Skills and Training in the Wake
of the Second Industrial Revolution
An empirical study of experience and vocational training
among industrial workers in the engineering industry
in the Stockholm area (Sweden) around 1900.
Paper at the Sixth European Historical Economics Society Conference.
Istanbul, September 9-10, 2005.
Anders Nilsson, associate professor
Lund University
School of Economics and Management
Department of Economic History
E-mail: [email protected]
Fay Lundh Nilsson, PhD-student
Lund University
School of Economics and Management
Department of Economic History
E-mail: [email protected]
Skills and Training in the Wake of the Second Industrial Revolution.*
An empirical study of experience and vocational training among industrial workers in the
engineering industry in the Stockholm area (Sweden) around 1900.
Anders Nilsson and Fay Lundh Nilsson
1. Introduction
Background and purpose
The period 1890-1910 was characterised by substantial economic growth in most
industrialising countries with Sweden reaching on average 2.4 per cent growth in per capita
GPD (Schön 2000). The application of science and technology to industrial processes is often
considered to have played a crucial role in explaining the fast growth of the period. In that
process, good human capital endowment was a likely contributing factor. Possessing a stock
of human capital is in itself, however, not equivalent to putting it to economic use. Lars
Sandberg has pointed out that the substantial human capital stock that existed in Sweden
already before 1850 had little economic impact because the use of human capital in
production processes was limited (Sandberg 1979, 1982). Empirically, Sandberg was quite
possibly mistaken but his basic idea was fundamentally sound. It is of vital importance to
identify and quantify the mechanisms through which human capital is put to economic use.
This paper aims at clarifying some central aspects of the role of human capital in Sweden’s
fast economic growth round the turn of the century 1900.
To be more precise, the main purpose of this study is to examine changes in the demand for
skills and training during the initial phase of the second industrial revolution. We do so by
examining the determinants of the wage structure in the engineering industry, which, strictly
speaking, represents the interplay between labour demand and supply. A strong case can be
made, however, that wage determination in Sweden around 1900 was mainly demand-driven.
First, employers generally had the stronger bargaining position (Lundh 2002). Second,
population growth was quite intense at the time which implies, ceteris paribus, that the labour
supply was inelastic. Third, the dynamic forces acting on the demand side were powerful. A
large number of innovations, including milk separators, ball-bearings, Ericsson telephones,
*
Our research is financed by a grant from the Swedish Council for Working Life and Social research, grant no.
FAS 2002-0649. We have benefited from comments and suggestions by Kerstin Enflo and Joakim Appelqvist,
both at the Department of Economic History, and from Lars Edgren at the Department of History at Lund
University.
2
and other products that formed part of what is often called ‘the genius’ companies’
(Magnusson 1997). In combination with investments in new consumption goods and an
increasingly sophisticated urban industry the decade constituted the breakthrough of the
modern industrial society (Schön 2004). As a consequence, we consider it a reasonable claim
that the determinants of wages were by and large driven by demand-side forces.
Reis (2004) discusses the role of human capital in the development of the Portuguese
economy during the 1890’s by exploiting the fact that human capital is produced, in principle,
in two different processes. Part of the human capital is formed in formal schooling but another
part is produced by experience acquired during work. The extremely detailed data we have at
our disposal permit a detailed analysis of the relative importance of formal education and
experience in wage determination, using Mincer-equations. Further, it has been suggested that
access to human capital is particularly important in a modernising environment. A second aim
of the study is to investigate to what extent the human capital components of wages were
influenced by the degree of modernization in the various companies. It should be noted that
this part of the study is still in a preliminary phase.
We study the engineering industry which was one of the most important branches in the
transformation of the initial industrialisation. The Stockholm area has been chosen since that
was the largest and most diversified industrial region in Sweden at the time.
Theoretical considerations
Ever since the human capital theory was explicitly formulated around 1960 it has been
considered a powerful tool in the analysis of various phenomena in the labour market. For
modern times, there exists a vast literature which clearly demonstrates the existence of a
positive relationship between human capital and wages. For the nineteenth century however,
the evidence is less clear. To take but a few examples, David Mitch has argued that literacy
(the most basic form of human capital) was dispensable in nineteenth-century industrialisation
and that, as late as in 1891, 37 per cent of the men in Britain were employed in an occupation
where literacy was not useful (Mitch 1990, 1992). Moses Abramovitz claimed that physical
capital (rather than labour or other inputs or combination of inputs) was the main source of
growth in the nineteenth century (Abramowitz 1993). However, as Abramovitz also pointed
out, the 1890’s may have been a decisive decade in the sense that human capital started to
play a more important role. In an influential article Claudia Goldin and Lawrence Katz
3
emphasised the complementarities between technology and skills in the US 1900-1940. They
based their argument on the perception that manufacturing has two distinct stages: the
machine installation and maintenance stage and the assembly stage. Whereas skilled labour is
considered to always be complementary to capital in the machine-phase, unskilled labour is
used to create the final product in the assembly stage. Goldin & Katz found technology-skill
complementarities in the period 1909-1929 and associated it with technologies such as
continuous and batch processes and the adoption of electric motors. In particular, they
emphasise the role of electricity which became much cheaper during the period, but they also
mention the large array of new goods that emerged: the automobile, airplane, synthetic dyes,
household appliances, office machinery, and others. (Goldin & Katz 1996)
The Goldin & Katz study does not extend further back than 1909 but several of their
examples of new technologies were applied before then, also in Sweden. The fastest-growing
branches of manufacturing industries from 1890 to 1910 in this country included power
generating, pulp and paper, graphic, and mechanical engineering, all of which grew on
average by ten per cent or more annually (Schön 2000). In all of these, new technologies were
introduced in the 1880’s or 1890’s. As a consequence, we assume that technology-skill
complementarities were emerging in several branches of manufacturing industry and that this
was reflected in skill premiums in wages, in other words that human capital was rewarded in
the labour market of the engineering industry.
