BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRODUCTIVITY AND TECHNOLOGY 

 

Turbulence, Inequality, and Cheap Steel 

 

Peter B. Meyer, U.S. Bureau of Labor Statistics 

 Working Paper 375

 February 2005 

 

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the U.S. 

Bureau of Labor Statistics.
Turbulence, inequality, and cheap steel 

Peter B. Meyer 

Office of Productivity and Technology 

U.S. Bureau of Labor Statistics 

February 2, 2005 

Abstract 

Iron and steel production grew dramatically in the U.S. when mass production 

technologies for steel were adopted in the 1860s. According to new measures 

presented in this study, earnings inequality rose within the iron and steel 

industries about 1870, perhaps because technological uncertainty led to gambles 

and turbulence. Firms made a variety of technological choices and began formal 

research and development. Professional associations and journals for mechanical 

engineers and chemists appeared. A national market replaced local markets for 

iron and steel. An industrial union replaced craft unions. As new ore sources and 

cheap water transportation were introduced, new plants along the Great Lakes 

outcompeted existing plants elsewhere. Because new iron and steel plants in the 

1870s were larger than any U.S. plants had ever been, cost accounting appeared 

in the industry and grew in importance. Uncertainty explains the rise in inequality 

better than a skill bias account, according to which differences among individuals 

generate greater differences in wages. Analogous issues of inequality come up 

with respect to recent information technology. 

Feedback to meyer.peter@bls.gov is welcome. My thanks to Joseph Davis, Mercedes Delgado-Garcia, 

Joseph Ferrie, Thomas Geraghty, Knick Harley, Thomas Maloney, Joel Mokyr, Leo Sveikauskas, 

Christopher Taber, Walter Wessels, Thomas Wiseman, John Woodruff, many other colleagues and 

seminar participants, and especially to Mercedes Delgado-Garcia and Slil (Lynn) Siripong for entering 

and editing much of the data. All views expressed in this paper are those of the author and do not 

necessarily reflect the views or policies of the U.S. Bureau of Labor Statistics.
1 

1.0 Introduction 

This paper investigates earnings inequality in the context of the introduction and rapid 

growth of mass production methods for making steel in the U.S. after 1865. There is an analogy 

to the appearance and growth of new information technology since the 1960s. In both of these 

cases, major technological changes had macroeconomic effects: steel principally through its use 

in railroads, and semiconductors mainly through their application in computers. In both cases 

earnings inequality rose in an affected population of workers. 

In the context of rapid technological change, the Greenwood and Yorukoglu (1997) model 

and similar dynamic models make predictions which match the evidence presented here. A sharp 

decline in prices and costs attributable to technological change coincided with a productivity 

slowdown and a rise in earnings inequality. After the technology matured, productivity boomed. 

These stylized facts describe both the steel case and the information age case. 

This paper presents measures of the dispersion of wages within each of several industries for 

each year from 1850 to 1881, based on wage data from a survey of establishments conducted by 

the U.S. Census Bureau. This evidence shows that in the industries producing iron and steel, 

earnings dispersion increased as the new technologies were adopted. Earnings dispersion did not 

rise in the other industries at that time. 

Uncertainty about the new technology and its effects may have caused this increase in wage 

inequality in the iron industries. Since they did not know the future of the technology or the 

industry, employers and workers chose from a variety of possible strategies and made a variety of 

gambles, whose outcomes varied greatly. Greater variation in choices led to more variability in 

outcomes, including wages, so there was a greater variance in the distribution of wages at the 

time of technological turbulence. In this phase of adaptation to the new technology, new skills 

are developed. This technological uncertainty hypothesis fits the evidence on mass production 

steel well, and may also apply to the computer age. 

2.0 Iron and steel technology of 1870 

Useful iron products in 1870 took three general forms: pig iron, wrought iron, and steel. Pig 

iron has 2.5% to 6% carbon and is the easiest to make from most iron ore. It is brittle, meaning it 

can break when other materials might bend. It can be cast into molds to make, for example, 

stoves or pots, and so may be cast iron. When pig iron is heated to a high enough temperature 

that carbon and other elements separate away as slag, the chemically near-pure iron product that 

remains is wrought iron or bar iron. When bent, it tends to remain bent rather than break. 

Wrought iron can be forged (pounded) into shapes such as cans, hinges, and rails. 

A mixture of pig iron and wrought iron, with approximately 2% carbon, is steel. Steel is 

more elastic than other iron materials, so after being bent it tends to return to its previous shape. 

Various steels are ideal for cutlery, railroad ties, armor plating, and structural elements of 
2 

buildings. The labor intensive steel making techniques called crucible steel and cementation 

steel were too expensive to make rails profitably, but were used for products with high value per 

weight like edge tools and cutlery. Many useful variations of pig iron, wrought iron, and steel 

were based on the kinds of original ore used, the amount of carbon in the final product, the 

admixtures (such as manganese and nickel), and other details of the production process. 

In the 1850s and 1860s European inventors developed two new approaches to steel making 

which were suitable for mass production. In one, the Bessemer steel process, molten iron was 

poured into a giant vessel called a Bessemer converter. Then pressurized air was blown through 

the liquid. The heat and air burned carbon away, and if the air flow were cut off at the right time 

the resulting mixture would be steel. Bessemer steel technology was established in Britain by 

1859 and experiments with it began in the U.S. in 1863. After several years and many failures 

the technology became established and production grew quickly for the rest of the century. The 

Bessemer steel process worked quickly but did not create very high quality output. 

The open hearth processes took longer but made higher quality steel. The acid open hearth

process produces higher quality but more expensive steel. It was invented about 1868 and 

gradually grew in importance. The basic (meaning alkaline) process, introduced in 1878, made 

open-hearth steel from a different kind of ore. There were also efforts to use a similar method 

called puddled steel. 

Railroads made of steel could sustain heavier trains, and lasted eight or more times as long, as 

wrought iron rails. Demand for steel rails was so high that American plants sold all the good 

rails they could make, and more rails were imported from Britain. Even so, the price of steel fell 

during the first twenty years of mass production as the technology of production improved 

greatly. The menu of iron and steel materials expanded too. Some new materials were better 

than previous ones, and others had niche purposes. Industrial makers and users knew of many 

differences among them, but the chemical contents, properties, and mechanisms were not well 

understood. Railroads were willing to take some risks to work with steel. Many innovations in 

iron and steel production were put to use. By 1920 steel had replaced wrought iron in most 

applications including the railroads. 

Along with the dramatic effects of the introduction of the new steel-making processes, there 

were a cluster of related changes in the technology and industry. Iron ore was discovered and 

mined around the Great Lakes. Much bigger plants were built, usually near the Great Lakes 

because ore could be shipped cheaply over water. These changes in materials supply and 

technology took place in a context of an ongoing boom in railroad-building which created 

enormous but volatile demand for iron and steel. 

Every year in the 1860s and 1870s steel rails were imported from British firms, at first 

because their technology was superior, and thereafter because U.S. producers could not satisfy 

domestic demand. U.S. firms first sold Bessemer steel in the late 1860s and production became 
3 

more efficient until it had exceeded the best practices elsewhere.1 There were several causes for 

this. (1) There were high tariffs on imports, 45% for steel and most steel products until 1870, 

then even higher; Temin (p. 173) estimated a rate of 100% by 1877. (2) American producers 

benefited from enormous domestic demand, especially from the railroads as they crossed the 

continent. Economies of scale in the newer iron and steel technologies gave American plants an 

efficiency advantage as well. (3) Iron ore in the U.S. tended to be low in phosphorus, which 

was required for the Bessemer technology. American ores also had little silicon, which was 

needed for the open hearth technology. Low silicon ore had to be blown more rapidly through 

the converter. (Carr and Taplin, pp. 154-155) These input differences drove American designs 

in slightly different directions from British ones. (4) British safety regulations prevented most 

uses of steel in buildings from 1859-1877, to an extent that held back Bessemer steel production 

(Carr and Taplin, p. 162). (5) The U.S. environment supported greater financial risk taking. 

U.S. commercial production of Bessemer steel exploded from about 10,000 tons in 1869 to 

one million tons in 1880 and continued to grow thereafter, as shown in Figure 1. Around ninety 

percent of this steel went into railroads. The price of the steel fell by about 50% as shown in 

Figure 2 as production capacity grew and the technology improved. Employment in the steelmaking and steel-using industries rose sharply in this period, as shown in Table 10. 

Figures 1 and 2. U.S. steel production quantities and price Source is Historical Statistics of the US. Table 4 has details.

To summarize: The Bessemer and open hearth processes were invented in the period from 

1856 to 1878, and all cheap, mass produced steel from then through 1920 came from these 

processes. This introduced much variation into the materials and methods of production of iron 

 

1

 By 1874 British expert Isaac Lowthian Bell described some U.S. blast furnaces, Bessemer works, and rolling mills 

as better than the best British ones (Carr and Taplin, p. 44 and p. 53). French, German, and Belgian ironmakers were 

also near the technological frontier in 1860. British firms were the leaders, and in the simplified description here, 

producers outside the U.S. and Britain are left out. 

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

180.00

1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881

Year

Dollars per gross ton

Price of

steel rails

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

186718681869187018711872187318741875187618771878187918801881

Year

Short tons

Bessemer steel ingots (new technology)

Open-hearth steel production (new

technology)

Crucible steel production (old technology)
4 

and steel. The variation declined slowly after 1880 as standardization occurred and various 

aspects of the new production methods became common knowledge. 

3.0 Wage data from the Weeks report 

The statistical hypothesis of interest is that earnings inequality rose during the takeoff of cheap 

steel. The data to examine this hypothesis come from a survey on wages in manufacturing 

published with the 1880 Census. It is called the Weeks report for Joseph D. Weeks, the Census 

special agent who supervised the survey. In the survey, manufacturing establishments reported 

jobs and the wages paid retrospectively for some set of years up till the time they were asked. The 

data set has 104,413 observations of wages per job-year, mostly from 1860-1880, but including 

observations from 1801 to 1884. Colleagues and I have entered and edited the data, which is 

available online (Meyer, 2004). The survey covered the 48 industries listed in Table 5. 

In the Weeks report, establishments reported descriptive information about themselves, 

including a list of job titles and some information about the wages, salaries, or piece rates paid. 

Most establishments were also complete firms, but in a few cases two establishments of a single 

firm are reported to be in different industries. Usually the wage information is the average wage 

paid by job title, but other times it is the range of wages or a representative wage. Occasionally the 

number of employees with that job title were reported or implied or other information about them 

is given. For the purposes of measuring wage inequality, each observation has a wage, associated 

with a job title, industry, and city or state location. There is often other information about the 

establishment drawn from the Weeks report or other sources. 

The report lists daily wages where possible but some pay was reported on a weekly or monthly 

basis, or in piece rates such as a specified payment per bushel, ton, or other unit of output. The 

inequality measures used here require that wages be expressed in a common unit of measure. All 

the time unit measures were converted to daily rates. Another 3,500 observations, with piece rate 

wages (such as an amount of dollars per thousand nails produced) were excluded from this study. 

The data do not form a balanced panel. Because almost all of the data were reported by firms 

doing business in 1880, data reported in earlier years has been selected by survival bias and has 

retrospective bias to the degree that data from earlier years was reconstructed by the firms. The 

Weeks report did not sample individuals. It surveyed firms, only about half of whom responded, 

and did so with a partial list of job titles and years. Also one does not know whether a jobholder 

1880 was the same person who held the job in 1879. Few statistical adjustments were made here 

for these issues, which I believe do not bias comparisons of wage inequality between industries. 

A more serious problem for measuring earnings inequality is that unskilled laborers were 

underreported in the Weeks report data. Most establishments reported average wages of workers 

within a job title (like “Puddler” or “Laborer”), and did not report how many there were, or what 

the distribution of wages were within the job category. For example, at a particular firm there 

might be ten laborers but only one manager, yet the firm may have reported one observation per 
5 

year for each job title. A comparison of the job titles in the Weeks report to the 1880 Census job 

categories showed that unskilled laborers were definitely underrepresented so each laborer in the 

data was taken, in the inequality measures, to represent three laborers.2

 This weighting chosen 

improves the degree to which the data represents the population, but only roughly. It was not clear 

how much other jobs were underrepresented. 

The measure of inequality one would get from a complete census could therefore be 

systematically different from the measures used here. Some variation is lost because sometimes 

we have an average wage for a job in place of a full list of wages. Furthermore we do not know 

how much workers in various occupations were undersampled. The net effect on inequality 

measures is probably to bias them up, since workers in other low wage occupations, such as 

helpers, were probably undersampled. They inequality measures are therefore not commensurate 

with those coming from a representative panel, but it is not clear they are biased for comparisons 

between two industries at the same time, or the same industry in different years. 

3.1. Wages and wage predictors 

Wages in the Weeks report data averaged $1.92 per day for an adult man, $1.21 for a woman, 

$.67 for a boy, and $.78 for a girl. The regression in Table 7 shows that holding job title constant, 

wages rose 1.2% per year in nominal terms. This was similar to the real effect since inflation was 

near zero overall. During the Civil War there was high inflation and after the Panic of 1873 there 

was a substantial deflation. These effects are visible in most of the wage series in the Weeks data. 

