier
transition from artisaml shops to the factory with our data.
A wide variety of innovations dominated the relationships among capital, technology,
and skill. We often hear that ours is a time of extraordinary technological change, Yet those
that appeared or diffused in the two decades around 1915 may have been more consequential.
Manufacturing horsepower in the form of purchased electricity rose from 9°/0 in 1909 to 53~0 in
1929; similar changes swept residential use. New goods proliferated, such as the automobile,
airplane, commercial radio, aluminum, synthetic dyes, and rayon; household electric appliances
such as the refrigerator and washing machine; office machinery such as calculators, dictating
machines, and copying equipment. New techniques enhanced the production of rubber, plate
glass, gasoline, canned condensed milk, and factory-made butter. Of the goods mentioned all
but the auto are among the higher-education industries in 1940 and which appear to have been
the higher-skill industries in the 1910s and 1920s. Looking back at Table 2, the high-education
24
industries include: aircraft, office and store machines, petroleum refining, dairy products,
electrical machinery, paints and varnishes, miscellaneous machinery, and nonferrous metals.
Input price changes may have been reinforcing in enticing firms to adopt particular
technologies. Among the changes in input prices, the decline in the price of purchased electricity
was the greatest and perhaps most irnportant.52 Cheaper power, purchased electricity, and unit
drive meant that firms could adopt different types of capital and production processes.=
Although the existence of electric utilities meant that less capital was needed to generate power
within firms, production-capital (as opposed to power-generating capital) could run faster and
hotter, thereby requiring more skilled workers to maintain it.
Before its sharp curtailment with World War I, the continuing flood of unskilled
immigrants must have encouraged the adoption of technologies intensive in unskilled labor and
with the decrease in immigration, firms should have pursued an opposite course. We have
already noted that industries rapidly automated after 1916, eliminating scores of laborer
positions. But it was not just the diminution of immigration that may have altered the direction
of innovation and adoption. We have elsewhere demonstrated that high schools diffused rapidly
in the United States from 1910 to 1940 and that by the 1920s a substantial fraction of youths in
America had completed several years of secondary schooling (Goldin 1994, Goldin and Katz
1995). The vast majority of those who graduated high school in the 1910s but did not continue
to college, entered a host of ordinary white-collar occupations, such as clerks, bookkeepers,
secretaries, and various sales positions. The large premium to high-school educated workers that
existed at the turn of the century, and which may have prompted the high school expansion,
52According to Woolf (1980, table 2.1) nominal price declines for electricity were 3.4~0 and real declines
exceeded 57. average annually from 1910 to 1929.
53See Du Boff (1964) and Devine (1983) on why electricity diffused slowly throughout industry until
the 1920s and the role of the capital stock and unit drive (i.e., separate motors for each tool).
25
was markedly reduced by the 1920s, although it remained substantial, A once-expensive
commodity -- a high-school graduate -- suddenly became a more reasonably-priced input.
The technologically-forward industries also grew the fastest, not surprisingly. If we
define a high-education industry (see Table 2) as one in which more than one-third of the young
male blue-collar workers had a high-school degree in 1940, the share of manufacturing
employment in these industries rose from 2t).7~0 in 1909 to 26.770 in 1929. The share in loweducation
industries (defined as less than 23~0 with high school degrees in 1940) declined from
29.0% in 1909 to 20.570 in 1929. The share of manufacturing employment in the top five (2-digit)
industries by education (petroleum, chemicals, electrical machinery, printing & publishing, and
scientific instruments) continued to expand from 1910 to 1940 (from 107o to 1670). Similar shifts
exist today and reinforce the effects of capital- and technology-skill complementarily.
What did the various changes we detail imply for the wage structure? On the shop-floor
and in the office and executive suite the relative demand for skilled and educated labor must
have increased greatly from 1910 to 1940.54 But we have reported elsewhere that the wage of
ordinary white-collar workers fell relative to that for all production workers from the late 1910s
to the early 1920s with the increase in high schools (Goldin and Katz 1995). It now seems clear
that blue-collar employments absorbed some of the increased supply of the high-school educated.
The response to increased demand for educated workers was overwhelming: in thousands of
school districts across the United States high schools mushroomed, curricula changed, and
enrollments soared. The differential between the high-school educated blue or white-collar
employee and the less-educated worker may thereby have been kept in check.
54 Chandler (1977) notes that the managerial revolution was also brought about by many of the
technologies we believe increased the relative demand for educated labor on the shop-floor. The demand
for more educated workers on the farm must also have increased as farm machinery became more
complex. Perhaps that is why high schools expanded even more rapidly in the rural farm areas of the
Great Plains and Far West than elsewhere.