Human capital formation is normally equated with formal education. This is, however, out of
convenience rather than from strictly theoretical considerations. In his seminal work Human
Capital Gary Becker commences his analysis by discussing on-the-job training (Becker 1964,
1975). He further discussed the concept of general and specific training respectively in detail.
Becker found that there has to be some sort of trade-off between employers and employees so
that employees pay there own general on-the-job training with lower wages during the
training period and that employees and employers together pay for the specific training. In the
latter case that means that the employees, after completed training, get higher wages than they
would get elsewhere, but lower wages than their marginal productivity would suggest.
The analysis was taken one step further by Jacob Mincer whose direction of analysis is
suggested already in the title of his book Schooling, experience, and earnings (Mincer 1974).
Mincer was specifically interested in the distribution of income, both over the life time of the
4
individual and between different groups in the labour market. He found that there were great
income differences within groups with the same amount of education and concluded that
using only schooling as a measure of human capital is misleading. Though an increase in the
income is strictly proportional to an increase in years of schooling, the correlation between
income and formal schooling is weak because the model does not capture the impact of age.
Most employees continue to invest in their human capital after completing their formal
education. Because investment in training outside the formal schooling system is difficult to
measure Mincer advocated the use of experience, which was computed as the difference
between a person’s age and the number of years of schooling. The final result of his
consideration was the formulation of the earnings function that have since carried his name:
Y = f(S, E).
Mincer-equations have, in innumerable studies, demonstrated considerable explanatory
power. That is in itself a good reason also for us to use them. In addition, the material we have
at our disposal is extremely well suited for a Mincer-type analysis. In the earlier studies by
Mincer and others, experience was calculated as a residual: age minus years of schooling
minus six (or whatever age schooling was supposed to begin at). In other words, experience
included not only work experience but also possible spells of unemployment and unrecorded
activities (such as later studies, child-care, extended travelling etc.) In theoretical terms,
including also unrecorded activities is debatable. You could, on the one hand, argue that all
activities form experiences valuable in working life. Raising children, for example, gives you
experience from, among other things, giving and implementing orders and instructions and
from (endless) negotiations. On the other hand, experiences may provide negative as well as
positive experiences. Being unemployed could give you an opportunity to perfect you
gardening skills, but it could also traumatise you to apply unsuccessfully for job after job. Our
material is presented in detail below but already here it should be mentioned that it contains
information on actual, recorded experience from working in the engineering industry, in the
current occupation as well as with the current employer. In other words, we are able to
measure relevant experience in several dimensions.
The technology-skill complementarity hypothesis is in principle valid in all branches of
industry and indeed in every single company. It could be argued, however, that these
complementarities should be stronger in branches and companies where technological change
is strong. We put forward, as a working hypothesis, that such was the case and that this was
5
reflected in the wage structure. Engineering industry at the time consisted of companies that
used old as well as new technologies and more often than not a mixture of them. We have
constructed a typology with different indicators to capture to what extent the companies in the
sample used new technologies. We have labelled this the degree of modernization and we
expect to find that human capital, in particular formal human capital but also certain forms of
experience was in higher demand in firms with a high degree of modernization.
2. Forms for the acquisition of technical skills in Sweden around 1900.1
The unregulated apprenticeship system
For a young person wanting to acquire technical skills there existed in principle two roads that
were not mutually exclusive. First, there was the possibility to become an “apprentice” and
learn a trade from a “master”. Note that the terms are surrounded by quotation marks since
there did not exist a formal apprenticeship system at the time. In that respect, the situation in
Sweden stood in sharp contrast to most other North- and Central European countries where
some sort of regulation hade been re-introduced by the late nineteenth century. The absence of
a formal regulation implied that apprentices and masters faced “free rider” problems. The
apprentice had no guarantees that he would actually be taught the trade; instead he could be
exploited by the very low wages that were paid to apprentices. The master, on the other hand,
had no guarantee that the apprentice would stay with him long enough to justify the training
costs. There are some indications that the system was deteriorating at the turn of the century,
not least in the construction business, but the evidence is far from conclusive (Söderberg
1965). The evidence for apprentices’ wages is sparse and unsystematic. One example from
about 1900 concerns carriage-making, where a first-year apprentice earned fifty kronor per
year. The following years were paid with one hundred, one hundred and fifty, and two
hundred kronor respectively (Söderberg 1965).
The number of apprentices is difficult to estimate. When the guilds were dismantled in the
middle of the nineteenth century, the number of apprentices was about 12,000 which
corresponded to eighteen per cent of the entire workforce in the artisan sector. For the 1890’s,
two very rough estimates have been made which imply that the proportion hade diminished to
1
This section builds on Nilsson (2005) unless specific references are given.