By modern standards wages were volatile, and jobs and wages were not protected from 

depressions.3

 Wages of common jobs are graphed over time in Figure 6. 

Job titles were not standardized by the Census Bureau. Going through the data and reading 

secondary sources, I have created a second job title which is standardized as much as possible. 

Based on the standardized job titles, 173 jobs have 100 or more wage observations on adult white 

males. In total there here are approximately 85,000 such observations. Using this definition of 

occupation, the effect of occupation on wages are stable over time. Standard deviations around 

the coefficient on the dummy variable for a particular standardized occupations are usually near 

zero, which suggests we can know the average wage for each job with precision. Job effects by 

this definition are also stable over time – a regression of the job effects measured in the 1870-

1881 data on those in the 1850-1869 period and a constant explains 95% of the variation in the 

later period. The occupational titles make it possible to hold constant some facets of human 

capital, or skill, in statistical comparisons. In some regressions to follow, fixed effects are 

measured with dummy variables on standardized occupations. 

 

2

 Montgomery (1987, p. 64), cited an estimate that in the 1870s 10 to 20 percent of the workers around rolling mills 

and converters were day laborers. The fraction rose over the next two decades. The data set has 3211 wage 

observations on rolling mills in the 1870s, 148 of which are for laborers, so triple-counting each laborer matches 

both the Census data and Montgomery’s estimate. 

3

 For example, S. Allen (1987) documented that wage differences across industries vary from year to year ten times 

as much before 1890 as they did after World War II. Employment was minimally regulated, contracts were short, 

and turnover was high. Few employees earned steady salaries. 
6 

Workers paid piece rates (that is, linearly by some measures of output) were paid slightly more 

per day than workers paid by time worked, holding all else constant. Because piece rates align 

incentives so closely to output measures, it was believed at the time that piece rate pay systems 

generated more effort, output, and wages.4 When the survey reported piece rate wages without a 

daily earnings estimate, these observations had to be left out of this regression. 

The materials of work indicate particular roles and skills and therefore predict wages. Job 

titles were coded as involving wood, metal, or heat based on key words. Common woodworker 

titles were carpenter, cabinet-maker, wood machinist, wood-chopper, and wood-worker. Over 80 

jobs were metal-work jobs, including bar-roller, blacksmith, coppersmith, forger, iron machinist, 

molder’s apprentice, pattern-maker, puddler, sheet-mill roller, and wire-straightener. Both wood 

and metal categories were skilled work, whose wages show premiums according to the wage 

regression in Table 8. Woodworking machinery had reached a technological plateau by 18605

whereas iron work experienced substantial technological change afterward. Perhaps because of 

this, metal workers received a slight rise in pay relative to wood workers starting about 1870. 

Workers who dealt with heat were paid 16% more, holding all else constant, according to the 

regression results. The phrase “hot work” was used at the time. Pay premiums for the use of 

heat and metal indicated that proximity to new technology was correlated to higher pay. 

3.2. Wage dispersion 

The uncertainty hypothesis is about the worker’s technological environment, not a statement 

about the worker’s own attributes. It can be represented in an individual wage regression. Let 

there be two time periods, one shortly before and one shortly after a particular technological 

advance. Imagine a wage regression in which a matrix S has data on worker skills, data matrix X

contains other attributes of employer, worker, and job role, and α is a constant in the regression. 

The remaining errors in the model’s prediction have mean zero and denoted by u below. The 

econometrician estimates the regression separately for the two time periods: 

ln(wagej) = α1 + β1Xj + γ1Sj + u1j for the early period (1a) 

ln(wagek) = α2 + β2Xk + γ2Sk + u2k for the later period (1b) 

If one found that γ2>γ1, this would support a claim that a skill bias had risen. If variance of 

u2 were greater than the variance of u1, this would support the claim that there was more 

economic turbulence in the wage relationship in the second period. Therefore in principle skill 

bias and uncertainty-driven turbulence could occur simultaneously and be distinguished. 

 

4

 For example, in British steelwork, tonnage rates of pay came out higher than time rates of pay (Carr and Taplin, p. 

147). Clawson (1980, p.170) estimated piece rates produced 1/3 higher daily pay than day wages did. A U.S. 

Commissioner of Labor reported that employers found employees paid by piecework produced 15-25 percent more 

than those paid by the day, according to Montgomery (p. 150). 

5

 Rosenberg (1977) documents important improvements in wood machinery, which occurred mainly before 1850. 
7 

In practice, they are not easy to distinguish because the empirical meanings of skill or human 

capital are subject to dispute. In a wage regression which includes the number of years of formal 

education spent by each worker, some analysts may treat this as a good proxy for skills. But this 

measure also incorporates institutional advantages the worker has, unrelated to skill. It is a 

result, first, of favorable opportunities available to the worker; second, it signals abilities of the 

worker apart from anything learned through education, and third, it represents certifications that 

the worker received which make the worker formally suitable for an employment position apart 

from any ability, skill, or prior advantage in opportunities. 

Thus any misspecification of skills could lead an econometrician to misinterpret either 

phenomenon as the other kind. For example, missing data on a dimension of skill makes it 

appear that technological uncertainty is present. Going the other way, an econometrician may 

interpret, as measures of skill, variables which do not represent skill variations, in order to 

support a skill bias argument when the uncertainty argument is a better characterization. 

The same issues arise in this study, where we do not have education but have fairly precise 

information about job title and content. Job title too has a mixture of path dependent results of 

opportunity, ability, skills, and certification-like outcomes of a worker’s experience. Holding job 

title constant, as best we can, and comparing wage residuals, is one way of showing that some 

kind of turbulence increased from one period to the next. Apart from the wage evidence, we will 

see that previous structural relations and institutions were under strain, and that employers, 

workers, and investors were exploring new alternatives, opportunities, and risks. We can infer 

that the rules of their game were changing, and this produced winners and losers on many 

dimensions some of which appear random. Sometimes we can identify those dimensions, but 

conceptually some dimensions exist which we could understand but not measure. 

Here, we have only one good measure of skills, which is the job title. We can show that 

residuals from some wage regressions were larger in magnitude in the iron and steel industries 

after 1870 than before. This tests the hypothesis that wage dispersion in one group rose, with the 

idea that a noisier u is built into those wages than other wages during this later period. 

Earnings dispersion here is measured by the standard deviation of log-wages. This measure has 

the virtue that it is invariant to changes in the unit of measure, so if inflation caused all wages to 

grow by the same percentage, the measures of inequality would be unchanged. This makes it 

possible to avoid using any price index. The Gini index and the coefficient of variation of wages 

(which is a sample’s standard deviation divided by the sample average) are measures of inequality 

within a group also have these advantages. I have found that the results to be shown do not tend to 

vary substantively with the choice of inequality measure. 

The relevant industries in the data are blast furnaces, which make pig iron from ore, and rolling 

mills, which press iron or steel into useful shapes. Only establishments from these industries 

expanded into steel production. We can test directly whether the iron and steel wages were more 

unequal, statistically. In the next table we combine all iron and steel wage information into a 

before and after period, and compare this to all other wages in manufacturing. 
8 

First we regress all log-wages on year indicators, to detect cyclical and trend effects. The 

predicted values based on year are shown in Figure 7 (at the back). They show the effects of (a) 

rising wages with time, (b) the enormous inflation during the Civil War, and (c) the fall during 

the depressions after the panics of 1857 and 1873. If we subtract the wage predicted by the year 

dummy regression from the actual wage, what remains are residuals with mean zero each year, 

whose dispersion can be compared between groups of years. Dispersion is measured by 

variance here because the standard deviation of a sample variance is known.6 Table 1a compares 

these wage residuals in iron and steel before 1869, and after, to wage residuals in other industry 

groups. 

Table 1a Variation in iron and steel wages versus other manufacturing wages 

Average of wage residuals, 

after year effects removed 

(sample size in parentheses)

Variance of 

wage residuals (standard 

deviation in parentheses)

1850-1869 1870-1881 1850-1869 1870-1881

Iron and steel establishments 

 (blast furnaces and rolling mills) 

.079 

(N=2172)

.064 

(N=7251)

.198 

(.010) 

.284 

(.007) 

All other establishments combined -.005 

(N=31662) 

-.008 

(N=58614) 

.292 

(.003) 

.278 

(.002) 

 Other metal work establishments .171 

(N=5252) 

.109 

(N=10215) 

.163 

(.005) 

.161 

(.003) 

 Food/agriculture/forestry -.066 

(N=4082) 

-.063 

(N=9338)

.306 

(.009) 

.298 

(.006) 

 Textiles/leather -.211 

(N=12436) 

-.196 

(N=19396) 

.296 

(.005) 

.317 

(.004) 

 Wood work .231 

(N=6157) 

.156 

(N=12865) 

.180 

(.005) 

.185 

(.004) 

 Mining and other materials .107 

(N=3735) 

.117 

(N=6800) 

.338 

(.012) 

.301 

(.007) 

Finding: variation of wage residuals grew most in the iron and steel industries. 

 

6

 The last column has the standard deviation of the measured variance. The sample variance of an estimated variance 

statistic is computed in Greene (1997, pp. 128-129) by the delta method applied to the sample variance computation. 

Let w be an observation from a distribution of log-earnings, n be the size of a sample of observations of w, E[] be the 

expectations operator, μw be the mean of w, and σw be the standard deviation of w. Then for large n, the variance 

around the sample variance of w is estimated by: 

n

E w w w

w

4 4

2 [( ) ] AsymptoticVariance( ) μ σ

σ − − =

 Substituting sample analogs for E[(w-μw)

4

] , σw

4

 , and n gives an estimate of the sampling variance around the 

estimated variance of log-wages. The square root of that quantity is the standard deviation and 1.96 times the 

standard deviation defines bounds for a 95% confidence interval. The derivation of this statistic depends on the 

assumption that the wages are a random sample of the industry population when in fact they were selected from 

establishments, which were stable enough to reply to a survey. So using this measure of variance, statistical 

significance is exaggerated, and no formal statistical significance tests are applied here. 
9 

The variance of the residuals in bold toward the top right rose in the iron and steel group but 

drifted down in the rest of the industries taken together, and in the most of the clusters of industries 

shown. This shows the wage distribution spread out in iron and steel but not in other industries. 

Now consider a more substantive regression in which there are indicator variables for the most 

common occupations after the job titles have been standardized. Residuals from this regression are 

much smaller. The same effect exists in the residuals which are shown in Table 1b – again, the 

iron and steel residuals jump after 1870 but the others do not. 

Table 1b. Variation in iron and steel wages versus other manufacturing wages 

Average of wage residuals, 

after regression on year 

indicators and standardized 

job indicators 

(sample size in parentheses)

Variance of 

wage residuals (standard 

deviation in parentheses)

1850-1869 1870-1881 1850-1869 1870-1881

Iron and steel establishments -.002 

(N=1915)

-.018 

(N=5731)

.090 

(.004) 

.112 

(.003) 

All other establishments combined .000 

(N=27276) 

 .002 

(N=49932) 

.100 

(.001) 

.101 

(.001) 

 Other metal work establishments .044 

(N=4988) 

.019 

(N=9452) 

.073 

(.002) 

.063 

(.002) 

 Food/agriculture/forestry -.013 

(N=3392) 

-.025 

(N=7705)

.160 

(.006) 

.148 

(.004) 

 Textiles/leather -.029 

(N=10602) 

-.004 

(N=15945) 

.097 

(..002 

.106 

(.002) 

 Wood work .012 

(N=5699 

-.014 

(N=12168) 

.086 

(.003) 

.089 

(.002) 

 Mining and other materials .025 

(N=2595) 

.051 

(N=4862) 

.108 

(.004) 

.109 

(.005) 

Note: regression includes only white adult men in the standardized jobs. 

Changes in measurement methodology seem not change this basic finding. The use of some 

other metric of inequality makes only small differences. The use of the wage regression in Table 8, 

with many more substantive variables, reduces the residuals but still it jumps for the iron and steel 

sector and not elsewhere, as shown in Table 1c. 

Thus this effect appears robust to variations in the regressors which include proxies for skill. 

The remainder of this section considers alternatives in measurement and alternative causes for the 

result in Table 1. For example, the weighting of the laborers to match their frequency in the 

population does not make much difference, basically because the variation that affects this measure 

of dispersion is the variation among high wages. Skipping the step of removing year effects, and 

simply using a using a time trend, or no adjustment at all to log-wages over time, produces a 

substantively similar result. 
10 

Table 1c. Variation in iron and steel wages versus other manufacturing wages 

Average of wage residuals by

industry, after regression on 

year indicators and 

standardized job indicators, 

and other variables 

(sample size in parentheses)

Variance of 

wage residuals 

(standard deviation of the 

variance in parentheses)

1850-1869 1870-1881 1850-1869 1870-1881

Iron and steel establishments .006 

(N=2172)

.000 

(N=7251)

.097 

(.004) 

.145 

(.004) 

All other establishments combined -.002 

(N=31662) 

 .006 

(N=58614) 

.120 

(.002) 

.121 

(.001) 

 Other metal work establishments .045 

(N=5252) 

.027 

(N=10215) 

.075 

(.002) 

.069 

(.002) 

 Food/agriculture/forestry -.024 

(N=4082) 

-.022 

(N=9338)

.168 

(.006) 

.154 

(.003) 

 Textiles/clothing/leather -.049 

(N=12436) 

-.016 

(N=19396) 

.119 

(.002) 

.136 

(.002) 

 Wood work .034 

(N=6157) 

-.003 

(N=12865) 

.099 

(.003) 

.102 

(.002) 

 Mining and other materials .050 

(N=3735) 

.090 

(N=6800) 

.149 

(.005) 

.146 

(.005) 

The regression from which these residuals come is shown in Table 8. It includes all observations from 

1850-1880 with measures of daily earnings. R2

 of the regression is .60. Laborers were weighted as three 

observations each. 