26
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Anderson, H. Dewey, and Percy E. Davidson. 1940. Occupational Trends in the United States. Stanford, CA:
Stanford University Press.
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29
-..
m
“$
u
n
a
m
r
2
Table 1
Predictions of the Framework
Technological change K/Q K/(L, + L.) L,/(L, + L.)
(a) Shift from artisanal or hand trades (H) to
factory production (F)
(b) Shift from facto~ (F) to assembly-line (A)
production (Hicks-neutral technical change)
(c) Shift from assembly-line (A) to continuous
process (or batch) methods (C)
&
+
‘r
Notes:
K = capital stock
L, = skilled or more-educated labor
LU = unskilled or less-educated labor
aThe prediction is obtained when: (A~k/l~k)< [(1 - a~)/(1- a~)] . (r*~/r*~) mat is, considering the
restrictive case discussed in the text of equal r* for H and F, the prediction is correct only if the
higher K*-intensity for the H technology is outweighed by the greater use of K in the creation of
K* in the F technology.
bThe impact of (a) on [K/(~ + Lu)] is ambiguous in the case when [L,/(L, + LU)] declines.
c The prediction holds in the restrictive case of equal d for H and F. When the r’ differ, the
condition is: (r*~/r*~) < [(a~/a~)] - [(1 - ~)/(1 - a~)] - (Al~/Xl~),
Table 2
Percentage High School Graduates by Industry,
18 to 34-Year Old Male Blue-Collar Workers: 1940
2-Digit SIC Manufacturing % H.S. Number
Industries Grad, of Ohs.
High-education industries (from high to low)
Top 20% by employment
Aircraft and parts
Printing and publishing
Office machinery
Petroleum refining
Dairy products
Scientific and photographic
equipment
Electrical machinery
Misc. nonmetallic mineral products
Paints and varnishes
Ship building
Clocks, watches, jewelry
Misc. machinery
Nonferrous metals
52.4
44.7
43.4
43.2
42.9
40.5
40,3
35.6
35.5
34.0
34.0
33.4
32.6
540
1287
166
414
417
227
976
135
107
526
197
1669
341
2-Digit SIC Manufacturing % H.S. Number
Industries Grad. of Ohs.
Low-education industries (from low to high)
Bottom 20% by employment
Cotton manufactures
Logging
Tobacco
Sawmills and planing mills
Not specified textile mills
Silk and rayon manufactures
Carpets and rugs
Cut-stone and stone products
Misc. fabricated textiles
Misc. textile goods
Structural clay products
Cement and concrete, gypsum,
and plaster products
Hats, except cloth and millinery
Dyeing and finishing textiles
Misc. wooden goods
Footwear industries except
rubber
10.7
11.2
11.8
13.8
15.6
16.3
16.8
16.8
17.0
17.1
18.8
19.4
20.0
20.4
21.5
22.8
1510
705
144
1940
128
349
107
101
94
117
271
263
60
191
474
680
Notes: The sample is limited to 18 to 34-year old, currently-employed males in blue-collar occupations
(craft, operative, laborer, service) in manufacturing. The mean for the entire sample of 31~00 is 27.4%.
The industry names are those given in ICPSR (1984), The 20% figure used as the cutoff is for all workers,
not just male blue-collar workers 18-34 years old.
Source: 1940 Public Use Micro-data Sample, 1/100; ICPSR (1984).
Table 3
Education, Capital Intensity, and Electricity Usage, 1909 and 1919
log(K/L)
% hp purchased electricity
log(hp purchased electricity/L)
log(other horsepower/L)
log(total horsepower/L)
dlog(employment) lWg,~gzg
70 artisan
‘%Ofemale
% children
Constant
Number of observations
R2
Fraction High School Graduates among 18 to 34-Year Old Males in
Blue-Collar Occupations, by Industry
Capital Intensity Capital and Electrification
(1) (2) (3) (4)
1909 1919 1919 1919
.0556
(.0191)
.202
(.0377)
.109
(.0605)
-2.00
(551)
.215
(.0268)
57
,495
.0537
(.0226)
.204
(.0396)
.0610
(.0584)
-1.99
(.574)
.211
(,0403)
57
.452
(.0218)
.206
(.0597)
-.0058
(.0167)
.0391
(.0141)
.129
(.0332)
-.0464
(.0713)
-1.02
(.520)
.106
(.0412)
57
.719
.0517
(.0228)
.0397
(.0168)
-.0431
(.0098)
.0380
(.0143)
.132
(.0328)
-.0249
(.0726)
-1.20
(.542)
.203
(.0342)
57
.716
Notes: The unit of observation is a 1940-industry group. Industries from the 1909, 1919, and 1929
Censuses of Manufactures are aggregated up to the 1940 groupings, e.g., beverages in 1940 contains the
1909 categories of distilled, malt, and vinous liquors, and mineral and soda waters. Each observation is
weighted by the industry share of total blue-collar employment in manufacturing averaged over 1909,
1919, and 1929. The dependent variable is the percentage of 18 to 34-year old, male blue-collar workers
in the industry in 1940 who graduated high school. The mean for 1940 among all blue-collar workers in
this group is 0,274. Standard errors are in parentheses.