6
between ten and fifteen per cent2. That implies that the number of apprentices in the artisan
sector around 1900 was somewhere between 18 000 and 27 000. To that number should be
added those who were employed as apprentices in the (modern) manufacturing industry, but
the size of this group is totally unknown. If the estimate presented below in table 6 is valid for
other branches of industry and for the country as a whole (which is very dubious) the
proportion of apprentices in manufacturing industry was only about two per cent.
Technical schools
The alternative – or in many cases supplementary – way to acquire technical skills was
through the rudimentary system for vocational training. Here, the quantitatively most
important were the so called Lower Technical Schools that existed in most towns and had a
total of about 10 000 pupils in 1900. However, the name “technical schools” notwithstanding,
the subjects that most pupils took part in were of a general nature: mathematics, writing,
Swedish, drawing, and book-keeping. It was possible to follow also more directly vocational
courses, in particular at the Technical school in Stockholm where courses were held in, for
example, mechanics, machinery, and engraving. The number of pupils was always, however,
much smaller at these specialised courses than in the generally orientated ones. All but a few
of the lower technical schools were Sunday- and Evening schools. The basic idea was that the
youngsters should have an ordinary daytime job as an apprentice or as a factory worker where
they would learn all practical aspects of their trade. The evening studies were supposed to
provide them with general skills that were applicable in any trade or profession but they were
seen as a supplement to the practical training. In reality things were a little bit more
complicated. A survey in 1907 revealed that one pupil out of three at the lower technical
schools did not have any employment. Thus, a substantial number of youngsters could, at
least in principle, finish their schooling in the lower technical schools before entering the
labour market.
There also existed a few technical schools that offered a more substantial vocational education
and training. They included the mining schools in Filipstad and in Falun, the weaving school
in Borås, and the craft school for forging and metalwork in Eskilstuna. Even though these
schools were primarily intended for their respective local labour market, the skills they
produced were demanded also in other places. This was the case not least for the school in
2
The higher estimate is from Söderberg (1965), the lower from an unpublished manuscript and discussions with
Dr. Lars Edgren at the Department of History at Lund University.
7
Eskilstuna, situated only about 100 kilometres from Stockholm and where the skills acquired
in forging and metalwork were useful in the engineering industry in general. Other schools
that were of some interest include the machinist classes at the nautical schools (where the
pupils were trained between three and eight months) and, to some extent, the technical
secondary schools.
3. The empirical study- method, data, and variables
Method
The main purpose of this study is to examine changes in the demand of skills and training in
the engineering industry during the second industrial revolution. As instrument for the
analysis we have chosen the (at least in econometrics) most commonly used method of
estimating parameters, the OLS (Ordinary Least Squares)-method. The method makes use of
a minimization of the error sum of squares (ESS) that gives the best linear fit possible, i.e. the
straight line that is closest to our data. All regressions are tested for heteroskedasticity with
White’s heteroskedasticity test. Since heteroskedasticity seems to be an ever present problem
in most cross section data, all regressions in this paper have been tested with White’s test for
heteroskedasticity and then corrected with White’s heteroskedasticity consistent covariance
matrix. The probability values given before the correction is accounted for in brackets in the
probability column in each estimation.
The data is analysed in two steps. In the first step we make use of the above mentioned
Mincer-equation with the two common variables, education and experience in a model:
log hourly wagesi = β0 + β1lower technical educationi + β2experiencei + β3experience2 + εi,
where different forms of experience is tested as explanatory variables together with lower
technical education and where the diminishing return to experience is expressed as experience
squared.
Since our hypothesis is that companies with a higher degree of modernity demanded more
skilled labour than more old fashioned companies, we have developed a typological model to
be able to determine to which degree a certain company can be characterised as modern. The
8
model and its indicators are shown in table 1 below. The typological model is used in the
second step, where we augment the model to include variables for the degree of
modernization and skills as well as other variables such as experience from abroad, marital
status and family situation. All variables are discussed below.3
Table 1. Indicators used to fix the rate of modernity
Older companies
•
diversified production
•
mainly since before existing products or
variants of these
•
low degree of mechanization
•
few millers in proportion to tool and die
makers
•
use of steam as driving power
•
use of steam and gas for lighting
•
small proportion piece rate wages
Modern companies
•
specialized production
•
new products
•
•
high degree of mechanization
few tool and die makers in proportion to
millers
use of electricity as driving power
use of electricity for lighting
large proportion piece rate wages
•
•
•
As would be expected the companies showed a very heterogeneous pattern with regard to
degree of modernization, which means that it is not possible to use just a binary model if we
want to understand the complexity of the Swedish engineering industry around 1900. For that
reason we use a continuous model (where 0 indicates no modernization at all and 7 is a, for
the time in question, completely modern company) as is shown in figure 1.
Figure 1. The companies in the sample classified by degree of modernization
De Laval
Luth
&
Rosé
Moberg
Bergsund
0
3
Rapid
1
Per From
2
3
Alpha
Separator
Per
Persson
Ahrens
Telefonfabriken
4
5
6
7
The model and the indicators are discussed in detail in a previous paper (Lundh Nilsson 2005).
9
Modernity in itself seems to influence the wages pretty much: A correlation test shows R =
0.55 and a simple estimation shows these results:
Table 2. Logged actual hourly wages and degree of modernization
Model:
Dependent variable
Sample
Number of observations
2
R
2.1
log_ah_wage
full
4
1569
0.30
Constant
Modernity
Coeff.