Perhaps any substantively sensible wage regression on this data will have this property – that 

its residuals will be larger for the iron and steel industry after 1870 than before, and that this is not 

the case for other industries. This suggests there is a link to the technological change. We consider 

below how instead it might be caused by misspecifications. 

One would get similar results in such comparisons if there were a long-lasting trend toward 

increasing inequality in the iron and steel business, even if there were no special change in or 

around 1869 in particular. Atack, Bateman, and Margo (2000) find in the Census of Manufactures 

that the presence of larger and larger firms seemed to be related to rising income inequality in this 

period. Certainly this is possible among the iron and steel firms, whose average size grew rapidly 

throughout the period. The Weeks report does not have enough direct information about firm size 

to address this hypothesis in particular, but Table 2 addresses the general possibility that there was 

a long lasting trend rise in iron and steel in particular. It has the results of a regression in which 

wage inequality, measured by the standard deviation of log-wages within an industry in one year, is 

the dependent variable. Holding constant a time trend, and a post-1869 indicator variable, and a 

time trend for the iron industries, the iron-making industries had a rise in earnings inequality as of 

about 1869. This evidence supports the finding of a structural break in 1869-70 rather than a trend. 
11 

Table 2. Predictors of wage dispersion within industry-years 

Predictor Coefficient 

Robust 

standard 

errors 

p-value 

Year (1855 to 1881) minus 1869 .0003 .001 .813 

Post-1869 indicator .022 .018 .206 

Minerals and mining industries -.062 .021 .003 

Woodwork industries -.044 .019 .023 

Agricultural or food processing industries -.028 .021 .180 

Textiles and clothing industries .075 .022 .001 

Metal production and use industry (incl. iron and steel) -.083 .019 .000 

Iron and steel (blast furnace or rolling mill) .027 .033 .414 

Year trend for iron and steel only -.004 .005 .354 

Iron blast furnace or rolling mill after 1869 .120 .050 .017 

Notes: The dependent variable is standard deviation of weighted log-wages within industries each year. There are 

1047 industry-year observations of this variable (27 years of 48 industries, excluding industry-years with fewer than 10 

wage observations). An excluded category of industries is those making building materials. The coefficient on the 

constant is not shown. R-squared: 0.14. 

Earnings inequality was higher in the industries making iron or steel, defined in the last row of 

Table 2, than in those same industries in the past or in other metals industries. If there were a 

longstanding trend in the inequality within these industries it should appear in the second to last 

row, but there does not seem to be any significant trend. 

Many establishments appear first in the data in 1870 and the apparent break at 1870 could be 

partly an artifact of the data. Partly this is because so many firms reported retrospectively back 

precisely to 1870, both in iron and steel and in other industries. Outside iron and steel, there is a 

rise of 31% in the numbers of firms and wage observations in the data from 1869 to 1870. Within 

iron and steel, 84% more establishments and 145% more wage observations are reported in 1870 

than in 1869. See Tables 5 and 6 for more specifics on the distribution of the data by industry and 

year. I have not adjusted for this, partly because new plants starting up in the context of a new 

steel technology are part of the phenomenon of interest. Analogously, new information 

technologies in the 1970s and 1980s created opportunities for a wave of startups. 

3.3 Labor demand, not labor supply 

One might think there were great changes in labor supply and that this effect has to do with the 

kinds of workers available. Indeed there was great immigration to the United States at this time. 

Estimates made in 1890 found that 55% of iron and steel workers were U.S.-born whites, 4% were 

U.S.-born blacks and 30% were born in Germany, Britain, or Ireland (Montgomery, p.25). 
12 

Immigrants from eastern European made up a growing but still small fraction of the iron and steel 

work force. 

 It is not clear whether immigration affected the iron and steel sector more than other 

industries. Table 9 compares the literacy levels of various subsets of workers based on 1850 

through 1880 Census data. By any measure, the iron sector employed a growing fraction of the 

workforce, as shown in Table 10. Iron workers were somewhat more literate than the working 

population. Table 9 shows that the generally speaking, the populations of iron workers and of 

American workers in general became more literate with time. Too few respondents in these 

Censuses answered questions about citizenship and birthplace to make clear whether there was a 

change in the extent to which iron workers or the population at large were native born or citizens.7

The evidence does not suggest there were distinctive changes in the workforce of iron and steel. 

Instead the inequality change is likely to be related to the technology change. 

 To summarize: earnings inequality rose in the industries making iron and steel around 1870, 

when the new technologies of cheap steel arrived. Inequality did not rise in other manufacturing 

industries generally. This seems not to be a result of changes in the workforce, but could be a 

result of changes related to the technology. 

4.0 Sources of the rise in inequality 

We have seen that there was dramatic technological change in the iron and steel sector, and a 

contemporaneous rise in earnings dispersion within just these industries. There are overlapping 

theories about why inequality would rise in response to technological change. Two of these ideas 

are skill-biased technological change, and the uncertainty hypothesis.

8

 

Both can be framed in the Greenwood-Yorukoglu (1997) model. It describes how a 

technology change which continually lowers capital equipment prices over time could cause 

employers to temporarily (a) invest more in capital, (b) exhibit lower productivity and earn lower 

profits, and (c) increase earnings inequality in the workforce. In that model, the rise in earnings 

inequality comes from variation in skill that makes workers differ in their capacity to adapt to the 

new technology. Knowing this, employers using new technology strongly prefer to hire skilled 

workers. This generates a rise in earnings inequality. Investment in wages for these individuals, 

 

7

 This evidence is not shown here but is available from the author. 

8 There is also the Kuznets curve. Kuznets (1955) characterized industrialization as a process where a population 

shifts from low-wage agriculture to higher-wage industrial work. Workers all have similar low wages at the 

beginning of the process and similar high wages at the end, so only in the transition phase is there a high measure of 

inequality. Findings of this kind regarding nationwide income distributions have been reported and also disputed. In 

any case, an analogous transition could occur on the microeconomic, industry level if there was a transition from a 

well-defined previous earnings distribution for 1860 iron and steel work to a well defined one after the diffusion of 

new technologies (Rogers, 1995). A Kuznets curve may be fairly predictable if it is clear what the previous and 

future earnings distributions are. The situation described in this paper was too much in flux for a Kuznets-type 

description to apply, since that would describe a transition between two stable distributions. 
13 

and associated investment in experimentation with fast-obsolescing equipment raises costs 

without raising immediate revenue. Thus measured productivity declines at first; there is a 

productivity slowdown. Still, investments in equipment is optimal for the employer because it 

maximizes long run profits, compared to taking higher short run profits but becoming 

technologically obsolete later. The Greenwood-Yorukoglu account matches the evidence 

presented here – there was great investment into the iron sector, a rise in earnings inequality in 

that sector, and at least some evidence of a productivity slowdown, to be discussed later. 

Since in the model employers preferred to hire skilled individuals, the rise in inequality of 

earnings was generated by a skill bias assumption: that workers with certain preexisting skills 

apply the new technology more productively than individuals without the skills. Another way 

this has been put is to take skills and employers’ understanding as given, and then say that the 

technology is skill-biased. In modern regressions, skill is often measured by years of formal 

education. We have no such information on individuals in the Weeks report, only what is 

implicit in their job titles. To some analysts “skill” is defined to include the ability to adapt to 

new circumstances, but if so it is hard to measure it or know if it was learned, and so here we do 

not call that a skill.9

 A skill-bias might not be accompanied by technological or market 

uncertainty. It may be predictable, and a worker may be able to prepare. Skill is an attribute of 

the worker, not of the worker’s context. Only demand for skill would vary by context. 

This story can remain almost the same with an uncertainty hypothesis. Uncertainty is the 

state of not having enough knowledge to predict the future. The term technological uncertainty

describes an environment in which the future production process is hard to predict. The 

uncertainty associated with innovative activities is greater than that associated with other 

economic activities such as trading, according to Dosi (1988): “[Innovation] involves not only 

lack of knowledge of the precise cost and outcomes of different alternatives, but often also lack 

of knowledge of what the alternatives are.” The relevant uncertainty is greater if the players 

think future profits could be high. If instead profits are sure to be low, choices matter less. 

Malerba (1985, p. 32) characterized technological uncertainty well: “In a situation of uncertainty 

and change, firms cannot have a perfect knowledge of either the objective random distributions 

of technological opportunities in the industry, or of the change of these distributions over time.” 

They depend on prior probability distributions “which are highly subjective and based on few, if 

any, observations.” Not knowing what is possible, they guess, and place bets. 

Here one assumes that because the environment has changed and adaptation is required, that 

there will be differentiation in productivity and wages among the population, whether correlated 

to individual attributes or not. Participants foresee that new technologies are likely to 

 

9

 Therefore this account of skill bias is restrictive, and does not account for all versions that appear in the economic 

literature. Recent works by Bresnahan, Brynjolfsson, and Hitt, for example, treat worker skills as useful for 

adaptation. This definition partly bridges the gap between what the description here calls the skill bias account and 

the uncertainty account. Because it depends on skills associated with workers, without institutional factors, it looks 

like the skill bias story depicted above. Because it is a temporary phenomenon, linked to the transition not the 

subsequent long-lasting state, it looks like the uncertainty account. 
14 

outcompete existing ones but cannot take over yet because too little is known.10 A learning 

process gradually reduces the uncertainty – improving each technology through experience, and 

testing out and selecting among technologies, materials, and ways of organizing work processes 

within and among firms. So if in the history of the period we see various players (investors, 

firms, or workers) making extra efforts to obtain information, we have evidence that they were 

responding to uncertainty. One difference between the hypotheses is that skill-bias might be 

entirely predictable, whereas if technological uncertainty were also important, then productivity 

and wages are uncertain too.11 

A story of the social process of technology change underlies the uncertainty hypothesis. In 

the Tushman and Anderson (1991) account, a technological discontinuity is a break between 

underlying technologies that creates a new product class, or has the prospect of radically 

improving the performance of existing products or processes. A period of technological 

uncertainty follows. There is a kind of gold rush toward new opportunities. New businesses start 

up and bid for employees. They try to make relevant products as the new technology becomes 

the basis of an industry. In this period of ferment, various players face new opportunities, 

choices, and problems. Their ability to forecast the outcomes is poor. Because they make 

divergent choices, some do poorly and others well. Underlying the wage determination process, 

for example, beliefs vary about the probability of possible outcomes. During this period, early 

customers get a sense of what the technology is useful for and what can be expected from it. 

Eventually a standardized dominant design emerges for the product category (Abernathy and 

Utterback, 1978) and there is a shakeout of producers in the industry. With time the dominant 

design becomes well understood and user-friendly. Reliable forms of the technology are 

incorporated into standard operating procedure. Producers and users then experiment less, so 

outcomes and wages are more predictable than they were during the uncertainty phase. 

Variations in outcomes could be a function of the circumstances, not differences among 

workers per se, since different forecasts about tomorrow’s market, customers, technologies, 

products, or prices would drive employers to make different choices. Some take new 

opportunities while others compete against them. The appearance of new opportunities and 

varied responses produces winners and losers, bringing about a rise in wage inequality among 

workers who may be similar ex ante. 

 

10 The term comes from Tushman and Anderson (1986, p. 44) and Rosenberg (1996). Tushman and Anderson 

(1986) measure this uncertainty by the forecasting errors of industry analysts predicting demand growth in the 

industry. They show that a technological discontinuity is followed by an expansion in these errors. After the 

dominant design appears, further innovations change the production process more than the product design. 

 The word “uncertainty” applies when there is qualitative or unanticipated variation in possible outcomes such as 

the form of a product or process. The more common economic language of risk presumes that the outcomes are 

quantitatively measurable and comparable to one another. (Rosenberg, 1996; Dosi, 1988). 

11 Other formal models using an uncertainty-like assumption, under various names, include Caselli (1997), 

Rubenstein and Tsiddon (1998), Galor and Moav (2000), and Aghion (2001). In all these, something like variation 

in ability leads to a rise in the dispersion of productivity and wages among workers during a new technology’s 

takeoff. Schultz (1975) discusses the ability to adapt to economic disequilibrium situations, perhaps like 

technological uncertainty. Evidence presented there implies that education predicts this ability. 
15 

To sum up, the skill-bias hypothesis depicted above is that: 

New technology appears Æ Better-skilled workers make relative wage gains 

Whereas the technological uncertainty prediction is: 

New technology produces divergent choices, under uncertainty Æ earnings inequality rises 

As the technology grows older Æ Producers standardize and customers have more complete 

information Æ Earnings inequality declines 

Levels of inequality need not be the same before and afterward. Only an increase in 

inequality, then a decrease, is predicted by any of these paradigms.12

These theories are not specified sharply enough to distinguish between them with a single 

statistical test. But the uncertainty hypothesis suggests that productivity measures would be 

noisy, and wages hard to predict. A skill-bias hypothesis instead suggests that if we could 

identify the key skills, the importance of those skills would rise, but wages need not be less 

predictable. So we will look at residuals from wage regressions. 