Table 3, continued
log(K/L) = log of capital stock (000, in current dollars) per wage earner in each industry for 1909 and 1919
respectively, as indicated by the column headings;
% hp purchased electricity= fraction total horsepower run by purchased electricity, averaged over 1909,
1919, and 1929;
log(hp purchased electricity/L) = log of horsepower run by purchased electricity per wage earner
averaged over 1909, 1919, and 1929;
log(other horsepower/L) = log of total horsepower minus horsepower run by purchased electricity per
wage earner averaged over 1909, 1919, and 1929;
log(total horsepower/L) = log of total horsepower per wage earner averaged over 1909, 1919, and 1929;
dlog(employment)lgw, ~~zg= change in log of total employment from 1909 to 1929;
% artisan = fraction of wage earners in the 1940 indust~ categories who were in an artisanal trade
averaged over 1909, 1919, and 1929. Artisan is defined as working in: gold and silver, leaf& foil; jewelry;
photo-engraving; stereotyping &electrotyping; cardcut design; wood& die engraving; glass; glass-cutting,
staining & ornamenting; instruments; optical goods; statuary art. Printing & publishing is also included
because of its special feature of demanding a literate labor force;
% female = average fraction of wage earners female in 1909, 1919, and 1929;
% child = average fraction of wage earners child in 1909 and 1919;
Sources: U.S. Department of Commerce, Bureau of the Census (1913, 1923, 1933) supplemented with data
provided by Arthur Woolf. See Woolf (1980).
1940 Public-Use Microdata Sample, 1/100; ICPSR (1984).
Table 4
Relationship between Earnings (1909, 1919, 1929) and Education (1940)
for 18 to 34-Year Old Blue-Collar Males, by Industry
Average years schooling among blue-collar,
18 to 34-year old males in 1940 industry
grouping
Percentage (females + children) among wage
earners in 1909 or 1919 industry; percentage
female in 1929 industry
Constant
Number of observations
Mean of weighted dependent variable
R2
Log (Average Annual, Current $, Wage)
in 1909, 1919, or 1929 Industry
1909 1919 1929
.124 .0984 .175
(.0111) (.0123) (.0192)
-.495 -.605 -,497
(.0681) (,110) (.0918)
5.22 6.25 5.65
(.102) (,109) (.185)
191 191 191
6,22 7.02 7.14
.698 .643 .656
.117 .133 .151
Notes: The number of 1940 industries is less than the number of 1909, 1919, or 1929 industries.
Regressions are weighted by the number of wage-earners in each 1909, 1919, 1929 indust~. Numbers in
parentheses are Huber (White) standard errors allowing for grouped errors within 1940 industries. The
education variable (average number of years of school) is from the 1940 PUMS. The average wage is
computed as the wage bill/ (average annual number of wage earners) for all years,
Source: US. Department of Commerce, Bureau of the Census (1913, 1923, 1933) supplemented with data
provided by Arthur Woolf. See Woolf (1980).
1940 Public Use Micro-data Sample, 1/100; ICPSR (1984).
Table 5
Relationship between Production-Worker Earnings and Industry Characteristics, 1909, 1919, 1929
log(K/L)
% hp purchased electricity
log(total horsepower/L)
log(establishment size)
70 female
Y. child
Artisan
Constant
Number of observations
Mean of weighted dependent variable
R2
&
Log (Average Annual, Current $, Wage)
in 1909, 1919, or 1929 Industry
1909 1919 1929 Means, 1929
(se.) (se.) (se.) (s.d,)
,0910
(.0151)
.439
(.0556)
-.0213
(.0115)
.0622
(.00633)
-,427
(.0563)
-3.41
(.377)
.136
(.0338)
5.99
(.0306)
228
6.23
.791
.0991
.0417
(.0169)
.266
(.0374)
-.00149
(.0131)
.0780
(.00577)
-.308
(.0613)
-6.91
(.697)
.144
(.0336)
6.65
(.0386)
225
7.03
.813
.0973
.0480
(.0262)
.546
(.0548)
.0184
(.0189)
.0638
(.0103)
-.307
(.0881)
-6.41
(.927)
.273
(.0481)
6.56
(.0666)
228
7,15
.667
.148
1.44
(.510)
.637
(.211)
1.09
(1.07)
4.51
(1.08)
.210
(.225)
.014
(.016)
.0563
(.231)
Notes: The average wage is computed as the (production-worker wage bill) /(average annual number of
wage earners) for all years. Independent variables are defined in Table 3. Log(K/L) by indust~ for 1919
is used in the 1929 regression; YO child for 1919 is used in the 1929 regression. Regressions are weighted
by the number of wage earners in each year, as are the means for 1929.