3.411636
0.107465
Prob.
0.0000 [0.0000]
0.0000 [0.0000]
.
Data
Around the turn of the century 1900 the Kommerskollegium (the Swedish National Board of
Trade) conducted a survey of the Swedish engineering industry in two steps (1899 and 1901),
henceforth abbreviated NTB. Together these two surveys covered nearly the entirely
engineering branch, which meant 138 engineering companies with about 23 000 employed
blue collar workers. The owners of the companies were supposed to answer questions, in
writing, about the operation of the company and to give very detailed information about the
wages and working hours for each individual for a whole year, week by week. These details
allow us to calculate an actual hourly wage for each worker (see below). A most interesting
thing is that each individual blue collar worker was interviewed in detail about, among other
things, hourly wages, occupation, education, experience, benefits, as well as personal
characteristics, such as birth year, marital status, and number of children. Today almost all
23 000 forms are preserved in the National Archives in Stockholm. Due to economic
restrictions it has been necessary to make a sample of about 2 000 individuals. To manage the
problem with different wages in different regions and to get the most comprehensive picture
of the engineering industry we decided to chose the region of Stockholm.5 The sample
comprises of eleven companies with together 1 576 blue collar workers for whom it has been
possible to compute actual hourly wages and it will later be complemented with a smaller
time series study for the period 1892-1920.
4
5
Seven extreme outliers (workers with exceptionally high hourly wages) were omitted.
For a more extensive presentation of the data and the sample, see Lundh Nilsson, (forthcoming).
10
Variables:
Wages
We use the concept of “actual hourly wages” throughout the paper. The wages are computed
from the detailed information of weekly working hours during one year (which often differs
substantially from the weekly working hours of the firm) and from hourly wages as well as
piece rate wages (it was not unusual that a worker was paid in both ways). The wages are
expressed in öre (one Swedish krona is equivalent to 100 öre). As table 3 demonstrates actual
wages varied quite a lot in the sample.
Table 3. Actual hourly wages (öre)
Mean
ah_wage
44.98
Source: Primary sources.
Median
45.38
Maximum
82.39
Minimum
10.52
Education
Already about the turn of the century 1800 almost all adult Swedes were able to read. By
1900 the entire population was literate, i.e. in possession of both reading and writing skills
(Johansson 1975). Probably there was a difference between workers around 1900 regarding
the length of their elementary schooling so that older workers had fewer years than the
younger ones but we can not know for sure that this applies to everybody in the sample. As a
consequence, we consider all workers to have the same amount of elementary schooling.
Lower technical education was much less common. About 12 per cent of the Swedish blue
collar workers in the engineering industry as a whole and about 17 per cent of those in the
Stockholm area had attended lower technical education. As we would expect only a very
small share of the workers, about 2-3 per cent, had attended secondary education or some
other education. For more detailed information, see table 4.
11
Table 4. Education among blue collar workers in the Swedish engineering industry around
1900
Type of education
General secondary
education
Lower technical education
Engineering industry
(n = 23 207)
n
%
526
2,3
Technical secondary and
higher education
Other education6
Stockholm area
(n = 5 908)
n
186
Present study
(n =1 576 )
n
37
%
3,2
%
2,4
2 846
12,3
1 009
17,1
261
16,6
54
565
0,02
2,4
10
280
0,02
4,7
3
37
0,02
2,4
Sources: Elmquist (1901) and (1904).
As mentioned above, we ought to expect that younger persons have more education than older
ones. Figure 2 shows that this is true, although there is considerable variation between
different cohorts.
Figure 2. Share of workers with lower technical education
45
40
35
percent
30
25
20
15
10
5
65
63
61
59
57
55
53
51
49
47
45
43
41
39
37
35
33
31
29
27
25
23
21
19
17
15
13
0
age
Source: Primary sources
Training
Section 2 pointed out the existence of a substantial if unregulated – and unrecorded apprenticeship system. Our material gives some possibility to estimate the extent of
apprenticeship or apprenticeship-like employment. It is only in a few cases, however, that
apprentices enter explicitly into the material. There are only 29 (explicit) apprentices (ranging
in age from 15 to 20)7 in the sample, which indicates that the system is drawing to a close in
the engineering industry. The majority of the young workers were called either craftsmen
6
This is a very heterogeneous group including all types of education that can not be classified in the other
groups, such as lower military education, agricultural education etc.
7
The sample also includes one apprentice aged 14 and two apprentices aged 22.
12
(104 in this age-group) or helpers (33). They were employed to fulfil work assignments that
could be learnt quickly and they would consequently be paid full wages (relative their age)
from the start. See Figure 3.
Figure 3. Mean hourly wages: apprentices, helpers and craftsmen (age 15-20)
35
30
25
öre
apprentices
helpers
craftsmen
20
15
10
15
16
17
18
19
20
age
Source: Primary sources
Figure 3 clearly shows that apprentices were paid special (i.e. low) wages up to and including
age 18. Compared to persons of the same age but employed as helpers or craftsmen the wages
for apprentices did not differ much at age 15 but at ages 17-18 the difference was 15-25 per
cent, which represents a substantial investment. At age 19 and later, however, apprentices
were paid “normal” wages.