4.1 A statistical test of uncertainty 

During times of technological uncertainty, measures of skill level should predict wages less 

well than during times of technological stability. To test this, Figures 2a and 2b display the 

average squared deviation of the log wage from the amount predicted by the regression in Table 

8, which had measures of year, state, business-cyclical factors, job content, and rank. These 

residuals will be high when the predictive power of the wage regression is low. 

Figures 3a and 3b show that in the industries in which the Bessemer process was applied -- 

blast furnaces and rolling mills -- the wage predictions fit less well after 1870; but this is not the 

case in other industries. If employers had different information sets, and guessed at production 

technologies and wages, wages would be less predictable, analogous perhaps to a period of 

volatility in asset prices. In a predictable transition as described by a Kuznets curve or a pure 

skill-bias theory, this would not be the case. 

 

12 For completeness, the Kuznets curve prediction is: Earnings distribution is stable, then new technology appears 

Æ Workers on new technology earn more Æ Old technology is abandoned Æ All workers use new technology and 

earnings distribution is stable.
16 

log-wage residual squared

Average squared residuals from log-wage regression Year of observation

1855 1860 1870 1880

0

.4

 Iron blast-furnace Rolling-mills and nails Car-wheel foundery Stove foundery

 General foundery Hardware and cutlery Machinery Agricultural implements

log-wage residual squared

Average squared residuals from log-wage regression Year of observation

1850 1860 1870 1880

0

.4

 Iron blast-furnace Rolling-mills and nails Agriculture/forestry/food processing

 Textiles Wood work industries Other materials and mining

Figure 3a and 3b. Yearly-averaged squared wage residuals in (a) metalwork industries, (b) iron and 

steel versus other sectors. Blast furnaces and rolling mills are the industries with lines connecting 

them. In rolling mills particularly, wages were less well predicted by the regression in Table 8 after 

1869 than they had been before. We do not see the same phenomenon for other industries. 

This fall in the predictive power of the wage regression could have been evident using data 

up through 1874. A forecaster applying the Greenwood-Yorukoglu idea could have foreseen a 

productivity slowdown, even during a period where prices were falling and output growing. 

Taking the steel case as a prototype, a model of uncertainty could be applied prospectively. 

5.0 Occupations and institutions relevant to the steel market

5.1 Ambiguity about the product 

The definition of steel was ambiguous in the 1870s. By one definition, steel could be 

distinguished from wrought iron by chemical attributes -- these material would be defined by 

their content of iron, carbon, and other elements. An alternative, physical definition, was that a 

material would be considered steel if it responded in certain ways to compression, twisting, and 

other mechanical tests. The U.S. military preferred this approach and took a lead in mechanical 

testing. A third possibility was a process definition according to which an iron material would be 

called steel if it had been completely molten (“fused”) during its creation.13 

There was substantive debate among chemists, metallurgists, and engineers about which 

definition was appropriate. Financial forces were in play too. Interested firms took explicit 

positions in the debate, and funded advocates for their preferred position. For example, railroad 

 

13 This discussion is drawn from Misa (1995) and Gordon (1996), pp 11-25. There are analogous definitional 

problems in the information age, such as whether algorithms and genetic material are patentable, whether 

cryptographic programs represent freedom of speech and are legal to export, and whether software-making should be 

categorized as manufacturing or service. After standardization, definitions are clearer. 
17 

interests were served by a process definition since the key feature at issue for rails was whether 

they would crack, and a “fused” (homogeneously melted) material would tend not to crack, 

whether it had the carbon content we now associate with steel or the lower carbon content of 

wrought iron. Whether a material was iron or steel would affect the import duties levied on it, 

which led to some allegedly scientific opinions. 

It was clear however that British-made Bessemer steel rails were definitely better than iron 

ones. A related financial effect was that a material would have a much higher price if it could be 

labeled steel, and a railroad's appeal to investors would increase if the rails it laid were steel. 

“Adopting steel rails,” noted one financial analyst, “was an effective strategy to inflate the value of 

rail stock for speculative purposes.”14 

To steel producers, quality control was a pressing issue. Steel makers were among the early 

users of chemical tests of iron inputs. By contrast, foundries still had experienced employees 

(“blenders”) who selected and mixed the iron inputs on the basis of experience, not chemical 

analysis, for many years afterward.15 

By 1880, steel had a standard definition -- the fusion definition distinguished between steel and 

other categories of iron, but steels would be distinguished from one another by chemical 

attributes.16 Railroads also specified a chemical composition in their contracts to buy rails. In 

1881 Carnegie's firm published a handbook that defined standard steel product lines (Misa, 1995, 

p. 73), clarifying a previously chaotic market. Iron columns were sometimes used in structures, but 

it was not still well understood which steels were safe to build structures and ships with until the 

1880s (Misa, 1995, and Nelson, 1995, p. 15). 

Since steel was not well defined at the beginning of this period, there was literally 

technological uncertainty about the product. Many purchasers of iron or steel could not test its 

chemistry or microstructure but would buy on the basis of what ore had been used and who the iron 

master was (Gordon, pp. 5 and 11). These were only meaningful locally, and declined in 

importance over time in an increasingly national-scale market. Thus the price of steel probably had 

more variation than it did later, and its market may not have been well defined as it was later. 

 

14 Misa, p. 37. Likewise in the dot-com boom, a company could show its commitment to the Web by putting “.com” 

in its name. This alone might lift its stock price. Cooper et al (2001) found that “A mere association with the 

Internet seems enough to provide a firm with a large and permanent value increase.” The value attributed by 

investors in both cases may reflect their recognition that in unpredictable ways these technologies were likely to 

improve dramatically. The official commitment to the new technology represented by the name change is then 

informative and could signal an actual change in value. 

15 Foundries use pig iron to make wrought iron or castings. In 1893 it was forecast that foundries would follow 

steelmaking and replace instincts of blenders with chemical analysis (Gordon, 1983, p. 624-5). 16 A 1913 metallurgy textbook did not find the subject closed yet. “At the best, however, the definition of steel is in 

a shockingly bad condition, and has been brought to it by a series of events which shows the carelessness of the 

buying public and the greed of men who will appropriate the name for their product that will bring them the best 

price without regard to whether the name really fits or not.” (Stoughton, 1913, p. 7) 
18 

5.2 Innovators: from hobbyists to formal R&D 

No American iron and steel firms had a research department in 1866. The great inventions in 

the technology if iron and steel during this period came from isolated inventors. During the period 

under study, shared institutions of collective invention arose (Meyer, 2003). By the end, 

systematic firm-sponsored research and development was established at every large iron or steel 

firm. 

The new steel technologies were first invented by isolated or idiosyncratically trained 

inventors, principally in Britain, who were not backed by major firms. Their financial returns from 

these inventions varied greatly. Examples: 

 British inventor Henry Bessemer had no history in the iron industry and little experience 

with iron production, but he was inspired in 1856 to adapt an invention he had applied to 

glassmaking. It happened that Bessemer used a particular iron input with the right mixture 

of elements to produce a good output. Partly because of his determined self-promotion, his 

invention was recognized. It did not always work however, and a number of licensees gave 

up. Bessemer started his own firm in Sheffield, England, improved the invention, 

undersold competitors, and became a millionaire. 

 Kentucky ironmaster William Kelly “boiled” iron like Bessemer, without actually making 

steel. He held an essential patent. 

 Metallurgist Robert Mushet solved a problem in Bessemer’s process which made its output 

brittle. Mushet depended on a Welsh ironmaking firm to renew his British patent but the 

firm blundered and did not. Mushet lost all subsequent British proceeds from his invention, 

and was poor the rest of his life.17 

 Sidney Thomas worked as a London court clerk full time, but was fascinated by chemistry. 

He became determined to find a way to make open hearth steel out of iron with phosphorus 

impurities, though experts had failed at this. He ran experiments in the evenings at home. 

After six years he persuaded an ironworks chemist to help him. By 1878 the process 

worked.

18 

“Thomas solved a problem that had engaged the attention of some of Europe’s most highly 

trained engineers for years. He was one of the last and perhaps the most important of the line of 

tinkerers that had made the Industrial Revolution. After him, the professionals just about had the 

field to themselves.” (Landes, 1969, pp 258-9) 

 

17 Carr and Taplin, p. 23. Mushet’s struggle is starkly illustrative. His contribution was essential and yet he lost out 

almost totally while Bessemer made millions. If Mushet had run his own firm or been an established professional 

employee, perhaps he could have protected himself. In recent high tech period, Tim Paterson’s case is analogous. 

Paterson wrote an early PC operating system and sold it for $50,000 in 1980 to Microsoft. Microsoft adapted it to 

make MS-DOS which became enormously profitable, eventually earning billions of dollars and market power. Also 

analogously, Tim Berners-Lee invented the World Wide Web, but did not own it, and the financial benefits went 

mainly to others. Uncertain environments characteristically produce these extremely dispersed outcomes. When 

well-established institutions dominate, outcomes vary less. That happened in the Bessemer steel case after 1880. 

18 A prestigious metallurgist did not accept that Thomas had done it. (Carr and Taplin, 1962, p. 99. 
19 

Relatedly, in the 1860s, the general perception in the U.S. industry was that chemistry was not 

of any industrial use. No chemists were employed in the U.S. iron and steel business, and 

metallurgical and chemical knowledge there was poor. Two U.S. books by experts from the 1850s 

asserted incorrectly that phosphorus impurities in iron would not prevent steel from being made 

from it (Gordon, 1983, p. 616). British experts knew the opposite was true, and that this fact was 

crucial in choosing ores and technologies in steelmaking. 

In the U.S. a number of institutions arose starting in 1866 to make it possible for various 

organizations and individuals to work together to advance the new steel technologies. Among 

the pathways of shared effort were: 

 a pool of patents held in common by the new Bessemer Association 

 an expert engineering consultant, Alexander Holley, working for and representing the 

Association’s. Holley helped design most of the early Bessemer plants, and 

recommended employees to employers and vice versa (McHugh, p. 260). 

 other common consultants, meetings19, and high turnover between plants which resulted 

in shared information, and 

 several new professional associations and technical journals related to iron.20

Such institutions of collective invention or aspects of the environment sustained investment, 

experimentation, and discovery.21 During this intermediate period chemists first appeared in the 

iron and steel business. One reason they were needed was that the processing of materials was 

increasingly mechanical. There was less and less human participation to correct imbalances in 

materials. For example, puddling is a mechanical craft in which an expert carefully watches the 

bubbling iron, but in a Bessemer converter the operators have little ability to see what is happening 

or to control it. Yet it was not clear that chemists, examining the input materials, could help in 

time. The variation of opinion is illustrated by two key quotes. In 1872, the general manager of a 

substantial iron works wrote 

The president of our company thinks we ought to follow the fashion and have a chemist. 

To my mind it is a waste of money. When I want an analysis I can have it made – and 

that is very seldom; for the furnace manager who needs a chemist to tell him the quality 

of ore or limestone, or whether his pig-iron is soft or hard, had better resign and go to 

farming. However, if the president says chemist, chemist it is. My object in writing is to 

know if you can recommend a young man competent to fit up a laboratory and take 

charge of it. . . . [It] is desirable that he should be a gentleman. My wife plays the piano 

and I do a little on the flute; and if we can get a chemist who plays the violin, we could 

have some music evenings. If you can suggest a man who combines these qualifications, 

 

19 Managers and technical experts moved between firms and “the five or six top engineers of the industry [met 

frequently] to discuss common problems.” (Temin, 1964, p. 133). 

20 In 1871 the American Institute of Mining Engineers began its journal Transactions, which was the most important 

one for steel development. In 1879 a professional journal of mechanical engineers began publication, and in 1880 a 

journal of charcoal iron workers began. 

21 This argument is made in detail in Meyer (2003a) which makes the case that the phenomenon has occurred in 

several cases of technological uncertainty, and that when the technology is better understood, such institutions tend 

to be replaced by private research and development. The term collective invention comes from Allen (1983). 
20 

I could employ him. I do not know what a chemist would expect; but I should not care to 

pay more than $10 a week.22 

This point of view was dying out. Around the same time Andrew Carnegie wrote: 

We found . . . a learned German, Dr. Fricke, and great secrets did the doctor open up to 

us. [Iron ore] from mines that had a high reputation was found to contain ten, fifteen, and 

even twenty per cent less iron than it had been credited with. Mines that hitherto had a 

poor reputation we found to be now yielding superior ore. . . . Nine-tenths of all the 

uncertainties of pig iron making were dispelled under the burning sun of chemical 

knowledge. 