Source: U.S. Department of Commerce, Bureau of the Census (1913, 1923, 1933) supplemented with data
provided by Arthur Woolf. See Woolf (1980),
Table 6
Change in the Nonproduction-Worker Share of Wage Bill, 1909 to 1919
Variable (1) (2) (3) (4) (5) (6) (7) (8)
dln(K/Q)
din(Q)
dln(K/VA)
dln(VA)
dln(w./wP)
dln(wCy wP)
d(% elec.)
Constant
R2
6
.059 .051
(.011) (.012)
-.008
(.004)
.051 .042
(.010) (.011)
-.0080
(.0044)
.017 .018 .016 .017
(1003) (.003) (.003) (.003)
.100 .111 .093 .105
.0367 .0366 .0368 .0366
.044
(.010)
.032
(.010)
.094 .092
(.013) (.014)
.034 .032
(.004) (.004)
.248 .227
.0336 .0340
.054 .048
(.010) (.012)
-.013
(.005)
.099
(.014)
.046
(,020)
.016 .008
(.003) (.005)
.247 .108
.0336 .0357
Notes: The dependent variable is the log change in the nonproduction-worker share of the wage bill
between 1909 and 1919. The number of observations is 256 for COIS. (l)-(7) and 253 for CO1.(8). An
observation is an industry, generally at the (pseudo for 1909 and 1919) 4-digit SIC level. Regressions are
weighted by the average wage bill share between 1909 and 1919. Standard errors are in parentheses. The
weighted mean of the dependent variable is 0.006.
K = capital stock
Q = shipments
VA = value added
Wn = average wage of non-production workers
WP = average wage of production workers
WC= a~erage wage of clerical workers
dln(wC /WP) = change in the (log) wage of clerical and productive workers adjusted for the change in the
percentage female and percentage child workers of production workers
d(% elec.) = change in the fraction of total horsepower run by purchased electricity
Sources: US. Department of Commerce, Bureau of the Census (1913, 1923).
Table 7
Change in Nonproduction-Worker Share of Wage Bill:
1909-19, 1959-69, 1969-79, 1979-89
1909-19 1959-69 1969-79 1979-89
Variable (1) (2) (3) (4) (5) (6) (7) (8)
dln(K/Q)
din(Q)
Constant
R2
6
Number of
observations
Mean of weighted
dependent variable
.059
(!011)
.017
(.003)
.100
.0367
256
.006
.051
(.012)
-.008
(.004)
.018
(.003)
.111
.0366
256
.006
.005 ,018
(.005) (.006)
.026
(.006)
.010 -.003
(.002) (.004)
.002 .040
.0354 .0348
450 450
.011 .011
.023
(.006)
.010
(.002)
.026
.0415
450
.013
.040
(.009)
.021
(.008)
.005
(.0Q3)
.040
.0412
450
.013
.024
(.008)
.037
(.002)
.018
.0438
450
.037
.061
(.011)
.036
(.007)
.030
(.002)
.071
.0427
450
.037
Notes: The dependent variable is the log change in the nonproduction-worker share of the wage bill
between the years given. An observation is an industry, generally at the (pseudo) 4-digit SIC level.
Regressions are weighted by the average wage bill share between the years given. Standard errors are
in parentheses. din(Q) is deflated by the WPI for 1909-19 and by the PPI for finished goods in the other
three periods.
K = capital stock
Q = shipments
Sources: US. Department of Commerce, Bureau of the Census (1913, 1923).
NBER Manufacturing Productivity Database, Annual Survey of Manufactures, as described in Bartelsrnan
and Gray (1994).
Deflators. 1909, 1919: U.S. Department of Commerce, Bureau of the Census (1975), series E-40.
All other years: EconomicReportof the President(1995),table B-64, p. 347.