In most cases, training was not completed at age 20 but there is no direct information
available about its extent. However, the wage structure exhibits certain traits that provide
some indirect information. Our material permits the calculation of actually paid minimum
wages that could be thought of as the compensation paid to “raw” (i.e. unqualified) labour.
The difference between the minimum and the median wages can thus be regarded as some
kind of skill premium. The minimum and median hourly wages for all ages are represented in
Figure 4 below.
13
Figure 4. Minimum and median hourly wages in the engineering industry around 1900
70
60
50
40
öre
Median
Minimum
30
20
10
70
66
68
63
59
61
55
57
53
49
51
47
45
43
41
39
37
33
35
29
31
27
23
25
21
17
19
15
13
0
age
Source: Primary sources.
Figure 4 demonstrates that the median wage increased steadily with age up to about age 30,
thereafter remained stable until about 50 and then declined with age (the irregular behaviour
of the graphs after about age 55 is due to few observations in each age category). Whereas the
increasing mean wages at the very early age (14-20) to some extent can be explained by
increasing body strength, the continued increase after that is a clear indication that training
continued at least until about age 30. It is of some interest that the wage profile even for the
minimum wage increases up to about age 30. This indicates that some training took place also
in that category (and that the term “raw labour” is somewhat misleading).
Experience
Some part of the difference between minimum or raw wages and median wages is in all
probability due to increasing productivity and skill over the years, even outside the explicit
learning situation. In the present study experience can be calculated in a more detailed way
because all blue collar workers in the survey made by the NBT were asked in depth about
their experience. The data set used contains data about experience in the engineering industry
and in the occupation as well as period of employment in the current company. That means
that we have three different sets of data concerning experience of mechanical engineering.
The workers were also asked about what they did before their entry to the engineering
industry and if they had any experience from abroad.
14
Table 5. The experience variables (years)
Mean
exp_eng
12.04
exp_occ
10.91
exp_emp
6.36
Sources: Primary sources
Median
10.00
9.00
4.00
Maximum
50.00
50.00
46.00
Minimum
0
0
0
Experience from the engineering industry (exp_eng)
This is in most cases the broadest form of experience that we can calculate meticulously.8
Most young men in the lower classes started to work full time directly after completing
elementary schooling, at the age of 11-14. That does not, however, mean that all of the
workers in the investigation went straight to the engineering industry. About 60 per cent of all
blue collar workers started to work in the engineering industry at the age of 18 or later and out
of these about 25 per cent were 25 years old or older when they got their first job in the
branch, which indicates that they had some other experience from outside the engineering
industry.
Experience in the current occupation (exp_occ)
The NBT also asked about experience in the current occupation. According to the account of
NBT it was not unusual that workers begun their career in the engineering industry as
blacksmiths or sheet-metal workers but later on proceeded to some other department.9 Also,
among workers in the machine department it was rather common that workers turned from
one occupation or activity to another.10 Both examples indicates that the workers must have
shared at least some basic training or education that was relatively transferable from one
activity to another. To get a picture of to which extent these transfers took place we have done
some calculation of how much experience in the current occupation differs from that in the
engineering industry. For the biggest proportion of workers there was no difference at all
between experience in the engineering industry and experience in the current occupation
(72.43 %). A small share of workers (4.22 %) had more experience in the current occupation
than in the engineering industry while a fairly big share of workers (23.35 %) had more
8
The workers told the agents who interviewed them which year their career in the engineering industry started,
so the numbers of year is simply calculated with the year of the investigation as a starting point. This method
applies to all our forms of experience, except experience from abroad which is a binary variable.
9
In NBTs’ investigations there were almost as many helpers as craftsmen in both occupations. Among
blacksmiths 909 were named craftsmen while 891 were called helpers.
10
Elmquist (1901).
15
experience in the engineering industry than in the current occupation.11 The latter numbers
indicates the existence of some form of internal labour market, which will be discussed later.
Experience with the current employer (exp_emp)
When NBT carried out their two investigations most employers were of the opinion that the
widespread mobility of the industrial workers constituted a problem. NBT could confirm the
high mobility, especially in their first survey in 1899, but preferred to explain it, at least
partially, as a consequence of high demand for labour during the boom in the engineering
industry which took place during the 1890s. In their second survey, however, which took
place in 1901, the mobility was found to be considerably lower, which is just in line with
diminishing demand of labour due to a lower growth rate in the engineering industry. A
calculation for the present study shows that 20.62 per cent of the workers had been employed
by the current company for less than two years, 58.31 per cent for two to ten years, and 21.07
per cent had been employed by the current employer for more than ten years. There was, in
other words, a quite substantial share of workers with longstanding employment periods.
Experience from abroad (exp_for)
Quite a lot of the blue collar workers, about six per cent, in the Swedish engineering industry
around 1900 had some experience from abroad, most of them for more than a year. About 50
per cent had been working in another Scandinavian country but many also had experience
from the American engineering industry. In the Stockholm case the share of workers with
experience from abroad was even bigger and in the current study 9.45 per cent of the workers
had been abroad working. This kind of experience can be regarded as a kind of human capital
complementing formal or informal education, but it also signals to the employer that you have
a special capacity or go-ahead. The available data makes it possible to study the impact of this
variable more closely but in this paper we use only a simple binary variable.