What fools we had been! But then there was this consolation: we were not as great fools 

as our competitors. . . . [They] said they could not afford to employ a chemist. Had they 

known the truth then, they would have known they could not afford to be without one.23

Carnegie was overoptimistic – a chemist could not dispel 90% of the uncertainty – but had 

identified a basic truth. The previously quoted manager seems dramatically mistaken. By the end 

of the decade managers would not write so flippantly about the issue. Hiring chemists was a 

gamble which sometimes paid off. Proper adaptation might require hiring experts.24 The 

Wyandotte, Michigan company attempting to use Kelly’s patent and process employed a chemist 

briefly (Temin, p. 156). Pennsylvania Steel hired a chemist by 1868 (Misa, 1995, p. 34). A 

Connecticut crucible steelworks in 1868 seeked to hire “a qualified ‘scientific’ man to operate [the] 

steelworks.” (Gordon, p. 628) Carnegie’s firm hired a chemist by 1872. These were early 

adopters. An observer reported that “until about 1875 the fact that iron smelting was a chemical 

process was not generally accepted.” (Temin, 1964, p. 156) Instead some thought the process was 

strictly physical – that iron and other metals were just melting out of the ore under tremendous 

heat. In fact, several chemical reactions create slag, gases, and iron from the ore. It was not well 

understood at this time, but both its chemistry and its microstructure of the resulting metal affect its 

strength and other properties. (Gordon, 1996) 

Once the technology progressed to be profitable on a large scale, the large firms had a 

financial incentive to form their own research laboratories. This changed the innovative 

environment permanently. After 1880, all significant inventions in this field came from experts 

in the major firms and laboratories.

25 Chemists conducted systematic research at the leading iron 

and steel works and many had been promoted into management.26 Uncertainty about 

 

22 McHugh, p. 185. This wage seems implausibly low. There are no chemists in the Weeks data to compare it to. 

23 Misa attributes this to Livesay’s 1975 book on Andrew Carnegie, p. 114. 

24 In the recent period this is better understood. Bartel and Lichtenberg (1987) looked for implications of the 

assumption that “when a new product or process has been recently introduced, there is ‘more (remaining) to be 

learned’ about the technology, and there is a greater premium on the superior ‘signal-extraction’ capability of 

educated labor.” They reported empirical results in which the introduction of new technology (measured by the 

introduction of new equipment) correlates with relative demand for educated workers, and that this difference was 

seen more strongly in R&D-intensive industries. 

25 An analogous change occurred in recent years. There was a flow of desktop computer hardware innovations from 

hobbyists and startup firms in the 1970s and 1980s but few since then.

26 Swank, p. 154; Temin; McHugh. 
21 

technological direction had been reduced by strategic investments in research and development, 

with well-defined training and professional standards and practices. Technological change 

continued and productivity and industry profits improved in the succeeding decades. 

To review: in the mid-1860s, lab chemistry was perceived to be unimportant and the great 

inventions came from amateurs by surprise. In an intervening period, professional associations and 

journals arose and innovations were generated collectively. Toward the end of the period, chemists 

were established at all the large firms. Chemists arrived in response to opportunities and dangers 

their employers perceived. Employers did not share a common expectation in advance about 

whether the gamble would work out well. 

5.3 Occupations: puddlers, rollers, and inside contracting 

Extreme variation in incomes was possible because of a special kind of labor contract. 

Skilled workers in iron and steel (including puddlers and rollers, discussed below) were usually 

inside contractors. Negotiations between contractors or their union and the firms determined the 

piece rates of pay to the group. Contractors hired and organized their own employees, agreeing 

to pay each one some percentage of the income. 

The inside contracting system was common in the iron and steel industry and in 

interchangeable parts manufacturing, possibly because the contracting system was flexible in 

response to circumstances and these were the most technologically advanced kinds of 

manufacturing. Because they were paid by the piece, contractors had strong financial incentives 

to improve the technology and organization of work. They made many innovations. As prices 

fell and production efficiency improved over the years, establishment managers negotiated the 

pay rates down. Contractors attempted to hide their technology and cost information so as to 

improve their bargaining position and worsen the outcome for the firm if they failed to come to 

agreement. But the establishment could hire the contractor's employees away from him and get 

the information that way. Negotiations usually occurred annually, but when demand was high, 

employers allowed inside contractors to make extraordinary incomes, and waited to renegotiate 

until demand was lower and the contractor was in a weaker position.27

Consider now two skilled iron and steel production jobs. The job of a puddler was to stir 

liquefied pig iron in a furnace for hours while impurities either burned off or separated away as 

slag. The resulting bar iron was highly valued. Puddling was the iron and steel craft requiring 

the highest skill, by many accounts.28 If pure skill bias were an important factor in the 

technology change, an observer at the time might incorrectly have predicted puddlers to gain 

from it. In fact, improving mass production technologies competed against them.29

 

27 This section on inside contracting is drawn from especially from Clawson (1980), and also from Nelson pp 38-39, 

Montgomery, Stone, Fitch, Brody, [and Englander]. These sources generally agree on the facts and issues.

28 Jardini estimated the numbers of years of apprenticeship and training various iron workers had. Puddlers had 

more than any other occupation. All sources seem to agree, e.g., Clawson pp 139-141; Nuwer; Gordon; Davis. 

29 In Jardini’s study (pp 177-182) the skill levels of the Bessemer converter crew were measured to be much lower 

than those of wrought iron workers (puddlers). 
22 

The new steel methods did not directly affect the craft of puddling except competing with its 

output, bar iron. Bessemer steel was sometimes cheap enough, and open hearth steel was usually 

good enough, to compete against bar iron. At the time it was widely believed that that open 

hearth steel would displace bar iron for most purposes. This was correct, but it took longer than 

many people expected. By 1905, puddling was permanently in decline. 

Rollers had another skilled job. They received iron or steel slabs which were usually still hot 

from their creation in a furnace or Bessemer converter. The rollers sent this input material slabs 

through slits between turning rolls of metal to compress the input material, squeeze away slag, 

and shape it into rails for the railroads, sheets, or some other product. Rollers and coworkers 

sent the material several times through the rolls, controlling it with tongs, and adjusted the gap 

between the rolls by hand or with hand tools. Technology changes affected them and they 

affected the technology change in several ways. The new steel materials affected them directly, 

because steel was tougher than bar iron and broke the rolls designed for iron in the early efforts 

(McHugh, 1980). There were several ways of adapting. One was to make larger, heavier, rolls. 

Another was to leave more room between the rolls so that the input material would not be 

reshaped as much in a single pass. This then required many more passes between the rolls. In 

the long run, the equipment was designed to have many rolls with pre-set gaps between them, on 

a long conveyor belt so less individual handling of each slab was required. Rollers were 

intimately tied up in these technological changes. 

In the Weeks report one firm distinguished iron rollers from steel rollers, and the steel rollers 

were paid much higher – about twenty percent more, per day. The piece rate difference was 

greater than that, because steel rollers made less output per day. 

Figures 4a and 4b display nominal wages of puddlers and rollers, including their helpers, in 

the Weeks data. The dispersion of pay rose visibly among the rollers in the later part of the 

period. For comparison, Figure 6 compares these to other jobs, and Figure 7 shows that high 

inflation during the Civil War period raised nominal wages generally and the depression 

following the Panic of 1873 lowered wages through 1878. Wage in dollars per day

Wages of iron puddlers Y ear of observ ation

1860 1870 1880

0

12

Wage in dollars per day

Wages of iron and steel rollers Y ear of observ ation

1860 1870 1880

0

12

Figures 4a and 4b. Wages of puddlers and rollers, including their helpers 
23 

The Weeks report is not the only evidence of this change. In 1882 a puddler near Pittsburgh 

complained to the Pittsburgh Post that puddlers made $3.00 to $3.50 per day, but rollers made 

$10.00 to $15.00.30 The Pennsylvania Bureau of Industrial Statistics reported that rollers were 

paid an average of 50% more than puddlers in 1874, but 200% more in 1887, and that rollers 

made 100% more than roughers in 1874 but 250% more in 1887.31 

This interaction can be analyzed as an information game. After each negotiation was 

complete, contractors had strong incentives to improve the technology and organization of work. 

If they succeeded at this, they could earn a lot in the short run. But as production rose, output 

prices fell, and furthermore any innovations by the contractors seeped into the possession of 

management. So external circumstances pressed against contractors in succeeding negotiations. 

Thus technological or organizational improvements made by contractors gradually and 

permanently became the property of the establishment. The short-run negotiation produced as a 

side effect a permanent change in the technology in general use.32

After 1880, inside contracting was decreasingly common. One possible explanation is that 

the technology had stabilized and establishment managers did not gain further from trying to 

extract further discoveries from the inside contractors. (Clawson, 1980, p. 28). Instead of giving 

production workers extraordinary incentives, managers carefully controlled the production 

process, and expanded formal research efforts. 

The work content of rolling and steel production changed with time, away from handmanaging the process. With greater mechanization and speed, Bessemer converter men and 

rollers had to monitor continuous or frequent mechanical processes, rather than use manual skills. 

Costs were high if they failed since breakdowns could affect processes before and after it in the 

production line.33 Historians of technology Michael Nuwer and David Jardini tried to define 

“diagnostic skills” to characterize this (Nuwer; Jardini, 1997, p. 171) but the term “skills” here 

breaks down under analysis since the key issues were whether the men were attentive, loyal, and 

careful with the equipment. Their accounts include the idea of “strategic workers” who were 

somehow well positioned, or used their positions well as managers and monitors of machines.34 

Here an efficiency wage thesis fits the qualitative evidence better than skill bias 

 

30 Montgomery, p. 32, citing the April 14, 1882 Pittsburgh Post. 31 Montgomery, p. 34. Roughers assisted rollers. They received something from a rolling mill and might send it 

back through the mill. They were usually employees of the roller in the inside contracting system. See also Jardini, 

pp 186-191.

32 Like inside contracts, modern-day stock options give strong incentives to work well with a team, and to produce 

technology which will become the employer’s property. The inside contract has short-term incentives. Stock 

options, too, have explicit time limits and also as firms mature, they give out fewer options, and these are less 

valuable since the stock’s greatest rise will be over, and its volatility normally declines. Both inside contracts and 

stock options are special labor contracts which give financial incentives to workers to improve the firm's technology 

and have been used at times of special technological opportunity. 

33 With computerization a hundred years later steel work took another step in this direction. For example, by one 

account, when processes like these are computerized the operator is no longer even physically looking at the steel 

material directly but monitors production processes through video screens and instrument panels. (Shaw, 2002) 

34 Jardini, p. 191. 
24 

At the time, puddling was the most highly skilled of the iron professions. Skill bias did not 

help them. A clearer relationship between the technological change and the earning inequality is 

that both puddlers and rollers had strong incentives to improve the technology under inside 

contracting, but for the puddlers it was not possible to improve it much. Rollers had more 

opportunities, risks, and random outcomes. 

Each of the several skilled iron and steel occupations had a long history as a craft, and had its 

own union in the 1860s. Because of the turbulence and reorganization in the industry, the 

various craft unions combined in 1876 to form the Amalgamated Association of Iron and Steel 

Workers, which continued to expand its membership to include less and less skilled workers, and 

to include blacks in 1881. This was an early industrial union, replacing the very concept of a 

craft by a larger entity, more flexible in response to technological change, and potentially more 

powerful in bargaining.35 Though this change from craft to industrial unions seems to be a 

significant indicator of technological uncertainty it is not clear how it affects the available wage 

data directly. Clearly rollers were not prevented from forming favorable contracts. 

5.4 Location effects 

U.S. iron and steel plants supplied local markets in the 1860s. Colonial and state 

governments had taken an active interest in getting a local iron industry started. Practically every 

state had iron furnaces, forges, rolling mills, or foundries. Small-scale enterprises did not need 

the most advanced technology to be viable. Charcoal-fueled furnaces, which tended to be small, 

produced more and more during the 1800s even as mineral coal fuel was taking over. 36 During 

the 1800s, growing fractions of the production came from large firms and firms using recently 

developed technologies. 

New iron ore resources were discovered along the Great Lakes especially on Michigan’s upper 

peninsula. Michigan iron ore grew as a fraction of national production from 4% in 1860 to 23% in 

1879 (Warren, p. 43). Because transportation was a large fraction of iron and steel production 

costs, and water transportation was cheaper per mile than any kind of land transportation, it was 

cheaper to produce steel along the edge of the Great Lakes than elsewhere. Over this period, steel 

production grew around the Great Lakes cities of Pittsburgh, Cleveland, and Chicago, which took 

over from previously leading areas such as Scranton, PA, and Troy, New York. 

Pittsburgh was well located for iron and steel work because it was near great coal fields and 

because it had access to the Great Lakes for iron ore. More tons of coal than tons of iron ore is 

needed to make pig iron, and Pittsburgh producers benefited from nearby sources of coal.37 The 

Bessemer steel process did not require coal, but it was most efficient for the Bessemer converter to 

 

35 Montgomery (1987) and Fitch (1989) describe this turbulent transition in detail. 

36 Hundreds of blast furnaces and rolling mills started up in the 1800s, in practically every U.S. state. Rolling mills

press iron or steel into shapes to make a product, including rails for the railroads. 

37 Coke is the fuel produced from bituminous coal, and was the main fuel in the Pittsburgh area. Half of the nation’s 

coke production in 1890 came from the Connellsville area near Pittsburgh (Warren, pp 43-49). 
25 

receive hot pig iron from nearby blast furnaces which did need great amounts of coal. Making 

steel near Pittsburgh lowered transportation costs. 