Skill
The data set allow us to deepen the study of qualifications of labour since we have very
detailed information on current as well as former occupations. This will however not be done
in this paper. Here we concentrate on whether the workers are characterised as apprentices
11
The numbers are calculated from my own data set with 1576 individuals.
16
(category_1), helpers and odd-job men (category_2), craftsmen (category_3) and foremen
(category_4).
Table 6. Number of individuals in different work categories
Category
catgory_1
category_2
category_3
category_4
Included workers
apprentices
helpers, odd-job men
craftsmen
foremen
Number
33
200
1309
27
Share of total labour (%)
2.1
12.75
83.43
1.72
Other variables
The data set allows us to explore and discuss other variables that, more or less consistently,
have been found to influence wages. One of the most used variables in such studies is sex, but
this variable will not be used here for two reasons. Firstly, the engineering industry was at the
time almost totally dominated by male labour. Secondly, the NTB survey completely ignored
the few women employed in the industry even if the employer included them in their accounts
of wages.12 There are, however, other standard variables that presumably had effect on wages.
They include marital status and family situation (number of children).
Marital status (m_status). As mentioned above, most employers experienced problems due to
high mobility of industrial workers. According to earlier surveys (however not concerning the
engineering industry) employers seemed to prefer married employees since these were
expected to be more stable in their relations to the employers (Karlsson 2003). This could
sometimes be expressed through subventions of housing for married workers as was the case
with the biggest company in this study, Separator. All married ordinary workers got a
housing allowance of 60 kronor a year and all foremen got 250 kronor a year – with median
wages of about 56 öre this sum represented quite an important sum for most workers.
Therefore, it is quite obvious that the employers aimed at reducing resignations through these
subventions. Not all employers, however, used this method but we can not know for sure if
they rewarded their married workers in some other way, e.g. with higher wages. So, in this
study the variable used is marital status. The share of married workers can be studied in table
7.
12
There were e.g. 27 women employed in the telephone company and a few others in other companies.
17
Table 7. Share of married workers in different age categories
Age category
2125all
Sources: Primary sources.
Married workers (%)
68
76
61
Number of children at home (children_ah). This variable reflects perhaps more the interest of
the workers than the employers. A reasonable assumption seems to be that workers either
worked harder (especially if paid piece rate wages) or in some way managed to negotiate
about higher wages. Table 8 indicates that the average number of children at home was just
over one child, which must be considered a low value.
Table 8. Number of children at home
Mean
children_ah
1.19
Sources: Primary sources.
Median
0
Maximum
9
Minimum
0
4. The empirical study - results
Mincer-equations
A short look at the correlation coefficients in table 9 shows that experience from the
engineering industry and from the current occupation is substantially stronger correlated with
wages than lower technical education, but there is also a marked difference between the first
two and the third form of experience. We should, however, expect the correlation to be
weaker between hourly wages and experience with the current employer than in the other
cases, since a significant share of workers were employed in rather ‘young’ companies.13
13
14 per cent were employed in companies not older than eight years of age, 25 per cent in companies not older
than eleven years of age.
18
Table 9. Correlation coefficient: hourly wages, lower technical education and different kinds of
experience (n = 1569)
Wages +
Correlation coefficient
engineering industry
current occupation
current employer
lower technical education
0.25
0.24
0.08
0.09
As was mentioned earlier we have used the common Mincerian equation,
log hourly wagesi = β0 + β1lower technical educationi + β2experiencei + β3experience2 + εI,
to estimate the impact of education and experience on wages, by implication that wages
mirrors the demand of skill and training. In the following three regressions β2 and β3 has by
turns been replaced by the three different forms of experience. In these estimations, as well as
in the following, wages are expressed in öre, lower technical education is a dummy variable
and experience is expressed in years. The results of the estimations are shown in tables 10-12.
Table 10. Hourly wages, lower technical education and experience in the engineering industry
Dependent variable: log_ah_wage
Sample: full
Number of observations: 1565
R2: 0.19
White Heteroskedasticity-Consistent Standard Errors & Covariance
C
lower_tech
exp_eng
exp_eng2
Coeff.
3.47286
0.059801
0.045145
- 0.001018
Prob.
0.0000 [0.0000]
0.0078 [0.0079]
0.0000 [0.0000]
0.0000 [0.0000]
Table 11. Hourly wages, lower technical education and experience in the current occupation
Dependent variable: log_ah_wage
Sample: full
Number of observations: 1556
R2: 0.16
White Heteroskedasticity-Consistent Standard Errors & Covariance
coeff.
C
3.483934
lower_tech
0.056554
exp_occ
0.040741
exp_occ2
- 0.000927
prob.
0.0000 [0.0000]
0.0122 [0.0140]
0.0000 [0.0000]
0.0000 [0.0000]
19
Table 12. Hourly wages, lower technical education and experience with the current employer
Dependent variable: log_ah_wage
Sample: full
Number of observations: 1569
R2: 0.09
White Heteroskedasticity-Consistent Standard Errors & Covariance
coeff.
C
3.595210
lower_tech
0.087454
exp_emp
0.038990
exp_emp2
- 0.001271
prob.
0.0000 [0.0000]
0.0002 [0.0002]
0.0000 [0.0000]
0.0000 [0.0000]
As can be seen from table 10-12, experience matters and so does lower technical education,
however the latter to a much smaller extent than the former. If a worker has attended one or
more courses of lower technical education this will increase his wage with 6-9 per cent,
depending on which form of experience we use in the estimation. We remind the reader that
lower technical education can not be fully compared to that of e.g. elementary schooling and
therefore the relatively small impact of education in this case should not come as a surprise.