Iron and steel workers near the Great Lakes were paid much more than in the east. Some wage 

inequality can be accounted for by the difference between wages in this area and others. This is 

not principally a difference in skills; presumably there is no particular skill to living there. Rather 

the arrival of the industry and its workers around the Great Lakes was a response to opportunities 

that had been discovered. It is the result of a learning process, partly accidental, in which it 

became clear that Pittsburgh and the other cities would be steel centers.38

The location effects discussion illustrates that gambles taken under technological change may 

not have been actively chosen by an actor. Founders of establishments in the 1850s who were 

choosing locations probably did not systematically take into account the possible subsequent 

invention of steel mass production methods. They may not have forecast the growing availability 

of iron ore resources in Michigan and Minnesota, the rise of coal mining and processing in 

southwestern Pennsylvania, and the creation of a national market that followed from all these and 

the growth of railroads. These changes put them in the position of making a new gamble on their 

own preselected location. 

In the case of an iron plant, its location is a heavy institutional commitment. Physical location 

can also serve analysts of technological change as a metaphor for other institutional commitments, 

which can be subject to new strains under the turbulence brought by new technology. Institutions 

like firms, unions, professions and schools have rules, philosophies, or scripts which are subject to 

change in the new environment. The gambles that economic actors took under technological 

change were reframed (or more problematically “caused”) by the new environment which might 

dramatically change the constraints, competitors, and opportunities. The change in environment 

created new risks and opportunities even for players who had not changed their behavior. These 

must have been temporarily sources of differentiation and therefore greater inequality. 

One such example is that the circumstances of a worker or merchant familiar with iron, and 

continuing to work with iron, have changed once there is competition from steel. Small businesses 

in the iron and steel sector continued (Ingham), but in various ways adapted to make way for the 

large scale establishments. Another example is of the unions. In the iron and steel sector, craft 

unions were replaced by a national industrial union in the 1870s. Partly this was because workers 

had lost control of work rules and the length of the work day, and in other ways craft ethics seemed 

to be under strain. (Montgomery). It is not clear from the data at hand that we can measure any 

effect on wage inequality here, but there may exist data by which to detect the effects of 

institutions under strain, producing erratic outcomes, and therefore greater inequality. 

 

38 A regression predicting part of the rise in inequality overall as a result of higher wages along the Great Lakes and 

increasing numbers of workers there would be helpful. The data is complicated however. Not every establishment 

is identified with its city. More to the point, one finds positive effects on wage from being located in Pittsburgh, 

Cleveland, or Chicago regardless of industry, and negative effects in Buffalo. But Buffalo is also along the Great 

Lakes. Is there an explanation, or is the data noisy, or is this the outcome of a random process of location selection? 

I am continuing research on this point. 
26 

5.5 Strategy: Cost accounting, consultants, and the science of management 

One of the difficult issues in the iron industry at the time was whether and how to manage a 

large plant. Andrew Carnegie was a key player and innovator. Pittsburgh had already been the 

dominant center of iron production in the U.S. (Ingham; Warren) but had not made steel at all yet 

by 1875. Yet within five years it was a fast growing center and dominated steel production for 

many decades afterward.39 Part of the reason for this was its physical location, but another part, 

not easily distinguished, came from informational factors: the presence of so much skilled iron and 

steel labor and expertise, and also the great efficiency enforced in the Carnegie plants through cost 

accounting. Pittsburgh steelmakers also benefited from the experience that steel plant designers 

(particularly Holley) had developed by designing and studying other plants. Holley personally 

participated in the design of eleven of the first thirteen plants and was personally an informationsharing institution between each plant and its successors. 

Most iron firms at the start of the period were small, had local customers, and were organized 

on the basis of artisan or craft principles, without detailed attention to costs.40 Partly because of the 

presence of inside contractors, even large blast furnaces and rolling mills did not know their own 

costs well. (Nelson, p. 41) Workers made basic production decisions, including their use of 

materials, tools, and inventory. The history of the iron businesses had shown that this kind of 

management was entirely sufficient and it took some time for other managers in the industry to 

understand that the rules had changed. Partly the new situation came about because of the much 

larger plants, the resulting management challenges (for example the greater costs of breakdowns), 

and also the opportunities to be more efficient through economies of scale. (Nelson, pp 8-10). 

Carnegie, a railroad executive, acquired an iron rolling mill in 1864, then gradually over the 

course of decades took more and more control of the steel industry. The success of the Carnegie 

firms was due largely to their systematic study and management of production costs. Cost 

accounting was already standard in the railroads partly because they were such big employers. The 

Pennsylvania Railroad had one thousand managers and fifty thousand employees in the mid-1870s. 

The largest manufacturing establishments in American industry in the 1870s made iron and steel.41 

Cambria Iron was the largest, with about 4000 employees in the mid-1870s (Montgomery, 1987, 

p. 54). They had not adopted careful cost accounting. Part of the reason for this was that inside 

contracting already aligned incentives and it had not been seen necessary to do anything more 

precise. But in addition, both iron and steel had been regional businesses, not facing enormous 

 

39 Houpt (2002) carefully argues that the same learning process was prevented in Spain. There the monopoly steel 

producer was not well located but was too strong to be dislodged, so costs of making Bessemer steel there were always 

high. Houpt finds there was a negative effect on Spanish industrialization in general. In the U.S. context there was 

diverse competition and many locations were tried. 

40 Ingham; Warren. 

41 Firm sizes are discussed in Nelson, pp. 6-10; Chandler p. 259; and Montgomery p. 28. New iron and steel plant 

buildings were larger than any previous industrial buildings. They had internal railroads and used conveying devices 

and cranes to move materials. (Nelson, p. 19). Plant designers paid more and more attention to efficient design of 

these large plants (Chander, pp. 260-262, and McHugh, 1980). 
27 

competition. With larger and larger plants, the industry became national, facing more precise 

competition, but also with the potential for gigantic profits. 

Managers in Carnegie-controlled plants submitted regular reports to the top management with 

the amounts and costs of materials and labor used in their departments. Carnegie studied these and 

compared the performance of each plant with other plants, with its own past performance, and 

information on costs at competitors. This made it practical to distinguish between more and less 

profitable contracts. Carnegie’s rail mills were highly profitable in the late 1870s, partly because 

of the innovation of bringing this approach from the railroads to the factories.42

During the period under study, Carnegie’s plant paid much higher wages than other firms 

did. “[Carnegie] was willing to pay a higher scale and grant his workers the eight-hour day” 

(Ingham, p. 66). Workers at the Carnegie-owned Lucy Furnace were among the highest paid at 

blast furnaces, according to the Weeks report data. Carnegie carefully avoided labor trouble 

throughout the period of the data. Carnegie’s high profits represented a surplus, attributable to 

Carnegie’s systematic management of information and successful focus on profitability, which 

was shared with the workforce. Carnegie extracted part of this surplus by taking control away 

from workers who were accustomed to autonomous craft work but had to deal with invasive and 

controlling management. Carnegie’s understanding of costs and benefits was (correctly) that the 

costs of downtime for the plant was much greater than any possible gain from beating workers in 

a conflict. At least this was the case while the technology was new. In later decades he 

encouraged his superintendents to handle workers and unions harshly. 

Cost accounting is an approach to locating information and opportunity (Clawson, p. 186). 

According to the uncertainty interpretation, the workers received a temporary wage premium 

because of temporary opportunities. As the industry stabilized, these premiums declined. 

This interpretation is supported also by the beginning of a journal literature about 

management in the 1870s, in technical engineering journals (Litterer, 1961). Owners and 

managers discussed the problem of managing large, complex firms in writing. They did not all 

agree on its importance: “Articles on bookkeeping [began] by explaining why employers should 

keep records and how they could make use of them (since most employers did not understand 

bookkeeping and tended to dismiss it).” (Clawson, p. 183) 

Management consulting also arose out of steel work in the 1880s. Frederick Taylor was an 

apprentice steel patternmaker and machinist at Midvale Steel until 1878. Taylor became a 

manager and instituted a bureaucratic system of control there in the late 1870s (Chandler, p. 560). 

He then called himself the first management consultant, advocating “scientific management.” 

His approach, later called Taylorism, was to bring in a team analyze the exact tasks of workers in 

a production process and to reorganize the work to specialize each worker’s role to maximize 

speed and reduce the decisions the workers would have to make. Taylor wrote that the work of 

cutting metals at high speed, using the optimally chosen equipment, was a problem of such 

 

42 Chandler (1977), pp. 269 and 258. 
28 

complexity that individual workers could not be expected to solve it (Taylor, 1911). Workers 

usually found this to be alienating and many said it destroyed any satisfaction in craftwork. 

Taylorism was an extreme extension of cost accounting, and it arose after the period under study 

was over. Outside consultants compared costs and compensation between firms (Clawson, p. 

188). Thus management consultants were information carriers, whose role arose because the 

technology and necessary organization were new and uncertain. The consultants and managers 

were trying to standardize work processes and reduce uncertainty and idiosyncrasy. 

The Weeks data include wages of managers only from the glass-making industry and the iron 

blast furnace industry. Figures 5a and 5b compare them. There appears to be a rise in inequality 

in the iron sector. Presumably the high wages were paid in large firms though this is not clear from 

the Weeks data. The observations at the same level each year were stable salaries in particular 

firms. Taking this into account, there are not many independent observations. Wage in dollars per day

Wages of glass-making managers Year of observation 1860 1870 1880

0

3

6

9

12

15

Wage in dollars per day

Wages of blast furnace managers Year of observation 1860 1870 1880

0

3

6

9

12

15

Figures 5a and 5b. Wages of managers 

In a wage regression with fixed effects for each year and each job, one can add also dummy 

variables for each establishment. About .70 of the establishment effect in the later period is 

predicted by the establishment effect in the earlier period, both in the iron sector and outside it. 

Among new firms, variation in the establishment effects of the iron sector is not as great as the 

variation outside the iron sector. This data is not shown but is available by request. So 

establishment effects measured this way do not directly account for the differences in earnings 

dispersion of the different industries. 

In the case of the one Carnegie plant that is identified in the data, its manager was paid near the 

top of the scale, and the establishment effect is near the highest of them. This evidence conforms 

to the standard accounts about Carnegie’s approach, which was to pay high wages. The Weeks 

report has estimates of the labor cost portion of all production costs for eighteen blast furnaces. 

These are shown in Table 11. Here too we see the evidence supporting the standard account of the 

Carnegie plants – Carnegie’s Lucy furnace appears to have lower labor costs per unit of output than 

did other plants. The plant was efficiently designed and capital-intensive, which explains how it 

was possible to have low labor costs while paying high wages to managers and workers. This 

would turn out to be a winning structure. 
29 

5.6 Transition from craft unions to industrial unions 

Until 1876, there were separate unions for several of the skilled iron work crafts. Puddlers, 

rollers, molders, and nailers had separate unions. In 1876 they joined formed a single group 

called the Amalgamated Association of Iron and Steel Workers. In the next decade the 

Amalgamated union expanded much further to include more jobs, unskilled workers, and allowed 

blacks to join. This transition from small, craft-based unions to a large, industrial-scale union, 

arose as a response by union members to three related phenomena: 

• Bigger, higher-throughput plants were built. Some managers of larger plants (like 

Carnegie’s) insisted on more control over tools and working hours. These demands 

conflicted with the inherited craft rules. Furthermore, the high-volume producers were large 

employers, potentially having great bargaining power. 

• The national market for iron and steel grew because of the improving and expanding 

transportation networks, especially the railroads. Local, regional, or otherwise narrowlydefined institutions were therefore declining in importance. For example, local steel prices 

were affected very much by events in Pittsburgh, not by local events. 

• Plants applying new technologies of Bessemer and open-hearth steel might reshape 

ironwork jobs, by, for example, reducing the importance of puddlers, expanding the number 

of laborers who were not in the union (Montgomery, p. 24), and potentially mechanizing 

away some other existing kinds of work (although this had not much happened by 1876). 

Individual inside contractors, including union members, were actively experimenting with 

alternative labor arrangements (Montgomery, p. 9 and pp 20-21). 

Facing these forces, union members could see that small, narrowly based occupational or 

regional unions would lose its relevance and its bargaining power. Their response was to unite 

into the Amalgamated union and then to expand its membership further (to include more crafts, 

to include blacks, and to include the unskilled) from 1876-1891. 

All three of these forces were partly attributable to effects of the transition to Bessemer steel. 

Thus the technological changes resulted in reshaping of the unions, as well as other institutions. 

This change in the unions was not guaranteed to work; it was a gamble which was a response to a 

changing environment. Here too, the iron industry was a leading case. Other industries also 

developed more rationalized managements, national markets, and changing technologies. 

5.7 Summary of aspects of technological uncertainty 

After 1880, the U.S. industry was producing millions of tons of steel annually, and the industry 

was consolidating. Craft unions had declined and industrial-scale unions appeared in their place. 

The relevant professions were well defined, and technological changes in the production process 
30 

came primarily from the large firms or other formally certified research and development sources. 

Barriers to entry were raised and consolidation was underway. Outsiders were no longer sources of 

technological surprises. Large plants became the dominant design for steel production. 