For each year of experience, his wage increases with about 4-5 per cent, but this increase
tapers off over time. However, as could be seen from the equations above the explanatory
power of the estimations are still rather weak, between 9 and 18 per cent.14 Consequently
there are several reasons as to why we should look for other explanations for the
determination of wages.
An augmented model
Fortunately, the data set allow us to explore and discuss other variables of interest. In the next
step we have augmented the model and make use of five other explanatory variables besides
education and experience:
log_actual hourly wagesi = β0 + β1lower technical educationi + β2experience from the
engineering industryi + β3experience from the engineering industyr2i + β4experience with the
current employeri + β5experience with the current employer2i + β6experience from abroadi +
β7lower technical educationi + β8modernityi + β9occupational categoryi + β10marital statusi
+ β11children at homei + εi.
The results from the estimation are presented in table 13 below. As can be seen, all
probability values are significant at a lower level than 5 %. The estimation was corrected with
White’s heteroskedasticity-consistent covariance matrix and there are not any marked
14
Another problem occurs when testing for normality – the probability values are not significant, which disturbs
the White’s test for heteroskedasticity. However, we ignore this problem at the moment.
20
differences between the probability values before and after the correction was made
(probability values before correction in brackets). Also the explanatory power of the
estimation is quite good: R2 = 0.59.15
The variables used in this augmented estimation were discussed in Section 3. As can be seen
in table 13, lower technical education raises the wage about 3.6 per cent, which is a rather
small increase as against the results of the Mincer-equations. The same phenomenon applies
to the experience variable. In this estimation one year of experience in the engineering
industry increases hourly wages with about 2.2 per cent while one year of experience with the
current employer raises the hourly wages with 1 per cent. There is, however, still a strong
impact of experience, above all regarding experience from the engineering industry. On the
other hand, the influence of lower technical education is not too bad if we consider that it
raises wages almost as much as two years of experience. It is of special interest since much of
this education in was in fact general to its character.
As was discussed earlier experience from abroad can be regarded as a form for human capital
and in this estimation it raises earnings nearly as much as lower technical education. The
latter result implies that employers appreciated either the increased skill acquired during a
stay abroad or some extraordinary ability or ‘go’ among these workers.
15
A Jarque-Bera test for normality was applied to the log-linear estimation and showed problems with
unsignificant probability values. When tested with a non-logged dependent variable the test shows significant
results (prob. 0.38). We have also estimated the regression, omitting all apprentices, to see if the very low wages
of this group would affect the results in a substantial way. We found no such effect.
21
Table 13. Logged actual hourly wages and determinants
Dependent Variable: LOG_AH_WAGE
Method: Least Squares
Date: 06/17/05 Time: 10:33
Sample: 1 1569
Included observations: 1565
Excluded observations: 4
16
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOWER_TECH
EXP_ENG
EXP_ENG^2
EXP_FOR
EXP_EMP
EXP_EMP^2
CATEGORY_1
CATEGORY_3
CATEGORY_4
MODERNITY
M_STATUS
CHILDREN_AH
3.002073
0.035592
0.021466
-0.000456
0.040504
0.010232
-0.000257
-0.379403
0.132694
0.250744
0.109163
0.139007
0.009519
0.025963
0.016440
0.002396
6.28E-05
0.018856
0.002555
9.16E-05
0.068100
0.021725
0.042486
0.003500
0.014972
0.003640
115.6271
2.165025
8.959595
-7.261859
2.148053
4.004574
-2.801450
-5.571240
6.107770
5.901812
31.19078
9.284364
2.615428
0.0000
[0.0293] 0.0305
0.0000
0.0000
[0.0501] 0.0319
0.0001
[0.0025] 0.0052
0.0000
0.0000
0.0000
0.0000
0.0000
[0.0359] 0.0090
0.590872
0.587709
0.234792
85.55784
53.65721
1.321553
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
3.745894
0.365664
-0.051958
-0.007470
186.7863
0.000000
To estimate the impact of skill we have used dummy variables for different categories of
workers. The reference category (category 2) is helpers and odd-job men. Category 1 includes
apprentices only. As could be seen from the estimation there is quite a difference between the
reference category and this group. Being an apprentice means substantially lower wages, 38
per cent, than being a helper or odd-job man. There ought to be two reasonable explanations
to this. Firstly, as was discussed earlier, apprentices paid for their vocational training with
very low wages. Secondly, also helpers and odd-job men could acquire some human capital at
the shop floor, either in the form of physical strength or in the form of occupational skill and
at least some of the workers in category 2 should be regarded as semi-skilled. The latter
especially applies to helpers in the forges and among sheet-metal workers; two occupations
with almost as many helpers as craftsmen and where long experience as a helper is common.
In comparison with helpers and odd-job men, craftsmen (category 3) earn 13 per cent more.
The difference in wages of being a category- 2 worker or a category -3 worker is apparently
relatively small. One explanation is that many of the helpers in those occupations just
mentioned (among others), were relatively skilled in comparison to those who called
themselves craftsmen but were employed in occupations not demanding the same amount of
training and skill. Another explanation is that at the time for the NBT-surveys the unions were
16
Probability values before correction in brackets.
22
launching the concept of craftsman as means of improving wages in general so the workers
may have been eager to negotiate for the right to call themselves craftsmen as soon as they
had completed an elementary training. The last category comprises the foremen (category 4).
In comparison with helpers and odd-job men they earned 25 per cent more. If it is plausible to
regard the differences in earnings between apprentices and category-2 and 3 workers as a skill
premium, it seems that the difference between category-3 and 4 could, if anything, be
regarded as an additional premium for leadership and supervision.
To estimate the impact of modernity we have used the results from figure 1 and 2. The results
from our first estimation (See table 2) are confirmed in this augmented estimation and it
seems as if modernity, as defined in table 1 (right-hand column), is a very strong and highly
significant determinant which raises wages quite substantially. According to the results given
in table 13, the difference between wages in an old fashioned company and a fully modern
company, respectively, is about 60 per cent. Also a much smaller step, just from one (full)
level of modernization to another, raises wages with 11 per cent. These results seems to
confirm our hypothesis that more modern companies demanded (and was prepared to pay for
it) more skilled labour than the lesser modern companies.
Another strong determinant seems to be the variable for marital status. According to the
estimation above a married worker got about 14 per cent higher wages as against an
unmarried worker, all other things being equal. The result seems to confirm our suspicion that
employers regarded married workers as more valuable because of their supposed higher
stability. The family situation besides marital status, estimated as number of children at home
seems, however, to play only a marginal role in the determination of wages since wages
increase only by about 1 per cent per child.
23
5. Discussion
The engineering industry experienced substantial growth around the turn of the century 1900
with an average annual growth in output of 10.6 per cent 1890-1910. This growth was
initiated by significant technological change that resulted in a large number of new or
modified products that were demanded in the domestic as well as in foreign markets. The
point of departure for this paper is that this implied an increasing demand for skilled labour
that, in turn, was reflected in the wage structure.
A first issue to be addressed is where the workers acquired their skills. We have demonstrated
that formal technical education was of a limited extent in general and also that rather few
workers in our sample had acquired such education. It is further corroborated by the low
returns to technical education of about three to four per cent. So we can rule out formal
vocational schools as primary providers of skills. Something similar goes for apprenticeships.
Although not formally regulated, the remnants of an earlier apprentice system still prevailed
in many working places. The companies in our sample, however, had only a few apprentices
who constituted a mere two per cent of the working force. The existing systems for
apprenticeship training and for vocational education were too limited and, in the case of lower
technical schools, provided a far too general education, for them to have a major impact on
the formation of skills for manufacturing industry. In this respect the situation in Sweden
resembled the better known English case with numerous Evening schools and an unregulated
apprentice system that was in disrepute.
In the absence of an extensive system for vocational training the workers must have acquired
most of their skills through on-the-job training. This is not just a “residual argument”; our
material presents some positive evidence as well. The wage profiles in Figure 4 clearly
indicate that the learning curve continued at least until age 30. The fact that wage increases
are discernible in the minimum wages as well as in the mean wages further indicates that at
least some training took place also in less demanding tasks. When we combine this with our
finding that the workers had, on average, twice as much experience from work in the
engineering industry as with their current employer we get the impression of a work force that
acquired skills not only in one but in several work places. This was rewarded; the effects on
wages from work experience in the engineering industry were twice as big as experience from
the current employer.
24
Our results so far are consistent with the existence of a local – or perhaps better, regional –
labour market with a high degree of mobility were workers acquired skills at several working
places. In some cases that process was preceded by more formalised learning and training, in
some cases supplemented by working periods abroad. All these skills were rewarded and
above all they were rewarded in modernising companies. In fact, our results are to a large
extent driven by the difference between “old” and “modern” companies. We interpret this
finding to signify that the demand for skilled labour came, above all, from the modernising
companies.
That result, however tentative and preliminary as it may be, could be brought into a wider
context. Sweden was a late-comer in the industrialisation process that had, up to the late
nineteenth century, benefited from the demand from Western Europe for a few primary
products such as timber and iron. Based on these a sophisticated industrial sector had evolved
by 1900 with products of higher value added such as pulp, paper, and high quality steel. The
remainder of manufacturing industry had to cope with the restraints in energy supply (no
exploitable coal deposits) and in effective demand (no large urban concentration). As a
consequence, it was small-scale, geographically dispersed, and oriented towards the home
market. This not very sophisticated manufacturing industry seems to have coped well with the
limited supply of vocationally trained labour prior to the 1890’s. The introduction of new
products and methods on a large scale in connection with the Second Industrial Revolution
also implied an increasing demand for skills that we see reflected in the skill premium
discussed above. A similar development took place in several other European countries at the
same time, but in most of them, at least in Central and Northern Europe this “skills challenge”
was met by a re-organisation of the apprentice system (Thelen 2004). In Sweden, although the
Artisans’ Confederation repeatedly demanded a similar re-organisation, this never took place.
It remained the responsibility of the companies themselves, and of individual workers, to
provide the necessary skills. That “system” was successful for a while but it also led to
prolonged discussions and a detailed investigation of how a more comprehensive system
could be organised. However, organised vocational training on a large place did not take place
until the Trade Union Confederation and the Employers Federation agreed on the terms, in the
late 1930’s.
25
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