Table 3. Environment of strained or reconstructed institutions

Late 1860s Around 1880

No mass steel production in the U.S., though the 

Bessemer converter invention was known 

Over one million tons of Bessemer steel 

produced in 1880, and still growing 

Industrial definitions of steel ambiguous Industrial definition clear 

Major inventions and discoveries made by 

isolated nonspecialists (Henry Bessemer, Sidney 

Thomas) 

Chemists and mechanical engineers do R&D in 

the big firms; all improvements are made by 

specialists with established sources of funding 

Bessemer patents dispute results in licensing 

Association (1867) with low barriers to entry 

Association raises barriers to entry (1877). 

Association sets prices (1881). Carnegie 

controls basic (alkaline) patents (1879). 

Holley’s Association newsletter distributed 

freely, starting 1868 

Association newsletter distribution restricted to 

members (1877) 

New startups make iron or steel (late 1860s); 

Railroad executive Carnegie branched out, 

acquiring iron plant (1864) 

Industry consolidating and integrating vertically 

into mining, railroads, and shipping. 

Merger forms Carnegie Steel (1881) 

Scattered iron and coal mining. Mineral coal 

moved to the iron ore. 

Huge, established, ore extraction industry 

around Great Lakes and growing shipping 

industry to move the iron ore to the coal. 

Increasingly diverse methods of steel-making 

(Bessemer, puddled, cementation, crucible, acid 

open-hearth (1867), basic open-hearth (1878)) 

All were understood; Bessemer and open-hearth 

were growing industries; puddled and 

cementation process may have disappeared 

No full time chemists in the iron industry Large firms have chemists doing R&D 

No design standards for iron/steel structures 1881 handbook defines widely accepted 

standards 

Separate craft unions for puddlers, rollers, 

nailers, molders, and other crafts; no union for 

laborers 

Lasting national cross-job and cross-industry 

Amalgamated union formed (1876); includes 

laborers in the early 1880s 

Productivity growth sinking Productivity growth rising 

Thus we see that much redefinition of the iron and steel sector occurred, in resources, 

methods, products, firms, prices, and markets. It was a turbulent, hard-to-predict, period. 

6.0 The long run: inequality decline and productivity boom? 

If a temporary uncertainty phenomenon caused a rise in earnings inequality, earnings 

inequality should have fallen after some period when the technology becomes understood and 

standardized. The Weeks report data does not extend far enough to test this but there is evidence 

that after 1890 earnings inequality was declining in the industry. (Stone, pp. 126-7, citing 
31 

Doeringer.) One institutional cause for this was that after 1880, inside contractors were slowly 

replaced by employees. Most worked for piece rates, and there was an associated rise in 

managerial attention to measuring the output of each worker separately from the others (Stone, 

1973, 127-133). Such distinctions among workers become possible once the technology of 

production is standardized and worker innovations are not an important source of innovations or 

differences among workers. Other rollers were put on salary (Montgomery, p. 32). By 1902, an 

industry manager said the technology was straightforward enough that it would be easy to teach a 

laborer to be a steel melter. (Stone, p133) Brody and Jardini have further evidence supporting 

the proposition that wage dispersion declined with time. 

Regarding productivity, experimentation with new technology may improve the capital 

equipment of establishments without contributing directly to output. For example, firms pay 

research and development costs of developing new technologies before the new better technology 

is available, and if they are not carefully distinguished in the accounting, exaggerate the costs 

associated with the earlier technology. More importantly, an organization may reorganize to use a 

new technology, then discover that because it did not have experience, it did not make a good 

choice, and must reorganize again. These costs can be large. External to a plant, different methods 

and markets compete with one another before standardization of products, markets, methods, and 

customers is worked out. This design competition is necessary but delays the benefits of some 

economies of scale, scope, and interoperability of suppliers, competitors, and customers. 

For these kinds of reasons, in the Greenwood-Yorukoglu (1997) model a new technology 

brings about a temporary productivity slowdown along with a rise in income inequality. 

Experimentation also exposes information useful to people searching for jobs, such as where one 

had to live to work in the developing steel plants, and which jobs were safe. Broadly, society 

collectively explored the effects of the new form of steelmaking to steel on new supply, demand, 

and search in labor markets, product markets, and so forth. Gambles and mistakes meant much of 

the effort did not directly raise output so measured productivity seemed low. 

There is scattered evidence of a temporary productivity slowdown in the steel episode. It took 

years for a Bessemer plant to come up to speed even after the technology was firmly established 

and understood (Jardini p. 177-179). Part of this delay came from learning by doing and 

adjustment costs. Allen (1979, pp. 916-7) measured total factor productivity growth of the industry 

by output growth not directly accountable for by changes in employee count, capital services as 

measured by mechanical horsepower installed, metallic inputs, or fuel. By this measure output 

productivity growth was low in rolling mills from 1860 to 1879, rising only 2.6% in the 1860s and 

less than 2% in the 1870s. Then it jumped 15% in the 1880s. Labor productivity improved faster, 

29% from 1860-1879, then 50% in the 1880s, partly because of sharp rises in measured capital. 

These measures illustrate a possible productivity slowdown during this period of technological 

turbulence perhaps analogous to the measured U.S. productivity slowdown from 1973-1996. 

During these periods, a variety of technologies and organizational designs competed with one 

another. With time and standardization, the improvements in steel had a large effect on the 

economy. Fishlow (1966, esp. pp 630-645) evaluated the effect of various innovations on the 
32 

productivity of railroads in the late 1800s and found that improvements in steel were more 

influential than any other identifiable innovation. Steel railroads survived ten times as much stress 

as iron ones (p. 639) and did not have to be replaced as often. They could also handle heavier and 

more efficient trains which became standard over the period 1880-1910. Railroad productivity rose 

sharply during this period and this affected indirectly the price and productivity of all other goods. 

Here too there is an analogy to recent semiconductors, which lowered prices and raised 

productivity through a variety of external effects.. For both cheap steel and semiconductors, the 

productivity boom arrived long after the inventions. 

7.0 Conclusion 

With the arrival of the new steel technologies to the U.S, the quantity of steel produced grew 

dramatically and its price fell. Earnings inequality rose within the iron and steel industries but 

not within other industries. Residuals from wage regression in the iron sector were greater in 

magnitude in the 1870-1881 period than in the 1850-1869 period. Table 2 showed that this was 

not a trend in the iron sector but rather a one-time change, and Table 1 showed that no 

comparable change occurred in the other measured sectors. Technological change was a likely 

cause for this since the quantity of steel rails produced grew dramatically while they fell in price, 

and improved in quality.

Players in the iron and steel production game – suppliers, investors, employees, managers, 

and customers – faced various kinds of turbulence during this period. The product called steel 

was growing in importance at the beginning of the period but was not well defined. The greatest 

innovations (Bessemer steel and both acid and basic open hearth steel) came from outside the 

industry. Scientific investigation did not yet play a regular, consistent role within the industry. 

Craft unions and small firms predominated. Large, centralized firms were not yet the iconic 

institutions they would become, and cost accounting was not yet common. Iron, steel, and coal 

production was scattered, not yet centered on the Great Lakes region. Inside contractors were 

paid a lot if they improved the production technology. This had the effect of encouraging them 

to experiment, and invent new skills and ways of organizing. All these aspects of the industry 

changed fundamentally in the 1866-1881 period, in ways that surprised some people. 

The uncertainty argument is that workers, employers, investors, and customers responded to 

new opportunities with diverse choices, gambles, and adaptations. At the beginning they did not 

know which kinds of work were safe and efficient, and which institutions would last. They chose 

among technologies such as Bessemer steel, open hearth steel, crucible steel, puddled steel, and 

wrought iron for various applications. Their predictions and choices varied from one another. 

The work of rollers changed over time and among plants, with varying ways of organizing the 

workers and various kinds of equipment. Ore and coal sources were discovered and applied. 

Gambles were intrinsic to the changing environment, which partly explains the increased wage 

inequality. 
33 

The uncertainty idea links this evidence on wages to the breakdown of existing institutions 

and the rise of new ones. Here are examples. Larger plants were built than any that had ever 

been built in the U.S. Previously acceptable locations were discovered to be uncompetitive, and 

new plants were built in places with water access to the Great Lakes. New professional 

associations appeared. Craft unions merged into an industrial union. The inherited craft and 

management traditions were partly outmoded -- instead, cost accounting and industrial research 

were perceived as essential in a large plant. 

The players understood that key technologies were changing dramatically and that steel 

prices would continue to fall. This informational situation fits the Greenwood-Yorukoglu type of 

theory in which earnings inequality rises at the same time that there is heavy investment in the 

industry and lowered productivity. 

Consider again the skill bias idea. Perhaps pre-existing skills received higher payoffs 

because of the changing technologies such as the new steel methods, new sources of ore, and 

improved metallurgy. But the investigations in this paper did not find this. It does not appear for 

example that the high pay some rollers received was a systematic return to being a roller. 

Instead, it was a chaotic result of insider contracting and its strong incentives and the 

opportunities available for new technology and reorganization. The increase in professional 

research and development brought in new skills, not higher-paid existing skills. One might say 

there was a set of analytic or diagnostic skills or abilities that received a greater payoff. But ex 

ante, the players did not know this. They confronted an environment of technological change in 

which they did not share a common expectation of what skills would pay off. So skill bias does 

not help us understand the behavior of the players at the time. Perhaps the most cogent point 

about skill bias is that it does not suggest an association to institutional breakdown. 

The term “skill bias” also creates a rhetorical conflict. Mass production methods reduced the 

value of previous craft skills, so historians have described them as “de-skilling” (Nuwer, 1988 p. 

833). But the skill bias story is that technology change enhances the productivity and payoff of 

skills. One resolution is to reject skill bias, and say instead that technological uncertainty 

produces a turbulent adaptation process which temporarily induces more inequality, because the 

transition from one production and industrial structure to another involves creative destruction, 

experimentation and gambling. Eventually new institutions, skills, and pay levels stabilize. 

Thus the concept of technological uncertainty has some value for labor economics and the 

history of technology. It describes an economic environment in which there is a flow of new 

information which could permanently change production. Players respond opportunistically. 

Their responses are gambles whether they wished to gamble or not, and we have evidence that 

this phenomenon brings about episodes of increased earnings inequality. 
34 

Table 4. U.S. iron production 

 Compiled by AISA, and drawn from Historical Statistics and the series cited in Swank (1883) 

pp. 127-9 (which is pp. 863-5 of the larger Census volume it is contained in). 

 Bessemer 

steel ingots, 

in net tons 

Bessemer 

steel rails, 

in net tons 

Total openhearth steel 

production, 

in net tons 

Crucible steel 

production, net 

tons 

1867 3,000 2,550 19,000 

1868 9,000 7,225 22,000 

1869 12,000 9,650 1,000 22,000 

1870 42,000 34,000 2,000 34,000 

1871 45,000 38,250 2,000 35,000 

1872 120,018 94,070 3,000 37,000 

1873 170,652 129,015 4,000 49,000 

1874 191,933 144,944 7,000 43,000 

1875 375,517 290,868 9,050 52,000 

1876 525,996 412,461 21,490 50,000 

1877 560,587 432,169 25,031 52,000 

1878 732,226 550,398 36,126 51,000 

1879 928,972 683,964 56,290 62,000 

1880 1,203,173 954,460 112,953 81,000 

1881 1,539,000 147,000 93,000 
35 

Table 5. Industries represented in the Weeks report sample 

Industry 

group Industry name Number of establishments 

Number 

of wage 

observations 

 Iron blast furnaces 41 5496 Iron and steel 

 Rolling-mills and nails 25 6762 

 Pins 2 148 

 Car-wheel founderies 8 421 

 Stove founderies 13 2650 

 General founderies 6 677 

 Hardware, cutlery, and edge tools 17 2357 

 Machinery 39 7623 

 Tin and sheet iron works 3 304 

 Agricultural implements (and Bells) 12 1422 

Other metal work 

 Bridgebuilding 2 140 

 Canning 3 121 

 Cigars/tobacco 22 2048 

 Flour and grist mills 46 3500 

 Pork packing 3 89 

 Ice 2 326 

 Paper manufacture 36 6800 

 Sugar-refining 1 216 

Agricultural and 

forestry products 

processing 

 Breweries and distilleries 9 406 

 Carpets 3 742 

 Cotton manufacture 37 13596 

 Hemp and jute manufacture 1 160 

 Silk 4 1159 

 Wool 36 10385 

 Tanneries 22 2620 

 Hats 3 661 

 Clothing 5 605 

 Boots and shoes 13 2278 

Textiles , 

clothing, 

and leather 

 Belting 4 151 

 Cooperage 4 93 

 Furniture 41 5807 

 Saw-mills and planing-mills 52 6835 

 Ship-carpentry 4 437 

 Pianos and organs 4 1142 

 Carriage and wagon works 26 4411 

Wood work 

 Car-works 6 476 

 Brickmaking 10 1638 

 Iron mining 6 262 

 Coal mining 8 624 

 Copper mining 4 228 

 Silver mining 2 96 

 Stone quarrying 11 486 

 Powder (explosive) 2 23 

 Paints and white lead 5 321 

 Marble 3 299 

 Glass 17 5191 

 Gas/gas coke 6 303 

Mining and 

other 

materials work 

 Pottery and earthenware 23 1793 

Total: 6 groups 48 industries 652 establishments 104413 observations 
36 

Table 6. Reports by year in the Weeks data 

The text argues that there is a change in the inequality measures in iron and steel around 

1870. There are many more reports from establishments starting then, especially in iron and 

steel. Comparing the 1870-1881 period to the 1850-1869 period for iron and steel, 84% more 

establishments report and 145% more wage observations are available in the later period. In 

other industries taken together, 31% more establishments and wage observations are available in 

the later period. The number of observations would not bias the inequality measures but the new 

firms appear to be substantively different from the earlier ones. 

Iron and steel industries 

(Blast furnaces and rolling mills) All other industries 

Year Establishments 

reporting 

Wage 

observations 

Establishments 

reporting 

Wage 

observations 

1850 5 72 66 761 

1851 3 21 58 653 

1852 2 20 63 676 

1853 2 20 73 745 

1854 4 27 79 845 

1855 6 83 100 1000 

1856 6 51 96 980 

1857 9 67 103 1035 

1858 8 102 111 1145 

1859 7 54 118 1218 

1860 10 148 157 1740 

1861 8 99 148 1626 

1862 8 90 154 1632 

1863 15 173 163 1716 

1864 17 199 185 2005 

1865 21 230 218 2345 

1866 14 135 234 2687 

1867 14 144 245 2809 

1868 21 257 260 2968 

1869 19 180 268 3067 

Total 1850-1869 28 2172 307 31662 

1870 35 442 350 3996 

1871 38 529 377 4200 

1872 45 565 414 4615 

1873 48 690 437 4803 

1874 48 654 462 5096 

1875 50 735 502 5607 

1876 47 678 512 5778 

1877 50 669 522 5861 

1878 51 705 525 5956 

1879 54 756 527 5991 

1880 57 801 552 6502 

1881 3 27 23 209 

Total 1870-1881 66 7251 576 58614 

Grand total 66 9423 577 90276 
37 

Table 7. Trend in wages, holding job title fixed 

This regression had a year trend, 173 job category indicators, and a constant. 

N=99906. R2 was .48. Source: Weeks report data 

Predictor of log(daywage) Coefficient Robust standard 

error 

pvalue 

Year of observation (1850 to 1882) .012 .0002 0.00 

173 job categories Not shown 

Constant Not shown 

Conclusion: Holding job titles fixed, wages trended up about 1.2% per year. 

Table 8. Wage predictors 

Dependent variable is log of daily wage of weighted sample. Coefficient on constant is not shown. 

There are 99699 observations, including a number which are not in one of the standardized jobs. R2

 of 

regression: .60. Figures especially relevant to iron or steel work are highlighted. 

Predictor of log(daywage) Coefficient Robust 

standard error

p-value 

Year indicators (1850 to 1881) Not shown 

Job indicators (173 jobs) Not shown 

Months worked (a business cycle indicator) .012 .001 0.00 

 Payment practices 

Worker receives pay not counted here -.028 .002 0.00 

Piece rate (pay by measured output, not time) .029 .007 0.00 

Paid partly in merchandise ("in truck") -.060 .005 0.00 

 Region (as distinct from South) 

In Northeast (in Maryland or northeast of Pennsylvania) .022 .005 0.00 

In Pennsylvania .034 .005 0.00 

In Midwest or West .059 .005 0.00 

Urban (one of 1880's largest nine cities) .126 .004 0.00 

 Job content 

Wood work (job title contains “wood,” “carpentry,” etc.) .127 .025 0.00 

Metal work (job title contains “bar”, “metal”, “iron”, “shear”, “smith”, “roller”, etc.) .098 .016 0.00 

Hot work .123 .027 0.00 

Paper work (bookkeeper, clerk, draughtsman) .282 .038 0.00 

Worker demographics 

Is a woman -.356 .016 0.00 

Is a girl -.583 .013 0.00 

Is a boy -.965 .011 0.00 
38 

Table 9. Literacy of workers, from U.S. Censuses 1850-1880 

Three cross-cutting definitions of the groups are used in the table: 

 the occupation determined at the time the Census was taken. 

 the individual’s industry category as recoded by the Census according to their 1950 industry definitions; 

 the occupation category per the 1950 Census occupation definitions; 

Respondent was described in the Census as literate (able to read and write), not literate (could not read or write) or 

sometimes in an intermediate category (could read or write, or did not answer). Literacy was higher than average in 

the iron sector, and increased over time. Laboreres and the general working-age population are shown in the last 

rows for comparison with iron and steel workers. Data source: online IPUMS U.S. Census data at 

http://www.ipums.umn.edu. Figures shown are unweighted, and would be slightly changed if Census weights on 

individual observations were incorporated. 

1850 1860 1870 1880

yes 85.8% 85.2% 85.7% 91.1% 

other 8.8% 10.9% 4.0% 6.5% 

no 5.4% 3.9% 10.3% 2.4% 

Iron/steel works operatives 

(occupation 191) 

N 204 359 700 1070 

yes 92.8% 76.0% 74.8% 93.3% 

other 4.4% 10.4% 5.1% 2.4% 

no 2.9% 13.6% 20.1% 4.4% 

blast furnace, steel works, and 

rolling mill operators (industry 

336) N 69 336 472 757 

yes 86.5% 84.0% 71.1% 88.2% 

other 9.4% 12.0% 3.9% 4.1% 

no 4.2% 4.0% 25.0% 7.7% 

furnacemen, smelters, and 

pourers (occ1950 641) 

N 96 75 128 416 

yes 83.3% 88.2% 97.6% 97.0% 

other 10.0% 10.7% 1.4% 0.5% 

no 6.7% 1.1% 1.0% 2.5% Molders (occ1950 561) 

N 90 187 289 403 

yes 87.7% 93.3% 98.1% 98.6% 

other 11.4% 6.8% 0.4% 0.2% 

no 0.9% 0.0% 1.4% 1.2% Machinists (occ 197) 

N 228 474 700 934 

yes 85.0% 88.0% 92.9% 97.4% 

other 15.0% 5.0% 3.5% 0.9% 

no 0.0% 7.5% 3.5% 1.7% Steam boiler makers (occ 248) 

N 20 40 85 117 

yes 69.4% 73.4% 68.4% 66.8% 

other 14.3% 10.7% 5.3% 4.0% 

no 16.3% 16.0% 26.4% 29.2% Laborers (occ 39) 

N 6015 7953 9766 17270 

yes 72.4% 74.6% 79.7% 83.0% 

other 18.9% 18.4% 3.9% 3.1% 

no 8.7% 7.1% 16.3% 13.9% 

general population 

aged 15-70 

N 114,328 161,504 227,717 302,450 
39 

Table 10. Numbers of iron workers and metal workers 

Three overlapping definitions of the groups are used in the table: the individual’s industry 

category according to the 1950 Census industry definitions, the occupation category per the 1950 

Census occupation definitions, and the 1880 occupation definition. 

 Percentage of U.S. population 

Group 1860 1870 1880 

Blast furnace, steel works, 

and rolling mill workers 

(industry 336) 

.11% .14% .15% 

Furnace men, smelters, and 

pourers 

(1950 occupation 641)

.02% .03% .08% 

Iron/steel works operatives 

(occupation 191) 

.15% .16% .22% 

Machinists (occupation 197) .17% .16% .19% 

Molders (1950 occupation 561) .07% .08% .08% 

Steam boiler makers 

(occ 248 or 1950 occ 503) 

.01% .02% .02% 

Occupations affected by innovations in iron and steel grew faster than the population. 

Source: IPUMS (Ruggles, Sobek, et al 1997) 
40 

Table 11. Labor costs in iron blast furnaces 

Some iron blast furnaces reported the fraction of their production costs that went to wages of furnace labor. The 

respondents may have interpreted the question somewhat differently from one another, since the reports vary so 

much. Reported charcoal furnace costs seem to be related to the difficulty of obtaining charcoal from wood, which 

is not strictly furnace labor. The non-charcoal furnaces used mineral coal fuels -- anthracite or bituminous coal. The 

table leaves out years in which only one establishment reported. Six additional establishments, not included, 

reported labor costs as a fraction of the price of the output. Source: Weeks report (1886). 

The point: The Lucy Furnace Company, co-owned by Andrew Carnegie, had among the lowest labor costs per unit 

of output although it paid high wages. This evidence is consistent with other historical evidence that the Carnegie 

firms used capital-intensive methods. 

1860 1865 1866 1868 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880

Cherrie & Co., 

Menominee, Michigan 

(charcoal furnace) 

 

7.8% 7.8% 9.9% 9.9% 9.9% 7.5% 7.5% 6.5% 

Lucy Furnace Co., 

Pittsburgh, Pennsylvania

 7.0%  8.0% 

Benwood Iron Works, 

Martin's Ferry, Ohio 

 

10.0%

 

9.0% 

 

8.0% 

An establishment in 

Pennsylvania 

 

9.0% 9.0% 9.0% 

Struthers Iron Co., 

Struthers, Ohio 

 

9.0% 

Thomas Iron Co., 

Hokendauqua, 

Pennsylvania 

10.9%

 

13.2%

 

10.5% 10.1% 11.1% 

 

11.4% 9.7% 

Jefferson Iron Works, 

Steubenville, Ohio 

 23.2%  21.2% 11.3% 11.4%

An establishment in 

Alabama (charcoal 

furnace) 

 

11.2%

 

12.9%

An establishment in 

Pennsylvania 

 

16.0% 16.0% 16.0% 16.0% 16.0% 13.0% 13.0% 13.0% 13.0% 13.0%

Himrod Furnace Co., 

Youngstown, Ohio 

 

11.3% 13.1% 10.8% 11.1% 13.1% 12.9% 13.6%

North Western Iron Co., 

Mayville, Wisconsin 

(charcoal furnace) 

 

14.0%

Pratt & Co., Buffalo, NY 

12.7% 12.7% 12.7% 12.7% 12.7% 14.0% 14.0% 14.6% 14.6% 14.6% 

An establishment in the 

state of New York 14.0% 14.0% 10.0% 13.0% 12.0% 11.5% 11.0% 10.0% 9.0% 10.0% 10.0% 12.0% 11.0% 13.0% 

William Ellicott & Son, 

Baltimore, Maryland 

(charcoal furnace) 

30.0%

 

18.5%

The Cherokee Iron Co., 

Cedartown, Georgia 

(charcoal furnace) 

 

56+% 

D. Hillman, Centre 

Furnace, Kentucky 

(charcoal furnace) 

 

75.0% 

 

80.0%

 

90.0%

 

65.0%

Salisbury Iron 

Manufacturing Co., 

Sailsbury Furnace, 

Virginia (charcoal furnace)

 

90.0%

 

Eastery Kentucky Railway, 

Greenup, Kentucky 

(charcoal furnace) 

 

95.0%

 

95.0%

 

95.0%
41 

Figure 6a. These are among the most common occupations in the data set. The figures for N are the 

numbers of observations since 1855 of the named occupation in the sample from the Weeks report data set. 

Some observations: 

 The highest paid occupations were in metal work – pattern-maker, molder, and blacksmith. 

 Wages in the data rose by 1.5% per year. 

 Nominal wages rose sharply during the Civil War inflation of 1861-65. 

 Nominal wages fell in most occupations during the depression of 1873-1879. 

 Apprentices were paid much less than persons in other listed occupation and their wages were 

relatively unresponsive to business conditions. Perhaps a major part of an apprentice’s earnings 

were in the form of human capital or social capital, in the form of the right to be promoted into a 

craft or profession. 

this phenomenon induces episodes of increased earnings inequality. 

Figure 6b. Average wages behaved similarly over time in the different industrial sectors. Log-wage average by year

Year of observation 1850 1881

-.2

1

 Iron metal agr

 cloth woodwork other materials and mining

Dollars per day

Mean wages of common occupations Year of observation

1855 1861 1865 1873 1880

0

1

2

3

 Pattern-maker (N=867) Molder (N= 1160) Blacksmith (N=2346) Engineer (N= 2903) Carpenter (N= 1976)

 Painter (N=1014) Teamster (N= 2092) Laborer (N=5015) Apprentice (N=798)
42 

Figure 7a. Predicted log-wages based on year indicators alone. 

These predicted values show the effects of (a) rising wages with time, (b) the enormous inflation during 

the Civil War (1861-1865), and (c) the fall during the depression after the Panic of 1873. There was a boom 

starting in 1878. The text takes out these time effects by subtracting this predicted log-wage level from all 

log-wages, in order to isolate technological effects by industry. Adjusted R2

 of this regression is 0.55. 

Average log-wage by year

Year of observation 1850 1861 1865 1873 1881

-.2

1

Effects of inflation and depression on wages did not vary much by industry. This supports the technique 

used later of having year indicators draw out business cycle effects. Iron work seemed to have responded 

more strongly to depressions and booms than others did, perhaps because iron went into durable goods. 

Food and forestry industries and clothing industries paid less than average throughout the period. Mining 

and minerals, woodwork, and metal work paid more than average. Points representing fewer than 30 

observations are not shown. 

Figure 7b. Predicted log-wages by industry based on year indicators alone. Log-wage average by year

Year of observation 1850 1881

-.2

1

 Iron metal agr cloth

 woodwork construction minerals
43 

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